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2
README.md
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@ -10,7 +10,7 @@ state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.<
[![python](https://img.shields.io/badge/python-3.10-green)](https://www.python.org/downloads/release/python-31012/)
[![cuda](https://img.shields.io/badge/cuda-13.0.0-green)](https://developer.nvidia.com/cuda-downloads)
[![torch](https://img.shields.io/badge/torch-2.9.0-green)](https://pytorch.org)
[![version](https://img.shields.io/badge/release-1.2.0rc7-green)](https://github.com/NVIDIA/TensorRT-LLM/blob/main/tensorrt_llm/version.py)
[![version](https://img.shields.io/badge/release-1.2.0rc8-green)](https://github.com/NVIDIA/TensorRT-LLM/blob/main/tensorrt_llm/version.py)
[![license](https://img.shields.io/badge/license-Apache%202-blue)](https://github.com/NVIDIA/TensorRT-LLM/blob/main/LICENSE)
[Architecture](https://nvidia.github.io/TensorRT-LLM/developer-guide/overview.html)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[Performance](https://nvidia.github.io/TensorRT-LLM/developer-guide/perf-overview.html)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[Examples](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[Documentation](https://nvidia.github.io/TensorRT-LLM/)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[Roadmap](https://github.com/NVIDIA/TensorRT-LLM/issues?q=is%3Aissue%20state%3Aopen%20label%3Aroadmap)

3
docs/source/models/supported-models.md
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@ -40,10 +40,11 @@ Note: Support for other models may vary. Features marked "N/A" are not applicabl
| `Qwen3MoeForCausalLM` | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | Yes | Yes | N/A | Yes | Yes |
| `Qwen3NextForCausalLM` | Yes | Yes | No | Untested | Yes | No | No | No | Yes | Yes | No | No | Untested | Untested |
| `Llama4ForConditionalGeneration` | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | Yes | Untested | N/A | Yes | Yes |
| `GptOssForCausalLM` | Yes | Yes | Yes | Yes | No | No | Yes | No | Yes | Yes | No | N/A | Yes | Yes |
| `GptOssForCausalLM` | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes [^3] | Yes | Yes | Yes | N/A | Yes | Yes |
[^1]: Chunked Prefill for MLA can only be enabled on SM100/SM103.
[^2]: KV cache reuse for MLA can only be enabled on SM90/SM100/SM103 and in BF16/FP8 KV cache dtype.
[^3]: Overlap scheduler isn't supported when using EAGLE-3(Two Model Engine) for GPT-OSS.
# Multimodal Feature Support Matrix (PyTorch Backend)

2
examples/constraints.txt
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@ -1,3 +1,3 @@
tensorrt_llm==1.2.0rc7
tensorrt_llm==1.2.0rc8
evaluate~=0.4.1
rouge_score~=0.1.2

3
examples/models/core/mistral_large_3/README.md
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@ -19,7 +19,8 @@ mpirun -n 1 --allow-run-as-root --oversubscribe python3 examples/llm-api/quickst
--max_tokens 100 \
--checkpoint_format mistral \
--model_type mistral_large_3 \
--moe_backend TRTLLM
--moe_backend TRTLLM \
--image_format pil
```
## LLM-only run

2
jenkins/L0_Test.groovy
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@ -808,7 +808,7 @@ def getPytestBaseCommandLine(
portEnvVars,
pytestUtil,
"pytest",
"-v",
"-vv",
testFilter[(DETAILED_LOG)] ? "-s" : "",
"--timeout-method=thread",
"--apply-test-list-correction",

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security_scanning/docs/poetry.lock generated
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6
security_scanning/examples/draft_target_model/poetry.lock generated
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6
security_scanning/examples/eagle/poetry.lock generated
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6
security_scanning/examples/llm-eval/lm-eval-harness/poetry.lock generated
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security_scanning/examples/lookahead/poetry.lock generated
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6
security_scanning/examples/medusa/poetry.lock generated
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20
security_scanning/examples/models/contrib/baichuan/poetry.lock generated
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@ -1888,13 +1888,13 @@ telegram = ["requests"]
[[package]]
name = "transformers"
version = "5.0.0rc1"
version = "5.0.0rc2"
description = "Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training."
