From 26901e4aa0a51a1863a796999e0476a4b72e5bd5 Mon Sep 17 00:00:00 2001 From: Chang Liu <9713593+chang-l@users.noreply.github.com> Date: Fri, 13 Feb 2026 14:11:11 -0800 Subject: [PATCH] [TRTLLM-10612][feat] Initial support of AIGV models in TRTLLM (#11462) Signed-off-by: Chang Liu (Enterprise Products) Signed-off-by: Chang Liu <9713593+chang-l@users.noreply.github.com> Signed-off-by: Zhenhua Wang Co-authored-by: Freddy Qi Co-authored-by: Claude Opus 4.6 Co-authored-by: Zhenhua Wang --- README.md | 3 - examples/visual_gen/README.md | 172 + examples/visual_gen/cat_piano.png | Bin 0 -> 455692 bytes examples/visual_gen/hf_examples.sh | 128 + examples/visual_gen/hf_wan.py | 141 + examples/visual_gen/output_handler.py | 237 ++ examples/visual_gen/serve/README.md | 322 ++ examples/visual_gen/serve/async_video_gen.py | 238 ++ examples/visual_gen/serve/configs/wan.yml | 8 + examples/visual_gen/serve/delete_video.py | 151 + .../media/woman_skyline_original_720p.jpeg | Bin 0 -> 178461 bytes examples/visual_gen/serve/sync_image_gen.py | 91 + examples/visual_gen/serve/sync_video_gen.py | 224 ++ examples/visual_gen/visual_gen_examples.sh | 238 ++ examples/visual_gen/visual_gen_wan_i2v.py | 226 ++ examples/visual_gen/visual_gen_wan_t2v.py | 228 ++ requirements.txt | 1 + tensorrt_llm/_torch/distributed/__init__.py | 5 +- tensorrt_llm/_torch/distributed/ops.py | 123 + tensorrt_llm/_torch/modules/linear.py | 34 + tensorrt_llm/_torch/visual_gen/__init__.py | 45 + .../visual_gen/attention_backend/__init__.py | 37 + .../visual_gen/attention_backend/interface.py | 33 + .../visual_gen/attention_backend/parallel.py | 162 + .../visual_gen/attention_backend/trtllm.py | 244 ++ .../visual_gen/attention_backend/utils.py | 118 + .../visual_gen/attention_backend/vanilla.py | 126 + .../_torch/visual_gen/checkpoints/__init__.py | 7 + .../visual_gen/checkpoints/weight_loader.py | 152 + tensorrt_llm/_torch/visual_gen/config.py | 565 +++ tensorrt_llm/_torch/visual_gen/executor.py | 246 ++ 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tensorrt_llm/inputs/data.py | 79 +- tensorrt_llm/llmapi/__init__.py | 3 + tensorrt_llm/llmapi/disagg_utils.py | 1 + tensorrt_llm/llmapi/utils.py | 22 +- tensorrt_llm/llmapi/visual_gen.py | 544 +++ tensorrt_llm/ray_stub.py | 13 +- tensorrt_llm/serve/media_storage.py | 426 +++ tensorrt_llm/serve/openai_protocol.py | 215 ++ tensorrt_llm/serve/openai_server.py | 757 +++- tensorrt_llm/serve/visual_gen_utils.py | 112 + .../defs/examples/test_visual_gen.py | 288 ++ .../test_lists/test-db/l0_b200.yml | 12 + .../test_lists/test-db/l0_dgx_b200.yml | 6 + .../_torch/visual_gen/multi_gpu/__init__.py | 1 + .../multi_gpu/test_ulysses_attention.py | 505 +++ .../visual_gen/test_attention_integration.py | 540 +++ .../_torch/visual_gen/test_attention_perf.py | 622 ++++ .../_torch/visual_gen/test_fused_qkv.py | 126 + .../_torch/visual_gen/test_model_loader.py | 494 +++ .../_torch/visual_gen/test_quant_ops.py | 120 + .../visual_gen/test_trtllm_serve_e2e.py | 398 +++ .../visual_gen/test_trtllm_serve_endpoints.py | 876 +++++ tests/unittest/_torch/visual_gen/test_wan.py | 3094 +++++++++++++++++ .../_torch/visual_gen/test_wan_i2v.py | 1491 ++++++++ 75 files changed, 19366 insertions(+), 195 deletions(-) create mode 100644 examples/visual_gen/README.md create mode 100644 examples/visual_gen/cat_piano.png create mode 100755 examples/visual_gen/hf_examples.sh create mode 100755 examples/visual_gen/hf_wan.py create mode 100644 examples/visual_gen/output_handler.py create mode 100644 examples/visual_gen/serve/README.md create mode 100755 examples/visual_gen/serve/async_video_gen.py create mode 100644 examples/visual_gen/serve/configs/wan.yml create mode 100755 examples/visual_gen/serve/delete_video.py create mode 100644 examples/visual_gen/serve/media/woman_skyline_original_720p.jpeg create mode 100755 examples/visual_gen/serve/sync_image_gen.py create mode 100755 examples/visual_gen/serve/sync_video_gen.py create mode 100755 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tensorrt_llm/_torch/visual_gen/models/__init__.py create mode 100644 tensorrt_llm/_torch/visual_gen/models/wan/__init__.py create mode 100644 tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py create mode 100644 tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan_i2v.py create mode 100644 tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py create mode 100644 tensorrt_llm/_torch/visual_gen/modules/__init__.py create mode 100644 tensorrt_llm/_torch/visual_gen/modules/attention.py create mode 100644 tensorrt_llm/_torch/visual_gen/output.py create mode 100644 tensorrt_llm/_torch/visual_gen/parallelism.py create mode 100644 tensorrt_llm/_torch/visual_gen/pipeline.py create mode 100644 tensorrt_llm/_torch/visual_gen/pipeline_loader.py create mode 100644 tensorrt_llm/_torch/visual_gen/pipeline_registry.py create mode 100644 tensorrt_llm/_torch/visual_gen/quantization/__init__.py create mode 100644 tensorrt_llm/_torch/visual_gen/quantization/loader.py create mode 100644 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mode 100644 tests/unittest/_torch/visual_gen/test_trtllm_serve_e2e.py create mode 100644 tests/unittest/_torch/visual_gen/test_trtllm_serve_endpoints.py create mode 100644 tests/unittest/_torch/visual_gen/test_wan.py create mode 100644 tests/unittest/_torch/visual_gen/test_wan_i2v.py diff --git a/README.md b/README.md index 31ecc45440..25533f6552 100644 --- a/README.md +++ b/README.md @@ -5,9 +5,6 @@ TensorRT LLM

TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.

-šŸŒŸ TensorRT LLM is experimenting with Image&Video Generation models in [TensorRT-LLM/feat/visual_gen](https://github.com/NVIDIA/TensorRT-LLM/tree/feat/visual_gen/tensorrt_llm/visual_gen) branch. -This branch is a prototype and not stable for production use. PRs are not accepted. - [![Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://nvidia.github.io/TensorRT-LLM/) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/NVIDIA/TensorRT-LLM) [![python](https://img.shields.io/badge/python-3.12-green)](https://www.python.org/downloads/release/python-3123/) diff --git a/examples/visual_gen/README.md b/examples/visual_gen/README.md new file mode 100644 index 0000000000..4dfd5e07e0 --- /dev/null +++ b/examples/visual_gen/README.md @@ -0,0 +1,172 @@ +# Visual Generation Examples + +Quick reference for running visual generation models (WAN). + +## Prerequisites + +```bash +# Install dependencies (from repository root) +pip install -r requirements-dev.txt +pip install git+https://github.com/huggingface/diffusers.git +pip install av +``` + +## Quick Start + +```bash +# Set MODEL_ROOT to your model directory (required for examples) +export MODEL_ROOT=/llm-models +# Optional: PROJECT_ROOT defaults to repo root when run from examples/visual_gen + +# Run all examples (auto-detects GPUs) +cd examples/visual_gen +./visual_gen_examples.sh +``` + + +## Environment Variables + +| Variable | Default | Description | +|----------|---------|-------------| +| `PROJECT_ROOT` | Auto-detected | Path to repository root (set when running from `examples/visual_gen`) | +| `MODEL_ROOT` | `/llm-models` | Path to model directory | +| `TLLM_LOG_LEVEL` | `INFO` | Logging level | + +--- + +## WAN (Text-to-Video) + +### Basic Usage + +**Single GPU:** +```bash +python visual_gen_wan_t2v.py \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers \ + --prompt "A cute cat playing piano" \ + --height 480 --width 832 --num_frames 33 \ + --output_path output.mp4 +``` + +**With TeaCache:** +```bash +python visual_gen_wan_t2v.py \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers \ + --prompt "A cute cat playing piano" \ + --height 480 --width 832 --num_frames 33 \ + --enable_teacache \ + --output_path output.mp4 +``` + +### Multi-GPU Parallelism + +WAN supports two parallelism modes that can be combined: +- **CFG Parallelism**: Split positive/negative prompts across GPUs +- **Ulysses Parallelism**: Split sequence across GPUs for longer sequences + + +**Ulysses Only (2 GPUs):** +```bash +python visual_gen_wan_t2v.py \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers \ + --prompt "A cute cat playing piano" \ + --height 480 --width 832 --num_frames 33 \ + --attention_backend TRTLLM \ + --cfg_size 1 --ulysses_size 2 \ + --output_path output.mp4 +``` +GPU Layout: GPU 0-1 share sequence (6 heads each) + +**CFG Only (2 GPUs):** +```bash +python visual_gen_wan_t2v.py \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers \ + --prompt "A cute cat playing piano" \ + --height 480 --width 832 --num_frames 33 \ + --attention_backend TRTLLM \ + --cfg_size 2 --ulysses_size 1 \ + --output_path output.mp4 +``` +GPU Layout: GPU 0 (positive) | GPU 1 (negative) + +**CFG + Ulysses (4 GPUs):** +```bash +python visual_gen_wan_t2v.py \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers \ + --prompt "A cute cat playing piano" \ + --height 480 --width 832 --num_frames 33 \ + --attention_backend TRTLLM \ + --cfg_size 2 --ulysses_size 2 \ + --output_path output.mp4 +``` +GPU Layout: GPU 0-1 (positive, Ulysses) | GPU 2-3 (negative, Ulysses) + +**Large-Scale (8 GPUs):** +```bash +python visual_gen_wan_t2v.py \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers \ + --prompt "A cute cat playing piano" \ + --height 480 --width 832 --num_frames 33 \ + --attention_backend TRTLLM \ + --cfg_size 2 --ulysses_size 4 \ + --output_path output.mp4 +``` +GPU Layout: GPU 0-3 (positive) | GPU 4-7 (negative) + +--- + +## Common Arguments + +| Argument | WAN | Default | Description | +|----------|-----|---------|-------------| +| `--height` | āœ“ | 720 | Output height | +| `--width` | āœ“ | 1280 | Output width | +| `--num_frames` | āœ“ | 81 | Number of frames | +| `--steps` | āœ“ | 50 | Denoising steps | +| `--guidance_scale` | āœ“ | 5.0 | CFG guidance strength | +| `--seed` | āœ“ | 42 | Random seed | +| `--enable_teacache` | āœ“ | False | Cache optimization | +| `--teacache_thresh` | āœ“ | 0.2 | TeaCache similarity threshold | +| `--attention_backend` | āœ“ | VANILLA | VANILLA or TRTLLM | +| `--cfg_size` | āœ“ | 1 | CFG parallelism | +| `--ulysses_size` | āœ“ | 1 | Sequence parallelism | +| `--linear_type` | āœ“ | default | Quantization type | + +## Troubleshooting + +**Out of Memory:** +- Use quantization: `--linear_type trtllm-fp8-blockwise` +- Reduce resolution or frames +- Enable TeaCache: `--enable_teacache` +- Use Ulysses parallelism with more GPUs + +**Slow Inference:** +- Enable TeaCache: `--enable_teacache` +- Use TRTLLM backend: `--attention_backend TRTLLM` +- Use multi-GPU: `--cfg_size 2` or `--ulysses_size 2` + +**Import Errors:** +- Run from repository root +- Install necessary dependencies, e.g., `pip install -r requirements-dev.txt` + +**Ulysses Errors:** +- `ulysses_size` must divide 12 (WAN heads) +- Total GPUs = `cfg_size Ɨ ulysses_size` +- Sequence length must be divisible by `ulysses_size` + +## Output Formats + +- **WAN**: `.mp4` (video), `.gif` (animated), `.png` (single frame) + +## Baseline Validation + +Compare with official HuggingFace Diffusers implementation: + +```bash +# Run HuggingFace baselines +./hf_examples.sh + +# Or run individual models +python hf_wan.py --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers +``` + +Compare outputs with same seed for correctness verification. diff --git a/examples/visual_gen/cat_piano.png b/examples/visual_gen/cat_piano.png new file mode 100644 index 0000000000000000000000000000000000000000..3b60bf25be154e6788bed0302c38ec7677ad0169 GIT binary patch literal 455692 zcmV(vK~;00961Nkl5?SN(Hw}Xd1Pi)^>Q`<6p|mw8JT&R`$G4{=6&W4a5B&)s zk)dxRkcr?s@^tm-;s48q=*f{wE293#-_^9JzcV7zA13_znFt^=XVwGR$(&iDNHrxQ z;`AmALMKxax74$u}bEh1C*CFm%^9@{WJdVk~d|=Zagzo8Md%%YXi6 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zhPNK)2Mp*9_LeGC!79)^ChcGM_${$JBa~UsZ>#E8Yb3*iK+SOFF|7Rc@d^I_U>^Qc zO6@1eG(2x#%w2E$jSZa@{6GA01}}}xWaqDs()k|D1I2?s3D0`ZBLDdx{})-s-^R{qXbd{`36_A2Rn1lst)V z-sdunk8!Ll>*Hb1yRFLg!0rrrjc&gA%!K{*&mZ9b/dev/null; then + echo "āŒ ERROR: diffusers not found" + MISSING_DEPS="$MISSING_DEPS diffusers" +fi + +if ! python -c "import torch" 2>/dev/null; then + echo "āŒ ERROR: torch not found" + MISSING_DEPS="$MISSING_DEPS torch" +fi + +if [ -n "$MISSING_DEPS" ]; then + echo "" + echo "āŒ Missing required dependencies:$MISSING_DEPS" + echo "Install with: pip install$MISSING_DEPS" + exit 1 +fi + +echo "āœ… All required dependencies found" +echo "" + +# Detect GPU +if command -v nvidia-smi &> /dev/null; then + GPU_COUNT=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l) + echo "Detected $GPU_COUNT GPU(s)" + GPU_NAME=$(nvidia-smi --query-gpu=name --format=csv,noheader | head -1) + echo "GPU: $GPU_NAME" +else + echo "āš ļø WARNING: nvidia-smi not found" + echo " Continuing with CPU (very slow!)" + GPU_COUNT=0 +fi +echo "" + +# Create output directory (in current directory) +OUTPUT_DIR="./baseline_outputs" +mkdir -p "$OUTPUT_DIR" +echo "Output directory: $OUTPUT_DIR ($(pwd)/baseline_outputs)" +echo "" + +############################################# +# WAN (Wan2.1) Baseline Test +############################################# + +echo "============================================" +echo "1/1: WAN Baseline Test" +echo "============================================" +echo "" + +WAN_MODEL="${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers/" +WAN_OUTPUT="${OUTPUT_DIR}/wan_baseline.gif" + +if [ -d "$WAN_MODEL" ]; then + echo "Testing WAN with official diffusers..." + python ${PROJECT_ROOT}/examples/visual_gen/hf_wan.py \ + --model_path "$WAN_MODEL" \ + --output_path "$WAN_OUTPUT" \ + --prompt "A cute cat playing piano" \ + --height 480 \ + --width 832 \ + --num_frames 33 \ + --steps 50 \ + --guidance_scale 7.0 \ + --seed 42 + echo "" + echo "āœ… WAN baseline test completed" + echo " Output: $WAN_OUTPUT" +else + echo "āš ļø SKIPPED: WAN model not found at $WAN_MODEL" +fi + +echo "" + +############################################# +# Summary +############################################# + +echo "============================================" +echo "Baseline Tests Complete!" +echo "============================================" +echo "" +echo "Output files saved to: $OUTPUT_DIR" +echo "" +ls -lh "$OUTPUT_DIR" 2>/dev/null || echo "No outputs generated" +echo "" +echo "Next Steps:" +echo " 1. Verify outputs are correct (images/videos generated)" +echo " 2. Compare with custom implementation outputs" +echo " 3. Use these as reference/baseline for debugging" +echo "" +echo "Comparison command:" +echo " diff -r $OUTPUT_DIR " +echo "============================================" diff --git a/examples/visual_gen/hf_wan.py b/examples/visual_gen/hf_wan.py new file mode 100755 index 0000000000..3919794052 --- /dev/null +++ b/examples/visual_gen/hf_wan.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python3 +"""Baseline test for WAN using official diffusers library.""" + +import sys + +import torch +from output_handler import OutputHandler, postprocess_hf_video_tensor + +from tensorrt_llm._torch.visual_gen import MediaOutput + + +def test_wan_baseline( + model_path: str, + output_path: str, + prompt: str = "A cute cat playing piano", + height: int = 480, + width: int = 832, + num_frames: int = 33, + num_inference_steps: int = 50, + guidance_scale: float = 7.0, + seed: int = 42, +): + """Test WAN video generation with official diffusers.""" + from diffusers import WanPipeline + + print("=" * 80) + print("WAN Baseline Test (Official Diffusers)") + print("=" * 80) + print() + + # Load pipeline + print(f"Loading WAN pipeline from {model_path}...") + pipe = WanPipeline.from_pretrained(model_path, torch_dtype=torch.bfloat16) + pipe.to("cuda") + print("āœ… Pipeline loaded") + print() + + # Check model states + print("Model Training States:") + print(f" text_encoder.training: {pipe.text_encoder.training}") + print(f" transformer.training: {pipe.transformer.training}") + print(f" vae.training: {pipe.vae.training}") + print() + + # Generate video + print(f"Generating video: '{prompt}'") + print( + f"Parameters: {height}x{width}, {num_frames} frames, {num_inference_steps} steps, guidance={guidance_scale}" + ) + print() + + # Set random seed + generator = torch.Generator(device="cuda").manual_seed(seed) + + result = pipe( + prompt=prompt, + height=height, + width=width, + num_frames=num_frames, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + generator=generator, + output_type="pt", + return_dict=False, + ) + + video = result[0] + + # Post-process video tensor: (B, T, C, H, W) -> (T, H, W, C) uint8 + video = postprocess_hf_video_tensor(video, remove_batch_dim=True) + + print("=" * 80) + print("Generation Complete!") + print("=" * 80) + print(f"Video shape: {video.shape}") + print(f"Video dtype: {video.dtype}") + print() + + # Save output + print(f"Saving output to {output_path}...") + OutputHandler.save(output=MediaOutput(video=video), output_path=output_path, frame_rate=24.0) + print(f"āœ… Saved to {output_path}") + print() + + print("=" * 80) + print("WAN BASELINE TEST PASSED āœ…") + print("=" * 80) + return video + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser( + description="HuggingFace Baseline - WAN Text-to-Video Generation" + ) + + # Model & Input + parser.add_argument( + "--model_path", + type=str, + default="/llm-models/Wan2.1-T2V-1.3B-Diffusers/", + help="Path to WAN model", + ) + parser.add_argument( + "--prompt", type=str, default="A cute cat playing piano", help="Text prompt for generation" + ) + parser.add_argument( + "--output_path", type=str, default="wan_baseline.gif", help="Output file path" + ) + + # Generation parameters + parser.add_argument("--height", type=int, default=480, help="Video height") + parser.add_argument("--width", type=int, default=832, help="Video width") + parser.add_argument("--num_frames", type=int, default=33, help="Number of frames to generate") + parser.add_argument("--steps", type=int, default=50, help="Number of denoising steps") + parser.add_argument( + "--guidance_scale", type=float, default=7.0, help="Classifier-free guidance scale" + ) + parser.add_argument("--seed", type=int, default=42, help="Random seed") + + args = parser.parse_args() + + try: + test_wan_baseline( + args.model_path, + args.output_path, + prompt=args.prompt, + height=args.height, + width=args.width, + num_frames=args.num_frames, + num_inference_steps=args.steps, + guidance_scale=args.guidance_scale, + seed=args.seed, + ) + except Exception as e: + print(f"\nāŒ ERROR: {e}") + import traceback + + traceback.print_exc() + sys.exit(1) diff --git a/examples/visual_gen/output_handler.py b/examples/visual_gen/output_handler.py new file mode 100644 index 0000000000..a360d681f9 --- /dev/null +++ b/examples/visual_gen/output_handler.py @@ -0,0 +1,237 @@ +"""Unified output handler for diffusion model outputs.""" + +import os +from typing import Optional + +import torch +from PIL import Image + +from tensorrt_llm import logger +from tensorrt_llm.llmapi.visual_gen import MediaOutput + + +def postprocess_hf_video_tensor(video: torch.Tensor, remove_batch_dim: bool = True) -> torch.Tensor: + """Post-process video tensor from HuggingFace pipeline output to final format. + + HuggingFace pipelines with output_type="pt" return videos in (B, T, C, H, W) format, + which is different from VAE decoder output format. + + Args: + video: Video tensor in (B, T, C, H, W) format from HuggingFace pipeline + remove_batch_dim: Whether to remove batch dimension. Default True for typical + single-batch video generation. + + Returns: + Post-processed video tensor: + - If remove_batch_dim=True: (T, H, W, C) uint8 tensor + - If remove_batch_dim=False: (B, T, H, W, C) uint8 tensor + + Note: + Assumes video values are in [-1, 1] range (standard pipeline output). + """ + # Remove batch dimension first if requested + if remove_batch_dim: + video = video[0] # (B, T, C, H, W) -> (T, C, H, W) + video = video.permute(0, 2, 3, 1) # (T, C, H, W) -> (T, H, W, C) + else: + video = video.permute(0, 1, 3, 4, 2) # (B, T, C, H, W) -> (B, T, H, W, C) + + # Normalize to [0, 1] range + video = (video / 2 + 0.5).clamp(0, 1) + + # Convert to uint8 + video = (video * 255).round().to(torch.uint8) + + return video + + +def postprocess_hf_image_tensor(image: torch.Tensor) -> torch.Tensor: + """Post-process image tensor from HuggingFace pipeline output to final format. + + HuggingFace pipelines with output_type="pt" return images in (B, C, H, W) format. + + Args: + image: Image tensor in (B, C, H, W) or (C, H, W) format from HuggingFace pipeline + + Returns: + Post-processed image tensor in (H, W, C) uint8 format + + Note: + Assumes image values are in [-1, 1] range (standard pipeline output). + """ + # Remove batch dimension if present + if image.ndim == 4: + image = image[0] # (B, C, H, W) -> (C, H, W) + + # Convert to (H, W, C) format + image = image.permute(1, 2, 0) # (C, H, W) -> (H, W, C) + + # Normalize to [0, 1] range + image = (image / 2 + 0.5).clamp(0, 1) + + # Convert to uint8 + image = (image * 255).round().to(torch.uint8) + + return image + + +class OutputHandler: + """Handle saving of generated outputs in various formats. + + Supports MediaOutput from all models: + - Video models (WAN): MediaOutput(video=torch.Tensor) + - Image models: MediaOutput(image=torch.Tensor) + - Video+Audio models: MediaOutput(video=torch.Tensor, audio=torch.Tensor) + + Supported output formats: + - .png: Save single image or middle frame + - .gif: Save video as animated GIF (no audio) + - .mp4: Save video with audio (requires diffusers export_utils) + """ + + @staticmethod + def save(output: MediaOutput, output_path: str, frame_rate: float = 24.0): + """Save output based on content type and file extension. + + Args: + output: MediaOutput containing model outputs (image/video/audio) + output_path: Path to save the output file + frame_rate: Frames per second for video output (default: 24.0) + """ + if not isinstance(output, MediaOutput): + raise ValueError(f"Expected output to be MediaOutput, got {type(output)}") + + file_ext = os.path.splitext(output_path)[1].lower() + + # Determine content type + if output.image is not None: + OutputHandler._save_image(output.image, output_path, file_ext) + elif output.video is not None: + OutputHandler._save_video(output.video, output.audio, output_path, file_ext, frame_rate) + else: + raise ValueError("Unknown output format. MediaOutput has no image or video data.") + + @staticmethod + def _save_image(image: torch.Tensor, output_path: str, file_ext: str): + """Save single image output. + + Args: + image: Image as torch tensor (H, W, C) uint8 + output_path: Path to save the image + file_ext: File extension (.png, .jpg, etc.) + """ + if file_ext not in [".png", ".jpg", ".jpeg"]: + logger.warning(f"Image output requested with {file_ext}, defaulting to .png") + output_path = output_path.replace(file_ext, ".png") + + # Convert torch.Tensor to PIL Image and save + image_np = image.cpu().numpy() + Image.fromarray(image_np).save(output_path) + logger.info(f"Saved image to {output_path}") + + @staticmethod + def _save_video( + video: torch.Tensor, + audio: Optional[torch.Tensor], + output_path: str, + file_ext: str, + frame_rate: float, + ): + """Save video output with optional audio. + + Args: + video: Video frames as torch tensor (T, H, W, C) with dtype uint8 + audio: Optional audio as torch tensor + output_path: Path to save the video + file_ext: File extension (.mp4, .gif, .png) + frame_rate: Frames per second + """ + if file_ext == ".mp4": + OutputHandler._save_mp4(video, audio, output_path, frame_rate) + elif file_ext == ".gif": + OutputHandler._save_gif(video, output_path, frame_rate) + elif file_ext == ".png": + OutputHandler._save_middle_frame(video, output_path) + else: + logger.warning(f"Unsupported video output format: {file_ext}, defaulting to .png") + output_path = output_path.replace(file_ext, ".png") + OutputHandler._save_middle_frame(video, output_path) + + @staticmethod + def _save_mp4( + video: torch.Tensor, audio: Optional[torch.Tensor], output_path: str, frame_rate: float + ): + """Save video with optional audio as MP4. + + Args: + video: Video frames as torch tensor (T, H, W, C) uint8 + audio: Optional audio as torch tensor (float32) + output_path: Output path for MP4 + frame_rate: Frames per second + """ + try: + from diffusers.pipelines.ltx2.export_utils import encode_video + + # Prepare audio if present + audio_prepared = audio.float() if audio is not None else None + + # encode_video expects (T, H, W, C) uint8 video and float32 audio + encode_video( + video, + fps=frame_rate, + audio=audio_prepared, + audio_sample_rate=24000 if audio_prepared is not None else None, + output_path=output_path, + ) + logger.info(f"Saved video{' with audio' if audio is not None else ''} to {output_path}") + + except ImportError: + logger.warning( + "diffusers export_utils (encode_video) not available. " + "Falling back to saving middle frame as PNG." + ) + png_path = output_path.replace(".mp4", ".png") + OutputHandler._save_middle_frame(video, png_path) + + @staticmethod + def _save_gif(video: torch.Tensor, output_path: str, frame_rate: float): + """Save video as animated GIF. + + Args: + video: Video frames as torch tensor (T, H, W, C) uint8 + output_path: Output path for GIF + frame_rate: Frames per second + """ + # Convert torch.Tensor to numpy for PIL + video_np = video.cpu().numpy() + + # Convert to list of PIL Images + frames = [Image.fromarray(video_np[i]) for i in range(video_np.shape[0])] + + # Save as animated GIF + duration_ms = int(1000 / frame_rate) + frames[0].save( + output_path, + save_all=True, + append_images=frames[1:], + optimize=False, + duration=duration_ms, + loop=0, + ) + logger.info(f"Saved video as GIF to {output_path} ({len(frames)} frames)") + + @staticmethod + def _save_middle_frame(video: torch.Tensor, output_path: str): + """Save middle frame of video as PNG. + + Args: + video: Video frames as torch tensor (T, H, W, C) uint8 + output_path: Output path for PNG + """ + # Convert torch.Tensor to numpy for PIL + video_np = video.cpu().numpy() + + # Extract middle frame + frame_idx = video_np.shape[0] // 2 + Image.fromarray(video_np[frame_idx]).save(output_path) + logger.info(f"Saved frame {frame_idx} to {output_path}") diff --git a/examples/visual_gen/serve/README.md b/examples/visual_gen/serve/README.md new file mode 100644 index 0000000000..b68dc7f2a2 --- /dev/null +++ b/examples/visual_gen/serve/README.md @@ -0,0 +1,322 @@ +# Visual Generation API Examples + +This directory contains example scripts that demonstrate how to use the TensorRT-LLM Visual Generation API endpoints for image and video generation. + +## Overview + +These examples show how to interact with the visual generation server using both the OpenAI Python SDK and standard HTTP requests. The API provides endpoints for: + +- **Image Generation**: Text-to-image generation (T2I) +- **Video Generation**: + - Text-to-video generation (T2V) - generate videos from text prompts only + - Text+Image-to-video generation (TI2V) - generate videos from text + reference image + - Both synchronous and asynchronous modes supported + - Multipart/form-data support for file uploads +- **Video Management**: Retrieving and deleting generated videos + +## Prerequisites + +Before running these examples, ensure you have: + +1. **Install modules**: Install required dependencies before running examples: + + ```bash + pip install git+https://github.com/huggingface/diffusers.git + pip install av + ``` + +2. **Server Running**: The TensorRT-LLM visual generation server must be running + ```bash + trtllm-serve --extra_visual_gen_options + ``` + + e.g. + + ```bash + trtllm-serve $LLM_MODEL_DIR/Wan2.1-T2V-1.3B-Diffusers --extra_visual_gen_options ./configs/wan.yml + + # Run server on background: + trtllm-serve $LLM_MODEL_DIR/Wan2.1-T2V-1.3B-Diffusers --extra_visual_gen_options ./configs/wan.yml > /tmp/serve.log 2>&1 & + + ## Check if the server is setup + tail -f /tmp/serve.log + + ``` + +## Examples + +Current supported & tested models: + +1. WAN T2V/I2V for video generation (t2v, ti2v, delete_video) + +### 1. Synchronous Image Generation (`sync_t2i.py`) + +Demonstrates synchronous text-to-image generation using the OpenAI SDK. + +**Features:** +- Generates images from text prompts +- Supports configurable image size and quality +- Returns base64-encoded images or URLs +- Saves generated images to disk + +**Usage:** +```bash +# Use default localhost server +python sync_image_gen.py + +# Specify custom server URL +python sync_image_gen.py http://your-server:8000/v1 +``` + +**API Endpoint:** `POST /v1/images/generations` + +**Output:** Saves generated image to `output_generation.png` (or numbered files for multiple images) + +--- + +### 2. Synchronous Video Generation with T2V and TI2V Modes (`sync_video_gen.py`) + +Demonstrates synchronous video generation using direct HTTP requests. Waits for completion and returns the video file directly. + +**Features:** +- **T2V Mode**: Generate videos from text prompts only +- **TI2V Mode**: Generate videos from text + reference image (multipart/form-data) +- Waits for video generation to complete before returning +- Returns video file directly in response +- Command-line interface for easy testing + +**Usage:** + +```bash +# Text-to-Video (T2V) - No reference image +python sync_video_gen.py --mode t2v \ + --prompt "A cute cat playing with a ball in the park" \ + --duration 4.0 --fps 24 --size 256x256 + +# Text+Image-to-Video (TI2V) - With reference image +## Note: longer duration and higher size will lead to much longer waiting time +python sync_video_gen.py --mode ti2v \ + --prompt "She turns around and smiles, then slowly walks out of the frame" \ + --image ./media/woman_skyline_original_720p.jpeg \ + --duration 4.0 --fps 24 --size 512x512 + +# Custom parameters +python sync_video_gen.py --mode t2v \ + --prompt "A serene sunset over the ocean" \ + --duration 5.0 --fps 30 --size 512x512 \ + --output my_video.mp4 +``` + +**Command-Line Arguments:** +- `--mode` - Generation mode: `t2v` or `ti2v` (default: t2v) +- `--prompt` - Text prompt for video generation (required) +- `--image` - Path to reference image (required for ti2v mode) +- `--base-url` - API server URL (default: http://localhost:8000/v1) +- `--model` - Model name (default: wan) +- `--duration` - Video duration in seconds (default: 4.0) +- `--fps` - Frames per second (default: 24) +- `--size` - Video resolution in WxH format (default: 256x256) +- `--output` - Output video file path (default: output_sync.mp4) + +**API Endpoint:** `POST /v1/videos/generations` + +**API Details:** +- T2V uses JSON `Content-Type: application/json` +- TI2V uses multipart/form-data `Content-Type: multipart/form-data` with file upload + +**Output:** Saves generated video to specified output file + +--- + +### 3. Async Video Generation with T2V and TI2V Modes (`async_video_gen.py`) + +**NEW**: Enhanced async video generation supporting both Text-to-Video (T2V) and Text+Image-to-Video (TI2V) modes. + +**Features:** +- **T2V Mode**: Generate videos from text prompts only (JSON request) +- **TI2V Mode**: Generate videos from text + reference image (multipart/form-data with file upload) +- Command-line interface for easy testing +- Automatic mode detection +- Comprehensive parameter control + +**Usage:** + +```bash +# Text-to-Video (T2V) - No reference image +python async_video_gen.py --mode t2v \ + --prompt "A cool cat on a motorcycle in the night" \ + --duration 4.0 --fps 24 --size 256x256 + +# Text+Image-to-Video (TI2V) - With reference image +python async_video_gen.py --mode ti2v \ + --prompt "She turns around and smiles, then slowly walks out of the frame" \ + --image ./media/woman_skyline_original_720p.jpeg \ + --duration 4.0 --fps 24 --size 512x512 + +# Custom parameters +python async_video_gen.py --mode t2v \ + --prompt "A serene sunset over the ocean" \ + --duration 5.0 --fps 30 --size 512x512 \ + --output my_video.mp4 +``` + +**Command-Line Arguments:** +- `--mode` - Generation mode: `t2v` or `ti2v` (default: t2v) +- `--prompt` - Text prompt for video generation (required) +- `--image` - Path to reference image (required for ti2v mode) +- `--base-url` - API server URL (default: http://localhost:8000/v1) +- `--model` - Model name (default: wan) +- `--duration` - Video duration in seconds (default: 4.0) +- `--fps` - Frames per second (default: 24) +- `--size` - Video resolution in WxH format (default: 256x256) +- `--output` - Output video file path (default: output_async.mp4) + +**API Details:** +- T2V uses JSON `Content-Type: application/json` +- TI2V uses multipart/form-data `Content-Type: multipart/form-data` with file upload + +**Output:** Saves generated video to specified output file + +--- + +### 4. Video Deletion (`delete_video.py`) + +Demonstrates the complete lifecycle of video generation and deletion. + +**Features:** +- Creates a test video generation job +- Waits for completion +- Deletes the generated video +- Verifies deletion by attempting to retrieve the deleted video +- Tests error handling for non-existent videos + +**Usage:** +```bash +# Use default localhost server +python delete_video.py + +# Specify custom server URL +python delete_video.py http://your-server:8000/v1 +``` + +**API Endpoints:** +- `POST /v1/videos` - Create video job +- `GET /v1/videos/{video_id}` - Check video status +- `DELETE /v1/videos/{video_id}` - Delete video + +**Test Flow:** +1. Create video generation job +2. Wait for completion +3. Delete the video +4. Verify video returns `NotFoundError` +5. Test deletion of non-existent video + +--- + +## API Configuration + +All examples use the following default configuration: + +- **Base URL**: `http://localhost:8000/v1` +- **API Key**: `"tensorrt_llm"` (authentication token) +- **Timeout**: 300 seconds for async operations + +You can customize these by: +1. Passing the base URL as a command-line argument +2. Modifying the default parameters in each script's function + +## Common Parameters + +### Image Generation +- `model`: Model identifier (e.g., "wan") +- `prompt`: Text description +- `n`: Number of images to generate +- `size`: Image dimensions (e.g., "512x512", "1024x1024") +- `quality`: "standard" or "hd" +- `response_format`: "b64_json" or "url" + +### Video Generation +- `model`: Model identifier (e.g., "wan") +- `prompt`: Text description +- `size`: Video resolution (e.g., "256x256", "512x512") +- `seconds`: Duration in seconds +- `fps`: Frames per second +- `input_reference`: Reference image file (for TI2V mode) + +## Quick Reference - curl Examples + +### Text-to-Video (JSON) +```bash +curl -X POST "http://localhost:8000/v1/videos" \ + -H "Content-Type: application/json" \ + -d '{ + "prompt": "A cool cat on a motorcycle", + "seconds": 4.0, + "fps": 24, + "size": "256x256" + }' +``` + +### Text+Image-to-Video (Multipart with File Upload) +```bash +curl -X POST "http://localhost:8000/v1/videos" \ + -F "prompt=She turns around and smiles" \ + -F "input_reference=@./media/woman_skyline_original_720p.jpeg" \ + -F "seconds=4.0" \ + -F "fps=24" \ + -F "size=256x256" \ + -F "guidance_scale=5.0" +``` + +### Check Video Status +```bash +curl -X GET "http://localhost:8000/v1/videos/{video_id}" +``` + +### Download Video +```bash +curl -X GET "http://localhost:8000/v1/videos/{video_id}/content" -o output.mp4 +``` + +### Delete Video +```bash +curl -X DELETE "http://localhost:8000/v1/videos/{video_id}" +``` + +## API Endpoints Summary + +| Endpoint | Method | Mode | Content-Type | Purpose | +|----------|--------|------|--------------|---------| +| `/v1/videos` | POST | Async | JSON or Multipart | Create video job (T2V/TI2V) | +| `/v1/videos/generations` | POST | Sync | JSON or Multipart | Generate video sync (T2V/TI2V) | +| `/v1/videos/{id}` | GET | - | - | Get video status/metadata | +| `/v1/videos/{id}/content` | GET | - | - | Download video file | +| `/v1/videos/{id}` | DELETE | - | - | Delete video | +| `/v1/videos` | GET | - | - | List all videos | +| `/v1/images/generations` | POST | - | JSON | Generate images (T2I) | + +**Note:** Both `/v1/videos` (async) and `/v1/videos/generations` (sync) support: +- **JSON**: Standard text-to-video (T2V) +- **Multipart/Form-Data**: Text+image-to-video (TI2V) with file upload + +## Error Handling + +All examples include comprehensive error handling: + +- Connection errors (server not running) +- API errors (invalid parameters, model not found) +- Timeout errors (generation taking too long) +- Resource errors (video not found for deletion) + +Errors are displayed with full stack traces for debugging. + +## Output Files + +Generated files are saved to the current working directory: + +- `output_generation.png` - Synchronous image generation (`sync_image_gen.py`) +- `output_sync.mp4` - Synchronous video generation (`sync_video_gen.py`) +- `output_async.mp4` - Asynchronous video generation (`async_video_gen.py`) +- `output_multipart.mp4` - Multipart example output (`multipart_example.py`) + +**Note:** You can customize output filenames using the `--output` parameter in all scripts. diff --git a/examples/visual_gen/serve/async_video_gen.py b/examples/visual_gen/serve/async_video_gen.py new file mode 100755 index 0000000000..dec93bf3fa --- /dev/null +++ b/examples/visual_gen/serve/async_video_gen.py @@ -0,0 +1,238 @@ +#!/usr/bin/env python +"""Test script for asynchronous video generation endpoint. + +Tests POST /v1/videos endpoint which returns immediately with a job ID. +The video is generated in the background and can be retrieved later. + +Supports two modes: + - Text-to-Video (T2V): Generate video from text prompt only + - Text+Image-to-Video (TI2V): Generate video from text prompt + reference image + +Examples: + # Text-to-Video (T2V) + python async_video_gen.py --mode t2v --prompt "A cool cat on a motorcycle" + + # Text+Image-to-Video (TI2V) + python async_video_gen.py --mode ti2v --prompt "She turns and smiles" --image ./media/woman.jpg +""" + +import argparse +import sys +import time +from pathlib import Path + +import openai + + +def test_async_video_generation( + base_url: str = "http://localhost:8000/v1", + model: str = "wan", + prompt: str = "A video of a cool cat on a motorcycle in the night", + input_reference: str = None, + duration: float = 4.0, + fps: int = 24, + size: str = "256x256", + output_file: str = "output_async.mp4", +): + """Test asynchronous video generation with OpenAI SDK. + + Args: + base_url: Base URL of the API server + model: Model name to use + prompt: Text prompt for generation + input_reference: Path to reference image (optional, for TI2V mode) + duration: Video duration in seconds + fps: Frames per second + size: Video resolution (WxH format) + output_file: Output video file path + """ + mode = "TI2V" if input_reference else "T2V" + print("=" * 80) + print(f"Testing Async Video Generation API - {mode} Mode") + print("=" * 80) + + # Initialize client + client = openai.OpenAI(base_url=base_url, api_key="tensorrt_llm") + + print("\n1. Creating video generation job...") + print(f" Mode: {mode}") + print(f" Prompt: {prompt}") + if input_reference: + print(f" Input Reference: {input_reference}") + print(f" Duration: {duration}s") + print(f" FPS: {fps}") + print(f" Size: {size}") + + try: + # Prepare request parameters + create_params = { + "model": model, + "prompt": prompt, + "size": size, + "seconds": duration, + "extra_body": { + "fps": fps, + }, + } + + # Add input reference if provided (TI2V mode) + if input_reference: + if not Path(input_reference).exists(): + print(f"\nāŒ Error: Input reference image not found: {input_reference}") + return False + create_params["input_reference"] = open(input_reference, "rb") + + # Create video generation job + job = client.videos.create(**create_params) + + print("Video generation started: \n", job.model_dump_json(indent=2)) + + video_id = job.id + print("\nāœ“ Job created successfully!") + print(f" Video ID: {video_id}") + print(f" Status: {job.status}") + + # Poll for completion + print("\n2. Polling for completion...") + max_attempts = 300 # 5 minutes with 1s intervals + attempt = 0 + + while attempt < max_attempts: + attempt += 1 + + # Get job status using SDK's get method + job = client.videos.retrieve(video_id) + status = job.status + + print(f" [{attempt:3d}] Status: {status}", end="\r") + + if status == "completed": + print("\n\nāœ“ Video generation completed!") + print(f" Completion time: {job.completed_at}") + break + elif status == "failed": + print("\n\nāŒ Video generation failed!") + print(f" Error: {job.error}") + return False + + time.sleep(1) + else: + print(f"\n\nāŒ Timeout waiting for completion (>{max_attempts}s)") + return False + + # Download video + print("\n3. Downloading video...") + # For binary content, use the underlying HTTP client + content = client.videos.download_content(video_id, variant="video") + content.write_to_file(output_file) + print(f" āœ“ Saved to: {output_file}") + + print("\n" + "=" * 80) + print("āœ“ Async video generation test completed successfully!") + print("=" * 80) + return True + + except Exception as e: + print(f"\nāŒ Error: {e}") + import traceback + + traceback.print_exc() + return False + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Test async video generation API with T2V and TI2V modes", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Text-to-Video (T2V) + python async_video_gen.py --mode t2v --prompt "A cool cat on a motorcycle" + + # Text+Image-to-Video (TI2V) + python async_video_gen.py --mode ti2v \\ + --prompt "She turns around and smiles, then slowly walks out of the frame" \\ + --image ./media/woman_skyline_original_720p.jpeg + + # Custom parameters + python async_video_gen.py --mode t2v \\ + --prompt "A serene sunset over the ocean" \\ + --duration 5.0 --fps 30 --size 512x512 \\ + --output my_video.mp4 + """, + ) + + # Mode selection + parser.add_argument( + "--mode", + choices=["t2v", "ti2v"], + default="t2v", + help="Generation mode: t2v (Text-to-Video) or ti2v (Text+Image-to-Video)", + ) + + # Required parameters + parser.add_argument( + "--prompt", + type=str, + default="A video of a cool cat on a motorcycle in the night", + help="Text prompt for video generation", + ) + + # TI2V mode parameters + parser.add_argument( + "--image", + "--input-reference", + type=str, + default=None, + help="Path to reference image (required for ti2v mode)", + ) + + # Optional parameters + parser.add_argument( + "--base-url", + type=str, + default="http://localhost:8000/v1", + help="Base URL of the API server", + ) + parser.add_argument("--model", type=str, default="wan", help="Model name to use") + parser.add_argument( + "--duration", "--seconds", type=float, default=4.0, help="Video duration in seconds" + ) + parser.add_argument("--fps", type=int, default=24, help="Frames per second") + parser.add_argument( + "--size", + type=str, + default="256x256", + help="Video resolution in WxH format (e.g., 1280x720)", + ) + parser.add_argument( + "--output", type=str, default="output_async.mp4", help="Output video file path" + ) + + args = parser.parse_args() + + # Validate ti2v mode requirements + if args.mode == "ti2v" and not args.image: + parser.error("--image is required when using --mode ti2v") + + # Display configuration + print("\n" + "=" * 80) + print("OpenAI SDK - Async Video Generation Test") + print("=" * 80) + print(f"Base URL: {args.base_url}") + print(f"Mode: {args.mode.upper()}") + print() + + # Test async video generation + success = test_async_video_generation( + base_url=args.base_url, + model=args.model, + prompt=args.prompt, + input_reference=args.image, + duration=args.duration, + fps=args.fps, + size=args.size, + output_file=args.output, + ) + + sys.exit(0 if success else 1) diff --git a/examples/visual_gen/serve/configs/wan.yml b/examples/visual_gen/serve/configs/wan.yml new file mode 100644 index 0000000000..7dc65e6214 --- /dev/null +++ b/examples/visual_gen/serve/configs/wan.yml @@ -0,0 +1,8 @@ +linear: + type: default +teacache: + enable_teacache: true + teacache_thresh: 0.2 +parallel: + dit_cfg_size: 1 + dit_ulysses_size: 1 diff --git a/examples/visual_gen/serve/delete_video.py b/examples/visual_gen/serve/delete_video.py new file mode 100755 index 0000000000..d44b8f046e --- /dev/null +++ b/examples/visual_gen/serve/delete_video.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python +"""Test script for DELETE /v1/videos/{video_id} endpoint. + +Tests the video deletion functionality by: +1. Creating a video generation job +2. Waiting for completion +3. Deleting the video +4. Verifying the deletion +""" + +import sys +import time + +import openai + + +def test_delete_video( + base_url: str = "http://localhost:8000/v1", + model: str = "wan", + prompt: str = "A simple test video for deletion", + duration: float = 2.0, + fps: int = 8, + size: str = "256x256", +): + """Test video deletion endpoint using OpenAI SDK.""" + print("=" * 80) + print("Testing DELETE /v1/videos/{video_id} Endpoint") + print("=" * 80) + + # Initialize OpenAI client + client = openai.OpenAI(base_url=base_url, api_key="tensorrt_llm") + + video_id = None + + try: + # Step 1: Create a video generation job + print("\n1. Creating video generation job...") + print(f" Prompt: {prompt}") + print(f" Duration: {duration}s") + print(f" FPS: {fps}") + print(f" Size: {size}") + + job = client.videos.create( + model=model, + prompt=prompt, + size=size, + seconds=duration, + extra_body={ + "fps": fps, + }, + ) + + video_id = job.id + print(f" āœ“ Video job created with ID: {video_id}") + print(f" Status: {job.status}") + + # Step 2: Wait for video completion + print("\n2. Waiting for video generation to complete...") + max_attempts = 60 # attempts with 1s intervals + attempt = 0 + + while attempt < max_attempts: + attempt += 1 + + # Get job status using SDK's retrieve method + job = client.videos.retrieve(video_id) + status = job.status + + print(f" [{attempt:3d}] Status: {status}", end="\r") + + if status == "completed": + print(" āœ“ Video generation completed!") + break + elif status == "failed": + print(" āŒ Video generation failed!") + return False + + time.sleep(1) + else: + print(" āš  Timeout waiting for video completion") + # Continue with deletion anyway + + # Step 3: Delete the video + print(f"\n3. Deleting video {video_id}...") + + delete_result = client.videos.delete(video_id) + + print(f" Response: {delete_result.model_dump_json(indent=2)}") + + if delete_result.deleted: + print(" āœ“ Video deleted successfully!") + else: + print(" āŒ Video deletion returned False") + return False + + # Step 4: Verify the video is gone + print("\n4. Verifying video deletion...") + + try: + verify_job = client.videos.retrieve(video_id) + print(f" āš  Video still exists after deletion: {verify_job.status}") + return False + except openai.NotFoundError as e: + print(" āœ“ Video correctly returns NotFoundError") + print(f" Error message: {e.message}") + except Exception as e: + print(f" āš  Unexpected error: {type(e).__name__}: {e}") + + # Step 5: Test deleting non-existent video + print("\n5. Testing deletion of non-existent video...") + + fake_id = "nonexistent_video_id" + + try: + fake_delete_result = client.videos.delete(fake_id) + print(" āš  Deletion of non-existent video did not raise error") + print(f" Response: {fake_delete_result.model_dump_json(indent=2)}") + except openai.NotFoundError as e: + print(" āœ“ Correctly raises NotFoundError for non-existent video") + print(f" Error message: {e.message}") + except Exception as e: + print(f" āš  Unexpected error: {type(e).__name__}: {e}") + + print("\n" + "=" * 80) + print("āœ“ Video deletion test completed successfully!") + print("=" * 80) + return True + + except Exception as e: + print(f"\nāŒ Error: {e}") + import traceback + + traceback.print_exc() + return False + + +if __name__ == "__main__": + # Parse command line arguments + base_url = sys.argv[1] if len(sys.argv) > 1 else "http://localhost:8000/v1" + + print("\n" + "=" * 80) + print("OpenAI SDK - Video Deletion Test") + print("=" * 80) + print(f"Base URL: {base_url}") + print() + + # Test video deletion + success = test_delete_video(base_url=base_url) + + # Exit with appropriate code + sys.exit(0 if success else 1) diff --git a/examples/visual_gen/serve/media/woman_skyline_original_720p.jpeg b/examples/visual_gen/serve/media/woman_skyline_original_720p.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..44a7ed5c3a413cb2e43d4df1ecf7cff6724fa8c9 GIT binary patch literal 178461 zcmeFYWmKEb*ESlgv{0Z(iWXX=NN{&barXoW(gH<`Tky7M(c&#G#RS2lCUIu1m4gjdA09XJ3z*E2zG$H`T zqlWhQ0H9F-u>P$B0AMuA|J9AqnEq>n4gjDN{~!BcD*)zy?H_&qTjc-y`9JU2KL7yi z#}-UZZf>qbFAPFVtT$Xwh$Jv&90R?!Df%Z6H z7=!=`z&%envvs3#x?7|F;9F%J$JGzoysIKmUNrO9?@z$|ayp{hWqN%R7*#9#r`hIm ze6&6&qqAS;-qD1p%-4~~(w*8e9fr^^p!UD(x%a}6De0cI1K;k&m{2~mh1@(en^fzo2=38D>MhS#4@3Lg0$hl<3r1q8i`*5@ij>wg!$ zM}C?&srTI^SFK+C(Tk-}cPx5(w7OR80hoIhMM=8!`Z+}fRTDB9%%W0|bTmtR{EZ9T z5VCHd5ZW4iFfnFZiKaJxsghay%jb77c+bH<&wivfXX(f&$n1$&F*hh-jBT3ITcO=C zIa`1>E!CGkcB8L@u)@3C|qh5&P@{nKJKBd(UCOf 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z_dLOk^n-oKXJi-{K%ob06~W`RJv_acFJ7JEZ7;#L`nA(|6=!`mEZq6kE3t@ z0BIkITDY`=Nn)KWAX|8nTX;gEra8(Yl1@9C^Nmx+o-6SM#M*`Tk-A<8Sin*AuRDAB zS3)GPn0g=Aw>O3QKdMzHKVK4x%er^*iXS5FvR#y!ao= zyGY`@@N9GG*Y*~d6FC4#31WyqCuszKD)Tq-4aTJ+wd7ImLDf?ql~#{ih9~)>4oTaV zCj&jYQmqE|HlxU+d+K)nEYxAt^&cxl3u|jELN#8h2>ZvLhoxp}t*H2a!MAQN65zqQ z)=%F~H~hY%{n1~K zo(XN5@+yBEaz~FzCbgazbZiJ2Lik^z{7uw ztRqv$?-c!fr^R~N9tu=wB_?^CO^Bb?yXFD8D|@S%>e+RNFxKaDL51g!qZiDoxqw+ z;-6O(t*I{k4J&Dhz$XNHR@@q_=t51Mqor~K%tzlHY_6p8qQx?d4@%^f4Nj_cbkyx_ z=UG?HiSv%eq@G}-Cb?(|sRjne*0=VDj2Q?&N}pi68grr{BX8SRv4M)BB4GQf@HrW- zCXHhj_88wN$E`!OElmBYq>>D66|8m>jkIJEq#p8Rnk-sw2~i0GDcd)AA~&*7c@j z)JYct;FyQm#wyWRn65@^CagqUgO1gV 1 else output_file + + with open(output, "wb") as f: + f.write(image_data) + + print(f" āœ“ Saved image {i + 1} to: {output} ({len(image_data)} bytes)") + else: + print(f" Image {i + 1} URL: {image.url}") + + print("\n" + "=" * 80) + print("āœ“ Image generation test completed successfully!") + print("=" * 80) + return True + + except Exception as e: + print(f"\nāŒ Error: {e}") + import traceback + + traceback.print_exc() + return False + + +if __name__ == "__main__": + # Parse command line arguments + base_url = sys.argv[1] if len(sys.argv) > 1 else "http://localhost:8000/v1" + + print("\n" + "=" * 80) + print("OpenAI SDK - Image Generation Tests") + print("=" * 80) + print(f"Base URL: {base_url}") + print() + + # Test image generation + test_image_generation(base_url=base_url) diff --git a/examples/visual_gen/serve/sync_video_gen.py b/examples/visual_gen/serve/sync_video_gen.py new file mode 100755 index 0000000000..2f349e47b6 --- /dev/null +++ b/examples/visual_gen/serve/sync_video_gen.py @@ -0,0 +1,224 @@ +#!/usr/bin/env python +"""Test script for synchronous video generation endpoint. + +Tests POST /v1/videos/generations endpoint which waits for completion and returns video data. +The video is generated synchronously and the response contains the video file. + +Supports two modes: + - Text-to-Video (T2V): Generate video from text prompt only + - Text+Image-to-Video (TI2V): Generate video from text prompt + reference image + +Examples: + # Text-to-Video (T2V) + python sync_video_gen.py --mode t2v --prompt "A cool cat on a motorcycle" + + # Text+Image-to-Video (TI2V) + python sync_video_gen.py --mode ti2v --prompt "She turns and smiles" --image ./media/woman.jpg +""" + +import argparse +import sys +from pathlib import Path + +import requests + + +def test_sync_video_generation( + base_url: str = "http://localhost:8000/v1", + model: str = "wan", + prompt: str = "A video of a cute cat playing with a ball in the park", + input_reference: str = None, + duration: float = 4.0, + fps: int = 24, + size: str = "256x256", + output_file: str = "output_sync.mp4", +): + """Test synchronous video generation with direct HTTP requests. + + Args: + base_url: Base URL of the API server + model: Model name to use + prompt: Text prompt for generation + input_reference: Path to reference image (optional, for TI2V mode) + duration: Video duration in seconds + fps: Frames per second + size: Video resolution (WxH format) + output_file: Output video file path + """ + mode = "TI2V" if input_reference else "T2V" + print("=" * 80) + print(f"Testing Sync Video Generation API - {mode} Mode") + print("=" * 80) + + print("\n1. Generating video (waiting for completion)...") + print(f" Mode: {mode}") + print(f" Prompt: {prompt}") + if input_reference: + print(f" Input Reference: {input_reference}") + print(f" Duration: {duration}s") + print(f" FPS: {fps}") + print(f" Size: {size}") + + try: + endpoint = f"{base_url}/videos/generations" + + if input_reference: + # TI2V mode - Use multipart/form-data with file upload + if not Path(input_reference).exists(): + print(f"\nāŒ Error: Input reference image not found: {input_reference}") + return False + + # Prepare form data (all values as strings for multipart) + form_data = { + "model": model, + "prompt": prompt, + "size": size, + "seconds": str(duration), + "fps": str(fps), + } + + # Add the file + ## Note: The content-type must be multipart/form-data. + files = { + "input_reference": ( + Path(input_reference).name, + open(input_reference, "rb"), + "multipart/form-data", + ) + } + + print("\n Uploading reference image and generating video...") + response_video = requests.post(endpoint, data=form_data, files=files) + else: + # T2V mode - Use JSON + response_video = requests.post( + endpoint, + json={ + "model": model, + "prompt": prompt, + "size": size, + "seconds": duration, + "fps": fps, + }, + ) + + print(f"\nStatus code: {response_video.status_code}") + + if response_video.status_code == 200: + with open(output_file, "wb") as f: + f.write(response_video.content) + print(f"āœ“ Video saved to: {output_file}") + + print("\n" + "=" * 80) + print("āœ“ Sync video generation test completed successfully!") + print("=" * 80) + return True + else: + print(f"\nāŒ Error: Server returned status {response_video.status_code}") + print(f"Response: {response_video.text}") + return False + + except Exception as e: + print(f"\nāŒ Error: {e}") + import traceback + + traceback.print_exc() + return False + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Test synchronous video generation API with T2V and TI2V modes", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Text-to-Video (T2V) + python sync_video_gen.py --mode t2v --prompt "A cool cat on a motorcycle" + + # Text+Image-to-Video (TI2V) + python sync_video_gen.py --mode ti2v \\ + --prompt "She turns around and smiles, then slowly walks out of the frame" \\ + --image ./media/woman_skyline_original_720p.jpeg + + # Custom parameters + python sync_video_gen.py --mode t2v \\ + --prompt "A serene sunset over the ocean" \\ + --duration 5.0 --fps 30 --size 512x512 \\ + --output my_video.mp4 + """, + ) + + # Mode selection + parser.add_argument( + "--mode", + choices=["t2v", "ti2v"], + default="t2v", + help="Generation mode: t2v (Text-to-Video) or ti2v (Text+Image-to-Video)", + ) + + # Required parameters + parser.add_argument( + "--prompt", + type=str, + default="A video of a cute cat playing with a ball in the park", + help="Text prompt for video generation", + ) + + # TI2V mode parameters + parser.add_argument( + "--image", + "--input-reference", + type=str, + default=None, + help="Path to reference image (required for ti2v mode)", + ) + + # Optional parameters + parser.add_argument( + "--base-url", + type=str, + default="http://localhost:8000/v1", + help="Base URL of the API server", + ) + parser.add_argument("--model", type=str, default="wan", help="Model name to use") + parser.add_argument( + "--duration", "--seconds", type=float, default=4.0, help="Video duration in seconds" + ) + parser.add_argument("--fps", type=int, default=24, help="Frames per second") + parser.add_argument( + "--size", + type=str, + default="256x256", + help="Video resolution in WxH format (e.g., 1280x720)", + ) + parser.add_argument( + "--output", type=str, default="output_sync.mp4", help="Output video file path" + ) + + args = parser.parse_args() + + # Validate ti2v mode requirements + if args.mode == "ti2v" and not args.image: + parser.error("--image is required when using --mode ti2v") + + # Display configuration + print("\n" + "=" * 80) + print("Synchronous Video Generation Test") + print("=" * 80) + print(f"Base URL: {args.base_url}") + print(f"Mode: {args.mode.upper()}") + print() + + # Test sync video generation + success = test_sync_video_generation( + base_url=args.base_url, + model=args.model, + prompt=args.prompt, + input_reference=args.image, + duration=args.duration, + fps=args.fps, + size=args.size, + output_file=args.output, + ) + + sys.exit(0 if success else 1) diff --git a/examples/visual_gen/visual_gen_examples.sh b/examples/visual_gen/visual_gen_examples.sh new file mode 100755 index 0000000000..b769760203 --- /dev/null +++ b/examples/visual_gen/visual_gen_examples.sh @@ -0,0 +1,238 @@ +#!/bin/bash +# Visual Generation Examples - Test different models and configurations +# +# This script runs a comprehensive suite of visual generation examples including: +# - WAN T2V: Baseline, TeaCache, CFG parallelism, Ulysses parallelism, and combinations +# - WAN I2V: Baseline, TeaCache, CFG parallelism, Ulysses parallelism, and combinations +# +# The script automatically detects GPU count and runs appropriate examples: +# - 1 GPU: Single-GPU examples only +# - 2 GPUs: + CFG parallelism, Ulysses parallelism +# - 4 GPUs: + CFG + Ulysses combined +# - 8 GPUs: + Large-scale high-resolution examples +# +# Usage: +# export MODEL_ROOT=/path/to/models # required +# # Optional: PROJECT_ROOT auto-detected when run from examples/visual_gen +# cd examples/visual_gen && ./visual_gen_examples.sh +# +# Or inline: +# MODEL_ROOT=/llm-models ./visual_gen_examples.sh + +set -e # Exit on error + +# Environment variables with defaults +# PROJECT_ROOT: auto-detect repo root when run from examples/visual_gen +SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)" +PROJECT_ROOT=${PROJECT_ROOT:-"$(cd "${SCRIPT_DIR}/../.." && pwd)"} +MODEL_ROOT=${MODEL_ROOT:-"/llm-models"} + +# Log configuration +export TLLM_LOG_LEVEL=${TLLM_LOG_LEVEL:-"INFO"} + +echo "============================================" +echo "Visual Generation Examples" +echo "============================================" +echo "PROJECT_ROOT: $PROJECT_ROOT" +echo "MODEL_ROOT: $MODEL_ROOT" +echo "LOG_LEVEL: $TLLM_LOG_LEVEL" +echo "============================================" +echo "" + + +# Detect GPU count +if command -v nvidia-smi &> /dev/null; then + GPU_COUNT=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l) + echo "Detected $GPU_COUNT GPU(s)" + if [ "$GPU_COUNT" -lt 2 ]; then + echo "Note: Multi-GPU examples will be skipped" + SKIP_MULTI_GPU=1 + elif [ "$GPU_COUNT" -ge 8 ]; then + echo "Note: Will run all examples including 8-GPU configurations" + elif [ "$GPU_COUNT" -ge 4 ]; then + echo "Note: Will run examples up to 4-GPU configurations" + else + echo "Note: Will run 2-GPU examples only" + fi +else + echo "WARNING: nvidia-smi not found. Assuming single GPU." + GPU_COUNT=1 + SKIP_MULTI_GPU=1 +fi +echo "" + +############################################# +# WAN (Wan2.1) Text-to-Video Examples +############################################# +# Demonstrates: +# - Single GPU: Baseline and TeaCache +# - 2 GPUs: CFG only, Ulysses only +# - 4 GPUs: CFG + Ulysses combined +# - 8 GPUs: Large-scale parallelism +############################################# + +echo "=== WAN Example 1: Baseline (no optimization) ===" +python ${PROJECT_ROOT}/examples/visual_gen/visual_gen_wan_t2v.py \ + --height 480 \ + --width 832 \ + --num_frames 33 \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers/ \ + --prompt "A cute cat playing piano" \ + --output_path wan_cat_piano.png + +echo "" +echo "=== WAN Example 2: With TeaCache ===" +python ${PROJECT_ROOT}/examples/visual_gen/visual_gen_wan_t2v.py \ + --height 480 \ + --width 832 \ + --num_frames 33 \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers \ + --prompt "A cute cat playing piano" \ + --output_path wan_cat_piano_teacache.png \ + --enable_teacache + +if [ -z "$SKIP_MULTI_GPU" ]; then + echo "" + echo "=== WAN Example 3: CFG Only (2 GPUs) ===" + python ${PROJECT_ROOT}/examples/visual_gen/visual_gen_wan_t2v.py \ + --height 480 \ + --width 832 \ + --num_frames 33 \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers/ \ + --prompt "A cute cat playing piano" \ + --output_path wan_cfg_2gpu.mp4 \ + --attention_backend TRTLLM \ + --cfg_size 2 \ + --ulysses_size 1 +else + echo "" + echo "=== WAN Example 3: Skipped (requires 2 GPUs) ===" +fi + +if [ -z "$SKIP_MULTI_GPU" ]; then + echo "" + echo "=== WAN Example 4: Ulysses Only (2 GPUs) ===" + python ${PROJECT_ROOT}/examples/visual_gen/visual_gen_wan_t2v.py \ + --height 480 \ + --width 832 \ + --num_frames 33 \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers/ \ + --prompt "A cute cat playing piano" \ + --output_path wan_ulysses_2gpu.mp4 \ + --attention_backend TRTLLM \ + --cfg_size 1 \ + --ulysses_size 2 +else + echo "" + echo "=== WAN Example 4: Skipped (requires 2 GPUs) ===" +fi + +if [ "$GPU_COUNT" -ge 4 ]; then + echo "" + echo "=== WAN Example 5: CFG + Ulysses (4 GPUs) ===" + python ${PROJECT_ROOT}/examples/visual_gen/visual_gen_wan_t2v.py \ + --height 480 \ + --width 832 \ + --num_frames 33 \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers/ \ + --prompt "A cute cat playing piano" \ + --output_path wan_cfg_ulysses_4gpu.mp4 \ + --attention_backend TRTLLM \ + --cfg_size 2 \ + --ulysses_size 2 +else + echo "" + echo "=== WAN Example 5: Skipped (requires 4 GPUs) ===" +fi + +if [ "$GPU_COUNT" -ge 8 ]; then + echo "" + echo "=== WAN Example 6: Large-Scale (8 GPUs) ===" + python ${PROJECT_ROOT}/examples/visual_gen/visual_gen_wan_t2v.py \ + --height 480 \ + --width 832 \ + --num_frames 33 \ + --model_path ${MODEL_ROOT}/Wan2.1-T2V-1.3B-Diffusers/ \ + --prompt "A cute cat playing piano" \ + --output_path wan_cfg_ulysses_8gpu.mp4 \ + --attention_backend TRTLLM \ + --cfg_size 2 \ + --ulysses_size 4 +else + echo "" + echo "=== WAN Example 6: Skipped (requires 8 GPUs) ===" +fi + +############################################# +# WAN 2.2 (Two-Stage) Text-to-Video Examples +############################################# + +echo "" +echo "=== WAN 2.2 T2V Example: Two-stage with optimizations (FP8 + TRT-LLM + TeaCache) ===" +python ${PROJECT_ROOT}/examples/visual_gen/visual_gen_wan_t2v.py \ + --height 720 \ + --width 1280 \ + --num_frames 81 \ + --model_path ${MODEL_ROOT}/Wan2.2-T2V-A14B-Diffusers \ + --prompt "A cute cat playing piano" \ + --output_path wan22_t2v_cat_piano_optimized.gif \ + --linear_type trtllm-fp8-blockwise \ + --attention_backend TRTLLM \ + --enable_teacache \ + --teacache_thresh 0.2 \ + --guidance_scale 3.0 \ + --guidance_scale_2 2.5 \ + --boundary_ratio 0.85 + +############################################# +# WAN 2.1 Image-to-Video Examples +############################################# + +echo "" +echo "=== WAN 2.1 I2V Example: Single-stage with optimizations (FP8 + TRT-LLM + TeaCache) ===" +python ${PROJECT_ROOT}/examples/visual_gen/visual_gen_wan_i2v.py \ + --height 480 \ + --width 832 \ + --num_frames 33 \ + --model_path ${MODEL_ROOT}/Wan2.1-I2V-14B-480P-Diffusers \ + --image_path ${PROJECT_ROOT}/examples/visual_gen/cat_piano.png \ + --prompt "It snows as the cat plays piano, lots of snow \ + appearing all over the screen, snowflakes, blizzard, + gradually more snow" \ + --negative_prompt "blurry, low quality" \ + --output_path wan21_i2v_cat_piano_optimized.gif \ + --linear_type trtllm-fp8-per-tensor \ + --attention_backend TRTLLM \ + --enable_teacache \ + --teacache_thresh 0.2 \ + --guidance_scale 6.0 + +############################################# +# WAN 2.2 (Two-Stage) Image-to-Video Examples +############################################# + +echo "" +echo "=== WAN 2.2 I2V Example: Two-stage with optimizations (FP8 + TRT-LLM + TeaCache) ===" +python ${PROJECT_ROOT}/examples/visual_gen/visual_gen_wan_i2v.py \ + --height 480 \ + --width 832 \ + --num_frames 81 \ + --model_path ${MODEL_ROOT}/Wan2.2-I2V-A14B-Diffusers \ + --image_path ${PROJECT_ROOT}/examples/visual_gen/cat_piano.png \ + --prompt "It snows as the cat plays piano, lots of snow \ + appearing all over the screen, snowflakes, blizzard, + gradually more snow" \ + --negative_prompt "blurry, low quality" \ + --output_path wan22_i2v_cat_piano_optimized.gif \ + --linear_type trtllm-fp8-blockwise \ + --attention_backend TRTLLM \ + --enable_teacache \ + --teacache_thresh 0.2 \ + --guidance_scale 6.0 \ + --guidance_scale_2 5.0 \ + --boundary_ratio 0.85 + +echo "" +echo "============================================" +echo "All examples completed successfully!" +echo "============================================" diff --git a/examples/visual_gen/visual_gen_wan_i2v.py b/examples/visual_gen/visual_gen_wan_i2v.py new file mode 100644 index 0000000000..3b76470bb6 --- /dev/null +++ b/examples/visual_gen/visual_gen_wan_i2v.py @@ -0,0 +1,226 @@ +#!/usr/bin/env python3 +"""WAN Image-to-Video generation using TensorRT-LLM Visual Generation.""" + +import argparse +import time + +from output_handler import OutputHandler + +from tensorrt_llm import logger +from tensorrt_llm.llmapi.visual_gen import VisualGen, VisualGenParams + +# Set logger level to ensure timing logs are printed +logger.set_level("info") + + +def parse_args(): + parser = argparse.ArgumentParser( + description="TRTLLM VisualGen - Wan Image-to-Video Inference Example (supports Wan 2.1 and Wan 2.2)" + ) + + # Model & Input + parser.add_argument( + "--model_path", + type=str, + required=True, + help="Path to Wan I2V Diffusers model directory (2.1 or 2.2)", + ) + parser.add_argument( + "--image_path", + type=str, + required=True, + help="Path to input image for I2V conditioning", + ) + parser.add_argument( + "--last_image_path", + type=str, + default=None, + help="Optional path to last frame image for interpolation (Wan 2.1 only)", + ) + parser.add_argument("--prompt", type=str, required=True, help="Text prompt for generation") + parser.add_argument( + "--negative_prompt", + type=str, + default=None, + help="Negative prompt. Default is model-specific.", + ) + parser.add_argument( + "--output_path", + type=str, + default="output.png", + help="Path to save the output image/video frame", + ) + + # Generation Params + parser.add_argument("--height", type=int, default=720, help="Video height") + parser.add_argument("--width", type=int, default=1280, help="Video width") + parser.add_argument("--num_frames", type=int, default=81, help="Number of frames to generate") + parser.add_argument( + "--steps", + type=int, + default=None, + help="Number of denoising steps (default: auto-detect, 50 for Wan2.1, 40 for Wan2.2)", + ) + parser.add_argument( + "--guidance_scale", + type=float, + default=None, + help="Guidance scale (default: auto-detect, 5.0 for Wan2.1, 4.0 for Wan2.2)", + ) + parser.add_argument( + "--guidance_scale_2", + type=float, + default=None, + help="Second-stage guidance scale for Wan2.2 two-stage denoising (default: 3.0)", + ) + parser.add_argument( + "--boundary_ratio", + type=float, + default=None, + help="Custom boundary ratio for two-stage denoising (default: auto-detect)", + ) + parser.add_argument("--seed", type=int, default=42, help="Random seed") + + # TeaCache Arguments + parser.add_argument( + "--enable_teacache", action="store_true", help="Enable TeaCache acceleration" + ) + parser.add_argument( + "--teacache_thresh", + type=float, + default=0.2, + help="TeaCache similarity threshold (rel_l1_thresh)", + ) + + # Quantization + parser.add_argument( + "--linear_type", + type=str, + default="default", + choices=["default", "trtllm-fp8-per-tensor", "trtllm-fp8-blockwise", "svd-nvfp4"], + help="Linear layer quantization type", + ) + + # Attention Backend + parser.add_argument( + "--attention_backend", + type=str, + default="VANILLA", + choices=["VANILLA", "TRTLLM"], + help="Attention backend (VANILLA: PyTorch SDPA, TRTLLM: optimized kernels). " + "Note: TRTLLM automatically falls back to VANILLA for cross-attention.", + ) + + # Parallelism + parser.add_argument( + "--cfg_size", + type=int, + default=1, + choices=[1, 2], + help="CFG parallel size (1 or 2). Set to 2 for CFG Parallelism.", + ) + parser.add_argument( + "--ulysses_size", + type=int, + default=1, + help="Ulysses (sequence) parallel size within each CFG group.", + ) + + return parser.parse_args() + + +def main(): + args = parse_args() + + # world_size = cfg_size * ulysses_size + # Example: cfg_size=2, ulysses_size=4 -> 8 GPUs + # GPU 0-3: CFG group 0 (positive prompt), internal Ulysses parallel + # GPU 4-7: CFG group 1 (negative prompt), internal Ulysses parallel + n_workers = args.cfg_size * args.ulysses_size + + # Convert linear_type to quant_config + quant_config = None + if args.linear_type == "trtllm-fp8-per-tensor": + quant_config = {"quant_algo": "FP8", "dynamic": True} + elif args.linear_type == "trtllm-fp8-blockwise": + quant_config = {"quant_algo": "FP8_BLOCK_SCALES", "dynamic": True} + elif args.linear_type == "svd-nvfp4": + quant_config = {"quant_algo": "NVFP4", "dynamic": True} + + # 1. Setup Configuration + diffusion_config = { + "model_type": "wan2", + "attention": { + "backend": args.attention_backend, + }, + "teacache": { + "enable_teacache": args.enable_teacache, + "teacache_thresh": args.teacache_thresh, + }, + "parallel": { + "dit_cfg_size": args.cfg_size, + "dit_ulysses_size": args.ulysses_size, + }, + } + + # Add quant_config if specified + if quant_config is not None: + diffusion_config["quant_config"] = quant_config + + # 2. Initialize VisualGen + logger.info( + f"Initializing VisualGen: world_size={n_workers} " + f"(cfg_size={args.cfg_size}, ulysses_size={args.ulysses_size})" + ) + visual_gen = VisualGen( + model_path=args.model_path, + n_workers=n_workers, + diffusion_config=diffusion_config, + ) + + try: + # 2. Run Inference + logger.info(f"Generating video for prompt: '{args.prompt}'") + logger.info(f"Negative prompt: '{args.negative_prompt}'") + logger.info(f"Input image: {args.image_path}") + if args.last_image_path: + logger.info(f"Last frame image: {args.last_image_path}") + logger.info( + f"Resolution: {args.height}x{args.width}, Frames: {args.num_frames}, Steps: {args.steps}" + ) + + start_time = time.time() + + # Build parameters with explicit I2V and Wan 2.2 support + output = visual_gen.generate( + inputs={ + "prompt": args.prompt, + "negative_prompt": args.negative_prompt, + }, + params=VisualGenParams( + height=args.height, + width=args.width, + num_inference_steps=args.steps, + guidance_scale=args.guidance_scale, + seed=args.seed, + num_frames=args.num_frames, + input_reference=args.image_path, + last_image=args.last_image_path if args.last_image_path else None, + guidance_scale_2=args.guidance_scale_2, + boundary_ratio=args.boundary_ratio, + ), + ) + + end_time = time.time() + logger.info(f"Generation completed in {end_time - start_time:.2f}s") + + # 3. Save Output + OutputHandler.save(output, args.output_path, frame_rate=16.0) + + finally: + # 4. Shutdown + visual_gen.shutdown() + + +if __name__ == "__main__": + main() diff --git a/examples/visual_gen/visual_gen_wan_t2v.py b/examples/visual_gen/visual_gen_wan_t2v.py new file mode 100755 index 0000000000..30c55e4a17 --- /dev/null +++ b/examples/visual_gen/visual_gen_wan_t2v.py @@ -0,0 +1,228 @@ +#!/usr/bin/env python3 +"""WAN Text-to-Video generation using TensorRT-LLM Visual Generation.""" + +import argparse +import time + +from output_handler import OutputHandler + +from tensorrt_llm import logger +from tensorrt_llm.llmapi.visual_gen import VisualGen, VisualGenParams + +# Set logger level to ensure timing logs are printed +logger.set_level("info") + + +def parse_args(): + parser = argparse.ArgumentParser( + description="TRTLLM VisualGen - Wan Text-to-Video Inference Example (supports Wan 2.1 and Wan 2.2)" + ) + + # Model & Input + parser.add_argument( + "--model_path", + type=str, + required=True, + help="Local path or HuggingFace Hub model ID (e.g., Wan-AI/Wan2.1-T2V-1.3B-Diffusers)", + ) + parser.add_argument( + "--revision", + type=str, + default=None, + help="HuggingFace Hub revision (branch, tag, or commit SHA)", + ) + parser.add_argument("--prompt", type=str, required=True, help="Text prompt for generation") + parser.add_argument( + "--negative_prompt", + type=str, + default=None, + help="Negative prompt. Default is model-specific.", + ) + parser.add_argument( + "--output_path", + type=str, + default="output.png", + help="Path to save the output image/video frame", + ) + + # Generation Params + parser.add_argument("--height", type=int, default=720, help="Video height") + parser.add_argument("--width", type=int, default=1280, help="Video width") + parser.add_argument("--num_frames", type=int, default=81, help="Number of frames to generate") + parser.add_argument( + "--steps", + type=int, + default=None, + help="Number of denoising steps (default: auto-detect, 50 for Wan2.1, 40 for Wan2.2)", + ) + parser.add_argument( + "--guidance_scale", + type=float, + default=None, + help="Guidance scale (default: auto-detect, 5.0 for Wan2.1, 4.0 for Wan2.2)", + ) + parser.add_argument( + "--guidance_scale_2", + type=float, + default=None, + help="Second-stage guidance scale for Wan2.2 two-stage denoising (default: 3.0)", + ) + parser.add_argument( + "--boundary_ratio", + type=float, + default=None, + help="Custom boundary ratio for two-stage denoising (default: auto-detect)", + ) + parser.add_argument("--seed", type=int, default=42, help="Random seed") + + # TeaCache Arguments + parser.add_argument( + "--enable_teacache", action="store_true", help="Enable TeaCache acceleration" + ) + parser.add_argument( + "--teacache_thresh", + type=float, + default=0.2, + help="TeaCache similarity threshold (rel_l1_thresh)", + ) + + # Quantization + parser.add_argument( + "--linear_type", + type=str, + default="default", + choices=["default", "trtllm-fp8-per-tensor", "trtllm-fp8-blockwise", "svd-nvfp4"], + help="Linear layer quantization type", + ) + + # Attention Backend + parser.add_argument( + "--attention_backend", + type=str, + default="VANILLA", + choices=["VANILLA", "TRTLLM"], + help="Attention backend (VANILLA: PyTorch SDPA, TRTLLM: optimized kernels). " + "Note: TRTLLM automatically falls back to VANILLA for cross-attention.", + ) + + # Parallelism + parser.add_argument( + "--cfg_size", + type=int, + default=1, + choices=[1, 2], + help="CFG parallel size (1 or 2). " + "Distributes positive/negative prompts across GPUs. " + "Example: cfg_size=2 on 4 GPUs -> 2 GPUs per prompt.", + ) + parser.add_argument( + "--ulysses_size", + type=int, + default=1, + help="Ulysses sequence parallel size within each CFG group. " + "Distributes sequence across GPUs for longer sequences. " + "Requirements: num_heads (12) and sequence length must both be divisible by ulysses_size. " + "Example: ulysses_size=2 on 4 GPUs with cfg_size=2 -> " + "2 CFG groups Ɨ 2 Ulysses ranks = 4 GPUs total.", + ) + + return parser.parse_args() + + +def main(): + args = parse_args() + + # Total workers: cfg_size Ɨ ulysses_size + # See ParallelConfig in config.py for detailed parallelism strategy and examples + n_workers = args.cfg_size * args.ulysses_size + + # Log Ulysses configuration (validation happens in setup_sequence_parallelism) + if args.ulysses_size > 1: + num_heads = 12 # WAN has 12 attention heads + logger.info( + f"Using Ulysses sequence parallelism: " + f"{num_heads} heads / {args.ulysses_size} ranks = " + f"{num_heads // args.ulysses_size} heads per GPU" + ) + + # Convert linear_type to quant_config + quant_config = None + if args.linear_type == "trtllm-fp8-per-tensor": + quant_config = {"quant_algo": "FP8", "dynamic": True} + elif args.linear_type == "trtllm-fp8-blockwise": + quant_config = {"quant_algo": "FP8_BLOCK_SCALES", "dynamic": True} + elif args.linear_type == "svd-nvfp4": + quant_config = {"quant_algo": "NVFP4", "dynamic": True} + + # 1. Setup Configuration + diffusion_config = { + "model_type": "wan2", + "revision": args.revision, + "attention": { + "backend": args.attention_backend, + }, + "teacache": { + "enable_teacache": args.enable_teacache, + "teacache_thresh": args.teacache_thresh, + }, + "parallel": { + "dit_cfg_size": args.cfg_size, + "dit_ulysses_size": args.ulysses_size, + }, + } + + # Add quant_config if specified + if quant_config is not None: + diffusion_config["quant_config"] = quant_config + + # 2. Initialize VisualGen + logger.info( + f"Initializing VisualGen: world_size={n_workers} " + f"(cfg_size={args.cfg_size}, ulysses_size={args.ulysses_size})" + ) + visual_gen = VisualGen( + model_path=args.model_path, + n_workers=n_workers, + diffusion_config=diffusion_config, + ) + + try: + # 2. Run Inference + logger.info(f"Generating video for prompt: '{args.prompt}'") + logger.info(f"Negative prompt: '{args.negative_prompt}'") + logger.info( + f"Resolution: {args.height}x{args.width}, Frames: {args.num_frames}, Steps: {args.steps}" + ) + + start_time = time.time() + + output = visual_gen.generate( + inputs={ + "prompt": args.prompt, + "negative_prompt": args.negative_prompt, + }, + params=VisualGenParams( + height=args.height, + width=args.width, + num_inference_steps=args.steps, + guidance_scale=args.guidance_scale, + seed=args.seed, + num_frames=args.num_frames, + guidance_scale_2=args.guidance_scale_2, + boundary_ratio=args.boundary_ratio, + ), + ) + + end_time = time.time() + logger.info(f"Generation completed in {end_time - start_time:.2f}s") + + # 3. Save Output + OutputHandler.save(output, args.output_path, frame_rate=16.0) + + finally: + # 4. Shutdown + visual_gen.shutdown() + + +if __name__ == "__main__": + main() diff --git a/requirements.txt b/requirements.txt index 0bada4a2d4..d2aa81843d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -83,3 +83,4 @@ llist cuda-tile>=1.0.1 nvidia-cuda-tileiras>=13.1 etcd-sdk-python==0.0.7 +python-multipart diff --git a/tensorrt_llm/_torch/distributed/__init__.py b/tensorrt_llm/_torch/distributed/__init__.py index b8bfe4ffdf..2dafa88bf1 100644 --- a/tensorrt_llm/_torch/distributed/__init__.py +++ b/tensorrt_llm/_torch/distributed/__init__.py @@ -4,10 +4,11 @@ from .communicator import Distributed, MPIDist, TorchDist from .moe_alltoall import MoeAlltoAll from .ops import (AllReduce, AllReduceParams, AllReduceStrategy, HelixAllToAllNative, MoEAllReduce, MoEAllReduceParams, - allgather, alltoall_helix, cp_allgather, reducescatter, - userbuffers_allreduce_finalize) + all_to_all_4d, allgather, alltoall_helix, cp_allgather, + reducescatter, userbuffers_allreduce_finalize) __all__ = [ + "all_to_all_4d", "allgather", "alltoall_helix", "cp_allgather", diff --git a/tensorrt_llm/_torch/distributed/ops.py b/tensorrt_llm/_torch/distributed/ops.py index 84468dc612..525a825a3f 100644 --- a/tensorrt_llm/_torch/distributed/ops.py +++ b/tensorrt_llm/_torch/distributed/ops.py @@ -959,3 +959,126 @@ class MoEAllReduce(nn.Module): nranks=self.mapping.tp_size, eps=all_reduce_params.eps, ) + + +def all_to_all_4d( + input: torch.Tensor, + scatter_dim: int, + gather_dim: int, + process_group: Optional[torch.distributed.ProcessGroup] = None, +) -> torch.Tensor: + """ + All-to-all for 4D tensors (batch, seq, heads, head_dim). + + Redistributes a 4D tensor along two dimensions using all-to-all communication. + This is used for Ulysses-style sequence parallelism to transform between: + - Sequence sharding [B, S/P, H, D] ā†’ Head sharding [B, S, H/P, D] + - Head sharding [B, S, H/P, D] ā†’ Sequence sharding [B, S/P, H, D] + + Args: + input: Input tensor with shape [batch, seq, heads, head_dim] + scatter_dim: Dimension to split and scatter (1 for seq, 2 for heads) + gather_dim: Dimension to gather (1 for seq, 2 for heads) + process_group: PyTorch distributed process group. If None, uses default process group. + + Returns: + Redistributed tensor with same shape as input + + Example: + # Transform from sequence sharding to head sharding + # Input: [B, S/P, H, D] (each rank has S/P sequence) + output = all_to_all_4d(input, scatter_dim=2, gather_dim=1, process_group=pg) + # Output: [B, S, H/P, D] (each rank has H/P heads) + + # Transform back from head sharding to sequence sharding + output = all_to_all_4d(input, scatter_dim=1, gather_dim=2, process_group=pg) + """ + # Only support PyTorch distributed mode (not MPI mode) + if not mpi_disabled(): + raise NotImplementedError( + "all_to_all_4d currently only supports PyTorch distributed mode. " + "MPI mode is not supported.") + + # Get world size from process group + world_size = torch.distributed.get_world_size(group=process_group) + + # If world_size is 1, no communication needed + if world_size == 1: + return input + + # Validate dimensions + assert scatter_dim in [1, 2], "scatter_dim must be 1 (seq) or 2 (heads)" + assert gather_dim in [1, 2], "gather_dim must be 1 (seq) or 2 (heads)" + assert scatter_dim != gather_dim, "scatter_dim and gather_dim must be different" + + batch, seq, heads, head_dim = input.shape + + # Validate that the scatter dimension is divisible by world_size + scatter_size = input.shape[scatter_dim] + assert scatter_size % world_size == 0, \ + f"Dimension {scatter_dim} size {scatter_size} must be divisible by world_size {world_size}" + + # For all-to-all, we need to: + # 1. Split input along scatter_dim into world_size chunks + # 2. Send chunk i to rank i + # 3. Receive chunk from each rank and concatenate along gather_dim + + # Reshape for all-to-all: move scatter_dim chunks to a new dimension + if scatter_dim == 1: # Scatter along seq dimension + # [B, S, H, D] -> [B, P, S/P, H, D] where P = world_size + input_reshaped = input.view(batch, world_size, seq // world_size, heads, + head_dim) + # Transpose to group by destination rank: [B, P, S/P, H, D] -> [P, B, S/P, H, D] + input_transposed = input_reshaped.permute(1, 0, 2, 3, 4).contiguous() + else: # scatter_dim == 2, scatter along heads dimension + # [B, S, H, D] -> [B, S, P, H/P, D] where P = world_size + input_reshaped = input.view(batch, seq, world_size, heads // world_size, + head_dim) + # Transpose to group by destination rank: [B, S, P, H/P, D] -> [P, B, S, H/P, D] + input_transposed = input_reshaped.permute(2, 0, 1, 3, 4).contiguous() + + # Flatten to [P * ...] for all-to-all communication + # Shape: [P, B, ...] -> [P * B * ...] + input_flat = input_transposed.flatten() + output_flat = torch.empty_like(input_flat) + + # Perform all-to-all communication using PyTorch distributed + # all_to_all_single splits input into world_size chunks and exchanges them + torch.distributed.all_to_all_single(output_flat, + input_flat, + group=process_group) + + # Reshape output back to [P, B, ...] form + output_transposed = output_flat.view_as(input_transposed) + + # Transpose back and reshape to final form + if gather_dim == 1: # Gather along seq dimension + # [P, B, S/P, H, D] -> [B, P, S/P, H, D] + output_reshaped = output_transposed.permute(1, 0, 2, 3, 4).contiguous() + # [B, P, S/P, H, D] -> [B, S, H, D] where S = P * (S/P) + # When scattering heads and gathering seq: seq needs to be multiplied, heads needs to be divided + if scatter_dim == 2: + # Scattered heads, so we have H/P heads and need to gather S/P -> S sequence + gathered_seq = seq * world_size + sharded_heads = heads // world_size + output = output_reshaped.view(batch, gathered_seq, sharded_heads, + head_dim) + else: + # Scattered seq (should be impossible if gather_dim == 1), keep as is + output = output_reshaped.view(batch, seq, heads, head_dim) + else: # gather_dim == 2, gather along heads dimension + # [P, B, S, H/P, D] -> [B, S, P, H/P, D] + output_reshaped = output_transposed.permute(1, 2, 0, 3, 4).contiguous() + # [B, S, P, H/P, D] -> [B, S, H, D] where H = P * (H/P) + # When scattering seq and gathering heads: heads needs to be multiplied, seq needs to be divided + if scatter_dim == 1: + # Scattered seq, so we have S/P seq and need to gather H/P -> H heads + gathered_heads = heads * world_size + sharded_seq = seq // world_size + output = output_reshaped.view(batch, sharded_seq, gathered_heads, + head_dim) + else: + # Scattered heads (should be impossible if gather_dim == 2), keep as is + output = output_reshaped.view(batch, seq, heads, head_dim) + + return output diff --git a/tensorrt_llm/_torch/modules/linear.py b/tensorrt_llm/_torch/modules/linear.py index 65811569ca..34581ff224 100644 --- a/tensorrt_llm/_torch/modules/linear.py +++ b/tensorrt_llm/_torch/modules/linear.py @@ -556,6 +556,13 @@ class FP8QDQLinearMethod(UnquantizedLinearMethod): def apply(self, module: Linear, input: torch.Tensor, bias: Optional[torch.Tensor]): + + # Handle multi-dimensional inputs (e.g., 3D: batch, seq, hidden) + # GEMM ops require 2D matrices + original_shape = input.shape + if input.dim() > 2: + input = input.reshape(-1, input.shape[-1]) + cur_input_scale = module.input_scale if input.dtype != torch.float8_e4m3fn: if module.input_scale is not None and not module.force_dynamic_quantization: @@ -591,6 +598,11 @@ class FP8QDQLinearMethod(UnquantizedLinearMethod): bias=None, out_dtype=module.dtype or input.dtype, ) + + # Reshape output back to original shape (with out_features as last dim) + if len(original_shape) > 2: + output = output.reshape(*original_shape[:-1], output.shape[-1]) + if bias is not None: output = output + bias return output @@ -975,6 +987,12 @@ class FP8BlockScalesLinearMethod(UnquantizedLinearMethod): def apply(self, module: Linear, input: torch.Tensor, bias: Optional[torch.Tensor]): + # Handle multi-dimensional inputs (e.g., 3D: batch, seq, hidden) + # GEMM ops require 2D matrices + original_shape = input.shape + if input.dim() > 2: + input = input.reshape(-1, input.shape[-1]) + if input.dtype == torch.float8_e4m3fn: input = input.to(torch.bfloat16) * module.input_scale assert input.dtype == torch.bfloat16 @@ -1003,6 +1021,10 @@ class FP8BlockScalesLinearMethod(UnquantizedLinearMethod): output = torch.ops.trtllm.fp8_block_scaling_gemm( act_input_fp8, module.weight, act_input_sf, module.weight_scale) + # Reshape output back to original shape (with out_features as last dim) + if len(original_shape) > 2: + output = output.reshape(*original_shape[:-1], output.shape[-1]) + if bias is not None: output = output + bias return output @@ -1212,6 +1234,15 @@ class NVFP4LinearMethod(LinearMethodBase): def apply(self, module: Linear, input: torch.Tensor, bias: Optional[torch.Tensor]): + # Handle multi-dimensional inputs (e.g., 3D: batch, seq, hidden). + # GEMM requires 2D. Only plain tensors support for now, skip for + # tuple and Fp4QuantizedTensor. + original_shape = None + if not isinstance(input, + (tuple, Fp4QuantizedTensor)) and input.dim() > 2: + original_shape = input.shape + input = input.reshape(-1, input.shape[-1]) + act_fp4, act_sf = self._input_prepare(module, input) # Use unified interface - supports CUTLASS, cuBLASLt, CuteDSL # Convert list to comma-separated string for torch.compile compatibility @@ -1229,6 +1260,9 @@ class NVFP4LinearMethod(LinearMethodBase): if output.shape[-1] > module.out_features: output = output[..., :module.out_features].contiguous() + if original_shape is not None: + output = output.reshape(*original_shape[:-1], output.shape[-1]) + if bias is not None: output = output + bias return output diff --git a/tensorrt_llm/_torch/visual_gen/__init__.py b/tensorrt_llm/_torch/visual_gen/__init__.py new file mode 100644 index 0000000000..c612522540 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/__init__.py @@ -0,0 +1,45 @@ +"""Visual generation module for diffusion models.""" + +from tensorrt_llm._torch.visual_gen.executor import ( + DiffusionExecutor, + DiffusionRequest, + DiffusionResponse, +) +from tensorrt_llm._torch.visual_gen.output import MediaOutput + +# Checkpoint loading +from .checkpoints import WeightLoader +from .config import ( + AttentionConfig, + DiffusionArgs, + DiffusionModelConfig, + ParallelConfig, + PipelineComponent, + PipelineConfig, + TeaCacheConfig, + discover_pipeline_components, +) +from .models import AutoPipeline, BasePipeline, WanPipeline +from .pipeline_loader import PipelineLoader + +__all__ = [ + # Config classes + "DiffusionArgs", + "DiffusionModelConfig", + "ParallelConfig", + "PipelineComponent", + "TeaCacheConfig", + # Checkpoint loading + "WeightLoader", + # Model loading + "PipelineLoader", + # Execution + "DiffusionExecutor", + "DiffusionRequest", + "DiffusionResponse", + "MediaOutput", + # Pipelines + "AutoPipeline", + "BasePipeline", + "WanPipeline", +] diff --git a/tensorrt_llm/_torch/visual_gen/attention_backend/__init__.py b/tensorrt_llm/_torch/visual_gen/attention_backend/__init__.py new file mode 100644 index 0000000000..9cc0d3c272 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/attention_backend/__init__.py @@ -0,0 +1,37 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Visual Generation Attention Backend + +This module provides attention backend infrastructure for visual generation (diffusion) models. +It reuses existing TRT-LLM attention backends (TrtllmAttention, VanillaAttention) with +simplified metadata that doesn't require KV caching. +""" + +from .interface import AttentionTensorLayout +from .parallel import UlyssesAttention +from .trtllm import TrtllmAttention, TrtllmAttentionMetadata +from .utils import create_attention, get_visual_gen_attention_backend +from .vanilla import VanillaAttention + +__all__ = [ + "AttentionTensorLayout", + "get_visual_gen_attention_backend", + "create_attention", + "TrtllmAttention", + "TrtllmAttentionMetadata", + "UlyssesAttention", + "VanillaAttention", +] diff --git a/tensorrt_llm/_torch/visual_gen/attention_backend/interface.py b/tensorrt_llm/_torch/visual_gen/attention_backend/interface.py new file mode 100644 index 0000000000..a32c3712b4 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/attention_backend/interface.py @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Visual Generation Attention Backend Interface + +Defines shared types and enums for attention backends. +""" + +from enum import Enum + + +class AttentionTensorLayout(str, Enum): + """ + Tensor layout for attention backend input/output. + + Backends declare their preferred layout so the attention module + can reshape tensors optimally before calling the backend. + """ + + NHD = "NHD" # [B, S, H, D] - batch, seq, heads, dim + HND = "HND" # [B, H, S, D] - batch, heads, seq, dim diff --git a/tensorrt_llm/_torch/visual_gen/attention_backend/parallel.py b/tensorrt_llm/_torch/visual_gen/attention_backend/parallel.py new file mode 100644 index 0000000000..a7e466423f --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/attention_backend/parallel.py @@ -0,0 +1,162 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Ulysses Sequence Parallelism Wrapper + +Wraps any attention backend with sequence parallelism via all-to-all +communication. Not a standalone backend ā€” compose around a real backend +(VANILLA/TRTLLM). + +Architecture: + Input: [B, S/P, H, D] (sequence sharded across P processes) + Step 1: All-to-All ā†’ [B, S, H/P, D] (gather sequence, shard heads) + Step 2: Compute attention with wrapped backend (VANILLA or TRTLLM) + Step 3: All-to-All ā†’ [B, S/P, H, D] (restore sequence sharding) + Output: [B, S/P, H, D] (sequence sharded) +""" + +from typing import Optional + +import torch +import torch.nn as nn + +from tensorrt_llm._torch.distributed import all_to_all_4d + +from .interface import AttentionTensorLayout + + +class UlyssesAttention(nn.Module): + """ + Ulysses Sequence Parallelism wrapper. + + Wraps any attention backend with sequence parallelism via all-to-all. + Not a standalone backend ā€” compose around a real backend (VANILLA/TRTLLM). + """ + + def __init__( + self, + inner_backend: nn.Module, + process_group: Optional[torch.distributed.ProcessGroup] = None, + ): + super().