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[package.dependencies]
@ -1912,15 +1912,15 @@ typer-slim = "*"
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benchmark = ["optimum-benchmark (>=0.3.0)"]
chat-template = ["jinja2 (>=3.1.0)", "jmespath (>=1.0.1)"]
codecarbon = ["codecarbon (>=2.8.1)"]
deepspeed = ["accelerate (>=1.1.0)", "deepspeed (>=0.9.3)"]
deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=1.1.0)", "accelerate (>=1.1.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.4.6)", "faiss-cpu", "fastapi", "libcst", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "openai (>=1.98.0)", "optuna", "parameterized (>=0.9)", "protobuf", "psutil", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0,<9.0.0)", "pytest-asyncio (>=1.2.0)", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "tensorboard", "timeout-decorator", "torch (>=2.2)", "uvicorn"]
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security_scanning/examples/models/contrib/gptj/poetry.lock generated
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security_scanning/examples/models/core/mixtral/poetry.lock generated
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security_scanning/examples/models/core/mllama/poetry.lock generated
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security_scanning/examples/models/core/nemotron/poetry.lock generated
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6
security_scanning/examples/models/core/phi/poetry.lock generated
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6
security_scanning/examples/models/core/qwen/poetry.lock generated
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6
security_scanning/examples/models/core/qwen2audio/poetry.lock generated
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20
security_scanning/examples/models/core/qwenvl/poetry.lock generated
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@ -2927,13 +2927,13 @@ telegram = ["requests"]
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6
security_scanning/examples/models/core/recurrentgemma/poetry.lock generated
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security_scanning/examples/models/core/whisper/poetry.lock generated
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security_scanning/examples/ngram/poetry.lock generated
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20
security_scanning/examples/quantization/poetry.lock generated
View File

@ -1855,13 +1855,13 @@ telegram = ["requests"]
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24
security_scanning/examples/ray_orchestrator/poetry.lock generated
View File

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6
security_scanning/examples/redrafter/poetry.lock generated
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6
security_scanning/examples/serve/poetry.lock generated
View File

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{file = "urllib3-2.6.2.tar.gz", hash = "sha256:016f9c98bb7e98085cb2b4b17b87d2c702975664e4f060c6532e64d1c1a5e797"},
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{file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
]
[package.extras]

6
security_scanning/examples/trtllm-eval/poetry.lock generated
View File

@ -3275,13 +3275,13 @@ files = [
[[package]]
name = "urllib3"
version = "2.6.2"
version = "2.6.3"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.9"
files = [
{file = "urllib3-2.6.2-py3-none-any.whl", hash = "sha256:ec21cddfe7724fc7cb4ba4bea7aa8e2ef36f607a4bab81aa6ce42a13dc3f03dd"},
{file = "urllib3-2.6.2.tar.gz", hash = "sha256:016f9c98bb7e98085cb2b4b17b87d2c702975664e4f060c6532e64d1c1a5e797"},
{file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
{file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
]
[package.extras]

4
security_scanning/metadata.json
View File

@ -1,4 +1,4 @@
{
"commit_hash": "6095c80e560c612b8a8f4ff70e8930edf7798d21",
"timestamp": "2026-01-07T02:39:54Z"