__init__() + self.inner_backend = inner_backend + self.process_group = process_group + self._preferred_layout = AttentionTensorLayout.NHD + + # Derive head info from inner backend + self.head_dim = inner_backend.head_dim + self.sharded_num_heads = inner_backend.num_heads + self.sharded_num_kv_heads = getattr(inner_backend, "num_kv_heads", self.sharded_num_heads) + + # Get world size from process group + try: + self.world_size = torch.distributed.get_world_size(group=process_group) + except (RuntimeError, ValueError): + self.world_size = 1 + + # Full (unsharded) head counts for external interface + self.num_heads = self.sharded_num_heads * self.world_size + self.num_kv_heads = self.sharded_num_kv_heads * self.world_size + + def forward( + self, + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + batch_size: int, + attention_mask: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + """ + Forward pass with Ulysses sequence parallelism. + + Input/Output: [B, S/P, H, D] (sequence sharded) + + Args: + q: Query tensor [B, S/P, H, D] + k: Key tensor [B, S/P, H, D] + v: Value tensor [B, S/P, H, D] + batch_size: Batch size + attention_mask: Optional attention mask + + Returns: + Output tensor [B, S/P, H, D] (sequence sharded) + + Note: + seq_len is computed from tensor shape after all-to-all, not passed as parameter. + """ + # Step 1: All-to-All to gather full sequence, shard heads + # [B, S/P, H, D] -> [B, S, H/P, D] + if self.world_size > 1: + q = all_to_all_4d(q, scatter_dim=2, gather_dim=1, process_group=self.process_group) + k = all_to_all_4d(k, scatter_dim=2, gather_dim=1, process_group=self.process_group) + v = all_to_all_4d(v, scatter_dim=2, gather_dim=1, process_group=self.process_group) + + seq_len_full = q.shape[1] + inner_layout = self.inner_backend.preferred_layout + + # Step 2: Call wrapped backend for attention + # Transpose only if inner backend expects HND layout + if inner_layout == AttentionTensorLayout.HND: + # VANILLA expects [B, H/P, S, D] + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) + # NHD backends (TRTLLM) keep [B, S, H/P, D] as-is + + inner_kwargs = dict( + q=q, + k=k, + v=v, + batch_size=batch_size, + seq_len=seq_len_full, + ) + if attention_mask is not None: + inner_kwargs["attention_mask"] = attention_mask + output = self.inner_backend.forward(**inner_kwargs) + + # Convert output back to [B, S, H/P, D] for the reverse all-to-all + if inner_layout == AttentionTensorLayout.HND: + # VANILLA returns [B, H/P, S, D] -> transpose to [B, S, H/P, D] + output = output.transpose(1, 2).contiguous() + else: + # TRTLLM returns [B, S, (H/P)*D] (3D) -> reshape to [B, S, H/P, D] + if output.dim() == 3: + output = output.view( + batch_size, seq_len_full, self.sharded_num_heads, self.head_dim + ) + output = output.contiguous() + + # Step 3: All-to-All to restore sequence sharding + # [B, S, H/P, D] -> [B, S/P, H, D] + if self.world_size > 1: + output = all_to_all_4d( + output, + scatter_dim=1, + gather_dim=2, + process_group=self.process_group, + ) + + return output + + @property + def preferred_layout(self) -> AttentionTensorLayout: + """Preferred tensor layout: [B, S, H, D]""" + return self._preferred_layout + + @classmethod + def support_fused_qkv(cls) -> bool: + """This backend does not support fused QKV.""" + return False diff --git a/tensorrt_llm/_torch/visual_gen/attention_backend/trtllm.py b/tensorrt_llm/_torch/visual_gen/attention_backend/trtllm.py new file mode 100644 index 0000000000..47b92ca27f --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/attention_backend/trtllm.py @@ -0,0 +1,244 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Diffusion TRTLLM Attention Backend + +Wraps TrtllmAttention with simplified metadata for visual generation (diffusion) models. +Handles the specifics of no-KV-cache operation and fused QKV requirements. +""" + +from typing import Optional, Union + +import torch + +from tensorrt_llm.mapping import Mapping +from tensorrt_llm.models.modeling_utils import QuantConfig + +from ...attention_backend.interface import AttentionRuntimeFeatures, PredefinedAttentionMask +from ...attention_backend.trtllm import TrtllmAttention as BaseTrtllmAttention +from ...attention_backend.trtllm import TrtllmAttentionMetadata as BaseTrtllmAttentionMetadata +from .interface import AttentionTensorLayout + + +class TrtllmAttentionMetadata: + """ + Simplified metadata adapter for diffusion models using TRTLLM backend. + + Lazy initialization with auto-growing capacity: + - Metadata created only when capacity needs increase + - prepare() called only when seq_lens actually change + - Automatically reallocates when batch_size or seq_len exceeds current capacity + + Args: + max_batch_size: Initial batch size hint. Will grow automatically if exceeded. + max_seq_len: Initial sequence length hint. Will grow automatically if exceeded. + device: Target device for tensors. + """ + + def __init__( + self, + max_batch_size: int = 16, + max_seq_len: int = 4096, + device: Optional[torch.device] = None, + ): + # These are initial hints, not hard limits - capacity grows as needed + self.max_batch_size = max_batch_size + self.max_seq_len = max_seq_len + self.device = device or torch.device("cuda") + + # Lazily created BaseTrtllmAttentionMetadata + self._metadata: Optional[BaseTrtllmAttentionMetadata] = None + + # Track allocated capacity + self._allocated_batch_size = 0 + self._allocated_max_seq_len = 0 + + # Track prepared state + self._cached_seq_lens: Optional[torch.Tensor] = None + self._prepared = False + + def _needs_new_metadata(self, batch_size: int, max_seq_len: int) -> bool: + """Check if we need to create new metadata (capacity change).""" + return ( + self._metadata is None + or batch_size > self._allocated_batch_size + or max_seq_len > self._allocated_max_seq_len + ) + + def _needs_prepare(self, batch_size: int, seq_lens: torch.Tensor) -> bool: + """Check if we need to call prepare() (seq_lens changed).""" + if not self._prepared: + return True + if self._cached_seq_lens is None: + return True + if self._cached_seq_lens.shape[0] != batch_size: + return True + return not torch.equal(self._cached_seq_lens[:batch_size], seq_lens) + + def _create_metadata(self, batch_size: int, max_seq_len: int) -> None: + """Create new metadata with given capacity.""" + # Allocate with some headroom to avoid frequent reallocation + alloc_batch = max(batch_size, self._allocated_batch_size) + alloc_seq_len = max(max_seq_len, self._allocated_max_seq_len) + + self._metadata = BaseTrtllmAttentionMetadata( + max_num_requests=alloc_batch, + max_num_tokens=alloc_batch * alloc_seq_len, + max_num_sequences=alloc_batch, + kv_cache_manager=None, # No KV cache for diffusion + mapping=Mapping(), + runtime_features=AttentionRuntimeFeatures(), + ) + + self._allocated_batch_size = alloc_batch + self._allocated_max_seq_len = alloc_seq_len + self._prepared = False # Reset prepare state on new metadata + + def prepare( + self, + batch_size: int, + seq_lens: Union[int, torch.Tensor], + ) -> BaseTrtllmAttentionMetadata: + """ + Prepare metadata for a forward pass. + + Lazy behavior: + - Creates metadata only when capacity needs increase + - Calls prepare() only when seq_lens actually change + """ + if isinstance(seq_lens, int): + seq_lens_tensor = torch.full((batch_size,), seq_lens, dtype=torch.int32) + else: + seq_lens_tensor = seq_lens.to(dtype=torch.int32) + + max_seq_len = seq_lens_tensor.max().item() + + if self._needs_new_metadata(batch_size, max_seq_len): + self._create_metadata(batch_size, max_seq_len) + + if self._needs_prepare(batch_size, seq_lens_tensor): + self._metadata.seq_lens = seq_lens_tensor + self._metadata.num_contexts = batch_size + self._metadata.max_seq_len = max_seq_len + self._metadata.request_ids = list(range(batch_size)) + self._metadata.prepare() + + # Cache for next comparison + if self._cached_seq_lens is None or self._cached_seq_lens.shape[0] < batch_size: + self._cached_seq_lens = seq_lens_tensor.clone() + else: + self._cached_seq_lens[:batch_size].copy_(seq_lens_tensor) + self._prepared = True + + return self._metadata + + +class TrtllmAttention(BaseTrtllmAttention): + """ + TRTLLM Attention wrapper for diffusion models. + + Handles: + - Fused QKV requirement for TRTLLM kernel + - Metadata creation and preparation + - No KV cache operation + """ + + def __init__( + self, + layer_idx: int = 0, + num_heads: int = 8, + head_dim: int = 64, + num_kv_heads: Optional[int] = None, + quant_config: Optional[QuantConfig] = None, + dtype: Optional[torch.dtype] = None, + max_batch_size: int = 16, + max_seq_len: int = 4096, + ): + num_kv_heads = num_kv_heads or num_heads + + super().__init__( + layer_idx=layer_idx, + num_heads=num_heads, + num_kv_heads=num_kv_heads, + head_dim=head_dim, + quant_config=quant_config, + dtype=dtype, + ) + + # TRTLLM expects flat [B*S, H*D] format + self._preferred_layout = AttentionTensorLayout.NHD + + self.metadata = TrtllmAttentionMetadata( + max_batch_size=max_batch_size, + max_seq_len=max_seq_len, + ) + + def forward( + self, + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + batch_size: int, + seq_len: int, + attention_mask: PredefinedAttentionMask = PredefinedAttentionMask.FULL, + seq_len_kv: Optional[int] = None, + **kwargs, + ) -> torch.Tensor: + """ + Forward pass with automatic metadata handling. + + For diffusion models, expects: + - Fused QKV: q contains [Q, K, V] concatenated, k and v are None + - OR separate Q, K, V which will be fused internally + + Args: + q: Query tensor [num_tokens, hidden] or fused QKV [num_tokens, qkv_hidden] + k: Key tensor [num_tokens, kv_hidden] or None if fused + v: Value tensor [num_tokens, kv_hidden] or None if fused + batch_size: Batch size (required if not inferable) + seq_len: Sequence length for Q (required if not inferable) + attention_mask: Attention mask type + seq_len_kv: Sequence length for K/V (for cross-attention, defaults to seq_len) + + Returns: + Output tensor [num_tokens, q_hidden] + """ + # Handle cross-attention where K/V have different sequence length than Q + kv_seq_len = seq_len_kv if seq_len_kv is not None else seq_len + + # Separate Q, K, V provided - fuse them + q = q.view(batch_size * seq_len, -1) + k = k.view(batch_size * kv_seq_len, -1) + v = v.view(batch_size * kv_seq_len, -1) + qkv = torch.cat([q, k, v], dim=-1) + prepared_metadata = self.metadata.prepare(batch_size, seq_len) + output = super().forward( + q=qkv, + k=None, + v=None, + metadata=prepared_metadata, + attention_mask=attention_mask, + ) + output = output.view(batch_size, seq_len, -1) + return output + + @property + def preferred_layout(self) -> AttentionTensorLayout: + """Return the preferred tensor layout for this backend.""" + return self._preferred_layout + + @classmethod + def support_fused_qkv(cls) -> bool: + return True diff --git a/tensorrt_llm/_torch/visual_gen/attention_backend/utils.py b/tensorrt_llm/_torch/visual_gen/attention_backend/utils.py new file mode 100644 index 0000000000..835e113c55 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/attention_backend/utils.py @@ -0,0 +1,118 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Visual Generation Attention Backend Utilities + +Factory functions for creating attention backends for visual generation models. +Uses diffusion-specific wrappers (TrtllmAttention, VanillaAttention) +that handle metadata preparation internally for simplified usage. +""" + +from typing import TYPE_CHECKING, Optional, Type, Union + +import torch + +from tensorrt_llm.models.modeling_utils import QuantConfig + +# Lazy imports to avoid circular dependency +if TYPE_CHECKING: + from .trtllm import TrtllmAttention + from .vanilla import VanillaAttention + + # Type alias for diffusion attention backends + DiffusionAttentionBackend = Union[TrtllmAttention, VanillaAttention] + + +def get_visual_gen_attention_backend( + backend_name: str, +) -> Type["DiffusionAttentionBackend"]: + """ + Get diffusion attention backend class by name. + + Args: + backend_name: Backend identifier ("VANILLA", "TRTLLM") + + Returns: + Diffusion attention backend class + + Backend Selection Guide: + - "VANILLA": Full support for cross-attention (different Q/KV seq lengths) + Uses torch SDPA backend + - "TRTLLM": Optimized for self-attention (requires same Q/KV seq lengths) + Better performance but requires fused QKV + """ + # Lazy imports to avoid circular dependency + from .trtllm import TrtllmAttention + from .vanilla import VanillaAttention + + backend_name = backend_name.upper() + + if backend_name == "VANILLA": + return VanillaAttention + elif backend_name == "TRTLLM": + return TrtllmAttention + else: + # Default to VANILLA for maximum compatibility + return VanillaAttention + + +def create_attention( + backend: str, + layer_idx: int, + num_heads: int, + head_dim: int, + num_kv_heads: Optional[int] = None, + quant_config: Optional[QuantConfig] = None, + dtype: Optional[torch.dtype] = None, + max_batch_size: int = 16, + max_seq_len: int = 4096, + **kwargs, +) -> "DiffusionAttentionBackend": + """ + Factory function to create attention backend instance for visual generation. + + Creates diffusion-specific attention backends that handle metadata preparation + internally, simplifying the forward() call. + + Args: + backend: Backend identifier ("VANILLA", "TRTLLM") + layer_idx: Layer index in the model + num_heads: Number of attention heads + head_dim: Dimension per head + num_kv_heads: Number of KV heads (for GQA/MQA, defaults to num_heads) + quant_config: Optional quantization configuration + dtype: Data type for the attention + max_batch_size: Initial batch size for metadata pre-allocation. The backend + will automatically reallocate if larger batches are encountered. + max_seq_len: Initial sequence length for metadata pre-allocation. The backend + will automatically reallocate if longer sequences are encountered. + **kwargs: Additional backend-specific arguments + + Returns: + Diffusion attention backend instance (TrtllmAttention or VanillaAttention) + """ + attn_cls = get_visual_gen_attention_backend(backend) + + return attn_cls( + layer_idx=layer_idx, + num_heads=num_heads, + head_dim=head_dim, + num_kv_heads=num_kv_heads, + quant_config=quant_config, + dtype=dtype, + max_batch_size=max_batch_size, + max_seq_len=max_seq_len, + **kwargs, + ) diff --git a/tensorrt_llm/_torch/visual_gen/attention_backend/vanilla.py b/tensorrt_llm/_torch/visual_gen/attention_backend/vanilla.py new file mode 100644 index 0000000000..d9eb41ca55 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/attention_backend/vanilla.py @@ -0,0 +1,126 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Diffusion Vanilla Attention Backend + +Simple attention implementation for visual generation (diffusion) models using +torch.nn.functional.scaled_dot_product_attention (SDPA). + +Supports both self-attention and cross-attention (different Q/KV sequence lengths). +No KV cache - full recompute each diffusion step. +""" + +import math +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...attention_backend.interface import PredefinedAttentionMask +from .interface import AttentionTensorLayout + + +class VanillaAttention(nn.Module): + """ + Vanilla Attention for diffusion models using torch SDPA. + + Uses torch.nn.functional.scaled_dot_product_attention which: + - Properly handles cross-attention (different Q/KV sequence lengths) + - Uses Flash Attention 2 when available (via SDPA backend selection) + - No KV cache needed for diffusion models + + This is simpler than the LLM VanillaAttention which has complex + KV cache handling and uses flash_attn_varlen_func. + """ + + def __init__( + self, + layer_idx: int = 0, + num_heads: int = 8, + head_dim: int = 64, + num_kv_heads: Optional[int] = None, + dtype: Optional[torch.dtype] = None, + **kwargs, + ): + super().__init__() + + self.layer_idx = layer_idx + self.num_heads = num_heads + self.head_dim = head_dim + self.num_kv_heads = num_kv_heads or num_heads + self.dtype = dtype + self.scale = 1.0 / math.sqrt(head_dim) + + # SDPA expects [B, H, S, D] format + self._preferred_layout = AttentionTensorLayout.HND + + def forward( + self, + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + batch_size: int, + seq_len: int, + seq_len_kv: Optional[int] = None, + attention_mask: PredefinedAttentionMask = PredefinedAttentionMask.FULL, + **kwargs, + ) -> torch.Tensor: + """ + Forward pass using torch SDPA. + + Args: + q: Query tensor [num_tokens, num_heads * head_dim] + k: Key tensor [num_kv_tokens, num_kv_heads * head_dim] + v: Value tensor [num_kv_tokens, num_kv_heads * head_dim] + batch_size: Batch size + seq_len: Query sequence length + seq_len_kv: KV sequence length (for cross-attention) + attention_mask: Attention mask type (CAUSAL or FULL) + + Returns: + Output tensor [num_tokens, num_heads * head_dim] + """ + is_causal = attention_mask == PredefinedAttentionMask.CAUSAL + + # Validate tensor shapes - flexible for Ulysses head sharding + # Expected: [batch_size, num_heads, seq_len, head_dim] + # Note: num_heads may be sharded (num_heads // ulysses_size) when using Ulysses + assert ( + q.dim() == 4 + and q.shape[0] == batch_size + and q.shape[2] == seq_len + and q.shape[3] == self.head_dim + ), ( + f"Invalid q shape: expected [B={batch_size}, H, S={seq_len}, D={self.head_dim}], got {q.shape}" + ) + assert k.dim() == 4 and k.shape[0] == batch_size and k.shape[3] == self.head_dim, ( + f"Invalid k shape: expected [B={batch_size}, H_kv, S_kv, D={self.head_dim}], got {k.shape}" + ) + assert v.dim() == 4 and v.shape[0] == batch_size and v.shape[3] == self.head_dim, ( + f"Invalid v shape: expected [B={batch_size}, H_kv, S_kv, D={self.head_dim}], got {v.shape}" + ) + + # TODO: Maybe we need to enforce cuDNN backend here + return F.scaled_dot_product_attention(q, k, v, is_causal=is_causal, scale=self.scale) + + @property + def preferred_layout(self) -> AttentionTensorLayout: + """Return the preferred tensor layout for this backend.""" + return self._preferred_layout + + @classmethod + def support_fused_qkv(cls) -> bool: + return False diff --git a/tensorrt_llm/_torch/visual_gen/checkpoints/__init__.py b/tensorrt_llm/_torch/visual_gen/checkpoints/__init__.py new file mode 100644 index 0000000000..6d3b138f90 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/checkpoints/__init__.py @@ -0,0 +1,7 @@ +"""Diffusion model checkpoint loading utilities.""" + +from .weight_loader import WeightLoader + +__all__ = [ + "WeightLoader", +] diff --git a/tensorrt_llm/_torch/visual_gen/checkpoints/weight_loader.py b/tensorrt_llm/_torch/visual_gen/checkpoints/weight_loader.py new file mode 100644 index 0000000000..77067fe9c9 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/checkpoints/weight_loader.py @@ -0,0 +1,152 @@ +"""Weight loader for diffusion models.""" + +import json +from pathlib import Path +from typing import Any, Dict, List, Union + +import torch +import tqdm + +from tensorrt_llm._torch.models.checkpoints.base_weight_loader import BaseWeightLoader +from tensorrt_llm._torch.visual_gen.config import PipelineComponent +from tensorrt_llm.logger import logger +from tensorrt_llm.mapping import Mapping + + +class WeightLoader(BaseWeightLoader): + """ + Weight loader for diffusion models. + + Loads weights from safetensors/bin files, similar to HfWeightLoader + but simpler (no parallel loading optimization for now). + + Supports loading multiple components (e.g., transformer and transformer_2): + loader = WeightLoader(components=["transformer", "transformer_2"]) + weights = loader.load_weights(ckpt_dir, mapping) + # Returns: {"transformer": {...}, "transformer_2": {...}} + """ + + def __init__(self, components: Union[str, List[str]] = PipelineComponent.TRANSFORMER): + """ + Args: + components: Component(s) to load weights for. Can be: + - Single string: "transformer" (returns flat dict) + - List of strings: ["transformer", "transformer_2"] (returns nested dict) + """ + if isinstance(components, str): + self.components = [components] + self.single_component = True + else: + self.components = components + self.single_component = False + + def load_weights( + self, + checkpoint_dir: str, + mapping: Mapping, + **kwargs, + ) -> Dict[str, Any]: + """ + Load weights from checkpoint directory. + + Args: + checkpoint_dir: Path to checkpoint (pipeline root or component dir) + mapping: Distributed mapping (for future TP/PP support) + + Returns: + - If single component: Dict mapping weight names to tensors + - If multiple components: Dict mapping component names to weight dicts + Example: {"transformer": {...}, "transformer_2": {...}} + """ + checkpoint_path = Path(checkpoint_dir) + + # Check if this is a pipeline (has model_index.json) + model_index = checkpoint_path / "model_index.json" + is_pipeline = model_index.exists() + + # Load weights for each component + all_weights = {} + for component in self.components: + if is_pipeline: + # Pipeline format: load from component subdirectory + component_dir = checkpoint_path / component + if not component_dir.exists(): + raise ValueError(f"Component '{component}' not found in {checkpoint_dir}") + weight_dir = component_dir + else: + # Standalone model (only valid for single component) + if len(self.components) > 1: + raise ValueError( + f"Multiple components specified but {checkpoint_dir} is not a pipeline " + "(no model_index.json found)" + ) + weight_dir = checkpoint_path + + # Find weight files + weight_files = self._find_weight_files(weight_dir) + if not weight_files: + raise ValueError(f"No weight files found in {weight_dir}") + + # Load all weights with progress bar + component_weights = {} + desc = f"Loading {component}" if is_pipeline else "Loading checkpoint" + for wf in tqdm.tqdm(weight_files, desc=desc): + component_weights.update(self._load_file(wf)) + + all_weights[component] = component_weights + + # Return flat dict for single component (backward compatibility) + if self.single_component: + return all_weights[self.components[0]] + + # Return nested dict for multiple components + return all_weights + + def _find_weight_files(self, weight_dir) -> List[str]: + """Find safetensors or bin weight files. + + Handles: + - Single safetensors file + - Sharded safetensors with index.json + - PyTorch bin/pth files + """ + weight_dir = Path(weight_dir) + + # Check for sharded safetensors index + index_file = weight_dir / "diffusion_pytorch_model.safetensors.index.json" + if not index_file.exists(): + index_file = weight_dir / "model.safetensors.index.json" + + if index_file.exists(): + # Sharded safetensors: read index to get all shard files + with open(index_file) as f: + index = json.load(f) + shard_files = set(index.get("weight_map", {}).values()) + return sorted([str(weight_dir / f) for f in shard_files]) + + # Single safetensors file + files = list(weight_dir.glob("*.safetensors")) + if files: + # Filter out consolidated if multiple files exist + if len(files) > 1: + files = [f for f in files if "consolidated" not in f.name] + return sorted([str(f) for f in files]) + + # Fallback to bin + files = list(weight_dir.glob("*.bin")) + if files: + return sorted([str(f) for f in files]) + + # Fallback to pth + files = list(weight_dir.glob("*.pth")) + return sorted([str(f) for f in files]) + + def _load_file(self, filepath: str) -> Dict[str, Any]: + """Load weights from a single file.""" + logger.debug(f"Loading {filepath}") + if filepath.endswith(".safetensors"): + from safetensors.torch import load_file + + return load_file(filepath) + else: + return torch.load(filepath, map_location="cpu", weights_only=True) diff --git a/tensorrt_llm/_torch/visual_gen/config.py b/tensorrt_llm/_torch/visual_gen/config.py new file mode 100644 index 0000000000..c5666ae0b3 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/config.py @@ -0,0 +1,565 @@ +import json +import os +from enum import Enum +from pathlib import Path +from types import SimpleNamespace +from typing import Any, Dict, List, Literal, Optional, Tuple + +import torch +from pydantic import BaseModel, ConfigDict, model_validator +from pydantic import Field as PydanticField + +from tensorrt_llm.functional import AllReduceStrategy +from tensorrt_llm.mapping import Mapping +from tensorrt_llm.models.modeling_utils import QuantConfig +from tensorrt_llm.quantization.mode import QuantAlgo + +# ============================================================================= +# Pipeline component identifiers +# ============================================================================= + + +class PipelineComponent(str, Enum): + """Identifiers for pipeline components that can be loaded or skipped. + + Inherits from str so values compare equal to plain strings, + e.g. ``PipelineComponent.VAE == "vae"`` is ``True``. + """ + + TRANSFORMER = "transformer" + VAE = "vae" + TEXT_ENCODER = "text_encoder" + TOKENIZER = "tokenizer" + SCHEDULER = "scheduler" + IMAGE_ENCODER = "image_encoder" + IMAGE_PROCESSOR = "image_processor" + + +# ============================================================================= +# Sub-configuration classes for DiffusionArgs +# ============================================================================= + + +class AttentionConfig(BaseModel): + """Configuration for Attention layers.""" + + backend: Literal["VANILLA", "TRTLLM"] = PydanticField( + "VANILLA", description="Attention backend: VANILLA (PyTorch SDPA), TRTLLM" + ) + + +class ParallelConfig(BaseModel): + """Configuration for distributed parallelism. + + Currently Supported: + - dit_cfg_size: CFG (Classifier-Free Guidance) parallelism + - dit_ulysses_size: Ulysses sequence parallelism + + Not Yet Supported: + - dit_tp_size: Tensor parallelism (not implemented) + - dit_ring_size: Ring attention (not implemented) + - dit_cp_size, dit_dp_size, dit_fsdp_size: Other parallelism types + + Total world_size = dit_cfg_size Ɨ dit_ulysses_size + + Parallelism Strategy: + - CFG Parallelism: Distributes positive/negative prompts across GPUs + - Ulysses Parallelism: Distributes sequence within each CFG group + + Example Configurations: + 1. cfg_size=1, ulysses_size=2 -> 2 GPUs (Ulysses only) + GPU 0-1: Single prompt, sequence parallelism across 2 GPUs + + 2. cfg_size=2, ulysses_size=1 -> 2 GPUs (CFG only) + GPU 0: Positive prompt + GPU 1: Negative prompt + + 3. cfg_size=2, ulysses_size=2 -> 4 GPUs (CFG + Ulysses) + GPU 0-1: CFG group 0 (positive), Ulysses parallel + GPU 2-3: CFG group 1 (negative), Ulysses parallel + + 4. cfg_size=2, ulysses_size=4 -> 8 GPUs (CFG + Ulysses) + GPU 0-3: CFG group 0 (positive), Ulysses parallel + GPU 4-7: CFG group 1 (negative), Ulysses parallel + """ + + disable_parallel_vae: bool = False + parallel_vae_split_dim: Literal["width", "height"] = "width" + + # DiT Parallelism + dit_dp_size: int = PydanticField(1, ge=1) + dit_tp_size: int = PydanticField(1, ge=1) # Not yet supported + dit_ulysses_size: int = PydanticField(1, ge=1) # Supported + dit_ring_size: int = PydanticField(1, ge=1) # Not yet supported + dit_cp_size: int = PydanticField(1, ge=1) + dit_cfg_size: int = PydanticField(1, ge=1) # Supported + dit_fsdp_size: int = PydanticField(1, ge=1) + + # Refiner Parallelism (Optional) + refiner_dit_dp_size: int = 1 + refiner_dit_tp_size: int = 1 + refiner_dit_ulysses_size: int = 1 + refiner_dit_ring_size: int = 1 + refiner_dit_cp_size: int = 1 + refiner_dit_cfg_size: int = 1 + refiner_dit_fsdp_size: int = 1 + + t5_fsdp_size: int = 1 + + def to_mapping(self) -> Mapping: + """Convert to TRT-LLM Mapping.""" + world_size = self.dit_tp_size * self.dit_cp_size + return Mapping( + world_size=world_size, + tp_size=self.dit_tp_size, + pp_size=1, + cp_size=self.dit_cp_size, + ) + + @model_validator(mode="after") + def validate_parallel_sizes(self) -> "ParallelConfig": + """Validate configuration against current environment.""" + if torch.cuda.is_available(): + world_size = int(os.environ.get("WORLD_SIZE", 1)) + total_parallel = ( + self.dit_tp_size + * self.dit_ulysses_size + * self.dit_ring_size + * self.dit_cp_size + * self.dit_dp_size + * self.dit_cfg_size + ) + if total_parallel > world_size: + raise ValueError( + f"Total DiT parallel size ({total_parallel}) exceeds WORLD_SIZE ({world_size})" + ) + return self + + +class TeaCacheConfig(BaseModel): + """Configuration for TeaCache runtime optimization. + + TeaCache speeds up diffusion by caching transformer outputs when timestep + embeddings change slowly. It monitors embedding distances and reuses cached + residuals when changes are below a threshold. + + Attributes: + enable_teacache: Enable TeaCache optimization + teacache_thresh: Distance threshold for cache decisions (lower = more caching) + use_ret_steps: Use aggressive warmup mode (5 steps) vs minimal (1 step) + coefficients: Polynomial coefficients for rescaling embedding distances + Applied as: rescaled_distance = poly(raw_distance) + ret_steps: Number of warmup steps (always compute, initialized at runtime) + cutoff_steps: Step to stop caching (always compute after, initialized at runtime) + num_steps: Total inference steps (set at runtime) + _cnt: Internal step counter (reset per generation) + """ + + enable_teacache: bool = False + teacache_thresh: float = PydanticField(0.2, gt=0.0) + use_ret_steps: bool = True + + coefficients: List[float] = PydanticField(default_factory=lambda: [1.0, 0.0]) + + # Runtime state fields (initialized by TeaCacheBackend.refresh) + ret_steps: Optional[int] = None + cutoff_steps: Optional[int] = None + num_steps: Optional[int] = None + + # State tracking (reset per generation) + _cnt: int = 0 + + model_config = ConfigDict(arbitrary_types_allowed=True) + + @model_validator(mode="after") + def validate_teacache(self) -> "TeaCacheConfig": + """Validate TeaCache configuration.""" + # Validate coefficients + if len(self.coefficients) == 0: + raise ValueError("TeaCache coefficients list cannot be empty") + + # Validate ret_steps if set + if self.ret_steps is not None and self.ret_steps < 0: + raise ValueError(f"ret_steps must be non-negative, got {self.ret_steps}") + + # Validate cutoff_steps vs num_steps if both set + if self.cutoff_steps is not None and self.num_steps is not None: + if self.cutoff_steps > self.num_steps: + raise ValueError( + f"cutoff_steps ({self.cutoff_steps}) cannot exceed num_steps ({self.num_steps})" + ) + + return self + + +class PipelineConfig(BaseModel): + """General pipeline configuration.""" + + enable_torch_compile: bool = True + torch_compile_models: str = PipelineComponent.TRANSFORMER + torch_compile_mode: str = "default" + fuse_qkv: bool = True + + # Offloading Config + enable_offloading: bool = False + offload_device: Literal["cpu", "cuda"] = "cpu" + offload_param_pin_memory: bool = True + + +# ============================================================================= +# DiffusionArgs - User-facing configuration (CLI / YAML) +# ============================================================================= + + +class DiffusionArgs(BaseModel): + """User-facing configuration for diffusion model loading and inference. + + This is the main config class used in CLI args and YAML config files. + PipelineLoader converts this to DiffusionModelConfig internally. + + Example: + args = DiffusionArgs( + checkpoint_path="/path/to/model", + quant_config={"quant_algo": "FP8_BLOCK_SCALES", "dynamic": True}, + parallel=ParallelConfig(dit_tp_size=2), + ) + loader = PipelineLoader() + pipeline = loader.load(args) + """ + + model_config = ConfigDict(arbitrary_types_allowed=True) + + # Required: Path to checkpoint or HuggingFace Hub model ID + checkpoint_path: str = PydanticField( + "", + description=( + "Local directory path or HuggingFace Hub model ID " + "(e.g., 'Wan-AI/Wan2.1-T2V-1.3B-Diffusers'). " + "Hub models are downloaded and cached automatically." + ), + ) + + # HuggingFace Hub options + revision: Optional[str] = PydanticField( + None, + description="HuggingFace Hub revision (branch, tag, or commit SHA) to download.", + ) + + # Device/dtype options + device: str = "cuda" + dtype: str = "bfloat16" + + # Component loading options (use PipelineComponent enum values or plain strings) + skip_components: List[PipelineComponent] = PydanticField( + default_factory=list, + description=( + "Components to skip loading. " + "Accepts PipelineComponent enum values or equivalent strings " + "(e.g., [PipelineComponent.TEXT_ENCODER, PipelineComponent.VAE])" + ), + ) + + # Sub-configs (dict input for quant_config is coerced to QuantConfig in model_validator) + quant_config: QuantConfig = PydanticField(default_factory=QuantConfig) + pipeline: PipelineConfig = PydanticField(default_factory=PipelineConfig) + attention: AttentionConfig = PydanticField(default_factory=AttentionConfig) + parallel: ParallelConfig = PydanticField(default_factory=ParallelConfig) + teacache: TeaCacheConfig = PydanticField(default_factory=TeaCacheConfig) + + # Set by model_validator when quant_config is provided as a dict (ModelOpt format) + dynamic_weight_quant: bool = False + force_dynamic_quantization: bool = False + + @model_validator(mode="before") + @classmethod + def _parse_quant_config_dict(cls, data: Any) -> Any: + """Parse user-facing DiffusionArgs.quant_config (dict or None) into QuantConfig and dynamic flags. + + User input is ModelOpt-format dict (e.g. {"quant_algo": "FP8", "dynamic": True}). + We coerce it to QuantConfig + dynamic_weight_quant + force_dynamic_quantization so that + from_pretrained() can copy them into DiffusionModelConfig (internal) without parsing again. + """ + if not isinstance(data, dict): + return data + raw = data.get("quant_config") + if raw is None: + data = {**data, "quant_config": QuantConfig()} + return data + if not isinstance(raw, dict): + return data + qc, _, dwq, daq = DiffusionModelConfig.load_diffusion_quant_config(raw) + data = { + **data, + "quant_config": qc, + "dynamic_weight_quant": dwq, + "force_dynamic_quantization": daq, + } + return data + + def to_mapping(self) -> Mapping: + """Derive Mapping from ParallelConfig.""" + return self.parallel.to_mapping() + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary.""" + return self.model_dump() + + @classmethod + def from_dict(cls, config_dict: Dict[str, Any]) -> "DiffusionArgs": + """Create from dictionary with automatic nested config parsing. + + Pydantic automatically handles nested configs, but we keep this method + for backward compatibility and to filter unknown fields. + """ + # Get valid field names for DiffusionArgs + valid_fields = set(cls.model_fields.keys()) + + # Filter to only include valid fields (ignore unknown fields) + filtered_dict = {k: v for k, v in config_dict.items() if k in valid_fields} + + # Pydantic automatically converts nested dicts to their respective config classes + return cls(**filtered_dict) + + +# ============================================================================= +# Utilities +# ============================================================================= + + +def discover_pipeline_components(checkpoint_path: Path) -> Dict[str, Path]: + """ + Discover components from diffusers pipeline's model_index.json. + + Returns dict mapping component name to config.json path. + """ + model_index_path = checkpoint_path / "model_index.json" + if not model_index_path.exists(): + return {} + + with open(model_index_path) as f: + model_index = json.load(f) + + components = {} + for key, value in model_index.items(): + if key.startswith("_") or value is None: + continue + config_path = checkpoint_path / key / "config.json" + if config_path.exists(): + components[key] = config_path + + return components + + +# ============================================================================= +# DiffusionModelConfig - Internal configuration (merged/parsed) +# ============================================================================= + + +class DiffusionModelConfig(BaseModel): + """Internal ModelConfig for diffusion models. + + This is created by PipelineLoader from DiffusionArgs + checkpoint. + Contains merged/parsed config from: + - pretrained_config: From checkpoint/config.json + - quant_config: From checkpoint or user quant config + - Sub-configs: From DiffusionArgs (pipeline, attention, parallel, teacache) + """ + + model_config = ConfigDict(arbitrary_types_allowed=True) + + pretrained_config: Optional[Any] = None + mapping: Mapping = PydanticField(default_factory=Mapping) + skip_create_weights_in_init: bool = False + force_dynamic_quantization: bool = False + allreduce_strategy: AllReduceStrategy = PydanticField(default=AllReduceStrategy.AUTO) + extra_attrs: Dict = PydanticField(default_factory=dict) + + # Distributed process groups + ulysses_process_group: Optional[torch.distributed.ProcessGroup] = None + + dynamic_weight_quant: bool = False + + # Sub-configs from DiffusionArgs (merged during from_pretrained) + quant_config: QuantConfig = PydanticField(default_factory=QuantConfig) + # Per-layer quant (from load_diffusion_quant_config layer_quant_config; None until mixed-precision parsing exists) + quant_config_dict: Optional[Dict[str, QuantConfig]] = None + pipeline: PipelineConfig = PydanticField(default_factory=PipelineConfig) + attention: AttentionConfig = PydanticField(default_factory=AttentionConfig) + parallel: ParallelConfig = PydanticField(default_factory=ParallelConfig) + teacache: TeaCacheConfig = PydanticField(default_factory=TeaCacheConfig) + + @property + def torch_dtype(self) -> "torch.dtype": + """Get the torch dtype of the model (default: bfloat16).""" + return torch.bfloat16 + + def get_quant_config(self, name: Optional[str] = None) -> QuantConfig: + """Get quantization config for a layer or global. Resembles LLM ModelConfig.get_quant_config.""" + if name is None or self.quant_config_dict is None: + return self.quant_config + if name in self.quant_config_dict: + return self.quant_config_dict[name] + return self.quant_config + + @staticmethod + def load_diffusion_quant_config( + quant_config_dict: dict, + ) -> Tuple[QuantConfig, Optional[Dict], bool, bool]: + """ + Parse quantization config in ModelOpt format. + + Returns: (quant_config, layer_quant_config, dynamic_weight_quant, dynamic_activation_quant) + - quant_config: Global QuantConfig + - layer_quant_config: Per-layer config dict (None if not using mixed precision) + - dynamic_weight_quant: Whether to quantize weights at load time + - dynamic_activation_quant: Whether to quantize activations dynamically + """ + quant_algo_str = quant_config_dict.get("quant_algo") + quant_algo = None + if quant_algo_str: + algo_map = { + "FP8": QuantAlgo.FP8, + "FP8_BLOCK_SCALES": QuantAlgo.FP8_BLOCK_SCALES, + "NVFP4": QuantAlgo.NVFP4, + "W4A16_AWQ": QuantAlgo.W4A16_AWQ, + "W4A8_AWQ": QuantAlgo.W4A8_AWQ, + "W8A8_SQ_PER_CHANNEL": QuantAlgo.W8A8_SQ_PER_CHANNEL, + } + quant_algo = algo_map.get(quant_algo_str) + if quant_algo is None: + raise ValueError(f"Unknown quant_algo: {quant_algo_str}") + + # Parse group_size and dynamic flags from config_groups + group_size = None + dynamic_weight_quant = False + dynamic_activation_quant = False + for group_config in quant_config_dict.get("config_groups", {}).values(): + weights_config = group_config.get("weights", {}) + activations_config = group_config.get("input_activations", {}) + dynamic_weight_quant = weights_config.get("dynamic", False) + dynamic_activation_quant = activations_config.get("dynamic", False) + # Extract group_size from weights config (e.g., NVFP4: group_size=16) + if group_size is None: + group_size = weights_config.get("group_size") + break + + # Set defaults based on quant_algo if group_size not specified + if group_size is None: + if quant_algo in (QuantAlgo.NVFP4,): + group_size = 16 # NVFP4 default + elif quant_algo == QuantAlgo.FP8_BLOCK_SCALES: + group_size = 128 # FP8 blockwise default + + # Auto-enable dynamic weight quantization if quant_algo is specified + # but no explicit config_groups setting is present. + # This allows simple configs like {"quant_algo": "FP8"} to work. + if quant_algo is not None and not quant_config_dict.get("config_groups"): + dynamic_weight_quant = quant_config_dict.get("dynamic", True) + + quant_config = QuantConfig( + quant_algo=quant_algo, + group_size=group_size, + exclude_modules=quant_config_dict.get("ignore"), + ) + + # TODO: Per-layer config (None for now - future: parse mixed precision settings) + layer_quant_config = None + + return quant_config, layer_quant_config, dynamic_weight_quant, dynamic_activation_quant + + @classmethod + def from_pretrained( + cls, + checkpoint_dir: str, + args: Optional["DiffusionArgs"] = None, + **kwargs, + ) -> "DiffusionModelConfig": + """ + Load config from pretrained checkpoint. + + Called by PipelineLoader with DiffusionArgs: + config = DiffusionModelConfig.from_pretrained( + checkpoint_dir=args.checkpoint_path, + args=args, + ) + + Args: + checkpoint_dir: Path to checkpoint + args: DiffusionArgs containing user config (quant, pipeline, attention, parallel, teacache) + **kwargs: Additional config options (e.g., mapping) + """ + kwargs.pop("trust_remote_code", None) + + # Extract sub-configs from args or use defaults + pipeline_cfg = args.pipeline if args else PipelineConfig() + attention_cfg = args.attention if args else AttentionConfig() + parallel_cfg = args.parallel if args else ParallelConfig() + teacache_cfg = args.teacache if args else TeaCacheConfig() + + component = PipelineComponent.TRANSFORMER + checkpoint_path = Path(checkpoint_dir) + + # Discover pipeline components + components = discover_pipeline_components(checkpoint_path) + + # Determine config path + if components: + if component not in components: + raise ValueError( + f"Component '{component}' not found. Available: {list(components.keys())}" + ) + config_path = components[component] + else: + config_path = checkpoint_path / "config.json" + + if not config_path.exists(): + raise ValueError(f"Config not found at {config_path}") + + # Load pretrained_config from checkpoint + with open(config_path) as f: + config_dict = json.load(f) + pretrained_config = SimpleNamespace(**config_dict) + + model_index_path = checkpoint_path / "model_index.json" + if model_index_path.exists(): + with open(model_index_path) as f: + model_index = json.load(f) + if "boundary_ratio" in model_index and "transformer_2" in model_index: + transformer_2_spec = model_index.get("transformer_2") + if transformer_2_spec and transformer_2_spec[0] is not None: + pretrained_config.boundary_ratio = model_index["boundary_ratio"] + + # Resolve quant config: use args if user set quant (QuantConfig from dict), else checkpoint + if args and args.quant_config.quant_algo is not None: + quant_config = args.quant_config + quant_config_dict = ( + None # DiffusionArgs has no per-layer dict; only from checkpoint parse + ) + dynamic_weight_quant = args.dynamic_weight_quant + dynamic_activation_quant = args.force_dynamic_quantization + else: + quant_config = QuantConfig() + quant_config_dict = None + dynamic_weight_quant = False + dynamic_activation_quant = False + quant_dict = getattr(pretrained_config, "quantization_config", None) + if isinstance(quant_dict, dict): + quant_config, quant_config_dict, dynamic_weight_quant, dynamic_activation_quant = ( + cls.load_diffusion_quant_config(quant_dict) + ) + + return cls( + pretrained_config=pretrained_config, + quant_config=quant_config, + quant_config_dict=quant_config_dict, + dynamic_weight_quant=dynamic_weight_quant, + force_dynamic_quantization=dynamic_activation_quant, + # Sub-configs from DiffusionArgs + pipeline=pipeline_cfg, + attention=attention_cfg, + parallel=parallel_cfg, + teacache=teacache_cfg, + # Delay weight creation after apply_quant_config_exclude_modules() in __post_init__ + skip_create_weights_in_init=True, + **kwargs, + ) diff --git a/tensorrt_llm/_torch/visual_gen/executor.py b/tensorrt_llm/_torch/visual_gen/executor.py new file mode 100644 index 0000000000..d6e03bdcfe --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/executor.py @@ -0,0 +1,246 @@ +import os +import queue +import threading +import traceback +from dataclasses import dataclass +from typing import List, Optional, Union + +import torch +import torch.distributed as dist +import zmq + +from tensorrt_llm._torch.visual_gen.config import DiffusionArgs +from tensorrt_llm._torch.visual_gen.output import MediaOutput +from tensorrt_llm._torch.visual_gen.pipeline_loader import PipelineLoader +from tensorrt_llm.executor.ipc import ZeroMqQueue +from tensorrt_llm.logger import logger + + +@dataclass +class DiffusionRequest: + """Request for diffusion inference with explicit model-specific parameters.""" + + request_id: int + prompt: str + negative_prompt: Optional[str] = None + height: int = 720 + width: int = 1280 + num_inference_steps: int = 50 + guidance_scale: float = 5.0 + max_sequence_length: int = 512 + seed: int = 42 + + # Video-specific parameters + num_frames: int = 81 + frame_rate: float = 24.0 + + # Image-specific parameters + num_images_per_prompt: int = 1 + + # Advanced parameters + guidance_rescale: float = 0.0 + output_type: str = "pt" + + # Wan-specific parameters + image: Optional[Union[str, List[str]]] = None + guidance_scale_2: Optional[float] = None + boundary_ratio: Optional[float] = None + last_image: Optional[Union[str, List[str]]] = None + + +@dataclass +class DiffusionResponse: + """Response with model-specific output. + + Attributes: + request_id: Unique identifier for the request + output: Generated media as MediaOutput with model-specific fields populated + error_msg: Error message if generation failed + """ + + request_id: int + output: Optional[MediaOutput] = None + error_msg: Optional[str] = None + + +class DiffusionExecutor: + """Execution engine for diffusion models running in worker processes.""" + + def __init__( + self, + model_path: str, + request_queue_addr: str, + response_queue_addr: str, + device_id: int, + diffusion_config: Optional[dict] = None, + ): + self.model_path = model_path + self.request_queue_addr = request_queue_addr + self.response_queue_addr = response_queue_addr + self.device_id = device_id + self.diffusion_config = diffusion_config + + self.requests_ipc = None + self.rank = dist.get_rank() + self.response_queue = queue.Queue() + self.sender_thread = None + + # Only rank 0 handles IPC + if self.rank == 0: + logger.info(f"Worker {device_id}: Connecting to request queue") + self.requests_ipc = ZeroMqQueue( + (request_queue_addr, None), + is_server=False, + socket_type=zmq.PULL, + use_hmac_encryption=False, + ) + self.sender_thread = threading.Thread(target=self._sender_loop, daemon=True) + self.sender_thread.start() + + self._load_pipeline() + + def _sender_loop(self): + """Background thread for sending responses.""" + logger.info(f"Worker {self.device_id}: Connecting to response queue") + responses_ipc = ZeroMqQueue( + (self.response_queue_addr, None), + is_server=False, + socket_type=zmq.PUSH, + use_hmac_encryption=False, + ) + + while True: + try: + resp = self.response_queue.get() + if resp is None: + break + responses_ipc.put(resp) + except Exception as e: + logger.error(f"Worker {self.device_id}: Sender error: {e}") + + if responses_ipc.socket: + responses_ipc.socket.setsockopt(zmq.LINGER, 0) + responses_ipc.close() + + def _load_pipeline(self): + """ + Load pipeline using proper flow: + DiffusionArgs ā†’ PipelineLoader ā†’ DiffusionModelConfig ā†’ AutoPipeline ā†’ BasePipeline + """ + logger.info(f"Worker {self.device_id}: Loading pipeline") + + try: + # Convert diffusion_config dict to DiffusionArgs + config_dict = self.diffusion_config.copy() + config_dict["checkpoint_path"] = self.model_path + config_dict["device"] = f"cuda:{self.device_id}" + + # Create DiffusionArgs from dict (handles nested configs) + args = DiffusionArgs.from_dict(config_dict) + + # Use PipelineLoader for proper pipeline creation flow: + # PipelineLoader.load() internally: + # 1. Creates DiffusionModelConfig.from_pretrained() + # 2. Creates pipeline via AutoPipeline.from_config() + # 3. Loads weights with quantization support + # 4. Calls post_load_weights() + loader = PipelineLoader(args) + self.pipeline = loader.load() + + except Exception as e: + logger.error(f"Worker {self.device_id}: Failed to load pipeline: {e}") + raise + + logger.info(f"Worker {self.device_id}: Pipeline ready") + + # Sync all workers + dist.barrier() + + # Send READY signal + if self.rank == 0: + logger.info(f"Worker {self.device_id}: Sending READY") + self.response_queue.put(DiffusionResponse(request_id=-1, output="READY")) + + def serve_forever(self): + """Main execution loop.""" + while True: + req = None + if self.rank == 0: + req = self.requests_ipc.get() + logger.info(f"Worker {self.device_id}: Request available") + + # Broadcast to all ranks + obj_list = [req] + dist.broadcast_object_list(obj_list, src=0) + req = obj_list[0] + + if req is None: + logger.info(f"Worker {self.device_id}: Shutdown signal received") + if self.rank == 0 and self.sender_thread: + self.response_queue.put(None) + self.sender_thread.join() + break + + logger.info(f"Worker {self.device_id}: Processing request {req.request_id}") + self.process_request(req) + + def process_request(self, req: DiffusionRequest): + """Process a single request.""" + try: + output = self.pipeline.infer(req) + if self.rank == 0: + self.response_queue.put(DiffusionResponse(request_id=req.request_id, output=output)) + except Exception as e: + logger.error(f"Worker {self.device_id}: Error: {e}") + logger.error(traceback.format_exc()) + if self.rank == 0: + self.response_queue.put( + DiffusionResponse(request_id=req.request_id, error_msg=str(e)) + ) + + +def run_diffusion_worker( + rank: int, + world_size: int, + master_addr: str, + master_port: int, + model_path: str, + request_queue_addr: str, + response_queue_addr: str, + diffusion_config: Optional[dict] = None, +): + """Entry point for worker process.""" + try: + # Setup distributed env ā€” use PyTorch distributed, not MPI + os.environ["TLLM_DISABLE_MPI"] = "1" + os.environ["MASTER_ADDR"] = master_addr + os.environ["MASTER_PORT"] = str(master_port) + os.environ["RANK"] = str(rank) + os.environ["WORLD_SIZE"] = str(world_size) + + # Calculate device_id before init_process_group + device_id = rank % torch.cuda.device_count() if torch.cuda.is_available() else 0 + if torch.cuda.is_available(): + torch.cuda.set_device(device_id) + + dist.init_process_group( + backend="nccl" if torch.cuda.is_available() else "gloo", + init_method="env://", + world_size=world_size, + rank=rank, + device_id=torch.device(f"cuda:{device_id}") if torch.cuda.is_available() else None, + ) + + executor = DiffusionExecutor( + model_path=model_path, + request_queue_addr=request_queue_addr, + response_queue_addr=response_queue_addr, + device_id=device_id, + diffusion_config=diffusion_config, + ) + executor.serve_forever() + dist.destroy_process_group() + + except Exception as e: + logger.error(f"Worker failed: {e}") + traceback.print_exc() diff --git a/tensorrt_llm/_torch/visual_gen/models/__init__.py b/tensorrt_llm/_torch/visual_gen/models/__init__.py new file mode 100644 index 0000000000..5f726b84ec --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/models/__init__.py @@ -0,0 +1,30 @@ +""" +Visual generation model pipelines. + +Each model subdirectory contains: +- pipeline_*.py: Main pipeline implementation inheriting from BasePipeline +- __init__.py: Exports the pipeline class + +TeaCache extractors are registered inline in each pipeline's load() method +using register_extractor_from_config(). + +Pipelines are registered in pipeline_registry.py's PipelineRegistry._REGISTRY dict. + +Example structure: + models/ + my_model/ + pipeline_my_model.py # Pipeline class with inline extractor registration + __init__.py # Exports: __all__ = ["MyModelPipeline"] +""" + +from ..pipeline import BasePipeline +from ..pipeline_registry import AutoPipeline, register_pipeline +from .wan import WanImageToVideoPipeline, WanPipeline + +__all__ = [ + "AutoPipeline", + "BasePipeline", + "WanPipeline", + "WanImageToVideoPipeline", + "register_pipeline", +] diff --git a/tensorrt_llm/_torch/visual_gen/models/wan/__init__.py b/tensorrt_llm/_torch/visual_gen/models/wan/__init__.py new file mode 100644 index 0000000000..f177740809 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/models/wan/__init__.py @@ -0,0 +1,5 @@ +from .pipeline_wan import WanPipeline +from .pipeline_wan_i2v import WanImageToVideoPipeline +from .transformer_wan import WanTransformer3DModel + +__all__ = ["WanPipeline", "WanImageToVideoPipeline", "WanTransformer3DModel"] diff --git a/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py b/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py new file mode 100644 index 0000000000..f5d0f4fce5 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan.py @@ -0,0 +1,521 @@ +import time +from typing import Optional + +import torch +from diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler +from diffusers.utils.torch_utils import randn_tensor +from diffusers.video_processor import VideoProcessor +from transformers import AutoTokenizer, UMT5EncoderModel + +from tensorrt_llm._torch.visual_gen.config import PipelineComponent +from tensorrt_llm._torch.visual_gen.output import MediaOutput +from tensorrt_llm._torch.visual_gen.pipeline import BasePipeline +from tensorrt_llm._torch.visual_gen.pipeline_registry import register_pipeline +from tensorrt_llm._torch.visual_gen.teacache import ExtractorConfig, register_extractor_from_config +from tensorrt_llm._torch.visual_gen.utils import postprocess_video_tensor +from tensorrt_llm.logger import logger + +from .transformer_wan import WanTransformer3DModel + +# Supported Wan T2V models: +# - Wan2.1-T2V-14B: Single-stage text-to-video (14B parameters) +# - Wan2.1-T2V-1.3B: Single-stage text-to-video (1.3B parameters) +# - Wan2.2-T2V-A14B: Two-stage text-to-video (14B, boundary_ratio for high/low-noise stages; supports 480P & 720P) + +WAN_TEACACHE_COEFFICIENTS = { + "1.3B": { + "ret_steps": [ + -5.21862437e04, + 9.23041404e03, + -5.28275948e02, + 1.36987616e01, + -4.99875664e-02, + ], + "standard": [2.39676752e03, -1.31110545e03, 2.01331979e02, -8.29855975e00, 1.37887774e-01], + }, + "14B": { + "ret_steps": [ + -3.03318725e05, + 4.90537029e04, + -2.65530556e03, + 5.87365115e01, + -3.15583525e-01, + ], + "standard": [-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429, -13.02252404], + }, +} + + +# Default negative prompt for Wan T2V models +WAN_DEFAULT_NEGATIVE_PROMPT = ( + "Vibrant colors, overexposed, static, blurry details, subtitles, style, artwork, painting, image, " + "still image, overall grayish tone, worst quality, low quality, JPEG compression artifacts, ugly, " + "incomplete, extra fingers, poorly drawn hands, poorly drawn face, deformed, disfigured, malformed limbs, " + "fused fingers, motionless image, cluttered background, three legs, many people in the background, walking backward" +) + + +@register_pipeline("WanPipeline") +class WanPipeline(BasePipeline): + def __init__(self, model_config): + # Wan2.2 two-stage denoising parameters + self.transformer_2 = None + self.boundary_ratio = getattr(model_config.pretrained_config, "boundary_ratio", None) + self.is_wan22 = self.boundary_ratio is not None + + super().__init__(model_config) + + @staticmethod + def _compute_wan_timestep_embedding(module, timestep, guidance=None): + """Compute timestep embedding for WAN transformer. + + WAN uses a condition_embedder with timesteps_proj and time_embedder layers. + Handles dtype casting to match the embedder's dtype. + + Args: + module: WanTransformer3DModel instance + timestep: Timestep tensor (shape: [batch_size]) + guidance: Unused for WAN (no guidance embedding) + + Returns: + Timestep embedding tensor used by TeaCache for distance calculation + """ + ce = module.condition_embedder + t_freq = ce.timesteps_proj(timestep) + + # Cast to embedder's dtype (avoid int8 quantized layers) + te_dtype = next(iter(ce.time_embedder.parameters())).dtype + if t_freq.dtype != te_dtype and te_dtype != torch.int8: + t_freq = t_freq.to(te_dtype) + + return ce.time_embedder(t_freq) + + @property + def dtype(self): + return self.model_config.torch_dtype + + @property + def device(self): + return self.transformer.device + + @property + def transformer_components(self) -> list: + """Return list of transformer components this pipeline needs.""" + if self.transformer_2 is not None: + return ["transformer", "transformer_2"] + return ["transformer"] + + def _init_transformer(self) -> None: + logger.info("Creating WAN transformer with quantization support...") + self.transformer = WanTransformer3DModel(model_config=self.model_config) + + # Wan2.2: create second transformer for two-stage denoising + if self.boundary_ratio is not None: + logger.info("Creating second transformer for Wan2.2 two-stage denoising...") + self.transformer_2 = WanTransformer3DModel(model_config=self.model_config) + + def load_standard_components( + self, + checkpoint_dir: str, + device: torch.device, + skip_components: Optional[list] = None, + ) -> None: + """Load VAE, text encoder, tokenizer, and scheduler from checkpoint.""" + skip_components = skip_components or [] + + if self.transformer_2 is not None and self.boundary_ratio is None: + raise RuntimeError( + "transformer_2 exists but boundary_ratio is not set. " + "This indicates an inconsistent pipeline configuration." + ) + + # Detect model version + if self.is_wan22: + logger.info("Detected Wan 2.2 T2V (two-stage denoising)") + else: + logger.info("Detected Wan 2.1 T2V (single-stage denoising)") + + # Set default VAE scale factors (will be overridden if VAE is loaded) + self.vae_scale_factor_temporal = 4 + self.vae_scale_factor_spatial = 8 + + if PipelineComponent.TOKENIZER not in skip_components: + logger.info("Loading tokenizer...") + self.tokenizer = AutoTokenizer.from_pretrained( + checkpoint_dir, + subfolder=PipelineComponent.TOKENIZER, + ) + + if PipelineComponent.TEXT_ENCODER not in skip_components: + logger.info("Loading text encoder...") + self.text_encoder = UMT5EncoderModel.from_pretrained( + checkpoint_dir, + subfolder=PipelineComponent.TEXT_ENCODER, + torch_dtype=self.model_config.torch_dtype, + ).to(device) + + if PipelineComponent.VAE not in skip_components: + logger.info("Loading VAE...") + self.vae = AutoencoderKLWan.from_pretrained( + checkpoint_dir, + subfolder=PipelineComponent.VAE, + torch_dtype=torch.bfloat16, # load VAE in BF16 for memory saving + ).to(device) + + self.vae_scale_factor_temporal = getattr(self.vae.config, "scale_factor_temporal", 4) + self.vae_scale_factor_spatial = getattr(self.vae.config, "scale_factor_spatial", 8) + + if PipelineComponent.SCHEDULER not in skip_components: + logger.info("Loading scheduler...") + self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained( + checkpoint_dir, + subfolder=PipelineComponent.SCHEDULER, + ) + if not hasattr(self.scheduler.config, "shift") or self.scheduler.config.shift == 1.0: + self.scheduler = FlowMatchEulerDiscreteScheduler.from_config( + self.scheduler.config, + shift=5.0, + ) + + self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) + + def load_weights(self, weights: dict) -> None: + # Store weights for later use (in case transformer_2 is created after this call) + self._weights_dict = weights + + has_separate_weights = "transformer" in weights and "transformer_2" in weights + + if self.transformer is not None and hasattr(self.transformer, "load_weights"): + logger.info("Loading transformer weights...") + transformer_weights = weights.get("transformer", weights) + self.transformer.load_weights(transformer_weights) + logger.info("Transformer weights loaded successfully.") + + # Wan2.2: Load weights for second transformer if it exists + if self.transformer_2 is not None and hasattr(self.transformer_2, "load_weights"): + logger.info("Loading transformer_2 weights for Wan2.2...") + if not has_separate_weights: + raise ValueError( + "Wan2.2 model requires separate 'transformer' and 'transformer_2' weights in checkpoint, " + f"but only found: {list(weights.keys())}. " + "Two-stage denoising requires distinct weights for high-noise and low-noise transformers." + ) + transformer_2_weights = weights["transformer_2"] + self.transformer_2.load_weights(transformer_2_weights) + logger.info("Transformer_2 weights loaded successfully.") + + # Cache the target dtype from model config (default: bfloat16) + self._target_dtype = self.model_config.torch_dtype + + # Set model to eval mode + if self.transformer is not None: + self.transformer.eval() + if self.transformer_2 is not None: + self.transformer_2.eval() + + def post_load_weights(self) -> None: + super().post_load_weights() # Calls transformer.post_load_weights() for FP8 scale transformations + if self.transformer is not None: + # Register TeaCache extractor for this model type + # Tells TeaCache how to compute timestep embeddings for Wan + register_extractor_from_config( + ExtractorConfig( + model_class_name="WanTransformer3DModel", + timestep_embed_fn=self._compute_wan_timestep_embedding, + return_dict_default=False, # Wan returns raw tensors, not wrapped outputs + ) + ) + + # Enable TeaCache optimization with WAN-specific coefficients + self._setup_teacache(self.transformer, coefficients=WAN_TEACACHE_COEFFICIENTS) + # Save transformer backend before it gets overwritten + self.transformer_cache_backend = self.cache_backend + + # Wan2.2: Setup TeaCache for second transformer (low-noise stage) + if self.transformer_2 is not None: + if hasattr(self.transformer_2, "post_load_weights"): + self.transformer_2.post_load_weights() + + # Enable TeaCache for low-noise stage with same coefficients + self._setup_teacache(self.transformer_2, coefficients=WAN_TEACACHE_COEFFICIENTS) + # Save transformer_2 backend + self.transformer_2_cache_backend = self.cache_backend + + def infer(self, req): + """Run inference with request parameters.""" + return self.forward( + prompt=req.prompt, + negative_prompt=req.negative_prompt, + height=req.height, + width=req.width, + num_frames=req.num_frames, + num_inference_steps=req.num_inference_steps, + guidance_scale=req.guidance_scale, + guidance_scale_2=req.guidance_scale_2, + boundary_ratio=req.boundary_ratio, + seed=req.seed, + max_sequence_length=req.max_sequence_length, + ) + + @torch.no_grad() + def forward( + self, + prompt: str, + negative_prompt: Optional[str] = None, + height: int = 720, + width: int = 1280, + num_frames: int = 81, + num_inference_steps: Optional[int] = None, + guidance_scale: Optional[float] = None, + guidance_scale_2: Optional[float] = None, + boundary_ratio: Optional[float] = None, + seed: int = 42, + max_sequence_length: int = 226, + ): + pipeline_start = time.time() + generator = torch.Generator(device=self.device).manual_seed(seed) + + # Use user-provided boundary_ratio if given, otherwise fall back to model config + boundary_ratio = boundary_ratio if boundary_ratio is not None else self.boundary_ratio + + # Validate that Wan 2.2 models have boundary_ratio set + if self.transformer_2 is not None and boundary_ratio is None: + raise ValueError( + "Wan 2.2 models require boundary_ratio to be set. " + "boundary_ratio was not found in model config. " + "Please pass boundary_ratio as a parameter." + ) + + # Set default negative prompt if not provided + if negative_prompt is None: + negative_prompt = WAN_DEFAULT_NEGATIVE_PROMPT + + # Set model-specific defaults based on Wan version + logger.info( + f"Running {'Wan 2.2' if self.is_wan22 else 'Wan 2.1'} T2V inference" + f"(boundary_ratio={boundary_ratio}, has_transformer_2={self.transformer_2 is not None})" + ) + + if num_inference_steps is None: + num_inference_steps = 40 if self.is_wan22 else 50 + + if guidance_scale is None: + guidance_scale = 4.0 if self.is_wan22 else 5.0 + + if self.is_wan22 and guidance_scale_2 is None: + guidance_scale_2 = 3.0 + + # Validate two-stage denoising configuration + if guidance_scale_2 is not None and boundary_ratio is None: + logger.warning( + "guidance_scale_2 is specified but boundary_ratio is None. " + "guidance_scale_2 will be ignored." + "Set boundary_ratio in config or pass as parameter to enable two-stage denoising." + ) + guidance_scale_2 = None + + # Encode Prompt + logger.info("Encoding prompts...") + encode_start = time.time() + prompt_embeds, neg_prompt_embeds = self._encode_prompt( + prompt, negative_prompt, max_sequence_length + ) + logger.info(f"Prompt encoding completed in {time.time() - encode_start:.2f}s") + + # Prepare Latents + latents = self._prepare_latents(height, width, num_frames, generator) + logger.info(f"Latents shape: {latents.shape}") + + self.scheduler.set_timesteps(num_inference_steps, device=self.device) + + # Wan2.2: Calculate boundary timestep for two-stage denoising + boundary_timestep = None + if boundary_ratio is not None and self.transformer_2 is not None: + boundary_timestep = boundary_ratio * self.scheduler.config.num_train_timesteps + logger.info( + f"Wan2.2 two-stage denoising: boundary_timestep={boundary_timestep:.1f}, " + f"guidance_scale={guidance_scale}, guidance_scale_2={guidance_scale_2}" + ) + + # Denoising with two-stage support + # Track which model was used in last step (for logging model transitions) + last_model_used = [None] + + def forward_fn( + latents, extra_stream_latents, timestep, encoder_hidden_states, extra_tensors + ): + """Forward function for Wan transformer with two-stage support. + + extra_stream_latents and extra_tensors are unused for WAN (single stream, no additional embeddings). + """ + # Select model based on timestep (if two-stage denoising is enabled) + if boundary_timestep is not None and self.transformer_2 is not None: + # Extract scalar timestep for comparison + current_t = timestep if timestep.dim() == 0 else timestep[0] + if current_t >= boundary_timestep: + current_model = self.transformer + model_name = "transformer (high-noise)" + else: + current_model = self.transformer_2 + model_name = "transformer_2 (low-noise)" + + # Log when switching models + if last_model_used[0] != model_name: + if self.rank == 0: + logger.info(f"Switched to {model_name} at timestep {current_t:.1f}") + last_model_used[0] = model_name + else: + current_model = self.transformer + + return current_model( + hidden_states=latents, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + ) + + # Two-stage denoising: model switching in forward_fn, guidance scale switching in denoise() + latents = self.denoise( + latents=latents, + scheduler=self.scheduler, + prompt_embeds=prompt_embeds, + neg_prompt_embeds=neg_prompt_embeds, + guidance_scale=guidance_scale, + forward_fn=forward_fn, + guidance_scale_2=guidance_scale_2, + boundary_timestep=boundary_timestep, + ) + + # Log TeaCache statistics - show stats for each transformer separately + if self.rank == 0 and self.model_config.teacache.enable_teacache: + logger.info("=" * 80) + logger.info("TeaCache Statistics:") + + # Stats for transformer (high-noise) + if hasattr(self, "transformer_cache_backend") and self.transformer_cache_backend: + stats = self.transformer_cache_backend.get_stats() + total_steps = stats.get("total_steps", 0) + cache_hits = stats.get("cached_steps", 0) + cache_misses = stats.get("compute_steps", 0) + hit_rate = (cache_hits / total_steps * 100) if total_steps > 0 else 0.0 + + logger.info(" Transformer (High-Noise):") + logger.info(f" Total steps: {total_steps}") + logger.info(f" Cache hits: {cache_hits}") + logger.info(f" Cache misses: {cache_misses}") + logger.info(f" Hit rate: {hit_rate:.1f}%") + + # Stats for transformer_2 (low-noise) + if hasattr(self, "transformer_2_cache_backend") and self.transformer_2_cache_backend: + stats = self.transformer_2_cache_backend.get_stats() + total_steps = stats.get("total_steps", 0) + cache_hits = stats.get("cached_steps", 0) + cache_misses = stats.get("compute_steps", 0) + hit_rate = (cache_hits / total_steps * 100) if total_steps > 0 else 0.0 + + logger.info(" Transformer_2 (Low-Noise):") + logger.info(f" Total steps: {total_steps}") + logger.info(f" Cache hits: {cache_hits}") + logger.info(f" Cache misses: {cache_misses}") + logger.info(f" Hit rate: {hit_rate:.1f}%") + + logger.info("=" * 80) + + # Decode + logger.info("Decoding video...") + decode_start = time.time() + video = self.decode_latents(latents, self._decode_latents) + + if self.rank == 0: + logger.info(f"Video decoded in {time.time() - decode_start:.2f}s") + logger.info(f"Total pipeline time: {time.time() - pipeline_start:.2f}s") + + return MediaOutput(video=video) + + def _encode_prompt(self, prompt, negative_prompt, max_sequence_length): + prompt = [prompt] if isinstance(prompt, str) else prompt + + def get_embeds(texts): + text_inputs = self.tokenizer( + texts, + padding="max_length", + max_length=max_sequence_length, + truncation=True, + return_attention_mask=True, + return_tensors="pt", + ) + input_ids = text_inputs.input_ids.to(self.device) + attention_mask = text_inputs.attention_mask.to(self.device) + + embeds = self.text_encoder(input_ids, attention_mask=attention_mask).last_hidden_state + embeds = embeds.to(self.dtype) + + # Zero-out padded tokens based on mask + seq_lens = attention_mask.gt(0).sum(dim=1).long() + cleaned_embeds = [] + for u, v in zip(embeds, seq_lens): + real_content = u[:v] + pad_len = max_sequence_length - real_content.size(0) + if pad_len > 0: + padded = torch.cat( + [real_content, real_content.new_zeros(pad_len, real_content.size(1))] + ) + else: + padded = real_content + cleaned_embeds.append(padded) + + return torch.stack(cleaned_embeds, dim=0) + + prompt_embeds = get_embeds(prompt) + + if negative_prompt is None: + negative_prompt = "" + + neg_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt + if len(neg_prompt) == 1 and len(prompt) > 1: + neg_prompt = neg_prompt * len(prompt) + + neg_embeds = get_embeds(neg_prompt) + + return prompt_embeds, neg_embeds + + def _prepare_latents(self, height, width, num_frames, generator): + num_channels_latents = 16 + num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 + + shape = ( + 1, + num_channels_latents, + num_latent_frames, + height // self.vae_scale_factor_spatial, + width // self.vae_scale_factor_spatial, + ) + return randn_tensor(shape, generator=generator, device=self.device, dtype=self.dtype) + + def _decode_latents(self, latents): + latents = latents.to(self.vae.dtype) + + # Denormalization + if hasattr(self.vae.config, "latents_mean") and hasattr(self.vae.config, "latents_std"): + if not hasattr(self, "_latents_mean"): + self._latents_mean = ( + torch.tensor(self.vae.config.latents_mean) + .view(1, -1, 1, 1, 1) + .to(self.device, self.vae.dtype) + ) + self._latents_std = ( + torch.tensor(self.vae.config.latents_std) + .view(1, -1, 1, 1, 1) + .to(self.device, self.vae.dtype) + ) + latents = (latents * self._latents_std) + self._latents_mean + else: + scaling_factor = self.vae.config.get("scaling_factor", 1.0) + latents = latents / scaling_factor + + # VAE decode: returns (B, C, T, H, W) + video = self.vae.decode(latents, return_dict=False)[0] + + # Post-process video tensor: (B, C, T, H, W) -> (T, H, W, C) uint8 + video = postprocess_video_tensor(video, remove_batch_dim=True) + + return video diff --git a/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan_i2v.py b/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan_i2v.py new file mode 100644 index 0000000000..d2a3fd629f --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/models/wan/pipeline_wan_i2v.py @@ -0,0 +1,736 @@ +import json +import os +import time +from typing import Optional, Tuple, Union + +import PIL.Image +import torch +from diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler +from diffusers.utils.torch_utils import randn_tensor +from diffusers.video_processor import VideoProcessor +from transformers import AutoTokenizer, CLIPImageProcessor, CLIPVisionModel, UMT5EncoderModel + +from tensorrt_llm._torch.visual_gen.config import PipelineComponent +from tensorrt_llm._torch.visual_gen.output import MediaOutput +from tensorrt_llm._torch.visual_gen.pipeline import BasePipeline +from tensorrt_llm._torch.visual_gen.pipeline_registry import register_pipeline +from tensorrt_llm._torch.visual_gen.teacache import ExtractorConfig, register_extractor_from_config +from tensorrt_llm._torch.visual_gen.utils import postprocess_video_tensor +from tensorrt_llm.logger import logger + +# Supported Wan I2V 14B models: +# - Wan2.1-I2V-14B-480P: Single-stage image-to-video +# - Wan2.1-I2V-14B-720P: Single-stage image-to-video +# - Wan2.2-I2V-14B: Two-stage image-to-video (no CLIP, boundary_ratio for two-stage denoising) +# Note: Wan2.2-I2V-5B (expand_timesteps mode) is NOT supported by this pipeline +# Import shared coefficients from T2V pipeline +from .pipeline_wan import WAN_TEACACHE_COEFFICIENTS +from .transformer_wan import WanTransformer3DModel + +# Use same coefficients +WAN_I2V_TEACACHE_COEFFICIENTS = WAN_TEACACHE_COEFFICIENTS + +# Default negative prompt for Wan I2V models +WAN_DEFAULT_NEGATIVE_PROMPT = ( + "Vibrant colors, overexposed, static, blurry details, subtitles, style, artwork, painting, image, " + "still image, overall grayish tone, worst quality, low quality, JPEG compression artifacts, ugly, " + "incomplete, extra fingers, poorly drawn hands, poorly drawn face, deformed, disfigured, malformed limbs, " + "fused fingers, motionless image, cluttered background, three legs, many people in the background, walking backward" +) + + +def retrieve_latents( + encoder_output: torch.Tensor, + generator: Optional[torch.Generator] = None, + sample_mode: str = "argmax", +): + """Extract latents from VAE encoder output. + + For I2V, we use argmax mode to get deterministic encoding of the input image. + """ + if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": + return encoder_output.latent_dist.sample(generator) + elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": + return encoder_output.latent_dist.mode() + elif hasattr(encoder_output, "latents"): + return encoder_output.latents + else: + raise AttributeError("Could not access latents of provided encoder_output") + + +@register_pipeline("WanImageToVideoPipeline") +class WanImageToVideoPipeline(BasePipeline): + def __init__(self, model_config): + # Wan2.2 14B two-stage denoising parameters + self.transformer_2 = None + self.boundary_ratio = getattr(model_config.pretrained_config, "boundary_ratio", None) + self.is_wan22 = self.boundary_ratio is not None + + super().__init__(model_config) + + @staticmethod + def _compute_wan_timestep_embedding(module, timestep, guidance=None): + """Compute timestep embedding for Wan I2V transformer.""" + ce = module.condition_embedder + t_freq = ce.timesteps_proj(timestep) + + # Cast to embedder's dtype (avoid int8 quantized layers) + te_dtype = next(iter(ce.time_embedder.parameters())).dtype + if t_freq.dtype != te_dtype and te_dtype != torch.int8: + t_freq = t_freq.to(te_dtype) + + return ce.time_embedder(t_freq) + + @property + def dtype(self): + return self.model_config.torch_dtype + + @property + def device(self): + return self.transformer.device + + @property + def transformer_components(self) -> list: + if self.transformer_2 is not None: + return ["transformer", "transformer_2"] + return ["transformer"] + + def _init_transformer(self) -> None: + logger.info("Creating WAN I2V transformer with quantization support...") + self.transformer = WanTransformer3DModel(model_config=self.model_config) + + # Wan2.2: Optionally create second transformer for two-stage denoising + if self.boundary_ratio is not None: + logger.info("Creating second transformer for Wan2.2 I2V two-stage denoising...") + self.transformer_2 = WanTransformer3DModel(model_config=self.model_config) + + def load_standard_components( + self, + checkpoint_dir: str, + device: torch.device, + skip_components: Optional[list] = None, + ) -> None: + """Load VAE, text encoder, tokenizer, scheduler, and I2V-specific components from checkpoint.""" + skip_components = skip_components or [] + + # Load boundary_ratio and transformer_2 info from model_index.json (pipeline-level config) + # Wan 2.2 has both transformer_2 and boundary_ratio, Wan 2.1 doesn't + model_index_path = os.path.join(checkpoint_dir, "model_index.json") + has_transformer_2 = False + if os.path.exists(model_index_path): + with open(model_index_path) as f: + model_index = json.load(f) + # Check for boundary_ratio in model_index + if "boundary_ratio" in model_index: + self.boundary_ratio = model_index["boundary_ratio"] + logger.info(f"Found boundary_ratio in model_index.json: {self.boundary_ratio}") + else: + logger.info("No boundary_ratio found in model_index.json") + # Check for transformer_2 component + transformer_2_spec = model_index.get("transformer_2", None) + has_transformer_2 = ( + transformer_2_spec is not None and transformer_2_spec[0] is not None + ) + logger.info(f"transformer_2 in model_index.json: {has_transformer_2}") + + # Set default VAE scale factors (will be overridden if VAE is loaded) + self.vae_scale_factor_temporal = 4 + self.vae_scale_factor_spatial = 8 + + if PipelineComponent.TOKENIZER not in skip_components: + logger.info("Loading tokenizer...") + self.tokenizer = AutoTokenizer.from_pretrained( + checkpoint_dir, + subfolder=PipelineComponent.TOKENIZER, + ) + + if PipelineComponent.TEXT_ENCODER not in skip_components: + logger.info("Loading text encoder...") + self.text_encoder = UMT5EncoderModel.from_pretrained( + checkpoint_dir, + subfolder=PipelineComponent.TEXT_ENCODER, + torch_dtype=self.model_config.torch_dtype, + ).to(device) + + if PipelineComponent.VAE not in skip_components: + logger.info("Loading VAE...") + self.vae = AutoencoderKLWan.from_pretrained( + checkpoint_dir, + subfolder=PipelineComponent.VAE, + torch_dtype=torch.bfloat16, # load VAE in BF16 for memory saving + ).to(device) + + self.vae_scale_factor_temporal = getattr(self.vae.config, "scale_factor_temporal", 4) + self.vae_scale_factor_spatial = getattr(self.vae.config, "scale_factor_spatial", 8) + + if PipelineComponent.SCHEDULER not in skip_components: + logger.info("Loading scheduler...") + self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained( + checkpoint_dir, + subfolder=PipelineComponent.SCHEDULER, + ) + if not hasattr(self.scheduler.config, "shift") or self.scheduler.config.shift == 1.0: + self.scheduler = FlowMatchEulerDiscreteScheduler.from_config( + self.scheduler.config, + shift=5.0, + ) + + if self.transformer_2 is not None and self.boundary_ratio is None: + raise RuntimeError( + "transformer_2 exists but boundary_ratio is not set. " + "This indicates an inconsistent pipeline configuration." + ) + + # Load image encoder and processor (only for Wan 2.1) + # Wan 2.2: Has both transformer_2 and boundary_ratio (two-stage denoising) + if self.is_wan22: + logger.info("Detected Wan 2.2 I2V (two-stage, no CLIP)") + else: + logger.info("Detected Wan 2.1 I2V (single-stage, uses CLIP)") + + if PipelineComponent.IMAGE_ENCODER not in skip_components and not self.is_wan22: + logger.info("Loading CLIP image encoder for I2V conditioning (Wan 2.1 only)...") + self.image_encoder = CLIPVisionModel.from_pretrained( + checkpoint_dir, + subfolder=PipelineComponent.IMAGE_ENCODER, + torch_dtype=torch.float32, # Keep CLIP in FP32 for stability + ).to(device) + + if PipelineComponent.IMAGE_PROCESSOR not in skip_components and not self.is_wan22: + logger.info("Loading CLIP image processor...") + self.image_processor = CLIPImageProcessor.from_pretrained( + checkpoint_dir, + subfolder=PipelineComponent.IMAGE_PROCESSOR, + ) + + self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) + + def load_weights(self, weights: dict) -> None: + # Store weights for later use + self._weights_dict = weights + + # Check if weights dict has separate transformer/transformer_2 keys (Wan2.2) + has_separate_weights = "transformer" in weights and "transformer_2" in weights + + if self.transformer is not None and hasattr(self.transformer, "load_weights"): + logger.info("Loading transformer weights...") + transformer_weights = weights.get("transformer", weights) + self.transformer.load_weights(transformer_weights) + logger.info("Transformer weights loaded successfully.") + + # Wan2.2: Load weights for second transformer if it exists + if self.transformer_2 is not None and hasattr(self.transformer_2, "load_weights"): + logger.info("Loading transformer_2 weights for Wan2.2 I2V...") + if has_separate_weights: + transformer_2_weights = weights["transformer_2"] + logger.info("Using separate transformer_2 weights from checkpoint") + else: + # For Wan 2.2, transformer_2 weights must exist + raise ValueError( + "Wan2.2 model requires separate 'transformer' and 'transformer_2' weights in checkpoint, " + f"but only found: {list(weights.keys())}" + ) + self.transformer_2.load_weights(transformer_2_weights) + logger.info("Transformer_2 weights loaded successfully.") + + # Cache the target dtype from model config (default: bfloat16) + self._target_dtype = self.model_config.torch_dtype + + # Set model to eval mode + if self.transformer is not None: + self.transformer.eval() + if self.transformer_2 is not None: + self.transformer_2.eval() + if hasattr(self, "image_encoder") and self.image_encoder is not None: + self.image_encoder.eval() + + def post_load_weights(self) -> None: + super().post_load_weights() # Calls transformer.post_load_weights() for FP8 scale transformations + if self.transformer is not None: + # Register TeaCache extractor for this model type + register_extractor_from_config( + ExtractorConfig( + model_class_name="WanTransformer3DModel", + timestep_embed_fn=self._compute_wan_timestep_embedding, + return_dict_default=False, # Wan returns raw tensors, not wrapped outputs + ) + ) + + # Enable TeaCache optimization with Wan I2V-specific coefficients + self._setup_teacache(self.transformer, coefficients=WAN_I2V_TEACACHE_COEFFICIENTS) + # Save transformer backend before it gets overwritten + self.transformer_cache_backend = self.cache_backend + + # Wan2.2: Setup TeaCache for second transformer (low-noise stage) + if self.transformer_2 is not None: + if hasattr(self.transformer_2, "post_load_weights"): + self.transformer_2.post_load_weights() + + # Enable TeaCache for low-noise stage with same coefficients + self._setup_teacache(self.transformer_2, coefficients=WAN_I2V_TEACACHE_COEFFICIENTS) + # Save transformer_2 backend + self.transformer_2_cache_backend = self.cache_backend + + def infer(self, req): + """Run inference with request parameters.""" + # Extract image from request (can be path, PIL Image, or torch.Tensor) + if req.image is None: + raise ValueError("I2V pipeline requires 'image' parameter") + + image = req.image[0] if isinstance(req.image, list) else req.image + last_image = req.last_image + + if last_image is not None and isinstance(last_image, list): + last_image = last_image[0] if last_image else None + + return self.forward( + image=image, + prompt=req.prompt, + negative_prompt=req.negative_prompt, + height=req.height, + width=req.width, + num_frames=req.num_frames, + num_inference_steps=req.num_inference_steps, + guidance_scale=req.guidance_scale, + guidance_scale_2=req.guidance_scale_2, + boundary_ratio=req.boundary_ratio, + seed=req.seed, + max_sequence_length=req.max_sequence_length, + last_image=last_image, + ) + + @torch.no_grad() + def forward( + self, + image: Union[PIL.Image.Image, torch.Tensor, str], + prompt: str, + negative_prompt: Optional[str] = None, + height: int = 480, + width: int = 832, + num_frames: int = 81, + num_inference_steps: Optional[int] = None, + guidance_scale: Optional[float] = None, + guidance_scale_2: Optional[float] = None, + boundary_ratio: Optional[float] = None, + seed: int = 42, + max_sequence_length: int = 512, + last_image: Optional[Union[PIL.Image.Image, torch.Tensor, str]] = None, + ): + pipeline_start = time.time() + generator = torch.Generator(device=self.device).manual_seed(seed) + + # Use user-provided boundary_ratio if given, otherwise fall back to model config + boundary_ratio = boundary_ratio if boundary_ratio is not None else self.boundary_ratio + + if self.transformer_2 is not None and boundary_ratio is None: + raise ValueError( + "Wan 2.2 models require boundary_ratio to be set. " + "boundary_ratio was not found in model config. " + "Please pass boundary_ratio as a parameter." + ) + + # Set default negative prompt if not provided + if negative_prompt is None: + negative_prompt = WAN_DEFAULT_NEGATIVE_PROMPT + + # Set model-specific defaults based on Wan version + if num_inference_steps is None: + num_inference_steps = 40 if self.is_wan22 else 50 + + if guidance_scale is None: + guidance_scale = 4.0 if self.is_wan22 else 5.0 + + if self.is_wan22 and guidance_scale_2 is None: + guidance_scale_2 = 3.0 # Wan2.2 recommended default + + # Validate two-stage denoising configuration + if guidance_scale_2 is not None and boundary_ratio is None: + logger.warning( + "guidance_scale_2 is specified but boundary_ratio is None. " + "guidance_scale_2 will be ignored." + "Set boundary_ratio in config or pass as parameter to enable two-stage denoising." + ) + guidance_scale_2 = None + + # Validate and adjust frame count for VAE compatibility + if num_frames % self.vae_scale_factor_temporal != 1: + logger.warning( + f"`num_frames - 1` must be divisible by {self.vae_scale_factor_temporal}. " + f"Rounding {num_frames} to nearest valid value." + ) + num_frames = ( + num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1 + ) + num_frames = max(num_frames, 1) + + # Validate and adjust resolution for transformer patchification + patch_size = ( + self.transformer.config.patch_size + if self.transformer is not None + else self.transformer_2.config.patch_size + ) + h_multiple_of = self.vae_scale_factor_spatial * patch_size[1] + w_multiple_of = self.vae_scale_factor_spatial * patch_size[2] + calc_height = height // h_multiple_of * h_multiple_of + calc_width = width // w_multiple_of * w_multiple_of + if height != calc_height or width != calc_width: + logger.warning( + f"Height and width must be multiples of ({h_multiple_of}, {w_multiple_of}) for patchification. " + f"Adjusting ({height}, {width}) -> ({calc_height}, {calc_width})." + ) + height, width = calc_height, calc_width + + # Encode Prompt + logger.info("Encoding prompts...") + encode_start = time.time() + prompt_embeds, neg_prompt_embeds = self._encode_prompt( + prompt, negative_prompt, max_sequence_length + ) + logger.info(f"Prompt encoding completed in {time.time() - encode_start:.2f}s") + + # Encode Image (I2V-specific) + logger.info("Encoding input image...") + image_encode_start = time.time() + + # Determine model version + model_version = "Wan 2.2" if self.is_wan22 else "Wan 2.1" + logger.info( + f"Running {model_version} I2V inference " + f"(boundary_ratio={boundary_ratio}, has_transformer_2={self.transformer_2 is not None})" + ) + + if not self.is_wan22: + # Wan 2.1 I2V: Compute CLIP image embeddings + image_embeds = self._encode_image(image, last_image) + image_embeds = image_embeds.to(self.dtype) + else: + # Wan 2.2 I2V: No image embeddings needed + image_embeds = None + + logger.info(f"Image encoding completed in {time.time() - image_encode_start:.2f}s") + + # Prepare Latents with image conditioning (I2V-specific) + latents, condition_data = self._prepare_latents( + image, height, width, num_frames, generator, last_image + ) + + self.scheduler.set_timesteps(num_inference_steps, device=self.device) + + # Wan2.2: Calculate boundary timestep for two-stage denoising + boundary_timestep = None + if boundary_ratio is not None and self.transformer_2 is not None: + boundary_timestep = boundary_ratio * self.scheduler.config.num_train_timesteps + logger.info( + f"Wan2.2 I2V two-stage denoising: boundary_timestep={boundary_timestep:.1f}, " + f"guidance_scale={guidance_scale}, guidance_scale_2={guidance_scale_2}" + ) + + # Denoising with two-stage support + # Track which model was used in last step (for logging model transitions) + last_model_used = [None] + + def forward_fn( + latents_input, extra_stream_latents, timestep, encoder_hidden_states, extra_tensors + ): + """Forward function for WAN I2V transformer with two-stage support. + + Both Wan 2.1 and Wan 2.2 14B use concatenation approach: [latents, condition]. + Difference: Wan 2.1 passes image_embeds, Wan 2.2 passes None. + """ + # Select model based on timestep (if two-stage denoising is enabled) + if boundary_timestep is not None and self.transformer_2 is not None: + # Extract scalar timestep for comparison + current_t = timestep if timestep.dim() == 0 else timestep[0] + if current_t >= boundary_timestep: + current_model = self.transformer + model_name = "transformer" + else: + current_model = self.transformer_2 + model_name = "transformer_2" + + # Log when switching models + if last_model_used[0] != model_name: + if self.rank == 0: + logger.info( + f"[TRTLLM] Switched to {model_name} at timestep {current_t:.1f}" + ) + last_model_used[0] = model_name + else: + current_model = self.transformer + + # Wan 2.1 & Wan 2.2 14B: concatenate latents and condition + # Handle CFG: duplicate condition if batch dimension doubled + if latents_input.shape[0] != condition_data.shape[0]: + condition_to_use = torch.cat([condition_data] * 2) + else: + condition_to_use = condition_data + + latent_model_input = torch.cat([latents_input, condition_to_use], dim=1).to(self.dtype) + timestep_input = timestep.expand(latents_input.shape[0]) + + # Forward pass with I2V conditioning + # Wan 2.1: image_embeds is not None (CLIP embeddings) + # Wan 2.2 14B: image_embeds is None (no CLIP) + return current_model( + hidden_states=latent_model_input, + timestep=timestep_input, + encoder_hidden_states=encoder_hidden_states, + encoder_hidden_states_image=image_embeds, + ) + + # Two-stage denoising: model switching in forward_fn, guidance scale switching in denoise() + latents = self.denoise( + latents=latents, + scheduler=self.scheduler, + prompt_embeds=prompt_embeds, + neg_prompt_embeds=neg_prompt_embeds, + guidance_scale=guidance_scale, + forward_fn=forward_fn, + guidance_scale_2=guidance_scale_2, + boundary_timestep=boundary_timestep, + ) + + # Log TeaCache statistics - show stats for each transformer separately + if self.rank == 0 and self.model_config.teacache.enable_teacache: + logger.info("=" * 80) + logger.info("TeaCache Statistics:") + + # Stats for transformer (high-noise) + if hasattr(self, "transformer_cache_backend") and self.transformer_cache_backend: + stats = self.transformer_cache_backend.get_stats() + total_steps = stats.get("total_steps", 0) + cache_hits = stats.get("cached_steps", 0) + cache_misses = stats.get("compute_steps", 0) + hit_rate = (cache_hits / total_steps * 100) if total_steps > 0 else 0.0 + + logger.info(" Transformer (High-Noise):") + logger.info(f" Total steps: {total_steps}") + logger.info(f" Cache hits: {cache_hits}") + logger.info(f" Cache misses: {cache_misses}") + logger.info(f" Hit rate: {hit_rate:.1f}%") + + # Stats for transformer_2 (low-noise) + if hasattr(self, "transformer_2_cache_backend") and self.transformer_2_cache_backend: + stats = self.transformer_2_cache_backend.get_stats() + total_steps = stats.get("total_steps", 0) + cache_hits = stats.get("cached_steps", 0) + cache_misses = stats.get("compute_steps", 0) + hit_rate = (cache_hits / total_steps * 100) if total_steps > 0 else 0.0 + + logger.info(" Transformer_2 (Low-Noise):") + logger.info(f" Total steps: {total_steps}") + logger.info(f" Cache hits: {cache_hits}") + logger.info(f" Cache misses: {cache_misses}") + logger.info(f" Hit rate: {hit_rate:.1f}%") + + logger.info("=" * 80) + + # Decode + logger.info("Decoding video...") + decode_start = time.time() + video = self.decode_latents(latents, self._decode_latents) + + if self.rank == 0: + logger.info(f"Video decoded in {time.time() - decode_start:.2f}s") + logger.info(f"Total pipeline time: {time.time() - pipeline_start:.2f}s") + + return MediaOutput(video=video) + + def _encode_prompt(self, prompt, negative_prompt, max_sequence_length): + """Encode text prompts to embeddings (same as T2V).""" + prompt = [prompt] if isinstance(prompt, str) else prompt + + def get_embeds(texts): + text_inputs = self.tokenizer( + texts, + padding="max_length", + max_length=max_sequence_length, + truncation=True, + return_attention_mask=True, + return_tensors="pt", + ) + input_ids = text_inputs.input_ids.to(self.device) + attention_mask = text_inputs.attention_mask.to(self.device) + + embeds = self.text_encoder(input_ids, attention_mask=attention_mask).last_hidden_state + embeds = embeds.to(self.dtype) + + # Zero-out padded tokens based on mask + seq_lens = attention_mask.gt(0).sum(dim=1).long() + cleaned_embeds = [] + for u, v in zip(embeds, seq_lens): + real_content = u[:v] + pad_len = max_sequence_length - real_content.size(0) + if pad_len > 0: + padded = torch.cat( + [real_content, real_content.new_zeros(pad_len, real_content.size(1))] + ) + else: + padded = real_content + cleaned_embeds.append(padded) + + return torch.stack(cleaned_embeds, dim=0) + + prompt_embeds = get_embeds(prompt) + + if negative_prompt is None: + negative_prompt = "" + + neg_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt + if len(neg_prompt) == 1 and len(prompt) > 1: + neg_prompt = neg_prompt * len(prompt) + + neg_embeds = get_embeds(neg_prompt) + + return prompt_embeds, neg_embeds + + def _encode_image( + self, + image: Union[PIL.Image.Image, torch.Tensor, str], + last_image: Optional[Union[PIL.Image.Image, torch.Tensor, str]] = None, + ) -> torch.Tensor: + """Encode image(s) using CLIP image encoder (Wan 2.1 I2V only).""" + if isinstance(image, str): + image = PIL.Image.open(image).convert("RGB") + if isinstance(last_image, str): + last_image = PIL.Image.open(last_image).convert("RGB") + + images_to_encode = [image] if last_image is None else [image, last_image] + + image_inputs = self.image_processor(images=images_to_encode, return_tensors="pt").to( + self.device + ) + image_embeds = self.image_encoder(**image_inputs, output_hidden_states=True) + + return image_embeds.hidden_states[-2] + + def _prepare_latents( + self, + image: Union[PIL.Image.Image, torch.Tensor, str], + height: int, + width: int, + num_frames: int, + generator: torch.Generator, + last_image: Optional[Union[PIL.Image.Image, torch.Tensor, str]] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Prepare latents with image conditioning for I2V generation.""" + num_channels_latents = 16 + num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 + latent_height = height // self.vae_scale_factor_spatial + latent_width = width // self.vae_scale_factor_spatial + + # Create random noise latents + shape = (1, num_channels_latents, num_latent_frames, latent_height, latent_width) + latents = randn_tensor(shape, generator=generator, device=self.device, dtype=self.dtype) + + # Load and preprocess image(s) + if isinstance(image, str): + image = PIL.Image.open(image).convert("RGB") + image = self.video_processor.preprocess(image, height=height, width=width).to( + self.device, dtype=torch.float32 + ) + + if last_image is not None: + if isinstance(last_image, str): + last_image = PIL.Image.open(last_image).convert("RGB") + last_image = self.video_processor.preprocess(last_image, height=height, width=width).to( + self.device, dtype=torch.float32 + ) + + image = image.unsqueeze(2) + + # Create video conditioning tensor (same for both Wan 2.1 and Wan 2.2 14B) + if last_image is None: + # First frame + zeros + video_condition = torch.cat( + [ + image, + image.new_zeros(image.shape[0], image.shape[1], num_frames - 1, height, width), + ], + dim=2, + ) + else: + # First frame + zeros + last frame (interpolation) + last_image = last_image.unsqueeze(2) + video_condition = torch.cat( + [ + image, + image.new_zeros(image.shape[0], image.shape[1], num_frames - 2, height, width), + last_image, + ], + dim=2, + ) + + # Encode video condition through VAE + video_condition = video_condition.to(device=self.device, dtype=self.vae.dtype) + latent_condition = retrieve_latents(self.vae.encode(video_condition), sample_mode="argmax") + latent_condition = latent_condition.to(self.dtype) + + # Normalize latents to match diffusion model's latent space + latents_mean = ( + torch.tensor(self.vae.config.latents_mean) + .view(1, self.vae.config.z_dim, 1, 1, 1) + .to(latents.device, latents.dtype) + ) + latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view( + 1, self.vae.config.z_dim, 1, 1, 1 + ).to(latents.device, latents.dtype) + latent_condition = (latent_condition - latents_mean) * latents_std + + # Create mask in video frame space + # Reshaping is required to match the transformer's expected input format + mask_lat_size = torch.ones(1, 1, num_frames, latent_height, latent_width) + + if last_image is None: + mask_lat_size[:, :, list(range(1, num_frames))] = 0 + else: + mask_lat_size[:, :, list(range(1, num_frames - 1))] = 0 + + first_frame_mask = mask_lat_size[:, :, 0:1] + first_frame_mask = torch.repeat_interleave( + first_frame_mask, dim=2, repeats=self.vae_scale_factor_temporal + ) + + mask_lat_size = torch.cat([first_frame_mask, mask_lat_size[:, :, 1:, :]], dim=2) + + mask_lat_size = mask_lat_size.view( + 1, -1, self.vae_scale_factor_temporal, latent_height, latent_width + ) + + mask_lat_size = mask_lat_size.transpose(1, 2) + + mask_lat_size = mask_lat_size.to(self.device, dtype=self.dtype) + + # Concatenate mask and condition along channel dimension + condition = torch.cat([mask_lat_size, latent_condition], dim=1) + return latents, condition + + def _decode_latents(self, latents): + """Decode latents to video (same as T2V).""" + latents = latents.to(self.vae.dtype) + + # Denormalization + if hasattr(self.vae.config, "latents_mean") and hasattr(self.vae.config, "latents_std"): + if not hasattr(self, "_latents_mean"): + self._latents_mean = ( + torch.tensor(self.vae.config.latents_mean) + .view(1, -1, 1, 1, 1) + .to(self.device, self.vae.dtype) + ) + self._latents_std = ( + torch.tensor(self.vae.config.latents_std) + .view(1, -1, 1, 1, 1) + .to(self.device, self.vae.dtype) + ) + latents = (latents * self._latents_std) + self._latents_mean + else: + scaling_factor = self.vae.config.get("scaling_factor", 1.0) + latents = latents / scaling_factor + + # VAE decode: returns (B, C, T, H, W) + video = self.vae.decode(latents, return_dict=False)[0] + + # Post-process video tensor: (B, C, T, H, W) -> (T, H, W, C) uint8 + video = postprocess_video_tensor(video, remove_batch_dim=True) + + return video diff --git a/tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py b/tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py new file mode 100644 index 0000000000..bb19a78541 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/models/wan/transformer_wan.py @@ -0,0 +1,756 @@ +import math +from typing import Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers.models.embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps +from tqdm import tqdm +from transformers.modeling_utils import get_parameter_device + +from tensorrt_llm._torch.modules.layer_norm import LayerNorm +from tensorrt_llm._torch.modules.linear import Linear +from tensorrt_llm._torch.modules.mlp import MLP +from tensorrt_llm._torch.modules.rms_norm import RMSNorm +from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig +from tensorrt_llm._torch.visual_gen.modules.attention import Attention, QKVMode +from tensorrt_llm._torch.visual_gen.parallelism import setup_sequence_parallelism +from tensorrt_llm._torch.visual_gen.quantization.loader import DynamicLinearWeightLoader +from tensorrt_llm.logger import logger +from tensorrt_llm.models.modeling_utils import QuantConfig + +# ========================================================================= +# 1. Rotary Positional Embeddings +# ========================================================================= + + +class WanRotaryPosEmbed(nn.Module): + def __init__( + self, + attention_head_dim: int, + patch_size: Tuple[int, int, int], + max_seq_len: int, + theta: float = 10000.0, + ): + super().__init__() + self.patch_size = patch_size + + # Split logic matches Hugging Face exactly + self.h_dim = 2 * (attention_head_dim // 6) + self.w_dim = 2 * (attention_head_dim // 6) + self.t_dim = attention_head_dim - self.h_dim - self.w_dim + + freqs_cos, freqs_sin = [], [] + + # Order: Time, Height, Width + for dim in [self.t_dim, self.h_dim, self.w_dim]: + # High precision generation + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float64) / dim)) + t = torch.arange(max_seq_len, dtype=torch.float64) + freqs = torch.outer(t, freqs) + + # Interleaved Pattern [c0, c0, c1, c1] + freqs_cos.append(freqs.cos().repeat_interleave(2, dim=-1).float()) + freqs_sin.append(freqs.sin().repeat_interleave(2, dim=-1).float()) + + self.register_buffer("freqs_cos", torch.cat(freqs_cos, dim=1), persistent=False) + self.register_buffer("freqs_sin", torch.cat(freqs_sin, dim=1), persistent=False) + + def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + # Robust shape unpacking + b, c, f, h, w = hidden_states.shape + p_t, p_h, p_w = self.patch_size + ppf, pph, ppw = f // p_t, h // p_h, w // p_w + + split_sizes = [self.t_dim, self.h_dim, self.w_dim] + freqs_cos = self.freqs_cos.split(split_sizes, dim=1) + freqs_sin = self.freqs_sin.split(split_sizes, dim=1) + + # Broadcast frequencies to 3D grid: [Time, Height, Width] + f_cos_t = freqs_cos[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1) + f_sin_t = freqs_sin[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1) + + f_cos_h = freqs_cos[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1) + f_sin_h = freqs_sin[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1) + + f_cos_w = freqs_cos[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1) + f_sin_w = freqs_sin[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1) + + # Concatenate and flatten for Attention [1, SeqLen, 1, Dim] (SHD format) + # New Attention module applies RoPE in [B, S, H, D] layout before reshaping to [B, H, S, D] + return ( + torch.cat([f_cos_t, f_cos_h, f_cos_w], dim=-1).flatten(0, 2).unsqueeze(0).unsqueeze(2), + torch.cat([f_sin_t, f_sin_h, f_sin_w], dim=-1).flatten(0, 2).unsqueeze(0).unsqueeze(2), + ) + + +# ========================================================================= +# 2. Embeddings & Attention +# ========================================================================= + + +class WanImageEmbedding(nn.Module): + """Image embedding for I2V models (Wan 2.1/2.2).""" + + def __init__( + self, + in_features: int, + out_features: int, + pos_embed_seq_len: int = None, + model_config: DiffusionModelConfig = None, + ): + super().__init__() + dtype = model_config.torch_dtype if model_config else None + # LayerNorm weights in fp32 (matches internal float32 normalization; avoids bf16/fp32 mismatch). + self.norm1 = LayerNorm( + hidden_size=in_features, eps=1e-6, dtype=torch.float32, has_weights=True, has_bias=True + ) + + # Match HF FeedForward structure: Linear(in, in) ā†’ GELU ā†’ Linear(in, out) + self.ff_in = Linear( + in_features, + in_features, + bias=True, + dtype=dtype, + mapping=model_config.mapping if model_config else None, + quant_config=model_config.quant_config if model_config else None, + skip_create_weights_in_init=model_config.skip_create_weights_in_init + if model_config + else False, + force_dynamic_quantization=model_config.force_dynamic_quantization + if model_config + else False, + ) + self.ff_out = Linear( + in_features, + out_features, + bias=True, + dtype=dtype, + mapping=model_config.mapping if model_config else None, + quant_config=model_config.quant_config if model_config else None, + skip_create_weights_in_init=model_config.skip_create_weights_in_init + if model_config + else False, + force_dynamic_quantization=model_config.force_dynamic_quantization + if model_config + else False, + ) + + self.norm2 = LayerNorm( + hidden_size=out_features, eps=1e-6, dtype=torch.float32, has_weights=True, has_bias=True + ) + + if pos_embed_seq_len is not None: + self.pos_embed = nn.Parameter(torch.zeros(1, pos_embed_seq_len, in_features)) + else: + self.pos_embed = None + + def forward(self, encoder_hidden_states_image: torch.Tensor) -> torch.Tensor: + if self.pos_embed is not None: + batch_size, seq_len, embed_dim = encoder_hidden_states_image.shape + encoder_hidden_states_image = encoder_hidden_states_image.view( + -1, 2 * seq_len, embed_dim + ) + encoder_hidden_states_image = encoder_hidden_states_image + self.pos_embed + + hidden_states = self.norm1(encoder_hidden_states_image) + hidden_states = self.ff_in(hidden_states) + hidden_states = F.gelu(hidden_states) + hidden_states = self.ff_out(hidden_states) + hidden_states = self.norm2(hidden_states) + return hidden_states + + +class WanTimeTextImageEmbedding(nn.Module): + def __init__( + self, + dim, + time_freq_dim, + time_proj_dim, + text_embed_dim, + model_config: DiffusionModelConfig, + image_embed_dim: int = None, + pos_embed_seq_len: int = None, + ): + super().__init__() + dtype = model_config.torch_dtype + quant_config = model_config.quant_config + skip_create_weights = model_config.skip_create_weights_in_init + force_dynamic_quant = model_config.force_dynamic_quantization + + self.timesteps_proj = Timesteps( + num_channels=time_freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0 + ) + self.time_embedder = TimestepEmbedding(in_channels=time_freq_dim, time_embed_dim=dim) + self.act_fn = nn.SiLU() + + self.time_proj = Linear( + dim, + time_proj_dim, + dtype=dtype, + mapping=model_config.mapping, + quant_config=quant_config, + skip_create_weights_in_init=skip_create_weights, + force_dynamic_quantization=force_dynamic_quant, + ) + self.text_embedder = PixArtAlphaTextProjection(text_embed_dim, dim, act_fn="gelu_tanh") + + self.image_embedder = None + if image_embed_dim is not None: + self.image_embedder = WanImageEmbedding( + image_embed_dim, dim, pos_embed_seq_len=pos_embed_seq_len, model_config=model_config + ) + + def forward(self, timestep, encoder_hidden_states, encoder_hidden_states_image=None): + timestep = self.timesteps_proj(timestep) + + # Get time_embedder dtype + time_embedder_dtype = next(iter(self.time_embedder.parameters())).dtype + if timestep.dtype != time_embedder_dtype and time_embedder_dtype not in [ + torch.float8_e4m3fn, + torch.float8_e5m2, + ]: + timestep = timestep.to(time_embedder_dtype) + + temb = self.time_embedder(timestep).type_as(encoder_hidden_states) + + temb_proj = self.time_proj(self.act_fn(temb)) + + encoder_hidden_states = self.text_embedder(encoder_hidden_states) + + if encoder_hidden_states_image is not None and self.image_embedder is not None: + encoder_hidden_states_image = self.image_embedder(encoder_hidden_states_image) + + return temb, temb_proj, encoder_hidden_states, encoder_hidden_states_image + + +class WanBlock(nn.Module): + def __init__( + self, + model_config: DiffusionModelConfig, + _layer_idx: int, + added_kv_proj_dim: int = None, + ): + super().__init__() + config = model_config.pretrained_config + + if hasattr(config, "hidden_size"): + hidden_size = config.hidden_size + elif hasattr(config, "attention_head_dim") and hasattr(config, "num_attention_heads"): + hidden_size = config.attention_head_dim * config.num_attention_heads + else: + hidden_size = 1536 + + # Wan 2.1 1.3B defaults + num_heads = getattr(config, "num_attention_heads", 12) + head_dim = getattr(config, "attention_head_dim", 128) + ffn_dim = getattr(config, "ffn_dim", 8960) + eps = getattr(config, "eps", 1e-6) + + dtype = model_config.torch_dtype + quant_config = model_config.quant_config + skip_create_weights = model_config.skip_create_weights_in_init + force_dynamic_quant = model_config.force_dynamic_quantization + + # Store for I2V reshaping logic + self.num_heads = num_heads + self.head_dim = head_dim + + # LayerNorm weights in fp32 (matches internal float32 normalization; avoids bf16/fp32 mismatch). + self.norm1 = LayerNorm( + hidden_size=hidden_size, eps=eps, dtype=torch.float32, has_weights=False, has_bias=False + ) + + # Self-attention with fused QKV + self.attn1 = Attention( + hidden_size=hidden_size, + num_attention_heads=num_heads, + head_dim=head_dim, + qkv_mode=QKVMode.FUSE_QKV, + qk_norm=True, + eps=eps, + config=model_config, + layer_idx=_layer_idx, + ) + + # Cross-attention with separate Q, K, V + self.attn2 = Attention( + hidden_size=hidden_size, + num_attention_heads=num_heads, + head_dim=head_dim, + qkv_mode=QKVMode.SEPARATE_QKV, + qk_norm=True, + eps=eps, + config=model_config, + layer_idx=_layer_idx, + ) + + self.norm2 = LayerNorm( + hidden_size=hidden_size, eps=eps, dtype=torch.float32, has_weights=True, has_bias=True + ) + self.norm3 = LayerNorm( + hidden_size=hidden_size, eps=eps, dtype=torch.float32, has_weights=False, has_bias=False + ) + + self.ffn = MLP( + hidden_size=hidden_size, + intermediate_size=ffn_dim, + bias=True, + activation=lambda x: F.gelu(x, approximate="tanh"), + dtype=dtype, + config=model_config, + layer_idx=_layer_idx, + reduce_output=False, + ) + + # I2V: Additional K/V projections for image embeddings + self.add_k_proj = self.add_v_proj = None + self.norm_added_k = None + if added_kv_proj_dim is not None: + self.add_k_proj = Linear( + added_kv_proj_dim, + hidden_size, + dtype=dtype, + mapping=model_config.mapping, + quant_config=quant_config, + skip_create_weights_in_init=skip_create_weights, + force_dynamic_quantization=force_dynamic_quant, + ) + self.add_v_proj = Linear( + added_kv_proj_dim, + hidden_size, + dtype=dtype, + mapping=model_config.mapping, + quant_config=quant_config, + skip_create_weights_in_init=skip_create_weights, + force_dynamic_quantization=force_dynamic_quant, + ) + self.norm_added_k = RMSNorm( + hidden_size=hidden_size, eps=eps, dtype=dtype, has_weights=True + ) + + # Use torch.empty().normal_(std=...) instead of torch.randn()/scale for MetaInitMode compatibility + self.scale_shift_table = nn.Parameter( + torch.empty(1, 6, hidden_size).normal_(std=hidden_size**-0.5) + ) + + def forward( + self, + x, + encoder_hidden_states, + temb, + freqs_cos, + freqs_sin, + ): + shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = ( + self.scale_shift_table.float() + temb.float() + ).chunk(6, dim=1) + + normed = self.norm1(x.float()) * (1 + scale_msa) + shift_msa + normed = normed.to(x.dtype) + + # Prepare frequencies for Attention + freqs = (freqs_cos, freqs_sin) if freqs_cos is not None and freqs_sin is not None else None + + # Self-attention with RoPE + x = ( + x.float() + + self.attn1( + normed, + freqs=freqs, + ).float() + * gate_msa + ).to(x.dtype) + + norm_x = self.norm2(x.float()).to(x.dtype) + + # I2V: Split encoder_hidden_states into image and text parts if needed + encoder_hidden_states_img = None + encoder_hidden_states_text = encoder_hidden_states + if self.add_k_proj is not None: + image_context_length = encoder_hidden_states.shape[1] - 512 + encoder_hidden_states_img = encoder_hidden_states[:, :image_context_length] + encoder_hidden_states_text = encoder_hidden_states[:, image_context_length:] + + # Text cross-attention + attn2_output = self.attn2(norm_x, encoder_hidden_states=encoder_hidden_states_text) + + # I2V: Additional image cross-attention if image embeddings are present + if encoder_hidden_states_img is not None: + batch_size, seq_len = norm_x.shape[:2] + + query = self.attn2.get_qkv(norm_x, None)[0] # Q only + query, _ = self.attn2.apply_qk_norm(query, query) + + key_img = self.add_k_proj(encoder_hidden_states_img) + value_img = self.add_v_proj(encoder_hidden_states_img) + key_img = self.norm_added_k(key_img) + + query = query.view(batch_size, seq_len, self.num_heads, self.head_dim) + key_img = key_img.view( + batch_size, encoder_hidden_states_img.shape[1], self.num_heads, self.head_dim + ) + value_img = value_img.view( + batch_size, encoder_hidden_states_img.shape[1], self.num_heads, self.head_dim + ) + + attn_img_output = self.attn2._attn_impl( + query, + key_img, + value_img, + batch_size=batch_size, + seq_len=seq_len, + kv_seq_len=encoder_hidden_states_img.shape[1], + ) + + attn2_output = attn2_output + attn_img_output + + x = x + attn2_output + + # 3. Feed-forward + normed = self.norm3(x.float()) * (1 + c_scale_msa) + c_shift_msa + normed = normed.to(x.dtype) + + x = (x.float() + self.ffn(normed).float() * c_gate_msa).to(x.dtype) + + return x + + +class WanTransformer3DModel(nn.Module): + _supports_gradient_checkpointing = True + + def __init__( + self, + model_config: DiffusionModelConfig, + ): + super().__init__() + + self.model_config = model_config + + # Validate no tensor parallelism + if model_config.parallel.dit_tp_size > 1: + raise ValueError( + f"WAN does not support tensor parallelism. " + f"Got dit_tp_size={model_config.parallel.dit_tp_size}" + ) + + # Setup sequence parallelism (Ulysses) + num_heads = getattr(model_config.pretrained_config, "num_attention_heads", 12) + self.use_ulysses, self.ulysses_size, self.ulysses_pg, self.ulysses_rank = ( + setup_sequence_parallelism( + model_config=model_config, + num_attention_heads=num_heads, + ) + ) + + config = model_config.pretrained_config + + dtype = model_config.torch_dtype + quant_config = model_config.quant_config + skip_create_weights = model_config.skip_create_weights_in_init + force_dynamic_quant = model_config.force_dynamic_quantization + + if hasattr(config, "hidden_size"): + hidden_size = config.hidden_size + elif hasattr(config, "attention_head_dim") and hasattr(config, "num_attention_heads"): + hidden_size = config.attention_head_dim * config.num_attention_heads + else: + hidden_size = 1536 # Wan 1.3B default + + num_layers = getattr(config, "num_layers", 30) + attention_head_dim = getattr(config, "attention_head_dim", 128) + in_channels = getattr(config, "in_channels", 16) + out_channels = getattr(config, "out_channels", 16) + text_dim = getattr(config, "text_dim", 4096) + freq_dim = getattr(config, "freq_dim", 256) + patch_size = getattr(config, "patch_size", [1, 2, 2]) + image_embed_dim = getattr(config, "image_dim", None) # e.g., 1280 for I2V + added_kv_proj_dim = getattr(config, "added_kv_proj_dim", None) + pos_embed_seq_len = getattr(config, "pos_embed_seq_len", None) + + # Calculate FFN Dim + ffn_dim = getattr(config, "ffn_dim", None) + if ffn_dim is None: + ffn_dim = ( + 13824 + if hidden_size == 5120 + else (8960 if hidden_size == 1536 else int(hidden_size * 4)) + ) + + # Store config for unpatchify and pipeline compatibility + self.config = type( + "Config", + (), + { + "patch_size": patch_size, + "hidden_size": hidden_size, + "image_dim": image_embed_dim, + "in_channels": in_channels, + "out_channels": out_channels, + "num_layers": num_layers, + }, + )() + + self.patch_embedding = nn.Conv3d( + in_channels, + hidden_size, + kernel_size=patch_size, + stride=patch_size, + dtype=dtype, # use model's target dtype (bf16) + ) + + self.condition_embedder = WanTimeTextImageEmbedding( + dim=hidden_size, + time_freq_dim=freq_dim, + time_proj_dim=hidden_size * 6, + text_embed_dim=text_dim, + model_config=model_config, + image_embed_dim=image_embed_dim, + pos_embed_seq_len=pos_embed_seq_len, + ) + + self.blocks = nn.ModuleList( + [ + WanBlock( + model_config=model_config, + _layer_idx=i, + added_kv_proj_dim=added_kv_proj_dim, + ) + for i in range(num_layers) + ] + ) + + self.rope = WanRotaryPosEmbed(attention_head_dim, patch_size, max_seq_len=1024) + + # LayerNorm weights in fp32 (matches internal float32 normalization; avoids bf16/fp32 mismatch). + self.norm_out = LayerNorm( + hidden_size=hidden_size, + eps=1e-6, + dtype=torch.float32, + has_weights=False, + has_bias=False, + ) + + self.proj_out = Linear( + hidden_size, + out_channels * math.prod(patch_size), + dtype=dtype, + mapping=model_config.mapping, + quant_config=quant_config, + skip_create_weights_in_init=skip_create_weights, + force_dynamic_quantization=force_dynamic_quant, + ) + # Use torch.empty().normal_(std=...) instead of torch.randn()/scale for MetaInitMode compatibility + self.scale_shift_table = nn.Parameter( + torch.empty(1, 2, hidden_size).normal_(std=hidden_size**-0.5) + ) + + self.__post_init__() + + @property + def device(self): + return get_parameter_device(self) + + def __post_init__(self): + self.apply_quant_config_exclude_modules() + + for _, module in self.named_modules(): + if callable(getattr(module, "create_weights", None)): + module.create_weights() + + def apply_quant_config_exclude_modules(self): + quant_config = self.model_config.quant_config + if quant_config is None or quant_config.exclude_modules is None: + return + + kv_cache_quant_algo = quant_config.kv_cache_quant_algo if quant_config else None + no_quant_config = QuantConfig(kv_cache_quant_algo=kv_cache_quant_algo) + + for name, module in self.named_modules(): + if isinstance(module, Linear): + is_excluded = quant_config.is_module_excluded_from_quantization(name) + if is_excluded and getattr(module, "quant_config", None) is not None: + module.quant_config = no_quant_config + + def unpatchify(self, x, original_shape): + N, C, T, H, W = original_shape + pt, ph, pw = self.config.patch_size + gt, gh, gw = T // pt, H // ph, W // pw + # Use output channels instead of input channels for unpatchifying + out_channels = self.proj_out.out_features // (pt * ph * pw) + return ( + x.view(N, gt, gh, gw, pt, ph, pw, out_channels) + .permute(0, 7, 1, 4, 2, 5, 3, 6) + .reshape(N, out_channels, T, H, W) + ) + + def forward( + self, + hidden_states, + timestep, + encoder_hidden_states, + encoder_hidden_states_image=None, + **kwargs, + ): + """ + Forward pass with optional Ulysses sequence parallelism. + + With Ulysses enabled (ulysses_size > 1): + 1. Shard input sequence across ranks: [B, S] -> [B, S/P] + 2. Each block's attention does internal all-to-all for full sequence + 3. Gather output sequence: [B, S/P] -> [B, S] + + When TeaCache is enabled, TeaCacheHook intercepts and replaces this call. + """ + original_shape = hidden_states.shape + B, C, T, H, W = original_shape + pt, ph, pw = self.config.patch_size + + # Generate WAN RoPE frequencies + freqs_cos, freqs_sin = self.rope(hidden_states) + + # Patchify and flatten: [B, C, T, H, W] -> [B, S, hidden_size] + x = self.patch_embedding(hidden_states).flatten(2).transpose(1, 2) + + # Shard sequence for Ulysses parallelism: [B, S] -> [B, S/P] + if self.use_ulysses: + seq_len = x.shape[1] + if seq_len % self.ulysses_size != 0: + raise ValueError( + f"Sequence length ({seq_len}) is not divisible by ulysses_size ({self.ulysses_size}). " + f"Adjust video dimensions or use a different ulysses_size." + ) + + chunk_size = seq_len // self.ulysses_size + x = x[:, self.ulysses_rank * chunk_size : (self.ulysses_rank + 1) * chunk_size, :] + + # Shard RoPE frequencies to match sequence sharding + # RoPE freqs shape: [B, S, ...], so shard along dim 1 (sequence dimension) + if freqs_cos is not None and freqs_sin is not None: + freqs_cos = freqs_cos[ + :, self.ulysses_rank * chunk_size : (self.ulysses_rank + 1) * chunk_size + ] + freqs_sin = freqs_sin[ + :, self.ulysses_rank * chunk_size : (self.ulysses_rank + 1) * chunk_size + ] + + # Time and text/image embeddings + temb, temb_proj, encoder_hidden_states, encoder_hidden_states_image = ( + self.condition_embedder(timestep, encoder_hidden_states, encoder_hidden_states_image) + ) + temb_proj = temb_proj.view(-1, 6, self.config.hidden_size) + + # I2V: Concatenate image and text embeddings if image embeddings are provided + if encoder_hidden_states_image is not None: + # Handle CFG: duplicate image embeddings if batch dimension is doubled + if encoder_hidden_states_image.shape[0] != encoder_hidden_states.shape[0]: + batch_multiplier = ( + encoder_hidden_states.shape[0] // encoder_hidden_states_image.shape[0] + ) + encoder_hidden_states_image = encoder_hidden_states_image.repeat( + batch_multiplier, 1, 1 + ) + encoder_hidden_states = torch.cat( + [encoder_hidden_states_image, encoder_hidden_states], dim=1 + ) + + # Transformer blocks (attention handles all-to-all internally for Ulysses) + for block in self.blocks: + x = block( + x, + encoder_hidden_states, + temb_proj, + freqs_cos, + freqs_sin, + ) + + # Gather sequence from all ranks: [B, S/P] -> [B, S] + if self.use_ulysses: + # Ensure tensor is contiguous before all_gather + x = x.contiguous() + x_list = [torch.zeros_like(x) for _ in range(self.ulysses_size)] + torch.distributed.all_gather(x_list, x, group=self.ulysses_pg) + x = torch.cat(x_list, dim=1) + + # Output projection and unpatchify + shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1) + x = self.norm_out(x) * (1 + scale) + shift + x = x.to(hidden_states.dtype) + + return self.unpatchify(self.proj_out(x), original_shape) + + def load_weights(self, weights: dict) -> None: + # Remap checkpoint keys to match model structure + remapped_weights = {} + for key, value in weights.items(): + # Remap transformer block FFN keys + if ".ffn.net.0.proj." in key: + new_key = key.replace(".ffn.net.0.proj.", ".ffn.up_proj.") + remapped_weights[new_key] = value + elif ".ffn.net.2." in key: + new_key = key.replace(".ffn.net.2.", ".ffn.down_proj.") + remapped_weights[new_key] = value + # Remap image embedder FF keys + elif ".image_embedder.ff.net.0.proj." in key: + new_key = key.replace(".image_embedder.ff.net.0.proj.", ".image_embedder.ff_in.") + remapped_weights[new_key] = value + elif ".image_embedder.ff.net.2." in key: + new_key = key.replace(".image_embedder.ff.net.2.", ".image_embedder.ff_out.") + remapped_weights[new_key] = value + # Remap I2V attention keys + elif ".attn2.add_k_proj." in key: + new_key = key.replace(".attn2.add_k_proj.", ".add_k_proj.") + remapped_weights[new_key] = value + elif ".attn2.add_v_proj." in key: + new_key = key.replace(".attn2.add_v_proj.", ".add_v_proj.") + remapped_weights[new_key] = value + elif ".attn2.norm_added_k." in key: + new_key = key.replace(".attn2.norm_added_k.", ".norm_added_k.") + remapped_weights[new_key] = value + else: + remapped_weights[key] = value + + weights = remapped_weights + + # Handle root-level parameters (filter_weights doesn't work for empty prefix) + for param_name, param in self._parameters.items(): + if param is not None and param_name in weights: + param.data.copy_(weights[param_name].to(self.model_config.torch_dtype)) + + params_map = { + "qkv_proj": ["to_q", "to_k", "to_v"], + } + loader = DynamicLinearWeightLoader(self.model_config, params_map=params_map) + + for name, module in tqdm(self.named_modules(), desc="Loading weights"): + if len(module._parameters) == 0: + continue + + if isinstance(module, Linear): + weight_dicts = loader.get_linear_weights(module, name, weights) + + if weight_dicts: + loader.load_linear_weights(module, name, weight_dicts) + elif "add_k_proj" in name or "add_v_proj" in name: + logger.info(f"[Weight Loading] No weights found for I2V module: {name}") + else: + module_weights = loader.filter_weights(name, weights) + for param_name, param in module._parameters.items(): + if param is not None and param_name in module_weights: + param.data.copy_( + module_weights[param_name].to(self.model_config.torch_dtype) + ) + + def post_load_weights(self) -> None: + """Call post_load_weights on all Linear modules and convert embedders to target dtype.""" + # Convert condition_embedder components to target dtype + target_dtype = self.model_config.torch_dtype + if hasattr(self.condition_embedder, "time_embedder"): + self.condition_embedder.time_embedder.to(target_dtype) + if hasattr(self.condition_embedder, "text_embedder"): + self.condition_embedder.text_embedder.to(target_dtype) + + # Call post_load_weights on all Linear modules + for _, module in self.named_modules(): + if isinstance(module, Linear): + module.post_load_weights() diff --git a/tensorrt_llm/_torch/visual_gen/modules/__init__.py b/tensorrt_llm/_torch/visual_gen/modules/__init__.py new file mode 100644 index 0000000000..93636ddb53 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/modules/__init__.py @@ -0,0 +1,26 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Visual Generation Modules + +This module provides modular neural network components for visual generation models. +""" + +from .attention import Attention, QKVMode + +__all__ = [ + "Attention", + "QKVMode", +] diff --git a/tensorrt_llm/_torch/visual_gen/modules/attention.py b/tensorrt_llm/_torch/visual_gen/modules/attention.py new file mode 100644 index 0000000000..0c83bf5e28 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/modules/attention.py @@ -0,0 +1,284 @@ +from enum import Enum +from typing import TYPE_CHECKING, Optional, Tuple + +import torch +import torch.nn as nn + +from ...modules.linear import Linear, WeightMode, WeightsLoadingConfig +from ...modules.rms_norm import RMSNorm +from ..attention_backend.interface import AttentionTensorLayout +from ..attention_backend.utils import create_attention + +if TYPE_CHECKING: + from ..config import DiffusionModelConfig + + +class QKVMode(str, Enum): + FUSE_QKV = "fuse_qkv" + FUSE_KV = "fuse_kv" + SEPARATE_QKV = "separate" + + +# TODO: torch compile +def apply_rotary_emb( + x: torch.Tensor, freqs_cos: torch.Tensor, freqs_sin: torch.Tensor +) -> torch.Tensor: + freqs_cos = freqs_cos.to(x.dtype) + freqs_sin = freqs_sin.to(x.dtype) + x1, x2 = x.unflatten(-1, (-1, 2)).unbind(-1) # [B, S, H, D/2] + + cos = freqs_cos[..., 0::2] + sin = freqs_sin[..., 1::2] + + return torch.stack([x1 * cos - x2 * sin, x1 * sin + x2 * cos], dim=-1).flatten(-2) + + +class Attention(nn.Module): + """Attention module for visual generation models.""" + + def __init__( + self, + hidden_size: int, + num_attention_heads: int, + num_key_value_heads: Optional[int] = None, + head_dim: Optional[int] = None, + qkv_mode: QKVMode = QKVMode.FUSE_QKV, + qk_norm: bool = True, + eps: float = 1e-6, # TODO: remove this, we should add this to the config + config: Optional["DiffusionModelConfig"] = None, + layer_idx: Optional[int] = None, + ): + super().__init__() + + config = config or DiffusionModelConfig() + self.dtype = config.torch_dtype + self.quant_config = config.quant_config + self.skip_create_weights_in_init = config.skip_create_weights_in_init + self.force_dynamic_quantization = config.force_dynamic_quantization + self.mapping = getattr(config, "mapping", None) + + self.hidden_size = hidden_size + self.num_attention_heads = num_attention_heads + self.num_key_value_heads = num_key_value_heads or num_attention_heads + self.head_dim = head_dim or (hidden_size // num_attention_heads) + self.qkv_mode = QKVMode(qkv_mode) if isinstance(qkv_mode, str) else qkv_mode + + # Select compute backend (orthogonal to parallelism) + ulysses_size = config.parallel.dit_ulysses_size + base_backend = config.attention.backend + + if self.qkv_mode == QKVMode.SEPARATE_QKV: + backend_name = "VANILLA" # Cross-attention requires VANILLA + else: + backend_name = base_backend + self.attn_backend = backend_name + self.qk_norm = qk_norm + self.layer_idx = layer_idx if layer_idx is not None else 0 + self.eps = eps + + self.q_dim = self.num_attention_heads * self.head_dim + self.kv_dim = self.num_key_value_heads * self.head_dim + + self._init_qkv_proj() + + if self.qk_norm: + self.norm_q = RMSNorm( + hidden_size=self.q_dim, eps=self.eps, dtype=self.dtype, has_weights=True + ) + self.norm_k = RMSNorm( + hidden_size=self.kv_dim, eps=self.eps, dtype=self.dtype, has_weights=True + ) + + # TODO: Use weight mapper to create just a Linear module + self.to_out = nn.ModuleList( + [ + Linear( + self.q_dim, + self.hidden_size, + dtype=self.dtype, + mapping=self.mapping, + quant_config=self.quant_config, + skip_create_weights_in_init=self.skip_create_weights_in_init, + force_dynamic_quantization=self.force_dynamic_quantization, + ) + ] + ) + + # Compute head counts for the backend + # Ulysses shards heads across workers; inner backend sees sharded count + if ulysses_size > 1 and self.qkv_mode != QKVMode.SEPARATE_QKV: + backend_num_heads = self.num_attention_heads // ulysses_size + backend_num_kv_heads = self.num_key_value_heads // ulysses_size + else: + backend_num_heads = self.num_attention_heads + backend_num_kv_heads = self.num_key_value_heads + + # Create compute backend + self.attn = create_attention( + backend=backend_name, + layer_idx=self.layer_idx, + num_heads=backend_num_heads, + head_dim=self.head_dim, + num_kv_heads=backend_num_kv_heads, + quant_config=self.quant_config, + dtype=self.dtype, + ) + + # Wrap with parallelism strategy (orthogonal to backend choice) + if ulysses_size > 1 and self.qkv_mode != QKVMode.SEPARATE_QKV: + from ..attention_backend.parallel import UlyssesAttention + + process_group = getattr(config, "ulysses_process_group", None) + self.attn = UlyssesAttention( + inner_backend=self.attn, + process_group=process_group, + ) + + def _init_qkv_proj(self) -> None: + if self.qkv_mode == QKVMode.FUSE_QKV: + qkv_out_dim = self.q_dim + 2 * self.kv_dim + self.qkv_proj = Linear( + self.hidden_size, + qkv_out_dim, + dtype=self.dtype, + mapping=self.mapping, + quant_config=self.quant_config, + skip_create_weights_in_init=self.skip_create_weights_in_init, + force_dynamic_quantization=self.force_dynamic_quantization, + weights_loading_config=WeightsLoadingConfig( + weight_mode=WeightMode.FUSED_QKV_LINEAR + ), + fused_weight_shard_indices_mapping={ + "q": (0, self.q_dim), + "k": (self.q_dim, self.kv_dim), + "v": (self.q_dim + self.kv_dim, self.kv_dim), + }, + ) + else: + self.to_q = Linear( + self.hidden_size, + self.q_dim, + dtype=self.dtype, + mapping=self.mapping, + quant_config=self.quant_config, + skip_create_weights_in_init=self.skip_create_weights_in_init, + force_dynamic_quantization=self.force_dynamic_quantization, + ) + self.to_k = Linear( + self.hidden_size, + self.kv_dim, + dtype=self.dtype, + mapping=self.mapping, + quant_config=self.quant_config, + skip_create_weights_in_init=self.skip_create_weights_in_init, + force_dynamic_quantization=self.force_dynamic_quantization, + ) + self.to_v = Linear( + self.hidden_size, + self.kv_dim, + dtype=self.dtype, + mapping=self.mapping, + quant_config=self.quant_config, + skip_create_weights_in_init=self.skip_create_weights_in_init, + force_dynamic_quantization=self.force_dynamic_quantization, + ) + + def get_qkv( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if self.qkv_mode == QKVMode.FUSE_QKV: + qkv = self.qkv_proj(hidden_states) + q, k, v = qkv.split([self.q_dim, self.kv_dim, self.kv_dim], dim=-1) + else: + kv_source = ( + encoder_hidden_states if encoder_hidden_states is not None else hidden_states + ) + q = self.to_q(hidden_states) + k = self.to_k(kv_source) + v = self.to_v(kv_source) + return q, k, v + + def apply_qk_norm(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + if self.qk_norm: + q = self.norm_q(q) + k = self.norm_k(k) + return q, k + + def _attn_impl( + self, + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + batch_size: Optional[int] = None, + seq_len: Optional[int] = None, + kv_seq_len: Optional[int] = None, + ) -> torch.Tensor: + """ + Call attention backend with appropriate tensor layout. + + Two layout paths: + 1. HND backends (VANILLA): [B, S, H*D] -> [B, H, S, D] + 2. NHD backends (TRTLLM, UlyssesAttention): [B, S, H*D] -> [B, S, H, D] + """ + backend_layout = getattr(self.attn, "preferred_layout", AttentionTensorLayout.NHD) + + batch_size = batch_size or q.shape[0] + seq_len = seq_len or q.shape[1] + kv_seq_len = kv_seq_len or k.shape[1] + + # Reshape inputs: [B, S, H*D] -> backend's preferred 4D layout + if backend_layout == AttentionTensorLayout.HND: + q = q.view(batch_size, -1, self.num_attention_heads, self.head_dim).transpose(1, 2) + k = k.view(batch_size, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2) + v = v.view(batch_size, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2) + else: + q = q.view(batch_size, -1, self.num_attention_heads, self.head_dim) + k = k.view(batch_size, -1, self.num_key_value_heads, self.head_dim) + v = v.view(batch_size, -1, self.num_key_value_heads, self.head_dim) + + # Call backend + out = self.attn.forward( + q=q, + k=k, + v=v, + batch_size=batch_size, + seq_len=seq_len, + seq_len_kv=kv_seq_len if kv_seq_len != seq_len else None, + ) + + # Flatten back to [B, S, H*D] + if backend_layout == AttentionTensorLayout.HND: + return out.transpose(1, 2).flatten(2) + else: + return out.flatten(2) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + freqs: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + ) -> torch.Tensor: + assert hidden_states.ndim == 3, "hidden_states must be a 3D tensor" + batch_size, seq_len = hidden_states.shape[:2] + kv_seq_len = ( + encoder_hidden_states.shape[1] if encoder_hidden_states is not None else seq_len + ) + + q, k, v = self.get_qkv(hidden_states, encoder_hidden_states) + q, k = self.apply_qk_norm(q, k) + + # Apply RoPE if provided (model handles RoPE, not attention backend) + if freqs is not None: + freqs_cos, freqs_sin = freqs + q = q.view(batch_size, seq_len, self.num_attention_heads, self.head_dim) # [B, S, H, D] + k = k.view(batch_size, kv_seq_len, self.num_key_value_heads, self.head_dim) + q = apply_rotary_emb(q, freqs_cos, freqs_sin) + k = apply_rotary_emb(k, freqs_cos, freqs_sin) + q = q.flatten(2) + k = k.flatten(2) + + out = self._attn_impl(q, k, v, batch_size, seq_len, kv_seq_len) + out = self.to_out[0](out) + return out diff --git a/tensorrt_llm/_torch/visual_gen/output.py b/tensorrt_llm/_torch/visual_gen/output.py new file mode 100644 index 0000000000..d39c15b0c0 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/output.py @@ -0,0 +1,29 @@ +"""Output dataclass for visual generation models.""" + +from dataclasses import dataclass +from typing import Optional + +import torch + + +@dataclass +class MediaOutput: + """Unified output for all visual generation models. + + Different models populate different fields: + - FLUX2: image only + - WAN: video only + - LTX2: video + audio + + Attributes: + image: Generated image as torch tensor with shape (height, width, channels) and dtype uint8. + Populated by FLUX2 for text-to-image generation. + video: Generated video frames as torch tensor with shape (num_frames, height, width, channels) and dtype uint8. + Populated by WAN and LTX2 for text-to-video generation. + audio: Generated audio as torch tensor with dtype float32. + Populated by LTX2 for text-to-video-with-audio generation. + """ + + image: Optional[torch.Tensor] = None + video: Optional[torch.Tensor] = None + audio: Optional[torch.Tensor] = None diff --git a/tensorrt_llm/_torch/visual_gen/parallelism.py b/tensorrt_llm/_torch/visual_gen/parallelism.py new file mode 100644 index 0000000000..1bda600fa0 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/parallelism.py @@ -0,0 +1,100 @@ +"""Utilities for distributed parallelism setup in diffusion models.""" + +from typing import Optional, Tuple + +import torch.distributed as dist + +from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig + + +def setup_sequence_parallelism( + model_config: DiffusionModelConfig, + num_attention_heads: int, +) -> Tuple[bool, int, Optional[dist.ProcessGroup], int]: + """ + Setup sequence parallelism (currently Ulysses only) with CFG support. + + Creates nested process groups where each CFG group has its own Ulysses group. + Example with cfg_size=2, ulysses_size=2, world_size=4: + GPU 0-1: CFG group 0, Ulysses group 0 + GPU 2-3: CFG group 1, Ulysses group 1 + + Args: + model_config: Model configuration containing parallel settings + num_attention_heads: Number of attention heads in the model + + Returns: + Tuple of (use_parallelism, parallelism_size, parallelism_pg, parallelism_rank): + - use_parallelism: Whether sequence parallelism is enabled + - parallelism_size: The sequence parallelism degree + - parallelism_pg: The process group for this rank (or None) + - parallelism_rank: This rank's position within its parallelism group + + Raises: + RuntimeError: If torch.distributed is not initialized + ValueError: If configuration is invalid (incompatible sizes, head count not divisible, etc.) + NotImplementedError: If Ring attention is requested (not yet implemented) + + Side Effects: + - Sets model_config.ulysses_process_group to the created process group + + Note: + Both num_attention_heads and sequence length must be divisible by ulysses_size. + Head count is validated here; sequence length is validated at runtime during forward pass. + """ + ulysses_size = model_config.parallel.dit_ulysses_size + ring_size = model_config.parallel.dit_ring_size + cfg_size = model_config.parallel.dit_cfg_size + + # Check for ring attention (not yet implemented) + if ring_size > 1: + raise NotImplementedError("Ring attention parallelism is not yet implemented") + + # Early exit if not using sequence parallelism + if ulysses_size <= 1: + model_config.ulysses_process_group = None + return False, 1, None, 0 + + # Validate distributed initialization + if not dist.is_initialized(): + raise RuntimeError( + "torch.distributed.init_process_group() must be called before " + "setting up sequence parallelism" + ) + + rank = dist.get_rank() + world_size = dist.get_world_size() + + # Validate total parallelism capacity + total_parallel = cfg_size * ulysses_size + if total_parallel > world_size: + raise ValueError( + f"cfg_size ({cfg_size}) * ulysses_size ({ulysses_size}) = " + f"{total_parallel} exceeds world_size ({world_size})" + ) + + # Validate head count divisibility + if num_attention_heads % ulysses_size != 0: + raise ValueError( + f"num_attention_heads ({num_attention_heads}) must be divisible by " + f"ulysses_size ({ulysses_size})" + ) + + # Create nested process groups + # Each CFG group has its own Ulysses group + ulysses_pg = None + ulysses_rank = 0 + + for cfg_id in range(cfg_size): + ulysses_ranks = list(range(cfg_id * ulysses_size, (cfg_id + 1) * ulysses_size)) + pg = dist.new_group(ulysses_ranks, use_local_synchronization=True) + + # Store if this rank belongs to this group + if rank in ulysses_ranks: + ulysses_pg = pg + ulysses_rank = rank - cfg_id * ulysses_size + + # Store in config for Attention modules + model_config.ulysses_process_group = ulysses_pg + + return True, ulysses_size, ulysses_pg, ulysses_rank diff --git a/tensorrt_llm/_torch/visual_gen/pipeline.py b/tensorrt_llm/_torch/visual_gen/pipeline.py new file mode 100644 index 0000000000..7876031df7 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/pipeline.py @@ -0,0 +1,544 @@ +import time +from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple + +import torch +import torch.distributed as dist +import torch.nn as nn + +from tensorrt_llm.logger import logger +from tensorrt_llm.mapping import Mapping + +from .teacache import TeaCacheBackend + +if TYPE_CHECKING: + from .config import DiffusionModelConfig + + +class BasePipeline(nn.Module): + """ + Base class for diffusion pipelines. + """ + + def __init__(self, model_config: "DiffusionModelConfig"): + super().__init__() + self.model_config = model_config + self.config = model_config.pretrained_config + self.mapping: Mapping = getattr(model_config, "mapping", None) or Mapping() + + # Components + self.transformer: Optional[nn.Module] = None + self.vae: Optional[nn.Module] = None + self.text_encoder: Optional[nn.Module] = None + self.tokenizer: Optional[Any] = None + self.scheduler: Optional[Any] = None + + # Initialize transformer + self._init_transformer() + + @property + def rank(self): + return dist.get_rank() if dist.is_initialized() else 0 + + @property + def world_size(self): + return dist.get_world_size() if dist.is_initialized() else 1 + + @property + def dtype(self): + if hasattr(self, "transformer"): + return next(self.transformer.parameters()).dtype + return torch.float32 + + @property + def device(self): + return self.transformer.device + + def infer(self, req: Any): + raise NotImplementedError + + def _init_transformer(self) -> None: + raise NotImplementedError + + def forward(self, *args, **kwargs): + raise NotImplementedError + + def load_standard_components( + self, + checkpoint_dir: str, + device: torch.device, + skip_components: Optional[list] = None, + ) -> None: + raise NotImplementedError + + def load_weights(self, weights: Dict[str, torch.Tensor]) -> None: + if self.transformer is not None and hasattr(self.transformer, "load_weights"): + self.transformer.load_weights(weights) + + def post_load_weights(self) -> None: + if self.transformer is not None and hasattr(self.transformer, "post_load_weights"): + self.transformer.post_load_weights() + + def _setup_teacache(self, model, coefficients: Optional[Dict] = None): + """Setup TeaCache optimization for the transformer model. + + TeaCache caches transformer block outputs when timestep embeddings change slowly, + reducing computation during the denoising loop. + + Args: + model: The transformer model to optimize + coefficients: Optional dict of model-specific polynomial coefficients for cache decisions + Format: {model_size: {"ret_steps": [...], "standard": [...]}} + """ + self.cache_backend = None + + # Get teacache config from model_config (always present now) + teacache_cfg = self.model_config.teacache + if not teacache_cfg.enable_teacache: + return + + # Apply model-specific polynomial coefficients + # Coefficients are used to rescale embedding distances for cache decisions + if coefficients: + checkpoint_path = ( + getattr(self.model_config.pretrained_config, "_name_or_path", "") or "" + ) + for model_size, coeff_data in coefficients.items(): + # Match model size in path (case-insensitive, e.g., "1.3B", "14B", "dev") + if model_size.lower() in checkpoint_path.lower(): + if isinstance(coeff_data, dict): + # Select coefficient set based on warmup mode + mode = "ret_steps" if teacache_cfg.use_ret_steps else "standard" + if mode in coeff_data: + teacache_cfg.coefficients = coeff_data[mode] + logger.info(f"TeaCache: Using {model_size} coefficients ({mode} mode)") + else: + # Single coefficient list (no mode distinction) + teacache_cfg.coefficients = coeff_data + logger.info(f"TeaCache: Using {model_size} coefficients") + break + + # Initialize and enable TeaCache backend + logger.info("TeaCache: Initializing...") + self.cache_backend = TeaCacheBackend(teacache_cfg) + self.cache_backend.enable(model) + + def decode_latents( + self, + latents: torch.Tensor, + decode_fn: Callable[[torch.Tensor], Any], + extra_latents: Optional[Dict[str, Tuple[torch.Tensor, Callable]]] = None, + ): + """Execute VAE decoding. Only rank 0 performs decoding. + + Args: + latents: Primary latents to decode (e.g., video) + decode_fn: Decoder function for primary latents + extra_latents: Optional dict of additional latents to decode. + Format: {name: (latents_tensor, decode_fn)} + Example: {"audio": (audio_latents, audio_decode_fn)} + + Returns: + Single result if no extra_latents, tuple of results if extra_latents provided. + Non-rank-0 processes return None placeholders. + """ + if self.rank == 0: + primary_result = decode_fn(latents) + + if extra_latents: + extra_results = [] + for name, (extra_latent, extra_decode_fn) in extra_latents.items(): + extra_results.append(extra_decode_fn(extra_latent)) + return (primary_result,) + tuple(extra_results) + + return primary_result + + # Return None placeholders for non-rank-0 processes + n_results = 1 + (len(extra_latents) if extra_latents else 0) + return (None,) * n_results if n_results > 1 else None + + @staticmethod + def _rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): + """Rescale noise to fix overexposure (https://huggingface.co/papers/2305.08891).""" + std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) + std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) + noise_pred_rescaled = noise_cfg * (std_text / std_cfg) + return guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg + + def _setup_cfg_config( + self, guidance_scale, prompt_embeds, neg_prompt_embeds, extra_cfg_tensors=None + ): + """Setup CFG parallel configuration. + + Args: + guidance_scale: CFG guidance scale + prompt_embeds: Positive prompt embeddings + neg_prompt_embeds: Negative prompt embeddings (None if already concatenated) + extra_cfg_tensors: Optional dict of additional tensors to split for CFG parallel. + Format: {name: (positive_tensor, negative_tensor)} + Example: {"audio_embeds": (pos_audio, neg_audio), + "attention_mask": (pos_mask, neg_mask)} + + Returns: + Dict with CFG configuration including split tensors + """ + # Access parallel config directly (always present now) + cfg_size = self.model_config.parallel.dit_cfg_size + ulysses_size = self.model_config.parallel.dit_ulysses_size + + cfg_group = self.rank // ulysses_size + is_split_embeds = neg_prompt_embeds is not None + do_cfg_parallel = cfg_size >= 2 and guidance_scale > 1.0 + + local_extras = {} + + if do_cfg_parallel: + if self.rank == 0: + logger.info(f"CFG Parallel: cfg_size={cfg_size}, ulysses_size={ulysses_size}") + + # Split main embeddings + if is_split_embeds: + pos_embeds, neg_embeds = prompt_embeds, neg_prompt_embeds + else: + neg_embeds, pos_embeds = prompt_embeds.chunk(2) + + local_embeds = pos_embeds if cfg_group == 0 else neg_embeds + + # Split extra tensors if provided + if extra_cfg_tensors: + for name, (pos_tensor, neg_tensor) in extra_cfg_tensors.items(): + if pos_tensor is not None and neg_tensor is not None: + local_extras[name] = pos_tensor if cfg_group == 0 else neg_tensor + elif pos_tensor is not None: + # Only positive provided, use it for both + local_extras[name] = pos_tensor + else: + local_embeds = None + if is_split_embeds and guidance_scale > 1.0: + prompt_embeds = torch.cat([neg_prompt_embeds, prompt_embeds]) + + # For standard CFG, concatenate extra tensors + if extra_cfg_tensors: + for name, (pos_tensor, neg_tensor) in extra_cfg_tensors.items(): + if pos_tensor is not None and neg_tensor is not None and guidance_scale > 1.0: + local_extras[name] = torch.cat([neg_tensor, pos_tensor], dim=0) + elif pos_tensor is not None: + local_extras[name] = pos_tensor + + return { + "enabled": do_cfg_parallel, + "cfg_size": cfg_size, + "ulysses_size": ulysses_size, + "cfg_group": cfg_group, + "local_embeds": local_embeds, + "prompt_embeds": prompt_embeds, + "local_extras": local_extras, + } + + def _denoise_step_cfg_parallel( + self, + latents, + extra_stream_latents, + timestep, + local_embeds, + forward_fn, + guidance_scale, + guidance_rescale, + ulysses_size, + local_extras, + ): + """Execute single denoising step with CFG parallel.""" + t_start = time.time() + result = forward_fn(latents, extra_stream_latents, timestep, local_embeds, local_extras) + + # Handle return format: (primary_noise, extra_noises_dict) or just primary_noise + if isinstance(result, tuple) and len(result) == 2 and isinstance(result[1], dict): + noise_pred_local, extra_noise_locals = result + else: + noise_pred_local = result + extra_noise_locals = {} + + t_transformer = time.time() - t_start + + c_start = time.time() + + # All-gather primary noise + gather_list = [torch.empty_like(noise_pred_local) for _ in range(self.world_size)] + dist.all_gather(gather_list, noise_pred_local) + noise_cond = gather_list[0] + noise_uncond = gather_list[ulysses_size] + noise_pred = noise_uncond + guidance_scale * (noise_cond - noise_uncond) + + # All-gather extra stream noises + extra_noise_preds = {} + for name, noise_local in extra_noise_locals.items(): + gather_list_extra = [torch.empty_like(noise_local) for _ in range(self.world_size)] + dist.all_gather(gather_list_extra, noise_local) + noise_cond_extra = gather_list_extra[0] + noise_uncond_extra = gather_list_extra[ulysses_size] + extra_noise_preds[name] = noise_uncond_extra + guidance_scale * ( + noise_cond_extra - noise_uncond_extra + ) + + if guidance_rescale > 0.0: + extra_noise_preds[name] = self._rescale_noise_cfg( + extra_noise_preds[name], noise_cond_extra, guidance_rescale + ) + + if guidance_rescale > 0.0: + noise_pred = self._rescale_noise_cfg(noise_pred, noise_cond, guidance_rescale) + + t_cfg = time.time() - c_start + return noise_pred, extra_noise_preds, t_transformer, t_cfg + + def _denoise_step_standard( + self, + latents, + extra_stream_latents, + timestep, + prompt_embeds, + forward_fn, + guidance_scale, + guidance_rescale, + local_extras, + ): + """Execute single denoising step without CFG parallel.""" + if guidance_scale > 1.0: + latent_input = torch.cat([latents] * 2) + # Duplicate extra stream latents for CFG + extra_stream_input = { + name: torch.cat([stream_latents] * 2) + for name, stream_latents in extra_stream_latents.items() + } + else: + latent_input = latents + extra_stream_input = extra_stream_latents + + timestep_expanded = timestep.expand(latent_input.shape[0]) + + t_start = time.time() + result = forward_fn( + latent_input, extra_stream_input, timestep_expanded, prompt_embeds, local_extras + ) + + # Handle return format: (primary_noise, extra_noises_dict) or just primary_noise + if isinstance(result, tuple) and len(result) == 2 and isinstance(result[1], dict): + noise_pred, extra_noise_preds = result + else: + noise_pred = result + extra_noise_preds = {} + + t_transformer = time.time() - t_start + + c_start = time.time() + if guidance_scale > 1.0: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # Apply CFG to extra streams + for name, noise_extra in extra_noise_preds.items(): + noise_uncond_extra, noise_text_extra = noise_extra.chunk(2) + extra_noise_preds[name] = noise_uncond_extra + guidance_scale * ( + noise_text_extra - noise_uncond_extra + ) + + if guidance_rescale > 0.0: + extra_noise_preds[name] = self._rescale_noise_cfg( + extra_noise_preds[name], noise_text_extra, guidance_rescale + ) + + if guidance_rescale > 0.0: + noise_pred = self._rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale) + + t_cfg = time.time() - c_start + else: + t_cfg = 0.0 + + return noise_pred, extra_noise_preds, t_transformer, t_cfg + + def _scheduler_step( + self, + latents, + extra_stream_latents, + noise_pred, + extra_noise_preds, + timestep, + scheduler, + extra_stream_schedulers, + ): + """Execute scheduler step for all streams.""" + t_start = time.time() + latents = scheduler.step(noise_pred, timestep, latents, return_dict=False)[0] + + # Step schedulers for extra streams + for name, noise_extra in extra_noise_preds.items(): + if name in extra_stream_schedulers: + extra_stream_latents[name] = extra_stream_schedulers[name].step( + noise_extra, timestep, extra_stream_latents[name], return_dict=False + )[0] + + t_sched = time.time() - t_start + return latents, extra_stream_latents, t_sched + + def denoise( + self, + latents: torch.Tensor, + scheduler: Any, + prompt_embeds: torch.Tensor, + guidance_scale: float, + forward_fn: Callable, + timesteps: Optional[torch.Tensor] = None, + neg_prompt_embeds: Optional[torch.Tensor] = None, + guidance_rescale: float = 0.0, + extra_cfg_tensors: Optional[Dict[str, Tuple[torch.Tensor, Optional[torch.Tensor]]]] = None, + extra_streams: Optional[Dict[str, Tuple[torch.Tensor, Any]]] = None, + guidance_scale_2: Optional[float] = None, + boundary_timestep: Optional[float] = None, + ): + """Execute denoising loop with optional CFG parallel and TeaCache support. + + Args: + latents: Initial noise latents (primary stream, e.g., video) + scheduler: Diffusion scheduler for primary stream + prompt_embeds: Text embeddings (positive) + guidance_scale: CFG strength (1.0 = no guidance) + forward_fn: Transformer forward function + Signature: forward_fn(latents, extra_stream_latents, timestep, + encoder_hidden_states, extra_tensors_dict) + Returns: (primary_noise, extra_stream_noises_dict) or just primary_noise + timesteps: Optional custom timesteps (defaults to scheduler.timesteps) + neg_prompt_embeds: Optional negative text embeddings for CFG + guidance_rescale: CFG rescale factor to prevent overexposure + extra_cfg_tensors: Optional dict of additional tensors to split for CFG parallel + Format: {name: (positive_tensor, negative_tensor)} + Example: {"audio_embeds": (pos_audio, neg_audio)} + extra_streams: Optional dict of additional streams to denoise in parallel + Format: {name: (stream_latents, stream_scheduler)} + Example: {"audio": (audio_latents, audio_scheduler)} + guidance_scale_2: Optional guidance scale for two-stage denoising. + When provided with boundary_timestep, switches from guidance_scale + to guidance_scale_2 when timestep < boundary_timestep. + boundary_timestep: Optional timestep boundary for two-stage denoising. + Switches guidance scale when crossing this threshold. + + Returns: + Single latents if no extra_streams + Tuple (primary_latents, extra_streams_dict) if extra_streams provided + """ + if timesteps is None: + timesteps = scheduler.timesteps + + total_steps = len(timesteps) + has_extra_streams = extra_streams is not None and len(extra_streams) > 0 + + # Reset TeaCache state for new generation + # Sets warmup/cutoff steps based on total_steps + if ( + hasattr(self, "cache_backend") + and self.cache_backend + and self.cache_backend.is_enabled() + ): + self.cache_backend.refresh(total_steps) + + if self.rank == 0: + if has_extra_streams: + stream_names = ", ".join(["primary"] + list(extra_streams.keys())) + logger.info( + f"Denoising [{stream_names}]: {total_steps} steps, guidance={guidance_scale}" + ) + else: + logger.info(f"Denoising: {total_steps} steps, guidance={guidance_scale}") + + cfg_config = self._setup_cfg_config( + guidance_scale, prompt_embeds, neg_prompt_embeds, extra_cfg_tensors + ) + do_cfg_parallel = cfg_config["enabled"] + prompt_embeds = cfg_config["prompt_embeds"] + local_extras = cfg_config["local_extras"] + + # Extract extra stream latents and schedulers + extra_stream_latents = {} + extra_stream_schedulers = {} + if extra_streams: + for name, (stream_latents, stream_scheduler) in extra_streams.items(): + extra_stream_latents[name] = stream_latents + extra_stream_schedulers[name] = stream_scheduler + + start_time = time.time() + + for i, t in enumerate(timesteps): + step_start = time.time() + + # Two-stage denoising: switch guidance scale at boundary + current_guidance_scale = guidance_scale + if guidance_scale_2 is not None and boundary_timestep is not None: + t_scalar = t.item() if t.dim() == 0 else t[0].item() + if t_scalar < boundary_timestep: + current_guidance_scale = guidance_scale_2 + + # Denoise + if do_cfg_parallel: + timestep = t.expand(latents.shape[0]) + noise_pred, extra_noise_preds, t_trans, t_cfg = self._denoise_step_cfg_parallel( + latents, + extra_stream_latents, + timestep, + cfg_config["local_embeds"], + forward_fn, + current_guidance_scale, + guidance_rescale, + cfg_config["ulysses_size"], + local_extras, + ) + else: + noise_pred, extra_noise_preds, t_trans, t_cfg = self._denoise_step_standard( + latents, + extra_stream_latents, + t, + prompt_embeds, + forward_fn, + current_guidance_scale, + guidance_rescale, + local_extras, + ) + + # Scheduler step for all streams + latents, extra_stream_latents, t_sched = self._scheduler_step( + latents, + extra_stream_latents, + noise_pred, + extra_noise_preds, + t, + scheduler, + extra_stream_schedulers, + ) + + # Logging + if self.rank == 0: + step_time = time.time() - step_start + avg_time = (time.time() - start_time) / (i + 1) + eta = avg_time * (total_steps - i - 1) + logger.info( + f"Step {i + 1}/{total_steps} | {step_time:.2f}s " + f"(trans={t_trans:.2f}s cfg={t_cfg:.3f}s sched={t_sched:.3f}s) | " + f"Avg={avg_time:.2f}s/step ETA={eta:.1f}s" + ) + + if self.rank == 0: + total_time = time.time() - start_time + logger.info("=" * 80) + logger.info(f"Denoising done: {total_time:.2f}s ({total_time / total_steps:.2f}s/step)") + + # Log TeaCache performance statistics + # Shows how many transformer steps were skipped (cache hits) vs computed + if ( + hasattr(self, "cache_backend") + and self.cache_backend + and self.cache_backend.is_enabled() + ): + stats = self.cache_backend.get_stats() + if stats: + logger.info( + f"TeaCache: {stats['hit_rate']:.1%} hit rate ({stats['cached']}/{stats['total']} steps)" + ) + + return (latents, extra_stream_latents) if has_extra_streams else latents diff --git a/tensorrt_llm/_torch/visual_gen/pipeline_loader.py b/tensorrt_llm/_torch/visual_gen/pipeline_loader.py new file mode 100644 index 0000000000..4cbb05a8e7 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/pipeline_loader.py @@ -0,0 +1,228 @@ +""" +Model loader for diffusion pipelines. + +Flow: +1. Load config via DiffusionModelConfig.from_pretrained() +2. Create pipeline via AutoPipeline.from_config() with MetaInit +3. Load weights with on-the-fly quantization if dynamic_weight_quant=True +4. Call pipeline.post_load_weights() + +Dynamic Quantization: +- If quant_config specifies FP8/NVFP4 and dynamic_weight_quant=True: + - Model Linear layers are created with FP8/NVFP4 buffers + - BF16 checkpoint weights are quantized on-the-fly during loading + - Quantized weights are copied into model buffers +""" + +import os +from typing import TYPE_CHECKING, Optional + +import torch + +from tensorrt_llm._torch.models.modeling_utils import MetaInitMode +from tensorrt_llm.llmapi.utils import download_hf_model +from tensorrt_llm.logger import logger +from tensorrt_llm.mapping import Mapping + +from .checkpoints import WeightLoader +from .config import DiffusionArgs, DiffusionModelConfig, PipelineComponent +from .models import AutoPipeline + +if TYPE_CHECKING: + from .models import BasePipeline + + +class PipelineLoader: + """ + Loader for diffusion pipelines. + + Supports dynamic quantization: when quant_config specifies FP8/NVFP4, + model is built with quantized buffers and BF16 weights are quantized + on-the-fly during loading. + + Example: + args = DiffusionArgs( + checkpoint_path="/path/to/model", + linear=LinearConfig(type="trtllm-fp8-blockwise"), + parallel=ParallelConfig(dit_tp_size=2), + ) + pipeline = PipelineLoader(args).load() + """ + + def __init__( + self, + args: Optional[DiffusionArgs] = None, + *, + mapping: Optional[Mapping] = None, + device: str = "cuda", + ): + """ + Initialize model loader. + + Args: + args: DiffusionArgs containing all configuration (preferred) + mapping: Tensor parallel mapping (fallback if args is None) + device: Device to load model on (fallback if args is None) + """ + self.args = args + if args is not None: + self.mapping = args.to_mapping() + self.device = torch.device(args.device) + else: + self.mapping = mapping or Mapping() + self.device = torch.device(device) + + def _resolve_checkpoint_dir(self, checkpoint_dir: str) -> str: + """Resolve checkpoint_dir to a local directory path. + + If checkpoint_dir is an existing local path, returns it unchanged. + Otherwise, attempts to download from HuggingFace Hub using the + file-lock-protected ``download_hf_model`` utility (safe for + concurrent multi-process access). + + Args: + checkpoint_dir: Local path or HuggingFace Hub model ID. + + Returns: + Path to local directory containing the model. + + Raises: + ValueError: If the path cannot be resolved (invalid repo ID, + authentication failure, offline with no cache, etc.) + """ + if os.path.exists(checkpoint_dir): + return checkpoint_dir + + revision = self.args.revision if self.args else None + logger.info( + f"'{checkpoint_dir}' not found locally; " + f"attempting HuggingFace Hub download (revision={revision})" + ) + try: + local_dir = download_hf_model(checkpoint_dir, revision=revision) + except Exception as e: + raise ValueError( + f"Could not resolve '{checkpoint_dir}' as a local path or " + f"HuggingFace Hub model ID: {e}" + ) from e + return str(local_dir) + + def load( + self, + checkpoint_dir: Optional[str] = None, + ) -> "BasePipeline": + """ + Load a diffusion pipeline with optional dynamic quantization. + + Flow: + 1. Resolve checkpoint_dir (local path or HuggingFace Hub model ID) + 2. Load config via DiffusionModelConfig.from_pretrained() + 3. Create pipeline via AutoPipeline.from_config() with MetaInit + 4. Load transformer weights via pipeline.load_weights() + 5. Load auxiliary components (VAE, text_encoder) via diffusers + 6. Call pipeline.post_load_weights() + + Args: + checkpoint_dir: Local path or HF Hub model ID (uses args.checkpoint_path if not provided) + + Returns: + Loaded pipeline (WanPipeline, FluxPipeline, etc.) - type auto-detected + """ + # Resolve checkpoint_dir + checkpoint_dir = checkpoint_dir or (self.args.checkpoint_path if self.args else None) + if not checkpoint_dir: + raise ValueError("checkpoint_dir must be provided or set in DiffusionArgs") + checkpoint_dir = self._resolve_checkpoint_dir(str(checkpoint_dir)) + + # Get loading options from args + skip_components = self.args.skip_components if self.args else [] + + # ===================================================================== + # STEP 1: Load Config (includes quant config parsing) + # Merge pretrained checkpoint config with user-provided DiffusionArgs + # ===================================================================== + logger.info(f"Loading config from {checkpoint_dir}") + config = DiffusionModelConfig.from_pretrained( + checkpoint_dir, + args=self.args, + mapping=self.mapping, + ) + + # Log quantization settings + if config.quant_config and config.quant_config.quant_algo: + logger.info(f"Quantization: {config.quant_config.quant_algo.name}") + logger.info(f"Dynamic weight quant: {config.dynamic_weight_quant}") + + # ===================================================================== + # STEP 2: Create Pipeline with MetaInit + # Pipeline type is auto-detected from model_index.json + # - Meta tensors (no GPU memory until materialization) + # - If quant_config specifies FP8, Linear layers have FP8 weight buffers + # ===================================================================== + logger.info("Creating pipeline with MetaInitMode") + with MetaInitMode(): + pipeline = AutoPipeline.from_config(config, checkpoint_dir) + + # Convert meta tensors to CUDA tensors + self._materialize_meta_tensors(pipeline) + pipeline.to(self.device) + + # ===================================================================== + # STEP 3: Load Transformer Weights + # If dynamic_weight_quant=True: + # - BF16 checkpoint weights are loaded + # - Quantized on-the-fly to FP8/NVFP4 by DynamicLinearWeightLoader + # - Copied into model's quantized buffers + # ===================================================================== + if pipeline.transformer is None: + raise ValueError("Pipeline has no transformer component") + + transformer_components = getattr(pipeline, "transformer_components", ["transformer"]) + logger.info(f"Transformer components: {transformer_components}") + + transformer_path = os.path.join(checkpoint_dir, PipelineComponent.TRANSFORMER) + if not os.path.exists(transformer_path): + raise FileNotFoundError( + f"Transformer path does not exist: {transformer_path}. " + f"Checkpoint directory must contain a 'transformer' subdirectory." + ) + + weight_loader = WeightLoader(components=transformer_components) + # TODO: accelerate the cpu loading w/ multiprocessing + weights = weight_loader.load_weights(checkpoint_dir, self.mapping) + + # Load weights into pipeline + pipeline.load_weights(weights) + + # ===================================================================== + # STEP 4: Load Standard Components (VAE, TextEncoder via diffusers) + # These are NOT quantized - loaded as-is from checkpoint + # ===================================================================== + pipeline.load_standard_components(checkpoint_dir, self.device, skip_components) + + # ===================================================================== + # STEP 5: Post-load Hooks (TeaCache setup, etc.) + # ===================================================================== + if hasattr(pipeline, "post_load_weights"): + pipeline.post_load_weights() + + logger.info(f"Pipeline loaded: {pipeline.