"commit_hash": "b85c447ceb1ff91c5d4df6b71de2256a5fabfe9d",
"timestamp": "2026-01-08T02:42:38Z"
}

24
security_scanning/poetry.lock generated
View File

@ -1838,20 +1838,20 @@ files = [
[[package]]
name = "jsonschema"
version = "4.25.1"
version = "4.26.0"
description = "An implementation of JSON Schema validation for Python"
optional = false
python-versions = ">=3.9"
python-versions = ">=3.10"
files = [
{file = "jsonschema-4.25.1-py3-none-any.whl", hash = "sha256:3fba0169e345c7175110351d456342c364814cfcf3b964ba4587f22915230a63"},
{file = "jsonschema-4.25.1.tar.gz", hash = "sha256:e4a9655ce0da0c0b67a085847e00a3a51449e1157f4f75e9fb5aa545e122eb85"},
{file = "jsonschema-4.26.0-py3-none-any.whl", hash = "sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce"},
{file = "jsonschema-4.26.0.tar.gz", hash = "sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326"},
]
[package.dependencies]
attrs = ">=22.2.0"
jsonschema-specifications = ">=2023.03.6"
referencing = ">=0.28.4"
rpds-py = ">=0.7.1"
rpds-py = ">=0.25.0"
[package.extras]
format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
@ -3852,13 +3852,13 @@ xmp = ["defusedxml"]
[[package]]
name = "plotly"
version = "6.5.0"
version = "6.5.1"
description = "An open-source interactive data visualization library for Python"
optional = false
python-versions = ">=3.8"
files = [
{file = "plotly-6.5.0-py3-none-any.whl", hash = "sha256:5ac851e100367735250206788a2b1325412aa4a4917a4fe3e6f0bc5aa6f3d90a"},
{file = "plotly-6.5.0.tar.gz", hash = "sha256:d5d38224883fd38c1409bef7d6a8dc32b74348d39313f3c52ca998b8e447f5c8"},
{file = "plotly-6.5.1-py3-none-any.whl", hash = "sha256:5adad4f58c360612b6c5ce11a308cdbc4fd38ceb1d40594a614f0062e227abe1"},
{file = "plotly-6.5.1.tar.gz", hash = "sha256:b0478c8d5ada0c8756bce15315bcbfec7d3ab8d24614e34af9aff7bfcfea9281"},
]
[package.dependencies]
@ -5930,13 +5930,13 @@ files = [
[[package]]
name = "urllib3"
version = "2.6.2"
version = "2.6.3"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.9"
files = [
{file = "urllib3-2.6.2-py3-none-any.whl", hash = "sha256:ec21cddfe7724fc7cb4ba4bea7aa8e2ef36f607a4bab81aa6ce42a13dc3f03dd"},
{file = "urllib3-2.6.2.tar.gz", hash = "sha256:016f9c98bb7e98085cb2b4b17b87d2c702975664e4f060c6532e64d1c1a5e797"},
{file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
{file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
]
[package.extras]
@ -6339,4 +6339,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.13"
content-hash = "04c93699fd51f42c19b5111e408e5240d4dc0616a9d2501c7d159c01b3331ca2"
content-hash = "dab9694d64d1c91b512eb62bbd31da9d0cdb8c93e99941a7022f2f46aea905e3"

4
security_scanning/pyproject.toml
View File

@ -59,7 +59,7 @@ einops = "^0.8.1"
flashinfer-python = ">=0.3.0,<0.4.0"
xgrammar = "0.1.25"
llguidance = "0.7.29"
jsonschema = "^4.25.1"
jsonschema = "^4.26.0"
backoff = "^2.2.1"
nvtx = "^0.2.14"
matplotlib = "^3.10.8"
@ -73,7 +73,7 @@ tiktoken = "^0.12.0"
blobfile = "^3.1.0"
openai-harmony = "0.0.4"
nvidia-cutlass-dsl = "4.3.4"
plotly = "^6.5.0"
plotly = "^6.5.1"
numexpr = "<2.14.0"
partial-json-parser = "^0.2.1.1.post7"
torch-c-dlpack-ext = "0.1.3"

6
security_scanning/triton_backend/poetry.lock generated
View File

@ -982,13 +982,13 @@ files = [
[[package]]
name = "urllib3"
version = "2.6.2"
version = "2.6.3"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.9"
files = [
{file = "urllib3-2.6.2-py3-none-any.whl", hash = "sha256:ec21cddfe7724fc7cb4ba4bea7aa8e2ef36f607a4bab81aa6ce42a13dc3f03dd"},
{file = "urllib3-2.6.2.tar.gz", hash = "sha256:016f9c98bb7e98085cb2b4b17b87d2c702975664e4f060c6532e64d1c1a5e797"},
{file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
{file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
]
[package.extras]

13
tensorrt_llm/_torch/auto_deploy/models/custom/modeling_nemotron_h.py
View File

@ -113,11 +113,6 @@ class NemotronHMamba2Mixer(nn.Module):
A = torch.arange(1, self.num_heads + 1)
self.A_log = nn.Parameter(torch.log(A))