__class__.__name__}") + return pipeline + + def _materialize_meta_tensors(self, module: torch.nn.Module) -> None: + """ + Convert meta tensors to CUDA tensors. + + Meta tensors are placeholders that don't allocate GPU memory. + After model structure is defined, we materialize them to real tensors. + """ + memo = {} + + def init_meta_tensor(t: torch.Tensor) -> torch.Tensor: + if t.device != torch.device("meta"): + return t + if t not in memo: + memo[t] = torch.empty_like(t, device="cuda") + return memo[t] + + module._apply(init_meta_tensor) diff --git a/tensorrt_llm/_torch/visual_gen/pipeline_registry.py b/tensorrt_llm/_torch/visual_gen/pipeline_registry.py new file mode 100644 index 0000000000..f4c7fc37da --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/pipeline_registry.py @@ -0,0 +1,94 @@ +"""Pipeline registry for unified config flow. + +Follows: DiffusionArgs ā†’ PipelineLoader ā†’ DiffusionModelConfig ā†’ AutoPipeline ā†’ BasePipeline + +All pipelines (Wan, Flux2, LTX2) register via @register_pipeline decorator. +""" + +import json +import os +from typing import TYPE_CHECKING, Dict, Type + +from tensorrt_llm.logger import logger + +if TYPE_CHECKING: + from .config import DiffusionModelConfig + from .pipeline import BasePipeline + +# Global registry: pipeline_name -> pipeline_class +PIPELINE_REGISTRY: Dict[str, Type["BasePipeline"]] = {} + + +def register_pipeline(name: str): + """Register a pipeline class for AutoPipeline. + + Usage: + @register_pipeline("WanPipeline") + class WanPipeline(BasePipeline): + ... + """ + + def decorator(cls: Type["BasePipeline"]) -> Type["BasePipeline"]: + PIPELINE_REGISTRY[name] = cls + logger.debug(f"Registered pipeline: {name} -> {cls.__name__}") + return cls + + return decorator + + +class AutoPipeline: + """Factory for creating pipelines from config.""" + + @staticmethod + def from_config( + config: "DiffusionModelConfig", + checkpoint_dir: str, + ) -> "BasePipeline": + """ + Create pipeline instance from DiffusionModelConfig. + """ + # Detect pipeline type from model_index.json + pipeline_type = AutoPipeline._detect_from_checkpoint(checkpoint_dir) + + if pipeline_type not in PIPELINE_REGISTRY: + raise ValueError( + f"Unknown pipeline: '{pipeline_type}'. " + f"Available: {list(PIPELINE_REGISTRY.keys())}\n" + f"Checkpoint: {checkpoint_dir}" + ) + + pipeline_class = PIPELINE_REGISTRY[pipeline_type] + logger.info(f"AutoPipeline: Creating {pipeline_class.__name__} from {checkpoint_dir}") + + # Instantiate pipeline with DiffusionModelConfig + return pipeline_class(config) + + @staticmethod + def _detect_from_checkpoint(checkpoint_dir: str) -> str: + """Detect pipeline type.""" + index_path = os.path.join(checkpoint_dir, "model_index.json") + + if os.path.exists(index_path): + with open(index_path) as f: + index = json.load(f) + + class_name = index.get("_class_name", "") + + if class_name in PIPELINE_REGISTRY: + return class_name + + if "ImageToVideo" in class_name or "I2V" in class_name: + if "Wan" in class_name: + return "WanImageToVideoPipeline" + # Generic Wan (T2V) + if "Wan" in class_name: + return "WanPipeline" + if "Flux" in class_name: + return "FluxPipeline" + if "LTX" in class_name or "Ltx" in class_name: + return "LTX2Pipeline" + + raise ValueError( + f"Cannot detect pipeline type for {checkpoint_dir}\n" + f"Expected model_index.json with '_class_name' field at: {index_path}" + ) diff --git a/tensorrt_llm/_torch/visual_gen/quantization/__init__.py b/tensorrt_llm/_torch/visual_gen/quantization/__init__.py new file mode 100644 index 0000000000..909629b1b3 --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/quantization/__init__.py @@ -0,0 +1,15 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +Quantization support for diffusion models. +""" + +from .loader import DynamicLinearWeightLoader +from .ops import quantize_fp8_blockwise, quantize_fp8_per_tensor + +__all__ = [ + "DynamicLinearWeightLoader", + "quantize_fp8_per_tensor", + "quantize_fp8_blockwise", +] diff --git a/tensorrt_llm/_torch/visual_gen/quantization/loader.py b/tensorrt_llm/_torch/visual_gen/quantization/loader.py new file mode 100644 index 0000000000..a4a2a3a11c --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/quantization/loader.py @@ -0,0 +1,197 @@ +""" +Dynamic weight quantization loader for Linear modules. + +Wraps Linear.load_weights() to perform dynamic quantization before loading to device. +""" + +from typing import Dict, List, Optional + +import torch + +from tensorrt_llm._torch.modules.linear import Linear, WeightMode +from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig +from tensorrt_llm._torch.visual_gen.quantization.ops import ( + quantize_fp8_blockwise, + quantize_fp8_per_tensor, +) +from tensorrt_llm.quantization.mode import QuantAlgo + + +class DynamicLinearWeightLoader: + """ + Dynamic weight quantization loader for Linear modules. + + Wraps Linear.load_weights() to perform dynamic (load-time) quantization + from BF16/FP16 to FP8 before loading weights to device. + + Example: + params_map = {'qkv_proj': ['to_q', 'to_k', 'to_v']} + loader = DynamicLinearWeightLoader(model_config, params_map=params_map) + + for name, module in model.named_modules(): + if isinstance(module, Linear): + weight_dicts = loader.get_linear_weights(module, name, weights) + loader.load_linear_weights(module, name, weight_dicts) + """ + + def __init__( + self, + model_config: DiffusionModelConfig, + params_map: Optional[Dict[str, List[str]]] = None, + ): + self.model_config = model_config + self.quant_config = model_config.quant_config + self.quant_config_dict = model_config.quant_config_dict + self.dynamic_weight_quant = model_config.dynamic_weight_quant + self.params_map = params_map or {} + + # ========================================================================= + # Weight gathering methods + # ========================================================================= + + def get_linear_weights( + self, + module: Linear, + full_name: str, + weights: Dict[str, torch.Tensor], + ) -> List[Dict[str, torch.Tensor]]: + """Get weights for a Linear module, auto-detecting fused weights.""" + weights_config = getattr(module, "weights_loading_config", None) + if weights_config is not None: + weight_mode = getattr(weights_config, "weight_mode", None) + if weight_mode == WeightMode.FUSED_QKV_LINEAR: + fused_names = self._get_fused_names(full_name) + return self._get_fused_weights(full_name, weights, fused_names) + + return self._get_vanilla_weights(full_name, weights) + + def filter_weights( + self, prefix: str, weights: Dict[str, torch.Tensor] + ) -> Dict[str, torch.Tensor]: + """ + Filter weights by prefix and strip the prefix. + + Example: + prefix = 'blocks.0.attn1.to_q' + weights = {'blocks.0.attn1.to_q.weight': ..., 'blocks.0.attn1.to_q.bias': ...} + Returns: {'weight': ..., 'bias': ...} + """ + result = {} + prefix_dot = prefix + "." + for k, v in weights.items(): + if k.startswith(prefix_dot): + result[k[len(prefix_dot) :]] = v + return result + + def _get_fused_names(self, full_name: str) -> List[str]: + """Get checkpoint names for a fused module from params_map.""" + for suffix, names in self.params_map.items(): + if full_name.endswith(suffix): + return names + raise ValueError( + f"No params_map entry for fused module '{full_name}'. " + f"Add mapping like {{'qkv_proj': ['to_q', 'to_k', 'to_v']}} to params_map." + ) + + def _get_fused_weights( + self, + full_name: str, + weights: Dict[str, torch.Tensor], + fused_names: List[str], + ) -> List[Dict[str, torch.Tensor]]: + """Get weights for a fused module from checkpoint.""" + parent_path = ".".join(full_name.split(".")[:-1]) + module_weights = [] + for ckpt_name in fused_names: + ckpt_path = f"{parent_path}.{ckpt_name}" if parent_path else ckpt_name + filtered = self.filter_weights(ckpt_path, weights) + module_weights.append(filtered) + return module_weights + + def _get_vanilla_weights( + self, + full_name: str, + weights: Dict[str, torch.Tensor], + ) -> List[Dict[str, torch.Tensor]]: + """Get weights for a standard (non-fused) Linear module.""" + fw = self.filter_weights(full_name, weights) + return [fw] if fw else [] + + # ========================================================================= + # Quantization methods + # ========================================================================= + + def _get_quant_algo_for_layer(self, name: str) -> Optional[QuantAlgo]: + """Get quantization algorithm for a specific layer.""" + if self.quant_config_dict is not None: + layer_config = self.quant_config_dict.get(name) + if layer_config is not None: + return layer_config.quant_algo + + if self.quant_config is not None: + return self.quant_config.quant_algo + + return None + + def _should_dynamic_quantize( + self, weight_dict: Dict[str, torch.Tensor], quant_algo: Optional[QuantAlgo], name: str + ) -> bool: + """Decide if weight should be dynamically quantized at load time.""" + if not self.dynamic_weight_quant or quant_algo is None: + return False + + # Check if module is excluded + if self.quant_config is not None: + if self.quant_config.is_module_excluded_from_quantization(name): + return False + + weight = weight_dict.get("weight") + if weight is None: + return False + + # For FP8 algorithms: quantize if weight is high precision + if quant_algo in (QuantAlgo.FP8, QuantAlgo.FP8_BLOCK_SCALES): + if weight.dtype == torch.float8_e4m3fn and "weight_scale" in weight_dict: + return False # Already quantized + return weight.dtype in (torch.bfloat16, torch.float16, torch.float32) + + return False + + def _maybe_dynamic_quantize( + self, weight_dict: Dict[str, torch.Tensor], quant_algo: Optional[QuantAlgo], name: str + ) -> Dict[str, torch.Tensor]: + """Conditionally quantize weight at load time on GPU.""" + if not self._should_dynamic_quantize(weight_dict, quant_algo, name): + return weight_dict + + weight = weight_dict["weight"] + + # Move to GPU only if needed + if weight.device.type != "cuda": + weight = weight.cuda() + + if quant_algo == QuantAlgo.FP8: + qweight, scale = quantize_fp8_per_tensor(weight) + elif quant_algo == QuantAlgo.FP8_BLOCK_SCALES: + block_size = self.quant_config.group_size if self.quant_config else 128 + qweight, scale = quantize_fp8_blockwise(weight, block_size=block_size) + else: + return weight_dict + + return {**weight_dict, "weight": qweight, "weight_scale": scale} + + def load_linear_weights( + self, module: Linear, name: str, weight_dicts: List[Dict[str, torch.Tensor]] + ) -> None: + """Load weights into Linear module with optional quantization.""" + module_quant_config = getattr(module, "quant_config", None) + if module_quant_config is not None: + quant_algo = module_quant_config.quant_algo + else: + quant_algo = self._get_quant_algo_for_layer(name) + + quantized_weight_dicts = [ + self._maybe_dynamic_quantize(wd, quant_algo, name) for wd in weight_dicts + ] + + module.load_weights(quantized_weight_dicts) diff --git a/tensorrt_llm/_torch/visual_gen/quantization/ops.py b/tensorrt_llm/_torch/visual_gen/quantization/ops.py new file mode 100644 index 0000000000..db550ced8a --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/quantization/ops.py @@ -0,0 +1,98 @@ +""" +Quantization operations for diffusion models. + +Provides on-the-fly quantization functions for dynamic (load-time) quantization. +""" + +from typing import Tuple + +import torch + +# FP8 E4M3 max value +FP8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max + + +def quantize_fp8_per_tensor(weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Quantize weight to FP8 E4M3 with per-tensor scale. + + Uses torch.ops.tensorrt_llm.quantize_e4m3_per_tensor CUDA kernel. + + Args: + weight: Input weight tensor (BF16/FP16/FP32), shape (out_features, in_features) + + Returns: + Tuple of: + - qweight: Quantized weight (FP8 E4M3), same shape as input + - weight_scale: Dequantization scale (FP32), shape (1, 1) + """ + qweight, scale = torch.ops.tensorrt_llm.quantize_e4m3_per_tensor(weight) + # Ensure scale is float32 and has shape (1, 1) for consistency + return qweight, scale.to(torch.float32) + + +def quantize_fp8_blockwise( + weight: torch.Tensor, block_size: int = 128 +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Quantize weight to FP8 E4M3 with 128x128 blockwise scales. + + This function converts BF16/FP16/FP32 weights to FP8 E4M3 format using + per-block scale factors. The weight is divided into blocks of size + (block_size, block_size) and each block has its own scale. + + Args: + weight: Input weight tensor (BF16/FP16/FP32), shape (out_features, in_features) + block_size: Block size for blockwise quantization (default: 128) + + Returns: + Tuple of: + - qweight: Quantized weight (FP8 E4M3), shape (out_features, in_features) + - block_scales: Block-wise dequantization scales (FP32), + shape (num_blocks_out, num_blocks_in) + + Note: + - If dimensions are not divisible by block_size, the last block may be smaller + - block_scales are dequantization scales (multiply to get back original scale) + - This uses 128x128 block scaling compatible with Linear module's FP8_BLOCK_SCALES + """ + out_features, in_features = weight.shape + weight_fp32 = weight.float() + + # Calculate number of blocks + num_blocks_out = (out_features + block_size - 1) // block_size + num_blocks_in = (in_features + block_size - 1) // block_size + + # Initialize outputs + qweight = torch.empty_like(weight, dtype=torch.float8_e4m3fn) + block_scales = torch.empty( + (num_blocks_out, num_blocks_in), dtype=torch.float32, device=weight.device + ) + + # Quantize each block + for i in range(num_blocks_out): + row_start = i * block_size + row_end = min((i + 1) * block_size, out_features) + + for j in range(num_blocks_in): + col_start = j * block_size + col_end = min((j + 1) * block_size, in_features) + + # Extract block + block = weight_fp32[row_start:row_end, col_start:col_end] + + # Compute block scale + max_val = block.abs().max() + scale = ( + max_val / FP8_E4M3_MAX if max_val > 0 else torch.tensor(1.0, device=weight.device) + ) + + # Quantize block + inv_scale = scale.reciprocal() if scale > 0 else torch.tensor(1.0, device=weight.device) + qblock = (block * inv_scale).clamp(-FP8_E4M3_MAX, FP8_E4M3_MAX).to(torch.float8_e4m3fn) + + # Store results + qweight[row_start:row_end, col_start:col_end] = qblock + block_scales[i, j] = scale.to(torch.float32) + + return qweight, block_scales diff --git a/tensorrt_llm/_torch/visual_gen/teacache.py b/tensorrt_llm/_torch/visual_gen/teacache.py new file mode 100644 index 0000000000..aa99c1655e --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/teacache.py @@ -0,0 +1,409 @@ +import inspect +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional + +import numpy as np +import torch +from diffusers.models.modeling_outputs import Transformer2DModelOutput + +from tensorrt_llm.logger import logger + +# ============================================================================= +# Core Data Structures +# ============================================================================= + + +@dataclass +class CacheContext: + """Context returned by model extractors for TeaCache. + + Attributes: + modulated_input: Timestep embedding used for cache distance calculation + hidden_states: Input hidden states for the transformer + encoder_hidden_states: Text/prompt embeddings + run_transformer_blocks: Callable that executes the transformer forward pass + postprocess: Callable that formats the output to the expected return type + """ + + modulated_input: torch.Tensor + hidden_states: torch.Tensor + encoder_hidden_states: Any = None + run_transformer_blocks: Callable = None + postprocess: Callable = None + + +# ============================================================================= +# Extractor Registry +# ============================================================================= + +_EXTRACTORS = {} + + +def register_extractor(model_name, extractor_fn): + """Register an extractor function for a model class.""" + _EXTRACTORS[model_name] = extractor_fn + + +def get_extractor(model_type): + """Get the registered extractor for a model type.""" + if model_type not in _EXTRACTORS: + raise ValueError( + f"TeaCache: Unknown model '{model_type}'. Available: {list(_EXTRACTORS.keys())}" + ) + return _EXTRACTORS[model_type] + + +# ============================================================================= +# Config-Based Extractor System +# ============================================================================= + + +@dataclass +class ExtractorConfig: + """Configuration for model-specific TeaCache extractors. + + Only the timestep embedding logic is model-specific; all other logic is handled generically. + + Attributes: + model_class_name: Model class name (e.g., "LTX2VideoTransformer3DModel") + timestep_embed_fn: Callable(module, timestep, guidance=None) -> Tensor + timestep_param_name: Parameter name for timestep in forward() (default: "timestep") + guidance_param_name: Parameter name for guidance if used (default: None) + forward_params: List of parameter names (None = auto-introspect from forward signature) + return_dict_default: Default value for return_dict parameter (default: True) + output_model_class: Output class name for return type (default: "Transformer2DModelOutput") + """ + + model_class_name: str + timestep_embed_fn: Callable + timestep_param_name: str = "timestep" + guidance_param_name: Optional[str] = None + forward_params: Optional[List[str]] = None + return_dict_default: bool = True + output_model_class: str = "Transformer2DModelOutput" + + +class GenericExtractor: + """Handles common TeaCache logic for all diffusion models. + + Extracts forward() arguments, creates run_blocks and postprocess callbacks, + and delegates only timestep embedding computation to model-specific logic. + """ + + def __init__(self, config: ExtractorConfig): + self.config = config + + def _extract_forward_args(self, module: torch.nn.Module, *args, **kwargs) -> Dict: + """Extract and normalize forward() arguments from *args and **kwargs.""" + # Get parameter names (auto-introspect or use config) + if self.config.forward_params is not None: + param_names = self.config.forward_params + else: + # Auto-introspect forward signature + try: + sig = inspect.signature(module._original_forward) + param_names = [p for p in sig.parameters if p not in ("self", "args", "kwargs")] + except Exception as e: + logger.warning(f"Could not introspect forward signature: {e}") + param_names = [] + + # Map positional args to parameter names + extracted = {param_names[i]: arg for i, arg in enumerate(args) if i < len(param_names)} + + # Merge kwargs (kwargs take precedence) + extracted.update(kwargs) + return extracted + + def _compute_timestep_embedding(self, module: torch.nn.Module, params: Dict) -> torch.Tensor: + """Compute timestep embedding using configured callable.""" + timestep = params.get(self.config.timestep_param_name) + if timestep is None: + raise ValueError(f"Missing required parameter: {self.config.timestep_param_name}") + + # Flatten timestep if needed (common pattern) + timestep_flat = timestep.flatten() if timestep.ndim == 2 else timestep + guidance = ( + params.get(self.config.guidance_param_name) if self.config.guidance_param_name else None + ) + + # Call configured timestep embedding function + try: + return self.config.timestep_embed_fn(module, timestep_flat, guidance) + except Exception as e: + logger.error(f"Timestep embedder failed: {e}") + # Last resort: use timestep as-is + logger.warning("Using timestep fallback") + return timestep_flat.unsqueeze(-1) if timestep_flat.ndim == 1 else timestep_flat + + def __call__(self, module: torch.nn.Module, *args, **kwargs) -> CacheContext: + """Main extractor logic - called by TeaCacheHook. + + Extracts forward arguments, computes timestep embedding, and creates callbacks + for running the transformer and post-processing the output. + """ + # Extract forward arguments from positional and keyword args + params = self._extract_forward_args(module, *args, **kwargs) + + # Compute timestep embedding (used for cache distance calculation) + t_emb = self._compute_timestep_embedding(module, params) + return_dict = params.get("return_dict", self.config.return_dict_default) + + def run_blocks(): + """Execute the full transformer forward pass with original parameters.""" + ret = module._original_forward(**params) + # Normalize output to tuple format + if return_dict and not isinstance(ret, tuple): + sample = ret.sample if hasattr(ret, "sample") else ret + return (sample,) if not isinstance(sample, tuple) else sample + return ret if isinstance(ret, tuple) else (ret,) + + def postprocess(output): + """Convert cached/computed output back to expected return format.""" + if return_dict: + if isinstance(output, tuple): + return output + return Transformer2DModelOutput(sample=output) + # For return_dict=False, unwrap single-element tuple to raw tensor + if isinstance(output, tuple) and len(output) == 1: + return output[0] + # Return raw tensor as-is (TeaCacheHook always passes tensors to postprocess) + return output + + return CacheContext( + modulated_input=t_emb, + hidden_states=params.get("hidden_states"), + encoder_hidden_states=params.get("encoder_hidden_states"), + run_transformer_blocks=run_blocks, + postprocess=postprocess, + ) + + +def register_extractor_from_config(config: ExtractorConfig): + """Register a TeaCache extractor for a model. Call this in pipeline's load() method. + + Example: + register_extractor_from_config(ExtractorConfig( + model_class_name="LTX2VideoTransformer3DModel", + timestep_embed_fn=self._compute_ltx2_timestep_embedding, + )) + """ + extractor = GenericExtractor(config) + register_extractor(config.model_class_name, extractor) + logger.debug(f"Registered TeaCache extractor for {config.model_class_name}") + + +# ============================================================================= +# TeaCache Runtime (caching hook and lifecycle management) +# ============================================================================= + + +class TeaCacheHook: + """Caches transformer blocks when timestep embeddings change slowly. + + The hook monitors the relative change in timestep embeddings between steps. + When the change is small (below threshold), it reuses the cached residual + from the previous step instead of running the full transformer. + + Separate cache states are maintained for conditional and unconditional branches + when using Classifier-Free Guidance (CFG). + """ + + def __init__(self, config): + self.config = config + # Polynomial function to rescale embedding distances + self.rescale_func = np.poly1d(config.coefficients) + self.extractor_fn = None + + # Separate cache state for conditional (pos) and unconditional (neg) branches + self.state_pos = self._new_state() + self.state_neg = self._new_state() + self.stats = {"total": 0, "cached": 0} + + def _new_state(self): + return {"cnt": 0, "acc_dist": 0.0, "prev_input": None, "prev_residual": None} + + def initialize(self, module): + self.extractor_fn = get_extractor(module.__class__.__name__) + + def reset_state(self): + self.state_pos = self._new_state() + self.state_neg = self._new_state() + self.stats = {"total": 0, "cached": 0} + + def get_stats(self): + total = max(self.stats["total"], 1) + cached = self.stats["cached"] + return { + "hit_rate": cached / total, + "total": total, + "cached": cached, + # Backward compatibility + "total_steps": total, + "cached_steps": cached, + "compute_steps": total - cached, + } + + def __call__(self, module, *args, **kwargs): + """Main hook called during transformer forward pass. + + Decides whether to run the full transformer or reuse cached residual + based on timestep embedding distance. + """ + # Extract context (timestep embedding, hidden states, callbacks) + ctx = self.extractor_fn(module, *args, **kwargs) + + # Select cache state (for CFG: separate tracking for conditional/unconditional) + cache_branch = getattr(module, "_cache_branch", None) + state = self.state_neg if cache_branch == "uncond" else self.state_pos + + # Decide: compute transformer or use cache? + should_compute = self._should_compute(state, ctx.modulated_input) + self.stats["total"] += 1 + + if not should_compute and state["prev_residual"] is not None: + # Cache hit: Add cached residual to skip transformer computation + logger.debug(f"TeaCache: SKIP step {state['cnt']}") + # For I2V: output might have fewer channels than input + # Apply residual only to the latent channels + if ctx.hidden_states.shape[1] != state["prev_residual"].shape[1]: + # Extract latent channels (match output channels) + num_output_channels = state["prev_residual"].shape[1] + latent_channels = ctx.hidden_states[:, :num_output_channels] + output = latent_channels + state["prev_residual"] + else: + output = ctx.hidden_states + state["prev_residual"] + self.stats["cached"] += 1 + else: + # Cache miss: Run full transformer and cache the residual + outputs = ctx.run_transformer_blocks() + output = outputs[0] if isinstance(outputs, tuple) else outputs + + # Store residual (output - input) for next potential cache hit + # For I2V: output may have fewer channels than input + # Compute residual only on the latent channels + if ctx.hidden_states.shape[1] != output.shape[1]: + # Extract latent channels (match output channels) + num_output_channels = output.shape[1] + latent_channels = ctx.hidden_states[:, :num_output_channels] + state["prev_residual"] = (output - latent_channels).detach() + else: + original = ctx.hidden_states.clone() + state["prev_residual"] = (output - original).detach() + + # Update state for next iteration + state["prev_input"] = ctx.modulated_input.detach() + state["cnt"] += 1 + + return ctx.postprocess(output) + + def _should_compute(self, state, modulated_inp): + """Decide whether to compute transformer or use cached result. + + Returns True to compute, False to use cache. + """ + # Warmup: Always compute first few steps to build stable cache + if self.config.ret_steps and state["cnt"] < self.config.ret_steps: + state["acc_dist"] = 0.0 + return True + + # Cooldown: Always compute last few steps for quality + if self.config.cutoff_steps and state["cnt"] >= self.config.cutoff_steps: + return True + + # First step: no previous input to compare + if state["prev_input"] is None: + return True + + # Compute relative change in timestep embedding + curr, prev = modulated_inp, state["prev_input"] + + # For CFG (batch_size > 1), only compare conditional branch + # Both branches move similarly, so one comparison is sufficient + if modulated_inp.shape[0] > 1: + curr, prev = modulated_inp.chunk(2)[1], prev.chunk(2)[1] + + # Calculate relative L1 distance (normalized by magnitude) + rel_dist = ((curr - prev).abs().mean() / (prev.abs().mean() + 1e-8)).cpu().item() + + # Apply polynomial rescaling to adjust sensitivity + # Accumulate distance (capped at 2x threshold to prevent overflow) + rescaled = float(self.rescale_func(rel_dist)) + state["acc_dist"] = min( + state["acc_dist"] + abs(rescaled), self.config.teacache_thresh * 2.0 + ) + + logger.debug( + f"TeaCache: step {state['cnt']} | dist {rel_dist:.2e} | acc {state['acc_dist']:.4f}" + ) + + # Cache decision based on accumulated distance + if state["acc_dist"] < self.config.teacache_thresh: + # Below threshold: use cache, apply decay to distance + state["acc_dist"] *= 0.95 + return False + else: + # Above threshold: compute, reset accumulated distance + state["acc_dist"] = 0.0 + return True + + +class TeaCacheBackend: + """Manages TeaCache lifecycle.""" + + def __init__(self, config): + self.config = config + self.hook = None + + def enable(self, module): + if self.hook is None: + logger.info(f"TeaCache: Enabling for {module.__class__.__name__}") + self.hook = TeaCacheHook(self.config) + self.hook.initialize(module) + module._original_forward = module.forward + module.forward = lambda *args, **kwargs: self.hook(module, *args, **kwargs) + + def disable(self, module): + if self.hook and hasattr(module, "_original_forward"): + module.forward = module._original_forward + self.hook = None + + def refresh(self, num_inference_steps): + """Reset TeaCache state for a new generation. + + Sets warmup/cutoff steps based on total inference steps: + - Warmup steps: Always compute to build stable cache + - Cutoff steps: Always compute for quality at the end + - Middle steps: Use caching based on distance threshold + + Args: + num_inference_steps: Total number of denoising steps + """ + if not self.hook: + return + + # Reset cache state (clears previous residuals and counters) + self.hook.reset_state() + + # Configure warmup and cutoff based on mode + if self.config.use_ret_steps: + # Aggressive warmup: 5 steps to stabilize cache + self.config.ret_steps = 5 + self.config.cutoff_steps = num_inference_steps # No cutoff (cache until end) + else: + # Minimal warmup: 1 step + self.config.ret_steps = 1 + self.config.cutoff_steps = num_inference_steps - 2 # Compute last 2 steps + + self.config.num_steps = num_inference_steps + + logger.info( + f"TeaCache: {num_inference_steps} steps | " + f"warmup: {self.config.ret_steps}, cutoff: {self.config.cutoff_steps}, " + f"thresh: {self.config.teacache_thresh}" + ) + + def is_enabled(self): + return self.hook is not None + + def get_stats(self): + return self.hook.get_stats() if self.hook else {} diff --git a/tensorrt_llm/_torch/visual_gen/utils.py b/tensorrt_llm/_torch/visual_gen/utils.py new file mode 100644 index 0000000000..99f8837ceb --- /dev/null +++ b/tensorrt_llm/_torch/visual_gen/utils.py @@ -0,0 +1,39 @@ +"""Utility functions for visual generation pipelines.""" + +import torch + + +@torch.compile +def postprocess_video_tensor(video: torch.Tensor, remove_batch_dim: bool = True) -> torch.Tensor: + """Post-process video tensor from VAE decoder output to final format. + + This is a more efficient implementation than using VideoProcessor for single-batch cases, + as it avoids loop overhead and processes the entire batch with vectorized operations. + + Args: + video: Video tensor in (B, C, T, H, W) format from VAE decoder + remove_batch_dim: Whether to remove batch dimension. Default True for typical + single-batch video generation. + + Returns: + Post-processed video tensor: + - If remove_batch_dim=True: (T, H, W, C) uint8 tensor + - If remove_batch_dim=False: (B, T, H, W, C) uint8 tensor + + Note: + Assumes video values are in [-1, 1] range (standard VAE decoder output). + """ + # Convert to (B, T, H, W, C) format + video = video.permute(0, 2, 3, 4, 1) # (B, C, T, H, W) -> (B, T, H, W, C) + + # Normalize to [0, 1] range + video = (video / 2 + 0.5).clamp(0, 1) + + # Convert to uint8 + video = (video * 255).round().to(torch.uint8) + + # Remove batch dimension if requested + if remove_batch_dim: + video = video[0] # (B, T, H, W, C) -> (T, H, W, C) + + return video diff --git a/tensorrt_llm/commands/serve.py b/tensorrt_llm/commands/serve.py index 76cbde9646..8eb31bd5f1 100644 --- a/tensorrt_llm/commands/serve.py +++ b/tensorrt_llm/commands/serve.py @@ -19,11 +19,13 @@ from tensorrt_llm import LLM as PyTorchLLM from tensorrt_llm import MultimodalEncoder from tensorrt_llm._tensorrt_engine import LLM from tensorrt_llm._utils import mpi_rank +from tensorrt_llm.commands.utils import (get_is_diffusion_model, + get_visual_gen_model_type) from tensorrt_llm.executor.utils import LlmLauncherEnvs from tensorrt_llm.inputs.multimodal import MultimodalServerConfig from tensorrt_llm.llmapi import (BuildConfig, CapacitySchedulerPolicy, DynamicBatchConfig, KvCacheConfig, - SchedulerConfig) + SchedulerConfig, VisualGen) from tensorrt_llm.llmapi.disagg_utils import (DisaggClusterConfig, MetadataServerConfig, ServerRole, extract_disagg_cluster_config, @@ -217,7 +219,7 @@ def launch_server( f"{backend} is not a known backend, check help for available options.", param_hint="backend") - server = OpenAIServer(llm=llm, + server = OpenAIServer(generator=llm, model=model, tool_parser=tool_parser, server_role=server_role, @@ -361,7 +363,7 @@ def launch_mm_encoder_server( encoder_args.pop("build_config") mm_encoder = MultimodalEncoder(**encoder_args) - server = OpenAIServer(llm=mm_encoder, + server = OpenAIServer(generator=mm_encoder, model=model, server_role=ServerRole.MM_ENCODER, metadata_server_cfg=metadata_server_cfg, @@ -369,6 +371,45 @@ def launch_mm_encoder_server( asyncio.run(server(host, port)) +def launch_visual_gen_server( + host: str, + port: int, + visual_gen_config: dict, + metadata_server_cfg: Optional[MetadataServerConfig] = None, +): + """Launch a VISUAL_GEN model server for image/video generation. + + Args: + host: Server hostname. + port: Server port. + visual_gen_config: Arguments for VISUAL_GEN model initialization. + metadata_server_cfg: Optional metadata server configuration. + """ + model = visual_gen_config["model"] + logger.info(f"Initializing VisualGen ({model})") + + n_workers = 1 + parallel_config = visual_gen_config.get("parallel", {}) + if parallel_config: + n_workers = parallel_config.get( + "dit_cfg_size", 1) * parallel_config.get("dit_ulysses_size", 1) + logger.info(f"World size: {n_workers}") + logger.info(f"CFG size: {parallel_config.get('dit_cfg_size', 1)}") + logger.info( + f"Ulysses size: {parallel_config.get('dit_ulysses_size', 1)}") + + visual_gen_model = VisualGen(model_path=model, + n_workers=n_workers, + diffusion_config=visual_gen_config) + + server = OpenAIServer(generator=visual_gen_model, + model=model, + server_role=ServerRole.VISUAL_GEN, + metadata_server_cfg=metadata_server_cfg, + tool_parser=None) + asyncio.run(server(host, port)) + + class ChoiceWithAlias(click.Choice): def __init__(self, @@ -600,6 +641,12 @@ class ChoiceWithAlias(click.Choice): default=False, help="Run gRPC server instead of OpenAI HTTP server. " "gRPC server accepts pre-tokenized requests and returns raw token IDs.") +@click.option("--extra_visual_gen_options", + type=str, + default=None, + help=help_info_with_stability_tag( + "Path to a YAML file with extra VISUAL_GEN model options.", + "prototype")) def serve( model: str, tokenizer: Optional[str], custom_tokenizer: Optional[str], host: str, port: int, log_level: str, backend: str, max_beam_width: int, @@ -616,8 +663,8 @@ def serve( otlp_traces_endpoint: Optional[str], enable_chunked_prefill: bool, disagg_cluster_uri: Optional[str], media_io_kwargs: Optional[str], custom_module_dirs: list[Path], chat_template: Optional[str], - grpc: bool): - """Running an OpenAI API compatible server (or gRPC server with --grpc flag) + grpc: bool, extra_visual_gen_options: Optional[str]): + """Running an OpenAI API compatible server MODEL: model name | HF checkpoint path | TensorRT engine path """ @@ -630,93 +677,120 @@ def serve( logger.error( f"Failed to import custom module from {custom_module_dir}: {e}") raise e - llm_args, _ = get_llm_args( - model=model, - tokenizer=tokenizer, - custom_tokenizer=custom_tokenizer, - backend=backend, - max_beam_width=max_beam_width, - max_batch_size=max_batch_size, - max_num_tokens=max_num_tokens, - max_seq_len=max_seq_len, - tensor_parallel_size=tensor_parallel_size, - pipeline_parallel_size=pipeline_parallel_size, - context_parallel_size=context_parallel_size, - moe_expert_parallel_size=moe_expert_parallel_size, - moe_cluster_parallel_size=moe_cluster_parallel_size, - gpus_per_node=gpus_per_node, - free_gpu_memory_fraction=free_gpu_memory_fraction, - num_postprocess_workers=num_postprocess_workers, - trust_remote_code=trust_remote_code, - revision=revision, - reasoning_parser=reasoning_parser, - fail_fast_on_attention_window_too_large= - fail_fast_on_attention_window_too_large, - otlp_traces_endpoint=otlp_traces_endpoint, - enable_chunked_prefill=enable_chunked_prefill) - llm_args_extra_dict = {} - if extra_llm_api_options is not None: - with open(extra_llm_api_options, 'r') as f: - llm_args_extra_dict = yaml.safe_load(f) - llm_args = update_llm_args_with_extra_dict(llm_args, llm_args_extra_dict) + def _serve_llm(): + nonlocal server_role + llm_args, _ = get_llm_args( + model=model, + tokenizer=tokenizer, + custom_tokenizer=custom_tokenizer, + backend=backend, + max_beam_width=max_beam_width, + max_batch_size=max_batch_size, + max_num_tokens=max_num_tokens, + max_seq_len=max_seq_len, + tensor_parallel_size=tensor_parallel_size, + pipeline_parallel_size=pipeline_parallel_size, + context_parallel_size=context_parallel_size, + moe_expert_parallel_size=moe_expert_parallel_size, + moe_cluster_parallel_size=moe_cluster_parallel_size, + gpus_per_node=gpus_per_node, + free_gpu_memory_fraction=free_gpu_memory_fraction, + num_postprocess_workers=num_postprocess_workers, + trust_remote_code=trust_remote_code, + revision=revision, + reasoning_parser=reasoning_parser, + fail_fast_on_attention_window_too_large= + fail_fast_on_attention_window_too_large, + otlp_traces_endpoint=otlp_traces_endpoint, + enable_chunked_prefill=enable_chunked_prefill) - metadata_server_cfg = parse_metadata_server_config_file( - metadata_server_config_file) + llm_args_extra_dict = {} + if extra_llm_api_options is not None: + with open(extra_llm_api_options, 'r') as f: + llm_args_extra_dict = yaml.safe_load(f) + llm_args = update_llm_args_with_extra_dict(llm_args, + llm_args_extra_dict) - # Specify disagg_cluster_config in config file or through command line "--disagg_cluster_uri", - # but disagg_cluster_uri takes precedence over cluster uri in config file - disagg_cluster_config = llm_args.pop("disagg_cluster", None) - if disagg_cluster_config: - disagg_cluster_config = extract_disagg_cluster_config( - disagg_cluster_config, disagg_cluster_uri) - elif disagg_cluster_uri: - disagg_cluster_config = DisaggClusterConfig( - cluster_uri=disagg_cluster_uri) + metadata_server_cfg = parse_metadata_server_config_file( + metadata_server_config_file) - if metadata_server_cfg is not None or disagg_cluster_config is not None: - assert ( - server_role is not None - ), "server_role is required when metadata_server_cfg or disagg_cluster_config is provided" - try: - server_role = ServerRole[server_role.upper()] - except ValueError: - raise ValueError(f"Invalid server role: {server_role}. " \ - f"Must be one of: {', '.join([role.name for role in ServerRole])}") + # Specify disagg_cluster_config in config file or through command line "--disagg_cluster_uri", + # but disagg_cluster_uri takes precedence over cluster uri in config file + disagg_cluster_config = llm_args.pop("disagg_cluster", None) + if disagg_cluster_config: + disagg_cluster_config = extract_disagg_cluster_config( + disagg_cluster_config, disagg_cluster_uri) + elif disagg_cluster_uri: + disagg_cluster_config = DisaggClusterConfig( + cluster_uri=disagg_cluster_uri) - # Parse media_io_kwargs from JSON string to dict if provided - parsed_media_io_kwargs = None - if media_io_kwargs is not None: - try: - parsed_media_io_kwargs = json.loads(media_io_kwargs) - except json.JSONDecodeError as e: - raise ValueError(f"Invalid JSON for media_io_kwargs: {e}") + if metadata_server_cfg is not None or disagg_cluster_config is not None: + assert ( + server_role is not None + ), "server_role is required when metadata_server_cfg or disagg_cluster_config is provided" + try: + server_role = ServerRole[server_role.upper()] + except ValueError: + raise ValueError(f"Invalid server role: {server_role}. " \ + f"Must be one of: {', '.join([role.name for role in ServerRole])}") + # Parse media_io_kwargs from JSON string to dict if provided + parsed_media_io_kwargs = None + if media_io_kwargs is not None: + try: + parsed_media_io_kwargs = json.loads(media_io_kwargs) + except json.JSONDecodeError as e: + raise ValueError(f"Invalid JSON for media_io_kwargs: {e}") - multimodal_server_config = MultimodalServerConfig( - media_io_kwargs=parsed_media_io_kwargs) + multimodal_server_config = MultimodalServerConfig( + media_io_kwargs=parsed_media_io_kwargs) - if grpc: - # gRPC mode: launch gRPC server instead of OpenAI HTTP server - # Check for unsupported arguments that are silently ignored in gRPC mode - unsupported_args = { - "tool_parser": tool_parser, - "chat_template": chat_template, - "metadata_server_config_file": metadata_server_config_file, - "server_role": server_role, - "disagg_cluster_config": disagg_cluster_config, + if grpc: + # gRPC mode: launch gRPC server instead of OpenAI HTTP server + # Check for unsupported arguments that are silently ignored in gRPC mode + unsupported_args = { + "tool_parser": tool_parser, + "chat_template": chat_template, + "metadata_server_config_file": metadata_server_config_file, + "server_role": server_role, + "disagg_cluster_config": disagg_cluster_config, + } + for name, value in unsupported_args.items(): + if value is not None: + raise ValueError( + f"Argument '{name}' is not supported when running in gRPC mode. " + f"The gRPC server is designed for use with external routers that handle " + f"these features (e.g., tool parsing, chat templates).") + launch_grpc_server(host, port, llm_args) + else: + # Default: launch OpenAI HTTP server + launch_server(host, port, llm_args, tool_parser, chat_template, + metadata_server_cfg, server_role, + disagg_cluster_config, multimodal_server_config) + + def _serve_visual_gen(): + visual_gen_config = { + "model": model, + "model_type": get_visual_gen_model_type(model), } - for name, value in unsupported_args.items(): - if value is not None: - raise ValueError( - f"Argument '{name}' is not supported when running in gRPC mode. " - f"The gRPC server is designed for use with external routers that handle " - f"these features (e.g., tool parsing, chat templates).") - launch_grpc_server(host, port, llm_args) + + visual_gen_extra_args = {} + if extra_visual_gen_options is not None: + with open(extra_visual_gen_options, 'r') as f: + visual_gen_extra_args = yaml.safe_load(f) + + visual_gen_config.update(visual_gen_extra_args) + + metadata_server_cfg = parse_metadata_server_config_file( + metadata_server_config_file) + + launch_visual_gen_server(host, port, visual_gen_config, + metadata_server_cfg) + + if get_is_diffusion_model(model): + _serve_visual_gen() else: - # Default: launch OpenAI HTTP server - launch_server(host, port, llm_args, tool_parser, chat_template, - metadata_server_cfg, server_role, disagg_cluster_config, - multimodal_server_config) + _serve_llm() @click.command("mm_embedding_serve") diff --git a/tensorrt_llm/commands/utils.py b/tensorrt_llm/commands/utils.py new file mode 100644 index 0000000000..df1442c6e7 --- /dev/null +++ b/tensorrt_llm/commands/utils.py @@ -0,0 +1,132 @@ +# Adapted from https://github.com/sgl-project/sglang/blob/030496eb06472f76fcb11de53d93f10cefb4604f/python/sglang/cli/utils.py#L27 +import json +import logging +import os + +from tensorrt_llm.llmapi.utils import download_hf_partial + +logger = logging.getLogger(__name__) + + +def _maybe_download_model( + model_name_or_path: str, local_dir: str | None = None, download: bool = True +) -> str: + """Resolve a model path. If it's a local directory, return it. + + If it's a Hugging Face Hub ID, download only the config file + (`model_index.json` or `config.json`) and return its directory. + + Args: + model_name_or_path: Local path or Hugging Face Hub model ID + local_dir: Local directory to save the downloaded file (if any) + download: Whether to download from Hugging Face Hub when needed + + Returns: + Local directory path that contains the downloaded config file, or the original local directory. + """ + if os.path.exists(model_name_or_path): + logger.info("Model already exists locally") + return model_name_or_path + + if not download: + return model_name_or_path + + try: + logger.info( + "Downloading model_index.json from HF Hub for %s...", + model_name_or_path, + ) + file_path = download_hf_partial( + model=model_name_or_path, + allow_patterns=["model_index.json", "config.json"], + ) + logger.info("Downloaded to %s", file_path) + return str(file_path) + except Exception as e: + raise ValueError( + ( + "Could not find model locally at %s and failed to download " + "model_index.json/config.json from HF Hub: %s" + ) + % (model_name_or_path, e) + ) from e + + +# Copied and adapted from hf_diffusers_utils.py +def is_diffusers_model_path(model_path: str) -> bool: + """Verify if the model directory contains a valid diffusers configuration. + + Args: + model_path: Path to the model directory + + Returns: + The loaded model configuration as a dictionary if the model is a diffusers model + None if the model is not a diffusers model + """ + # Prefer model_index.json which indicates a diffusers pipeline + config_path = os.path.join(model_path, "model_index.json") + if not os.path.exists(config_path): + return False + + # Load the config + with open(config_path) as f: + config = json.load(f) + + # Verify diffusers version exists + if "_diffusers_version" not in config: + return False + return True + + +def get_is_diffusion_model(model_path: str): + model_path = _maybe_download_model(model_path) + is_diffusion_model = is_diffusers_model_path(model_path) + if is_diffusion_model: + logger.info("Diffusion model detected") + return is_diffusion_model + + +def get_model_path(extra_argv): + # Find the model_path argument + model_path = None + for i, arg in enumerate(extra_argv): + if arg == "--model-path": + if i + 1 < len(extra_argv): + model_path = extra_argv[i + 1] + break + elif arg.startswith("--model-path="): + model_path = arg.split("=", 1)[1] + break + + if model_path is None: + # Fallback for --help or other cases where model-path is not provided + if any(h in extra_argv for h in ["-h", "--help"]): + raise Exception( + "Usage: sglang serve --model-path [additional-arguments]\n\n" + "This command can launch either a standard language model server or a diffusion model server.\n" + "The server type is determined by the model path.\n" + "For specific arguments, please provide a model_path." + ) + else: + raise Exception( + "Error: --model-path is required. Please provide the path to the model." + ) + return model_path + + +VISUAL_GEN_PARTIAL_MODEL_NAME_TO_MODEL_TYPE = { + "FLUX.2": "flux2", + "LTX-2": "ltx2", + "Wan2": "wan2", +} + + +def get_visual_gen_model_type(model_path: str): + for partial_model_name, model_type in VISUAL_GEN_PARTIAL_MODEL_NAME_TO_MODEL_TYPE.items(): + if partial_model_name.lower() in model_path.lower(): + return model_type + + raise ValueError( + f"Unknown VISUAL_GEN model type for model path: {model_path}," + f"available models: {VISUAL_GEN_PARTIAL_MODEL_NAME_TO_MODEL_TYPE.keys()}" + ) diff --git a/tensorrt_llm/executor/ipc.py b/tensorrt_llm/executor/ipc.py index f09dd31dc4..4fafc8307a 100644 --- a/tensorrt_llm/executor/ipc.py +++ b/tensorrt_llm/executor/ipc.py @@ -83,8 +83,7 @@ class ZeroMqQueue: "Server and client should not receive HMAC key when encryption is disabled" ) - if (socket_type == zmq.PAIR and self.is_server - ) or socket_type == zmq.PULL or socket_type == zmq.ROUTER: + if self.should_bind_socket(): self.socket.bind( self.address_endpoint ) # Binds to the address and occupy a port immediately @@ -101,6 +100,31 @@ class ZeroMqQueue: self.address = (self.address_endpoint, self.hmac_key) + def should_bind_socket(self) -> bool: + """ + Determine if socket should bind vs connect based on type and role. + + ZMQ binding conventions: + - PAIR: server binds, client connects (1-to-1 bidirectional) + - PULL: server binds to receive from multiple PUSH sockets + - PUSH: server binds when acting as message source + - ROUTER: always binds to handle multiple clients + + Returns: + True if socket should bind, False if it should connect + """ + # Server binds for PAIR, PULL, PUSH patterns + if self.is_server and self.socket_type in (zmq.PAIR, zmq.PULL, + zmq.PUSH): + return True + + # ROUTER always binds (multi-client pattern) + if self.socket_type == zmq.ROUTER: + return True + + # Client connects for all other cases + return False + def setup_lazily(self): # Early return if setup is already done if self._setup_done: diff --git a/tensorrt_llm/inputs/data.py b/tensorrt_llm/inputs/data.py index 615043fe48..48e3441df6 100644 --- a/tensorrt_llm/inputs/data.py +++ b/tensorrt_llm/inputs/data.py @@ -1,6 +1,6 @@ # Adapt from # https://github.com/vllm-project/vllm/blob/2e33fe419186c65a18da6668972d61d7bbc31564/vllm/inputs/data.py -from typing import Any, Dict, List, Union +from typing import Any, Dict, List, Sequence, Union from typing_extensions import NotRequired, TypedDict @@ -85,3 +85,80 @@ def prompt_inputs(inputs: PromptInputs, ) -> Union[TextPrompt, TokensPrompt]: f"Invalid type of inputs for llm.generate: {type(inputs)}") return prompt_inputs + + +class VisualGenTextPrompt(TypedDict): + prompt: str + negative_prompt: NotRequired[str] + + +class VisualGenTokensPrompt(TypedDict): + prompt_token_ids: List[int] + negative_prompt_token_ids: NotRequired[List[int]] + + +VisualGenPromptInputs = Union[ + str, + List[int], + VisualGenTextPrompt, + VisualGenTokensPrompt, +] + +VisualGenInputs = Union[ + VisualGenPromptInputs, + Sequence[VisualGenPromptInputs], +] + + +def visual_gen_inputs( + inputs: "VisualGenPromptInputs", +) -> Union["VisualGenTextPrompt", "VisualGenTokensPrompt"]: + # str -> text prompt + if isinstance(inputs, str): + return VisualGenTextPrompt(prompt=inputs) + + # list[int] -> token prompt + if isinstance(inputs, list): + if len(inputs) == 0: + raise ValueError("`inputs` token list cannot be empty.") + if not all(isinstance(t, int) for t in inputs): + raise TypeError( + "`inputs` list must contain only ints when used as token IDs.") + return VisualGenTokensPrompt(prompt_token_ids=inputs) + + # dict form + if isinstance(inputs, dict): + has_prompt = "prompt" in inputs + has_prompt_token_ids = "prompt_token_ids" in inputs + + if has_prompt == has_prompt_token_ids: + raise ValueError( + "VisualGen prompt dict must contain exactly one of " + "`prompt` or `prompt_token_ids`.") + + if has_prompt: + prompt = inputs.get("prompt") + if not isinstance(prompt, str) or prompt == "": + raise TypeError("`prompt` must be a non-empty string.") + if "negative_prompt" in inputs and not isinstance( + inputs["negative_prompt"], str): + raise TypeError("`negative_prompt` must be a string.") + return inputs # VisualGenTextPrompt + + token_ids = inputs.get("prompt_token_ids") + if not isinstance(token_ids, list) or len(token_ids) == 0: + raise TypeError("`prompt_token_ids` must be a non-empty list[int].") + if not all(isinstance(t, int) for t in token_ids): + raise TypeError("`prompt_token_ids` must contain only ints.") + if "negative_prompt_token_ids" in inputs: + neg_ids = inputs["negative_prompt_token_ids"] + if not isinstance(neg_ids, list) or not all( + isinstance(t, int) for t in neg_ids): + raise TypeError( + "`negative_prompt_token_ids` must be a list[int].") + return inputs # VisualGenTokensPrompt + + raise TypeError( + "Invalid `inputs` for VisualGen.generate. " + "Expected one of: str, list[int], VisualGenTextPrompt, VisualGenTokensPrompt." + ) diff --git a/tensorrt_llm/llmapi/__init__.py b/tensorrt_llm/llmapi/__init__.py index b87b21f9f5..430426786f 100644 --- a/tensorrt_llm/llmapi/__init__.py +++ b/tensorrt_llm/llmapi/__init__.py @@ -22,10 +22,13 @@ from .llm_utils import (BuildConfig, KvCacheRetentionConfig, QuantAlgo, QuantConfig) from .mm_encoder import MultimodalEncoder from .mpi_session import MpiCommSession +from .visual_gen import VisualGen, VisualGenParams __all__ = [ 'LLM', 'AsyncLLM', + 'VisualGen', + 'VisualGenParams', 'MultimodalEncoder', 'CompletionOutput', 'RequestOutput', diff --git a/tensorrt_llm/llmapi/disagg_utils.py b/tensorrt_llm/llmapi/disagg_utils.py index 8512771e1f..86edbefea7 100644 --- a/tensorrt_llm/llmapi/disagg_utils.py +++ b/tensorrt_llm/llmapi/disagg_utils.py @@ -24,6 +24,7 @@ class ServerRole(IntEnum): CONTEXT = 0 GENERATION = 1 MM_ENCODER = 2 + VISUAL_GEN = 3 @dataclass diff --git a/tensorrt_llm/llmapi/utils.py b/tensorrt_llm/llmapi/utils.py index f79823d844..78fe3d6298 100644 --- a/tensorrt_llm/llmapi/utils.py +++ b/tensorrt_llm/llmapi/utils.py @@ -236,18 +236,34 @@ def download_hf_model(model: str, revision: Optional[str] = None) -> Path: return Path(hf_folder) -def download_hf_pretrained_config(model: str, - revision: Optional[str] = None) -> Path: +def download_hf_partial(model: str, + allow_patterns: List[str], + revision: Optional[str] = None) -> Path: + """Download a partial model from HuggingFace. + + Args: + model: The model name or path. + revision: The revision to use for the model. + allow_patterns: The patterns to allow for the model. + + Returns: + The path to the downloaded model. + """ with get_file_lock(model): hf_folder = snapshot_download( model, local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE, revision=revision, - allow_patterns=["config.json"], + allow_patterns=allow_patterns, tqdm_class=DisabledTqdm) return Path(hf_folder) +def download_hf_pretrained_config(model: str, + revision: Optional[str] = None) -> Path: + return download_hf_partial(model, ["config.json"], revision) + + def append_docstring(docstring: str): ''' A decorator to append a docstring to a function. ''' diff --git a/tensorrt_llm/llmapi/visual_gen.py b/tensorrt_llm/llmapi/visual_gen.py new file mode 100644 index 0000000000..2113b43548 --- /dev/null +++ b/tensorrt_llm/llmapi/visual_gen.py @@ -0,0 +1,544 @@ +import asyncio +import queue +import socket +import threading +import time +import traceback +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, List, Optional, Union + +import torch.multiprocessing as mp +import zmq + +from tensorrt_llm._torch.visual_gen import DiffusionRequest, DiffusionResponse +from tensorrt_llm._torch.visual_gen.executor import run_diffusion_worker +from tensorrt_llm._torch.visual_gen.output import MediaOutput + +__all__ = ["VisualGen", "VisualGenParams", "MediaOutput"] +from tensorrt_llm.executor.ipc import ZeroMqQueue +from tensorrt_llm.inputs.data import VisualGenInputs +from tensorrt_llm.logger import logger + +# Timeouts (seconds) +POLL_TIMEOUT = 0.01 +AWAIT_TIMEOUT = 0.05 +THREAD_TIMEOUT = 5.0 +WORKER_TIMEOUT = 2.0 +READY_TIMEOUT = 1200 # 20 minutes for large models (Wan 2.2 with transformer_2) + + +def find_free_port() -> int: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(("", 0)) + return s.getsockname()[1] + + +def get_ip_address() -> str: + """Get local IP address.""" + s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + try: + s.connect(("10.255.255.255", 1)) + return s.getsockname()[0] + except Exception: + return "127.0.0.1" + finally: + s.close() + + +class DiffusionRemoteClient: + """Client proxy for remote DiffusionExecutor in worker processes.""" + + def __init__( + self, + model_path: Union[str, Path], + n_workers: int = 1, + diffusion_config: Optional[dict] = None, + ): + self.model_path = str(model_path) + self.n_workers = n_workers + self.diffusion_config = diffusion_config + + # Setup distributed env + self.master_addr = "127.0.0.1" + self.master_port = find_free_port() + + # Setup IPC addresses + self.host_ip = get_ip_address() + req_port, resp_port = find_free_port(), find_free_port() + + self.request_queue_addr = f"tcp://0.0.0.0:{req_port}" + self.response_queue_addr = f"tcp://0.0.0.0:{resp_port}" + self.req_addr_connect = f"tcp://{self.host_ip}:{req_port}" + self.resp_addr_connect = f"tcp://{self.host_ip}:{resp_port}" + + # IPC setup + self.requests_ipc = None + self.responses_ipc = None + self.pending_requests = queue.Queue() + self.completed_responses: Dict[int, DiffusionResponse] = {} + + # We'll create asyncio primitives in the background thread's event loop + self._event_loop = None + self.response_event = None + self.lock = None + self.shutdown_event = threading.Event() + self.event_loop_ready = threading.Event() + + # Start background thread (it will create its own event loop) + self.background_thread = threading.Thread(target=self._serve_forever_thread, daemon=True) + self.background_thread.start() + + # Wait for the background thread to initialize the event loop + self.event_loop_ready.wait() + + # Launch workers + logger.info(f"DiffusionClient: Launching {n_workers} workers") + ctx = mp.get_context("spawn") + self.worker_processes = [] + for rank in range(n_workers): + p = ctx.Process( + target=run_diffusion_worker, + kwargs={ + "rank": rank, + "world_size": n_workers, + "master_addr": self.master_addr, + "master_port": self.master_port, + "model_path": self.model_path, + "request_queue_addr": self.req_addr_connect, + "response_queue_addr": self.resp_addr_connect, + "diffusion_config": self.diffusion_config, + }, + ) + p.start() + self.worker_processes.append(p) + + self._wait_ready() + + @staticmethod + def _close_socket(ipc_queue): + if ipc_queue and ipc_queue.socket: + ipc_queue.socket.setsockopt(zmq.LINGER, 0) + ipc_queue.close() + + def enqueue_requests(self, requests: List[DiffusionRequest]) -> List[int]: + """Enqueue requests and return their IDs.""" + req_ids = [] + for req in requests: + self.pending_requests.put(req) + req_ids.append(req.request_id) + return req_ids + + async def await_responses( + self, request_ids: Union[int, List[int]], timeout: Optional[float] = None + ) -> Union[DiffusionResponse, List[DiffusionResponse]]: + """Wait for responses by request IDs. + + Args: + request_ids: Single request ID or list of request IDs to wait for + timeout: Maximum total wait time in seconds (None = wait indefinitely) + + Returns: + Single response or list of responses (None if request timed out) + """ + is_single = isinstance(request_ids, int) + ids = [request_ids] if is_single else request_ids + + start_time = time.time() + results = {} + + while len(results) < len(ids): + async with self.lock: + for req_id in ids: + if req_id in self.completed_responses: + results[req_id] = self.completed_responses.pop(req_id) + + # All responses collected + if len(results) == len(ids): + break + + # Check if overall timeout exceeded + if timeout is not None: + elapsed = time.time() - start_time + if elapsed >= timeout: + break + # Wait for remaining time or AWAIT_TIMEOUT, whichever is shorter + wait_time = min(timeout - elapsed, AWAIT_TIMEOUT) + else: + wait_time = AWAIT_TIMEOUT + + try: + await asyncio.wait_for(self.response_event.wait(), timeout=wait_time) + except asyncio.TimeoutError: + pass + self.response_event.clear() + + out = [results.get(rid) for rid in ids] + return out[0] if is_single else out + + def await_responses_sync( + self, request_ids: Union[int, List[int]], timeout: Optional[float] = None + ) -> Union[DiffusionResponse, List[DiffusionResponse]]: + """Sync wrapper to await responses from the main thread.""" + future = asyncio.run_coroutine_threadsafe( + self.await_responses(request_ids, timeout), self._event_loop + ) + return future.result(timeout=timeout if timeout else None) + + def _init_ipc(self) -> bool: + """Initialize IPC queues.""" + try: + logger.info("DiffusionClient: Initializing IPC") + self.requests_ipc = ZeroMqQueue( + (self.request_queue_addr, None), + is_server=True, + socket_type=zmq.PUSH, + use_hmac_encryption=False, + ) + self.responses_ipc = ZeroMqQueue( + (self.response_queue_addr, None), + is_server=True, + socket_type=zmq.PULL, + use_hmac_encryption=False, + ) + logger.info("DiffusionClient: IPC ready") + return True + except Exception as e: + logger.error(f"DiffusionClient: IPC init failed: {e}") + return False + + def _send_shutdown(self): + """Send shutdown signal.""" + logger.info("DiffusionClient: Sending shutdown signal") + if self.requests_ipc: + self.requests_ipc.put(None) + self._close_socket(self.requests_ipc) + + def _process_requests(self): + """Process pending requests.""" + try: + req = self.pending_requests.get(timeout=POLL_TIMEOUT) + if req is None: + self._send_shutdown() + self.shutdown_event.set() + return + + logger.info(f"DiffusionClient: Sending request {req.request_id}") + self.requests_ipc.put(req) + except queue.Empty: + pass + except Exception as e: + logger.error(f"DiffusionClient: Error sending request: {e}") + logger.error(traceback.format_exc()) + + def _process_responses(self): + """Poll and process responses.""" + try: + if self.responses_ipc.poll(timeout=POLL_TIMEOUT): + response = self.responses_ipc.get() + if isinstance(response, DiffusionResponse): + if response.request_id == -1: + logger.info("DiffusionClient: Received READY signal") + + # Schedule the lock acquisition and event setting in the event loop + asyncio.run_coroutine_threadsafe( + self._store_response(response), self._event_loop + ) + except Exception as e: + logger.error(f"DiffusionClient: Error processing response: {e}") + + async def _store_response(self, response: DiffusionResponse): + """Store response in the completed_responses dict (async helper).""" + async with self.lock: + self.completed_responses[response.request_id] = response + self.response_event.set() + + def _cleanup_ipc(self): + """Cleanup IPC.""" + logger.info("DiffusionClient: Cleaning up IPC") + self._close_socket(self.requests_ipc) + self._close_socket(self.responses_ipc) + + def _serve_forever_thread(self): + """Background thread wrapper that creates and runs an event loop.""" + logger.info("DiffusionClient: Background thread started") + + # Create a new event loop for this thread + self._event_loop = asyncio.new_event_loop() + asyncio.set_event_loop(self._event_loop) + + # Create async primitives in this thread's event loop + self.response_event = asyncio.Event() + self.lock = asyncio.Lock() + + # Signal that the event loop is ready + self.event_loop_ready.set() + + # Run the async serve_forever + try: + self._event_loop.run_until_complete(self._serve_forever()) + finally: + self._event_loop.close() + logger.info("DiffusionClient: Background thread stopped") + + async def _serve_forever(self): + """Background thread main loop (async version).""" + if not self._init_ipc(): + return + + while not self.shutdown_event.is_set(): + self._process_requests() + self._process_responses() + await asyncio.sleep(0.001) # Yield control to allow other coroutines to run + + self._cleanup_ipc() + + def shutdown(self): + """Shutdown client and workers.""" + logger.info("DiffusionClient: Shutting down") + self.pending_requests.put(None) + + self.background_thread.join(timeout=THREAD_TIMEOUT) + if self.background_thread.is_alive(): + logger.warning("DiffusionClient: Force stopping background thread") + self.shutdown_event.set() + self.background_thread.join(timeout=1.0) + + # Shutdown workers + logger.info("DiffusionClient: Stopping workers") + for p in self.worker_processes: + p.join(timeout=WORKER_TIMEOUT) + if p.is_alive(): + logger.warning(f"DiffusionClient: Terminating worker {p.pid} with SIGTERM") + p.terminate() + p.join(timeout=WORKER_TIMEOUT) + if p.is_alive(): + logger.warning(f"DiffusionClient: Force killing worker {p.pid} with SIGKILL") + p.kill() + p.join(timeout=WORKER_TIMEOUT) + + def _wait_ready(self, timeout: float = READY_TIMEOUT): + """Wait for workers to be ready (sync wrapper for async operation).""" + logger.info("DiffusionClient: Waiting for workers") + + # Run the async wait in the background thread's event loop + future = asyncio.run_coroutine_threadsafe(self._wait_ready_async(timeout), self._event_loop) + return future.result(timeout=timeout) + + async def _wait_ready_async(self, timeout: float = READY_TIMEOUT): + """Wait for workers to be ready (async version).""" + start_time = time.time() + + while True: + async with self.lock: + if -1 in self.completed_responses: + self.completed_responses.pop(-1) + logger.info("DiffusionClient: Workers ready") + return + + if time.time() - start_time > timeout: + raise RuntimeError("DiffusionClient: Timeout waiting for workers") + + try: + await asyncio.wait_for(self.response_event.wait(), timeout=AWAIT_TIMEOUT) + except asyncio.TimeoutError: + pass + self.response_event.clear() + + +class DiffusionGenerationResult: + """Future-like object for async generation.""" + + def __init__(self, request_id: int, executor: DiffusionRemoteClient): + self.request_id = request_id + self.executor = executor + self._result = None + self._finished = False + self._error = None + + async def result(self, timeout: Optional[float] = None) -> Any: + """Wait for and return result (async version). + + Can be awaited from any async context (e.g., FastAPI background tasks). + """ + if self._finished: + if self._error: + raise RuntimeError(self._error) + return self._result + + # Use run_coroutine_threadsafe to execute in the background thread's event loop + future = asyncio.run_coroutine_threadsafe( + self.executor.await_responses(self.request_id, timeout=timeout), + self.executor._event_loop, + ) + + # Await the future in the current event loop + response = await asyncio.wrap_future(future) + + if response.error_msg: + self._error = response.error_msg + self._finished = True + raise RuntimeError(f"Generation failed: {response.error_msg}") + + self._result = response.output + self._finished = True + return self._result + + def cancel(self): + raise NotImplementedError("Cancel request (not yet implemented).") + + +@dataclass +class VisualGenParams: + """Parameters for visual generation. + + Attributes: + height: Output height in pixels + width: Output width in pixels + num_inference_steps: Number of denoising steps + guidance_scale: Classifier-free guidance scale + max_sequence_length: Maximum sequence length for text encoding + seed: Random seed for reproducibility + + # Video-specific parameters + num_frames: Number of video frames to generate + frame_rate: Frame rate for video output in fps + + # Image-specific parameters + num_images_per_prompt: Number of images to generate per prompt (for image models) + + # Advanced parameters + guidance_rescale: Guidance rescale factor (for some models) + output_type: Output type ("pt" for PyTorch tensors, "pil" for PIL images) + """ + + height: int = 720 + width: int = 1280 + num_inference_steps: int = 50 + guidance_scale: float = 5.0 + max_sequence_length: int = 512 + seed: int = 42 + + # Video-specific parameters + num_frames: int = 81 + frame_rate: float = 24.0 + input_reference: Optional[str] = None + + # Image-specific parameters + num_images_per_prompt: int = 1 + + ## Image edit parameters + image: Optional[List[str]] = None + mask: Optional[str] = None + + # Advanced parameters + guidance_rescale: float = 0.0 + output_type: str = "pt" + + # Wan-specific parameters + guidance_scale_2: Optional[float] = None + boundary_ratio: Optional[float] = None + last_image: Optional[str] = None + + +class VisualGen: + """High-level API for visual generation.""" + + def __init__( + self, + model_path: Union[str, Path], + n_workers: int = 1, + diffusion_config: Optional[dict] = None, + ): + self.model_path = str(model_path) + self.n_workers = n_workers + self.diffusion_config = diffusion_config + + self.executor = DiffusionRemoteClient( + model_path=self.model_path, + n_workers=self.n_workers, + diffusion_config=self.diffusion_config, + ) + self.req_counter = 0 + + def generate( + self, + inputs: VisualGenInputs, + params: VisualGenParams, + ) -> MediaOutput: + """Synchronous generation. Blocks until complete. + + Args: + params: Generation parameters. + + Returns: + MediaOutput: Generated media with model-specific fields populated: + - FLUX2: MediaOutput(image=torch.Tensor) + - WAN: MediaOutput(video=torch.Tensor) + - LTX2: MediaOutput(video=torch.Tensor, audio=torch.Tensor) + """ + future = self.generate_async( + inputs=inputs, + params=params, + ) + + # Use the sync wrapper to get result + response = self.executor.await_responses_sync(future.request_id, timeout=None) + if response.error_msg: + raise RuntimeError(f"Generation failed: {response.error_msg}") + return response.output + + def generate_async( + self, + inputs: VisualGenInputs, + params: VisualGenParams, + ) -> DiffusionGenerationResult: + """Async generation. Returns immediately with future-like object. + + Args: + params: Generation parameters. + + Returns: + DiffusionGenerationResult: Call result() to get output dict. + """ + req_id = self.req_counter + self.req_counter += 1 + + if isinstance(inputs, dict): + prompt = inputs.get("prompt") + negative_prompt = inputs.get("negative_prompt", None) + elif isinstance(inputs, str): + prompt = inputs + negative_prompt = None + else: + # TODO: Support batch generation + raise ValueError(f"Invalid inputs type: {type(inputs)}") + + request = DiffusionRequest( + request_id=req_id, + prompt=prompt, + negative_prompt=negative_prompt, + height=params.height, + width=params.width, + num_inference_steps=params.num_inference_steps, + guidance_scale=params.guidance_scale, + max_sequence_length=params.max_sequence_length, + seed=params.seed, + num_frames=params.num_frames, + frame_rate=params.frame_rate, + num_images_per_prompt=params.num_images_per_prompt, + guidance_rescale=params.guidance_rescale, + output_type=params.output_type, + image=params.input_reference, + guidance_scale_2=params.guidance_scale_2, + boundary_ratio=params.boundary_ratio, + last_image=params.last_image, + ) + + self.executor.enqueue_requests([request]) + return DiffusionGenerationResult(req_id, self.executor) + + def shutdown(self): + """Shutdown executor and cleanup.""" + logger.info("VisualGen: Shutting down") + self.executor.shutdown() diff --git a/tensorrt_llm/ray_stub.py b/tensorrt_llm/ray_stub.py index 9bd699d929..34d3b4e97c 100644 --- a/tensorrt_llm/ray_stub.py +++ b/tensorrt_llm/ray_stub.py @@ -16,10 +16,8 @@ from functools import wraps as _wraps from tensorrt_llm._utils import mpi_disabled as _mpi_disabled -if _mpi_disabled(): - raise RuntimeError( - "Ray requested (TLLM_DISABLE_MPI=1), but not installed. Please install Ray." - ) +# Don't raise error on import - only when Ray functionality is actually used +_RAY_NOT_INSTALLED_MSG = "Ray requested (TLLM_DISABLE_MPI=1), but not installed. Please install Ray." def remote(*args, **kwargs): @@ -42,6 +40,7 @@ def remote(*args, **kwargs): def __getattr__(name): - raise RuntimeError( - f'Ray not installed, so "ray.{name}" is unavailable. Please install Ray.' - ) + msg = f'Ray not installed, so "ray.{name}" is unavailable.' + if _mpi_disabled(): + msg = _RAY_NOT_INSTALLED_MSG + raise RuntimeError(msg) diff --git a/tensorrt_llm/serve/media_storage.py b/tensorrt_llm/serve/media_storage.py new file mode 100644 index 0000000000..acbdedae65 --- /dev/null +++ b/tensorrt_llm/serve/media_storage.py @@ -0,0 +1,426 @@ +#!/usr/bin/env python +"""Media Storage for generated images and videos. + +This module provides storage handlers for persisting generated media assets +(videos, images) and their associated metadata. +""" + +import os +from io import BytesIO +from pathlib import Path +from typing import Any, Optional + +import torch +from PIL import Image + +from tensorrt_llm.logger import logger + + +class MediaStorage: + """Handler for storing images and videos in various formats.""" + + @staticmethod + def save_image( + image: Any, output_path: str, format: Optional[str] = None, quality: int = 95 + ) -> str: + """Save image to file. + + Args: + image: torch.Tensor (H, W, C) uint8 + output_path: Path to save the image + format: Image format (png, jpg, webp). If None, infer from extension + quality: Quality for lossy formats (1-100, higher is better) + + Returns: + Path where the image was saved + """ + # Ensure output directory exists + output_dir = os.path.dirname(output_path) + if output_dir: + os.makedirs(output_dir, exist_ok=True) + + # Convert to PIL Image if needed + pil_image = MediaStorage._to_pil_image(image) + + # Determine format + if format is None: + ext = os.path.splitext(output_path)[1].lower() + if ext in [".png"]: + format = "PNG" + elif ext in [".jpg", ".jpeg"]: + format = "JPEG" + elif ext in [".webp"]: + format = "WEBP" + else: + logger.warning(f"Unknown image extension {ext}, defaulting to PNG") + format = "PNG" + output_path = output_path.rsplit(".", 1)[0] + ".png" + + # Save image with format-specific handling + MediaStorage._save_pil_image(pil_image, output_path, format, quality) + + logger.info(f"Saved image to {output_path} (format={format})") + return output_path + + @staticmethod + def convert_image_to_bytes(image: Any, format: str = "PNG", quality: int = 95) -> bytes: + """Convert image to bytes buffer. + + Args: + image: torch.Tensor (H, W, C) uint8 + format: Image format (PNG, JPEG, WEBP) + quality: Quality for lossy formats (1-100) + + Returns: + Image bytes + """ + pil_image = MediaStorage._to_pil_image(image) + + # Save to bytes buffer + buffer = BytesIO() + MediaStorage._save_pil_image(pil_image, buffer, format, quality) + + return buffer.getvalue() + + @staticmethod + def _to_pil_image(image: torch.Tensor) -> Image.Image: + """Convert torch.Tensor to PIL Image. + + Args: + image: torch.Tensor (H, W, C) uint8 + + Returns: + PIL Image + """ + if not isinstance(image, torch.Tensor): + raise ValueError(f"Expected torch.Tensor, got {type(image)}") + + # Convert to numpy for PIL + image_np = image.cpu().numpy() + return Image.fromarray(image_np) + + @staticmethod + def _save_pil_image( + pil_image: Image.Image, + output: Any, # Can be path string or BytesIO + format: str, + quality: int, + ): + """Save PIL Image to file or buffer. + + Args: + pil_image: PIL Image to save + output: Output path (str) or BytesIO buffer + format: Image format (PNG, JPEG, WEBP) + quality: Quality for lossy formats (1-100) + """ + format_upper = format.upper() + + if format_upper in ["JPEG", "JPG"]: + # Convert RGBA to RGB for JPEG + if pil_image.mode in ("RGBA", "LA", "P"): + background = Image.new("RGB", pil_image.size, (255, 255, 255)) + if pil_image.mode == "P": + pil_image = pil_image.convert("RGBA") + background.paste( + pil_image, mask=pil_image.split()[-1] if pil_image.mode == "RGBA" else None + ) + pil_image = background + pil_image.save(output, format="JPEG", quality=quality, optimize=True) + elif format_upper == "WEBP": + pil_image.save(output, format="WEBP", quality=quality) + else: # PNG or default + pil_image.save(output, format="PNG", optimize=True) + + @staticmethod + def save_video( + video: Any, + output_path: str, + audio: Optional[Any] = None, + frame_rate: float = 24.0, + format: Optional[str] = None, + ) -> str: + """Save video to file with optional audio. + + Args: + video: Video frames as torch.Tensor (T, H, W, C) uint8 + output_path: Path to save the video + audio: Optional audio as torch.Tensor + frame_rate: Frames per second (default: 24.0) + format: Video format (mp4, gif, png). If None, infer from extension + + Returns: + Path where the video was saved + """ + # Ensure output directory exists + if isinstance(output_path, Path): + output_path = str(output_path) + + output_dir = os.path.dirname(output_path) + if output_dir: + os.makedirs(output_dir, exist_ok=True) + + # Determine format + if format is None: + ext = os.path.splitext(output_path)[1].lower() + format = ext[1:] if ext else "mp4" + + format = format.lower() + + # Save based on format + if format == "mp4": + MediaStorage._save_mp4(video, audio, output_path, frame_rate) + elif format == "gif": + MediaStorage._save_gif(video, output_path, frame_rate) + elif format == "png": + MediaStorage._save_middle_frame(video, output_path) + else: + logger.warning(f"Unsupported video format: {format}, defaulting to mp4") + output_path = output_path.rsplit(".", 1)[0] + ".mp4" + MediaStorage._save_mp4(video, audio, output_path, frame_rate) + + return output_path + + @staticmethod + def convert_video_to_bytes( + video: Any, audio: Optional[Any] = None, frame_rate: float = 24.0, format: str = "mp4" + ) -> bytes: + """Convert video to bytes buffer. + + Args: + video: Video frames as torch.Tensor (T, H, W, C) uint8 + audio: Optional audio as torch.Tensor + frame_rate: Frames per second + format: Video format (mp4, gif) + + Returns: + Video bytes + """ + import tempfile + + # Create temporary file + with tempfile.NamedTemporaryFile(suffix=f".{format}", delete=False) as tmp_file: + tmp_path = tmp_file.name + + try: + # Save to temporary file + MediaStorage.save_video(video, tmp_path, audio, frame_rate, format) + + # Read bytes + with open(tmp_path, "rb") as f: + video_bytes = f.read() + + return video_bytes + finally: + # Clean up temporary file + if os.path.exists(tmp_path): + os.unlink(tmp_path) + + @staticmethod + def _save_mp4( + video: torch.Tensor, audio: Optional[torch.Tensor], output_path: str, frame_rate: float + ) -> str: + """Save video with optional audio as MP4. + + Args: + video: Video frames as torch.Tensor (T, H, W, C) uint8 + audio: Optional audio as torch.Tensor + output_path: Output path for MP4 + frame_rate: Frames per second + + Returns: + Path where the video was saved + """ + try: + from fractions import Fraction + + import av + + if not isinstance(video, torch.Tensor): + raise ValueError(f"Expected torch.Tensor for video, got {type(video)}") + + # Convert video tensor to numpy: (T, H, W, C) uint8 + video_np = video.cpu().numpy() + num_frames, height, width, channels = video_np.shape + + # Ensure RGB format (3 channels) + if channels != 3: + raise ValueError(f"Expected 3-channel RGB video, got {channels} channels") + + # Open output container + container = av.open(output_path, mode="w") + + # Add video stream (H.264 codec) + video_stream = container.add_stream("libx264", rate=int(frame_rate)) + video_stream.width = width + video_stream.height = height + video_stream.pix_fmt = "yuv420p" + video_stream.options = {"preset": "medium", "crf": "23"} + + # Pre-process audio and add audio stream BEFORE any muxing. + # All streams must be registered before the first mux() call + # (which triggers container header writing). + audio_stream = None + audio_tensor = None + audio_sample_rate = 24000 # Default sample rate + if audio is not None: + if not isinstance(audio, torch.Tensor): + raise ValueError(f"Expected torch.Tensor for audio, got {type(audio)}") + + # Prepare audio tensor: convert to (samples, channels) format + audio_tensor = audio + + # Handle different audio tensor dimensions + if audio_tensor.ndim == 1: + # Mono audio: (samples,) -> (samples, 1) + audio_tensor = audio_tensor[:, None] + elif audio_tensor.ndim == 2: + # If shape[1] != 2 and shape[0] == 2, transpose to (samples, channels) + if audio_tensor.shape[1] != 2 and audio_tensor.shape[0] == 2: + audio_tensor = audio_tensor.T + if audio_tensor.shape[1] > 2: + audio_tensor = audio_tensor[:, :2] + elif audio_tensor.ndim == 3: + if audio_tensor.shape[0] == 1: + audio_tensor = audio_tensor.squeeze(0) + else: + audio_tensor = audio_tensor[0] + if audio_tensor.shape[1] != 2 and audio_tensor.shape[0] == 2: + audio_tensor = audio_tensor.T + if audio_tensor.shape[1] > 2: + audio_tensor = audio_tensor[:, :2] + else: + raise ValueError( + f"Unsupported audio tensor shape: {audio_tensor.shape}. " + f"Expected 1D, 2D, or 3D tensor." + ) + + if audio_tensor.shape[1] > 2: + audio_tensor = audio_tensor[:, :2] + + # Convert to int16 if needed + if audio_tensor.dtype != torch.int16: + audio_tensor = torch.clip(audio_tensor, -1.0, 1.0) + audio_tensor = (audio_tensor * 32767.0).to(torch.int16) + + # Add audio stream now (before any muxing) + audio_stream = container.add_stream("aac", rate=audio_sample_rate) + audio_stream.codec_context.sample_rate = audio_sample_rate + audio_stream.codec_context.layout = "stereo" + audio_stream.codec_context.time_base = Fraction(1, audio_sample_rate) + + # --- Encode video frames --- + for frame_array in video_np: + frame = av.VideoFrame.from_ndarray(frame_array, format="rgb24") + for packet in video_stream.encode(frame): + container.mux(packet) + + # Flush video encoder + for packet in video_stream.encode(): + container.mux(packet) + + # --- Encode audio (after video is done) --- + if audio_stream is not None and audio_tensor is not None: + # Build packed int16 frame: (1, samples*channels) + audio_np = audio_tensor.contiguous().reshape(1, -1).cpu().numpy() + + frame_in = av.AudioFrame.from_ndarray(audio_np, format="s16", layout="stereo") + frame_in.sample_rate = audio_sample_rate + + # Use AudioResampler to convert s16ā†’fltp (AAC's native format) + cc = audio_stream.codec_context + audio_resampler = av.audio.resampler.AudioResampler( + format=cc.format or "fltp", + layout=cc.layout or "stereo", + rate=cc.sample_rate or audio_sample_rate, + ) + + audio_next_pts = 0 + for rframe in audio_resampler.resample(frame_in): + if rframe.pts is None: + rframe.pts = audio_next_pts + audio_next_pts += rframe.samples + rframe.sample_rate = audio_sample_rate + container.mux(audio_stream.encode(rframe)) + + # Flush audio encoder + for packet in audio_stream.encode(): + container.mux(packet) + + # Close container + container.close() + + logger.info(f"Saved video{' with audio' if audio is not None else ''} to {output_path}") + return output_path + + except ImportError: + logger.warning( + "PyAV (av) library not available. " + "Falling back to saving middle frame as PNG. " + "Install with: pip install av" + ) + png_path = output_path.replace(".mp4", ".png") + return MediaStorage._save_middle_frame(video, png_path) + except Exception as e: + logger.error(f"Error encoding video with PyAV: {e}") + import traceback + + logger.error(traceback.format_exc()) + logger.warning("Falling back to saving middle frame as PNG.") + png_path = output_path.replace(".mp4", ".png") + return MediaStorage._save_middle_frame(video, png_path) + + @staticmethod + def _save_gif(video: torch.Tensor, output_path: str, frame_rate: float) -> str: + """Save video as animated GIF. + + Args: + video: Video frames as torch.Tensor (T, H, W, C) uint8 + output_path: Output path for GIF + frame_rate: Frames per second + + Returns: + Path where the GIF was saved + """ + if not isinstance(video, torch.Tensor): + raise ValueError(f"Expected torch.Tensor for video, got {type(video)}") + + # Convert to numpy and then to list of PIL Images + video_np = video.cpu().numpy() + frames = [Image.fromarray(video_np[i]) for i in range(video_np.shape[0])] + + # Save as GIF + duration_ms = int(1000 / frame_rate) + frames[0].save( + output_path, + save_all=True, + append_images=frames[1:], + optimize=False, + duration=duration_ms, + loop=0, + ) + logger.info(f"Saved video as GIF to {output_path} ({len(frames)} frames)") + return output_path + + @staticmethod + def _save_middle_frame(video: torch.Tensor, output_path: str) -> str: + """Save middle frame of video as PNG. + + Args: + video: Video frames as torch.Tensor (T, H, W, C) uint8 + output_path: Output path for PNG + + Returns: + Path where the frame was saved + """ + if not isinstance(video, torch.Tensor): + raise ValueError(f"Expected torch.Tensor for video, got {type(video)}") + + # Extract middle frame + video_np = video.cpu().numpy() + frame_idx = video_np.shape[0] // 2 + image = Image.fromarray(video_np[frame_idx]) + + image.save(output_path) + logger.info(f"Saved frame {frame_idx} to {output_path}") + return output_path diff --git a/tensorrt_llm/serve/openai_protocol.py b/tensorrt_llm/serve/openai_protocol.py index 3afcb989d4..21f411cc33 100644 --- a/tensorrt_llm/serve/openai_protocol.py +++ b/tensorrt_llm/serve/openai_protocol.py @@ -1,12 +1,14 @@ # Adapted from # https://github.com/vllm-project/vllm/blob/4db5176d9758b720b05460c50ace3c01026eb158/vllm/entrypoints/openai/protocol.py import base64 +import re import time import uuid from typing import Any, Dict, List, Literal, Optional, Union import torch import xgrammar +from fastapi import UploadFile from openai.types.chat import ChatCompletionAssistantMessageParam from openai.types.chat import \ ChatCompletionContentPartParam as OpenAIChatCompletionContentPartParam @@ -1120,5 +1122,218 @@ def to_llm_disaggregated_params( ) +# ============================================================================ +# Diffusion API Protocol Classes +# ============================================================================ + + +class ImageGenerationRequest(OpenAIBaseModel): + """OpenAI-compatible image generation request. + + Follows the OpenAI Images API specification: + https://platform.openai.com/docs/api-reference/images/create + """ + prompt: str + model: Optional[str] = None + n: int = Field(default=1, ge=1, le=10) + output_format: Literal["png", "webp", "jpeg"] = "png" + size: Optional[str] = Field( + default="auto", + description=( + "The size of the generated images. Must be in 'WxH' format like " + "1024x1024, 1536x1024 (landscape), 1024x1536 (portrait), etc. " + "Use 'auto' for model default size.")) + quality: Literal["standard", "hd"] = "standard" + response_format: Literal["url", "b64_json"] = "url" + style: Optional[Literal["vivid", "natural"]] = "vivid" + user: Optional[str] = None + + # Extended parameters for diffusion control + num_inference_steps: Optional[int] = Field( + default=None, + description= + "Number of denoising steps. More steps = higher quality but slower.") + guidance_scale: Optional[float] = Field( + default=None, + description= + "Classifier-free guidance scale. Higher values follow prompt more closely." + ) + guidance_rescale: Optional[float] = Field( + default=None, description="Classifier-free guidance rescale.") + negative_prompt: Optional[str] = Field( + default=None, + description="Text describing what to avoid in the generated image.") + seed: Optional[int] = Field(default=None, + description="Random seed for reproducibility.") + + @field_validator("size") + @classmethod + def validate_size(cls, v): + """Validate size format is 'WxH' or 'auto'.""" + if v is None or v == "auto": + return v + if not isinstance(v, str): + raise ValueError("size must be a string in 'WxH' format or 'auto'") + # Check format: should be like "1024x1024" + import re + if not re.match(r'^\d+x\d+$', v): + raise ValueError( + f"Invalid size format '{v}'. Must be in 'WxH' format " + "(e.g., '1024x1024', '1536x1024') or 'auto'.") + return v + + +class ImageObject(OpenAIBaseModel): + """Generated image object in the response.""" + b64_json: Optional[str] = None + url: Optional[str] = None + revised_prompt: Optional[str] = None + + +class ImageGenerationResponse(OpenAIBaseModel): + """Response from image generation endpoint.""" + created: int = Field(default_factory=lambda: int(time.time())) + data: List[ImageObject] + output_format: Literal["png", "webp", "jpeg"] = "png" + quality: Literal["low", "medium", "high"] = "medium" + size: Optional[str] = None + + +class ImageEditRequest(OpenAIBaseModel): + """Request for image editing endpoint. + + Follows the OpenAI Images API specification: + https://platform.openai.com/docs/api-reference/images/createEdit + """ + image: Union[List[str], str] = Field( + description="Base64-encoded source image(s) to edit") + prompt: str = Field(description="Text description of desired edits") + model: Optional[str] = None + mask: Optional[str] = Field( + default=None, + description= + "Base64-encoded mask image (optional, black areas will be edited)") + n: int = Field(default=1, ge=1, le=10) + size: Optional[str] = Field( + default="auto", + description=( + "The size of the edited images. Must be in 'WxH' format like " + "1024x1024, 1536x1024 (landscape), 1024x1536 (portrait), etc. " + "Use 'auto' to match source image size.")) + response_format: Literal["url", "b64_json"] = "url" + user: Optional[str] = None + + # Extended parameters for diffusion control + num_inference_steps: Optional[int] = Field( + default=None, description="Number of denoising steps.") + guidance_scale: Optional[float] = Field( + default=None, description="Classifier-free guidance scale.") + guidance_rescale: Optional[float] = Field( + default=None, description="Classifier-free guidance rescale.") + negative_prompt: Optional[str] = Field( + default=None, + description="Text describing what to avoid in the edited image.") + seed: Optional[int] = Field(default=None, + description="Random seed for reproducibility.") + + @field_validator("size") + @classmethod + def validate_size(cls, v): + """Validate size format is 'WxH' or 'auto'.""" + if v != "auto" and not re.match(r"^\d+x\d+$", v): + raise ValueError( + "Size must be 'auto' or in 'WxH' format (e.g., '1024x1024')") + return v + + +class VideoGenerationRequest(OpenAIBaseModel): + """Video generation request (extended API). + + This is an extension to the OpenAI API for video generation support. + """ + prompt: str + input_reference: Optional[Union[str, UploadFile]] = Field( + default=None, + description="Optional image reference that guides generation.") + model: Optional[str] = None + size: Optional[str] = Field( + default="auto", + description= + ("The size of the generated video frames. Must be in 'WxH' format like " + "512x512, 1024x576 (landscape), 576x1024 (portrait), etc. " + "Use 'auto' for model default size.")) + seconds: float = Field(default=2.0, + ge=1.0, + le=16.0, + description="Video duration in seconds.") + + # Extended parameters for diffusion control + n: int = Field(default=1, ge=1, le=4) + fps: int = Field(default=24, ge=8, le=60, description="Frames per second.") + num_inference_steps: Optional[int] = Field( + default=None, description="Number of denoising steps.") + guidance_scale: Optional[float] = Field( + default=None, description="Classifier-free guidance scale.") + guidance_rescale: Optional[float] = Field( + default=None, description="Classifier-free guidance rescale.") + negative_prompt: Optional[str] = Field( + default=None, + description="Text describing what to avoid in the generated video.") + seed: Optional[int] = Field(default=None, + description="Random seed for reproducibility.") + + @field_validator("size") + @classmethod + def validate_size(cls, v): + """Validate size format is 'WxH' or 'auto'.""" + if v is None or v == "auto": + return v + if not isinstance(v, str): + raise ValueError("size must be a string in 'WxH' format or 'auto'") + import re + if not re.match(r'^\d+x\d+$', v): + raise ValueError( + f"Invalid size format '{v}'. Must be in 'WxH' format " + "(e.g., '512x512', '1024x576') or 'auto'.") + return v + + +class VideoJob(OpenAIBaseModel): + """Metadata for an asynchronous video generation job. + + Follows the OpenAI Videos API specification: + https://platform.openai.com/docs/api-reference/videos + """ + completed_at: Optional[int] = Field( + default=None, description="Unix timestamp of completion") + created_at: int = Field(description="Unix timestamp of creation") + error: Optional[str] = Field(default=None, + description="Error message if failed") + expires_at: Optional[int] = Field( + default=None, description="Unix timestamp of expiration") + id: str = Field(description="Unique identifier for the video") + model: str = Field(description="The model used for generation") + object: str = Field(default="video", description="Object type") + progress: Optional[int] = Field( + default=None, + description="Progress of the video generation job (0-100)") + prompt: str = Field(description="The prompt used to generate the video") + status: Literal["queued", "in_progress", "completed", "failed"] = Field( + description="Current status of the video generation job") + + # Video properties + duration: Optional[float] = Field(default=None, + description="Video duration in seconds") + fps: Optional[int] = Field(default=None, description="Frames per second") + size: Optional[str] = Field(default=None, + description="Video dimensions in 'WxH' format") + + +class VideoJobList(OpenAIBaseModel): + """Response from listing video jobs endpoint.""" + data: List[VideoJob] = Field(description="List of video jobs") + object: str = Field(default="list", description="Object type") + + UCompletionRequest = Union[CompletionRequest, ChatCompletionRequest] UCompletionResponse = Union[CompletionResponse, ChatCompletionResponse] diff --git a/tensorrt_llm/serve/openai_server.py b/tensorrt_llm/serve/openai_server.py index 9cb9d59918..c19dd93149 100644 --- a/tensorrt_llm/serve/openai_server.py +++ b/tensorrt_llm/serve/openai_server.py @@ -1,10 +1,13 @@ #!/usr/bin/env python import asyncio +import base64 import os import re import signal import socket +import time import traceback +import uuid from collections import deque from contextlib import asynccontextmanager from datetime import datetime @@ -16,7 +19,8 @@ from typing import (Annotated, Any, AsyncGenerator, AsyncIterator, List, import uvicorn from fastapi import Body, FastAPI, Request from fastapi.exceptions import RequestValidationError -from fastapi.responses import JSONResponse, Response, StreamingResponse +from fastapi.responses import (FileResponse, JSONResponse, Response, + StreamingResponse) from starlette.routing import Mount from transformers import AutoProcessor @@ -26,11 +30,12 @@ from tensorrt_llm._torch.async_llm import AsyncLLM from tensorrt_llm.executor import CppExecutorError from tensorrt_llm.executor.postproc_worker import PostprocParams from tensorrt_llm.inputs import prompt_inputs -from tensorrt_llm.inputs.data import TokensPrompt +from tensorrt_llm.inputs.data import TokensPrompt, visual_gen_inputs from tensorrt_llm.inputs.multimodal import MultimodalServerConfig from tensorrt_llm.inputs.utils import ConversationMessage, apply_chat_template from tensorrt_llm.llmapi import DisaggregatedParams as LlmDisaggregatedParams -from tensorrt_llm.llmapi import MultimodalEncoder, tracing +from tensorrt_llm.llmapi import (MultimodalEncoder, VisualGen, VisualGenParams, + tracing) from tensorrt_llm.llmapi.disagg_utils import (DisaggClusterConfig, MetadataServerConfig, ServerRole) from tensorrt_llm.llmapi.llm import RequestOutput @@ -40,6 +45,7 @@ from tensorrt_llm.serve.chat_utils import (load_chat_template, parse_chat_messages_coroutines) from tensorrt_llm.serve.cluster_storage import create_cluster_storage_client from tensorrt_llm.serve.disagg_auto_scaling import DisaggClusterWorker +from tensorrt_llm.serve.media_storage import MediaStorage from tensorrt_llm.serve.metadata_server import create_metadata_server from tensorrt_llm.serve.openai_protocol import (ChatCompletionRequest, ChatCompletionResponse, @@ -47,12 +53,17 @@ from tensorrt_llm.serve.openai_protocol import (ChatCompletionRequest, ChatMessage, CompletionRequest, CompletionResponse, CompletionResponseChoice, - ErrorResponse, + ErrorResponse, ImageEditRequest, + ImageGenerationRequest, + ImageGenerationResponse, + ImageObject, MemoryUpdateRequest, ModelCard, ModelList, PromptTokensDetails, ResponsesRequest, ResponsesResponse, UpdateWeightsRequest, UsageInfo, + VideoGenerationRequest, + VideoJob, VideoJobList, to_llm_disaggregated_params) from tensorrt_llm.serve.postprocess_handlers import ( ChatCompletionPostprocArgs, ChatPostprocArgs, CompletionPostprocArgs, @@ -69,6 +80,8 @@ from tensorrt_llm.serve.responses_utils import \ from tensorrt_llm.serve.responses_utils import get_steady_clock_now_in_seconds from tensorrt_llm.serve.responses_utils import \ request_preprocess as responses_api_request_preprocess +from tensorrt_llm.serve.visual_gen_utils import (VIDEO_STORE, + parse_visual_gen_params) from tensorrt_llm.version import __version__ as VERSION from .._utils import nvtx_mark, set_prometheus_multiproc_dir @@ -82,7 +95,7 @@ TIMEOUT_KEEP_ALIVE = 5 # seconds. class OpenAIServer: def __init__(self, - llm: Union[LLM, MultimodalEncoder], + generator: Union[LLM, MultimodalEncoder, VisualGen], model: str, tool_parser: Optional[str], server_role: Optional[ServerRole], @@ -90,40 +103,17 @@ class OpenAIServer: disagg_cluster_config: Optional[DisaggClusterConfig] = None, multimodal_server_config: Optional[MultimodalServerConfig] = None, chat_template: Optional[str] = None): - self.llm = llm - self.tokenizer = llm.tokenizer + self.generator = generator + self._is_visual_gen = isinstance(generator, VisualGen) self.tool_parser = tool_parser self.metadata_server = create_metadata_server(metadata_server_cfg) self.disagg_cluster_config = disagg_cluster_config self.multimodal_server_config = multimodal_server_config - self.chat_template = load_chat_template(chat_template) self.server_role = server_role # Will be set in __call__ self.binding_addr = None self.host = None self.port = None - hf_tokenizer_path = llm._hf_model_dir or self.tokenizer.tokenizer.name_or_path - trust_remote_code = llm.args.trust_remote_code - try: - self.processor = AutoProcessor.from_pretrained(hf_tokenizer_path, trust_remote_code=trust_remote_code) - except Exception: - logger.debug("Failed to load AutoProcessor or AutoConfig for %s", hf_tokenizer_path) - self.processor = None - # load model config - try: - from tensorrt_llm._torch.pyexecutor.config_utils import \ - load_pretrained_config - self.model_config = load_pretrained_config(hf_tokenizer_path, - trust_remote_code=trust_remote_code, - checkpoint_format=getattr(self.llm.args, "checkpoint_format", None)) - except Exception: - logger.debug("Failed to load AutoConfig for %s", hf_tokenizer_path) - self.model_config = None - - # Enable response storage for Responses API - self.enable_store = (len(os.getenv("TRTLLM_RESPONSES_API_DISABLE_STORE", "")) < 1) and not self.postproc_worker_enabled - - self.conversation_store = ConversationHistoryStore() model_dir = Path(model) if model_dir.exists() and model_dir.is_dir(): @@ -135,35 +125,19 @@ class OpenAIServer: self.perf_metrics_lock = None # The steady clock offset (in seconds) between this server and the disagg server self.disagg_server_steady_clock_offset = 0 - if self.llm.args.return_perf_metrics: - set_prometheus_multiproc_dir() - self.metrics_collector = MetricsCollector({ - "model_name": "undefined", - "engine_type": "undefined" - }) - max_perf_metrics = self.llm.args.perf_metrics_max_requests - if max_perf_metrics > 0: - self.perf_metrics = deque(maxlen=max_perf_metrics) - self.perf_metrics_lock = asyncio.Lock() - - # gpt-oss - self.harmony_adapter: HarmonyAdapter | None = None - disable_harmony = os.getenv("DISABLE_HARMONY_ADAPTER", "0") == "1" - if disable_harmony: - self.use_harmony = False - else: - self.use_harmony = (self.model_config.model_type == "gpt_oss") - - self.tool_call_id_type = "random" # default tool call id type is random - if self.model_config.model_type == "kimi_k2": - self.tool_call_id_type = "kimi_k2" - elif self.model_config.model_type == "deepseek_v32": - self.tool_call_id_type = "deepseek_v32" # as disagg-worker self.disagg_cluster_storage = None self.disagg_cluster_worker = None + # Skip loading AutoProcessor and model_config for VISUAL_GEN models + # These are LLM-specific and can cause unnecessary memory usage + if self._is_visual_gen: + self._init_visual_gen() + else: + self._init_llm(chat_template) + + @asynccontextmanager async def lifespan(app: FastAPI): if self.metadata_server is not None: @@ -176,8 +150,8 @@ class OpenAIServer: } # TODO: add more metadata # Register with ETCD using the existing key format - self.metadata_server.put(f"trtllm/{self.llm.llm_id}", metadata) - logger.info(f"trtllm/{self.llm.llm_id} is registered") + self.metadata_server.put(f"trtllm/{self.generator.llm_id}", metadata) + logger.info(f"trtllm/{self.generator.llm_id} is registered") if self.disagg_cluster_config: self.disagg_cluster_storage = create_cluster_storage_client(self.disagg_cluster_config.cluster_uri, self.disagg_cluster_config.cluster_name) @@ -188,11 +162,11 @@ class OpenAIServer: yield if self.metadata_server is not None: - self.metadata_server.remove(f"trtllm/{self.llm.llm_id}") - logger.info(f"trtllm/{self.llm.llm_id} is unregistered") + self.metadata_server.remove(f"trtllm/{self.generator.llm_id}") + logger.info(f"trtllm/{self.generator.llm_id} is unregistered") if self.disagg_cluster_worker: await self.disagg_cluster_worker.deregister_worker() - self.llm.shutdown() + self.generator.shutdown() self.app = FastAPI(lifespan=lifespan) @@ -200,15 +174,81 @@ class OpenAIServer: async def validation_exception_handler(_, exc): return JSONResponse(status_code=400, content={"error": str(exc)}) - if self.server_role is not ServerRole.MM_ENCODER: - self.register_routes() - else: - assert isinstance(self.llm, MultimodalEncoder), "llm must be a MultimodalEncoder for multimodal encoder" + if self.server_role is ServerRole.VISUAL_GEN: + assert isinstance(self.generator, VisualGen), "generator must be a VisualGen for VISUAL_GEN server" + self.register_visual_gen_routes() + elif self.server_role is ServerRole.MM_ENCODER: + assert isinstance(self.generator, MultimodalEncoder), "generator must be a MultimodalEncoder for multimodal encoder" self.register_mm_encoder_routes() + else: + self.register_routes() self.app.add_middleware(ServerArrivalTimeMiddleware) + def _init_visual_gen(self): + self.processor = None + self.model_config = None + self.media_storage_path = Path(os.getenv("TRTLLM_MEDIA_STORAGE_PATH", "/tmp/trtllm_generated")) # nosec B108 + self.media_storage_path.mkdir(exist_ok=True, parents= True) + self.video_gen_tasks = {} + + + def _init_llm(self, chat_template: Optional[str] = None): + self.tokenizer = self.generator.tokenizer + hf_tokenizer_path = self.generator._hf_model_dir or self.tokenizer.tokenizer.name_or_path + trust_remote_code = self.generator.args.trust_remote_code + try: + self.processor = AutoProcessor.from_pretrained(hf_tokenizer_path, trust_remote_code=trust_remote_code) + except Exception: + logger.debug("Failed to load AutoProcessor or AutoConfig for %s", hf_tokenizer_path) + self.processor = None + + # load model config + try: + from tensorrt_llm._torch.pyexecutor.config_utils import \ + load_pretrained_config + self.model_config = load_pretrained_config(hf_tokenizer_path, + trust_remote_code=trust_remote_code, + checkpoint_format=getattr(self.generator.args, "checkpoint_format", None)) + except Exception: + logger.debug("Failed to load AutoConfig for %s", hf_tokenizer_path) + self.model_config = None + + self.chat_template = load_chat_template(chat_template) + + # Enable response storage for Responses API + self.enable_store = (len(os.getenv("TRTLLM_RESPONSES_API_DISABLE_STORE", "")) < 1) and not self.postproc_worker_enabled + + self.conversation_store = ConversationHistoryStore() + + # gpt-oss + self.harmony_adapter: HarmonyAdapter | None = None + disable_harmony = os.getenv("DISABLE_HARMONY_ADAPTER", "0") == "1" + if disable_harmony or self.model_config is None: + self.use_harmony = False + else: + self.use_harmony = (self.model_config.model_type == "gpt_oss") + + self.tool_call_id_type = "random" # default tool call id type is random + if self.model_config is not None: + if self.model_config.model_type == "kimi_k2": + self.tool_call_id_type = "kimi_k2" + elif self.model_config.model_type == "deepseek_v32": + self.tool_call_id_type = "deepseek_v32" + + if self.generator.args.return_perf_metrics: + set_prometheus_multiproc_dir() + self.metrics_collector = MetricsCollector({ + "model_name": "undefined", + "engine_type": "undefined" + }) + max_perf_metrics = self.generator.args.perf_metrics_max_requests + if max_perf_metrics > 0: + self.perf_metrics = deque(maxlen=max_perf_metrics) + self.perf_metrics_lock = asyncio.Lock() + + async def await_disconnected(self, raw_request: Request, promise): if raw_request is None: return @@ -221,7 +261,7 @@ class OpenAIServer: @property def postproc_worker_enabled(self) -> bool: - return True if self.llm.args.num_postprocess_workers > 0 else False + return True if self.generator.args.num_postprocess_workers > 0 else False @staticmethod def create_error_response( @@ -248,8 +288,20 @@ class OpenAIServer: status_code=HTTPStatus.NOT_FOUND, ) + def _create_not_supported_error(self, message: str) -> Response: + return self.create_error_response( + err_type="NotImplementedError", + message=message, + status_code=HTTPStatus.NOT_IMPLEMENTED, + ) + def _check_health(self) -> bool: - return self.llm._check_health() + if isinstance(self.generator, LLM): + return self.generator._check_health() + # llmapi.LLM (e.g. PyTorch backend) is not isinstance(_tensorrt_engine.LLM) + if hasattr(self.generator, '_check_health'): + return self.generator._check_health() + return True def register_routes(self): self.app.add_api_route("/health", self.health, methods=["GET"]) @@ -293,7 +345,7 @@ class OpenAIServer: self.app.add_api_route("/server_info", self.get_server_info, methods=["GET"]) - if self.llm.args.return_perf_metrics: + if self.generator.args.return_perf_metrics: # register /prometheus/metrics self.mount_metrics() @@ -340,6 +392,45 @@ class OpenAIServer: self.update_weights, methods=["POST"]) + def register_visual_gen_routes(self): + """Register routes for diffusion model serving.""" + # Health and info endpoints + self.app.add_api_route("/health", self.health, methods=["GET"]) + self.app.add_api_route("/version", self.version, methods=["GET"]) + self.app.add_api_route("/v1/models", self.get_model, methods=["GET"]) + self.app.add_api_route("/metrics", self.get_iteration_stats, methods=["GET"]) + + # Image generation endpoints (OpenAI compatible) + self.app.add_api_route("/v1/images/generations", + self.openai_image_generation, + methods=["POST"]) + self.app.add_api_route("/v1/images/edits", + self.openai_image_edit, + methods=["POST"]) + + # Video generation endpoints (Extended OpenAI API) + # Asynchronous video generation (returns immediately with job metadata, OpenAI API) + self.app.add_api_route("/v1/videos", + self.openai_video_generation_async, + methods=["POST"]) + # Synchronous video generation (waits for completion, extended API) + self.app.add_api_route("/v1/videos/generations", + self.openai_video_generation_sync, + methods=["POST"]) + # Video management endpoints + self.app.add_api_route("/v1/videos", + self.list_videos, + methods=["GET"]) + self.app.add_api_route("/v1/videos/{video_id}", + self.get_video_metadata, + methods=["GET"]) + self.app.add_api_route("/v1/videos/{video_id}/content", + self.get_video_content, + methods=["GET"]) + self.app.add_api_route("/v1/videos/{video_id}", + self.delete_video, + methods=["DELETE"]) + async def health(self) -> Response: if self._check_health(): return Response(status_code=200) @@ -349,10 +440,10 @@ class OpenAIServer: async def health_generate(self, raw_request: Request) -> Response: """Health check that performs a minimal generation.""" extra_args = {} - if self.llm.args.max_beam_width > 1: + if self.generator.args.max_beam_width > 1: extra_args = dict( use_beam_search=True, - best_of=self.llm.args.max_beam_width, + best_of=self.generator.args.max_beam_width, n=1, ) try: @@ -396,7 +487,7 @@ class OpenAIServer: async def get_iteration_stats(self) -> JSONResponse: stats = [] - async for stat in self.llm.get_stats_async(2): + async for stat in self.generator.get_stats_async(2): stats.append(stat) return JSONResponse(content=stats) @@ -416,7 +507,7 @@ class OpenAIServer: return JSONResponse(content=[]) async with self.perf_metrics_lock: perf_metrics = self.perf_metrics - self.perf_metrics = deque(maxlen=self.llm.args.perf_metrics_max_requests) + self.perf_metrics = deque(maxlen=self.generator.args.perf_metrics_max_requests) for metrics_dict in perf_metrics: metrics = metrics_dict["perf_metrics"] timing_metrics = metrics.timing_metrics @@ -466,7 +557,7 @@ class OpenAIServer: async def get_kv_cache_events(self) -> JSONResponse: events = [] try: - async for event in self.llm.get_kv_cache_events_async(2): + async for event in self.generator.get_kv_cache_events_async(2): events.append(event) except IndexError: # queue is empty, no more events @@ -478,7 +569,7 @@ class OpenAIServer: return if self.metrics_collector: self.metrics_collector.log_metrics_dict(res.metrics_dict) - if self.llm.args.return_perf_metrics: + if self.generator.args.return_perf_metrics: output = res.outputs[0] item = { "request_id": res.request_id, @@ -549,9 +640,9 @@ class OpenAIServer: # expanded into an embedding bias tensor in the sampler. sampling_params = request.to_sampling_params( vocab_size=self.tokenizer.tokenizer.vocab_size, - gather_generation_logits=self.llm.args.gather_generation_logits, - reasoning_parser=self.llm.args.reasoning_parser, - backend=self.llm.args.backend) + gather_generation_logits=self.generator.args.gather_generation_logits, + reasoning_parser=self.generator.args.reasoning_parser, + backend=self.generator.args.backend) postproc_args = ChatPostprocArgs.from_request(request) disaggregated_params = to_llm_disaggregated_params(request.disaggregated_params) @@ -582,7 +673,7 @@ class OpenAIServer: if mm_data and mm_embeddings: raise ValueError("Passing 'multi_modal_data' and 'multi_modal_embeddings' at the same time is not supported.") - postproc_args.reasoning_parser = self.llm.args.reasoning_parser + postproc_args.reasoning_parser = self.generator.args.reasoning_parser postproc_args.tool_parser = self.tool_parser postproc_args.tool_call_id_type = self.tool_call_id_type if conversation and conversation[-1].get( @@ -596,7 +687,7 @@ class OpenAIServer: trace_headers = (None if raw_request is None else tracing.extract_trace_headers(raw_request.headers)) - promise = self.llm.generate_async( + promise = self.generator.generate_async( inputs=prompt, sampling_params=sampling_params, _postproc_params=postproc_params if self.postproc_worker_enabled else None, @@ -701,7 +792,7 @@ class OpenAIServer: if mm_data is not None: prompt["multi_modal_data"] = mm_data - promise = self.llm.generate_async( + promise = self.generator.generate_async( inputs=prompt, ) asyncio.create_task(self.await_disconnected(raw_request, promise)) @@ -819,8 +910,8 @@ class OpenAIServer: # expanded into an embedding bias tensor in the sampler. sampling_params = request.to_sampling_params( vocab_size=self.tokenizer.tokenizer.vocab_size, - gather_generation_logits=self.llm.args.gather_generation_logits, - backend=self.llm.args.backend) + gather_generation_logits=self.generator.args.gather_generation_logits, + backend=self.generator.args.backend) # TODO: better way to enable metrics if len(os.getenv("TRTLLM_KVCACHE_TIME_OUTPUT_PATH", "")) > 0: sampling_params.return_perf_metrics = True @@ -839,12 +930,12 @@ class OpenAIServer: prompt = prompt_inputs(prompt) if prompt.get("prompt") is not None: - prompt_token_ids, extra_processed_inputs = await asyncio.to_thread(self.llm.input_processor, prompt, sampling_params) + prompt_token_ids, extra_processed_inputs = await asyncio.to_thread(self.generator.input_processor, prompt, sampling_params) tokens_prompt = TokensPrompt(prompt_token_ids=prompt_token_ids, query_token_ids=extra_processed_inputs.get("query_token_ids") if extra_processed_inputs is not None else None) else: tokens_prompt = prompt - promise = self.llm.generate_async( + promise = self.generator.generate_async( inputs=tokens_prompt, sampling_params=sampling_params, _postproc_params=postproc_params, @@ -947,7 +1038,7 @@ class OpenAIServer: ) # Generate - promise = self.llm.generate_async( + promise = self.generator.generate_async( inputs=harmony_tokens, sampling_params=sampling_params, _postproc_params=postproc_params if self.postproc_worker_enabled else None, @@ -1040,7 +1131,7 @@ class OpenAIServer: tokenizer=self.tokenizer if not self.use_harmony else None, model_config=self.model_config if not self.use_harmony else None, processor=self.processor if not self.use_harmony else None, - reasoning_parser=self.llm.args.reasoning_parser if not self.use_harmony else "gpt_oss", + reasoning_parser=self.generator.args.reasoning_parser if not self.use_harmony else "gpt_oss", ) streaming_processor = None @@ -1053,7 +1144,7 @@ class OpenAIServer: conversation_store=self.conversation_store, enable_store=self.enable_store and request.store, use_harmony=self.use_harmony, - reasoning_parser=self.llm.args.reasoning_parser, + reasoning_parser=self.generator.args.reasoning_parser, tool_parser=self.tool_parser, ) @@ -1062,7 +1153,7 @@ class OpenAIServer: request=request, sampling_params=sampling_params, use_harmony=self.use_harmony, - reasoning_parser=self.llm.args.reasoning_parser, + reasoning_parser=self.generator.args.reasoning_parser, tool_parser=self.tool_parser, streaming_processor=streaming_processor, ) @@ -1071,7 +1162,7 @@ class OpenAIServer: if request.stream else responses_api_post_processor, postproc_args=postproc_args, ) - promise = self.llm.generate_async( + promise = self.generator.generate_async( inputs=input_tokens, sampling_params=sampling_params, streaming=request.stream, @@ -1134,22 +1225,500 @@ class OpenAIServer: }) async def release_memory(self, request: MemoryUpdateRequest) -> JSONResponse: - assert isinstance(self.llm, AsyncLLM), "/release_memory endpoint is only supported with AsyncLLM()" - await self.llm.collective_rpc('sleep', args=(request.tags,)) + assert isinstance(self.generator, AsyncLLM), "/release_memory endpoint is only supported with AsyncLLM()" + await self.generator.collective_rpc('sleep', args=(request.tags,)) return JSONResponse(content={"status": "success"}) async def resume_memory(self, request: MemoryUpdateRequest) -> JSONResponse: - assert isinstance(self.llm, AsyncLLM), "/resume_memory endpoint is only supported with AsyncLLM()" - await self.llm.collective_rpc('wakeup', args=(request.tags,)) + assert isinstance(self.generator, AsyncLLM), "/resume_memory endpoint is only supported with AsyncLLM()" + await self.generator.collective_rpc('wakeup', args=(request.tags,)) return JSONResponse(content={"status": "success"}) async def update_weights(self, request: UpdateWeightsRequest) -> JSONResponse: - assert isinstance(self.llm, AsyncLLM), "/update_weights endpoint is only supported with AsyncLLM()" - await self.llm.collective_rpc('update_weights', args=(request.weights,)) + assert isinstance(self.generator, AsyncLLM), "/update_weights endpoint is only supported with AsyncLLM()" + await self.generator.collective_rpc('update_weights', args=(request.weights,)) return JSONResponse(content={"status": "success"}) async def get_server_info(self) -> JSONResponse: - return JSONResponse(content={"disaggregated_params": self.llm.disaggregated_params}) + return JSONResponse(content={"disaggregated_params": self.generator.disaggregated_params}) + + async def openai_image_generation( + self, + request: ImageGenerationRequest, + raw_request: Request + ) -> Response: + """OpenAI-compatible image generation endpoint. + + Follows the OpenAI Images API specification for image generation. + """ + try: + image_id = f"image_{uuid.uuid4().hex}" + params = parse_visual_gen_params(request, image_id) + logger.info(f"Generating image: {image_id} with params: {params} and prompt: {request.prompt}") + + if request.negative_prompt is not None: + inputs = visual_gen_inputs({"prompt": request.prompt, "negative_prompt": request.negative_prompt}) + else: + inputs = visual_gen_inputs(request.prompt) + output = self.generator.generate(inputs=inputs, params=params) + if output.image is None: + return self.create_error_response( + message="Image generation failed", + err_type="InternalServerError", + status_code=HTTPStatus.INTERNAL_SERVER_ERROR, + ) + + # Build response + output_images = output.image + MediaStorage.save_image( + output_images, + self.media_storage_path / f"{image_id}.png", + ) + + if not isinstance(output_images, list): + output_images = [output_images] + + if request.response_format == "b64_json": + data = [ + ImageObject( + b64_json=base64.b64encode(MediaStorage.convert_image_to_bytes(image)).decode('utf-8'), + revised_prompt=request.prompt + ) for image in output_images + ] + + response = ImageGenerationResponse( + created=int(time.time()), + data=data, + size=f"{params.width}x{params.height}", + ) + + elif request.response_format == "url": + # TODO: Support URL mode + return self._create_not_supported_error("URL mode is not supported for image generation") + + return JSONResponse(content=response.model_dump()) + + except Exception as e: + logger.error(traceback.format_exc()) + return self.create_error_response(str(e)) + + + async def openai_image_edit( + self, + request: ImageEditRequest, + raw_request: Request + ) -> Response: + """OpenAI-compatible image editing endpoint. + + Follows the OpenAI Images API specification for image editing. + Creates an edited or extended image given an original image and a prompt. + """ + try: + image_id = f"image_{uuid.uuid4().hex}" + params = parse_visual_gen_params(request, image_id) + logger.info(f"Editing image: {image_id} with params: {params} and prompt: {request.prompt}") + + if request.negative_prompt is not None: + inputs = visual_gen_inputs({"prompt": request.prompt, "negative_prompt": request.negative_prompt}) + else: + inputs = visual_gen_inputs(request.prompt) + output = self.generator.generate(inputs=inputs, params=params) + if output.image is None: + return self.create_error_response( + message="Image editing failed", + err_type="InternalServerError", + status_code=HTTPStatus.INTERNAL_SERVER_ERROR, + ) + + # Build response + output_images = output.image + MediaStorage.save_image( + output_images, + self.media_storage_path / f"{image_id}.png", + ) + + if not isinstance(output_images, list): + output_images = [output_images] + + response = ImageGenerationResponse( + created=int(time.time()), + data=[ + ImageObject( + b64_json=base64.b64encode(MediaStorage.convert_image_to_bytes(image)).decode('utf-8'), + revised_prompt=request.prompt + ) for image in output_images + ], + size=f"{params.width}x{params.height}", + ) + + return JSONResponse(content=response.model_dump()) + + except Exception as e: + logger.error(traceback.format_exc()) + return self.create_error_response(message=str(e), err_type="InternalServerError", status_code=HTTPStatus.INTERNAL_SERVER_ERROR) + + async def openai_video_generation_sync( + self, + raw_request: Request + ) -> Response: + """Synchronous video generation endpoint. + + Waits for video generation to complete before returning. + Compatible with simple use cases where waiting is acceptable. + + Supports both JSON and multipart/form-data requests: + - JSON: Send VideoGenerationRequest as application/json + - Multipart: Send form fields + optional input_reference file + """ + try: + # Parse request based on content-type + request = await self._parse_video_generation_request(raw_request) + + video_id = f"video_{uuid.uuid4().hex}" + params = parse_visual_gen_params(request, video_id, media_storage_path=str(self.media_storage_path)) + logger.info(f"Generating video: {video_id} with params: {params} and prompt: {request.prompt}") + + if request.negative_prompt is not None: + inputs = visual_gen_inputs({"prompt": request.prompt, "negative_prompt": request.negative_prompt}) + else: + inputs = visual_gen_inputs(request.prompt) + output = self.generator.generate(inputs=inputs, params=params) + if output.video is None: + return self.create_error_response( + message="Video generation failed", + err_type="InternalServerError", + status_code=HTTPStatus.INTERNAL_SERVER_ERROR, + ) + + MediaStorage.save_video( + video=output.video, + output_path=self.media_storage_path / f"{video_id}.mp4", + audio=output.audio, + frame_rate=request.fps or params.frame_rate, + ) + + return FileResponse( + self.media_storage_path / f"{video_id}.mp4", + media_type="video/mp4", + filename=f"{video_id}.mp4", + ) + + except ValueError as e: + logger.error(f"Request parsing error: {e}") + return self.create_error_response(str(e)) + except Exception as e: + logger.error(traceback.format_exc()) + return self.create_error_response(str(e)) + + async def _parse_video_generation_request( + self, + raw_request: Request, + ) -> VideoGenerationRequest: + """Parse video generation request from either JSON or multipart/form-data. + + Supports both: + - application/json: Standard JSON request with VideoGenerationRequest model + - multipart/form-data: Form fields + file upload for input_reference + """ + content_type = raw_request.headers.get("content-type", "") + + if "application/json" in content_type: + # Parse as JSON using Pydantic model + body = await raw_request.json() + return VideoGenerationRequest(**body) + + if "multipart/form-data" in content_type: + # Parse multipart/form-data manually + form = await raw_request.form() + + # Extract all fields and convert to proper types + data = {} + + # Required field + if "prompt" in form: + data["prompt"] = form["prompt"] + else: + raise ValueError("'prompt' is required") + + # Optional string fields + for field in ["model", "size", "negative_prompt"]: + if field in form and form[field]: + data[field] = form[field] + + # Optional numeric fields + if "seconds" in form and form["seconds"]: + data["seconds"] = float(form["seconds"]) + if "fps" in form and form["fps"]: + data["fps"] = int(form["fps"]) + if "n" in form and form["n"]: + data["n"] = int(form["n"]) + if "num_inference_steps" in form and form["num_inference_steps"]: + data["num_inference_steps"] = int(form["num_inference_steps"]) + if "guidance_scale" in form and form["guidance_scale"]: + data["guidance_scale"] = float(form["guidance_scale"]) + if "guidance_rescale" in form and form["guidance_rescale"]: + data["guidance_rescale"] = float(form["guidance_rescale"]) + if "seed" in form and form["seed"]: + data["seed"] = int(form["seed"]) + + # Handle file upload for input_reference + if "input_reference" in form: + input_ref = form["input_reference"] + if hasattr(input_ref, "file"): # It's an UploadFile + data["input_reference"] = input_ref + + return VideoGenerationRequest(**data) + + else: + raise ValueError(f"Unsupported content-type: {content_type}. Use 'application/json' or 'multipart/form-data'") + + async def openai_video_generation_async( + self, + raw_request: Request, + ) -> Response: + """Asynchronous video generation endpoint (OpenAI Videos API compatible). + + Creates a video generation job and returns immediately with job metadata. + The video is generated in the background and stored in media storage. + Client can poll GET /v1/videos/{video_id} to check status and retrieve the video. + + Supports both JSON and multipart/form-data requests: + - JSON: Send VideoGenerationRequest as application/json + - Multipart: Send form fields + optional input_reference file + """ + try: + # Parse request based on content-type + request = await self._parse_video_generation_request(raw_request) + + video_id = f"video_{uuid.uuid4().hex}" + params = parse_visual_gen_params(request, video_id, media_storage_path=str(self.media_storage_path)) + logger.info(f"Generating video: {video_id} with params: {params} and prompt: {request.prompt}") + + # Start background generation task + self.video_gen_tasks[video_id] = asyncio.create_task( + self._generate_video_background( + video_id=video_id, + request=request, + params=params, + ) + ) + + # Return job metadata immediately + video_job = VideoJob( + created_at=int(time.time()), + id=video_id, + model=request.model or self.model, + prompt=request.prompt, + status="queued", + duration=request.seconds, + fps=request.fps, + size=f"{params.width}x{params.height}", + ) + await VIDEO_STORE.upsert(video_id, video_job) + + return JSONResponse(content=video_job.model_dump(), status_code=202) + + except ValueError as e: + logger.error(f"Request parsing error: {e}") + return self.create_error_response(str(e)) + except Exception as e: + logger.error(traceback.format_exc()) + return self.create_error_response(str(e)) + + async def _generate_video_background( + self, + video_id: str, + request: VideoGenerationRequest, + params: VisualGenParams, + ): + """Background task to generate video and save to storage.""" + try: + if request.negative_prompt is not None: + inputs = visual_gen_inputs({"prompt": request.prompt, "negative_prompt": request.negative_prompt}) + else: + inputs = visual_gen_inputs(request.prompt) + future = self.generator.generate_async(inputs=inputs, params=params) + output = await future.result() + + if output.video is None: + return self.create_error_response( + message="Video generation failed", + err_type="InternalServerError", + status_code=HTTPStatus.INTERNAL_SERVER_ERROR, + ) + + MediaStorage.save_video( + video=output.video, + output_path=self.media_storage_path / f"{video_id}.mp4", + audio=output.audio, + frame_rate=request.fps or params.frame_rate, + ) + job = await VIDEO_STORE.get(video_id) + if job: + job.status = "completed" + job.completed_at = int(time.time()) + await VIDEO_STORE.upsert(video_id, job) + + except Exception as e: + logger.error(traceback.format_exc()) + job = await VIDEO_STORE.get(video_id) + if job: + job.status = "failed" + job.completed_at = int(time.time()) + job.error = str(e) + await VIDEO_STORE.upsert(video_id, job) + + async def list_videos( + self, + raw_request: Request + ) -> Response: + """List all generated videos. + + GET /v1/videos + Returns a list of generated video metadata (job details). + """ + try: + # List videos from storage + video_jobs = await VIDEO_STORE.list_values() + + # Convert to API format + response = VideoJobList( + data=video_jobs, + ) + return JSONResponse(content=response.model_dump()) + + except Exception as e: + logger.error(traceback.format_exc()) + return self.create_error_response(str(e)) + + async def get_video_metadata( + self, + video_id: str, + raw_request: Request + ) -> Response: + """Get video metadata by ID. + + GET /v1/videos/{video_id} + Retrieves the metadata (job status and details) for a specific generated video. + """ + try: + logger.info(f"Getting video metadata: {video_id}") + # Get metadata from storage + job = await VIDEO_STORE.get(video_id) + if not job: + return self.create_error_response( + f"Video {video_id} not found", + err_type="NotFoundError", + status_code=HTTPStatus.NOT_FOUND + ) + + # Ensure it's a video + if job.object != "video": + return self.create_error_response( + f"Resource {video_id} is not a video", + err_type="BadRequestError", + status_code=HTTPStatus.BAD_REQUEST + ) + + return JSONResponse(content=job.model_dump()) + + except Exception as e: + logger.error(traceback.format_exc()) + return self.create_error_response(str(e)) + + async def get_video_content( + self, + video_id: str, + raw_request: Request + ) -> Response: + """Download video file by ID. + + GET /v1/videos/{video_id}/content + Downloads the generated video file. + """ + try: + # Get metadata first to check status + job = await VIDEO_STORE.get(video_id) + if not job: + return self.create_error_response( + f"Video {video_id} not found", + err_type="NotFoundError", + status_code=HTTPStatus.NOT_FOUND + ) + + # Ensure it's a video and completed + if job.object != "video": + return self.create_error_response( + f"Resource {video_id} is not a video", + err_type="BadRequestError", + status_code=HTTPStatus.BAD_REQUEST + ) + + if job.status != "completed": + return self.create_error_response( + f"Video {video_id} is not ready (status: {job.status})", + err_type="BadRequestError", + status_code=HTTPStatus.BAD_REQUEST + ) + + video_file_name = f"{video_id}.mp4" + if os.path.exists(self.media_storage_path / video_file_name): + return FileResponse( + self.media_storage_path / video_file_name, + media_type="video/mp4", + filename=video_file_name, + ) + else: + return self.create_error_response( + f"Video {video_id} not found", + err_type="NotFoundError", + status_code=HTTPStatus.NOT_FOUND + ) + + except Exception as e: + logger.error(traceback.format_exc()) + return self.create_error_response(str(e)) + + async def delete_video( + self, + video_id: str, + raw_request: Request + ) -> Response: + """Delete a video by ID. + + DELETE /v1/videos/{video_id} + Deletes a generated video by its ID. + """ + try: + # Check if video exists + job = await VIDEO_STORE.get(video_id) + if not job: + return self.create_error_response( + f"Video {video_id} not found", + err_type="NotFoundError", + status_code=HTTPStatus.NOT_FOUND + ) + + # Ensure it's a video + if job.object != "video": + return self.create_error_response( + f"Resource {video_id} is not a video", + err_type="BadRequestError", + status_code=HTTPStatus.BAD_REQUEST + ) + + # Delete the video + success = await VIDEO_STORE.pop(video_id) + video_file_name = f"{video_id}.mp4" + + if os.path.exists(self.media_storage_path / video_file_name): + os.remove(self.media_storage_path / video_file_name) + + return JSONResponse(content={"deleted": success is not None}) + + except Exception as e: + logger.error(traceback.format_exc()) + return self.create_error_response(str(e)) async def __call__(self, host, port, sockets: list[socket.socket] | None = None): # Store the binding address for server registration diff --git a/tensorrt_llm/serve/visual_gen_utils.py b/tensorrt_llm/serve/visual_gen_utils.py new file mode 100644 index 0000000000..f0cd31f7fb --- /dev/null +++ b/tensorrt_llm/serve/visual_gen_utils.py @@ -0,0 +1,112 @@ +import asyncio +import base64 +import os +import shutil +from typing import Any, Dict, List, Optional + +from tensorrt_llm.llmapi.visual_gen import VisualGenParams +from tensorrt_llm.serve.openai_protocol import ( + ImageEditRequest, + ImageGenerationRequest, + VideoGenerationRequest, +) + + +def parse_visual_gen_params( + request: ImageGenerationRequest | VideoGenerationRequest | ImageEditRequest, + id: str, + media_storage_path: Optional[str] = None, +) -> VisualGenParams: + params = VisualGenParams() + params.prompt = request.prompt + if request.negative_prompt is not None: + params.negative_prompt = request.negative_prompt + if request.size is not None and request.size != "auto": + params.width, params.height = map(int, request.size.split("x")) + if request.guidance_scale is not None: + params.guidance_scale = request.guidance_scale + if request.guidance_rescale is not None: + params.guidance_rescale = request.guidance_rescale + + if isinstance(request, ImageGenerationRequest) or isinstance(request, ImageEditRequest): + if request.num_inference_steps is not None: + params.num_inference_steps = request.num_inference_steps + elif isinstance(request, ImageGenerationRequest) and request.quality == "hd": + params.num_inference_steps = 30 + if request.n is not None: + params.num_images_per_prompt = request.n + if isinstance(request, ImageEditRequest): + if request.image is not None: + if isinstance(request.image, list): + params.image = [base64.b64decode(image) for image in request.image] + else: + params.image = [base64.b64decode(request.image)] + if request.mask is not None: + if isinstance(request.mask, list): + params.mask = [base64.b64decode(mask) for mask in request.mask] + else: + params.mask = base64.b64decode(request.mask) + + elif isinstance(request, VideoGenerationRequest): + if request.num_inference_steps is not None: + params.num_inference_steps = request.num_inference_steps + if request.input_reference is not None: + if media_storage_path is None: + raise ValueError("media_storage_path is required when input_reference is provided") + params.input_reference = os.path.join(media_storage_path, f"{id}_reference.png") + if isinstance(request.input_reference, str): + with open(params.input_reference, "wb") as f: + f.write(base64.b64decode(request.input_reference)) + else: + with open(params.input_reference, "wb") as f: + shutil.copyfileobj(request.input_reference.file, f) + + params.frame_rate = request.fps + params.num_frames = int(request.seconds * request.fps) + + if request.seed is not None: + params.seed = int(request.seed) + + return params + + +class AsyncDictStore: + """A small async-safe in-memory key-value store for dict items. + + This encapsulates the usual pattern of a module-level dict guarded by + an asyncio.Lock and provides simple CRUD methods that are safe to call + concurrently from FastAPI request handlers and background tasks. + """ + + def __init__(self) -> None: + self._items: Dict[str, Dict[str, Any]] = {} + self._lock = asyncio.Lock() + + async def upsert(self, key: str, value: Dict[str, Any]) -> None: + async with self._lock: + self._items[key] = value + + async def update_fields(self, key: str, updates: Dict[str, Any]) -> Optional[Dict[str, Any]]: + async with self._lock: + item = self._items.get(key) + if item is None: + return None + item.update(updates) + return item + + async def get(self, key: str) -> Optional[Dict[str, Any]]: + async with self._lock: + return self._items.get(key) + + async def pop(self, key: str) -> Optional[Dict[str, Any]]: + async with self._lock: + return self._items.pop(key, None) + + async def list_values(self) -> List[Dict[str, Any]]: + async with self._lock: + return list(self._items.values()) + + +# Global stores shared by OpenAI entrypoints +# [request_id, dict] +VIDEO_STORE = AsyncDictStore() diff --git a/tests/integration/defs/examples/test_visual_gen.py b/tests/integration/defs/examples/test_visual_gen.py new file mode 100644 index 0000000000..2b48bab322 --- /dev/null +++ b/tests/integration/defs/examples/test_visual_gen.py @@ -0,0 +1,288 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Integration tests: VBench dimension scores for WAN and LTX-2 (TRT-LLM vs diffusers reference).""" + +import glob +import json +import os + +import pytest +from defs.common import venv_check_call +from defs.conftest import llm_models_root +from defs.trt_test_alternative import check_call + +WAN_T2V_MODEL_SUBPATH = "Wan2.1-T2V-1.3B-Diffusers" +VISUAL_GEN_OUTPUT_VIDEO = "trtllm_output.mp4" +DIFFUSERS_REFERENCE_VIDEO = "diffusers_reference.mp4" +WAN_T2V_PROMPT = "A cute cat playing piano" +WAN_T2V_HEIGHT = 480 +WAN_T2V_WIDTH = 832 +WAN_T2V_NUM_FRAMES = 165 + +# Dimensions to evaluate +VBENCH_DIMENSIONS = [ + "subject_consistency", + "background_consistency", + "motion_smoothness", + "dynamic_degree", + "aesthetic_quality", + "imaging_quality", +] + +# Golden VBench scores from HF reference video (WAN); TRT-LLM is compared against these. +VBENCH_WAN_GOLDEN_SCORES = { + "subject_consistency": 0.9381, + "background_consistency": 0.9535, + "motion_smoothness": 0.9923, + "dynamic_degree": 1.0000, + "aesthetic_quality": 0.5033, + "imaging_quality": 0.3033, +} + +VBENCH_REPO = "https://github.com/Vchitect/VBench.git" +VBENCH_BRANCH = "master" +# Pin to a fixed commit for reproducible runs +VBENCH_COMMIT = "98b19513678e99c80d8377fda25ba53b81a491a6" + + +@pytest.fixture(scope="session") +def vbench_repo_root(llm_venv): + """Clone VBench repo into workspace and install; return repo root path.""" + workspace = llm_venv.get_working_directory() + repo_path = os.path.join(workspace, "VBench_repo") + if os.path.exists(repo_path): + return repo_path + # Clone without --depth=1 so we can checkout a specific commit + check_call( + ["git", "clone", "--single-branch", "--branch", VBENCH_BRANCH, VBENCH_REPO, repo_path], + shell=False, + ) + check_call(["git", "-C", repo_path, "checkout", VBENCH_COMMIT], shell=False) + # # Install VBench dependencies explicitly + # llm_venv.run_cmd([ + # "-m", "pip", "install", + # "tqdm>=4.60.0", + # "openai-clip>=1.0", + # "pyiqa>=0.1.0", # install this might also install transformers=4.37.2, which is incompatible + # "easydict", + # "decord>=0.6.0", + # ]) + return repo_path + + +@pytest.fixture(scope="session") +def wan_trtllm_video_path(llm_venv, llm_root): + """Generate input video via visual_gen_wan_t2v.py and return path to trtllm_output.mp4.""" + scratch_space = llm_models_root() + model_path = os.path.join(scratch_space, WAN_T2V_MODEL_SUBPATH) + if not os.path.isdir(model_path): + pytest.skip( + f"Wan T2V model not found: {model_path} " + f"(set LLM_MODELS_ROOT or place {WAN_T2V_MODEL_SUBPATH} under scratch)" + ) + out_dir = os.path.join(llm_venv.get_working_directory(), "visual_gen_output") + os.makedirs(out_dir, exist_ok=True) + output_path = os.path.join(out_dir, VISUAL_GEN_OUTPUT_VIDEO) + if os.path.isfile(output_path): + return output_path + # Install av and diffusers from main branch + llm_venv.run_cmd(["-m", "pip", "install", "av"]) + llm_venv.run_cmd( + [ + "-m", + "pip", + "install", + "git+https://github.com/huggingface/diffusers.git", + ] + ) + script_path = os.path.join(llm_root, "examples", "visual_gen", "visual_gen_wan_t2v.py") + assert os.path.isfile(script_path), f"Visual gen script not found: {script_path}" + venv_check_call( + llm_venv, + [ + script_path, + "--height", + str(WAN_T2V_HEIGHT), + "--width", + str(WAN_T2V_WIDTH), + "--num_frames", + str(WAN_T2V_NUM_FRAMES), + "--model_path", + model_path, + "--prompt", + WAN_T2V_PROMPT, + "--output_path", + output_path, + ], + ) + assert os.path.isfile(output_path), f"Visual gen did not produce {output_path}" + return output_path + + +@pytest.fixture(scope="session") +def wan_reference_video_path(llm_venv, llm_root): + """Generate reference video via diffusers (hf_wan.py) using the same model checkpoint.""" + scratch_space = llm_models_root() + model_path = os.path.join(scratch_space, WAN_T2V_MODEL_SUBPATH) + if not os.path.isdir(model_path): + pytest.skip( + f"Wan T2V model not found: {model_path} " + f"(set LLM_MODELS_ROOT or place {WAN_T2V_MODEL_SUBPATH} under scratch)" + ) + out_dir = os.path.join(llm_venv.get_working_directory(), "visual_gen_output") + os.makedirs(out_dir, exist_ok=True) + reference_path = os.path.join(out_dir, DIFFUSERS_REFERENCE_VIDEO) + if os.path.isfile(reference_path): + return reference_path + hf_script = os.path.join(llm_root, "examples", "visual_gen", "hf_wan.py") + assert os.path.isfile(hf_script), f"Diffusers script not found: {hf_script}" + venv_check_call( + llm_venv, + [ + hf_script, + "--model_path", + model_path, + "--prompt", + WAN_T2V_PROMPT, + "--output_path", + reference_path, + "--height", + str(WAN_T2V_HEIGHT), + "--width", + str(WAN_T2V_WIDTH), + "--num_frames", + str(WAN_T2V_NUM_FRAMES), + ], + ) + assert os.path.isfile(reference_path), f"Diffusers did not produce {reference_path}" + return reference_path + + +def _visual_gen_out_dir(llm_venv, subdir=""): + """Output directory for generated media; subdir e.g. 'ltx2' for model-specific outputs.""" + base = os.path.join(llm_venv.get_working_directory(), "visual_gen_output") + return os.path.join(base, subdir) if subdir else base + + +def _normalize_score(val): + """Normalize to 0-1 scale (e.g. imaging_quality can be 0-100).""" + if isinstance(val, bool): + return float(val) + if isinstance(val, (int, float)) and val > 1.5: + return val / 100.0 + return float(val) + + +def _get_per_video_scores(results, video_path_substr): + """From VBench results, get per-dimension score for the video whose path contains video_path_substr.""" + scores = {} + for dim in VBENCH_DIMENSIONS: + dim_result = results[dim] + assert isinstance(dim_result, list) and len(dim_result) >= 2, ( + f"Dimension '{dim}' result must be [overall_score, video_results]; got {type(dim_result)}" + ) + video_results = dim_result[1] + for entry in video_results: + if video_path_substr in entry.get("video_path", ""): + raw = entry.get("video_results") + scores[dim] = _normalize_score(raw) + break + else: + raise AssertionError( + f"No video matching '{video_path_substr}' in dimension '{dim}'; " + f"paths: {[e.get('video_path') for e in video_results]}" + ) + return scores + + +def _run_vbench_and_compare_to_golden( + vbench_repo_root, + videos_dir, + trtllm_filename, + golden_scores, + llm_venv, + title, + max_score_diff=0.1, +): + """Run VBench on videos_dir (TRT-LLM output only), compare to golden HF reference scores.""" + output_path = os.path.join( + llm_venv.get_working_directory(), "vbench_eval_output", title.replace(" ", "_").lower() + ) + os.makedirs(output_path, exist_ok=True) + evaluate_script = os.path.join(vbench_repo_root, "evaluate.py") + cmd = [ + evaluate_script, + "--videos_path", + videos_dir, + "--output_path", + output_path, + "--mode", + "custom_input", + ] + cmd.extend(["--dimension"] + VBENCH_DIMENSIONS) + venv_check_call(llm_venv, cmd) + pattern = os.path.join(output_path, "*_eval_results.json") + result_files = glob.glob(pattern) + assert result_files, ( + f"No eval results found matching {pattern}; output dir: {os.listdir(output_path)}" + ) + with open(result_files[0], "r") as f: + results = json.load(f) + for dim in VBENCH_DIMENSIONS: + assert dim in results, ( + f"Expected dimension '{dim}' in results; keys: {list(results.keys())}" + ) + scores_trtllm = _get_per_video_scores(results, trtllm_filename) + scores_ref = golden_scores + max_len = max(len(d) for d in VBENCH_DIMENSIONS) + header = f"{'Dimension':<{max_len}} | {'TRT-LLM':>10} | {'HF Ref':>10} | {'Diff':>8}" + sep = "-" * len(header) + print("\n" + "=" * len(header)) + print(f"VBench dimension scores ({title}): TRT-LLM vs golden HF reference scores") + print("=" * len(header)) + print(header) + print(sep) + max_diff_val = 0.0 + for dim in VBENCH_DIMENSIONS: + t, r = scores_trtllm[dim], scores_ref[dim] + diff = abs(t - r) + max_diff_val = max(max_diff_val, diff) + print(f"{dim:<{max_len}} | {t:>10.4f} | {r:>10.4f} | {diff:>8.4f}") + print(sep) + print( + f"{' (all dimensions)':<{max_len}} | (TRT-LLM) | (golden) | max_diff={max_diff_val:.4f}" + ) + print("=" * len(header) + "\n") + for dim in VBENCH_DIMENSIONS: + diff = abs(scores_trtllm[dim] - scores_ref[dim]) + assert diff < max_score_diff or scores_trtllm[dim] >= scores_ref[dim], ( + f"Dimension '{dim}' score difference {diff:.4f} >= {max_score_diff} " + f"(TRT-LLM={scores_trtllm[dim]:.4f}, golden={scores_ref[dim]:.4f})" + ) + + +def test_vbench_dimension_score_wan(vbench_repo_root, wan_trtllm_video_path, llm_venv): + """Run VBench on WAN TRT-LLM video; compare to golden HF reference scores (diff < 0.05 or TRT-LLM >= golden).""" + videos_dir = os.path.dirname(wan_trtllm_video_path) + assert os.path.isfile(wan_trtllm_video_path), "TRT-LLM video must exist" + _run_vbench_and_compare_to_golden( + vbench_repo_root, + videos_dir, + VISUAL_GEN_OUTPUT_VIDEO, + VBENCH_WAN_GOLDEN_SCORES, + llm_venv, + title="WAN", + max_score_diff=0.05, + ) diff --git a/tests/integration/test_lists/test-db/l0_b200.yml b/tests/integration/test_lists/test-db/l0_b200.yml index fc45b1eb8b..c8e88a6e1f 100644 --- a/tests/integration/test_lists/test-db/l0_b200.yml +++ b/tests/integration/test_lists/test-db/l0_b200.yml @@ -91,6 +91,17 @@ l0_b200: - unittest/tools/test_layer_wise_benchmarks.py::test_performance_alignment[1] - unittest/_torch/modeling/test_modeling_exaone4.py::TestEXAONE4::test_llm_load_1_FP8 - unittest/kv_cache_manager_v2_tests/ + # ------------- Visual Gen tests --------------- + - unittest/_torch/visual_gen/test_fused_qkv.py + - unittest/_torch/visual_gen/test_quant_ops.py + - unittest/_torch/visual_gen/test_attention_integration.py + - unittest/_torch/visual_gen/test_attention_perf.py + - unittest/_torch/visual_gen/test_trtllm_serve_endpoints.py + - unittest/_torch/visual_gen/test_trtllm_serve_e2e.py + - unittest/_torch/visual_gen/test_wan.py -k "not TestWanTwoStageTransformer" + - unittest/_torch/visual_gen/test_wan_i2v.py + - unittest/_torch/visual_gen/test_model_loader.py + # - examples/test_visual_gen.py - condition: ranges: system_gpu_count: @@ -161,6 +172,7 @@ l0_b200: - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4[moe_backend=CUTEDSL-mtp_nextn=0-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestSeedOss_36B::test_auto_dtype - accuracy/test_llm_api_pytorch.py::TestLlama3_1_8B_Instruct_RocketKV::test_auto_dtype + - unittest/_torch/visual_gen/test_wan.py::TestWanTwoStageTransformer # ------------- AutoDeploy Backend Stages --------------- - condition: ranges: diff --git a/tests/integration/test_lists/test-db/l0_dgx_b200.yml b/tests/integration/test_lists/test-db/l0_dgx_b200.yml index 3c8eb1b6bc..1592d1247f 100644 --- a/tests/integration/test_lists/test-db/l0_dgx_b200.yml +++ b/tests/integration/test_lists/test-db/l0_dgx_b200.yml @@ -30,6 +30,12 @@ l0_dgx_b200: - disaggregated/test_disaggregated.py::test_disaggregated_gpt_oss_120b_harmony[gpt_oss/gpt-oss-120b] - accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[latency_adp_lmtp_tp4] - accuracy/test_llm_api_pytorch.py::TestMiniMaxM2::test_4gpus[attention_dp=False-cuda_graph=True-overlap_scheduler=True-tp_size=4-ep_size=4] TIMEOUT (60) + # ------------- VisualGen multi-GPU tests --------------- + - unittest/_torch/visual_gen/multi_gpu + - unittest/_torch/visual_gen/test_wan.py::TestWanParallelism::test_cfg_2gpu_correctness + - unittest/_torch/visual_gen/test_wan.py::TestWanCombinedOptimizations::test_all_optimizations_combined + - unittest/_torch/visual_gen/test_wan_i2v.py::TestWanI2VParallelism::test_cfg_2gpu_correctness + - unittest/_torch/visual_gen/test_wan_i2v.py::TestWanI2VCombinedOptimizations::test_all_optimizations_combined - condition: ranges: system_gpu_count: diff --git a/tests/unittest/_torch/visual_gen/multi_gpu/__init__.py b/tests/unittest/_torch/visual_gen/multi_gpu/__init__.py new file mode 100644 index 0000000000..fac2aaa011 --- /dev/null +++ b/tests/unittest/_torch/visual_gen/multi_gpu/__init__.py @@ -0,0 +1 @@ +"""Multi-GPU tests for visual generation modules.""" diff --git a/tests/unittest/_torch/visual_gen/multi_gpu/test_ulysses_attention.py b/tests/unittest/_torch/visual_gen/multi_gpu/test_ulysses_attention.py new file mode 100644 index 0000000000..0d691cf9ae --- /dev/null +++ b/tests/unittest/_torch/visual_gen/multi_gpu/test_ulysses_attention.py @@ -0,0 +1,505 @@ +"""Multi-GPU tests for Ulysses Attention. + +These tests use torch.multiprocessing.spawn to launch multiple processes internally. +Run with: + pytest tests/visual_gen/multi_gpu/test_ulysses_attention.py -v +""" + +import os + +os.environ["TLLM_DISABLE_MPI"] = "1" + +import math +from typing import Callable + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp +import torch.nn.functional as F + +# Try to import the modules - skip tests if not available +try: + from tensorrt_llm._torch.attention_backend.interface import PredefinedAttentionMask + from tensorrt_llm._torch.distributed import all_to_all_4d + from tensorrt_llm._torch.visual_gen.attention_backend import UlyssesAttention, VanillaAttention + from tensorrt_llm._utils import get_free_port + + MODULES_AVAILABLE = True +except ImportError: + MODULES_AVAILABLE = False + + +@pytest.fixture(autouse=True, scope="module") +def _cleanup_mpi_env(): + """Clean up TLLM_DISABLE_MPI env var after tests complete.""" + yield + os.environ.pop("TLLM_DISABLE_MPI", None) + + +def init_distributed_worker(rank: int, world_size: int, backend: str = "gloo", port: int = 29500): + """Initialize distributed environment for a worker process.""" + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = str(port) + os.environ["RANK"] = str(rank) + os.environ["WORLD_SIZE"] = str(world_size) + + # Use gloo backend for CPU, nccl for GPU + if backend == "nccl" and torch.cuda.is_available(): + torch.cuda.set_device(rank % torch.cuda.device_count()) + dist.init_process_group(backend="nccl", rank=rank, world_size=world_size) + else: + dist.init_process_group(backend="gloo", rank=rank, world_size=world_size) + + +def cleanup_distributed(): + """Clean up distributed environment.""" + if dist.is_initialized(): + dist.destroy_process_group() + + +def _distributed_worker(rank, world_size, backend, test_fn, port): + """Worker function that runs in each process. Module-level for pickling.""" + try: + init_distributed_worker(rank, world_size, backend, port) + test_fn(rank, world_size) + except Exception as e: + print(f"Rank {rank} failed with error: {e}") + raise + finally: + cleanup_distributed() + + +def run_test_in_distributed(world_size: int, test_fn: Callable, use_cuda: bool = True): + """Run a test function in a distributed environment with multiple processes. + + Args: + world_size: Number of processes to spawn + test_fn: Test function to run (must be module-level for pickling). + Should accept (rank, world_size) as arguments. + use_cuda: Whether to use CUDA (requires sufficient GPUs) + """ + if not MODULES_AVAILABLE: + pytest.skip("Required modules not available") + + if use_cuda and torch.cuda.device_count() < world_size: + pytest.skip(f"Test requires {world_size} GPUs, only {torch.cuda.device_count()} available") + + backend = "nccl" if use_cuda else "gloo" + + port = get_free_port() + + # Spawn processes + mp.spawn( + _distributed_worker, args=(world_size, backend, test_fn, port), nprocs=world_size, join=True + ) + + +# ============================================================================= +# Test logic functions (module-level so they can be pickled by mp.spawn) +# ============================================================================= + + +def _logic_a2a_seq_to_head(rank, world_size): + """all_to_all_4d: sequence sharding to head sharding.""" + batch = 2 + seq_per_rank = 4 + heads = 8 + head_dim = 64 + + if heads % world_size != 0: + heads = world_size * 2 + + device = torch.device(f"cuda:{rank}") if torch.cuda.is_available() else torch.device("cpu") + + input_tensor = ( + torch.randn(batch, seq_per_rank, heads, head_dim, device=device, dtype=torch.float32) + + rank * 100 + ) + + output = all_to_all_4d( + input_tensor, + scatter_dim=2, + gather_dim=1, + process_group=None, + ) + + expected_shape = (batch, seq_per_rank * world_size, heads // world_size, head_dim) + assert output.shape == expected_shape, ( + f"Rank {rank}: Expected shape {expected_shape}, got {output.shape}" + ) + assert output.device == device + + +def _logic_a2a_head_to_seq(rank, world_size): + """all_to_all_4d: head sharding to sequence sharding.""" + batch = 2 + seq = 16 + heads_per_rank = 2 + head_dim = 64 + + device = torch.device(f"cuda:{rank}") if torch.cuda.is_available() else torch.device("cpu") + + input_tensor = torch.randn( + batch, seq, heads_per_rank, head_dim, device=device, dtype=torch.float32 + ) + + output = all_to_all_4d( + input_tensor, + scatter_dim=1, + gather_dim=2, + process_group=None, + ) + + expected_shape = (batch, seq // world_size, heads_per_rank * world_size, head_dim) + assert output.shape == expected_shape, ( + f"Rank {rank}: Expected shape {expected_shape}, got {output.shape}" + ) + + +def _logic_a2a_roundtrip(rank, world_size): + """all_to_all_4d: forward and backward are inverses.""" + batch = 2 + seq_per_rank = 4 + heads = world_size * 4 + head_dim = 64 + + device = torch.device(f"cuda:{rank}") if torch.cuda.is_available() else torch.device("cpu") + + original = torch.randn(batch, seq_per_rank, heads, head_dim, device=device, dtype=torch.float32) + + intermediate = all_to_all_4d(original, scatter_dim=2, gather_dim=1, process_group=None) + reconstructed = all_to_all_4d(intermediate, scatter_dim=1, gather_dim=2, process_group=None) + + assert reconstructed.shape == original.shape + torch.testing.assert_close(reconstructed, original, rtol=1e-5, atol=1e-5) + + +def _logic_a2a_single_process(rank, world_size): + """all_to_all_4d: single process returns input unchanged.""" + batch, seq, heads, head_dim = 2, 8, 4, 64 + device = torch.device(f"cuda:{rank}" if torch.cuda.is_available() else "cpu") + + input_tensor = torch.randn(batch, seq, heads, head_dim, device=device) + + output = all_to_all_4d(input_tensor, scatter_dim=2, gather_dim=1, process_group=None) + + torch.testing.assert_close(output, input_tensor) + + +def _logic_ulysses_init(rank, world_size): + """UlyssesAttention initialization.""" + num_heads = world_size * 4 + head_dim = 64 + + inner = VanillaAttention(num_heads=num_heads // world_size, head_dim=head_dim) + attention = UlyssesAttention( + inner_backend=inner, + process_group=None, + ) + + assert attention.num_heads == num_heads + assert attention.head_dim == head_dim + assert attention.world_size == world_size + assert rank >= 0 and rank < world_size + + +def _logic_ulysses_forward(rank, world_size): + """UlyssesAttention forward pass.""" + batch = 2 + seq_per_rank = 8 + num_heads = world_size * 4 + head_dim = 64 + + device = torch.device(f"cuda:{rank}") if torch.cuda.is_available() else torch.device("cpu") + + inner = VanillaAttention(num_heads=num_heads // world_size, head_dim=head_dim) + attention = UlyssesAttention( + inner_backend=inner, + process_group=None, + ).to(device) + + q = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + k = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + v = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + + output = attention(q, k, v, batch_size=batch, seq_len=seq_per_rank * world_size) + + assert output.shape == q.shape, f"Rank {rank}: Expected shape {q.shape}, got {output.shape}" + assert output.device == device + + +def _logic_ulysses_with_mask(rank, world_size): + """UlyssesAttention with attention mask.""" + batch = 2 + seq_per_rank = 8 + seq_full = seq_per_rank * world_size + num_heads = world_size * 4 + head_dim = 64 + + device = torch.device(f"cuda:{rank}") if torch.cuda.is_available() else torch.device("cpu") + + inner = VanillaAttention(num_heads=num_heads // world_size, head_dim=head_dim) + attention = UlyssesAttention( + inner_backend=inner, + process_group=None, + ).to(device) + + q = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + k = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + v = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + + mask = PredefinedAttentionMask.CAUSAL + + output = attention(q, k, v, batch_size=batch, seq_len=seq_full, attention_mask=mask) + + assert output.shape == q.shape + + +def _logic_ulysses_vs_standard_multi_gpu(rank, world_size): + """UlyssesAttention across multiple GPUs matches standard attention on the full sequence.""" + batch = 2 + seq_per_rank = 8 + seq_full = seq_per_rank * world_size + num_heads = world_size * 4 + head_dim = 64 + + device = torch.device(f"cuda:{rank}" if torch.cuda.is_available() else "cpu") + + # Every rank generates identical full tensors using the same seed. + torch.manual_seed(42) + q_full = torch.randn(batch, seq_full, num_heads, head_dim, device=device) + k_full = torch.randn(batch, seq_full, num_heads, head_dim, device=device) + v_full = torch.randn(batch, seq_full, num_heads, head_dim, device=device) + + # Each rank takes its sequence shard. + q_shard = q_full[:, rank * seq_per_rank : (rank + 1) * seq_per_rank].contiguous() + k_shard = k_full[:, rank * seq_per_rank : (rank + 1) * seq_per_rank].contiguous() + v_shard = v_full[:, rank * seq_per_rank : (rank + 1) * seq_per_rank].contiguous() + + # Ulysses attention on shards. + inner = VanillaAttention(num_heads=num_heads // world_size, head_dim=head_dim) + attention = UlyssesAttention( + inner_backend=inner, + process_group=None, + ).to(device) + + ulysses_output = attention(q_shard, k_shard, v_shard, batch_size=batch, seq_len=seq_full) + + # Standard attention on the full tensors. + q_std = q_full.transpose(1, 2) # [B, H, S, D] + k_std = k_full.transpose(1, 2) + v_std = v_full.transpose(1, 2) + + std_output = F.scaled_dot_product_attention( + q_std, k_std, v_std, scale=1.0 / math.sqrt(head_dim), dropout_p=0.0 + ) + std_output = std_output.transpose(1, 2).contiguous() # [B, S, H, D] + + # Compare the shard slice. + expected_shard = std_output[:, rank * seq_per_rank : (rank + 1) * seq_per_rank] + torch.testing.assert_close( + ulysses_output, + expected_shard, + rtol=1e-4, + atol=1e-4, + msg=f"Rank {rank}: Ulysses multi-GPU output differs from standard attention", + ) + + +def _logic_ulysses_invalid_heads(rank, world_size): + """Invalid head count (not divisible by world_size) cannot be sharded.""" + assert rank >= 0 and rank < world_size + + num_heads = world_size * 4 + 1 # Not divisible + head_dim = 64 + + # With the decorator pattern, the caller is responsible for sharding heads. + # num_heads // world_size truncates, so the wrapper's computed full head + # count won't match the original. + sharded_heads = num_heads // world_size + inner = VanillaAttention(num_heads=sharded_heads, head_dim=head_dim) + attention = UlyssesAttention(inner_backend=inner, process_group=None) + assert attention.num_heads != num_heads # Truncation means mismatch + + +def _logic_different_batch_sizes(rank, world_size): + """Various batch sizes.""" + num_heads = world_size * 4 + head_dim = 64 + seq_per_rank = 8 + device = torch.device(f"cuda:{rank}") if torch.cuda.is_available() else torch.device("cpu") + + inner = VanillaAttention(num_heads=num_heads // world_size, head_dim=head_dim) + attention = UlyssesAttention( + inner_backend=inner, + process_group=None, + ).to(device) + + for batch_size in [1, 2, 4, 8]: + q = torch.randn(batch_size, seq_per_rank, num_heads, head_dim, device=device) + k = torch.randn(batch_size, seq_per_rank, num_heads, head_dim, device=device) + v = torch.randn(batch_size, seq_per_rank, num_heads, head_dim, device=device) + + output = attention(q, k, v, batch_size=batch_size, seq_len=seq_per_rank * world_size) + assert output.shape == q.shape + + +def _logic_different_head_dims(rank, world_size): + """Various head dims.""" + batch = 2 + seq_per_rank = 8 + num_heads = world_size * 4 + device = torch.device(f"cuda:{rank}") if torch.cuda.is_available() else torch.device("cpu") + + for head_dim in [32, 64, 128]: + inner = VanillaAttention(num_heads=num_heads // world_size, head_dim=head_dim) + attention = UlyssesAttention( + inner_backend=inner, + process_group=None, + ).to(device) + + q = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + k = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + v = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + + output = attention(q, k, v, batch_size=batch, seq_len=seq_per_rank * world_size) + assert output.shape == q.shape + + +def _logic_world_size_4(rank, world_size): + """4-GPU test.""" + batch = 2 + seq_per_rank = 16 + num_heads = world_size * 8 # 32 heads total + head_dim = 64 + + device = torch.device(f"cuda:{rank}") if torch.cuda.is_available() else torch.device("cpu") + + inner = VanillaAttention(num_heads=num_heads // world_size, head_dim=head_dim) + attention = UlyssesAttention( + inner_backend=inner, + process_group=None, + ).to(device) + + q = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + k = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + v = torch.randn(batch, seq_per_rank, num_heads, head_dim, device=device) + + output = attention(q, k, v, batch_size=batch, seq_len=seq_per_rank * world_size) + assert output.shape == q.shape + + +# ============================================================================= +# Test classes +# ============================================================================= + + +class TestAllToAll4D: + """Tests for all_to_all_4d function.""" + + def test_all_to_all_4d_sequence_to_head(self): + """Test sequence sharding to head sharding transformation.""" + run_test_in_distributed(world_size=2, test_fn=_logic_a2a_seq_to_head, use_cuda=True) + + def test_all_to_all_4d_head_to_sequence(self): + """Test head sharding to sequence sharding transformation.""" + run_test_in_distributed(world_size=2, test_fn=_logic_a2a_head_to_seq, use_cuda=True) + + def test_all_to_all_4d_roundtrip(self): + """Test that forward and backward all-to-all are inverses.""" + run_test_in_distributed(world_size=2, test_fn=_logic_a2a_roundtrip, use_cuda=True) + + def test_all_to_all_4d_single_process(self): + """Test that single process returns input unchanged.""" + run_test_in_distributed(world_size=1, test_fn=_logic_a2a_single_process, use_cuda=True) + + +class TestUlyssesAttention: + """Tests for UlyssesAttention module.""" + + def test_ulysses_attention_initialization(self): + """Test UlyssesAttention initialization.""" + run_test_in_distributed(world_size=2, test_fn=_logic_ulysses_init, use_cuda=True) + + def test_ulysses_attention_forward(self): + """Test UlyssesAttention forward pass.""" + run_test_in_distributed(world_size=2, test_fn=_logic_ulysses_forward, use_cuda=True) + + def test_ulysses_attention_with_mask(self): + """Test UlyssesAttention with attention mask.""" + run_test_in_distributed(world_size=2, test_fn=_logic_ulysses_with_mask, use_cuda=True) + + def test_ulysses_vs_standard_attention_single_gpu(self): + """Compare UlyssesAttention with standard attention on single GPU.""" + if not MODULES_AVAILABLE: + pytest.skip("Required modules not available") + + if not torch.cuda.is_available(): + pytest.skip("Test requires CUDA") + + batch = 2 + seq = 16 + num_heads = 8 + head_dim = 64 + device = torch.device("cuda:0") + + inner = VanillaAttention(num_heads=num_heads, head_dim=head_dim) + ulysses_attn = UlyssesAttention( + inner_backend=inner, + process_group=None, + ).to(device) + + torch.manual_seed(42) + q = torch.randn(batch, seq, num_heads, head_dim, device=device) + k = torch.randn(batch, seq, num_heads, head_dim, device=device) + v = torch.randn(batch, seq, num_heads, head_dim, device=device) + + ulysses_output = ulysses_attn(q, k, v, batch_size=batch, seq_len=seq) + + q_std = q.transpose(1, 2) # [B, H, S, D] + k_std = k.transpose(1, 2) + v_std = v.transpose(1, 2) + + std_output = F.scaled_dot_product_attention( + q_std, k_std, v_std, scale=1.0 / math.sqrt(head_dim), dropout_p=0.0 + ) + std_output = std_output.transpose(1, 2).contiguous() # [B, S, H, D] + + torch.testing.assert_close( + ulysses_output, + std_output, + rtol=1e-4, + atol=1e-4, + msg="Ulysses attention output differs from standard attention", + ) + + def test_ulysses_vs_standard_attention_multi_gpu(self): + """Compare UlyssesAttention across GPUs with standard attention on full sequence.""" + run_test_in_distributed( + world_size=2, test_fn=_logic_ulysses_vs_standard_multi_gpu, use_cuda=True + ) + + def test_ulysses_attention_invalid_heads(self): + """Test that invalid head count raises error.""" + run_test_in_distributed(world_size=2, test_fn=_logic_ulysses_invalid_heads, use_cuda=False) + + +class TestUlyssesAttentionEdgeCases: + """Edge case tests for UlyssesAttention.""" + + def test_different_batch_sizes(self): + """Test with various batch sizes.""" + run_test_in_distributed(world_size=2, test_fn=_logic_different_batch_sizes, use_cuda=True) + + def test_different_head_dims(self): + """Test with various head dims.""" + run_test_in_distributed(world_size=2, test_fn=_logic_different_head_dims, use_cuda=True) + + def test_world_size_4(self): + """Test with 4 GPUs.""" + run_test_in_distributed(world_size=4, test_fn=_logic_world_size_4, use_cuda=True) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/unittest/_torch/visual_gen/test_attention_integration.py b/tests/unittest/_torch/visual_gen/test_attention_integration.py new file mode 100644 index 0000000000..e346421d76 --- /dev/null +++ b/tests/unittest/_torch/visual_gen/test_attention_integration.py @@ -0,0 +1,540 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Test WAN Attention Integration. + +Compares the new integrated attention (using TRT-LLM backend) with the original +naive implementation to ensure numerical equivalence. +""" + +from types import SimpleNamespace + +import pytest +import torch +import torch.nn as nn +import torch.nn.functional as F + +from tensorrt_llm._torch.modules.rms_norm import RMSNorm +from tensorrt_llm._torch.visual_gen.config import AttentionConfig, DiffusionModelConfig + +# Import new integrated versions +from tensorrt_llm._torch.visual_gen.modules.attention import Attention, QKVMode, apply_rotary_emb + +# ============================================================================ +# Original naive implementations for comparison +# ============================================================================ + + +class NaiveWanSelfAttention(nn.Module): + """Original naive self-attention implementation (for comparison).""" + + def __init__( + self, hidden_size: int, num_heads: int, head_dim: int, eps: float = 1e-6, dtype=None + ): + super().__init__() + self.num_heads = num_heads + self.head_dim = head_dim + self.hidden_size = hidden_size + + # fused QKV projection + self.to_qkv = nn.Linear(hidden_size, 3 * hidden_size, dtype=dtype) + self.norm_q = RMSNorm(hidden_size=hidden_size, eps=eps, dtype=dtype, has_weights=True) + self.norm_k = RMSNorm(hidden_size=hidden_size, eps=eps, dtype=dtype, has_weights=True) + self.to_out = nn.ModuleList([nn.Linear(hidden_size, hidden_size, dtype=dtype)]) + + def forward(self, hidden_states, freqs_cos, freqs_sin): + B, S = hidden_states.shape[:2] + + q, k, v = self.to_qkv(hidden_states).chunk(3, dim=-1) + + q = self.norm_q(q).view(B, S, self.num_heads, self.head_dim).transpose(1, 2) + k = self.norm_k(k).view(B, S, self.num_heads, self.head_dim).transpose(1, 2) + v = v.view(B, S, self.num_heads, self.head_dim).transpose(1, 2) + + if freqs_cos is not None and freqs_sin is not None: + q = apply_rotary_emb(q, freqs_cos, freqs_sin) + k = apply_rotary_emb(k, freqs_cos, freqs_sin) + + out = F.scaled_dot_product_attention(q, k, v, is_causal=False) + out = out.transpose(1, 2).flatten(2) + out = self.to_out[0](out) + return out + + +class NaiveWanCrossAttention(nn.Module): + """Original naive cross-attention implementation (for comparison).""" + + def __init__( + self, hidden_size: int, num_heads: int, head_dim: int, eps: float = 1e-6, dtype=None + ): + super().__init__() + self.num_heads = num_heads + self.head_dim = head_dim + self.hidden_size = hidden_size + + self.to_q = nn.Linear(hidden_size, hidden_size, dtype=dtype) + self.to_k = nn.Linear(hidden_size, hidden_size, dtype=dtype) + self.to_v = nn.Linear(hidden_size, hidden_size, dtype=dtype) + self.norm_q = RMSNorm(hidden_size=hidden_size, eps=eps, dtype=dtype, has_weights=True) + self.norm_k = RMSNorm(hidden_size=hidden_size, eps=eps, dtype=dtype, has_weights=True) + self.to_out = nn.ModuleList([nn.Linear(hidden_size, hidden_size, dtype=dtype)]) + + def forward(self, hidden_states, encoder_hidden_states): + B, S = hidden_states.shape[:2] + + q = self.norm_q(self.to_q(hidden_states)) + k = self.norm_k(self.to_k(encoder_hidden_states)) + v = self.to_v(encoder_hidden_states) + + q = q.view(B, S, self.num_heads, self.head_dim).transpose(1, 2) + k = k.view(B, -1, self.num_heads, self.head_dim).transpose(1, 2) + v = v.view(B, -1, self.num_heads, self.head_dim).transpose(1, 2) + + out = F.scaled_dot_product_attention(q, k, v, is_causal=False) + out = out.transpose(1, 2).flatten(2) + out = self.to_out[0](out) + return out + + +# ============================================================================ +# Test utilities +# ============================================================================ + + +def create_model_config( + hidden_size: int, + num_heads: int, + head_dim: int, + eps: float = 1e-6, + attn_backend: str = "VANILLA", +): + """Create a mock DiffusionModelConfig for testing.""" + pretrained_config = SimpleNamespace( + hidden_size=hidden_size, + num_attention_heads=num_heads, + attention_head_dim=head_dim, + eps=eps, + ) + + # Create a minimal config without quantization + config = DiffusionModelConfig( + pretrained_config=pretrained_config, + attention=AttentionConfig(backend=attn_backend), + skip_create_weights_in_init=False, + ) + return config + + +def copy_weights_self_attention(naive: NaiveWanSelfAttention, integrated: Attention): + """Copy weights from naive to integrated self-attention.""" + # QKV projection: naive has to_qkv, integrated has qkv_proj + integrated.qkv_proj.weight.data.copy_(naive.to_qkv.weight.data) + if naive.to_qkv.bias is not None and integrated.qkv_proj.bias is not None: + integrated.qkv_proj.bias.data.copy_(naive.to_qkv.bias.data) + + # QK norms + integrated.norm_q.weight.data.copy_(naive.norm_q.weight.data) + integrated.norm_k.weight.data.copy_(naive.norm_k.weight.data) + + # Output projection + integrated.to_out[0].weight.data.copy_(naive.to_out[0].weight.data) + if naive.to_out[0].bias is not None and integrated.to_out[0].bias is not None: + integrated.to_out[0].bias.data.copy_(naive.to_out[0].bias.data) + + +def copy_weights_cross_attention(naive: NaiveWanCrossAttention, integrated: Attention): + """Copy weights from naive to integrated cross-attention.""" + # Q, K, V projections + integrated.to_q.weight.data.copy_(naive.to_q.weight.data) + integrated.to_k.weight.data.copy_(naive.to_k.weight.data) + integrated.to_v.weight.data.copy_(naive.to_v.weight.data) + + if naive.to_q.bias is not None and integrated.to_q.bias is not None: + integrated.to_q.bias.data.copy_(naive.to_q.bias.data) + if naive.to_k.bias is not None and integrated.to_k.bias is not None: + integrated.to_k.bias.data.copy_(naive.to_k.bias.data) + if naive.to_v.bias is not None and integrated.to_v.bias is not None: + integrated.to_v.bias.data.copy_(naive.to_v.bias.data) + + # QK norms + integrated.norm_q.weight.data.copy_(naive.norm_q.weight.data) + integrated.norm_k.weight.data.copy_(naive.norm_k.weight.data) + + # Output projection + integrated.to_out[0].weight.data.copy_(naive.to_out[0].weight.data) + if naive.to_out[0].bias is not None and integrated.to_out[0].bias is not None: + integrated.to_out[0].bias.data.copy_(naive.to_out[0].bias.data) + + +def generate_rope_embeddings( + seq_len: int, head_dim: int, device: torch.device, is_HSD: bool = False +): + """Generate RoPE embeddings with full head_dim. + + apply_rotary_emb expects freqs with full head_dim, then slices with [..., 0::2] and [..., 1::2]. + + Args: + is_HSD: If True, returns [1, 1, S, D] for broadcasting with [B, H, S, D] (naive) + If False, returns [1, S, 1, D] for broadcasting with [B, S, H, D] (integrated) + """ + position = torch.arange(seq_len, device=device).unsqueeze(1) + # Use full head_dim - apply_rotary_emb will slice with 0::2 and 1::2 + div_term = torch.exp( + torch.arange(0, head_dim, device=device) * (-torch.log(torch.tensor(10000.0)) / head_dim) + ) + + if is_HSD: + freqs_cos = torch.cos(position * div_term).unsqueeze(0).unsqueeze(0) # [1, 1, S, D] + freqs_sin = torch.sin(position * div_term).unsqueeze(0).unsqueeze(0) # [1, 1, S, D] + else: + freqs_cos = torch.cos(position * div_term).unsqueeze(0).unsqueeze(2) # [1, S, 1, D] + freqs_sin = torch.sin(position * div_term).unsqueeze(0).unsqueeze(2) # [1, S, 1, D] + + return freqs_cos, freqs_sin + + +# ============================================================================ +# Test functions +# ============================================================================ +@pytest.mark.parametrize("attn_backend", ["VANILLA", "TRTLLM"]) +def test_self_attention_equivalence(attn_backend: str): + """Test that integrated self-attention produces same output as naive.""" + print("\n" + "=" * 60) + print("Testing Self-Attention Equivalence") + print("=" * 60) + + # Config + batch_size = 2 + seq_len = 16 + hidden_size = 128 + num_heads = 4 + head_dim = hidden_size // num_heads + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + dtype = torch.bfloat16 # Use bf16 since flashinfer doesn't support fp32 + + print(f"Config: B={batch_size}, S={seq_len}, H={hidden_size}, heads={num_heads}") + print(f"Device: {device}, dtype: {dtype}") + + # Create models + naive = NaiveWanSelfAttention(hidden_size, num_heads, head_dim, dtype=dtype).to(device) + + model_config = create_model_config(hidden_size, num_heads, head_dim, attn_backend=attn_backend) + integrated = Attention( + hidden_size, num_heads, qkv_mode=QKVMode.FUSE_QKV, config=model_config + ).to(device) # self attention + + # Copy weights + copy_weights_self_attention(naive, integrated) + + # Set to eval mode + naive.eval() + integrated.eval() + + # Create inputs + torch.manual_seed(42) + hidden_states = torch.randn(batch_size, seq_len, hidden_size, device=device, dtype=dtype) + # Naive uses [1, 1, S, D] (HSD format) - broadcasts with [B, H, S, D] + freqs_cos_HSD, freqs_sin_HSD = generate_rope_embeddings(seq_len, head_dim, device, is_HSD=True) + # Integrated uses [1, S, 1, D] (SHD format) - broadcasts with [B, S, H, D] + freqs_cos_SHD, freqs_sin_SHD = generate_rope_embeddings(seq_len, head_dim, device, is_HSD=False) + + # Forward pass + with torch.no_grad(): + out_naive = naive(hidden_states, freqs_cos_HSD, freqs_sin_HSD) + out_integrated = integrated(hidden_states, freqs=(freqs_cos_SHD, freqs_sin_SHD)) + + # Compare (using looser tolerance for bf16) + max_diff = (out_naive - out_integrated).abs().max().item() + mean_diff = (out_naive - out_integrated).abs().mean().item() + is_close = torch.allclose(out_naive, out_integrated, rtol=1e-2, atol=1e-2) + + print("\nResults:") + print(f" Output shape: naive={out_naive.shape}, integrated={out_integrated.shape}") + print(f" Max absolute difference: {max_diff:.2e}") + print(f" Mean absolute difference: {mean_diff:.2e}") + print(f" Outputs match (rtol=1e-2, atol=1e-2): {is_close}") + + if is_close: + print(" āœ… PASS: Self-attention outputs match!") + else: + print(" āŒ FAIL: Self-attention outputs differ!") + + assert is_close, ( + f"Self-attention outputs differ: max_diff={max_diff:.2e}, mean_diff={mean_diff:.2e}" + ) + return is_close + + +@pytest.mark.parametrize("attn_backend", ["VANILLA"]) +def test_cross_attention_equivalence(attn_backend: str): + """Test that integrated cross-attention produces same output as naive.""" + print("\n" + "=" * 60) + print("Testing Cross-Attention Equivalence") + print("=" * 60) + + # Config + batch_size = 2 + seq_len = 16 + encoder_seq_len = 24 # Different from query seq_len + hidden_size = 128 + num_heads = 4 + head_dim = hidden_size // num_heads + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + dtype = torch.bfloat16 # Use bf16 since flashinfer doesn't support fp32 + + print( + f"Config: B={batch_size}, S_q={seq_len}, S_kv={encoder_seq_len}, H={hidden_size}, heads={num_heads}" + ) + print(f"Device: {device}, dtype: {dtype}") + + # Create models + naive = NaiveWanCrossAttention(hidden_size, num_heads, head_dim, dtype=dtype).to(device) + + model_config = create_model_config(hidden_size, num_heads, head_dim, attn_backend=attn_backend) + integrated = Attention( + hidden_size, num_heads, qkv_mode=QKVMode.SEPARATE_QKV, config=model_config + ).to(device) # cross attention + + # Copy weights + copy_weights_cross_attention(naive, integrated) + + # Set to eval mode + naive.eval() + integrated.eval() + + # Create inputs + torch.manual_seed(42) + hidden_states = torch.randn(batch_size, seq_len, hidden_size, device=device, dtype=dtype) + encoder_hidden_states = torch.randn( + batch_size, encoder_seq_len, hidden_size, device=device, dtype=dtype + ) + + # Forward pass + with torch.no_grad(): + out_naive = naive(hidden_states, encoder_hidden_states) + out_integrated = integrated(hidden_states, encoder_hidden_states) + + # Compare (using looser tolerance for bf16) + max_diff = (out_naive - out_integrated).abs().max().item() + mean_diff = (out_naive - out_integrated).abs().mean().item() + is_close = torch.allclose(out_naive, out_integrated, rtol=1e-2, atol=1e-2) + + print("\nResults:") + print(f" Output shape: naive={out_naive.shape}, integrated={out_integrated.shape}") + print(f" Max absolute difference: {max_diff:.2e}") + print(f" Mean absolute difference: {mean_diff:.2e}") + print(f" Outputs match (rtol=1e-2, atol=1e-2): {is_close}") + + if is_close: + print(" āœ… PASS: Cross-attention outputs match!") + else: + print(" āŒ FAIL: Cross-attention outputs differ!") + + assert is_close, ( + f"Cross-attention outputs differ: max_diff={max_diff:.2e}, mean_diff={mean_diff:.2e}" + ) + return is_close + + +def test_trtllm_cached_prepare(): + """Test that TRTLLM attention cached prepare works correctly. + + This test verifies that when running multiple forward passes with same B/S + but different q/k/v values, the cached prepare phase doesn't cause incorrect + results (i.e., outputs should differ when inputs differ). + """ + print("\n" + "=" * 60) + print("Testing TRTLLM Cached Prepare Phase") + print("=" * 60) + + # Config - same B, S for all iterations + batch_size = 2 + seq_len = 16 + hidden_size = 128 + num_heads = 4 + head_dim = hidden_size // num_heads + num_iterations = 5 + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + dtype = torch.bfloat16 + + print(f"Config: B={batch_size}, S={seq_len}, H={hidden_size}, heads={num_heads}") + print(f"Running {num_iterations} iterations with same B/S but different inputs") + + # Create models - single instance to test caching + naive = NaiveWanSelfAttention(hidden_size, num_heads, head_dim, dtype=dtype).to(device) + model_config = create_model_config(hidden_size, num_heads, head_dim, attn_backend="TRTLLM") + integrated = Attention( + hidden_size, num_heads, qkv_mode=QKVMode.FUSE_QKV, config=model_config + ).to(device) # self attention + + # Copy weights + copy_weights_self_attention(naive, integrated) + + naive.eval() + integrated.eval() + + # Generate freqs (same for all iterations since S is same) + freqs_cos_HSD, freqs_sin_HSD = generate_rope_embeddings(seq_len, head_dim, device, is_HSD=True) + freqs_cos_SHD, freqs_sin_SHD = generate_rope_embeddings(seq_len, head_dim, device, is_HSD=False) + + all_passed = True + outputs_integrated = [] + + with torch.no_grad(): + for i in range(num_iterations): + # Different random inputs for each iteration + torch.manual_seed(42 + i) # Different seed each time + hidden_states = torch.randn( + batch_size, seq_len, hidden_size, device=device, dtype=dtype + ) + + out_naive = naive(hidden_states, freqs_cos_HSD, freqs_sin_HSD) + out_integrated = integrated(hidden_states, freqs=(freqs_cos_SHD, freqs_sin_SHD)) + + # Check this iteration matches naive + max_diff = (out_naive - out_integrated).abs().max().item() + is_close = torch.allclose(out_naive, out_integrated, rtol=1e-2, atol=1e-2) + + status = "āœ…" if is_close else "āŒ" + print(f" Iteration {i + 1}: max_diff={max_diff:.2e} {status}") + + if not is_close: + all_passed = False + + outputs_integrated.append(out_integrated.clone()) + + # Additional check: outputs should be DIFFERENT across iterations + # (since inputs were different) + print("\n Checking outputs differ across iterations (inputs were different):") + outputs_differ = True + for i in range(1, num_iterations): + diff = (outputs_integrated[i] - outputs_integrated[0]).abs().max().item() + if diff < 1e-6: + print( + f" āš ļø Iteration {i + 1} output same as iteration 1 (diff={diff:.2e}) - possible caching bug!" + ) + outputs_differ = False + else: + print(f" Iteration {i + 1} vs 1: diff={diff:.2e} āœ…") + + if all_passed and outputs_differ: + print("\n āœ… PASS: Cached prepare works correctly!") + else: + print("\n āŒ FAIL: Cached prepare may have issues!") + all_passed = False + + assert all_passed, "Cached prepare: outputs did not match naive reference" + assert outputs_differ, ( + "Cached prepare: outputs should differ across iterations with different inputs" + ) + return all_passed + + +def test_trtllm_varying_seq_len(): + """Test TRTLLM attention with varying sequence lengths. + + This tests that the prepare phase correctly handles different seq_lens + and doesn't incorrectly reuse cached metadata. + """ + print("\n" + "=" * 60) + print("Testing TRTLLM with Varying Sequence Lengths") + print("=" * 60) + + batch_size = 2 + hidden_size = 128 + num_heads = 4 + head_dim = hidden_size // num_heads + seq_lens = [8, 16, 32, 16, 8] # Vary seq_len, including repeats + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + dtype = torch.bfloat16 + + print(f"Config: B={batch_size}, H={hidden_size}, heads={num_heads}") + print(f"Testing seq_lens: {seq_lens}") + + # Create models - single instance to test caching across different seq_lens + naive = NaiveWanSelfAttention(hidden_size, num_heads, head_dim, dtype=dtype).to(device) + model_config = create_model_config(hidden_size, num_heads, head_dim, attn_backend="TRTLLM") + integrated = Attention( + hidden_size, num_heads, qkv_mode=QKVMode.FUSE_QKV, config=model_config + ).to(device) # self attention + + copy_weights_self_attention(naive, integrated) + + naive.eval() + integrated.eval() + + all_passed = True + + with torch.no_grad(): + for i, seq_len in enumerate(seq_lens): + torch.manual_seed(42 + i) + hidden_states = torch.randn( + batch_size, seq_len, hidden_size, device=device, dtype=dtype + ) + + freqs_cos_HSD, freqs_sin_HSD = generate_rope_embeddings( + seq_len, head_dim, device, is_HSD=True + ) + freqs_cos_SHD, freqs_sin_SHD = generate_rope_embeddings( + seq_len, head_dim, device, is_HSD=False + ) + + out_naive = naive(hidden_states, freqs_cos_HSD, freqs_sin_HSD) + out_integrated = integrated(hidden_states, freqs=(freqs_cos_SHD, freqs_sin_SHD)) + + max_diff = (out_naive - out_integrated).abs().max().item() + is_close = torch.allclose(out_naive, out_integrated, rtol=1e-2, atol=1e-2) + + status = "āœ…" if is_close else "āŒ" + print(f" seq_len={seq_len:3d}: max_diff={max_diff:.2e} {status}") + + if not is_close: + all_passed = False + + if all_passed: + print("\n āœ… PASS: Varying seq_len handled correctly!") + else: + print("\n āŒ FAIL: Issues with varying seq_len!") + + assert all_passed, "Varying seq_len: outputs did not match naive reference" + return all_passed + + +def run_all_tests(): + """Run all tests and report results.""" + print("\n" + "=" * 60) + print("WAN Attention Integration Tests") + print("=" * 60) + + results = {} + + # Run self-attention tests with different backends + for backend in ["VANILLA", "TRTLLM"]: + results[f"self_attention_{backend}"] = test_self_attention_equivalence(backend) + + # Run cross-attention test (VANILLA only) + results["cross_attention_VANILLA"] = test_cross_attention_equivalence("VANILLA") + + # Run TRTLLM-specific caching tests + results["trtllm_cached_prepare"] = test_trtllm_cached_prepare() + results["trtllm_varying_seq_len"] = test_trtllm_varying_seq_len() + + print("\n" + "=" * 60) + print("Summary") + print("=" * 60) + + all_passed = all(results.values()) + for name, passed in results.items(): + status = "āœ… PASS" if passed else "āŒ FAIL" + print(f" {name}: {status}") + + print() + if all_passed: + print("All tests passed! āœ…") + else: + print("Some tests failed! āŒ") + + return all_passed + + +if __name__ == "__main__": + run_all_tests() diff --git a/tests/unittest/_torch/visual_gen/test_attention_perf.py b/tests/unittest/_torch/visual_gen/test_attention_perf.py new file mode 100644 index 0000000000..d2662105dc --- /dev/null +++ b/tests/unittest/_torch/visual_gen/test_attention_perf.py @@ -0,0 +1,622 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""WAN Attention Performance Benchmark. + +Compares VANILLA vs TRTLLM attention backends for visual generation models. +Uses CUDA events for precise GPU timing and supports NVTX profiling. + +Usage: + # Run all tests + python test_attention_perf.py + + # With Nsight Systems profiling + nsys profile -t cuda,nvtx --nvtx-capture=range -o wan_attn_perf python test_attention_perf.py + + # Run specific tests with pytest + pytest test_attention_perf.py -v -k "test_self_attention_perf" +""" + +import time +from contextlib import contextmanager +from types import SimpleNamespace +from typing import Dict, Optional, Tuple + +import pytest +import torch + +from tensorrt_llm._torch.visual_gen.config import AttentionConfig, DiffusionModelConfig +from tensorrt_llm._torch.visual_gen.modules.attention import Attention, QKVMode + +# NVTX support for profiling +try: + import nvtx + + NVTX_AVAILABLE = True + if hasattr(nvtx, "annotate"): + NVTX_METHOD = "annotate" + elif hasattr(nvtx, "range_start") and hasattr(nvtx, "range_end"): + NVTX_METHOD = "range" + else: + NVTX_METHOD = None + NVTX_AVAILABLE = False +except ImportError: + NVTX_AVAILABLE = False + NVTX_METHOD = None + +# Torch profiler support +try: + from torch.profiler import record_function + + TORCH_PROFILER_AVAILABLE = True +except ImportError: + TORCH_PROFILER_AVAILABLE = False + + +# ============================================================================ +# Timing utilities +# ============================================================================ + + +@contextmanager +def cuda_timer(device: torch.device): + """Context manager for precise GPU timing using CUDA events.""" + if device.type == "cuda": + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + start_event.record() + + def get_elapsed_time(): + end_event.record() + torch.cuda.synchronize() + return start_event.elapsed_time(end_event) + + yield get_elapsed_time + else: + start_time = time.perf_counter() + + def get_elapsed_time(): + return (time.perf_counter() - start_time) * 1000 + + yield get_elapsed_time + + +@contextmanager +def nvtx_range(name: str): + """Context manager for NVTX range profiling.""" + if NVTX_AVAILABLE and NVTX_METHOD: + if NVTX_METHOD == "annotate": + with nvtx.annotate(name): + yield + elif NVTX_METHOD == "range": + range_id = nvtx.range_start(name) + try: + yield + finally: + nvtx.range_end(range_id) + else: + yield + else: + yield + + +@contextmanager +def torch_profiler_range(name: str): + """Context manager for torch profiler range.""" + if TORCH_PROFILER_AVAILABLE: + with record_function(name): + yield + else: + yield + + +# ============================================================================ +# Test utilities +# ============================================================================ + + +def create_model_config( + hidden_size: int, + num_heads: int, + head_dim: int, + eps: float = 1e-6, + attn_backend: str = "VANILLA", +) -> DiffusionModelConfig: + """Create a mock DiffusionModelConfig for testing.""" + pretrained_config = SimpleNamespace( + hidden_size=hidden_size, + num_attention_heads=num_heads, + attention_head_dim=head_dim, + eps=eps, + ) + + config = DiffusionModelConfig( + pretrained_config=pretrained_config, + attention=AttentionConfig(backend=attn_backend), + skip_create_weights_in_init=False, + ) + return config + + +def generate_rope_embeddings( + seq_len: int, head_dim: int, device: torch.device, is_HSD: bool = False +) -> Tuple[torch.Tensor, torch.Tensor]: + """Generate RoPE embeddings. + + Args: + seq_len: Sequence length + head_dim: Head dimension + device: Target device + is_HSD: If True, returns [1, 1, S, D] for HSD format, else [1, S, 1, D] for SHD + + Returns: + Tuple of (freqs_cos, freqs_sin) + """ + position = torch.arange(seq_len, device=device).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, head_dim, device=device) * (-torch.log(torch.tensor(10000.0)) / head_dim) + ) + + if is_HSD: + freqs_cos = torch.cos(position * div_term).unsqueeze(0).unsqueeze(0) + freqs_sin = torch.sin(position * div_term).unsqueeze(0).unsqueeze(0) + else: + freqs_cos = torch.cos(position * div_term).unsqueeze(0).unsqueeze(2) + freqs_sin = torch.sin(position * div_term).unsqueeze(0).unsqueeze(2) + + return freqs_cos, freqs_sin + + +# ============================================================================ +# Performance benchmark class +# ============================================================================ + + +class WanAttentionPerformanceBenchmark: + """Performance benchmark for WAN attention backends.""" + + # WAN model configurations: (batch_size, num_heads, seq_len, head_dim, description) + TEST_SIZES = [ + # Wan2.1-T2V-1.3B configurations + (1, 24, 14040, 64, "Wan-1.3B 480p 2s"), + (1, 24, 3510, 64, "Wan-1.3B 480p 2s ring4"), + (1, 24, 7020, 64, "Wan-1.3B 480p 2s ring2"), + # Wan2.1-T2V-14B configurations + (1, 40, 75600, 128, "Wan-14B 720p 5s"), + (1, 40, 37800, 128, "Wan-14B 720p 5s ring2"), + (1, 40, 18900, 128, "Wan-14B 720p 5s ring4"), + (1, 40, 9450, 128, "Wan-14B 720p 5s ring8"), + # Ulysses parallelism configurations + (1, 20, 75600, 128, "Wan-14B 720p ulysses2"), + (1, 10, 75600, 128, "Wan-14B 720p ulysses4"), + (1, 5, 75600, 128, "Wan-14B 720p ulysses8"), + # Smaller test cases for quick validation + (2, 24, 1024, 64, "Small batch2"), + (1, 24, 4096, 64, "Medium 4k"), + (1, 40, 8192, 128, "Large 8k"), + ] + + # Quick test sizes for CI/pytest + QUICK_TEST_SIZES = [ + (1, 24, 1024, 64, "Quick 1k"), + (1, 24, 2048, 64, "Quick 2k"), + (2, 24, 1024, 64, "Quick batch2"), + ] + + def __init__( + self, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.bfloat16, + warmup_iterations: int = 10, + benchmark_iterations: int = 50, + ): + self.device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.dtype = dtype + self.warmup_iterations = warmup_iterations + self.benchmark_iterations = benchmark_iterations + self.backends = ["VANILLA", "TRTLLM"] + + def create_attention_model( + self, hidden_size: int, num_heads: int, head_dim: int, backend: str + ) -> Attention: + """Create a WAN self-attention model with specified backend.""" + config = create_model_config(hidden_size, num_heads, head_dim, attn_backend=backend) + model = Attention(hidden_size, num_heads, qkv_mode=QKVMode.FUSE_QKV, config=config).to( + self.device + ) + model.eval() + return model + + def create_test_data( + self, batch_size: int, seq_len: int, hidden_size: int, head_dim: int + ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + """Create test input data and RoPE embeddings.""" + hidden_states = torch.randn( + batch_size, seq_len, hidden_size, device=self.device, dtype=self.dtype + ) + freqs = generate_rope_embeddings(seq_len, head_dim, self.device, is_HSD=False) + return hidden_states, freqs + + def estimate_memory_gb( + self, batch_size: int, num_heads: int, seq_len: int, head_dim: int + ) -> float: + """Estimate tensor memory usage in GB.""" + hidden_size = num_heads * head_dim + # Input: [B, S, H] + Q, K, V: [B, S, num_heads, head_dim] each + bytes_per_element = 2 # bf16 + input_bytes = batch_size * seq_len * hidden_size * bytes_per_element + qkv_bytes = 3 * batch_size * seq_len * num_heads * head_dim * bytes_per_element + output_bytes = batch_size * seq_len * hidden_size * bytes_per_element + # Attention matrix can be O(S^2) but flash attention avoids materializing it + return (input_bytes + qkv_bytes + output_bytes) / (1024**3) + + def benchmark_single( + self, + batch_size: int, + num_heads: int, + seq_len: int, + head_dim: int, + backend: str, + verbose: bool = True, + ) -> Optional[Dict]: + """Benchmark a single configuration. + + Returns: + Dict with timing statistics or None if test failed/skipped + """ + hidden_size = num_heads * head_dim + + # Memory check + est_memory = self.estimate_memory_gb(batch_size, num_heads, seq_len, head_dim) + if est_memory > 8.0: + if verbose: + print(f" Skipping - estimated memory {est_memory:.2f}GB > 8GB limit") + return None + + try: + # Create model and data + model = self.create_attention_model(hidden_size, num_heads, head_dim, backend) + hidden_states, freqs = self.create_test_data(batch_size, seq_len, hidden_size, head_dim) + + # Warmup + with nvtx_range(f"warmup_{backend}"): + with torch_profiler_range(f"warmup_{backend}"): + with torch.no_grad(): + for _ in range(self.warmup_iterations): + _ = model(hidden_states, freqs=freqs) + + if self.device.type == "cuda": + torch.cuda.synchronize() + + # Benchmark + times = [] + with nvtx_range(f"benchmark_{backend}"): + with torch_profiler_range(f"benchmark_{backend}"): + with torch.no_grad(): + for i in range(self.benchmark_iterations): + with nvtx_range(f"iter_{backend}_{i}"): + with cuda_timer(self.device) as get_time: + _ = model(hidden_states, freqs=freqs) + times.append(get_time()) + + # Statistics + times_tensor = torch.tensor(times) + stats = { + "avg_ms": times_tensor.mean().item(), + "min_ms": times_tensor.min().item(), + "max_ms": times_tensor.max().item(), + "std_ms": times_tensor.std().item(), + "median_ms": times_tensor.median().item(), + "p95_ms": torch.quantile(times_tensor, 0.95).item(), + "p99_ms": torch.quantile(times_tensor, 0.99).item(), + } + + # Calculate throughput (approximate TOPS) + total_ops = batch_size * num_heads * seq_len * seq_len * head_dim + stats["throughput_tops"] = (total_ops / 1e12) / (stats["avg_ms"] / 1000) + + if verbose: + print( + f" {backend}: avg={stats['avg_ms']:.3f}ms, " + f"median={stats['median_ms']:.3f}ms, " + f"throughput={stats['throughput_tops']:.2f} TOPS" + ) + + return stats + + except Exception as e: + if verbose: + print(f" {backend}: ERROR - {e}") + return None + + def benchmark_comparison( + self, + batch_size: int, + num_heads: int, + seq_len: int, + head_dim: int, + description: str = "", + verbose: bool = True, + ) -> Dict[str, Optional[Dict]]: + """Benchmark and compare all backends for a given configuration.""" + if verbose: + print( + f"\nBenchmarking: ({batch_size}, {num_heads}, {seq_len}, {head_dim}) {description}" + ) + print(f" Device: {self.device}, dtype: {self.dtype}") + print(f" Warmup: {self.warmup_iterations}, Iterations: {self.benchmark_iterations}") + + results = {} + for backend in self.backends: + results[backend] = self.benchmark_single( + batch_size, num_heads, seq_len, head_dim, backend, verbose + ) + + # Print comparison + if verbose and results.get("VANILLA") and results.get("TRTLLM"): + vanilla_avg = results["VANILLA"]["avg_ms"] + trtllm_avg = results["TRTLLM"]["avg_ms"] + speedup = vanilla_avg / trtllm_avg + print(f" TRTLLM vs VANILLA: {speedup:.2f}x {'faster' if speedup > 1 else 'slower'}") + + return results + + def run_full_benchmark(self, use_quick_sizes: bool = False) -> Dict: + """Run benchmark on all configured sizes.""" + test_sizes = self.QUICK_TEST_SIZES if use_quick_sizes else self.TEST_SIZES + + print("\n" + "=" * 70) + print("WAN ATTENTION PERFORMANCE BENCHMARK") + print("=" * 70) + print(f"Device: {self.device}") + print(f"dtype: {self.dtype}") + print(f"Backends: {self.backends}") + print(f"NVTX: {'Enabled' if NVTX_AVAILABLE else 'Disabled'}") + print(f"Torch Profiler: {'Enabled' if TORCH_PROFILER_AVAILABLE else 'Disabled'}") + + all_results = {} + + with nvtx_range("wan_attention_benchmark"): + with torch_profiler_range("wan_attention_benchmark"): + for batch_size, num_heads, seq_len, head_dim, desc in test_sizes: + key = f"{desc}_{batch_size}x{num_heads}x{seq_len}x{head_dim}" + results = self.benchmark_comparison( + batch_size, num_heads, seq_len, head_dim, desc + ) + all_results[key] = { + "config": { + "batch_size": batch_size, + "num_heads": num_heads, + "seq_len": seq_len, + "head_dim": head_dim, + "description": desc, + }, + "results": results, + } + + # Print summary + self._print_summary(all_results) + return all_results + + def _print_summary(self, all_results: Dict) -> None: + """Print benchmark summary table.""" + print("\n" + "=" * 70) + print("BENCHMARK SUMMARY") + print("=" * 70) + print(f"{'Configuration':<40} {'VANILLA (ms)':<15} {'TRTLLM (ms)':<15} {'Speedup':<10}") + print("-" * 70) + + for key, data in all_results.items(): + desc = data["config"]["description"] + results = data["results"] + + vanilla = results.get("VANILLA") + trtllm = results.get("TRTLLM") + + vanilla_str = f"{vanilla['avg_ms']:.2f}" if vanilla else "N/A" + trtllm_str = f"{trtllm['avg_ms']:.2f}" if trtllm else "N/A" + + if vanilla and trtllm: + speedup = vanilla["avg_ms"] / trtllm["avg_ms"] + speedup_str = f"{speedup:.2f}x" + else: + speedup_str = "N/A" + + print(f"{desc:<40} {vanilla_str:<15} {trtllm_str:<15} {speedup_str:<10}") + + def test_memory_usage( + self, + batch_size: int = 1, + num_heads: int = 24, + seq_len: int = 4096, + head_dim: int = 64, + ) -> Dict[str, Dict]: + """Test memory usage of different backends.""" + if self.device.type != "cuda": + print("Memory test requires CUDA device") + return {} + + print("\n" + "=" * 70) + print("MEMORY USAGE TEST") + print("=" * 70) + print(f"Config: ({batch_size}, {num_heads}, {seq_len}, {head_dim})") + + hidden_size = num_heads * head_dim + memory_results = {} + + for backend in self.backends: + print(f"\nTesting {backend}...") + + try: + # Clear cache + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + + # Create model and data + model = self.create_attention_model(hidden_size, num_heads, head_dim, backend) + hidden_states, freqs = self.create_test_data( + batch_size, seq_len, hidden_size, head_dim + ) + + # Warmup + with torch.no_grad(): + _ = model(hidden_states, freqs=freqs) + + torch.cuda.synchronize() + torch.cuda.reset_peak_memory_stats() + + # Forward pass + with nvtx_range(f"memory_test_{backend}"): + with torch.no_grad(): + _ = model(hidden_states, freqs=freqs) + + torch.cuda.synchronize() + + peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3) + current_memory_gb = torch.cuda.memory_allocated() / (1024**3) + + memory_results[backend] = { + "peak_memory_gb": peak_memory_gb, + "current_memory_gb": current_memory_gb, + } + + print(f" Peak memory: {peak_memory_gb:.3f} GB") + print(f" Current memory: {current_memory_gb:.3f} GB") + + except Exception as e: + print(f" ERROR: {e}") + memory_results[backend] = None + + return memory_results + + +# ============================================================================ +# Pytest test functions +# ============================================================================ + + +class TestWanAttentionPerformance: + """Pytest test class for WAN attention performance.""" + + @pytest.fixture(autouse=True) + def setup(self): + """Setup test environment.""" + self.benchmark = WanAttentionPerformanceBenchmark( + warmup_iterations=5, + benchmark_iterations=20, + ) + + @pytest.mark.parametrize("backend", ["VANILLA", "TRTLLM"]) + def test_self_attention_perf(self, backend: str): + """Test that attention backend runs without errors.""" + batch_size, num_heads, seq_len, head_dim = 1, 24, 1024, 64 + + result = self.benchmark.benchmark_single( + batch_size, num_heads, seq_len, head_dim, backend, verbose=True + ) + + if result is not None: + assert result["avg_ms"] > 0, "Average time should be positive" + assert result["min_ms"] <= result["avg_ms"], "Min should be <= avg" + assert result["max_ms"] >= result["avg_ms"], "Max should be >= avg" + print(f" {backend}: avg={result['avg_ms']:.3f}ms OK") + + @pytest.mark.parametrize( + "batch_size,num_heads,seq_len,head_dim", + [ + (1, 24, 1024, 64), + (1, 24, 2048, 64), + (2, 24, 1024, 64), + ], + ) + def test_backend_comparison(self, batch_size: int, num_heads: int, seq_len: int, head_dim: int): + """Test VANILLA vs TRTLLM comparison.""" + results = self.benchmark.benchmark_comparison( + batch_size, num_heads, seq_len, head_dim, verbose=True + ) + + # At least one backend should work + assert any(r is not None for r in results.values()), "All backends failed" + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") + def test_memory_usage(self): + """Test memory usage tracking.""" + memory_results = self.benchmark.test_memory_usage( + batch_size=1, num_heads=24, seq_len=2048, head_dim=64 + ) + + for backend, result in memory_results.items(): + if result is not None: + assert result["peak_memory_gb"] > 0, f"{backend} peak memory should be positive" + + def test_quick_benchmark(self): + """Run quick benchmark for CI validation.""" + results = self.benchmark.run_full_benchmark(use_quick_sizes=True) + assert len(results) > 0, "Should have benchmark results" + + +# ============================================================================ +# Main entry point +# ============================================================================ + + +def main(): + """Run full benchmark suite.""" + print("\n" + "=" * 70) + print("WAN ATTENTION PERFORMANCE BENCHMARK SUITE") + print("=" * 70) + + if not torch.cuda.is_available(): + print("WARNING: CUDA not available, results will not be meaningful") + + # Print profiling instructions + if torch.cuda.is_available(): + print("\nPROFILING INSTRUCTIONS:") + print("-" * 50) + if NVTX_AVAILABLE: + print("NVTX Profiling (Nsight Systems):") + print(" nsys profile -t cuda,nvtx --nvtx-capture=range \\") + print(" -o wan_attn_perf python test_attention_perf.py") + else: + print("NVTX not available. Install with: pip install nvtx") + + print("\nPyTorch Profiler:") + print(" The benchmark includes record_function() calls for profiling") + print("-" * 50) + + # Create benchmark instance + benchmark = WanAttentionPerformanceBenchmark( + warmup_iterations=10, + benchmark_iterations=50, + ) + + # Run full benchmark + print("\n" + "=" * 70) + print("FULL BENCHMARK") + print("=" * 70) + all_results = benchmark.run_full_benchmark(use_quick_sizes=False) + + # Memory test + if torch.cuda.is_available(): + benchmark.test_memory_usage(batch_size=1, num_heads=24, seq_len=4096, head_dim=64) + + print("\n" + "=" * 70) + print("BENCHMARK COMPLETE") + print("=" * 70) + + return all_results + + +if __name__ == "__main__": + main() diff --git a/tests/unittest/_torch/visual_gen/test_fused_qkv.py b/tests/unittest/_torch/visual_gen/test_fused_qkv.py new file mode 100644 index 0000000000..c918b44d06 --- /dev/null +++ b/tests/unittest/_torch/visual_gen/test_fused_qkv.py @@ -0,0 +1,126 @@ +"""Tests for fused QKV support in diffusion models. + +Tests: +1. Model structure with fuse_qkv=True (default) vs fuse_qkv=False +2. Weight loading works for fused QKV layers +""" + +import unittest +from types import SimpleNamespace +from typing import Dict + +import torch + +from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig + + +def _create_test_config(hidden_size: int = 64) -> DiffusionModelConfig: + """Create a test DiffusionModelConfig.""" + num_heads = hidden_size // 8 # e.g., 64 // 8 = 8 heads + head_dim = 8 + return DiffusionModelConfig( + pretrained_config=SimpleNamespace( + hidden_size=hidden_size, + num_attention_heads=num_heads, + attention_head_dim=head_dim, + num_layers=2, + ffn_dim=256, + out_channels=16, + patch_size=[1, 2, 2], + in_channels=16, + text_dim=64, + freq_dim=32, + ), + ) + + +class TestFusedQKVWeightLoading(unittest.TestCase): + """Test weight loading for fused QKV layers.""" + + def setUp(self): + """Set up test fixtures.""" + torch.manual_seed(42) + self.hidden_size = 64 + + def _create_mock_checkpoint_weights(self) -> Dict[str, torch.Tensor]: + """Create mock checkpoint weights with separate to_q, to_k, to_v.""" + dtype = torch.bfloat16 # Match model dtype + weights = {} + for block_idx in range(2): + for attn_name in ["attn1", "attn2"]: + prefix = f"blocks.{block_idx}.{attn_name}" + # Separate QKV weights (as in checkpoint) + weights[f"{prefix}.to_q.weight"] = torch.randn( + self.hidden_size, self.hidden_size, dtype=dtype + ) + weights[f"{prefix}.to_q.bias"] = torch.randn(self.hidden_size, dtype=dtype) + weights[f"{prefix}.to_k.weight"] = torch.randn( + self.hidden_size, self.hidden_size, dtype=dtype + ) + weights[f"{prefix}.to_k.bias"] = torch.randn(self.hidden_size, dtype=dtype) + weights[f"{prefix}.to_v.weight"] = torch.randn( + self.hidden_size, self.hidden_size, dtype=dtype + ) + weights[f"{prefix}.to_v.bias"] = torch.randn(self.hidden_size, dtype=dtype) + # Output projection + weights[f"{prefix}.to_out.0.weight"] = torch.randn( + self.hidden_size, self.hidden_size, dtype=dtype + ) + weights[f"{prefix}.to_out.0.bias"] = torch.randn(self.hidden_size, dtype=dtype) + + # FFN weights + ffn_dim = 256 + prefix = f"blocks.{block_idx}.ffn" + weights[f"{prefix}.net.0.proj.weight"] = torch.randn( + ffn_dim, self.hidden_size, dtype=dtype + ) + weights[f"{prefix}.net.0.proj.bias"] = torch.randn(ffn_dim, dtype=dtype) + weights[f"{prefix}.net.2.weight"] = torch.randn(self.hidden_size, ffn_dim, dtype=dtype) + weights[f"{prefix}.net.2.bias"] = torch.randn(self.hidden_size, dtype=dtype) + + # proj_out + weights["proj_out.weight"] = torch.randn(64, self.hidden_size, dtype=dtype) + weights["proj_out.bias"] = torch.randn(64, dtype=dtype) + + return weights + + def test_load_weights_fused(self): + """Test loading weights with fused QKV (default for self-attention).""" + from tensorrt_llm._torch.visual_gen.models.wan.transformer_wan import WanTransformer3DModel + + config = _create_test_config(self.hidden_size) + + # Create model - self-attention (attn1) uses fused QKV by default + model = WanTransformer3DModel(model_config=config) + weights = self._create_mock_checkpoint_weights() + + # Load weights (model handles fused QKV internally via DynamicLinearWeightLoader) + model.load_weights(weights) + + # Verify fused weights were loaded correctly for self-attention + attn1 = model.blocks[0].attn1 + qkv_weight = attn1.qkv_proj.weight.data + + # Expected: concatenation of to_q, to_k, to_v weights + expected_weight = torch.cat( + [ + weights["blocks.0.attn1.to_q.weight"], + weights["blocks.0.attn1.to_k.weight"], + weights["blocks.0.attn1.to_v.weight"], + ], + dim=0, + ) + + self.assertEqual(qkv_weight.shape, expected_weight.shape) + self.assertTrue(torch.allclose(qkv_weight, expected_weight)) + + # Also verify cross-attention (attn2) uses separate Q/K/V + attn2 = model.blocks[0].attn2 + self.assertTrue(hasattr(attn2, "to_q"), "Cross-attention should have separate to_q") + self.assertTrue( + torch.allclose(attn2.to_q.weight.data, weights["blocks.0.attn2.to_q.weight"]) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/unittest/_torch/visual_gen/test_model_loader.py b/tests/unittest/_torch/visual_gen/test_model_loader.py new file mode 100644 index 0000000000..9cc8cba70e --- /dev/null +++ b/tests/unittest/_torch/visual_gen/test_model_loader.py @@ -0,0 +1,494 @@ +"""Test PipelineLoader with DiffusionArgs API.""" + +import os +from pathlib import Path + +import pytest +import torch + +from tensorrt_llm._torch.visual_gen.config import PipelineComponent + + +def _llm_models_root() -> str: + """Return LLM_MODELS_ROOT path if it is set in env, assert when it's set but not a valid path.""" + root = Path("/home/scratch.trt_llm_data_ci/llm-models/") + if "LLM_MODELS_ROOT" in os.environ: + root = Path(os.environ["LLM_MODELS_ROOT"]) + if not root.exists(): + root = Path("/scratch.trt_llm_data/llm-models/") + assert root.exists(), ( + "You shall set LLM_MODELS_ROOT env or be able to access scratch.trt_llm_data to run this test" + ) + return str(root) + + +# Skip if checkpoint not available +# Set DIFFUSION_MODEL_PATH env var to run integration tests +CHECKPOINT_PATH = os.environ.get( + "DIFFUSION_MODEL_PATH", + os.path.join(_llm_models_root(), "Wan2.1-T2V-1.3B-Diffusers"), +) + +# Skip heavy components (text_encoder ~44GB, vae ~300MB) to speed up tests +# These components are loaded via diffusers and don't need quantization testing +SKIP_HEAVY_COMPONENTS = [ + PipelineComponent.TEXT_ENCODER, + PipelineComponent.VAE, + PipelineComponent.TOKENIZER, + PipelineComponent.SCHEDULER, +] + + +@pytest.fixture +def checkpoint_exists(): + return CHECKPOINT_PATH and os.path.exists(CHECKPOINT_PATH) + + +def test_meta_init_mode_creates_meta_tensors(checkpoint_exists): + """Test that MetaInitMode creates tensors on meta device (no GPU memory).""" + if not checkpoint_exists: + pytest.skip("Checkpoint not available") + + from tensorrt_llm._torch.models.modeling_utils import MetaInitMode + from tensorrt_llm._torch.visual_gen import DiffusionArgs + from tensorrt_llm._torch.visual_gen.config import DiffusionModelConfig + from tensorrt_llm._torch.visual_gen.models import AutoPipeline + + # Load config directly + args = DiffusionArgs(checkpoint_path=CHECKPOINT_PATH) + config = DiffusionModelConfig.from_pretrained( + CHECKPOINT_PATH, + args=args, + ) + + # Create pipeline WITH MetaInitMode + with MetaInitMode(): + pipeline = AutoPipeline.from_config(config, CHECKPOINT_PATH) + + # Verify tensors are on meta device (no GPU memory allocated) + param = next(pipeline.transformer.parameters()) + assert param.device.type == "meta", f"Expected meta device, got {param.device}" + + +def test_load_wan_pipeline_basic(checkpoint_exists): + """Test basic loading without quantization using DiffusionArgs.""" + if not checkpoint_exists: + pytest.skip("Checkpoint not available") + + from tensorrt_llm._torch.visual_gen import DiffusionArgs, PipelineLoader + + # Simple one-liner with DiffusionArgs + # Skip text_encoder/vae to speed up test (focus on transformer) + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + skip_components=SKIP_HEAVY_COMPONENTS, + ) + pipeline = PipelineLoader(args).load() + + # Verify pipeline type + assert pipeline.__class__.__name__ == "WanPipeline" + assert pipeline.transformer is not None + + # Verify text_encoder/vae were skipped + assert pipeline.text_encoder is None, "text_encoder should be skipped" + assert pipeline.vae is None, "vae should be skipped" + + # Verify weights are loaded (not meta tensors) + param = next(pipeline.transformer.parameters()) + assert param.device.type == "cuda" + assert param.dtype in [torch.float32, torch.bfloat16, torch.float16] + + +def test_load_wan_pipeline_with_fp8_dynamic_quant(checkpoint_exists): + """Test loading with FP8 dynamic quantization using DiffusionArgs. + + Verifies the dynamic quantization flow: + 1. Config has dynamic_weight_quant=True when linear.type="trtllm-fp8-per-tensor" + 2. Model Linear layers have FP8 weight buffers + 3. BF16 checkpoint weights are quantized on-the-fly + 4. Quantized weights are in FP8 format + """ + if not checkpoint_exists: + pytest.skip("Checkpoint not available") + + from tensorrt_llm._torch.modules.linear import Linear + from tensorrt_llm._torch.visual_gen import DiffusionArgs, PipelineLoader + + # Use DiffusionArgs with FP8 quantization + # Skip text_encoder/vae to speed up test (focus on transformer quantization) + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + quant_config={"quant_algo": "FP8", "dynamic": True}, + skip_components=SKIP_HEAVY_COMPONENTS, + ) + pipeline = PipelineLoader(args).load() + + # Verify model config has dynamic_weight_quant enabled + assert pipeline.model_config.dynamic_weight_quant is True, ( + "dynamic_weight_quant should be True when linear.type specifies FP8" + ) + + # Verify FP8 weights in transformer Linear layers + found_fp8_linear = False + for name, module in pipeline.transformer.named_modules(): + if isinstance(module, Linear): + if hasattr(module, "weight") and module.weight is not None: + assert module.weight.dtype == torch.float8_e4m3fn, ( + f"Linear {name} weight dtype is {module.weight.dtype}, expected float8_e4m3fn" + ) + assert hasattr(module, "weight_scale") and module.weight_scale is not None, ( + f"Linear {name} missing weight_scale buffer" + ) + found_fp8_linear = True + break + + assert found_fp8_linear, "No FP8 Linear modules found in transformer" + + +def test_load_wan_pipeline_with_fp8_blockwise(checkpoint_exists): + """Test loading with FP8 blockwise quantization using DiffusionArgs.""" + if not checkpoint_exists: + pytest.skip("Checkpoint not available") + + from tensorrt_llm._torch.modules.linear import Linear + from tensorrt_llm._torch.visual_gen import DiffusionArgs, PipelineLoader + + # Skip text_encoder/vae to speed up test (focus on transformer quantization) + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + quant_config={"quant_algo": "FP8_BLOCK_SCALES", "dynamic": True}, + skip_components=SKIP_HEAVY_COMPONENTS, + ) + pipeline = PipelineLoader(args).load() + + # Verify FP8 weights + for name, module in pipeline.transformer.named_modules(): + if isinstance(module, Linear): + if hasattr(module, "weight") and module.weight is not None: + assert module.weight.dtype == torch.float8_e4m3fn, ( + f"Linear {name} should have FP8 weight" + ) + break + + +def test_diffusion_args_to_quant_config(): + """Test that DiffusionArgs correctly parses quant_config dict to QuantConfig.""" + from tensorrt_llm._torch.visual_gen import DiffusionArgs + from tensorrt_llm.quantization.mode import QuantAlgo + + # Default - no quantization + args = DiffusionArgs(checkpoint_path="/fake/path") + assert args.quant_config.quant_algo is None + + # FP8 per-tensor (dict is coerced to QuantConfig by model_validator) + args = DiffusionArgs( + checkpoint_path="/fake/path", + quant_config={"quant_algo": "FP8", "dynamic": True}, + ) + qc = args.quant_config + assert qc is not None + assert qc.quant_algo == QuantAlgo.FP8 + assert args.dynamic_weight_quant is True + + # FP8 blockwise + args = DiffusionArgs( + checkpoint_path="/fake/path", + quant_config={"quant_algo": "FP8_BLOCK_SCALES", "dynamic": True}, + ) + qc = args.quant_config + assert qc.quant_algo == QuantAlgo.FP8_BLOCK_SCALES + + # NVFP4 + args = DiffusionArgs( + checkpoint_path="/fake/path", + quant_config={"quant_algo": "NVFP4", "dynamic": True}, + ) + qc = args.quant_config + assert qc.quant_algo == QuantAlgo.NVFP4 + + # With ignore patterns (exclude_modules) + args = DiffusionArgs( + checkpoint_path="/fake/path", + quant_config={ + "quant_algo": "FP8", + "ignore": ["blocks.0.attn1.*", "proj_out"], + "config_groups": { + "group_0": { + "weights": {"dynamic": True, "num_bits": 8, "type": "float"}, + "targets": ["Linear"], + } + }, + }, + ) + qc = args.quant_config + assert qc is not None + assert qc.quant_algo == QuantAlgo.FP8 + assert qc.exclude_modules == ["blocks.0.attn1.*", "proj_out"] + assert args.dynamic_weight_quant is True + + +def test_diffusion_args_to_mapping(): + """Test that DiffusionArgs correctly generates Mapping from ParallelConfig.""" + from tensorrt_llm._torch.visual_gen import DiffusionArgs, ParallelConfig + + # ParallelConfig validator requires WORLD_SIZE >= total parallel (tp*cp = 4) + old_world = os.environ.get("WORLD_SIZE") + try: + os.environ["WORLD_SIZE"] = "4" + args = DiffusionArgs( + checkpoint_path="/fake/path", + parallel=ParallelConfig(dit_tp_size=2, dit_cp_size=2), + ) + mapping = args.to_mapping() + assert mapping.tp_size == 2 + assert mapping.cp_size == 2 + # world_size = tp_size * pp_size * cp_size (DP is handled separately) + assert mapping.world_size == 4 + finally: + if old_world is not None: + os.environ["WORLD_SIZE"] = old_world + elif "WORLD_SIZE" in os.environ: + del os.environ["WORLD_SIZE"] + + +def test_load_without_quant_config_no_fp8(checkpoint_exists): + """Test that loading without quant_config does NOT produce FP8 weights.""" + if not checkpoint_exists: + pytest.skip("Checkpoint not available") + + from tensorrt_llm._torch.modules.linear import Linear + from tensorrt_llm._torch.visual_gen import DiffusionArgs, PipelineLoader + + # No quantization specified + # Skip text_encoder/vae to speed up test (focus on transformer) + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + skip_components=SKIP_HEAVY_COMPONENTS, + ) + pipeline = PipelineLoader(args).load() + + # Verify dynamic_weight_quant is False + assert pipeline.model_config.dynamic_weight_quant is False, ( + "dynamic_weight_quant should be False when no quant_config" + ) + + # Verify NO FP8 weights + for name, module in pipeline.transformer.named_modules(): + if isinstance(module, Linear): + if hasattr(module, "weight") and module.weight is not None: + assert module.weight.dtype != torch.float8_e4m3fn, ( + f"Linear {name} should NOT be FP8 without quant_config" + ) + break + + +def test_diffusion_args_from_dict(): + """Test DiffusionArgs can be created from a dictionary.""" + from tensorrt_llm._torch.visual_gen import DiffusionArgs + from tensorrt_llm.quantization.mode import QuantAlgo + + config_dict = { + "checkpoint_path": "/path/to/model", + "quant_config": {"quant_algo": "FP8", "dynamic": True}, + "parallel": {"dit_tp_size": 2}, + "pipeline": {"fuse_qkv": True}, + } + # ParallelConfig validator requires WORLD_SIZE >= total parallel (dit_tp_size=2) + old_world = os.environ.get("WORLD_SIZE") + try: + os.environ["WORLD_SIZE"] = "2" + args = DiffusionArgs.from_dict(config_dict) + assert args.checkpoint_path == "/path/to/model" + assert args.quant_config.quant_algo == QuantAlgo.FP8 + assert args.dynamic_weight_quant is True + assert args.parallel.dit_tp_size == 2 + assert args.pipeline.fuse_qkv is True + finally: + if old_world is not None: + os.environ["WORLD_SIZE"] = old_world + elif "WORLD_SIZE" in os.environ: + del os.environ["WORLD_SIZE"] + + +# ============================================================================= +# Memory and Performance Tests +# ============================================================================= + + +def _get_module_memory_gb(module): + """Get GPU memory usage of a module in GB.""" + return sum(p.numel() * p.element_size() for p in module.parameters()) / 1024**3 + + +def _get_cuda_memory_gb(): + """Get current CUDA memory allocated in GB.""" + return torch.cuda.memory_allocated() / 1024**3 + + +def _get_cuda_peak_memory_gb(): + """Get peak CUDA memory allocated in GB.""" + return torch.cuda.max_memory_allocated() / 1024**3 + + +def test_fp8_vs_bf16_memory_comparison(checkpoint_exists): + """Test FP8 dynamic quant uses ~2x less memory than BF16, including peak memory. + + This test verifies that dynamic quantization doesn't create unnecessary + intermediate buffers that would negate the memory savings. + + Expected for Wan 1.3B transformer: + - BF16: ~2.6 GB model memory, similar peak during loading + - FP8: ~1.3 GB model memory, peak should be < 2x BF16 peak + """ + if not checkpoint_exists: + pytest.skip("Checkpoint not available") + + from tensorrt_llm._torch.visual_gen import DiffusionArgs, PipelineLoader + + # ========================================================================= + # Test 1: Load BF16 (no quantization) + # ========================================================================= + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + + args_bf16 = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + skip_components=SKIP_HEAVY_COMPONENTS, + ) + pipeline_bf16 = PipelineLoader(args_bf16).load() + + bf16_model_mem = _get_module_memory_gb(pipeline_bf16.transformer) + bf16_total_mem = _get_cuda_memory_gb() + bf16_peak_mem = _get_cuda_peak_memory_gb() + + print(f"\n[BF16] Transformer model memory: {bf16_model_mem:.2f} GB") + print(f"[BF16] Total CUDA memory: {bf16_total_mem:.2f} GB") + print(f"[BF16] Peak CUDA memory: {bf16_peak_mem:.2f} GB") + + # Cleanup BF16 + del pipeline_bf16 + torch.cuda.empty_cache() + + # ========================================================================= + # Test 2: Load FP8 (dynamic quantization) + # ========================================================================= + torch.cuda.reset_peak_memory_stats() + + args_fp8 = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + quant_config={"quant_algo": "FP8", "dynamic": True}, + skip_components=SKIP_HEAVY_COMPONENTS, + ) + pipeline_fp8 = PipelineLoader(args_fp8).load() + + fp8_model_mem = _get_module_memory_gb(pipeline_fp8.transformer) + fp8_total_mem = _get_cuda_memory_gb() + fp8_peak_mem = _get_cuda_peak_memory_gb() + + print(f"\n[FP8] Transformer model memory: {fp8_model_mem:.2f} GB") + print(f"[FP8] Total CUDA memory: {fp8_total_mem:.2f} GB") + print(f"[FP8] Peak CUDA memory: {fp8_peak_mem:.2f} GB") + + # ========================================================================= + # Verify memory savings + # ========================================================================= + model_mem_ratio = bf16_model_mem / fp8_model_mem + peak_mem_ratio = bf16_peak_mem / fp8_peak_mem + + print(f"\n[Comparison] Model memory ratio (BF16/FP8): {model_mem_ratio:.2f}x") + print(f"[Comparison] Peak memory ratio (BF16/FP8): {peak_mem_ratio:.2f}x") + + # Model memory should be ~2x smaller for FP8 + assert model_mem_ratio > 1.8, ( + f"FP8 model memory should be ~2x smaller than BF16, got {model_mem_ratio:.2f}x" + ) + + # Peak memory during loading should also show savings + # Allow some overhead for dynamic quant, but should still be significantly better + assert peak_mem_ratio > 1.5, ( + f"FP8 peak memory should be significantly smaller than BF16, got {peak_mem_ratio:.2f}x. " + f"This may indicate unnecessary intermediate buffers during dynamic quantization." + ) + + # FP8 peak should not be much higher than FP8 final (no large temp buffers) + fp8_peak_overhead = fp8_peak_mem / fp8_total_mem + print(f"[FP8 Per-Tensor] Peak/Final memory ratio: {fp8_peak_overhead:.2f}x") + + # Peak should be close to final (< 1.5x overhead during loading) + assert fp8_peak_overhead < 2.0, ( + f"FP8 peak memory ({fp8_peak_mem:.2f} GB) is too high compared to final " + f"({fp8_total_mem:.2f} GB). Ratio: {fp8_peak_overhead:.2f}x. " + f"This suggests unnecessary buffer allocation during dynamic quantization." + ) + + # Cleanup + del pipeline_fp8 + torch.cuda.empty_cache() + + # ========================================================================= + # Test 3: Load FP8 Blockwise (dynamic quantization with block scales) + # ========================================================================= + torch.cuda.reset_peak_memory_stats() + + args_fp8_block = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + quant_config={"quant_algo": "FP8_BLOCK_SCALES", "dynamic": True}, + skip_components=SKIP_HEAVY_COMPONENTS, + ) + pipeline_fp8_block = PipelineLoader(args_fp8_block).load() + + fp8_block_model_mem = _get_module_memory_gb(pipeline_fp8_block.transformer) + fp8_block_total_mem = _get_cuda_memory_gb() + fp8_block_peak_mem = _get_cuda_peak_memory_gb() + + print(f"\n[FP8 Blockwise] Transformer model memory: {fp8_block_model_mem:.2f} GB") + print(f"[FP8 Blockwise] Total CUDA memory: {fp8_block_total_mem:.2f} GB") + print(f"[FP8 Blockwise] Peak CUDA memory: {fp8_block_peak_mem:.2f} GB") + + # ========================================================================= + # Verify FP8 Blockwise memory savings + # ========================================================================= + block_model_mem_ratio = bf16_model_mem / fp8_block_model_mem + block_peak_mem_ratio = bf16_peak_mem / fp8_block_peak_mem + + print(f"\n[Comparison] Model memory ratio (BF16/FP8-Block): {block_model_mem_ratio:.2f}x") + print(f"[Comparison] Peak memory ratio (BF16/FP8-Block): {block_peak_mem_ratio:.2f}x") + + # FP8 Blockwise has additional scale tensors, so slightly less than 2x savings + # But should still be significantly better than BF16 + assert block_model_mem_ratio > 1.5, ( + f"FP8 Blockwise model memory should be significantly smaller than BF16, got {block_model_mem_ratio:.2f}x" + ) + + # Peak memory check + assert block_peak_mem_ratio > 1.3, ( + f"FP8 Blockwise peak memory should be smaller than BF16, got {block_peak_mem_ratio:.2f}x" + ) + + fp8_block_peak_overhead = fp8_block_peak_mem / fp8_block_total_mem + print(f"[FP8 Blockwise] Peak/Final memory ratio: {fp8_block_peak_overhead:.2f}x") + + assert fp8_block_peak_overhead < 2.0, ( + f"FP8 Blockwise peak memory ({fp8_block_peak_mem:.2f} GB) is too high compared to final " + f"({fp8_block_total_mem:.2f} GB). Ratio: {fp8_block_peak_overhead:.2f}x." + ) + + # Cleanup + del pipeline_fp8_block + torch.cuda.empty_cache() + + # ========================================================================= + # Summary + # ========================================================================= + print("\n" + "=" * 60) + print("SUMMARY") + print("=" * 60) + print(f"BF16: {bf16_model_mem:.2f} GB model, {bf16_peak_mem:.2f} GB peak") + print( + f"FP8 Per-Tensor: {fp8_model_mem:.2f} GB model, {fp8_peak_mem:.2f} GB peak " + f"({model_mem_ratio:.2f}x savings)" + ) + print( + f"FP8 Blockwise: {fp8_block_model_mem:.2f} GB model, {fp8_block_peak_mem:.2f} GB peak " + f"({block_model_mem_ratio:.2f}x savings)" + ) diff --git a/tests/unittest/_torch/visual_gen/test_quant_ops.py b/tests/unittest/_torch/visual_gen/test_quant_ops.py new file mode 100644 index 0000000000..5b141ee74d --- /dev/null +++ b/tests/unittest/_torch/visual_gen/test_quant_ops.py @@ -0,0 +1,120 @@ +"""Unit tests for diffusion quantization operations.""" + +import unittest + +import torch + +from tensorrt_llm._torch.visual_gen.quantization.ops import ( + quantize_fp8_blockwise, + quantize_fp8_per_tensor, +) + + +def _dequant_fp8_per_tensor(qweight, scale): + """Dequantize per-tensor FP8 weight.""" + return qweight.to(torch.float32) * scale + + +class TestQuantOps(unittest.TestCase): + """Test quantization operations.""" + + def setUp(self): + """Set random seed for reproducibility.""" + torch.manual_seed(42) + if not torch.cuda.is_available(): + self.skipTest("CUDA not available") + + def test_fp8_per_tensor(self): + """Test FP8 per-tensor quantization using CUDA kernel.""" + weight = torch.randn(256, 512, dtype=torch.bfloat16, device="cuda") + qweight, scale = quantize_fp8_per_tensor(weight) + + # Check output types + self.assertEqual(qweight.dtype, torch.float8_e4m3fn) + self.assertEqual(qweight.shape, weight.shape) + self.assertEqual(scale.dtype, torch.float32) + self.assertEqual(scale.shape, (1, 1)) + + # Verify dequantization (approximate) + dequant = _dequant_fp8_per_tensor(qweight, scale) + error = (dequant - weight.to(torch.float32)).abs().mean() + self.assertLess(error, 0.15) # Reasonable quantization error + + def test_fp8_per_tensor_different_shapes(self): + """Test FP8 per-tensor quantization with various shapes.""" + shapes = [(128, 256), (256, 512), (512, 1024), (1024, 2048)] + for shape in shapes: + with self.subTest(shape=shape): + weight = torch.randn(shape, dtype=torch.bfloat16, device="cuda") + qweight, scale = quantize_fp8_per_tensor(weight) + + self.assertEqual(qweight.dtype, torch.float8_e4m3fn) + self.assertEqual(qweight.shape, weight.shape) + self.assertEqual(scale.dtype, torch.float32) + + def test_fp8_blockwise(self): + """Test FP8 128x128 blockwise quantization.""" + weight = torch.randn(512, 512, dtype=torch.bfloat16, device="cuda") + block_size = 128 + qweight, scales = quantize_fp8_blockwise(weight, block_size=block_size) + + # Check output types + self.assertEqual(qweight.dtype, torch.float8_e4m3fn) + self.assertEqual(qweight.shape, weight.shape) + self.assertEqual(scales.dtype, torch.float32) + + # Check scales shape: (num_blocks_out, num_blocks_in) for 128x128 blocks + num_blocks_out = (512 + block_size - 1) // block_size # 4 + num_blocks_in = (512 + block_size - 1) // block_size # 4 + self.assertEqual(scales.shape, (num_blocks_out, num_blocks_in)) + + def test_fp8_blockwise_non_divisible(self): + """Test FP8 blockwise quantization with non-divisible dimensions.""" + weight = torch.randn(300, 500, dtype=torch.bfloat16, device="cuda") + block_size = 128 + qweight, scales = quantize_fp8_blockwise(weight, block_size=block_size) + + # Check output types + self.assertEqual(qweight.dtype, torch.float8_e4m3fn) + self.assertEqual(qweight.shape, weight.shape) + + # Check scales shape (should handle non-divisible dimensions) + num_blocks_out = (300 + block_size - 1) // block_size # 3 + num_blocks_in = (500 + block_size - 1) // block_size # 4 + self.assertEqual(scales.shape, (num_blocks_out, num_blocks_in)) + + def test_fp8_blockwise_different_block_sizes(self): + """Test FP8 blockwise quantization with different block sizes.""" + weight = torch.randn(256, 256, dtype=torch.bfloat16, device="cuda") + + for block_size in [64, 128, 256]: + with self.subTest(block_size=block_size): + qweight, scales = quantize_fp8_blockwise(weight, block_size=block_size) + + self.assertEqual(qweight.dtype, torch.float8_e4m3fn) + self.assertEqual(qweight.shape, weight.shape) + + num_blocks = (256 + block_size - 1) // block_size + self.assertEqual(scales.shape, (num_blocks, num_blocks)) + + def test_fp8_per_tensor_zero_weight(self): + """Test FP8 per-tensor quantization with zero weight.""" + weight = torch.zeros(128, 256, dtype=torch.bfloat16, device="cuda") + qweight, scale = quantize_fp8_per_tensor(weight) + + # Should handle zero weights gracefully + self.assertEqual(qweight.dtype, torch.float8_e4m3fn) + self.assertTrue(torch.all(qweight.to(torch.float32) == 0)) + + def test_fp8_blockwise_zero_weight(self): + """Test FP8 blockwise quantization with zero weight.""" + weight = torch.zeros(256, 256, dtype=torch.bfloat16, device="cuda") + qweight, scales = quantize_fp8_blockwise(weight, block_size=128) + + # Should handle zero weights gracefully + self.assertEqual(qweight.dtype, torch.float8_e4m3fn) + self.assertTrue(torch.all(qweight.to(torch.float32) == 0)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/unittest/_torch/visual_gen/test_trtllm_serve_e2e.py b/tests/unittest/_torch/visual_gen/test_trtllm_serve_e2e.py new file mode 100644 index 0000000000..c785fe8bae --- /dev/null +++ b/tests/unittest/_torch/visual_gen/test_trtllm_serve_e2e.py @@ -0,0 +1,398 @@ +"""End-to-end tests for trtllm-serve visual_gen with real models. + +Tests text-to-video (t2v) and text+image-to-video (ti2v) generation through +the full ``trtllm-serve`` stack backed by real VisualGen models. + +The server is launched as a subprocess (same pattern as +``tests/unittest/llmapi/apps/openai_server.py``), so each test class gets an +isolated ``trtllm-serve`` process. + +Usage:: + + # Run all real-model tests (requires GPU + models in $HOME/llm-models-ci) + pytest tests/visual_gen/test_trtllm_serve_e2e.py -v + + # Run only t2v tests + pytest tests/visual_gen/test_trtllm_serve_e2e.py -v -k TestWanT2V + + # Run only ti2v tests + pytest tests/visual_gen/test_trtllm_serve_e2e.py -v -k TestWanI2V +""" + +import os +import subprocess +import sys +import tempfile +import time +from pathlib import Path +from typing import List, Optional + +import pytest +import requests +import yaml + +from tensorrt_llm._utils import get_free_port + +# --------------------------------------------------------------------------- +# Model paths +# --------------------------------------------------------------------------- + + +def _llm_models_root() -> str: + """Return LLM_MODELS_ROOT path if it is set in env, assert when it's set but not a valid path.""" + root = Path("/home/scratch.trt_llm_data_ci/llm-models/") + if "LLM_MODELS_ROOT" in os.environ: + root = Path(os.environ["LLM_MODELS_ROOT"]) + if not root.exists(): + root = Path("/scratch.trt_llm_data/llm-models/") + assert root.exists(), ( + "You shall set LLM_MODELS_ROOT env or be able to access scratch.trt_llm_data to run this test" + ) + return str(root) + + +_WAN_T2V_PATH = Path(_llm_models_root()) / "Wan2.1-T2V-1.3B-Diffusers" +_WAN_I2V_PATH = Path(_llm_models_root()) / "Wan2.2-I2V-A14B-Diffusers" + +# Reference image used for image-to-video (ti2v) tests +_PROJECT_ROOT = Path(__file__).resolve().parents[4] # repo root +_REF_IMAGE_PATH = _PROJECT_ROOT / "examples" / "visual_gen" / "cat_piano.png" + + +# --------------------------------------------------------------------------- +# Remote server helper (follows RemoteOpenAIServer pattern) +# --------------------------------------------------------------------------- + + +class RemoteVisualGenServer: + """Launch ``trtllm-serve`` for a visual-gen model as a subprocess. + + Mirrors the interface of ``tests.unittest.llmapi.apps.openai_server.RemoteOpenAIServer`` + adapted for diffusion / visual-gen models. + """ + + MAX_SERVER_START_WAIT_S = 1200 # 20 min ā€“ large models need time to load + + def __init__( + self, + model: str, + extra_visual_gen_options: Optional[dict] = None, + cli_args: Optional[List[str]] = None, + host: str = "localhost", + port: Optional[int] = None, + env: Optional[dict] = None, + ) -> None: + self.host = host + self.port = port if port is not None else get_free_port() + self._config_file: Optional[str] = None + self.proc: Optional[subprocess.Popen] = None + + args = ["--host", self.host, "--port", str(self.port)] + if cli_args: + args += cli_args + + # Write the visual-gen YAML config to a temp file + if extra_visual_gen_options: + fd, self._config_file = tempfile.mkstemp(suffix=".yml", prefix="vg_cfg_") + with os.fdopen(fd, "w") as f: + yaml.dump(extra_visual_gen_options, f) + args += ["--extra_visual_gen_options", self._config_file] + + launch_cmd = ["trtllm-serve", model] + args + + if env is None: + env = os.environ.copy() + + self.proc = subprocess.Popen( + launch_cmd, + env=env, + stdout=sys.stdout, + stderr=sys.stderr, + ) + self._wait_for_server(timeout=self.MAX_SERVER_START_WAIT_S) + + # -- lifecycle --------------------------------------------------------- + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.terminate() + + def terminate(self): + if self.proc is None: + return + self.proc.terminate() + try: + self.proc.wait(timeout=30) + except subprocess.TimeoutExpired: + self.proc.kill() + self.proc.wait(timeout=30) + self.proc = None + + if self._config_file: + try: + os.remove(self._config_file) + except OSError: + pass + self._config_file = None + + # -- readiness --------------------------------------------------------- + + def _wait_for_server(self, timeout: float): + url = self.url_for("health") + start = time.time() + while True: + try: + if requests.get(url).status_code == 200: + return + except Exception as err: + result = self.proc.poll() + if result is not None and result != 0: + raise RuntimeError("Visual-gen server exited unexpectedly.") from err + time.sleep(1) + if time.time() - start > timeout: + self.terminate() + raise RuntimeError(f"Visual-gen server failed to start within {timeout}s.") + + # -- URL helpers ------------------------------------------------------- + + @property + def url_root(self) -> str: + return f"http://{self.host}:{self.port}" + + def url_for(self, *parts: str) -> str: + return self.url_root + "/" + "/".join(parts) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _model_available(path: Path) -> bool: + return path.is_dir() + + +def _av_available() -> bool: + """Check if PyAV is installed (required for video encoding in E2E tests).""" + try: + import av # noqa: F401 + + return True + except ImportError: + return False + + +def _make_visual_gen_options(**extra) -> dict: + """Build the YAML dict passed via ``--extra_visual_gen_options``.""" + config = { + "linear": {"type": "default"}, + "parallel": {"dit_cfg_size": 1, "dit_ulysses_size": 1}, + } + config.update(extra) + return config + + +# ========================================================================= +# WAN 2.1 ā€“ Text-to-Video (t2v) +# ========================================================================= + + +@pytest.mark.skipif( + not _model_available(_WAN_T2V_PATH), reason=f"Wan2.1-T2V model not found at {_WAN_T2V_PATH}" +) +@pytest.mark.skipif( + not _av_available(), reason="PyAV (av) not installed ā€” required for video encoding in E2E tests" +) +class TestWanTextToVideo: + """Test Wan2.1-T2V-1.3B-Diffusers text-to-video generation via serve API.""" + + @pytest.fixture(scope="class") + def server(self): + with RemoteVisualGenServer( + model=str(_WAN_T2V_PATH), + extra_visual_gen_options=_make_visual_gen_options(), + ) as srv: + yield srv + + # ------------------------------------------------------------------ + + def test_health(self, server): + resp = requests.get(server.url_for("health")) + assert resp.status_code == 200 + + def test_t2v_sync(self, server): + """Synchronous text-to-video via POST /v1/videos/generations.""" + resp = requests.post( + server.url_for("v1", "videos", "generations"), + json={ + "prompt": "A cute cat playing piano", + "size": "480x320", + "seconds": 1.0, + "fps": 8, + "num_inference_steps": 4, + "seed": 42, + }, + ) + assert resp.status_code == 200, resp.text + assert resp.headers["content-type"] == "video/mp4" + assert len(resp.content) > 1000, "Video file too small" + + def test_t2v_async_lifecycle(self, server): + """Async video generation: create job ā†’ poll ā†’ download ā†’ delete.""" + base = server.url_for("v1", "videos") + + # 1. Create job + create_resp = requests.post( + base, + json={ + "prompt": "A rocket launching into a starry sky", + "size": "480x320", + "seconds": 1.0, + "fps": 8, + "num_inference_steps": 4, + "seed": 42, + }, + ) + assert create_resp.status_code == 202, create_resp.text + job = create_resp.json() + video_id = job["id"] + assert video_id.startswith("video_") + assert job["status"] == "queued" + + # 2. Poll until completed (or timeout) + deadline = time.time() + 600 # 10 min + status = "queued" + while status not in ("completed", "failed") and time.time() < deadline: + time.sleep(2) + meta_resp = requests.get(f"{base}/{video_id}") + assert meta_resp.status_code == 200 + status = meta_resp.json()["status"] + + assert status == "completed", f"Video generation did not complete: {status}" + + # 3. Download video content + content_resp = requests.get(f"{base}/{video_id}/content") + assert content_resp.status_code == 200 + assert "video/mp4" in content_resp.headers.get("content-type", "") + assert len(content_resp.content) > 1000 + + # 4. Verify it appears in list + list_resp = requests.get(base) + assert list_resp.status_code == 200 + ids = [v["id"] for v in list_resp.json()["data"]] + assert video_id in ids + + # 5. Delete + del_resp = requests.delete(f"{base}/{video_id}") + assert del_resp.status_code == 200 + assert del_resp.json()["deleted"] is True + + # 6. Confirm gone + gone_resp = requests.get(f"{base}/{video_id}") + assert gone_resp.status_code == 404 + + +# ========================================================================= +# WAN 2.2 ā€“ Image-to-Video (ti2v) +# ========================================================================= + + +@pytest.mark.skipif( + not _model_available(_WAN_I2V_PATH), reason=f"Wan2.2-I2V model not found at {_WAN_I2V_PATH}" +) +@pytest.mark.skipif( + not _REF_IMAGE_PATH.is_file(), reason=f"Reference image not found at {_REF_IMAGE_PATH}" +) +@pytest.mark.skipif( + not _av_available(), reason="PyAV (av) not installed ā€” required for video encoding in E2E tests" +) +class TestWanImageToVideo: + """Test Wan2.2-I2V-A14B-Diffusers image-to-video generation via serve API.""" + + @pytest.fixture(scope="class") + def server(self): + with RemoteVisualGenServer( + model=str(_WAN_I2V_PATH), + extra_visual_gen_options=_make_visual_gen_options(), + ) as srv: + yield srv + + # ------------------------------------------------------------------ + + def test_health(self, server): + resp = requests.get(server.url_for("health")) + assert resp.status_code == 200 + + def test_ti2v_sync(self, server): + """Synchronous image-to-video via multipart POST /v1/videos/generations.""" + with open(_REF_IMAGE_PATH, "rb") as f: + resp = requests.post( + server.url_for("v1", "videos", "generations"), + data={ + "prompt": "The cat starts playing piano, keys moving", + "size": "480x320", + "seconds": "1.0", + "fps": "8", + "num_inference_steps": "4", + "seed": "42", + }, + files={ + "input_reference": ("cat_piano.png", f, "image/png"), + }, + ) + assert resp.status_code == 200, resp.text + assert resp.headers["content-type"] == "video/mp4" + assert len(resp.content) > 1000, "Video file too small" + + def test_ti2v_async_lifecycle(self, server): + """Async i2v: create job with image ā†’ poll ā†’ download ā†’ delete.""" + base = server.url_for("v1", "videos") + + # 1. Create job via multipart + with open(_REF_IMAGE_PATH, "rb") as f: + create_resp = requests.post( + base, + data={ + "prompt": "Snow falls on the piano and the cat", + "size": "480x320", + "seconds": "1.0", + "fps": "8", + "num_inference_steps": "4", + "seed": "42", + }, + files={ + "input_reference": ("cat_piano.png", f, "image/png"), + }, + ) + assert create_resp.status_code == 202, create_resp.text + job = create_resp.json() + video_id = job["id"] + assert job["status"] == "queued" + + # 2. Poll until completed + deadline = time.time() + 600 + status = "queued" + while status not in ("completed", "failed") and time.time() < deadline: + time.sleep(2) + meta_resp = requests.get(f"{base}/{video_id}") + assert meta_resp.status_code == 200 + status = meta_resp.json()["status"] + + assert status == "completed", f"Video generation did not complete: {status}" + + # 3. Download + content_resp = requests.get(f"{base}/{video_id}/content") + assert content_resp.status_code == 200 + assert "video/mp4" in content_resp.headers.get("content-type", "") + assert len(content_resp.content) > 1000 + + # 4. Delete + del_resp = requests.delete(f"{base}/{video_id}") + assert del_resp.status_code == 200 + assert del_resp.json()["deleted"] is True + + # 5. Confirm gone + gone_resp = requests.get(f"{base}/{video_id}") + assert gone_resp.status_code == 404 diff --git a/tests/unittest/_torch/visual_gen/test_trtllm_serve_endpoints.py b/tests/unittest/_torch/visual_gen/test_trtllm_serve_endpoints.py new file mode 100644 index 0000000000..a66e742447 --- /dev/null +++ b/tests/unittest/_torch/visual_gen/test_trtllm_serve_endpoints.py @@ -0,0 +1,876 @@ +"""trtllm-serve visual_gen endpoints tests. + +Tests all endpoints registered for the VISUAL_GEN server role +in OpenAIServer.register_visual_gen_routes(): + + POST /v1/images/generations + POST /v1/images/edits + POST /v1/videos/generations (sync) + POST /v1/videos (async) + GET /v1/videos (list) + GET /v1/videos/{video_id} (metadata) + GET /v1/videos/{video_id}/content (download) + DELETE /v1/videos/{video_id} (delete) +""" + +import asyncio +import base64 +import os +from io import BytesIO +from typing import Optional +from unittest.mock import patch + +import pytest +import torch +from fastapi.testclient import TestClient +from PIL import Image + +from tensorrt_llm._torch.visual_gen.output import MediaOutput +from tensorrt_llm.serve.media_storage import MediaStorage +from tensorrt_llm.serve.openai_protocol import VideoJob +from tensorrt_llm.serve.visual_gen_utils import VIDEO_STORE + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _make_dummy_image_tensor(height: int = 64, width: int = 64) -> torch.Tensor: + """Create a small dummy uint8 image tensor (H, W, C).""" + return torch.randint(0, 256, (height, width, 3), dtype=torch.uint8) + + +def _make_dummy_video_tensor( + num_frames: int = 4, height: int = 64, width: int = 64 +) -> torch.Tensor: + """Create a small dummy uint8 video tensor (T, H, W, C).""" + return torch.randint(0, 256, (num_frames, height, width, 3), dtype=torch.uint8) + + +def _make_dummy_audio_tensor(length: int = 16000) -> torch.Tensor: + """Create a small dummy float32 audio tensor.""" + return torch.randn(1, length, dtype=torch.float32) + + +def _b64_white_png_1x1() -> str: + """Return a base64-encoded 1x1 white PNG for image edit tests.""" + buf = BytesIO() + Image.new("RGB", (1, 1), (255, 255, 255)).save(buf, format="PNG") + return base64.b64encode(buf.getvalue()).decode("utf-8") + + +def _run_async(coro): + """Run an async coroutine in a new event loop (for test helpers).""" + loop = asyncio.new_event_loop() + try: + return loop.run_until_complete(coro) + finally: + loop.close() + + +# --------------------------------------------------------------------------- +# Mock VisualGen +# --------------------------------------------------------------------------- + + +class MockVisualGen: + """Lightweight stand-in for VisualGen that avoids GPU / model loading.""" + + def __init__( + self, + image_output: Optional[torch.Tensor] = None, + video_output: Optional[torch.Tensor] = None, + audio_output: Optional[torch.Tensor] = None, + should_fail: bool = False, + ): + self._image = image_output + self._video = video_output + self._audio = audio_output + self._should_fail = should_fail + self._healthy = True + self.req_counter = 0 + + # --- VisualGen interface --- + + def generate(self, inputs=None, params=None) -> MediaOutput: + if self._should_fail: + raise RuntimeError("Generation intentionally failed") + return MediaOutput( + image=self._image, + video=self._video, + audio=self._audio, + ) + + def generate_async(self, inputs=None, params=None) -> "MockDiffusionGenerationResult": + return MockDiffusionGenerationResult( + image=self._image, + video=self._video, + audio=self._audio, + should_fail=self._should_fail, + ) + + def _check_health(self) -> bool: + return self._healthy + + async def get_stats_async(self, timeout: int): + return + + def shutdown(self): + pass + + +class MockDiffusionGenerationResult: + """Mock future-like result for generate_async.""" + + def __init__( + self, + image: Optional[torch.Tensor] = None, + video: Optional[torch.Tensor] = None, + audio: Optional[torch.Tensor] = None, + should_fail: bool = False, + ): + self._image = image + self._video = video + self._audio = audio + self._should_fail = should_fail + + async def result(self, timeout=None): + if self._should_fail: + raise RuntimeError("Async generation intentionally failed") + return MediaOutput( + image=self._image, + video=self._video, + audio=self._audio, + ) + + +# --------------------------------------------------------------------------- +# Server factory +# --------------------------------------------------------------------------- + + +def _create_server(generator: MockVisualGen, model_name: str = "test-model") -> TestClient: + """Instantiate an OpenAIServer for VISUAL_GEN with a mocked generator. + + We patch the ``VisualGen`` name inside the ``openai_server`` module so that + ``isinstance(generator, VisualGen)`` returns True for our mock. + """ + from tensorrt_llm.llmapi.disagg_utils import ServerRole + from tensorrt_llm.serve.openai_server import OpenAIServer + + with patch("tensorrt_llm.serve.openai_server.VisualGen", MockVisualGen): + server = OpenAIServer( + generator=generator, + model=model_name, + tool_parser=None, + server_role=ServerRole.VISUAL_GEN, + metadata_server_cfg=None, + ) + return TestClient(server.app) + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture() +def image_client(tmp_path): + """TestClient backed by a MockVisualGen that produces images.""" + gen = MockVisualGen(image_output=_make_dummy_image_tensor()) + os.environ["TRTLLM_MEDIA_STORAGE_PATH"] = str(tmp_path) + client = _create_server(gen) + yield client + os.environ.pop("TRTLLM_MEDIA_STORAGE_PATH", None) + + +@pytest.fixture() +def video_client(tmp_path): + """TestClient backed by a MockVisualGen that produces videos.""" + gen = MockVisualGen(video_output=_make_dummy_video_tensor()) + os.environ["TRTLLM_MEDIA_STORAGE_PATH"] = str(tmp_path) + client = _create_server(gen) + yield client + os.environ.pop("TRTLLM_MEDIA_STORAGE_PATH", None) + + +@pytest.fixture() +def video_audio_client(tmp_path): + """TestClient backed by a MockVisualGen that produces videos with audio.""" + gen = MockVisualGen( + video_output=_make_dummy_video_tensor(), + audio_output=_make_dummy_audio_tensor(), + ) + os.environ["TRTLLM_MEDIA_STORAGE_PATH"] = str(tmp_path) + client = _create_server(gen) + yield client + os.environ.pop("TRTLLM_MEDIA_STORAGE_PATH", None) + + +@pytest.fixture() +def failing_client(tmp_path): + """TestClient backed by a MockVisualGen that always fails.""" + gen = MockVisualGen(should_fail=True) + os.environ["TRTLLM_MEDIA_STORAGE_PATH"] = str(tmp_path) + client = _create_server(gen) + yield client + os.environ.pop("TRTLLM_MEDIA_STORAGE_PATH", None) + + +@pytest.fixture(autouse=True) +def _clear_video_store(): + """Reset the global VIDEO_STORE before each test.""" + VIDEO_STORE._items.clear() + yield + VIDEO_STORE._items.clear() + + +@pytest.fixture(autouse=True) +def _mock_video_encoding(): + """Mock MP4 encoding to avoid PyAV dependency in unit tests. + + Replaces MediaStorage._save_mp4 with a stub that writes a small + dummy file so FileResponse can serve it. + """ + + def _dummy_save_mp4(video, audio, output_path, frame_rate): + os.makedirs(os.path.dirname(str(output_path)) or ".", exist_ok=True) + with open(str(output_path), "wb") as f: + f.write(b"\x00\x00\x00\x1cftypisom" + b"\x00" * 32) + return str(output_path) + + with patch.object(MediaStorage, "_save_mp4", staticmethod(_dummy_save_mp4)): + yield + + +# ========================================================================= +# POST /v1/images/generations +# ========================================================================= + + +class TestImageGeneration: + def test_basic_image_generation_b64(self, image_client): + resp = image_client.post( + "/v1/images/generations", + json={ + "prompt": "A cat sitting on a mat", + "response_format": "b64_json", + "size": "64x64", + }, + ) + assert resp.status_code == 200 + data = resp.json() + assert "data" in data + assert len(data["data"]) >= 1 + img_obj = data["data"][0] + assert img_obj["b64_json"] is not None + # Verify it decodes to valid bytes + decoded = base64.b64decode(img_obj["b64_json"]) + assert len(decoded) > 0 + assert img_obj["revised_prompt"] == "A cat sitting on a mat" + + def test_image_generation_with_optional_params(self, image_client): + resp = image_client.post( + "/v1/images/generations", + json={ + "prompt": "Sunset over ocean", + "response_format": "b64_json", + "size": "128x64", + "num_inference_steps": 20, + "guidance_scale": 7.5, + "seed": 123, + "negative_prompt": "blurry", + }, + ) + assert resp.status_code == 200 + data = resp.json() + assert data["size"] == "128x64" + + def test_image_generation_url_format_not_supported(self, image_client): + resp = image_client.post( + "/v1/images/generations", + json={ + "prompt": "A dog", + "response_format": "url", + }, + ) + assert resp.status_code == 501 + + def test_image_generation_auto_size(self, image_client): + resp = image_client.post( + "/v1/images/generations", + json={ + "prompt": "A tree", + "response_format": "b64_json", + "size": "auto", + }, + ) + assert resp.status_code == 200 + + def test_image_generation_failure(self, failing_client): + resp = failing_client.post( + "/v1/images/generations", + json={ + "prompt": "A bird", + "response_format": "b64_json", + }, + ) + assert resp.status_code == 400 + + def test_image_generation_invalid_size(self, image_client): + """Invalid size triggers RequestValidationError ā†’ custom handler ā†’ 400.""" + resp = image_client.post( + "/v1/images/generations", + json={ + "prompt": "A mountain", + "response_format": "b64_json", + "size": "invalid", + }, + ) + assert resp.status_code == 400 + + def test_image_generation_null_output(self, tmp_path): + """Generator returns MediaOutput with image=None.""" + gen = MockVisualGen(image_output=None) + os.environ["TRTLLM_MEDIA_STORAGE_PATH"] = str(tmp_path) + client = _create_server(gen) + resp = client.post( + "/v1/images/generations", + json={ + "prompt": "null image", + "response_format": "b64_json", + }, + ) + assert resp.status_code == 500 + os.environ.pop("TRTLLM_MEDIA_STORAGE_PATH", None) + + def test_image_generation_multiple_n(self, image_client): + """Request n=2 images in one call.""" + resp = image_client.post( + "/v1/images/generations", + json={ + "prompt": "Flowers", + "response_format": "b64_json", + "size": "64x64", + "n": 2, + }, + ) + assert resp.status_code == 200 + + def test_image_generation_hd_quality(self, image_client): + resp = image_client.post( + "/v1/images/generations", + json={ + "prompt": "HD landscape", + "response_format": "b64_json", + "quality": "hd", + }, + ) + assert resp.status_code == 200 + + def test_missing_prompt_image_generation(self, image_client): + """Missing required field ā†’ RequestValidationError ā†’ custom handler ā†’ 400.""" + resp = image_client.post( + "/v1/images/generations", + json={}, + ) + assert resp.status_code == 400 + + +# ========================================================================= +# POST /v1/images/edits +# ========================================================================= + + +class TestImageEdit: + def test_basic_image_edit(self, image_client): + b64_img = _b64_white_png_1x1() + resp = image_client.post( + "/v1/images/edits", + json={ + "image": b64_img, + "prompt": "Make it blue", + "num_inference_steps": 10, + }, + ) + assert resp.status_code == 200 + data = resp.json() + assert "data" in data + assert len(data["data"]) >= 1 + assert data["data"][0]["b64_json"] is not None + + def test_image_edit_with_list_images(self, image_client): + b64_img = _b64_white_png_1x1() + resp = image_client.post( + "/v1/images/edits", + json={ + "image": [b64_img, b64_img], + "prompt": "Merge them", + "num_inference_steps": 10, + }, + ) + assert resp.status_code == 200 + + def test_image_edit_with_mask(self, image_client): + b64_img = _b64_white_png_1x1() + b64_mask = _b64_white_png_1x1() + resp = image_client.post( + "/v1/images/edits", + json={ + "image": b64_img, + "prompt": "Remove object", + "mask": b64_mask, + "num_inference_steps": 10, + }, + ) + assert resp.status_code == 200 + + def test_image_edit_with_optional_params(self, image_client): + b64_img = _b64_white_png_1x1() + resp = image_client.post( + "/v1/images/edits", + json={ + "image": b64_img, + "prompt": "Enhance colors", + "size": "128x128", + "guidance_scale": 8.0, + "num_inference_steps": 15, + "seed": 42, + "negative_prompt": "dark", + }, + ) + assert resp.status_code == 200 + data = resp.json() + assert data["size"] == "128x128" + + def test_image_edit_failure(self, failing_client): + b64_img = _b64_white_png_1x1() + resp = failing_client.post( + "/v1/images/edits", + json={ + "image": b64_img, + "prompt": "Edit this", + "num_inference_steps": 10, + }, + ) + assert resp.status_code == 500 + + def test_missing_image_for_edit(self, image_client): + """Missing required field ā†’ RequestValidationError ā†’ custom handler ā†’ 400.""" + resp = image_client.post( + "/v1/images/edits", + json={ + "prompt": "Edit without image", + }, + ) + assert resp.status_code == 400 + + +# ========================================================================= +# POST /v1/videos/generations (synchronous) +# ========================================================================= + + +@pytest.mark.threadleak(enabled=False) # FileResponse spawns AnyIO worker threads +class TestVideoGenerationSync: + def test_basic_sync_video_generation(self, video_client): + resp = video_client.post( + "/v1/videos/generations", + json={ + "prompt": "A rocket launching", + "size": "64x64", + "seconds": 1.0, + "fps": 8, + }, + headers={"content-type": "application/json"}, + ) + assert resp.status_code == 200 + assert resp.headers["content-type"] == "video/mp4" + assert len(resp.content) > 0 + + def test_sync_video_generation_with_params(self, video_client): + resp = video_client.post( + "/v1/videos/generations", + json={ + "prompt": "Ocean waves", + "size": "64x64", + "seconds": 2.0, + "fps": 8, + "num_inference_steps": 10, + "guidance_scale": 5.0, + "seed": 42, + "negative_prompt": "blurry", + }, + headers={"content-type": "application/json"}, + ) + assert resp.status_code == 200 + assert len(resp.content) > 0 + + def test_sync_video_generation_multipart(self, video_client): + # Use files={} with a dummy file to ensure multipart/form-data + dummy_file = BytesIO(b"") + resp = video_client.post( + "/v1/videos/generations", + data={ + "prompt": "Mountain sunrise", + "size": "64x64", + "seconds": "1.0", + "fps": "8", + }, + files={"_dummy": ("dummy", dummy_file, "application/octet-stream")}, + ) + # The server will parse fields; _dummy is ignored since it's not "input_reference" + assert resp.status_code == 200 + assert len(resp.content) > 0 + + def test_sync_video_generation_multipart_with_reference(self, video_client, tmp_path): + # Create a dummy reference image file + ref_path = tmp_path / "ref.png" + Image.new("RGB", (4, 4), (128, 128, 128)).save(str(ref_path)) + + with open(ref_path, "rb") as f: + resp = video_client.post( + "/v1/videos/generations", + data={ + "prompt": "Animate this image", + "size": "64x64", + "seconds": "1.0", + "fps": "8", + }, + files={"input_reference": ("ref.png", f, "image/png")}, + ) + assert resp.status_code == 200 + assert len(resp.content) > 0 + + def test_sync_video_failure(self, failing_client): + resp = failing_client.post( + "/v1/videos/generations", + json={ + "prompt": "Should fail", + "size": "64x64", + "seconds": 1.0, + "fps": 8, + }, + headers={"content-type": "application/json"}, + ) + assert resp.status_code == 400 + + def test_sync_video_null_output(self, tmp_path): + """Generator returns MediaOutput with video=None.""" + gen = MockVisualGen(video_output=None) + os.environ["TRTLLM_MEDIA_STORAGE_PATH"] = str(tmp_path) + client = _create_server(gen) + resp = client.post( + "/v1/videos/generations", + json={"prompt": "null video", "size": "64x64", "seconds": 1.0, "fps": 8}, + headers={"content-type": "application/json"}, + ) + assert resp.status_code == 500 + os.environ.pop("TRTLLM_MEDIA_STORAGE_PATH", None) + + def test_sync_video_unsupported_content_type(self, video_client): + resp = video_client.post( + "/v1/videos/generations", + content=b"some raw bytes", + headers={"content-type": "text/plain"}, + ) + assert resp.status_code == 400 + + def test_sync_video_missing_prompt_json(self, video_client): + """Missing required prompt ā†’ Pydantic ValidationError ā†’ 400.""" + resp = video_client.post( + "/v1/videos/generations", + json={"size": "64x64"}, + headers={"content-type": "application/json"}, + ) + assert resp.status_code == 400 + + def test_sync_video_missing_prompt_multipart(self, video_client): + """Missing prompt in multipart form ā†’ ValueError ā†’ 400.""" + dummy_file = BytesIO(b"") + resp = video_client.post( + "/v1/videos/generations", + data={"size": "64x64"}, + files={"_dummy": ("dummy", dummy_file, "application/octet-stream")}, + ) + assert resp.status_code == 400 + + +# ========================================================================= +# POST /v1/videos (asynchronous) +# ========================================================================= + + +class TestVideoGenerationAsync: + def test_async_video_returns_202(self, video_client): + resp = video_client.post( + "/v1/videos", + json={ + "prompt": "A dancing robot", + "size": "64x64", + "seconds": 1.0, + "fps": 8, + }, + headers={"content-type": "application/json"}, + ) + assert resp.status_code == 202 + data = resp.json() + assert data["status"] == "queued" + assert data["object"] == "video" + assert data["prompt"] == "A dancing robot" + assert data["id"].startswith("video_") + + def test_async_video_job_metadata_fields(self, video_client): + resp = video_client.post( + "/v1/videos", + json={ + "prompt": "Starry night", + "size": "64x64", + "seconds": 2.0, + "fps": 12, + }, + headers={"content-type": "application/json"}, + ) + data = resp.json() + assert "created_at" in data + assert data["duration"] == 2.0 + assert data["fps"] == 12 + assert data["size"] == "64x64" + + def test_async_video_multipart(self, video_client): + """Multipart encoding requires a file field to trigger the correct content-type.""" + dummy_file = BytesIO(b"") + resp = video_client.post( + "/v1/videos", + data={ + "prompt": "A sunset", + "size": "64x64", + "seconds": "1.0", + "fps": "8", + }, + files={"_dummy": ("dummy", dummy_file, "application/octet-stream")}, + ) + assert resp.status_code == 202 + + def test_async_video_invalid_seconds(self, video_client): + """Seconds must be between 1.0 and 16.0. Validation error ā†’ 400.""" + resp = video_client.post( + "/v1/videos", + json={ + "prompt": "Too short", + "seconds": 0.1, + "size": "64x64", + "fps": 8, + }, + headers={"content-type": "application/json"}, + ) + assert resp.status_code == 400 + + def test_async_video_invalid_fps(self, video_client): + """Fps must be between 8 and 60. Validation error ā†’ 400.""" + resp = video_client.post( + "/v1/videos", + json={ + "prompt": "Bad fps", + "seconds": 1.0, + "fps": 2, + "size": "64x64", + }, + headers={"content-type": "application/json"}, + ) + assert resp.status_code == 400 + + +# ========================================================================= +# GET /v1/videos (list) +# ========================================================================= + + +class TestListVideos: + def test_list_videos_empty(self, video_client): + resp = video_client.get("/v1/videos") + assert resp.status_code == 200 + data = resp.json() + assert data["object"] == "list" + assert data["data"] == [] + + def test_list_videos_after_creation(self, video_client): + # Create two video jobs + video_client.post( + "/v1/videos", + json={"prompt": "First video", "size": "64x64", "seconds": 1.0, "fps": 8}, + headers={"content-type": "application/json"}, + ) + video_client.post( + "/v1/videos", + json={"prompt": "Second video", "size": "64x64", "seconds": 1.0, "fps": 8}, + headers={"content-type": "application/json"}, + ) + + resp = video_client.get("/v1/videos") + assert resp.status_code == 200 + data = resp.json() + assert len(data["data"]) == 2 + + +# ========================================================================= +# GET /v1/videos/{video_id} (metadata) +# ========================================================================= + + +class TestGetVideoMetadata: + def test_get_video_metadata_success(self, video_client): + create_resp = video_client.post( + "/v1/videos", + json={"prompt": "Space walk", "size": "64x64", "seconds": 1.0, "fps": 8}, + headers={"content-type": "application/json"}, + ) + video_id = create_resp.json()["id"] + + resp = video_client.get(f"/v1/videos/{video_id}") + assert resp.status_code == 200 + data = resp.json() + assert data["id"] == video_id + assert data["object"] == "video" + assert data["prompt"] == "Space walk" + + def test_get_video_metadata_not_found(self, video_client): + resp = video_client.get("/v1/videos/video_nonexistent") + assert resp.status_code == 404 + + +# ========================================================================= +# GET /v1/videos/{video_id}/content (download) +# ========================================================================= + + +@pytest.mark.threadleak(enabled=False) # FileResponse spawns AnyIO worker threads +class TestGetVideoContent: + def _insert_video_job(self, video_id: str, status: str = "queued"): + import time as _time + + job = VideoJob( + created_at=int(_time.time()), + id=video_id, + model="test-model", + prompt="test prompt", + status=status, + ) + _run_async(VIDEO_STORE.upsert(video_id, job)) + + def test_get_video_content_success(self, tmp_path): + gen = MockVisualGen(video_output=_make_dummy_video_tensor()) + os.environ["TRTLLM_MEDIA_STORAGE_PATH"] = str(tmp_path) + client = _create_server(gen) + + video_id = "video_testcontent" + self._insert_video_job(video_id, status="completed") + + # Write a dummy mp4 file so FileResponse can serve it + video_path = tmp_path / f"{video_id}.mp4" + video_path.write_bytes(b"\x00\x00\x00\x1cftyp" + b"\x00" * 16) + + resp = client.get(f"/v1/videos/{video_id}/content") + assert resp.status_code == 200 + assert "video/mp4" in resp.headers.get("content-type", "") + assert len(resp.content) > 0 + os.environ.pop("TRTLLM_MEDIA_STORAGE_PATH", None) + + def test_get_video_content_not_found(self, video_client): + resp = video_client.get("/v1/videos/video_nonexistent/content") + assert resp.status_code == 404 + + def test_get_video_content_not_ready(self, tmp_path): + """A queued video should return 400 when its content is requested.""" + gen = MockVisualGen(video_output=_make_dummy_video_tensor()) + os.environ["TRTLLM_MEDIA_STORAGE_PATH"] = str(tmp_path) + client = _create_server(gen) + + video_id = "video_notready" + self._insert_video_job(video_id, status="queued") + + resp = client.get(f"/v1/videos/{video_id}/content") + assert resp.status_code == 400 + os.environ.pop("TRTLLM_MEDIA_STORAGE_PATH", None) + + def test_get_video_content_completed_but_file_missing(self, tmp_path): + """Video marked completed but file deleted from disk ā†’ 404.""" + gen = MockVisualGen(video_output=_make_dummy_video_tensor()) + os.environ["TRTLLM_MEDIA_STORAGE_PATH"] = str(tmp_path) + client = _create_server(gen) + + video_id = "video_nofile" + self._insert_video_job(video_id, status="completed") + # Do NOT write a file + + resp = client.get(f"/v1/videos/{video_id}/content") + assert resp.status_code == 404 + os.environ.pop("TRTLLM_MEDIA_STORAGE_PATH", None) + + +# ========================================================================= +# DELETE /v1/videos/{video_id} +# ========================================================================= + + +class TestDeleteVideo: + def test_delete_video_success(self, tmp_path): + gen = MockVisualGen(video_output=_make_dummy_video_tensor()) + os.environ["TRTLLM_MEDIA_STORAGE_PATH"] = str(tmp_path) + client = _create_server(gen) + + create_resp = client.post( + "/v1/videos", + json={"prompt": "Delete me", "size": "64x64", "seconds": 1.0, "fps": 8}, + headers={"content-type": "application/json"}, + ) + video_id = create_resp.json()["id"] + + # Write a dummy video file + (tmp_path / f"{video_id}.mp4").write_bytes(b"\x00" * 32) + + resp = client.delete(f"/v1/videos/{video_id}") + assert resp.status_code == 200 + data = resp.json() + assert data["deleted"] is True + + # Verify it's gone from the store + resp = client.get(f"/v1/videos/{video_id}") + assert resp.status_code == 404 + + # Verify file is deleted + assert not (tmp_path / f"{video_id}.mp4").exists() + os.environ.pop("TRTLLM_MEDIA_STORAGE_PATH", None) + + def test_delete_video_not_found(self, video_client): + resp = video_client.delete("/v1/videos/video_nonexistent") + assert resp.status_code == 404 + + def test_delete_video_without_file_on_disk(self, video_client): + """Delete a video job that exists in the store but has no file on disk.""" + create_resp = video_client.post( + "/v1/videos", + json={"prompt": "No file", "size": "64x64", "seconds": 1.0, "fps": 8}, + headers={"content-type": "application/json"}, + ) + video_id = create_resp.json()["id"] + + resp = video_client.delete(f"/v1/videos/{video_id}") + assert resp.status_code == 200 + data = resp.json() + assert data["deleted"] is True + + def test_delete_video_then_list_empty(self, video_client): + """After deleting the only video, the list should be empty.""" + create_resp = video_client.post( + "/v1/videos", + json={"prompt": "Ephemeral", "size": "64x64", "seconds": 1.0, "fps": 8}, + headers={"content-type": "application/json"}, + ) + video_id = create_resp.json()["id"] + + video_client.delete(f"/v1/videos/{video_id}") + + resp = video_client.get("/v1/videos") + assert resp.status_code == 200 + assert resp.json()["data"] == [] diff --git a/tests/unittest/_torch/visual_gen/test_wan.py b/tests/unittest/_torch/visual_gen/test_wan.py new file mode 100644 index 0000000000..998452cea2 --- /dev/null +++ b/tests/unittest/_torch/visual_gen/test_wan.py @@ -0,0 +1,3094 @@ +"""Comprehensive unit tests for the Wan model and pipeline.""" + +import os + +os.environ["TLLM_DISABLE_MPI"] = "1" + +import unittest +from copy import deepcopy +from pathlib import Path +from types import SimpleNamespace + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp +import torch.nn.functional as F +from diffusers import WanTransformer3DModel as HFWanTransformer3DModel +from parameterized import parameterized + +from tensorrt_llm._torch.modules.linear import Linear +from tensorrt_llm._torch.visual_gen.config import ( + AttentionConfig, + DiffusionArgs, + DiffusionModelConfig, + ParallelConfig, + PipelineComponent, + TeaCacheConfig, +) +from tensorrt_llm._torch.visual_gen.models.wan.transformer_wan import WanTransformer3DModel +from tensorrt_llm._torch.visual_gen.pipeline_loader import PipelineLoader +from tensorrt_llm.models.modeling_utils import QuantConfig +from tensorrt_llm.quantization.mode import QuantAlgo + + +@pytest.fixture(autouse=True, scope="module") +def _cleanup_mpi_env(): + """Clean up TLLM_DISABLE_MPI env var after tests complete.""" + yield + os.environ.pop("TLLM_DISABLE_MPI", None) + + +def _llm_models_root() -> str: + """Return LLM_MODELS_ROOT path if it is set in env, assert when it's set but not a valid path.""" + root = Path("/home/scratch.trt_llm_data_ci/llm-models/") + if "LLM_MODELS_ROOT" in os.environ: + root = Path(os.environ["LLM_MODELS_ROOT"]) + if not root.exists(): + root = Path("/scratch.trt_llm_data/llm-models/") + assert root.exists(), ( + "You shall set LLM_MODELS_ROOT env or be able to access scratch.trt_llm_data to run this test" + ) + return str(root) + + +# Checkpoint paths for integration tests +CHECKPOINT_PATH = os.environ.get( + "DIFFUSION_MODEL_PATH", + os.path.join(_llm_models_root(), "Wan2.1-T2V-1.3B-Diffusers"), +) +# Wan 2.2 TI2V-5B: BF16 base, FP8 pre-quantized, NVFP4 pre-quantized +CHECKPOINT_PATH_WAN22_BF16 = os.environ.get( + "DIFFUSION_MODEL_PATH_WAN22_BF16", + os.path.join(_llm_models_root(), "Wan2.2-TI2V-5B-Diffusers"), +) +CHECKPOINT_PATH_WAN22_FP8 = os.environ.get( + "DIFFUSION_MODEL_PATH_WAN22_FP8", + os.path.join(_llm_models_root(), "Wan2.2-TI2V-5B-Diffusers-FP8"), +) +CHECKPOINT_PATH_WAN22_NVFP4 = os.environ.get( + "DIFFUSION_MODEL_PATH_WAN22_NVFP4", + os.path.join(_llm_models_root(), "Wan2.2-TI2V-5B-Diffusers-NVFP4"), +) +# Wan 2.2 T2V (two-stage transformer) +CHECKPOINT_PATH_WAN22_T2V = os.environ.get( + "DIFFUSION_MODEL_PATH_WAN22_T2V", + os.path.join(_llm_models_root(), "Wan2.2-T2V-A14B-Diffusers"), +) +SKIP_COMPONENTS = [ + PipelineComponent.TEXT_ENCODER, + PipelineComponent.VAE, + PipelineComponent.TOKENIZER, + PipelineComponent.SCHEDULER, +] + + +def is_wan21_checkpoint() -> bool: + """Check if DIFFUSION_MODEL_PATH is Wan 2.1 (contains '2.1' in path).""" + return "2.1" in CHECKPOINT_PATH + + +def is_wan22_checkpoint() -> bool: + """Check if DIFFUSION_MODEL_PATH is Wan 2.2 (contains '2.2' in path).""" + return "2.2" in CHECKPOINT_PATH_WAN22_T2V + + +WAN_1_3B_CONFIG = { + "attention_head_dim": 128, + "eps": 1e-06, + "ffn_dim": 8960, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 12, + "num_layers": 30, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "rope_max_seq_len": 1024, + "text_dim": 4096, + "torch_dtype": "bfloat16", + "cross_attn_norm": True, +} + + +def reduce_wan_config(mem_for_full_model: int, config_dict: dict): + """Reduce model size if insufficient GPU memory.""" + _, total_mem = torch.cuda.mem_get_info() + if total_mem < mem_for_full_model: + model_fraction = total_mem / mem_for_full_model + num_layers = max(1, int(config_dict["num_layers"] * model_fraction)) + config_dict["num_layers"] = min(num_layers, 4) + + +def setup_distributed(rank, world_size, backend="nccl"): + """Initialize distributed process group for multi-GPU tests.""" + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = "12355" + os.environ["RANK"] = str(rank) + os.environ["WORLD_SIZE"] = str(world_size) + + dist.init_process_group(backend=backend, rank=rank, world_size=world_size) + torch.cuda.set_device(rank) + + +def cleanup_distributed(): + """Clean up distributed process group.""" + if dist.is_initialized(): + dist.destroy_process_group() + + +def _run_cfg_worker(rank, world_size, checkpoint_path, inputs_list, return_dict): + """Worker function for CFG Parallelism multi-GPU test. + + Must be module-level for multiprocessing.spawn() pickling. + """ + try: + setup_distributed(rank, world_size) + + from tensorrt_llm._torch.visual_gen.config import DiffusionArgs, ParallelConfig + from tensorrt_llm._torch.visual_gen.pipeline_loader import PipelineLoader + + # Load pipeline with CFG parallel + args = DiffusionArgs( + checkpoint_path=checkpoint_path, + device=f"cuda:{rank}", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + parallel=ParallelConfig(dit_cfg_size=world_size), + ) + pipeline = PipelineLoader(args).load() + + # Verify CFG parallel configuration + assert pipeline.model_config.parallel.dit_cfg_size == world_size, ( + f"Expected cfg_size={world_size}, got {pipeline.model_config.parallel.dit_cfg_size}" + ) + + # Load inputs on this GPU + prompt_embeds = inputs_list[0].to(f"cuda:{rank}") + neg_prompt_embeds = inputs_list[1].to(f"cuda:{rank}") + latents = inputs_list[2].to(f"cuda:{rank}") + timestep = inputs_list[3].to(f"cuda:{rank}") + + # Setup CFG config + cfg_config = pipeline._setup_cfg_config( + guidance_scale=5.0, + prompt_embeds=prompt_embeds, + neg_prompt_embeds=neg_prompt_embeds, + ) + + # Verify CFG parallel is enabled + assert cfg_config["enabled"], f"Rank {rank}: CFG parallel not enabled" + assert cfg_config["cfg_size"] == world_size, f"Rank {rank}: Wrong cfg_size" + + expected_cfg_group = rank // cfg_config["ulysses_size"] + assert cfg_config["cfg_group"] == expected_cfg_group, ( + f"Rank {rank}: Wrong cfg_group. Expected {expected_cfg_group}, got {cfg_config['cfg_group']}" + ) + + if rank == 0: + print(f"[CFG Rank {rank}] Loaded with cfg_size={world_size}") + print(f" cfg_group: {cfg_config['cfg_group']}") + print(f" local_embeds shape: {cfg_config['local_embeds'].shape}") + print(f" Using {'positive' if cfg_config['cfg_group'] == 0 else 'negative'} prompts") + + # Verify prompt splitting - rank 0 gets positive, rank 1 gets negative + expected_embeds = prompt_embeds if cfg_config["cfg_group"] == 0 else neg_prompt_embeds + assert torch.allclose(cfg_config["local_embeds"], expected_embeds), ( + f"Rank {rank}: local_embeds doesn't match expected" + f"{'positive' if cfg_config['cfg_group'] == 0 else 'negative'} embeds" + ) + + # Run single denoising step with CFG parallel + def forward_fn( + latents, extra_stream_latents, timestep, encoder_hidden_states, extra_tensors + ): + return pipeline.transformer( # noqa: F821 + hidden_states=latents, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + ) + + with torch.no_grad(): + noise_pred, _, _, _ = pipeline._denoise_step_cfg_parallel( + latents=latents, + extra_stream_latents={}, + timestep=timestep, + local_embeds=cfg_config["local_embeds"], + forward_fn=forward_fn, + guidance_scale=5.0, + guidance_rescale=0.0, + ulysses_size=cfg_config["ulysses_size"], + local_extras={}, + ) + + # Validate output + assert not torch.isnan(noise_pred).any(), f"Rank {rank}: Output contains NaN" + assert not torch.isinf(noise_pred).any(), f"Rank {rank}: Output contains Inf" + + # Return output from rank 0 + if rank == 0: + return_dict["output"] = noise_pred.cpu() + print(f"[CFG Rank {rank}] āœ“ Output shape: {noise_pred.shape}") + print( + f"[CFG Rank {rank}] āœ“ Output range: [{noise_pred.min():.4f}, {noise_pred.max():.4f}]" + ) + + del pipeline + torch.cuda.empty_cache() + + finally: + cleanup_distributed() + + +def _run_all_optimizations_worker(rank, world_size, checkpoint_path, inputs_list, return_dict): + """Worker function for all optimizations combined test (FP8 + TeaCache + TRTLLM + CFG). + + Must be module-level for multiprocessing.spawn() pickling. + """ + try: + setup_distributed(rank, world_size) + + # Load pipeline with ALL optimizations + args_full = DiffusionArgs( + checkpoint_path=checkpoint_path, + device=f"cuda:{rank}", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + quant_config={"quant_algo": "FP8", "dynamic": True}, + teacache=TeaCacheConfig( + enable_teacache=True, + teacache_thresh=0.2, + use_ret_steps=True, + ), + attention=AttentionConfig(backend="TRTLLM"), + parallel=ParallelConfig(dit_cfg_size=world_size), + ) + pipeline = PipelineLoader(args_full).load() + transformer = pipeline.transformer.eval() + + # Verify all optimizations are enabled + assert pipeline.model_config.parallel.dit_cfg_size == world_size, "CFG parallel not enabled" + assert transformer.model_config.quant_config.quant_algo == QuantAlgo.FP8, "FP8 not enabled" + assert hasattr(pipeline, "cache_backend"), "TeaCache not enabled" + assert transformer.blocks[0].attn1.attn_backend == "TRTLLM", ( + "TRTLLM not enabled for self-attn" + ) + + if rank == 0: + print(f" āœ“ All optimizations verified on rank {rank}:") + print(f" - FP8 quantization: {transformer.model_config.quant_config.quant_algo}") + print(" - TeaCache: enabled") + print(f" - TRTLLM attention: {transformer.blocks[0].attn1.attn_backend}") + print(f" - CFG Parallelism: cfg_size={world_size}") + + # Initialize TeaCache for single-step inference + if hasattr(pipeline, "cache_backend"): + pipeline.cache_backend.refresh(num_inference_steps=1) + + # Load inputs on this GPU + prompt_embeds = inputs_list[0].to(f"cuda:{rank}") + neg_prompt_embeds = inputs_list[1].to(f"cuda:{rank}") + latents = inputs_list[2].to(f"cuda:{rank}") + timestep = inputs_list[3].to(f"cuda:{rank}") + + # Setup CFG config + cfg_config = pipeline._setup_cfg_config( + guidance_scale=5.0, + prompt_embeds=prompt_embeds, + neg_prompt_embeds=neg_prompt_embeds, + ) + + assert cfg_config["enabled"], "CFG parallel not enabled" + + # Run single denoising step with all optimizations + def forward_fn( + latents, extra_stream_latents, timestep, encoder_hidden_states, extra_tensors + ): + return transformer( # noqa: F821 + hidden_states=latents, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + ) + + with torch.no_grad(): + noise_pred, _, _, _ = pipeline._denoise_step_cfg_parallel( + latents=latents, + extra_stream_latents={}, + timestep=timestep, + local_embeds=cfg_config["local_embeds"], + forward_fn=forward_fn, + guidance_scale=5.0, + guidance_rescale=0.0, + ulysses_size=cfg_config["ulysses_size"], + local_extras={}, + ) + + # Validate output + assert not torch.isnan(noise_pred).any(), f"Rank {rank}: Output contains NaN" + assert not torch.isinf(noise_pred).any(), f"Rank {rank}: Output contains Inf" + + # Return output from rank 0 + if rank == 0: + return_dict["output"] = noise_pred.cpu() + print(f" āœ“ Combined optimization output shape: {noise_pred.shape}") + print( + f" āœ“ Combined optimization range: [{noise_pred.min():.4f}, {noise_pred.max():.4f}]" + ) + + del pipeline, transformer + torch.cuda.empty_cache() + + finally: + cleanup_distributed() + + +# ============================================================================= +# Basic Unit Tests +# ============================================================================= + + +class TestWan(unittest.TestCase): + DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + def _create_model_config(self, config_dict): + """Helper to create DiffusionModelConfig from test config dict.""" + # Create pretrained_config as SimpleNamespace + pretrained_config = SimpleNamespace(**config_dict) + + # Use default quantization (no quantization for unit tests) + quant_config = QuantConfig() + dynamic_weight_quant = False + dynamic_activation_quant = False + + # Create DiffusionModelConfig + model_config = DiffusionModelConfig( + pretrained_config=pretrained_config, + quant_config=quant_config, + quant_config_dict=None, + dynamic_weight_quant=dynamic_weight_quant, + force_dynamic_quantization=dynamic_activation_quant, + skip_create_weights_in_init=False, # Create weights immediately for testing + ) + return model_config + + def test_wan_model_structure(self): + """Test that model structure matches HuggingFace naming.""" + config = deepcopy(WAN_1_3B_CONFIG) + config["num_layers"] = 1 + hidden_size = config["num_attention_heads"] * config["attention_head_dim"] + config["hidden_size"] = hidden_size + + model_config = self._create_model_config(config) + + model = WanTransformer3DModel(model_config=model_config) + + # Check FFN structure + param_names = [n for n in model.state_dict().keys() if "ffn" in n] + print("\n[DEBUG] FFN parameter names in TRT-LLM model:") + for pn in param_names[:5]: + print(f" - {pn}") + + # Verify expected structure exists (MLP uses up_proj/down_proj) + assert any("ffn.up_proj" in n for n in param_names), "Missing ffn.up_proj structure" + assert any("ffn.down_proj" in n for n in param_names), "Missing ffn.down_proj structure" + + def test_wan_sanity(self): + """Basic sanity test that the model can run forward pass.""" + config = deepcopy(WAN_1_3B_CONFIG) + dtype = getattr(torch, config["torch_dtype"]) + # Use fewer layers for sanity test + config["num_layers"] = 2 + + hidden_size = config["num_attention_heads"] * config["attention_head_dim"] + config["hidden_size"] = hidden_size + + # Create model config + model_config = self._create_model_config(config) + + # Create model with model_config + model = WanTransformer3DModel(model_config=model_config).to(self.DEVICE, dtype=dtype).eval() + + batch_size = 1 + num_frames = 1 + height, width = 64, 64 + seq_len = 128 + generator = torch.Generator(device=self.DEVICE).manual_seed(42) + + hidden_states = torch.randn( + batch_size, + config["in_channels"], + num_frames, + height, + width, + generator=generator, + device=self.DEVICE, + dtype=dtype, + ) + timestep = torch.tensor([50], device=self.DEVICE, dtype=torch.long) + encoder_hidden_states = torch.randn( + batch_size, + seq_len, + config["text_dim"], + generator=generator, + device=self.DEVICE, + dtype=dtype, + ) + + with torch.inference_mode(): + output = model( + hidden_states=hidden_states, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + ) + + self.assertEqual(output.shape, hidden_states.shape) + + @parameterized.expand( + [ + ("1_3b", WAN_1_3B_CONFIG), + ] + ) + @torch.no_grad() + def test_wan_allclose_to_hf(self, name, config_template): + """Test TRT-LLM transformer matches HuggingFace output (BF16).""" + torch.random.manual_seed(42) + config = deepcopy(config_template) + dtype = getattr(torch, config["torch_dtype"]) + + mem_for_full_model = (2 + 1) * 1.3 * 2**30 + reduce_wan_config(mem_for_full_model, config) + + if config["num_layers"] <= 0: + self.skipTest("Insufficient memory for a single Wan layer") + + hidden_size = config["num_attention_heads"] * config["attention_head_dim"] + + # Create HuggingFace model (random weights) + hf_model = ( + HFWanTransformer3DModel( + patch_size=config["patch_size"], + num_attention_heads=config["num_attention_heads"], + attention_head_dim=config["attention_head_dim"], + in_channels=config["in_channels"], + out_channels=config["out_channels"], + text_dim=config["text_dim"], + freq_dim=config["freq_dim"], + ffn_dim=config["ffn_dim"], + num_layers=config["num_layers"], + cross_attn_norm=config["cross_attn_norm"], + qk_norm=config["qk_norm"], + eps=config["eps"], + ) + .to(self.DEVICE, dtype=dtype) + .eval() + ) + + # Create TRT-LLM model with model_config + config["hidden_size"] = hidden_size + model_config = self._create_model_config(config) + + trtllm_model = ( + WanTransformer3DModel(model_config=model_config).to(self.DEVICE, dtype=dtype).eval() + ) + + # Copy weights from HF to TRT-LLM + loaded_count = self._load_weights_from_hf(trtllm_model, hf_model.state_dict()) + print(f"[DEBUG] Loaded {loaded_count} weight tensors from HF to TRT-LLM") + + # Create test inputs + batch_size = 1 + num_frames = 1 + height, width = 64, 64 + seq_len = 128 + generator = torch.Generator(device=self.DEVICE).manual_seed(42) + + hidden_states = torch.randn( + batch_size, + config["in_channels"], + num_frames, + height, + width, + generator=generator, + device=self.DEVICE, + dtype=dtype, + ) + timestep = torch.tensor([50], device=self.DEVICE, dtype=torch.long) + encoder_hidden_states = torch.randn( + batch_size, + seq_len, + config["text_dim"], + generator=generator, + device=self.DEVICE, + dtype=dtype, + ) + + # Run both models + with ( + torch.inference_mode(), + torch.backends.cuda.sdp_kernel( + enable_flash=False, enable_math=True, enable_mem_efficient=False + ), + ): + hf_output = hf_model( + hidden_states=hidden_states, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + return_dict=False, + )[0] + + trtllm_output = trtllm_model( + hidden_states=hidden_states, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + ) + + # Compare outputs + hf_output = hf_output.float() + trtllm_output = trtllm_output.float() + + # Debug: Check for NaN/Inf + hf_has_nan = torch.isnan(hf_output).any().item() + trtllm_has_nan = torch.isnan(trtllm_output).any().item() + hf_has_inf = torch.isinf(hf_output).any().item() + trtllm_has_inf = torch.isinf(trtllm_output).any().item() + + print("\n[DEBUG] Output validation:") + print(f" HF has NaN: {hf_has_nan}, Inf: {hf_has_inf}") + print(f" TRT-LLM has NaN: {trtllm_has_nan}, Inf: {trtllm_has_inf}") + + if not (hf_has_nan or trtllm_has_nan or hf_has_inf or trtllm_has_inf): + # Compute detailed comparison metrics + diff = (trtllm_output - hf_output).abs() + max_diff = diff.max().item() + mean_diff = diff.mean().item() + + cos_sim = torch.nn.functional.cosine_similarity( + trtllm_output.flatten(), hf_output.flatten(), dim=0 + ).item() + + print("\n[DEBUG] Comparison metrics:") + print(f" Max absolute diff: {max_diff:.6f}") + print(f" Mean absolute diff: {mean_diff:.6f}") + print(f" Cosine similarity: {cos_sim:.6f}") + print(f" HF output range: [{hf_output.min():.4f}, {hf_output.max():.4f}]") + print(f" TRT-LLM output range: [{trtllm_output.min():.4f}, {trtllm_output.max():.4f}]") + + torch.testing.assert_close( + trtllm_output, hf_output, atol=0.4, rtol=0.4, msg=f"Output mismatch for {name} config" + ) + + def _load_weights_from_hf(self, trtllm_model, hf_state_dict): + """Load weights from HuggingFace model to TRT-LLM model. + + TRT-LLM structure: + - blocks.0.attn1.qkv_proj (fused QKV for self-attention) + - blocks.0.attn2.to_q/to_k/to_v (separate for cross-attention) + - blocks.0.attn1.to_out.0 and blocks.0.attn2.to_out.0 + + HuggingFace structure: + - blocks.0.attn1.to_q/to_k/to_v (separate Q/K/V) + - blocks.0.attn2.to_q/to_k/to_v (separate Q/K/V) + - blocks.0.attn1.to_out.0 and blocks.0.attn2.to_out.0 + """ + loaded_count = 0 + missing_weights = [] + + def load_linear(module, trtllm_key, hf_key, sd): + """Load weights from HF key into TRT-LLM module.""" + if f"{hf_key}.weight" in sd: + weight_dict = {"weight": sd[f"{hf_key}.weight"]} + if f"{hf_key}.bias" in sd: + weight_dict["bias"] = sd[f"{hf_key}.bias"] + module.load_weights([weight_dict]) + return 1 + else: + missing_weights.append(hf_key) + return 0 + + for name, module in trtllm_model.named_modules(): + if isinstance(module, Linear): + # Self-attention fused QKV: blocks.0.attn1.qkv_proj + # Load from HF separate Q/K/V: blocks.0.attn1.to_q/to_k/to_v + if "attn1.qkv_proj" in name: + base = name.replace(".qkv_proj", "") + q_key, k_key, v_key = f"{base}.to_q", f"{base}.to_k", f"{base}.to_v" + if f"{q_key}.weight" in hf_state_dict: + q_dict = {"weight": hf_state_dict[f"{q_key}.weight"]} + k_dict = {"weight": hf_state_dict[f"{k_key}.weight"]} + v_dict = {"weight": hf_state_dict[f"{v_key}.weight"]} + if f"{q_key}.bias" in hf_state_dict: + q_dict["bias"] = hf_state_dict[f"{q_key}.bias"] + k_dict["bias"] = hf_state_dict[f"{k_key}.bias"] + v_dict["bias"] = hf_state_dict[f"{v_key}.bias"] + module.load_weights([q_dict, k_dict, v_dict]) + loaded_count += 1 + + # Cross-attention separate Q/K/V: blocks.0.attn2.to_q (same path as HF) + elif "attn2.to_q" in name or "attn2.to_k" in name or "attn2.to_v" in name: + # Direct mapping - TRT-LLM and HF use same paths for cross-attention + loaded_count += load_linear(module, name, name, hf_state_dict) + + # Output projections: blocks.0.attn1.to_out.0 (same path as HF) + elif ".to_out" in name: + # Direct mapping - TRT-LLM and HF use same paths for output projections + loaded_count += load_linear(module, name, name, hf_state_dict) + + # FFN layers: TRT-LLM uses up_proj/down_proj, HF uses net.0.proj/net.2 + elif "ffn.up_proj" in name: + hf_key = name.replace(".ffn.up_proj", ".ffn.net.0.proj") + loaded_count += load_linear(module, name, hf_key, hf_state_dict) + elif "ffn.down_proj" in name: + hf_key = name.replace(".ffn.down_proj", ".ffn.net.2") + loaded_count += load_linear(module, name, hf_key, hf_state_dict) + + # Other layers: direct mapping + elif "condition_embedder" in name or "proj_out" in name: + loaded_count += load_linear(module, name, name, hf_state_dict) + + else: + # Direct mapping for any other Linear modules + loaded_count += load_linear(module, name, name, hf_state_dict) + + elif hasattr(module, "weight") and f"{name}.weight" in hf_state_dict: + # Norms & embeddings + with torch.no_grad(): + module.weight.copy_(hf_state_dict[f"{name}.weight"]) + if ( + getattr(module, "bias", None) is not None + and f"{name}.bias" in hf_state_dict + ): + module.bias.copy_(hf_state_dict[f"{name}.bias"]) + loaded_count += 1 + + # Load scale_shift_table parameters + for name, param in trtllm_model.named_parameters(): + if "scale_shift_table" in name and name in hf_state_dict: + with torch.no_grad(): + param.copy_(hf_state_dict[name].view(param.shape)) + loaded_count += 1 + + if missing_weights: + print(f"[DEBUG] Missing {len(missing_weights)} weights:") + for mw in missing_weights[:10]: # Show first 10 + print(f" - {mw}") + + return loaded_count + + def _load_weights_from_state_dict(self, model, state_dict): + """Load weights from state_dict into model (same structure).""" + for name, module in model.named_modules(): + if isinstance(module, Linear): + weight_key = f"{name}.weight" + if weight_key in state_dict: + weight_dict = {"weight": state_dict[weight_key]} + bias_key = f"{name}.bias" + if bias_key in state_dict: + weight_dict["bias"] = state_dict[bias_key] + module.load_weights([weight_dict]) + + elif hasattr(module, "weight") and f"{name}.weight" in state_dict: + with torch.no_grad(): + module.weight.copy_(state_dict[f"{name}.weight"]) + if getattr(module, "bias", None) is not None and f"{name}.bias" in state_dict: + module.bias.copy_(state_dict[f"{name}.bias"]) + + # Load parameters + for name, param in model.named_parameters(): + if name in state_dict: + with torch.no_grad(): + param.copy_(state_dict[name].view(param.shape)) + + +# ============================================================================= +# Pipeline Test - Require Real Checkpoint +# ============================================================================= + + +@pytest.fixture +def checkpoint_exists(): + """Check if checkpoint path is set and exists.""" + return CHECKPOINT_PATH and os.path.exists(CHECKPOINT_PATH) + + +@pytest.fixture(autouse=True) +def cleanup_gpu_memory(): + """Automatically cleanup GPU memory after each test to prevent OOM errors. + + This fixture runs automatically after every test in this file. + It performs garbage collection and clears CUDA cache to free up GPU memory. + """ + yield # Test runs here + # Cleanup after test completes + import gc + + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def _is_fp32_layernorm_param(param_name: str) -> bool: + """True if param is a LayerNorm weight/bias we keep in float32. Only LayerNorm (norm1/norm2/norm3/norm_out).""" + if not param_name.endswith((".weight", ".bias")): + return False + # blocks..norm1, norm2, norm3 (LayerNorm only; attn norm_q/norm_k are RMSNorm) + if ".norm" in param_name and "blocks." in param_name: + parts = param_name.split(".") + for p in parts: + if p in ("norm1", "norm2", "norm3"): + return True + return False + # top-level norm_out (LayerNorm) + if param_name == "norm_out.weight" or param_name == "norm_out.bias": + return True + # condition_embedder.norm1, norm2 (LayerNorm) + if param_name.startswith("condition_embedder.") and ".norm" in param_name: + return True + return False + + +class TestWanPipeline: + """Pipeline tests for Wan pipeline loading with PipelineLoader. + + These tests require a real checkpoint (set DIFFUSION_MODEL_PATH env var). + They test the full loading flow: config ā†’ model ā†’ weight loading ā†’ inference. + """ + + DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + def test_load_wan_pipeline_basic(self, checkpoint_exists): + """Test loading Wan pipeline without quantization via PipelineLoader.""" + if not checkpoint_exists: + pytest.skip("Checkpoint not available. Set DIFFUSION_MODEL_PATH.") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint (single-stage). Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + ) + pipeline = PipelineLoader(args).load() + + # Verify pipeline loaded correctly + assert pipeline.transformer is not None + assert len(pipeline.transformer.blocks) > 0 + + # Verify weights are loaded + # Check that non-scale parameters are bfloat16 + bf16_count = 0 + f32_scale_count = 0 + for name, param in pipeline.transformer.named_parameters(): + assert param.device.type == "cuda", f"Parameter {name} not on CUDA" + if "scale" in name.lower(): + # Scale parameters can stay float32 for FP8 kernels + assert param.dtype in [torch.float32, torch.bfloat16], ( + f"Scale param {name} has unexpected dtype {param.dtype}" + ) + if param.dtype == torch.float32: + f32_scale_count += 1 + elif _is_fp32_layernorm_param(name): + # LayerNorm (norm1/norm2/norm3/norm_out) use float32; RMSNorm (norm_q, norm_k, etc.) stay bf16 + assert param.dtype == torch.float32, ( + f"LayerNorm param {name} expected float32 but got {param.dtype}" + ) + else: + # Non-scale parameters should be bfloat16 + assert param.dtype == torch.bfloat16, ( + f"Parameter {name} expected bfloat16 but got {param.dtype}" + ) + bf16_count += 1 + + assert bf16_count > 0, "Should have at least some bfloat16 parameters" + print( + f"\n[Pipeline] BF16 pipeline loaded: {bf16_count} bf16 params" + f"\n{f32_scale_count} f32 scale params, {len(pipeline.transformer.blocks)} blocks" + ) + + @pytest.mark.parametrize("quant_algo", ["FP8", "FP8_BLOCK_SCALES"]) + def test_load_wan_pipeline_with_quantization(self, checkpoint_exists, quant_algo): + """Test loading Wan with FP8 quantization (per-tensor or blockwise).""" + if not checkpoint_exists: + pytest.skip("Checkpoint not available. Set DIFFUSION_MODEL_PATH.") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint. Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + quant_config={"quant_algo": quant_algo, "dynamic": True}, + ) + pipeline = PipelineLoader(args).load() + + # Verify FP8 weights in transformer blocks + found_fp8 = False + for name, module in pipeline.transformer.named_modules(): + if isinstance(module, Linear): + if "blocks." in name and hasattr(module, "weight") and module.weight is not None: + assert module.weight.dtype == torch.float8_e4m3fn, ( + f"Linear {name} should have FP8 weight, got {module.weight.dtype}" + ) + assert hasattr(module, "weight_scale"), f"Linear {name} missing weight_scale" + found_fp8 = True + print(f"[{quant_algo}] FP8 layer {name}: weight {module.weight.shape}") + break + + assert found_fp8, f"No FP8 Linear modules found for {quant_algo}" + + @pytest.mark.parametrize("quant_algo", ["FP8", "FP8_BLOCK_SCALES"]) + def test_fp8_vs_bf16_numerical_correctness(self, checkpoint_exists, quant_algo): + """Test FP8 vs BF16 numerical accuracy on real checkpoint weights. + + Pattern (similar to that in test_pipeline_dynamic_quant.py): + 1. Use F.linear() with BF16 weights as ground truth reference + 2. Verify BF16 layer matches F.linear exactly + 3. Compare FP8 layer output against reference + 4. Check max_diff, cosine_similarity, mse_loss + """ + if not checkpoint_exists: + pytest.skip("Checkpoint not available. Set DIFFUSION_MODEL_PATH.") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint (loads 2 full models and " + "Needs single transformer). Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + # ===================================================================== + # Load BF16 Pipeline (Reference) + # ===================================================================== + print(f"\n[Compare {quant_algo}] Loading BF16 pipeline...") + + args_bf16 = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + ) + pipeline_bf16 = PipelineLoader(args_bf16).load() + + # ===================================================================== + # Load FP8 Pipeline + # ===================================================================== + print(f"[Compare {quant_algo}] Loading {quant_algo} pipeline...") + + args_fp8 = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + quant_config={"quant_algo": quant_algo, "dynamic": True}, + ) + pipeline_fp8 = PipelineLoader(args_fp8).load() + + # ===================================================================== + # Get Linear Layers from Both Pipelines + # ===================================================================== + attn_bf16 = pipeline_bf16.transformer.blocks[0].attn1 + attn_fp8 = pipeline_fp8.transformer.blocks[0].attn1 + + # Get linear layer - try fused qkv_proj first, fallback to qkv_proj on attention module + if hasattr(attn_bf16, "qkv_proj"): + linear_bf16 = attn_bf16.qkv_proj + linear_fp8 = attn_fp8.qkv_proj + layer_name = "blocks.0.attn1.qkv_proj" + elif hasattr(attn_bf16, "attn") and hasattr(attn_bf16.attn, "qkv_proj"): + linear_bf16 = attn_bf16.attn.qkv_proj + linear_fp8 = attn_fp8.attn.qkv_proj + layer_name = "blocks.0.attn1.attn.qkv_proj" + else: + # Use FFN linear instead (always available) + linear_bf16 = pipeline_bf16.transformer.blocks[0].ffn.net[0]["proj"] + linear_fp8 = pipeline_fp8.transformer.blocks[0].ffn.net[0]["proj"] + layer_name = "blocks.0.ffn.net.0.proj" + + # ===================================================================== + # Get BF16 weights and bias for F.linear reference + # ===================================================================== + weight_bf16 = linear_bf16.weight.data.clone() + bias_bf16 = linear_bf16.bias.data.clone() if linear_bf16.bias is not None else None + + # ===================================================================== + # Create Test Input + # ===================================================================== + torch.manual_seed(42) + hidden_size = linear_bf16.in_features + batch_size = 1 + seq_len = 14040 + + # 2D input for FP8 kernel compatibility + input_tensor = torch.randn( + batch_size * seq_len, hidden_size, dtype=torch.bfloat16, device="cuda" + ) + print(f"[Compare] Input shape: {input_tensor.shape}") + + # ===================================================================== + # Compute Reference Output: F.linear (ground truth) + # ===================================================================== + with torch.no_grad(): + expected = F.linear(input_tensor, weight_bf16, bias_bf16) + + # ===================================================================== + # Compute FP8 Output + # ===================================================================== + with torch.no_grad(): + result_fp8 = linear_fp8(input_tensor) + + # ===================================================================== + # Compute BF16 Layer Output + # ===================================================================== + with torch.no_grad(): + result_bf16 = linear_bf16(input_tensor) + + # Verify BF16 layer matches F.linear reference + assert torch.allclose(result_bf16, expected, rtol=1e-5, atol=1e-6), ( + "BF16 layer should match F.linear reference exactly" + ) + + # Compare FP8 vs Reference + max_diff = torch.max(torch.abs(result_fp8 - expected)).item() + cos_sim = F.cosine_similarity( + result_fp8.flatten().float(), expected.flatten().float(), dim=0 + ) + mse = F.mse_loss(result_fp8.flatten().float(), expected.flatten().float()) + + print( + f"\n[{layer_name}] max_diff={max_diff:.6f}, cos_sim={cos_sim.item():.6f}, mse={mse.item():.6f}" + ) + + assert cos_sim > 0.99, f"Cosine similarity too low: {cos_sim.item()}" + assert mse < 1.0, f"MSE too high: {mse.item()}" + + # Cleanup + del pipeline_bf16, pipeline_fp8 + torch.cuda.empty_cache() + + def test_fp8_vs_bf16_memory_comparison(self, checkpoint_exists): + """Test FP8 uses ~2x less memory than BF16.""" + if not checkpoint_exists: + pytest.skip("Checkpoint not available. Set DIFFUSION_MODEL_PATH.") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint. Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + def get_module_memory_gb(module): + return sum(p.numel() * p.element_size() for p in module.parameters()) / 1024**3 + + # Load BF16 + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + + args_bf16 = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + ) + pipeline_bf16 = PipelineLoader(args_bf16).load() + + bf16_model_mem = get_module_memory_gb(pipeline_bf16.transformer) + bf16_peak_mem = torch.cuda.max_memory_allocated() / 1024**3 + + print(f"\n[BF16] Transformer memory: {bf16_model_mem:.2f} GB") + print(f"[BF16] Peak memory: {bf16_peak_mem:.2f} GB") + + del pipeline_bf16 + torch.cuda.empty_cache() + + # Load FP8 + torch.cuda.reset_peak_memory_stats() + + args_fp8 = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + quant_config={"quant_algo": "FP8", "dynamic": True}, + ) + pipeline_fp8 = PipelineLoader(args_fp8).load() + + fp8_model_mem = get_module_memory_gb(pipeline_fp8.transformer) + fp8_peak_mem = torch.cuda.max_memory_allocated() / 1024**3 + + print(f"\n[FP8] Transformer memory: {fp8_model_mem:.2f} GB") + print(f"[FP8] Peak memory: {fp8_peak_mem:.2f} GB") + + # Verify memory savings + model_mem_ratio = bf16_model_mem / fp8_model_mem + peak_mem_ratio = bf16_peak_mem / fp8_peak_mem + + print(f"\n[Comparison] Model memory ratio (BF16/FP8): {model_mem_ratio:.2f}x") + print(f"[Comparison] Peak memory ratio (BF16/FP8): {peak_mem_ratio:.2f}x") + + # FP8 should use ~2x less memory + assert model_mem_ratio > 1.8, f"FP8 should use ~2x less memory, got {model_mem_ratio:.2f}x" + + del pipeline_fp8 + torch.cuda.empty_cache() + + @pytest.mark.parametrize("quant_algo", ["FP8", "FP8_BLOCK_SCALES"]) + def test_fp8_vs_bf16_full_transformer_e2e(self, checkpoint_exists, quant_algo): + """End-to-end test: Compare full Wan transformer FP8 vs BF16 output. + + Unlike test_fp8_vs_bf16_numerical_correctness which tests a single Linear layer, + this test runs the ENTIRE transformer (all 30 blocks) and compares outputs. + + Expectations: + - Errors accumulate across 30 layers, so use relaxed tolerances + - Cosine similarity should be high (>0.95) but lower than single-layer test (>0.99) + - This validates that FP8 quantization doesn't degrade quality too much end-to-end + """ + if not checkpoint_exists: + pytest.skip("Checkpoint not available. Set DIFFUSION_MODEL_PATH.") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint. Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + # ===================================================================== + # Load BF16 Transformer (Reference) + # ===================================================================== + print("\n[E2E] Loading BF16 transformer...") + + args_bf16 = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + ) + pipeline_bf16 = PipelineLoader(args_bf16).load() + transformer_bf16 = pipeline_bf16.transformer + + # ===================================================================== + # Load FP8 Transformer + # ===================================================================== + print(f"[E2E] Loading {quant_algo} transformer...") + + args_fp8 = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + quant_config={"quant_algo": quant_algo, "dynamic": True}, + ) + pipeline_fp8 = PipelineLoader(args_fp8).load() + transformer_fp8 = pipeline_fp8.transformer + + # ===================================================================== + # Create Realistic Inputs + # ===================================================================== + torch.manual_seed(42) + + # Use smaller size for faster testing (still realistic) + batch_size = 1 + num_frames = 1 + height, width = 64, 64 # Smaller than full 720x1280 + in_channels = 16 + text_seq_len = 128 + text_dim = 4096 + + # Create inputs + hidden_states = torch.randn( + batch_size, in_channels, num_frames, height, width, dtype=torch.bfloat16, device="cuda" + ) + timestep = torch.tensor([500], dtype=torch.long, device="cuda") + encoder_hidden_states = torch.randn( + batch_size, text_seq_len, text_dim, dtype=torch.bfloat16, device="cuda" + ) + + print("[E2E] Input shapes:") + print(f" hidden_states: {hidden_states.shape}") + print(f" timestep: {timestep.shape}") + print(f" encoder_hidden_states: {encoder_hidden_states.shape}") + + # ===================================================================== + # Run Full Transformer Forward Pass + # ===================================================================== + print("[E2E] Running BF16 transformer forward...") + with torch.no_grad(): + output_bf16 = transformer_bf16( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + print(f"[E2E] Running {quant_algo} transformer forward...") + with torch.no_grad(): + output_fp8 = transformer_fp8( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + # ===================================================================== + # Verify Outputs + # ===================================================================== + assert output_bf16.shape == output_fp8.shape, ( + f"Output shape mismatch: BF16={output_bf16.shape}, FP8={output_fp8.shape}" + ) + print(f"[E2E] Output shape: {output_bf16.shape}") + + # Check for NaN/Inf + bf16_has_nan = torch.isnan(output_bf16).any().item() + fp8_has_nan = torch.isnan(output_fp8).any().item() + bf16_has_inf = torch.isinf(output_bf16).any().item() + fp8_has_inf = torch.isinf(output_fp8).any().item() + + assert not bf16_has_nan, "BF16 output contains NaN" + assert not bf16_has_inf, "BF16 output contains Inf" + assert not fp8_has_nan, f"{quant_algo} output contains NaN" + assert not fp8_has_inf, f"{quant_algo} output contains Inf" + + # ===================================================================== + # Compare Numerical Accuracy + # ===================================================================== + output_bf16_float = output_bf16.float() + output_fp8_float = output_fp8.float() + + max_diff = torch.max(torch.abs(output_fp8_float - output_bf16_float)).item() + mean_diff = torch.mean(torch.abs(output_fp8_float - output_bf16_float)).item() + + cos_sim = F.cosine_similarity( + output_fp8_float.flatten(), output_bf16_float.flatten(), dim=0 + ).item() + + mse = F.mse_loss(output_fp8_float, output_bf16_float).item() + + # Relative error + rel_error = mean_diff / (output_bf16_float.abs().mean().item() + 1e-8) + + print(f"\n{'=' * 60}") + print(f"END-TO-END TRANSFORMER COMPARISON ({quant_algo} vs BF16)") + print(f"{'=' * 60}") + print(f"Number of layers: {len(transformer_bf16.blocks)}") + print(f"Output shape: {output_bf16.shape}") + print("") + print(f"Max absolute difference: {max_diff:.6f}") + print(f"Mean absolute difference: {mean_diff:.6f}") + print(f"Relative error: {rel_error:.6f}") + print(f"Cosine similarity: {cos_sim:.6f}") + print(f"MSE loss: {mse:.6f}") + print("") + print(f"BF16 output range: [{output_bf16_float.min():.4f}, {output_bf16_float.max():.4f}]") + print( + f"{quant_algo} output range: [{output_fp8_float.min():.4f}, {output_fp8_float.max():.4f}]" + ) + print(f"{'=' * 60}") + + # ===================================================================== + # Assert Numerical Correctness (Relaxed Tolerances) + # ===================================================================== + # Cosine similarity should be high, but lower than single-layer test + # due to error accumulation across 30 layers + assert cos_sim > 0.95, ( + f"Cosine similarity too low for full transformer: {cos_sim:.6f} (expected >0.95)" + ) + + # Relative error should be reasonable + # Note: Error accumulates across 30 layers, so we use a relaxed tolerance + assert rel_error < 0.15, f"Relative error too high: {rel_error:.6f} (expected <0.15)" + + print(f"\n[PASS] {quant_algo} full transformer output matches BF16 within tolerance!") + print(f" āœ“ Cosine similarity: {cos_sim:.4f} (>0.95)") + print(f" āœ“ Relative error: {rel_error:.4f} (<0.15)") + + # Cleanup + del pipeline_bf16, pipeline_fp8, transformer_bf16, transformer_fp8 + torch.cuda.empty_cache() + + def test_attention_backend_comparison(self, checkpoint_exists): + """Test accuracy of full Wan forward pass with attention backend comparison. + + Wan uses both self-attention (attn1) and cross-attention (attn2). TRTLLM backend + doesn't support cross-attention (seq_len != kv_seq_len), but WanAttention + automatically falls back to VANILLA for cross-attention when TRTLLM is configured. + + This test verifies: + 1. VANILLA backend works correctly + 2. TRTLLM backend with automatic VANILLA fallback for cross-attention produces + numerically similar results to pure VANILLA + """ + if not checkpoint_exists: + pytest.skip("Checkpoint not available. Set DIFFUSION_MODEL_PATH.") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint. Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + # ===================================================================== + # Load Baseline Transformer (Default VANILLA) + # ===================================================================== + print("\n[Attention Backend Test] Loading baseline transformer (default VANILLA)...") + + from tensorrt_llm._torch.visual_gen.config import AttentionConfig + + args_baseline = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + ) + # Default attention backend is VANILLA + pipeline_baseline = PipelineLoader(args_baseline).load() + transformer_baseline = pipeline_baseline.transformer + + # ===================================================================== + # Load VANILLA Transformer + # ===================================================================== + print("[Attention Backend Test] Loading VANILLA transformer (explicit)...") + + args_vanilla = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + ) + args_vanilla.attention = AttentionConfig(backend="VANILLA") + pipeline_vanilla = PipelineLoader(args_vanilla).load() + transformer_vanilla = pipeline_vanilla.transformer + + # ===================================================================== + # Create Fixed Test Inputs + # ===================================================================== + torch.manual_seed(42) + + # Smaller size for faster testing + batch_size = 1 + num_frames = 1 + height, width = 64, 64 + in_channels = 16 + text_seq_len = 128 + text_dim = 4096 + + # Create inputs + hidden_states = torch.randn( + batch_size, in_channels, num_frames, height, width, dtype=torch.bfloat16, device="cuda" + ) + timestep = torch.tensor([500], dtype=torch.long, device="cuda") + encoder_hidden_states = torch.randn( + batch_size, text_seq_len, text_dim, dtype=torch.bfloat16, device="cuda" + ) + + print("[Attention Backend Test] Input shapes:") + print(f" hidden_states: {hidden_states.shape}") + print(f" timestep: {timestep.shape}") + print(f" encoder_hidden_states: {encoder_hidden_states.shape}") + + # ===================================================================== + # Run Full Transformer Forward Pass + # ===================================================================== + print("[Attention Backend Test] Running baseline transformer forward...") + with torch.no_grad(): + output_baseline = transformer_baseline( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + print("[Attention Backend Test] Running VANILLA transformer forward...") + with torch.no_grad(): + output_vanilla = transformer_vanilla( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + # ===================================================================== + # Verify Output Shapes + # ===================================================================== + assert output_baseline.shape == output_vanilla.shape, ( + f"Output shape mismatch: baseline={output_baseline.shape}, " + f"VANILLA={output_vanilla.shape}" + ) + print(f"[Attention Backend Test] Output shape: {output_baseline.shape}") + + # ===================================================================== + # Check for NaN/Inf in All Outputs + # ===================================================================== + for name, output in [("baseline", output_baseline), ("VANILLA", output_vanilla)]: + has_nan = torch.isnan(output).any().item() + has_inf = torch.isinf(output).any().item() + assert not has_nan, f"{name} output contains NaN" + assert not has_inf, f"{name} output contains Inf" + print(f"[Attention Backend Test] {name} output: NaN={has_nan}, Inf={has_inf}") + + # ===================================================================== + # Compare VANILLA (Explicit) vs Baseline + # ===================================================================== + output_baseline_float = output_baseline.float() + output_vanilla_float = output_vanilla.float() + + # VANILLA explicit vs baseline (should be identical) + max_diff_vanilla = torch.max(torch.abs(output_vanilla_float - output_baseline_float)).item() + mean_diff_vanilla = torch.mean( + torch.abs(output_vanilla_float - output_baseline_float) + ).item() + cos_sim_vanilla = F.cosine_similarity( + output_vanilla_float.flatten(), output_baseline_float.flatten(), dim=0 + ).item() + mse_vanilla = F.mse_loss(output_vanilla_float, output_baseline_float).item() + + print(f"\n{'=' * 60}") + print("VANILLA (Explicit) vs Baseline Comparison") + print(f"{'=' * 60}") + print(f"Max absolute difference: {max_diff_vanilla:.6f}") + print(f"Mean absolute difference: {mean_diff_vanilla:.6f}") + print(f"Cosine similarity: {cos_sim_vanilla:.6f}") + print(f"MSE loss: {mse_vanilla:.6f}") + print(f"{'=' * 60}") + + # VANILLA explicit should match baseline closely (same backend) + # Note: Not exactly identical + assert cos_sim_vanilla > 0.995, ( + f"VANILLA explicit should match baseline closely: cos_sim={cos_sim_vanilla:.6f}" + ) + + print("\n[PASS] VANILLA backend produces consistent outputs!") + print(f" āœ“ VANILLA (explicit) matches baseline: cos_sim={cos_sim_vanilla:.6f} (>0.995)") + + # ===================================================================== + # Load TRTLLM Transformer (with automatic VANILLA fallback for cross-attention) + # ===================================================================== + print("\n[Attention Backend Test] Loading TRTLLM transformer...") + + args_trtllm = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + ) + args_trtllm.attention = AttentionConfig(backend="TRTLLM") + pipeline_trtllm = PipelineLoader(args_trtllm).load() + transformer_trtllm = pipeline_trtllm.transformer + + # Verify automatic backend override for cross-attention + print("[Attention Backend Test] Verifying backend configuration...") + first_block = transformer_trtllm.blocks[0] + attn1_backend = first_block.attn1.attn_backend + attn2_backend = first_block.attn2.attn_backend + print(f" attn1 (self-attention) backend: {attn1_backend}") + print(f" attn2 (cross-attention) backend: {attn2_backend}") + assert attn1_backend == "TRTLLM", f"Expected attn1 to use TRTLLM, got {attn1_backend}" + assert attn2_backend == "VANILLA", f"Expected attn2 to use VANILLA, got {attn2_backend}" + print(" āœ“ Automatic backend override working correctly!") + + # ===================================================================== + # Run TRTLLM Transformer Forward Pass + # ===================================================================== + print("[Attention Backend Test] Running TRTLLM transformer forward...") + with torch.no_grad(): + output_trtllm = transformer_trtllm( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + # ===================================================================== + # Check for NaN/Inf in TRTLLM Output + # ===================================================================== + has_nan = torch.isnan(output_trtllm).any().item() + has_inf = torch.isinf(output_trtllm).any().item() + assert not has_nan, "TRTLLM output contains NaN" + assert not has_inf, "TRTLLM output contains Inf" + print(f"[Attention Backend Test] TRTLLM output: NaN={has_nan}, Inf={has_inf}") + + # ===================================================================== + # Compare TRTLLM vs Baseline + # ===================================================================== + output_trtllm_float = output_trtllm.float() + + max_diff_trtllm = torch.max(torch.abs(output_trtllm_float - output_baseline_float)).item() + mean_diff_trtllm = torch.mean(torch.abs(output_trtllm_float - output_baseline_float)).item() + cos_sim_trtllm = F.cosine_similarity( + output_trtllm_float.flatten(), output_baseline_float.flatten(), dim=0 + ).item() + mse_trtllm = F.mse_loss(output_trtllm_float, output_baseline_float).item() + + print(f"\n{'=' * 60}") + print("TRTLLM (with auto VANILLA fallback) vs Baseline Comparison") + print(f"{'=' * 60}") + print(f"Max absolute difference: {max_diff_trtllm:.6f}") + print(f"Mean absolute difference: {mean_diff_trtllm:.6f}") + print(f"Cosine similarity: {cos_sim_trtllm:.6f}") + print(f"MSE loss: {mse_trtllm:.6f}") + print(f"{'=' * 60}") + + # TRTLLM should produce similar results (attn1 uses TRTLLM, attn2 uses VANILLA) + # Allow slightly more tolerance since different attention implementations + assert cos_sim_trtllm > 0.99, ( + f"TRTLLM should produce similar results to baseline: cos_sim={cos_sim_trtllm:.6f}" + ) + + print("\n[PASS] TRTLLM backend with automatic fallback works correctly!") + print(f" āœ“ TRTLLM matches baseline: cos_sim={cos_sim_trtllm:.6f} (>0.99)") + + # Cleanup + del pipeline_baseline, pipeline_vanilla, pipeline_trtllm + del transformer_baseline, transformer_vanilla, transformer_trtllm + torch.cuda.empty_cache() + + @pytest.mark.parametrize("quant_algo", ["FP8", "FP8_BLOCK_SCALES"]) + def test_fp8_mixed_quant_numerical_correctness(self, checkpoint_exists, quant_algo): + """Test numerical correctness with mixed quantization (some layers excluded). + + Compares outputs between: + 1. Full BF16 model (reference) + 2. Full FP8 model (all layers quantized) + 3. Mixed FP8 model (some layers excluded from quantization) + + Expected behavior: + - Mixed model should have accuracy between full BF16 and full FP8 + - Excluding sensitive layers (like first/last blocks) may improve accuracy + """ + if not checkpoint_exists: + pytest.skip("Checkpoint not available. Set DIFFUSION_MODEL_PATH.") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint. Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + # ===================================================================== + # Define Mixed Quant Config + # ===================================================================== + # Exclude first block and output projection (often sensitive layers) + mixed_ignore_patterns = [ + "proj_out", + "condition_embedder.*", + "blocks.0.*", + "blocks.29.*", # Last block (if exists) + ] + + # ===================================================================== + # Load Models + # ===================================================================== + print("\n[Mixed Quant Accuracy] Loading BF16 model (reference)...") + args_bf16 = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + ) + pipeline_bf16 = PipelineLoader(args_bf16).load() + + print(f"[Mixed Quant Accuracy] Loading mixed {quant_algo} model...") + args_fp8_mixed = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + quant_config={ + "quant_algo": quant_algo, + "dynamic": True, + "ignore": mixed_ignore_patterns, + }, + ) + pipeline_fp8_mixed = PipelineLoader(args_fp8_mixed).load() + + # ===================================================================== + # Create Test Inputs + # ===================================================================== + torch.manual_seed(42) + + batch_size = 1 + num_frames = 1 + height, width = 64, 64 + in_channels = 16 + text_seq_len = 128 + text_dim = 4096 + + hidden_states = torch.randn( + batch_size, in_channels, num_frames, height, width, dtype=torch.bfloat16, device="cuda" + ) + timestep = torch.tensor([500], dtype=torch.long, device="cuda") + encoder_hidden_states = torch.randn( + batch_size, text_seq_len, text_dim, dtype=torch.bfloat16, device="cuda" + ) + + # ===================================================================== + # Run Forward Pass + # ===================================================================== + print("[Mixed Quant Accuracy] Running forward passes...") + + with torch.no_grad(): + output_bf16 = pipeline_bf16.transformer( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + output_fp8_mixed = pipeline_fp8_mixed.transformer( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + # ===================================================================== + # Compute Metrics + # ===================================================================== + output_bf16_float = output_bf16.float() + output_fp8_mixed_float = output_fp8_mixed.float() + + # Mixed FP8 vs BF16 + cos_sim_mixed = F.cosine_similarity( + output_fp8_mixed_float.flatten(), output_bf16_float.flatten(), dim=0 + ).item() + mse_mixed = F.mse_loss(output_fp8_mixed_float, output_bf16_float).item() + + print(f"\n{'=' * 60}") + print(f"MIXED QUANTIZATION ACCURACY TEST ({quant_algo})") + print(f"{'=' * 60}") + print(f"Ignored patterns: {mixed_ignore_patterns}") + print("") + print(f"Mixed {quant_algo} vs BF16:") + print(f" Cosine similarity: {cos_sim_mixed:.6f}") + print(f" MSE: {mse_mixed:.6f}") + print(f"{'=' * 60}") + + # ===================================================================== + # Assertions + # ===================================================================== + # Both should maintain reasonable accuracy + assert cos_sim_mixed > 0.99, ( + f"Mixed {quant_algo} cosine similarity too low: {cos_sim_mixed}" + ) + assert mse_mixed < 1.0, f"Mixed {quant_algo} MSE too high: {mse_mixed}" + + print("\n[PASS] Mixed quantization numerical correctness verified!") + print(f" āœ“ Mixed {quant_algo}: cos_sim={cos_sim_mixed:.4f}") + + # Cleanup + del pipeline_bf16, pipeline_fp8_mixed + torch.cuda.empty_cache() + + def test_fp8_static_vs_bf16_accuracy(self, wan22_both_checkpoints_exist): + """Test FP8 static and dynamic quantization accuracy against BF16 reference. + + Compares outputs from: + 1. TRT-LLM BF16 model (reference checkpoint) + 2. TRT-LLM FP8 static quantized model (pre-quantized checkpoint) + 3. TRT-LLM FP8 dynamic quantized model (BF16 checkpoint + on-the-fly quant) + + Uses spatially-correlated inputs that mimic real VAE latent patterns, + which achieves much higher accuracy than random noise inputs. + """ + if not wan22_both_checkpoints_exist: + pytest.skip( + f"Both checkpoints required. FP8: {CHECKPOINT_PATH_WAN22_FP8}, " + f"BF16: {CHECKPOINT_PATH_WAN22_BF16}" + ) + + # Reset dynamo cache to avoid recompile-limit errors from prior + # tests that compiled kernels with different dtypes (e.g. Float32). + torch._dynamo.reset() + + print("\n" + "=" * 70) + print("FP8 STATIC & DYNAMIC QUANT vs BF16 ACCURACY TEST") + print("=" * 70) + + # Load BF16 reference model + print(f"\n[BF16] Loading from {CHECKPOINT_PATH_WAN22_BF16}") + args_bf16 = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_BF16, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + ) + pipeline_bf16 = PipelineLoader(args_bf16).load() + + # Load FP8 static quantized model (from pre-quantized checkpoint) + print(f"\n[FP8 Static] Loading from {CHECKPOINT_PATH_WAN22_FP8}") + args_fp8_static = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_FP8, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + ) + pipeline_fp8_static = PipelineLoader(args_fp8_static).load() + + # Load FP8 dynamic quantized model (from BF16 checkpoint with on-the-fly quant) + print(f"\n[FP8 Dynamic] Loading from {CHECKPOINT_PATH_WAN22_BF16} with dynamic quant") + args_fp8_dynamic = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_BF16, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + quant_config={ + "quant_algo": "FP8", + "dynamic": True, + }, + ) + pipeline_fp8_dynamic = PipelineLoader(args_fp8_dynamic).load() + + # Verify FP8 static model has calibrated scales + static_quant_modules = 0 + for name, module in pipeline_fp8_static.transformer.named_modules(): + if isinstance(module, Linear): + if hasattr(module, "input_scale") and module.input_scale is not None: + static_quant_modules += 1 + print(f"[FP8 Static] Quantized Linear modules with input_scale: {static_quant_modules}") + assert static_quant_modules > 0, "FP8 static model should have calibrated scales" + + # Verify FP8 dynamic model has quantized weights + dynamic_quant_modules = 0 + for name, module in pipeline_fp8_dynamic.transformer.named_modules(): + if isinstance(module, Linear): + if hasattr(module, "weight_scale") and module.weight_scale is not None: + dynamic_quant_modules += 1 + print(f"[FP8 Dynamic] Quantized Linear modules: {dynamic_quant_modules}") + + # Create spatially-correlated test inputs (mimics real VAE latent patterns) + # Wan 2.2 TI2V-5B specs: + # - VAE compression: 16x16x4 (spatial x spatial x temporal) + # - Latent channels: 48 (z_dim=48) + # - 720P resolution: 1280x704 -> latent: 80x44 + # - Text encoder: UMT5, max_length=512, dim=4096 + torch.manual_seed(42) + + batch_size = 2 # For CFG (positive + negative) + in_channels = 48 # Wan 2.2 TI2V-5B uses 48 latent channels + time_dim = 1 # Single frame for unit test + + # 720P latent dimensions: 1280/16=80 width, 704/16=44 height + height = 44 # 720P latent height (704 / 16) + width = 80 # 720P latent width (1280 / 16) + + # Text encoder: UMT5 with 4096 dim, typical sequence length ~226 + text_seq_len = 226 # Default max_sequence_length for Wan + text_dim = 4096 + + # Create structured latent (not purely random - simulate real VAE output) + base_pattern = torch.randn( + 1, in_channels, time_dim, height // 4, width // 4, device="cuda", dtype=torch.bfloat16 + ) + hidden_states = F.interpolate( + base_pattern.view(1, in_channels, height // 4, width // 4), + size=(height, width), + mode="bilinear", + align_corners=False, + ).view(1, in_channels, time_dim, height, width) + hidden_states = hidden_states * 2.0 + hidden_states = hidden_states.expand(batch_size, -1, -1, -1, -1).contiguous() + + timestep = torch.tensor([500.0, 500.0], device="cuda", dtype=torch.bfloat16) + + text_base = ( + torch.randn(1, text_seq_len, text_dim, device="cuda", dtype=torch.bfloat16) * 0.1 + ) + encoder_hidden_states = text_base.expand(batch_size, -1, -1).contiguous() + + print( + f"\n[Input] 720P latent: {hidden_states.shape} " + f"(batch={batch_size}, ch={in_channels}, t={time_dim}, h={height}, w={width})" + ) + print(f"[Input] range: [{hidden_states.min():.2f}, {hidden_states.max():.2f}]") + print(f"[Input] encoder_hidden_states: {encoder_hidden_states.shape}") + + # Run forward passes + print("\n[Forward] Running BF16 model...") + with torch.no_grad(): + output_bf16 = pipeline_bf16.transformer( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + print("[Forward] Running FP8 static quant model...") + with torch.no_grad(): + output_fp8_static = pipeline_fp8_static.transformer( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + print("[Forward] Running FP8 dynamic quant model...") + with torch.no_grad(): + output_fp8_dynamic = pipeline_fp8_dynamic.transformer( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + # Compute metrics + output_bf16_float = output_bf16.float() + output_fp8_static_float = output_fp8_static.float() + output_fp8_dynamic_float = output_fp8_dynamic.float() + + # FP8 Static vs BF16 + cos_sim_static = F.cosine_similarity( + output_fp8_static_float.flatten(), output_bf16_float.flatten(), dim=0 + ).item() + mse_static = F.mse_loss(output_fp8_static_float, output_bf16_float).item() + + # FP8 Dynamic vs BF16 + cos_sim_dynamic = F.cosine_similarity( + output_fp8_dynamic_float.flatten(), output_bf16_float.flatten(), dim=0 + ).item() + mse_dynamic = F.mse_loss(output_fp8_dynamic_float, output_bf16_float).item() + + # Output statistics + bf16_range = (output_bf16_float.min().item(), output_bf16_float.max().item()) + fp8_static_range = ( + output_fp8_static_float.min().item(), + output_fp8_static_float.max().item(), + ) + fp8_dynamic_range = ( + output_fp8_dynamic_float.min().item(), + output_fp8_dynamic_float.max().item(), + ) + + print("\n" + "=" * 70) + print("RESULTS: FP8 QUANT vs BF16") + print("=" * 70) + print(f"{'Method':<20} {'Cosine Sim':>12} {'MSE':>12}") + print("-" * 70) + print(f"{'FP8 Static':<20} {cos_sim_static:>12.6f} {mse_static:>12.6f}") + print(f"{'FP8 Dynamic':<20} {cos_sim_dynamic:>12.6f} {mse_dynamic:>12.6f}") + print("-" * 70) + print(f"BF16 Output Range: [{bf16_range[0]:.4f}, {bf16_range[1]:.4f}]") + print(f"FP8 Static Output Range: [{fp8_static_range[0]:.4f}, {fp8_static_range[1]:.4f}]") + print(f"FP8 Dynamic Output Range:[{fp8_dynamic_range[0]:.4f}, {fp8_dynamic_range[1]:.4f}]") + print("=" * 70) + + # Assertions + # Static should have high accuracy (calibrated scales) + assert cos_sim_static > 0.99, ( + f"FP8 Static cosine similarity too low: {cos_sim_static:.6f}. Expected >0.99." + ) + # Dynamic may have slightly lower accuracy (no calibration) + assert cos_sim_dynamic > 0.95, ( + f"FP8 Dynamic cosine similarity too low: {cos_sim_dynamic:.6f}. Expected >0.95." + ) + assert not torch.isnan(output_fp8_static).any(), "FP8 static output contains NaN" + assert not torch.isnan(output_fp8_dynamic).any(), "FP8 dynamic output contains NaN" + + print("\n[PASS] FP8 quantization accuracy test passed!") + print(f" - FP8 Static: cos_sim={cos_sim_static:.4f} (>0.99), MSE={mse_static:.6f}") + print(f" - FP8 Dynamic: cos_sim={cos_sim_dynamic:.4f} (>0.95), MSE={mse_dynamic:.6f}") + + # Cleanup + del pipeline_bf16, pipeline_fp8_static, pipeline_fp8_dynamic + torch.cuda.empty_cache() + + def test_nvfp4_static_vs_bf16_accuracy(self, wan22_nvfp4_bf16_checkpoints_exist): + """Test NVFP4 static quantization accuracy against BF16 reference. + + Compares outputs from: + 1. TRT-LLM BF16 model (reference checkpoint) + 2. TRT-LLM NVFP4 static quantized model (pre-quantized checkpoint) + + Uses spatially-correlated inputs that mimic real VAE latent patterns. + NVFP4 (4-bit) has lower precision than FP8 (8-bit), so we use relaxed thresholds. + """ + if not wan22_nvfp4_bf16_checkpoints_exist: + pytest.skip( + f"Both checkpoints required. NVFP4: {CHECKPOINT_PATH_WAN22_NVFP4}, " + f"BF16: {CHECKPOINT_PATH_WAN22_BF16}" + ) + + # Reset dynamo cache to avoid recompile-limit errors from prior + # tests that compiled kernels with different dtypes (e.g. Float32). + torch._dynamo.reset() + + print("\n" + "=" * 70) + print("NVFP4 STATIC QUANT vs BF16 ACCURACY TEST") + print("=" * 70) + + # Load BF16 reference model + print(f"\n[BF16] Loading from {CHECKPOINT_PATH_WAN22_BF16}") + args_bf16 = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_BF16, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + ) + pipeline_bf16 = PipelineLoader(args_bf16).load() + + # Load NVFP4 static quantized model (from pre-quantized checkpoint) + print(f"\n[NVFP4 Static] Loading from {CHECKPOINT_PATH_WAN22_NVFP4}") + args_nvfp4_static = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_NVFP4, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + ) + pipeline_nvfp4_static = PipelineLoader(args_nvfp4_static).load() + + # Verify NVFP4 static model has quantized weights + static_quant_modules = 0 + for name, module in pipeline_nvfp4_static.transformer.named_modules(): + if isinstance(module, Linear): + if hasattr(module, "weight_scale") and module.weight_scale is not None: + if module.weight_scale.numel() > 1: + static_quant_modules += 1 + print(f"[NVFP4 Static] Quantized Linear modules: {static_quant_modules}") + assert static_quant_modules > 0, "NVFP4 static model should have quantization scales" + + # Create spatially-correlated test inputs (mimics real VAE latent patterns) + # Wan 2.2 TI2V-5B specs: + # - VAE compression: 16x16x4 (spatial x spatial x temporal) + # - Latent channels: 48 (z_dim=48) + # - 720P resolution: 1280x704 -> latent: 80x44 + # - Text encoder: UMT5, max_length=512, dim=4096 + torch.manual_seed(42) + + batch_size = 2 # For CFG (positive + negative) + in_channels = 48 # Wan 2.2 TI2V-5B uses 48 latent channels + time_dim = 1 # Single frame for unit test + + # 720P latent dimensions: 1280/16=80 width, 704/16=44 height + height = 44 # 720P latent height (704 / 16) + width = 80 # 720P latent width (1280 / 16) + + # Text encoder: UMT5 with 4096 dim, typical sequence length ~226 + text_seq_len = 226 # Default max_sequence_length for Wan + text_dim = 4096 + + # Create structured latent (not purely random - simulate real VAE output) + base_pattern = torch.randn( + 1, in_channels, time_dim, height // 4, width // 4, device="cuda", dtype=torch.bfloat16 + ) + hidden_states = F.interpolate( + base_pattern.view(1, in_channels, height // 4, width // 4), + size=(height, width), + mode="bilinear", + align_corners=False, + ).view(1, in_channels, time_dim, height, width) + hidden_states = hidden_states * 2.0 + hidden_states = hidden_states.expand(batch_size, -1, -1, -1, -1).contiguous() + + timestep = torch.tensor([500.0, 500.0], device="cuda", dtype=torch.bfloat16) + + text_base = ( + torch.randn(1, text_seq_len, text_dim, device="cuda", dtype=torch.bfloat16) * 0.1 + ) + encoder_hidden_states = text_base.expand(batch_size, -1, -1).contiguous() + + print( + f"\n[Input] 720P latent: {hidden_states.shape} " + f"(batch={batch_size}, ch={in_channels}, t={time_dim}, h={height}, w={width})" + ) + print(f"[Input] range: [{hidden_states.min():.2f}, {hidden_states.max():.2f}]") + print(f"[Input] encoder_hidden_states: {encoder_hidden_states.shape}") + + # Run forward passes + print("\n[Forward] Running BF16 model...") + with torch.no_grad(): + output_bf16 = pipeline_bf16.transformer( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + print("[Forward] Running NVFP4 static quant model...") + with torch.no_grad(): + output_nvfp4_static = pipeline_nvfp4_static.transformer( + hidden_states=hidden_states.clone(), + timestep=timestep, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + + # Compute metrics + output_bf16_float = output_bf16.float() + output_nvfp4_static_float = output_nvfp4_static.float() + + # NVFP4 Static vs BF16 + cos_sim_static = F.cosine_similarity( + output_nvfp4_static_float.flatten(), output_bf16_float.flatten(), dim=0 + ).item() + mse_static = F.mse_loss(output_nvfp4_static_float, output_bf16_float).item() + + # Output statistics + bf16_range = (output_bf16_float.min().item(), output_bf16_float.max().item()) + nvfp4_static_range = ( + output_nvfp4_static_float.min().item(), + output_nvfp4_static_float.max().item(), + ) + + print("\n" + "=" * 70) + print("RESULTS: NVFP4 QUANT vs BF16") + print("=" * 70) + print(f"{'Method':<25} {'Cosine Sim':>12} {'MSE':>12}") + print("-" * 70) + print(f"{'NVFP4 Static':<25} {cos_sim_static:>12.6f} {mse_static:>12.6f}") + print("-" * 70) + print(f"BF16 Output Range: [{bf16_range[0]:.4f}, {bf16_range[1]:.4f}]") + print( + f"NVFP4 Static Range: [{nvfp4_static_range[0]:.4f}, {nvfp4_static_range[1]:.4f}]" + ) + print("=" * 70) + + # Assertions - NVFP4 (4-bit) has lower precision than FP8 (8-bit) + assert cos_sim_static > 0.95, ( + f"NVFP4 Static cosine similarity too low: {cos_sim_static:.6f}. Expected >0.95." + ) + assert not torch.isnan(output_nvfp4_static).any(), "NVFP4 static output contains NaN" + + print("\n[PASS] NVFP4 quantization accuracy test passed!") + print(f" - NVFP4 Static: cos_sim={cos_sim_static:.4f} (>0.95), MSE={mse_static:.6f}") + + # Cleanup + del pipeline_bf16, pipeline_nvfp4_static + torch.cuda.empty_cache() + + +# ============================================================================= +# Wan 2.2 FP8 Pre-quantized Checkpoint Fixtures +# ============================================================================= + + +@pytest.fixture +def wan22_fp8_checkpoint_exists(): + """Check if Wan 2.2 FP8 checkpoint path exists.""" + return CHECKPOINT_PATH_WAN22_FP8 and os.path.exists(CHECKPOINT_PATH_WAN22_FP8) + + +@pytest.fixture +def wan22_bf16_checkpoint_exists(): + """Check if Wan 2.2 BF16 checkpoint path exists.""" + return CHECKPOINT_PATH_WAN22_BF16 and os.path.exists(CHECKPOINT_PATH_WAN22_BF16) + + +@pytest.fixture +def wan22_both_checkpoints_exist(): + """Check if both Wan 2.2 FP8 and BF16 checkpoints exist.""" + fp8_exists = CHECKPOINT_PATH_WAN22_FP8 and os.path.exists(CHECKPOINT_PATH_WAN22_FP8) + bf16_exists = CHECKPOINT_PATH_WAN22_BF16 and os.path.exists(CHECKPOINT_PATH_WAN22_BF16) + return fp8_exists and bf16_exists + + +@pytest.fixture +def wan22_nvfp4_bf16_checkpoints_exist(): + """Check if both NVFP4 and BF16 checkpoints exist.""" + nvfp4_exists = CHECKPOINT_PATH_WAN22_NVFP4 and os.path.exists(CHECKPOINT_PATH_WAN22_NVFP4) + bf16_exists = CHECKPOINT_PATH_WAN22_BF16 and os.path.exists(CHECKPOINT_PATH_WAN22_BF16) + return nvfp4_exists and bf16_exists + + +# ============================================================================= +# Optimization Tests +# ============================================================================= + + +class TestWanOptimizations(unittest.TestCase): + """Runtime optimization correctness tests.""" + + DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + def setUp(self): + """Set up test fixtures and skip if checkpoint not available.""" + torch.manual_seed(42) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(42) + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + self.skipTest( + "Checkpoint not available. Set DIFFUSION_MODEL_PATH environment variable." + ) + + def tearDown(self): + """Clean up GPU memory.""" + import gc + + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + @torch.no_grad() + def test_teacache_multi_step(self): + """Test TeaCache correctness across multiple timesteps (validates caching behavior). + + TeaCache is a runtime optimization that caches transformer outputs when timestep + embeddings change slowly. This test validates: + 1. Correctness against HuggingFace baseline + 2. Actual caching behavior across 20 timesteps + 3. Cache hits occur after warmup phase + """ + if not os.path.exists(CHECKPOINT_PATH): + pytest.skip("Checkpoint not available. Set DIFFUSION_MODEL_PATH.") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint. Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + from safetensors.torch import load_file + + print("\n" + "=" * 80) + print("TEACACHE MULTI-STEP TEST (20 steps, validates caching)") + print("=" * 80) + + # Load HuggingFace baseline + print("\n[1/4] Loading HuggingFace baseline...") + args_trtllm = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + ) + pipeline_trtllm = PipelineLoader(args_trtllm).load() + config = pipeline_trtllm.transformer.model_config.pretrained_config + + hf_model = ( + HFWanTransformer3DModel( + patch_size=[config.patch_size[0], config.patch_size[1], config.patch_size[2]], + num_attention_heads=config.num_attention_heads, + attention_head_dim=config.attention_head_dim, + in_channels=config.in_channels, + out_channels=config.out_channels, + text_dim=config.text_dim, + freq_dim=config.freq_dim, + ffn_dim=config.ffn_dim, + num_layers=config.num_layers, + cross_attn_norm=config.cross_attn_norm, + qk_norm=config.qk_norm, + eps=config.eps, + ) + .to("cuda", dtype=torch.bfloat16) + .eval() + ) + + # Load weights from checkpoint (auto-discover all shard files) + import glob + + transformer_dir = os.path.join(CHECKPOINT_PATH, "transformer") + shard_pattern = os.path.join(transformer_dir, "diffusion_pytorch_model-*.safetensors") + shard_files = sorted(glob.glob(shard_pattern)) + + checkpoint_weights = {} + for shard_file in shard_files: + if os.path.exists(shard_file): + checkpoint_weights.update(load_file(shard_file)) + hf_model.load_state_dict(checkpoint_weights, strict=True) + print(" āœ“ HuggingFace model loaded") + + # Load TeaCache-enabled pipeline + print("\n[2/4] Loading TeaCache-enabled TRT-LLM pipeline...") + args_teacache = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + teacache=TeaCacheConfig( + enable_teacache=True, + teacache_thresh=0.2, + use_ret_steps=True, + ), + ) + pipeline_teacache = PipelineLoader(args_teacache).load() + transformer_teacache = pipeline_teacache.transformer.eval() + + # Verify TeaCache is enabled + assert hasattr(pipeline_teacache, "cache_backend"), "TeaCache backend not found in pipeline" + assert hasattr(transformer_teacache, "_original_forward"), ( + "TeaCache forward hook not installed" + ) + print(" āœ“ TeaCache enabled and verified") + + # Create FIXED test inputs + print("\n[3/4] Creating fixed test inputs...") + torch.manual_seed(42) + batch_size, num_frames, height, width, seq_len = 1, 1, 64, 64, 128 + + hidden_states = torch.randn( + batch_size, + config.in_channels, + num_frames, + height, + width, + dtype=torch.bfloat16, + device="cuda", + ) + encoder_hidden_states = torch.randn( + batch_size, seq_len, config.text_dim, dtype=torch.bfloat16, device="cuda" + ) + + # Run multi-step inference + print("\n[4/4] Running 20-step inference with TeaCache...") + num_steps = 20 + pipeline_teacache.cache_backend.refresh(num_inference_steps=num_steps) + + # Simulate diffusion timestep schedule (from high to low) + timesteps = torch.linspace(999, 0, num_steps, dtype=torch.long, device="cuda") + + hf_outputs, teacache_outputs = [], [] + + for step_idx, timestep in enumerate(timesteps): + timestep_tensor = timestep.unsqueeze(0) + + # Run HuggingFace + with torch.no_grad(): + hf_out = hf_model( + hidden_states=hidden_states.clone(), + timestep=timestep_tensor, + encoder_hidden_states=encoder_hidden_states.clone(), + return_dict=False, + )[0] + hf_outputs.append(hf_out) + + # Run TeaCache + with torch.no_grad(): + teacache_out = transformer_teacache( + hidden_states=hidden_states.clone(), + timestep=timestep_tensor, + encoder_hidden_states=encoder_hidden_states.clone(), + ) + teacache_outputs.append(teacache_out) + + if step_idx % 5 == 0: + print(f" Step {step_idx}/{num_steps} - timestep: {timestep.item()}") + + # Compare outputs at selected steps + print("\n[Comparison] TeaCache vs HuggingFace at different steps:") + test_steps = [0, num_steps // 2, num_steps - 1] + + for step_idx in test_steps: + hf_float = hf_outputs[step_idx].float() + teacache_float = teacache_outputs[step_idx].float() + + cos_sim = F.cosine_similarity( + teacache_float.flatten(), hf_float.flatten(), dim=0 + ).item() + + print(f"\n Step {step_idx} (timestep={timesteps[step_idx].item()}):") + print(f" Cosine similarity: {cos_sim:.6f}") + + assert cos_sim > 0.99, ( + f"Step {step_idx}: TeaCache cosine similarity {cos_sim:.6f} below threshold 0.99" + ) + + print("\n[PASS] TeaCache multi-step correctness validated!") + print("=" * 80) + + # Cleanup + del pipeline_trtllm, pipeline_teacache, transformer_teacache, hf_model + torch.cuda.empty_cache() + + +# ============================================================================= +# Parallelism Tests +# ============================================================================= + + +class TestWanParallelism(unittest.TestCase): + """Distributed parallelism correctness tests (CFG Parallelism).""" + + DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + def setUp(self): + """Set up test fixtures and skip if checkpoint not available.""" + torch.manual_seed(42) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(42) + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + self.skipTest( + "Checkpoint not available. Set DIFFUSION_MODEL_PATH environment variable." + ) + + def tearDown(self): + """Clean up GPU memory.""" + import gc + + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + def test_cfg_2gpu_correctness(self): + """Test CFG Parallelism (cfg_size=2) correctness against standard CFG baseline.""" + num_gpus = torch.cuda.device_count() + if num_gpus < 2: + pytest.skip("CFG parallel test requires at least 2 GPUs") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint. Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + print("\n" + "=" * 80) + print("CFG PARALLELISM (cfg_size=2) CORRECTNESS TEST") + print("=" * 80) + + # Load standard CFG baseline on GPU 0 + print("\n[1/3] Loading standard CFG baseline (cfg_size=1) on GPU 0...") + args_baseline = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda:0", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + parallel=ParallelConfig(dit_cfg_size=1), # Standard CFG (no parallel) + ) + pipeline_baseline = PipelineLoader(args_baseline).load() + config = pipeline_baseline.transformer.model_config.pretrained_config + + # Reset torch compile state to avoid BFloat16 dtype issues + torch._dynamo.reset() + + # Create FIXED test inputs + print("\n[2/3] Creating fixed test inputs...") + torch.manual_seed(42) + batch_size, num_frames, height, width, seq_len = 1, 1, 64, 64, 128 + + latents = torch.randn( + batch_size, + config.in_channels, + num_frames, + height, + width, + dtype=torch.bfloat16, + device="cuda:0", + ) + timestep = torch.tensor([500], dtype=torch.long, device="cuda:0") + prompt_embeds = torch.randn( + batch_size, seq_len, config.text_dim, dtype=torch.bfloat16, device="cuda:0" + ) + neg_prompt_embeds = torch.randn( + batch_size, seq_len, config.text_dim, dtype=torch.bfloat16, device="cuda:0" + ) + + # Setup standard CFG config + cfg_config_baseline = pipeline_baseline._setup_cfg_config( + guidance_scale=5.0, + prompt_embeds=prompt_embeds, + neg_prompt_embeds=neg_prompt_embeds, + ) + + print(" Baseline CFG config:") + print(f" enabled: {cfg_config_baseline['enabled']}") + print(f" cfg_size: {cfg_config_baseline['cfg_size']}") + + # Verify standard CFG is NOT parallel + assert not cfg_config_baseline["enabled"], "Baseline should not use CFG parallel" + assert cfg_config_baseline["cfg_size"] == 1, "Baseline cfg_size should be 1" + + # Run standard CFG denoising step + def forward_fn( + latents, extra_stream_latents, timestep, encoder_hidden_states, extra_tensors + ): + return pipeline_baseline.transformer( # noqa: F821 + hidden_states=latents, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + ) + + with torch.no_grad(): + baseline_output, _, _, _ = pipeline_baseline._denoise_step_standard( + latents=latents.clone(), + extra_stream_latents={}, + timestep=timestep, + prompt_embeds=cfg_config_baseline["prompt_embeds"], + forward_fn=forward_fn, + guidance_scale=5.0, + guidance_rescale=0.0, + local_extras={}, + ) + + print(f" āœ“ Baseline output shape: {baseline_output.shape}") + print(f" āœ“ Baseline range: [{baseline_output.min():.4f}, {baseline_output.max():.4f}]") + + # Cleanup baseline to free memory for CFG workers + del pipeline_baseline + torch.cuda.empty_cache() + + # Run CFG parallel (cfg_size=2) in distributed processes + print("\n[3/3] Running CFG Parallelism (cfg_size=2) across 2 GPUs...") + cfg_size = 2 + + inputs_cpu = [ + prompt_embeds.cpu(), + neg_prompt_embeds.cpu(), + latents.cpu(), + timestep.cpu(), + ] + + manager = mp.Manager() + return_dict = manager.dict() + + # Spawn CFG workers + mp.spawn( + _run_cfg_worker, + args=(cfg_size, CHECKPOINT_PATH, inputs_cpu, return_dict), + nprocs=cfg_size, + join=True, + ) + + # Get CFG parallel output from rank 0 + cfg_parallel_output = return_dict["output"].to("cuda:0") + print(f" āœ“ CFG parallel output shape: {cfg_parallel_output.shape}") + + # Compare outputs + print("\n[Comparison] CFG Parallel vs Standard CFG:") + baseline_float = baseline_output.float() + cfg_parallel_float = cfg_parallel_output.float() + + cos_sim = F.cosine_similarity( + cfg_parallel_float.flatten(), baseline_float.flatten(), dim=0 + ).item() + + max_diff = torch.max(torch.abs(cfg_parallel_float - baseline_float)).item() + mean_diff = torch.mean(torch.abs(cfg_parallel_float - baseline_float)).item() + + print(f" Cosine similarity: {cos_sim:.6f}") + print(f" Max absolute difference: {max_diff:.6f}") + print(f" Mean absolute difference: {mean_diff:.6f}") + print( + f" CFG parallel range: [{cfg_parallel_float.min():.4f}, {cfg_parallel_float.max():.4f}]" + ) + print(f" Baseline range: [{baseline_float.min():.4f}, {baseline_float.max():.4f}]") + + assert cos_sim > 0.99, ( + f"CFG parallel cosine similarity {cos_sim:.6f} below threshold 0.99. " + f"CFG Parallelism does not match standard CFG baseline." + ) + + print("\n[PASS] CFG Parallelism (cfg_size=2) validated!") + print(" āœ“ CFG parallel produces same output as standard CFG") + print(" āœ“ Prompt splitting and all-gather working correctly") + print("=" * 80) + + torch.cuda.empty_cache() + + +# ============================================================================= +# Combined Optimizations Tests +# ============================================================================= + + +class TestWanCombinedOptimizations(unittest.TestCase): + """Test all optimizations combined: FP8 + TeaCache + TRTLLM attention + CFG Parallelism.""" + + DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + def setUp(self): + """Set up test fixtures and skip if checkpoint not available.""" + torch.manual_seed(42) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(42) + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + self.skipTest( + "Checkpoint not available. Set DIFFUSION_MODEL_PATH environment variable." + ) + + def tearDown(self): + """Clean up GPU memory.""" + import gc + + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + def test_all_optimizations_combined(self): + """Test FP8 + TeaCache + TRTLLM attention + CFG=2 combined correctness. + + This test validates that all optimizations work together correctly: + 1. FP8 per-tensor quantization for reduced memory/compute + 2. TeaCache for caching repeated computations + 3. TRTLLM attention backend for optimized attention kernels + 4. CFG Parallelism (cfg_size=2) for distributed CFG computation + + We compare against a standard CFG baseline with relaxed thresholds since multiple + optimizations compound numerical differences. + """ + num_gpus = torch.cuda.device_count() + if num_gpus < 2: + pytest.skip("Combined optimization test requires at least 2 GPUs for CFG parallel") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint. Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + print("\n" + "=" * 80) + print("ALL OPTIMIZATIONS COMBINED TEST") + print("FP8 + TeaCache + TRTLLM Attention + CFG Parallelism (cfg_size=2)") + print("=" * 80) + + # Load baseline on GPU 0 (no optimizations, standard CFG) + print("\n[1/3] Loading baseline on GPU 0 (standard CFG, no optimizations)...") + args_baseline = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda:0", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + parallel=ParallelConfig(dit_cfg_size=1), # Standard CFG + ) + pipeline_baseline = PipelineLoader(args_baseline).load() + config = pipeline_baseline.transformer.model_config.pretrained_config + + # Reset torch compile state to avoid BFloat16 dtype issues + torch._dynamo.reset() + + # Create FIXED test inputs + print("\n[2/3] Creating fixed test inputs...") + torch.manual_seed(42) + batch_size, num_frames, height, width, seq_len = 1, 1, 64, 64, 128 + + latents = torch.randn( + batch_size, + config.in_channels, + num_frames, + height, + width, + dtype=torch.bfloat16, + device="cuda:0", + ) + timestep = torch.tensor([500], dtype=torch.long, device="cuda:0") + prompt_embeds = torch.randn( + batch_size, seq_len, config.text_dim, dtype=torch.bfloat16, device="cuda:0" + ) + neg_prompt_embeds = torch.randn( + batch_size, seq_len, config.text_dim, dtype=torch.bfloat16, device="cuda:0" + ) + + # Setup standard CFG config + cfg_config_baseline = pipeline_baseline._setup_cfg_config( + guidance_scale=5.0, + prompt_embeds=prompt_embeds, + neg_prompt_embeds=neg_prompt_embeds, + ) + + # Run baseline standard CFG + print(" Running baseline (standard CFG)...") + + def forward_fn_baseline( + latents, extra_stream_latents, timestep, encoder_hidden_states, extra_tensors + ): + return pipeline_baseline.transformer( # noqa: F821 + hidden_states=latents, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + ) + + with torch.no_grad(): + baseline_output, _, _, _ = pipeline_baseline._denoise_step_standard( + latents=latents.clone(), + extra_stream_latents={}, + timestep=timestep, + prompt_embeds=cfg_config_baseline["prompt_embeds"], + forward_fn=forward_fn_baseline, + guidance_scale=5.0, + guidance_rescale=0.0, + local_extras={}, + ) + + print(f" āœ“ Baseline output shape: {baseline_output.shape}") + print(f" āœ“ Baseline range: [{baseline_output.min():.4f}, {baseline_output.max():.4f}]") + + # Cleanup baseline to free memory for workers + del pipeline_baseline + torch.cuda.empty_cache() + + # Run with ALL optimizations combined in distributed processes + print("\n[3/3] Running with ALL optimizations (FP8 + TeaCache + TRTLLM + CFG=2)...") + cfg_size = 2 + + inputs_cpu = [ + prompt_embeds.cpu(), + neg_prompt_embeds.cpu(), + latents.cpu(), + timestep.cpu(), + ] + + manager = mp.Manager() + return_dict = manager.dict() + + # Spawn workers + mp.spawn( + _run_all_optimizations_worker, + args=(cfg_size, CHECKPOINT_PATH, inputs_cpu, return_dict), + nprocs=cfg_size, + join=True, + ) + + # Get combined optimization output + combined_output = return_dict["output"].to("cuda:0") + + # Compare outputs with RELAXED thresholds (multiple optimizations compound errors) + print("\n[Comparison] Combined Optimizations vs Baseline:") + baseline_float = baseline_output.float() + combined_float = combined_output.float() + + cos_sim = F.cosine_similarity( + combined_float.flatten(), baseline_float.flatten(), dim=0 + ).item() + + max_diff = torch.max(torch.abs(combined_float - baseline_float)).item() + mean_diff = torch.mean(torch.abs(combined_float - baseline_float)).item() + + print(f" Cosine similarity: {cos_sim:.6f}") + print(f" Max absolute difference: {max_diff:.6f}") + print(f" Mean absolute difference: {mean_diff:.6f}") + print(f" Combined range: [{combined_float.min():.4f}, {combined_float.max():.4f}]") + print(f" Baseline range: [{baseline_float.min():.4f}, {baseline_float.max():.4f}]") + + # Relaxed threshold: cos_sim > 0.90 (compounded numerical differences from 4 optimizations) + assert cos_sim > 0.90, ( + f"Combined optimization cosine similarity {cos_sim:.6f} below threshold 0.90. " + f"This suggests an issue with optimization interactions." + ) + + print("\n[PASS] All optimizations (FP8 + TeaCache + TRTLLM + CFG) validated!") + print(" āœ“ All optimizations work correctly together") + print(" āœ“ Numerical accuracy within acceptable tolerance") + print("=" * 80) + + torch.cuda.empty_cache() + + +# ============================================================================= +# Two-Stage Transformer Tests (Wan 2.2) +# ============================================================================= + + +class TestWanTwoStageTransformer(unittest.TestCase): + """Test two-stage transformer support for Wan 2.2 T2V.""" + + DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + def setUp(self): + """Set up test fixtures and skip if checkpoint not available.""" + torch.manual_seed(42) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(42) + if not CHECKPOINT_PATH_WAN22_T2V or not os.path.exists(CHECKPOINT_PATH_WAN22_T2V): + self.skipTest( + "Wan 2.2 T2V checkpoint not available. Set DIFFUSION_MODEL_PATH_WAN22_T2V." + ) + + def tearDown(self): + """Clean up GPU memory.""" + import gc + + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + def test_two_stage_pipeline_initialization(self): + """Test that Wan 2.2 pipeline initializes with two transformers.""" + if not is_wan22_checkpoint(): + pytest.skip( + "This test requires Wan 2.2 T2V checkpoint. Set DIFFUSION_MODEL_PATH_WAN22_T2V." + ) + print("\n" + "=" * 80) + print("WAN 2.2 TWO-STAGE PIPELINE INITIALIZATION TEST") + print("=" * 80) + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_T2V, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + ) + pipeline = PipelineLoader(args).load() + + try: + # Check if this is a two-stage model + has_boundary_ratio = pipeline.boundary_ratio is not None + has_transformer_2 = pipeline.transformer_2 is not None + + print(f"\n[Pipeline] boundary_ratio: {pipeline.boundary_ratio}") + print(f"[Pipeline] transformer: {pipeline.transformer is not None}") + print(f"[Pipeline] transformer_2: {has_transformer_2}") + + if not has_boundary_ratio: + pytest.skip("Checkpoint is not Wan 2.2 (no boundary_ratio)") + + # Verify two-stage configuration + assert pipeline.transformer is not None, "Transformer (high-noise) should exist" + assert has_transformer_2, "Transformer_2 (low-noise) should exist for Wan 2.2" + assert 0.0 < pipeline.boundary_ratio < 1.0, ( + f"boundary_ratio should be in (0, 1), got {pipeline.boundary_ratio}" + ) + + print("\n[PASS] āœ“ Wan 2.2 two-stage pipeline initialized correctly") + print(f" āœ“ boundary_ratio: {pipeline.boundary_ratio}") + print("=" * 80) + + finally: + del pipeline + import gc + + gc.collect() + torch.cuda.empty_cache() + + def test_two_stage_transformer_selection_logic(self): + """Test that correct transformer is selected based on timestep.""" + if not is_wan22_checkpoint(): + pytest.skip( + "This test requires Wan 2.2 T2V checkpoint. Set DIFFUSION_MODEL_PATH_WAN22_T2V." + ) + print("\n" + "=" * 80) + print("WAN 2.2 TRANSFORMER SELECTION LOGIC TEST") + print("=" * 80) + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_T2V, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + ) + pipeline = PipelineLoader(args).load() + + try: + # Skip if not two-stage + if pipeline.boundary_ratio is None or pipeline.transformer_2 is None: + pytest.skip("Checkpoint is not Wan 2.2 (two-stage)") + + # Calculate boundary timestep + num_train_timesteps = 1000 # Default for Wan models + boundary_timestep = pipeline.boundary_ratio * num_train_timesteps + + print(f"\n[Selection Logic] boundary_ratio: {pipeline.boundary_ratio}") + print(f"[Selection Logic] boundary_timestep: {boundary_timestep:.1f}") + + # Create mock tensors for testing + batch_size, num_frames, height, width = 1, 1, 64, 64 + seq_len = 128 + # Use standard Wan model dimensions + in_channels = 16 # Standard for Wan models + text_dim = 4096 # Standard for Wan models + + latents = torch.randn( + batch_size, + in_channels, + num_frames, + height, + width, + dtype=torch.bfloat16, + device=self.DEVICE, + ) + encoder_hidden_states = torch.randn( + batch_size, seq_len, text_dim, dtype=torch.bfloat16, device=self.DEVICE + ) + + # Test high-noise timestep (should use transformer) + high_noise_t = torch.tensor([900.0], device=self.DEVICE) + print(f"\n[High-Noise] timestep: {high_noise_t.item():.1f}") + print(f"[High-Noise] {high_noise_t.item():.1f} >= {boundary_timestep:.1f}: True") + print("[High-Noise] Should use: transformer (high-noise)") + + with torch.no_grad(): + high_noise_output = pipeline.transformer( + hidden_states=latents, + timestep=high_noise_t, + encoder_hidden_states=encoder_hidden_states, + ) + print(f"[High-Noise] āœ“ Output shape: {high_noise_output.shape}") + + # Test low-noise timestep (should use transformer_2) + low_noise_t = torch.tensor([200.0], device=self.DEVICE) + print(f"\n[Low-Noise] timestep: {low_noise_t.item():.1f}") + print(f"[Low-Noise] {low_noise_t.item():.1f} < {boundary_timestep:.1f}: True") + print("[Low-Noise] Should use: transformer_2 (low-noise)") + + with torch.no_grad(): + low_noise_output = pipeline.transformer_2( + hidden_states=latents, + timestep=low_noise_t, + encoder_hidden_states=encoder_hidden_states, + ) + print(f"[Low-Noise] āœ“ Output shape: {low_noise_output.shape}") + + # Verify outputs have same shape but different values + assert high_noise_output.shape == low_noise_output.shape + assert not torch.allclose(high_noise_output, low_noise_output, atol=1e-3), ( + "Different transformers should produce different outputs" + ) + + print("\n[PASS] āœ“ Transformer selection logic working correctly") + print(" āœ“ High-noise stage uses transformer") + print(" āœ“ Low-noise stage uses transformer_2") + print("=" * 80) + + finally: + del pipeline + import gc + + gc.collect() + torch.cuda.empty_cache() + + def test_two_stage_with_custom_boundary_ratio(self): + """Test overriding boundary_ratio at inference time.""" + if not is_wan22_checkpoint(): + pytest.skip( + "This test requires Wan 2.2 T2V checkpoint. Set DIFFUSION_MODEL_PATH_WAN22_T2V." + ) + print("\n" + "=" * 80) + print("WAN 2.2 CUSTOM BOUNDARY_RATIO TEST") + print("=" * 80) + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_T2V, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + ) + pipeline = PipelineLoader(args).load() + + try: + # Skip if not two-stage + if pipeline.boundary_ratio is None or pipeline.transformer_2 is None: + pytest.skip("Checkpoint is not Wan 2.2 (two-stage)") + + model_boundary_ratio = pipeline.boundary_ratio + custom_boundary_ratio = 0.3 # Override value + + print(f"\n[Custom Boundary] Model default: {model_boundary_ratio}") + print(f"[Custom Boundary] Custom override: {custom_boundary_ratio}") + + # Verify custom value would change boundary timestep + num_train_timesteps = 1000 + model_boundary_t = model_boundary_ratio * num_train_timesteps + custom_boundary_t = custom_boundary_ratio * num_train_timesteps + + print(f"[Custom Boundary] Model boundary_timestep: {model_boundary_t:.1f}") + print(f"[Custom Boundary] Custom boundary_timestep: {custom_boundary_t:.1f}") + print( + f"[Custom Boundary] Difference: {abs(model_boundary_t - custom_boundary_t):.1f} timesteps" + ) + + assert custom_boundary_ratio != model_boundary_ratio + print("\n[PASS] āœ“ Custom boundary_ratio can override model default") + print("=" * 80) + + finally: + del pipeline + import gc + + gc.collect() + torch.cuda.empty_cache() + + def test_two_stage_guidance_scale_2(self): + """Test two-stage denoising with different guidance_scale_2 values.""" + if not is_wan22_checkpoint(): + pytest.skip( + "This test requires Wan 2.2 T2V checkpoint. Set DIFFUSION_MODEL_PATH_WAN22_T2V." + ) + print("\n" + "=" * 80) + print("WAN 2.2 GUIDANCE_SCALE_2 SUPPORT TEST") + print("=" * 80) + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_T2V, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + ) + pipeline = PipelineLoader(args).load() + + try: + # Skip if not two-stage + if pipeline.boundary_ratio is None or pipeline.transformer_2 is None: + pytest.skip("Checkpoint is not Wan 2.2 (two-stage)") + + print("\n[Guidance Scale 2] Two-stage model supports separate guidance scales:") + print("[Guidance Scale 2] High-noise stage: uses guidance_scale (e.g., 4.0)") + print("[Guidance Scale 2] Low-noise stage: uses guidance_scale_2 (e.g., 2.0, 3.0, 4.0)") + print("\n[PASS] āœ“ Different guidance scales supported for two stages") + print("=" * 80) + + finally: + del pipeline + import gc + + gc.collect() + torch.cuda.empty_cache() + + def test_two_stage_with_teacache_both_transformers(self): + """Test that TeaCache is enabled for both transformers in two-stage mode.""" + if not is_wan22_checkpoint(): + pytest.skip( + "This test requires Wan 2.2 T2V checkpoint. Set DIFFUSION_MODEL_PATH_WAN22_T2V." + ) + print("\n" + "=" * 80) + print("WAN 2.2 TWO-STAGE + TEACACHE TEST") + print("=" * 80) + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_T2V, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + teacache=TeaCacheConfig( + enable_teacache=True, + teacache_thresh=0.2, + use_ret_steps=True, + ), + ) + pipeline = PipelineLoader(args).load() + + try: + # Skip if not two-stage + if pipeline.boundary_ratio is None or pipeline.transformer_2 is None: + pytest.skip("Checkpoint is not Wan 2.2 (two-stage)") + + # Verify TeaCache on transformer (high-noise) + assert hasattr(pipeline, "transformer_cache_backend"), ( + "Pipeline missing transformer_cache_backend" + ) + assert pipeline.transformer_cache_backend is not None + print("\n[TeaCache] āœ“ Transformer (high-noise): TeaCache enabled") + + # Verify TeaCache on transformer_2 (low-noise) + assert hasattr(pipeline, "transformer_2_cache_backend"), ( + "Pipeline missing transformer_2_cache_backend" + ) + assert pipeline.transformer_2_cache_backend is not None + print("[TeaCache] āœ“ Transformer_2 (low-noise): TeaCache enabled") + + # Verify both have get_stats method + assert hasattr(pipeline.transformer_cache_backend, "get_stats") + assert hasattr(pipeline.transformer_2_cache_backend, "get_stats") + print("[TeaCache] āœ“ Both transformers support statistics logging") + + print("\n[PASS] āœ“ TeaCache enabled for BOTH transformers") + print(" āœ“ Low-noise stage benefits MORE from TeaCache") + print("=" * 80) + + finally: + del pipeline + import gc + + gc.collect() + torch.cuda.empty_cache() + + def test_two_stage_with_fp8_quantization(self): + """Test two-stage with FP8 quantization on both transformers.""" + if not is_wan22_checkpoint(): + pytest.skip( + "This test requires Wan 2.2 T2V checkpoint. Set DIFFUSION_MODEL_PATH_WAN22_T2V." + ) + print("\n" + "=" * 80) + print("WAN 2.2 TWO-STAGE + FP8 QUANTIZATION TEST") + print("=" * 80) + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_T2V, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + quant_config={"quant_algo": "FP8", "dynamic": True}, + ) + pipeline = PipelineLoader(args).load() + + try: + # Skip if not two-stage + if pipeline.boundary_ratio is None or pipeline.transformer_2 is None: + pytest.skip("Checkpoint is not Wan 2.2 (two-stage)") + + # Verify FP8 in transformer (high-noise) + found_fp8_t1 = False + for name, param in pipeline.transformer.named_parameters(): + if "blocks.0" in name and "weight" in name and param.dtype == torch.float8_e4m3fn: + found_fp8_t1 = True + print(f"\n[FP8] āœ“ Transformer: Found FP8 weight in {name}") + break + assert found_fp8_t1, "No FP8 weights found in transformer" + + # Verify FP8 in transformer_2 (low-noise) + found_fp8_t2 = False + for name, param in pipeline.transformer_2.named_parameters(): + if "blocks.0" in name and "weight" in name and param.dtype == torch.float8_e4m3fn: + found_fp8_t2 = True + print(f"[FP8] āœ“ Transformer_2: Found FP8 weight in {name}") + break + assert found_fp8_t2, "No FP8 weights found in transformer_2" + + print("\n[PASS] āœ“ FP8 quantization enabled for BOTH transformers") + print("=" * 80) + + finally: + del pipeline + import gc + + gc.collect() + torch.cuda.empty_cache() + + def test_two_stage_with_trtllm_attention(self): + """Test two-stage with TRTLLM attention backend on both transformers.""" + if not is_wan22_checkpoint(): + pytest.skip( + "This test requires Wan 2.2 T2V checkpoint. Set DIFFUSION_MODEL_PATH_WAN22_T2V." + ) + print("\n" + "=" * 80) + print("WAN 2.2 TWO-STAGE + TRTLLM ATTENTION TEST") + print("=" * 80) + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_T2V, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + attention=AttentionConfig(backend="TRTLLM"), + ) + pipeline = PipelineLoader(args).load() + + try: + # Skip if not two-stage + if pipeline.boundary_ratio is None or pipeline.transformer_2 is None: + pytest.skip("Checkpoint is not Wan 2.2 (two-stage)") + + # Verify TRTLLM attention on transformer (high-noise) + first_block_t1 = pipeline.transformer.blocks[0] + attn1_backend_t1 = first_block_t1.attn1.attn_backend + attn2_backend_t1 = first_block_t1.attn2.attn_backend + + assert attn1_backend_t1 == "TRTLLM", ( + f"Expected TRTLLM for transformer self-attn, got {attn1_backend_t1}" + ) + assert attn2_backend_t1 == "VANILLA", ( + f"Expected VANILLA for transformer cross-attn, got {attn2_backend_t1}" + ) + + print("\n[Attention] Transformer (high-noise):") + print(f" āœ“ Self-attention: {attn1_backend_t1}") + print(f" āœ“ Cross-attention: {attn2_backend_t1}") + + # Verify TRTLLM attention on transformer_2 (low-noise) + first_block_t2 = pipeline.transformer_2.blocks[0] + attn1_backend_t2 = first_block_t2.attn1.attn_backend + attn2_backend_t2 = first_block_t2.attn2.attn_backend + + assert attn1_backend_t2 == "TRTLLM", ( + f"Expected TRTLLM for transformer_2 self-attn, got {attn1_backend_t2}" + ) + assert attn2_backend_t2 == "VANILLA", ( + f"Expected VANILLA for transformer_2 cross-attn, got {attn2_backend_t2}" + ) + + print("[Attention] Transformer_2 (low-noise):") + print(f" āœ“ Self-attention: {attn1_backend_t2}") + print(f" āœ“ Cross-attention: {attn2_backend_t2}") + + print("\n[PASS] āœ“ TRTLLM attention enabled for BOTH transformers") + print("=" * 80) + + finally: + del pipeline + import gc + + gc.collect() + torch.cuda.empty_cache() + + def test_two_stage_all_optimizations(self): + """Test two-stage with ALL optimizations: FP8 + TeaCache + TRTLLM.""" + if not is_wan22_checkpoint(): + pytest.skip( + "This test requires Wan 2.2 T2V checkpoint. Set DIFFUSION_MODEL_PATH_WAN22_T2V." + ) + print("\n" + "=" * 80) + print("WAN 2.2 TWO-STAGE + ALL OPTIMIZATIONS TEST") + print("FP8 + TeaCache + TRTLLM Attention") + print("=" * 80) + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH_WAN22_T2V, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + quant_config={"quant_algo": "FP8_BLOCK_SCALES", "dynamic": True}, + attention=AttentionConfig(backend="TRTLLM"), + teacache=TeaCacheConfig( + enable_teacache=True, + teacache_thresh=0.2, + use_ret_steps=True, + ), + ) + pipeline = PipelineLoader(args).load() + + try: + # Skip if not two-stage + if pipeline.boundary_ratio is None or pipeline.transformer_2 is None: + pytest.skip("Checkpoint is not Wan 2.2 (two-stage)") + + optimizations = [] + + # Check FP8 + for name, param in pipeline.transformer.named_parameters(): + if "blocks.0" in name and "weight" in name and param.dtype == torch.float8_e4m3fn: + optimizations.append("FP8") + break + + # Check TRTLLM + if pipeline.transformer.blocks[0].attn1.attn_backend == "TRTLLM": + optimizations.append("TRTLLM") + + # Check TeaCache + if ( + hasattr(pipeline, "transformer_cache_backend") + and pipeline.transformer_cache_backend is not None + ): + optimizations.append("TeaCache") + + # Check two-stage + optimizations.append("Two-Stage") + + print(f"\n[All Optimizations] Enabled: {', '.join(optimizations)}") + assert len(optimizations) == 4, ( + f"Expected 4 optimizations, got {len(optimizations)}: {optimizations}" + ) + + # Verify all optimizations on transformer_2 as well + for name, param in pipeline.transformer_2.named_parameters(): + if "blocks.0" in name and "weight" in name and param.dtype == torch.float8_e4m3fn: + print("[All Optimizations] āœ“ Transformer_2: FP8 enabled") + break + + if pipeline.transformer_2.blocks[0].attn1.attn_backend == "TRTLLM": + print("[All Optimizations] āœ“ Transformer_2: TRTLLM enabled") + + if ( + hasattr(pipeline, "transformer_2_cache_backend") + and pipeline.transformer_2_cache_backend is not None + ): + print("[All Optimizations] āœ“ Transformer_2: TeaCache enabled") + + print("\n[PASS] āœ“ All optimizations working on BOTH transformers") + print("=" * 80) + + finally: + del pipeline + import gc + + gc.collect() + torch.cuda.empty_cache() + + +# ============================================================================= +# Robustness Tests +# ============================================================================= + + +class TestWanRobustness(unittest.TestCase): + """Error handling and edge case tests.""" + + DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + def setUp(self): + """Set up test fixtures and skip if checkpoint not available.""" + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + self.skipTest( + "Checkpoint not available. Set DIFFUSION_MODEL_PATH environment variable." + ) + + def tearDown(self): + """Clean up GPU memory.""" + import gc + + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + def test_invalid_quant_config(self): + """Test that invalid quantization config raises appropriate error.""" + with pytest.raises((ValueError, KeyError)): + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_COMPONENTS, + quant_config={"quant_algo": "INVALID_ALGO"}, + ) + pipeline = PipelineLoader(args).load() # noqa: F841 + + print("\n[Error Handling] āœ“ Invalid quant_algo raises error as expected") + + +if __name__ == "__main__": + unittest.main(verbosity=2) diff --git a/tests/unittest/_torch/visual_gen/test_wan_i2v.py b/tests/unittest/_torch/visual_gen/test_wan_i2v.py new file mode 100644 index 0000000000..34d232893f --- /dev/null +++ b/tests/unittest/_torch/visual_gen/test_wan_i2v.py @@ -0,0 +1,1491 @@ +"""Optimized tests for Wan Image-to-Video (I2V) pipeline with module-scoped fixtures. + +Run with: + pytest tests/visual_gen/test_wan_i2v_2.py -v + + # With real checkpoint: + DIFFUSION_MODEL_PATH=/path/to/Wan-I2V-Diffusers pytest tests/visual_gen/test_wan_i2v_2.py -v + + # Run only smoke tests: + pytest tests/visual_gen/test_wan_i2v_2.py -v -m "unit and smoke" + + # Run only Wan 2.1 tests: + pytest tests/visual_gen/test_wan_i2v_2.py -v -m "wan21" + + # Run only Wan 2.2 tests: + pytest tests/visual_gen/test_wan_i2v_2.py -v -m "wan22" +""" + +import os + +os.environ["TLLM_DISABLE_MPI"] = "1" + +import unittest +from pathlib import Path +from types import SimpleNamespace + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp +import torch.nn.functional as F +from PIL import Image + +from tensorrt_llm._torch.visual_gen.config import ( + AttentionConfig, + DiffusionArgs, + DiffusionModelConfig, + ParallelConfig, + TeaCacheConfig, +) +from tensorrt_llm._torch.visual_gen.models.wan.pipeline_wan_i2v import WanImageToVideoPipeline +from tensorrt_llm._torch.visual_gen.pipeline_loader import PipelineLoader +from tensorrt_llm.models.modeling_utils import QuantConfig +from tensorrt_llm.quantization.mode import QuantAlgo + + +@pytest.fixture(autouse=True, scope="module") +def _cleanup_mpi_env(): + """Clean up TLLM_DISABLE_MPI env var after tests complete.""" + yield + os.environ.pop("TLLM_DISABLE_MPI", None) + + +def _llm_models_root() -> str: + """Return LLM_MODELS_ROOT path if it is set in env, assert when it's set but not a valid path.""" + root = Path("/home/scratch.trt_llm_data_ci/llm-models/") + if "LLM_MODELS_ROOT" in os.environ: + root = Path(os.environ["LLM_MODELS_ROOT"]) + if not root.exists(): + root = Path("/scratch.trt_llm_data/llm-models/") + assert root.exists(), ( + "You shall set LLM_MODELS_ROOT env or be able to access scratch.trt_llm_data to run this test" + ) + return str(root) + + +# Checkpoint paths +CHECKPOINT_PATH = os.environ.get( + "DIFFUSION_MODEL_PATH", + os.path.join(_llm_models_root(), "Wan2.2-I2V-A14B-Diffusers"), +) + +# Skip components for different test scenarios +SKIP_MINIMAL = ["text_encoder", "vae", "tokenizer", "scheduler", "image_encoder", "image_processor"] +SKIP_WITH_IMAGE = ["text_encoder", "vae", "tokenizer", "scheduler"] + + +# ============================================================================ +# VERSION DETECTION HELPERS +# ============================================================================ + + +def is_wan21_checkpoint() -> bool: + """Check if DIFFUSION_MODEL_PATH is Wan 2.1 (contains '2.1' in path).""" + return "2.1" in CHECKPOINT_PATH + + +def is_wan22_checkpoint() -> bool: + """Check if DIFFUSION_MODEL_PATH is Wan 2.2 (contains '2.2' in path).""" + return "2.2" in CHECKPOINT_PATH + + +# ============================================================================ +# MODULE-SCOPED FIXTURES +# ============================================================================ + + +@pytest.fixture(scope="module") +def wan21_i2v_pipeline_bf16(): + """Load Wan 2.1 I2V BF16 pipeline once per module.""" + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + pytest.skip("I2V checkpoint not available") + if not is_wan21_checkpoint(): + pytest.skip("This fixture requires Wan 2.1 checkpoint") + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + ) + pipeline = PipelineLoader(args).load() + yield pipeline + del pipeline + torch.cuda.empty_cache() + + +@pytest.fixture(scope="module") +def wan21_i2v_pipeline_fp8(): + """Load Wan 2.1 I2V FP8 pipeline once per module.""" + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + pytest.skip("I2V checkpoint not available") + if not is_wan21_checkpoint(): + pytest.skip("This fixture requires Wan 2.1 checkpoint") + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + quant_config={"quant_algo": "FP8", "dynamic": True}, + ) + pipeline = PipelineLoader(args).load() + yield pipeline + del pipeline + torch.cuda.empty_cache() + + +@pytest.fixture(scope="module") +def wan21_i2v_pipeline_fp8_blockwise(): + """Load Wan 2.1 I2V FP8 blockwise pipeline once per module.""" + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + pytest.skip("I2V checkpoint not available") + if not is_wan21_checkpoint(): + pytest.skip("This fixture requires Wan 2.1 checkpoint") + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + quant_config={"quant_algo": "FP8_BLOCK_SCALES", "dynamic": True}, + ) + pipeline = PipelineLoader(args).load() + yield pipeline + del pipeline + torch.cuda.empty_cache() + + +@pytest.fixture(scope="module") +def wan21_i2v_pipeline_with_image_encoder(): + """Load Wan 2.1 I2V pipeline with image encoder once per module.""" + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + pytest.skip("I2V checkpoint not available") + if not is_wan21_checkpoint(): + pytest.skip("This fixture requires Wan 2.1 checkpoint") + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_WITH_IMAGE, + ) + pipeline = PipelineLoader(args).load() + yield pipeline + del pipeline + torch.cuda.empty_cache() + + +@pytest.fixture(scope="module") +def wan22_i2v_pipeline_bf16(): + """Load Wan 2.2 I2V BF16 pipeline once per module.""" + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + pytest.skip("I2V checkpoint not available") + if not is_wan22_checkpoint(): + pytest.skip("This fixture requires Wan 2.2 checkpoint") + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + ) + pipeline = PipelineLoader(args).load() + yield pipeline + del pipeline + torch.cuda.empty_cache() + + +@pytest.fixture(scope="module") +def wan22_i2v_pipeline_fp8(): + """Load Wan 2.2 I2V FP8 pipeline once per module.""" + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + pytest.skip("I2V checkpoint not available") + if not is_wan22_checkpoint(): + pytest.skip("This fixture requires Wan 2.2 checkpoint") + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + quant_config={"quant_algo": "FP8_BLOCK_SCALES", "dynamic": True}, + ) + pipeline = PipelineLoader(args).load() + yield pipeline + del pipeline + torch.cuda.empty_cache() + + +@pytest.fixture(scope="module") +def test_image(): + """Create a shared test image for I2V tests.""" + import numpy as np + + img_array = np.zeros((480, 832, 3), dtype=np.uint8) + for i in range(480): + img_array[i, :, 0] = int((i / 480) * 255) + img_array[i, :, 1] = 128 + return Image.fromarray(img_array, mode="RGB") + + +@pytest.fixture(autouse=True) +def cleanup_gpu(): + """GPU cleanup fixture.""" + import gc + + gc.collect() + torch.cuda.empty_cache() + yield + gc.collect() + torch.cuda.empty_cache() + + +# ============================================================================ +# DISTRIBUTED HELPERS (for CFG Parallelism tests) +# ============================================================================ + + +def setup_distributed(rank, world_size, backend="nccl"): + """Initialize distributed process group for multi-GPU tests.""" + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = "12356" # Different port from T2V tests + os.environ["RANK"] = str(rank) + os.environ["WORLD_SIZE"] = str(world_size) + + dist.init_process_group(backend=backend, rank=rank, world_size=world_size) + torch.cuda.set_device(rank) + + +def cleanup_distributed(): + """Clean up distributed process group.""" + if dist.is_initialized(): + dist.destroy_process_group() + + +def _run_cfg_worker_i2v(rank, world_size, checkpoint_path, inputs_list, return_dict): + """Worker function for I2V CFG Parallelism multi-GPU test.""" + try: + setup_distributed(rank, world_size) + + from tensorrt_llm._torch.visual_gen.config import DiffusionArgs, ParallelConfig + from tensorrt_llm._torch.visual_gen.pipeline_loader import PipelineLoader + + # Load I2V pipeline with CFG parallel + args = DiffusionArgs( + checkpoint_path=checkpoint_path, + device=f"cuda:{rank}", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + parallel=ParallelConfig(dit_cfg_size=world_size), + ) + pipeline = PipelineLoader(args).load() + + # Verify CFG parallel configuration + assert pipeline.model_config.parallel.dit_cfg_size == world_size, ( + f"Expected cfg_size={world_size}, got {pipeline.model_config.parallel.dit_cfg_size}" + ) + + # Load inputs on this GPU + prompt_embeds = inputs_list[0].to(f"cuda:{rank}") + neg_prompt_embeds = inputs_list[1].to(f"cuda:{rank}") + latents = inputs_list[2].to(f"cuda:{rank}") + timestep = inputs_list[3].to(f"cuda:{rank}") + # I2V-specific: image embeddings (if present) + image_embeds = inputs_list[4].to(f"cuda:{rank}") if inputs_list[4] is not None else None + + # Setup CFG config + cfg_config = pipeline._setup_cfg_config( + guidance_scale=5.0, + prompt_embeds=prompt_embeds, + neg_prompt_embeds=neg_prompt_embeds, + ) + + # Verify CFG parallel is enabled + assert cfg_config["enabled"], f"Rank {rank}: CFG parallel not enabled" + assert cfg_config["cfg_size"] == world_size, f"Rank {rank}: Wrong cfg_size" + + expected_cfg_group = rank // cfg_config["ulysses_size"] + assert cfg_config["cfg_group"] == expected_cfg_group, ( + f"Rank {rank}: Wrong cfg_group. Expected {expected_cfg_group}, got {cfg_config['cfg_group']}" + ) + + if rank == 0: + print(f"[CFG I2V Rank {rank}] Loaded with cfg_size={world_size}") + print(f" cfg_group: {cfg_config['cfg_group']}") + print(f" local_embeds shape: {cfg_config['local_embeds'].shape}") + print(f" Using {'positive' if cfg_config['cfg_group'] == 0 else 'negative'} prompts") + print(f" Image embeds: {'present' if image_embeds is not None else 'None'}") + + # Verify prompt splitting + expected_embeds = prompt_embeds if cfg_config["cfg_group"] == 0 else neg_prompt_embeds + assert torch.allclose(cfg_config["local_embeds"], expected_embeds), ( + f"Rank {rank}: local_embeds doesn't match expected embeds" + ) + + # Run single denoising step with CFG parallel + def forward_fn( + latents, extra_stream_latents, timestep, encoder_hidden_states, extra_tensors + ): + # I2V-specific: include image embeddings in extra_tensors if present + return pipeline.transformer( # noqa: F821 + hidden_states=latents, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + encoder_hidden_states_image=extra_tensors.get("encoder_hidden_states_image"), + ) + + with torch.no_grad(): + local_extras = ( + {"encoder_hidden_states_image": image_embeds} if image_embeds is not None else {} + ) + noise_pred, _, _, _ = pipeline._denoise_step_cfg_parallel( + latents=latents, + extra_stream_latents={}, + timestep=timestep, + local_embeds=cfg_config["local_embeds"], + forward_fn=forward_fn, + guidance_scale=5.0, + guidance_rescale=0.0, + ulysses_size=cfg_config["ulysses_size"], + local_extras=local_extras, + ) + + # Validate output + assert not torch.isnan(noise_pred).any(), f"Rank {rank}: Output contains NaN" + assert not torch.isinf(noise_pred).any(), f"Rank {rank}: Output contains Inf" + + # Return output from rank 0 + if rank == 0: + return_dict["output"] = noise_pred.cpu() + print(f"[CFG I2V Rank {rank}] āœ“ Output shape: {noise_pred.shape}") + print( + f"[CFG I2V Rank {rank}] āœ“ Output range: [{noise_pred.min():.4f}, {noise_pred.max():.4f}]" + ) + + del pipeline + torch.cuda.empty_cache() + + finally: + cleanup_distributed() + + +def _run_all_optimizations_worker_i2v(rank, world_size, checkpoint_path, inputs_list, return_dict): + try: + setup_distributed(rank, world_size) + + # Load I2V pipeline with ALL optimizations + args_full = DiffusionArgs( + checkpoint_path=checkpoint_path, + device=f"cuda:{rank}", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + quant_config={"quant_algo": "FP8", "dynamic": True}, + teacache=TeaCacheConfig( + enable_teacache=True, + teacache_thresh=0.2, + use_ret_steps=True, + ), + attention=AttentionConfig(backend="TRTLLM"), + parallel=ParallelConfig(dit_cfg_size=world_size), + ) + pipeline = PipelineLoader(args_full).load() + transformer = pipeline.transformer.eval() + + # Verify all optimizations are enabled + assert pipeline.model_config.parallel.dit_cfg_size == world_size, "CFG parallel not enabled" + assert transformer.model_config.quant_config.quant_algo == QuantAlgo.FP8, "FP8 not enabled" + assert hasattr(pipeline, "transformer_cache_backend"), "TeaCache not enabled" + assert transformer.blocks[0].attn1.attn_backend == "TRTLLM", ( + "TRTLLM not enabled for self-attn" + ) + + if rank == 0: + print(f" āœ“ All optimizations verified on I2V rank {rank}:") + print(f" - FP8 quantization: {transformer.model_config.quant_config.quant_algo}") + print(" - TeaCache: enabled") + print(f" - TRTLLM attention: {transformer.blocks[0].attn1.attn_backend}") + print(f" - CFG Parallelism: cfg_size={world_size}") + + # Initialize TeaCache for single-step inference + if hasattr(pipeline, "transformer_cache_backend"): + pipeline.transformer_cache_backend.refresh(num_inference_steps=1) + + # Load inputs on this GPU + prompt_embeds = inputs_list[0].to(f"cuda:{rank}") + neg_prompt_embeds = inputs_list[1].to(f"cuda:{rank}") + latents = inputs_list[2].to(f"cuda:{rank}") + timestep = inputs_list[3].to(f"cuda:{rank}") + image_embeds = inputs_list[4].to(f"cuda:{rank}") if inputs_list[4] is not None else None + + # Setup CFG config + cfg_config = pipeline._setup_cfg_config( + guidance_scale=5.0, + prompt_embeds=prompt_embeds, + neg_prompt_embeds=neg_prompt_embeds, + ) + + assert cfg_config["enabled"], "CFG parallel not enabled" + + # Run single denoising step with all optimizations + def forward_fn( + latents, extra_stream_latents, timestep, encoder_hidden_states, extra_tensors + ): + return transformer( # noqa: F821 + hidden_states=latents, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + encoder_hidden_states_image=extra_tensors.get("encoder_hidden_states_image"), + ) + + with torch.no_grad(): + local_extras = ( + {"encoder_hidden_states_image": image_embeds} if image_embeds is not None else {} + ) + noise_pred, _, _, _ = pipeline._denoise_step_cfg_parallel( + latents=latents, + extra_stream_latents={}, + timestep=timestep, + local_embeds=cfg_config["local_embeds"], + forward_fn=forward_fn, + guidance_scale=5.0, + guidance_rescale=0.0, + ulysses_size=cfg_config["ulysses_size"], + local_extras=local_extras, + ) + + # Validate output + assert not torch.isnan(noise_pred).any(), f"Rank {rank}: Output contains NaN" + assert not torch.isinf(noise_pred).any(), f"Rank {rank}: Output contains Inf" + + # Return output from rank 0 + if rank == 0: + return_dict["output"] = noise_pred.cpu() + print(f" āœ“ Combined optimization I2V output shape: {noise_pred.shape}") + print( + f" āœ“ Combined optimization I2V range: [{noise_pred.min():.4f}, {noise_pred.max():.4f}]" + ) + + del pipeline, transformer + torch.cuda.empty_cache() + + finally: + cleanup_distributed() + + +# ============================================================================ +# SMOKE TESTS (No Checkpoint Required) +# ============================================================================ + + +@pytest.mark.unit +@pytest.mark.smoke +class TestWanI2VSmoke: + def _create_model_config(self, boundary_ratio=None): + """Helper to create test model config.""" + config_dict = { + "attention_head_dim": 128, + "in_channels": 16, + "out_channels": 16, + "num_attention_heads": 4, + "num_layers": 1, + "patch_size": [1, 2, 2], + "text_dim": 4096, + "freq_dim": 256, + "ffn_dim": 1024, + "torch_dtype": "bfloat16", + "hidden_size": 512, + "qk_norm": "rms_norm_across_heads", + "cross_attn_norm": "layer_norm", + "eps": 1e-06, + "image_dim": 1280, # CLIP dimension (HF naming convention) + "added_kv_proj_dim": 1280, # Added KV projection dimension for I2V + "boundary_ratio": boundary_ratio, + } + pretrained_config = SimpleNamespace(**config_dict) + quant_config = QuantConfig() + + return DiffusionModelConfig( + pretrained_config=pretrained_config, + quant_config=quant_config, + skip_create_weights_in_init=True, + ) + + def test_wan21_instantiation(self): + """Test Wan 2.1 I2V pipeline (single-stage).""" + model_config = self._create_model_config(boundary_ratio=None) + pipeline = WanImageToVideoPipeline(model_config) + + assert pipeline.transformer is not None + assert pipeline.transformer_2 is None # Single-stage + assert pipeline.boundary_ratio is None + + def test_wan22_instantiation(self): + """Test Wan 2.2 I2V pipeline (two-stage).""" + model_config = self._create_model_config(boundary_ratio=0.4) + pipeline = WanImageToVideoPipeline(model_config) + + assert pipeline.transformer is not None + assert pipeline.transformer_2 is not None # Two-stage + assert pipeline.boundary_ratio == 0.4 + + def test_retrieve_latents(self): + """Test retrieve_latents helper.""" + from tensorrt_llm._torch.visual_gen.models.wan.pipeline_wan_i2v import retrieve_latents + + class MockLatentDist: + def mode(self): + return torch.randn(1, 16, 1, 64, 64) + + def sample(self, generator=None): + return torch.randn(1, 16, 1, 64, 64) + + class MockEncoderOutput: + def __init__(self): + self.latent_dist = MockLatentDist() + + encoder_output = MockEncoderOutput() + + # Test argmax mode (I2V default for deterministic encoding) + latents_argmax = retrieve_latents(encoder_output, sample_mode="argmax") + assert latents_argmax.shape == (1, 16, 1, 64, 64) + + # Test sample mode + latents_sample = retrieve_latents(encoder_output, sample_mode="sample") + assert latents_sample.shape == (1, 16, 1, 64, 64) + + +# ============================================================================ +# INTEGRATION TESTS - WAN 2.1 (Require Wan 2.1 Checkpoint) +# ============================================================================ + + +@pytest.mark.integration +@pytest.mark.i2v +@pytest.mark.wan21 +class TestWanI2VIntegration: + """Integration tests with Wan 2.1 checkpoint.""" + + def test_load_pipeline(self, wan21_i2v_pipeline_bf16): + """Test loading I2V pipeline from checkpoint.""" + # Verify I2V pipeline + assert "ImageToVideo" in type(wan21_i2v_pipeline_bf16).__name__ + assert wan21_i2v_pipeline_bf16.transformer is not None + assert len(wan21_i2v_pipeline_bf16.transformer.blocks) > 0 + + # Detect version + is_two_stage = ( + wan21_i2v_pipeline_bf16.boundary_ratio is not None + and wan21_i2v_pipeline_bf16.transformer_2 is not None + ) + + print(f"\nāœ“ Pipeline: {type(wan21_i2v_pipeline_bf16).__name__}") + print(f"āœ“ Transformer blocks: {len(wan21_i2v_pipeline_bf16.transformer.blocks)}") + print(f"āœ“ boundary_ratio: {wan21_i2v_pipeline_bf16.boundary_ratio}") + print(f"āœ“ Two-stage: {is_two_stage}") + + def test_image_encoding(self, wan21_i2v_pipeline_with_image_encoder, test_image): + """Test CLIP image encoding (if model uses it).""" + # Check if model uses image encoder + if ( + not hasattr(wan21_i2v_pipeline_with_image_encoder, "image_encoder") + or wan21_i2v_pipeline_with_image_encoder.image_encoder is None + ): + pytest.skip("This checkpoint doesn't use image encoder") + + # Encode test image + image_embeds = wan21_i2v_pipeline_with_image_encoder._encode_image(test_image) + + assert image_embeds is not None + assert image_embeds.dim() == 3 # [batch, seq_len, embed_dim] + print(f"\nāœ“ Image embeddings: {image_embeds.shape}, dtype={image_embeds.dtype}") + + def test_fp8_per_tensor_quantization(self, wan21_i2v_pipeline_fp8): + """Test FP8 per-tensor dynamic quantization.""" + # Check transformer for FP8 weights + found_fp8 = any( + param.dtype == torch.float8_e4m3fn + for name, param in wan21_i2v_pipeline_fp8.transformer.named_parameters() + if "blocks.0" in name and "weight" in name + ) + assert found_fp8, "No FP8 weights found for FP8" + print("\nāœ“ FP8: FP8 weights found in transformer") + + # Check transformer_2 if two-stage + if wan21_i2v_pipeline_fp8.transformer_2 is not None: + found_fp8_t2 = any( + param.dtype == torch.float8_e4m3fn + for name, param in wan21_i2v_pipeline_fp8.transformer_2.named_parameters() + if "blocks.0" in name and "weight" in name + ) + assert found_fp8_t2, "No FP8 weights in transformer_2" + print("āœ“ FP8: FP8 weights found in transformer_2") + + def test_fp8_blockwise_quantization(self, wan21_i2v_pipeline_fp8_blockwise): + """Test FP8 blockwise dynamic quantization.""" + # Check transformer for FP8 weights + found_fp8 = any( + param.dtype == torch.float8_e4m3fn + for name, param in wan21_i2v_pipeline_fp8_blockwise.transformer.named_parameters() + if "blocks.0" in name and "weight" in name + ) + assert found_fp8, "No FP8 weights found for FP8_BLOCK_SCALES" + print("\nāœ“ FP8_BLOCK_SCALES: FP8 weights found in transformer") + + @pytest.mark.parametrize("backend", ["VANILLA", "TRTLLM"]) + def test_attention_backends(self, backend): + """Test different attention backends.""" + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + pytest.skip("DIFFUSION_MODEL_PATH not set") + if not is_wan21_checkpoint(): + pytest.skip("This test requires Wan 2.1 checkpoint") + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + attention=AttentionConfig(backend=backend), + ) + pipeline = PipelineLoader(args).load() + + try: + # Check transformer attention backend + first_block = pipeline.transformer.blocks[0] + attn1_backend = first_block.attn1.attn_backend + attn2_backend = first_block.attn2.attn_backend + + # TRTLLM for self-attention, VANILLA for cross-attention + if backend == "TRTLLM": + assert attn1_backend == "TRTLLM", f"Expected TRTLLM, got {attn1_backend}" + assert attn2_backend == "VANILLA", ( + f"Cross-attn should be VANILLA, got {attn2_backend}" + ) + else: + assert attn1_backend == "VANILLA" + assert attn2_backend == "VANILLA" + + print(f"\nāœ“ Attention backend: {backend}") + print(f" Self-attn: {attn1_backend}, Cross-attn: {attn2_backend}") + + # Check transformer_2 if two-stage + if pipeline.transformer_2 is not None: + first_block_t2 = pipeline.transformer_2.blocks[0] + attn1_backend_t2 = first_block_t2.attn1.attn_backend + attn2_backend_t2 = first_block_t2.attn2.attn_backend + + if backend == "TRTLLM": + assert attn1_backend_t2 == "TRTLLM" + assert attn2_backend_t2 == "VANILLA" + print( + f" Transformer_2 - Self-attn: {attn1_backend_t2}, Cross-attn: {attn2_backend_t2}" + ) + + finally: + del pipeline + torch.cuda.empty_cache() + + def test_teacache(self): + """Test TeaCache on both transformers.""" + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + pytest.skip("DIFFUSION_MODEL_PATH not set") + if not is_wan21_checkpoint(): + pytest.skip("This test requires Wan 2.1 checkpoint") + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + teacache=TeaCacheConfig( + enable_teacache=True, + teacache_thresh=0.2, + use_ret_steps=True, + ), + ) + pipeline = PipelineLoader(args).load() + + try: + # Verify TeaCache on transformer + assert hasattr(pipeline, "transformer_cache_backend") + assert pipeline.transformer_cache_backend is not None + print("\nāœ“ TeaCache enabled on transformer (high-noise)") + + # Verify get_stats method + stats = pipeline.transformer_cache_backend.get_stats() + assert "total_steps" in stats + assert "cached_steps" in stats + assert "compute_steps" in stats + print("āœ“ TeaCache stats available") + + # Check transformer_2 if two-stage + if pipeline.transformer_2 is not None: + assert hasattr(pipeline, "transformer_2_cache_backend") + assert pipeline.transformer_2_cache_backend is not None + stats2 = pipeline.transformer_2_cache_backend.get_stats() + assert "total_steps" in stats2 + print("āœ“ TeaCache enabled on transformer_2 (low-noise)") + + finally: + del pipeline + torch.cuda.empty_cache() + + def test_all_optimizations_combined(self): + """Test all optimizations enabled simultaneously.""" + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + pytest.skip("DIFFUSION_MODEL_PATH not set") + if not is_wan21_checkpoint(): + pytest.skip("This test requires Wan 2.1 checkpoint") + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + quant_config={"quant_algo": "FP8_BLOCK_SCALES", "dynamic": True}, + attention=AttentionConfig(backend="VANILLA"), # VANILLA more stable with all opts + teacache=TeaCacheConfig( + enable_teacache=True, + teacache_thresh=0.2, + use_ret_steps=True, + ), + ) + pipeline = PipelineLoader(args).load() + + try: + optimizations = [] + + # Check FP8 + if any(p.dtype == torch.float8_e4m3fn for p in pipeline.transformer.parameters()): + optimizations.append("FP8") + + # Check TeaCache + if ( + hasattr(pipeline, "transformer_cache_backend") + and pipeline.transformer_cache_backend + ): + optimizations.append("TeaCache") + + # Check two-stage + if pipeline.transformer_2 is not None: + optimizations.append("Two-Stage") + + # Check attention backend + optimizations.append(f"Attention={args.attention.backend}") + + print(f"\nāœ“ All optimizations: {', '.join(optimizations)}") + assert len(optimizations) >= 3 + + finally: + del pipeline + torch.cuda.empty_cache() + + def test_fp8_vs_bf16_numerical_correctness( + self, wan21_i2v_pipeline_bf16, wan21_i2v_pipeline_fp8 + ): + """Test FP8 vs BF16 numerical accuracy on I2V transformer.""" + # Get linear layers from first transformer + attn_bf16 = wan21_i2v_pipeline_bf16.transformer.blocks[0].attn1 + attn_fp8 = wan21_i2v_pipeline_fp8.transformer.blocks[0].attn1 + + # Get qkv_proj layer + if hasattr(attn_bf16, "qkv_proj"): + linear_bf16 = attn_bf16.qkv_proj + linear_fp8 = attn_fp8.qkv_proj + layer_name = "blocks.0.attn1.qkv_proj" + elif hasattr(attn_bf16, "attn") and hasattr(attn_bf16.attn, "qkv_proj"): + linear_bf16 = attn_bf16.attn.qkv_proj + linear_fp8 = attn_fp8.attn.qkv_proj + layer_name = "blocks.0.attn1.attn.qkv_proj" + else: + # Use FFN linear instead + linear_bf16 = wan21_i2v_pipeline_bf16.transformer.blocks[0].ffn.net[0]["proj"] + linear_fp8 = wan21_i2v_pipeline_fp8.transformer.blocks[0].ffn.net[0]["proj"] + layer_name = "blocks.0.ffn.net.0.proj" + + # Get weights + weight_bf16 = linear_bf16.weight.data.clone() + bias_bf16 = linear_bf16.bias.data.clone() if linear_bf16.bias is not None else None + + # Create test input + torch.manual_seed(42) + hidden_size = linear_bf16.in_features + batch_size = 1 + seq_len = 14040 + + input_tensor = torch.randn( + batch_size * seq_len, hidden_size, dtype=torch.bfloat16, device="cuda" + ) + print(f"\n[Compare] Input shape: {input_tensor.shape}") + + # Compute reference output + with torch.no_grad(): + expected = F.linear(input_tensor, weight_bf16, bias_bf16) + + # Compute FP8 output + with torch.no_grad(): + result_fp8 = linear_fp8(input_tensor) + + # Compute BF16 output + with torch.no_grad(): + result_bf16 = linear_bf16(input_tensor) + + # Verify BF16 matches reference + assert torch.allclose(result_bf16, expected, rtol=1e-5, atol=1e-6), ( + "BF16 layer should match F.linear reference exactly" + ) + + # Compare FP8 vs reference + max_diff = torch.max(torch.abs(result_fp8 - expected)).item() + cos_sim = F.cosine_similarity( + result_fp8.flatten().float(), expected.flatten().float(), dim=0 + ) + mse = F.mse_loss(result_fp8.flatten().float(), expected.flatten().float()) + + print( + f"\n[{layer_name}] max_diff={max_diff:.6f}, cos_sim={cos_sim.item():.6f}, mse={mse.item():.6f}" + ) + + assert cos_sim > 0.99, f"Cosine similarity too low: {cos_sim.item()}" + assert mse < 1.0, f"MSE too high: {mse.item()}" + + # Test transformer_2 if two-stage + if ( + wan21_i2v_pipeline_bf16.transformer_2 is not None + and wan21_i2v_pipeline_fp8.transformer_2 is not None + ): + print("\n[Testing transformer_2]") + attn2_bf16 = wan21_i2v_pipeline_bf16.transformer_2.blocks[0].attn1 + attn2_fp8 = wan21_i2v_pipeline_fp8.transformer_2.blocks[0].attn1 + + if hasattr(attn2_bf16, "qkv_proj"): + linear2_bf16 = attn2_bf16.qkv_proj + linear2_fp8 = attn2_fp8.qkv_proj + else: + linear2_bf16 = wan21_i2v_pipeline_bf16.transformer_2.blocks[0].ffn.net[0]["proj"] + linear2_fp8 = wan21_i2v_pipeline_fp8.transformer_2.blocks[0].ffn.net[0]["proj"] + + weight2_bf16 = linear2_bf16.weight.data.clone() + bias2_bf16 = linear2_bf16.bias.data.clone() if linear2_bf16.bias is not None else None + + with torch.no_grad(): + expected2 = F.linear(input_tensor, weight2_bf16, bias2_bf16) + result2_fp8 = linear2_fp8(input_tensor) + + cos_sim2 = F.cosine_similarity( + result2_fp8.flatten().float(), expected2.flatten().float(), dim=0 + ) + print(f"[transformer_2] cos_sim={cos_sim2.item():.6f}") + assert cos_sim2 > 0.99, f"Transformer_2 cosine similarity too low: {cos_sim2.item()}" + + def test_fp8_vs_bf16_memory_comparison(self, wan21_i2v_pipeline_bf16, wan21_i2v_pipeline_fp8): + """Test FP8 uses ~2x less memory than BF16 for I2V.""" + + def get_module_memory_gb(module): + return sum(p.numel() * p.element_size() for p in module.parameters()) / 1024**3 + + bf16_model_mem = get_module_memory_gb(wan21_i2v_pipeline_bf16.transformer) + if wan21_i2v_pipeline_bf16.transformer_2 is not None: + bf16_model_mem += get_module_memory_gb(wan21_i2v_pipeline_bf16.transformer_2) + + fp8_model_mem = get_module_memory_gb(wan21_i2v_pipeline_fp8.transformer) + if wan21_i2v_pipeline_fp8.transformer_2 is not None: + fp8_model_mem += get_module_memory_gb(wan21_i2v_pipeline_fp8.transformer_2) + + print(f"\n[BF16] Transformer(s) memory: {bf16_model_mem:.2f} GB") + print(f"[FP8] Transformer(s) memory: {fp8_model_mem:.2f} GB") + + # Verify memory savings + model_mem_ratio = bf16_model_mem / fp8_model_mem + + print(f"\n[Comparison] Model memory ratio (BF16/FP8): {model_mem_ratio:.2f}x") + + # FP8 should use ~2x less memory + assert model_mem_ratio > 1.8, f"FP8 should use ~2x less memory, got {model_mem_ratio:.2f}x" + + +# ============================================================================ +# TWO-STAGE SPECIFIC TESTS - WAN 2.2 (Require Wan 2.2 Checkpoint) +# ============================================================================ + + +@pytest.mark.integration +@pytest.mark.i2v +@pytest.mark.wan22 +class TestWanI2VTwoStage: + """Tests specific to Wan 2.2 two-stage denoising.""" + + def test_transformer_selection_logic(self, wan22_i2v_pipeline_bf16): + """Test boundary_timestep logic for transformer selection.""" + # Skip if not two-stage + if ( + wan22_i2v_pipeline_bf16.boundary_ratio is None + or wan22_i2v_pipeline_bf16.transformer_2 is None + ): + pytest.skip("Not a two-stage checkpoint") + + # Calculate boundary + num_train_timesteps = 1000 + boundary_timestep = wan22_i2v_pipeline_bf16.boundary_ratio * num_train_timesteps + + print(f"\nāœ“ boundary_ratio: {wan22_i2v_pipeline_bf16.boundary_ratio}") + print(f"āœ“ boundary_timestep: {boundary_timestep:.1f}") + print(f"āœ“ High-noise (t >= {boundary_timestep:.1f}): uses transformer") + print(f"āœ“ Low-noise (t < {boundary_timestep:.1f}): uses transformer_2") + + @pytest.mark.parametrize("guidance_scale_2", [2.0, 3.0, 4.0]) + def test_guidance_scale_2_parameter(self, wan22_i2v_pipeline_bf16, guidance_scale_2): + """Test guidance_scale_2 for low-noise stage.""" + # Skip if not two-stage + if ( + wan22_i2v_pipeline_bf16.boundary_ratio is None + or wan22_i2v_pipeline_bf16.transformer_2 is None + ): + pytest.skip("Not a two-stage checkpoint") + + print(f"\nāœ“ Two-stage model supports guidance_scale_2={guidance_scale_2}") + print("āœ“ High-noise: uses guidance_scale") + print(f"āœ“ Low-noise: uses guidance_scale_2={guidance_scale_2}") + + def test_custom_boundary_ratio(self, wan22_i2v_pipeline_bf16): + """Test overriding boundary_ratio at runtime.""" + # Skip if not two-stage + if ( + wan22_i2v_pipeline_bf16.boundary_ratio is None + or wan22_i2v_pipeline_bf16.transformer_2 is None + ): + pytest.skip("Not a two-stage checkpoint") + + default_ratio = wan22_i2v_pipeline_bf16.boundary_ratio + custom_ratio = 0.3 + + print(f"\nāœ“ Model default boundary_ratio: {default_ratio}") + print(f"āœ“ Custom override: {custom_ratio}") + print("āœ“ forward() accepts boundary_ratio parameter for runtime override") + + def test_two_stage_with_all_optimizations(self, wan22_i2v_pipeline_fp8): + """Test Wan 2.2 with FP8, TeaCache, and TRTLLM attention.""" + # Skip if not two-stage + if ( + wan22_i2v_pipeline_fp8.boundary_ratio is None + or wan22_i2v_pipeline_fp8.transformer_2 is None + ): + pytest.skip("Not a two-stage checkpoint") + + # Load pipeline with all optimizations + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + quant_config={"quant_algo": "FP8_BLOCK_SCALES", "dynamic": True}, + attention=AttentionConfig(backend="TRTLLM"), + teacache=TeaCacheConfig( + enable_teacache=True, + teacache_thresh=0.2, + use_ret_steps=True, + ), + ) + pipeline = PipelineLoader(args).load() + + try: + print("\n[Two-Stage + All Optimizations]") + + # Check FP8 on both transformers + fp8_t1 = any(p.dtype == torch.float8_e4m3fn for p in pipeline.transformer.parameters()) + fp8_t2 = any( + p.dtype == torch.float8_e4m3fn for p in pipeline.transformer_2.parameters() + ) + print(f"āœ“ FP8: transformer={fp8_t1}, transformer_2={fp8_t2}") + assert fp8_t1 and fp8_t2 + + # Check TeaCache on both transformers + has_cache_t1 = ( + hasattr(pipeline, "transformer_cache_backend") + and pipeline.transformer_cache_backend + ) + has_cache_t2 = ( + hasattr(pipeline, "transformer_2_cache_backend") + and pipeline.transformer_2_cache_backend + ) + print(f"āœ“ TeaCache: transformer={has_cache_t1}, transformer_2={has_cache_t2}") + assert has_cache_t1 and has_cache_t2 + + # Check TRTLLM attention + attn1_backend = pipeline.transformer.blocks[0].attn1.attn_backend + attn2_backend = pipeline.transformer_2.blocks[0].attn1.attn_backend + print(f"āœ“ TRTLLM: transformer={attn1_backend}, transformer_2={attn2_backend}") + assert attn1_backend == "TRTLLM" + assert attn2_backend == "TRTLLM" + + print("āœ“ All optimizations working on two-stage model!") + + finally: + del pipeline + torch.cuda.empty_cache() + + +# ============================================================================ +# ROBUSTNESS TESTS +# ============================================================================ + + +@pytest.mark.robustness +class TestWanI2VRobustness: + """Robustness and error handling tests.""" + + def test_invalid_quant_config(self): + """Test that invalid quantization config raises appropriate error.""" + with pytest.raises((ValueError, KeyError)): + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + quant_config={"quant_algo": "INVALID_ALGO", "dynamic": True}, + ) + pipeline = PipelineLoader(args).load() + del pipeline + + def test_mismatched_image_size(self, test_image): + """Test handling of unexpected image dimensions.""" + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + pytest.skip("DIFFUSION_MODEL_PATH not set") + + args = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda", + dtype="bfloat16", + skip_components=SKIP_WITH_IMAGE, + ) + pipeline = PipelineLoader(args).load() + + try: + # Check if model uses image encoder + if not hasattr(pipeline, "image_encoder") or pipeline.image_encoder is None: + pytest.skip("This checkpoint doesn't use image encoder") + + # Create image with unexpected size + import numpy as np + + small_img = np.zeros((224, 224, 3), dtype=np.uint8) + small_image = Image.fromarray(small_img, mode="RGB") + + # Should handle gracefully + try: + image_embeds = pipeline._encode_image(small_image) + assert image_embeds is not None + print("\nāœ“ Handled non-standard image size gracefully") + except Exception as e: + # Some error is expected + print(f"\nāœ“ Raised appropriate error for mismatched size: {type(e).__name__}") + + finally: + del pipeline + torch.cuda.empty_cache() + + +# ============================================================================ +# CFG PARALLELISM TESTS (Requires 2+ GPUs) +# ============================================================================ + + +@pytest.mark.parallelism +class TestWanI2VParallelism(unittest.TestCase): + """Distributed parallelism correctness tests for I2V (CFG Parallelism).""" + + DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + def setUp(self): + """Set up test fixtures and skip if checkpoint not available.""" + torch.manual_seed(42) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(42) + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + self.skipTest( + "Checkpoint not available. Set DIFFUSION_MODEL_PATH environment variable." + ) + + def tearDown(self): + """Clean up GPU memory.""" + import gc + + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + def test_cfg_2gpu_correctness(self): + """Test I2V CFG Parallelism (cfg_size=2) correctness against standard CFG baseline.""" + num_gpus = torch.cuda.device_count() + if num_gpus < 2: + pytest.skip("CFG parallel test requires at least 2 GPUs") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint. Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + print("\n" + "=" * 80) + print("I2V CFG PARALLELISM (cfg_size=2) CORRECTNESS TEST") + print("=" * 80) + + # Load standard CFG baseline on GPU 0 + print("\n[1/3] Loading standard CFG I2V baseline (cfg_size=1) on GPU 0...") + args_baseline = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda:0", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + parallel=ParallelConfig(dit_cfg_size=1), # Standard CFG (no parallel) + ) + pipeline_baseline = PipelineLoader(args_baseline).load() + config = pipeline_baseline.transformer.model_config.pretrained_config + + # Reset torch compile state + torch._dynamo.reset() + + # Create FIXED test inputs + print("\n[2/3] Creating fixed test inputs...") + torch.manual_seed(42) + batch_size, num_frames, height, width, seq_len = 1, 1, 64, 64, 128 + + latents = torch.randn( + batch_size, + config.in_channels, + num_frames, + height, + width, + dtype=torch.bfloat16, + device="cuda:0", + ) + timestep = torch.tensor([500], dtype=torch.long, device="cuda:0") + prompt_embeds = torch.randn( + batch_size, seq_len, config.text_dim, dtype=torch.bfloat16, device="cuda:0" + ) + neg_prompt_embeds = torch.randn( + batch_size, seq_len, config.text_dim, dtype=torch.bfloat16, device="cuda:0" + ) + + # I2V-specific: Create image embeddings (or None if Wan 2.2) + image_embeds = None + image_dim = getattr(config, "image_dim", getattr(config, "image_embed_dim", None)) + if image_dim is not None: + # Wan 2.1 uses CLIP image embeddings + image_seq_len = 256 # CLIP patch count + image_embeds = torch.randn( + batch_size, image_seq_len, image_dim, dtype=torch.bfloat16, device="cuda:0" + ) + print(f" āœ“ Created image embeddings: {image_embeds.shape}") + + # Setup standard CFG config + cfg_config_baseline = pipeline_baseline._setup_cfg_config( + guidance_scale=5.0, + prompt_embeds=prompt_embeds, + neg_prompt_embeds=neg_prompt_embeds, + ) + + print(" Baseline CFG config:") + print(f" enabled: {cfg_config_baseline['enabled']}") + print(f" cfg_size: {cfg_config_baseline['cfg_size']}") + + # Verify standard CFG is NOT parallel + assert not cfg_config_baseline["enabled"], "Baseline should not use CFG parallel" + assert cfg_config_baseline["cfg_size"] == 1, "Baseline cfg_size should be 1" + + # Run standard CFG denoising step + def forward_fn( + latents, extra_stream_latents, timestep, encoder_hidden_states, extra_tensors + ): + return pipeline_baseline.transformer( # noqa: F821 + hidden_states=latents, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + encoder_hidden_states_image=extra_tensors.get("encoder_hidden_states_image"), + ) + + with torch.no_grad(): + local_extras = ( + {"encoder_hidden_states_image": image_embeds} if image_embeds is not None else {} + ) + baseline_output, _, _, _ = pipeline_baseline._denoise_step_standard( + latents=latents.clone(), + extra_stream_latents={}, + timestep=timestep, + prompt_embeds=cfg_config_baseline["prompt_embeds"], + forward_fn=forward_fn, + guidance_scale=5.0, + guidance_rescale=0.0, + local_extras=local_extras, + ) + + print(f" āœ“ Baseline output shape: {baseline_output.shape}") + print(f" āœ“ Baseline range: [{baseline_output.min():.4f}, {baseline_output.max():.4f}]") + + # Cleanup baseline to free memory for CFG workers + del pipeline_baseline + torch.cuda.empty_cache() + + # Run CFG parallel (cfg_size=2) in distributed processes + print("\n[3/3] Running I2V CFG Parallelism (cfg_size=2) across 2 GPUs...") + cfg_size = 2 + + inputs_cpu = [ + prompt_embeds.cpu(), + neg_prompt_embeds.cpu(), + latents.cpu(), + timestep.cpu(), + image_embeds.cpu() if image_embeds is not None else None, + ] + + manager = mp.Manager() + return_dict = manager.dict() + + # Spawn CFG workers + mp.spawn( + _run_cfg_worker_i2v, + args=(cfg_size, CHECKPOINT_PATH, inputs_cpu, return_dict), + nprocs=cfg_size, + join=True, + ) + + # Get CFG parallel output from rank 0 + cfg_parallel_output = return_dict["output"].to("cuda:0") + print(f" āœ“ CFG parallel output shape: {cfg_parallel_output.shape}") + + # Compare outputs + print("\n[Comparison] I2V CFG Parallel vs Standard CFG:") + baseline_float = baseline_output.float() + cfg_parallel_float = cfg_parallel_output.float() + + cos_sim = F.cosine_similarity( + cfg_parallel_float.flatten(), baseline_float.flatten(), dim=0 + ).item() + + max_diff = torch.max(torch.abs(cfg_parallel_float - baseline_float)).item() + mean_diff = torch.mean(torch.abs(cfg_parallel_float - baseline_float)).item() + + print(f" Cosine similarity: {cos_sim:.6f}") + print(f" Max absolute difference: {max_diff:.6f}") + print(f" Mean absolute difference: {mean_diff:.6f}") + print( + f" CFG parallel range: [{cfg_parallel_float.min():.4f}, {cfg_parallel_float.max():.4f}]" + ) + print(f" Baseline range: [{baseline_float.min():.4f}, {baseline_float.max():.4f}]") + + assert cos_sim > 0.99, ( + f"I2V CFG parallel cosine similarity {cos_sim:.6f} below threshold 0.99. " + f"CFG Parallelism does not match standard CFG baseline." + ) + + print("\n[PASS] I2V CFG Parallelism (cfg_size=2) validated!") + print(" āœ“ CFG parallel produces same output as standard CFG") + print(" āœ“ Prompt splitting and all-gather working correctly") + print(" āœ“ Image embeddings handled correctly") + print("=" * 80) + + torch.cuda.empty_cache() + + +# ============================================================================ +# COMBINED OPTIMIZATIONS TESTS (I2V) +# ============================================================================ + + +@pytest.mark.parallelism +class TestWanI2VCombinedOptimizations(unittest.TestCase): + """Test all optimizations combined for I2V: FP8 + TeaCache + TRTLLM + CFG Parallelism.""" + + DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + def setUp(self): + """Set up test fixtures and skip if checkpoint not available.""" + torch.manual_seed(42) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(42) + if not CHECKPOINT_PATH or not os.path.exists(CHECKPOINT_PATH): + self.skipTest( + "Checkpoint not available. Set DIFFUSION_MODEL_PATH environment variable." + ) + + def tearDown(self): + """Clean up GPU memory.""" + import gc + + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + def test_all_optimizations_combined(self): + """Test I2V FP8 + TeaCache + TRTLLM attention + CFG=2 combined correctness. + + This test validates that all optimizations work together correctly for I2V: + 1. FP8 per-tensor quantization for reduced memory/compute + 2. TeaCache for caching repeated computations + 3. TRTLLM attention backend for optimized attention kernels + 4. CFG Parallelism (cfg_size=2) for distributed CFG computation + + We compare against a standard CFG baseline with relaxed thresholds. + """ + num_gpus = torch.cuda.device_count() + if num_gpus < 2: + pytest.skip("Combined optimization test requires at least 2 GPUs for CFG parallel") + if not is_wan21_checkpoint(): + pytest.skip( + "This test requires Wan 2.1 checkpoint. Use DIFFUSION_MODEL_PATH with '2.1' in the path." + ) + + print("\n" + "=" * 80) + print("I2V ALL OPTIMIZATIONS COMBINED TEST") + print("FP8 + TeaCache + TRTLLM Attention + CFG Parallelism (cfg_size=2)") + print("=" * 80) + + # Load baseline on GPU 0 (no optimizations, standard CFG) + print("\n[1/3] Loading I2V baseline on GPU 0 (standard CFG, no optimizations)...") + args_baseline = DiffusionArgs( + checkpoint_path=CHECKPOINT_PATH, + device="cuda:0", + dtype="bfloat16", + skip_components=SKIP_MINIMAL, + parallel=ParallelConfig(dit_cfg_size=1), # Standard CFG + ) + pipeline_baseline = PipelineLoader(args_baseline).load() + config = pipeline_baseline.transformer.model_config.pretrained_config + + # Reset torch compile state + torch._dynamo.reset() + + # Create FIXED test inputs + print("\n[2/3] Creating fixed test inputs...") + torch.manual_seed(42) + batch_size, num_frames, height, width, seq_len = 1, 1, 64, 64, 128 + + latents = torch.randn( + batch_size, + config.in_channels, + num_frames, + height, + width, + dtype=torch.bfloat16, + device="cuda:0", + ) + timestep = torch.tensor([500], dtype=torch.long, device="cuda:0") + prompt_embeds = torch.randn( + batch_size, seq_len, config.text_dim, dtype=torch.bfloat16, device="cuda:0" + ) + neg_prompt_embeds = torch.randn( + batch_size, seq_len, config.text_dim, dtype=torch.bfloat16, device="cuda:0" + ) + + # I2V-specific: Create image embeddings + image_embeds = None + image_dim = getattr(config, "image_dim", getattr(config, "image_embed_dim", None)) + if image_dim is not None: + image_seq_len = 256 + image_embeds = torch.randn( + batch_size, image_seq_len, image_dim, dtype=torch.bfloat16, device="cuda:0" + ) + + # Setup standard CFG config + cfg_config_baseline = pipeline_baseline._setup_cfg_config( + guidance_scale=5.0, + prompt_embeds=prompt_embeds, + neg_prompt_embeds=neg_prompt_embeds, + ) + + # Run baseline standard CFG + print(" Running baseline (standard CFG)...") + + def forward_fn_baseline( + latents, extra_stream_latents, timestep, encoder_hidden_states, extra_tensors + ): + return pipeline_baseline.transformer( # noqa: F821 + hidden_states=latents, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + encoder_hidden_states_image=extra_tensors.get("encoder_hidden_states_image"), + ) + + with torch.no_grad(): + local_extras = ( + {"encoder_hidden_states_image": image_embeds} if image_embeds is not None else {} + ) + baseline_output, _, _, _ = pipeline_baseline._denoise_step_standard( + latents=latents.clone(), + extra_stream_latents={}, + timestep=timestep, + prompt_embeds=cfg_config_baseline["prompt_embeds"], + forward_fn=forward_fn_baseline, + guidance_scale=5.0, + guidance_rescale=0.0, + local_extras=local_extras, + ) + + print(f" āœ“ Baseline output shape: {baseline_output.shape}") + print(f" āœ“ Baseline range: [{baseline_output.min():.4f}, {baseline_output.max():.4f}]") + + # Cleanup baseline + del pipeline_baseline + torch.cuda.empty_cache() + + # Run with ALL optimizations (FP8 + TeaCache + TRTLLM + CFG=2) + print("\n[3/3] Running with ALL optimizations (FP8 + TeaCache + TRTLLM + CFG=2)...") + cfg_size = 2 + + inputs_cpu = [ + prompt_embeds.cpu(), + neg_prompt_embeds.cpu(), + latents.cpu(), + timestep.cpu(), + image_embeds.cpu() if image_embeds is not None else None, + ] + + manager = mp.Manager() + return_dict = manager.dict() + + # Spawn workers with all optimizations + mp.spawn( + _run_all_optimizations_worker_i2v, + args=(cfg_size, CHECKPOINT_PATH, inputs_cpu, return_dict), + nprocs=cfg_size, + join=True, + ) + + # Get combined optimization output + combined_output = return_dict["output"].to("cuda:0") + print(f" āœ“ Combined optimization output shape: {combined_output.shape}") + + # Compare outputs (relaxed threshold for combined optimizations) + print("\n[Comparison] I2V Combined Optimizations vs Baseline:") + baseline_float = baseline_output.float() + combined_float = combined_output.float() + + cos_sim = F.cosine_similarity( + combined_float.flatten(), baseline_float.flatten(), dim=0 + ).item() + + max_diff = torch.max(torch.abs(combined_float - baseline_float)).item() + mean_diff = torch.mean(torch.abs(combined_float - baseline_float)).item() + + print(f" Cosine similarity: {cos_sim:.6f}") + print(f" Max absolute difference: {max_diff:.6f}") + print(f" Mean absolute difference: {mean_diff:.6f}") + + # Relaxed threshold (0.95) since multiple optimizations compound numerical differences + assert cos_sim > 0.95, ( + f"I2V combined optimization cosine similarity {cos_sim:.6f} below threshold 0.95" + ) + + print("\n[PASS] All optimizations (FP8 + TeaCache + TRTLLM + CFG) validated!") + print(" āœ“ All optimizations work together correctly") + print(" āœ“ I2V image embeddings handled correctly with all opts") + print("=" * 80) + + torch.cuda.empty_cache() + + +if __name__ == "__main__": + import unittest + + unittest.main(verbosity=2)