self.A_log._no_weight_decay = True
# Instead of recomputing `torch.exp(self.A_log.float())` on every forward pass, we will register a hook
# that sets this appropriately when loading weights.
# NOTE: we explicitly register this as a non-persistent buffer so that it does not appear in the state dict of
# this module, or an equivalent graph module trace from it, but still gets included in e.g. `to()` calls.
self.register_buffer("_minus_A", -A.float(), persistent=False)
self.norm = MambaRMSNormGated(
self.intermediate_size,
eps=self.layer_norm_epsilon,
@ -129,8 +124,6 @@ class NemotronHMamba2Mixer(nn.Module):
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias)
self.use_bias = config.use_bias
self.register_load_state_dict_post_hook(self._load_state_dict_post_hook)
def torch_forward(self, input_states):
batch_size, seq_len, _ = input_states.shape
dtype = input_states.dtype
@ -166,7 +159,7 @@ class NemotronHMamba2Mixer(nn.Module):
)
# 3. SSM transformation
A = self._minus_A
A = -torch.exp(self.A_log.float())
y = torch.ops.auto_deploy.torch_ssm(
hidden_states=hidden_states.view(batch_size, seq_len, -1, self.head_dim),
A=A,
@ -193,10 +186,6 @@ class NemotronHMamba2Mixer(nn.Module):
def forward(self, hidden_states):
return self.torch_forward(hidden_states)
@staticmethod
def _load_state_dict_post_hook(module, incompatible_keys) -> None:
module._minus_A.data = -torch.exp(module.A_log.float())
class NemotronHRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):

35
tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py
View File

@ -6,7 +6,7 @@ import torch
from tensorrt_llm.logger import logger
from ..._utils import get_sm_version
from ...math_utils import pad_up
from ...math_utils import ceil_div, pad_up
from ..autotuner import (AutoTuner, ConstraintSpec, DistributedTuningStrategy,
DynamicTensorSpec, OptimizationProfile, TunableRunner,
TuningConfig)
@ -314,6 +314,16 @@ class GatherGroupedGemmInputsHelper(GroupedGemmInputsHelper):
num_non_exiting_tiles, global_sf)
def get_dense_gemm_approximate_cta_nums(
M: int, N: int, tile_mn: Tuple[int, int],
cluster_shape_mn: Tuple[int, int]) -> int:
tile_m, tile_n = tile_mn
cluster_m, cluster_n = cluster_shape_mn
clustered_ctas_m = pad_up(ceil_div(M, tile_m), cluster_m)
clustered_ctas_n = pad_up(ceil_div(N, tile_n), cluster_n)
return clustered_ctas_m * clustered_ctas_n
if IS_CUTLASS_DSL_AVAILABLE:
import cutlass
@ -360,15 +370,6 @@ if IS_CUTLASS_DSL_AVAILABLE:
def unique_id(self):
return (self.output_dtype, self.to_userbuffers, self.use_tvm_ffi)
def __hash__(self):
return hash(
(self.output_dtype, self.to_userbuffers, self.use_tvm_ffi))
def __eq__(self, other):
if not isinstance(other, self.__class__):
return False
return self.output_dtype == other.output_dtype and self.to_userbuffers == other.to_userbuffers and self.use_tvm_ffi == other.use_tvm_ffi
def get_valid_tactics(
self,
inputs: List[torch.Tensor],
@ -454,6 +455,7 @@ if IS_CUTLASS_DSL_AVAILABLE:
(4, 4),
]
swap_ab_candidates = [True, False]
# prune: prefetch is beneficial only when K is large enough
use_prefetch_candidates = [True, False]
valid_tactics = []
@ -484,6 +486,19 @@ if IS_CUTLASS_DSL_AVAILABLE:
b_major,
c_major,
):
# Prefetch pruning to save tuning time
cta_nums = get_dense_gemm_approximate_cta_nums(
m, n, mma_tiler_mn, cluster_shape_mn)
cta_wave_ratio = cta_nums / torch.cuda.get_device_properties(
).multi_processor_count
if use_prefetch and not any((
# CTA waves ratio between 0.5 and 1.0
0.5 < cta_wave_ratio < 1.0,
# K is large enough
real_k >= 8192,
)):
continue
valid_tactics.append(
(mma_tiler_mn, cluster_shape_mn, swap_ab, use_prefetch))

11
tensorrt_llm/_torch/models/checkpoints/base_weight_mapper.py
View File

@ -29,9 +29,6 @@ class BaseWeightMapper(ABC):
raise ValueError("model must have a config attribute")
self._tp_size = 1 if model.model_config.mapping.enable_attention_dp else model.model_config.mapping.tp_size
self._head_dim = model.config.head_dim if hasattr(
model.config, 'head_dim'
) and model.config.head_dim is not None else model.config.hidden_size // model.config.num_attention_heads
self.map_weights()
@ -173,3 +170,11 @@ class BaseWeightMapper(ABC):
if self._model is None:
raise RuntimeError("Weight mapper is not initialized")
return self._model
@property
def _head_dim(self) -> int:
model = self.model
head_dim = model.config.head_dim if hasattr(
model.config, 'head_dim'
) and model.config.head_dim is not None else model.config.hidden_size // model.config.num_attention_heads
return head_dim

19
tensorrt_llm/_torch/models/checkpoints/hf/qwen3vl_weight_mapper.py
View File

@ -1,3 +1,8 @@
from transformers.models.qwen3_vl.configuration_qwen3_vl import (
Qwen3VLTextConfig,
Qwen3VLVisionConfig,
)
from tensorrt_llm._torch.models.checkpoints.hf.weight_mapper import HfWeightMapper
from tensorrt_llm._torch.models.modeling_utils import register_mapper
@ -6,3 +11,17 @@ from tensorrt_llm._torch.models.modeling_utils import register_mapper
class Qwen3VLHfWeightMapper(HfWeightMapper):
def preprocess_weights(self, weights: dict) -> dict:
return weights
@property
def _head_dim(self) -> int:
config = self.model.config
if (head_dim := getattr(config, "head_dim", None)) is not None:
return head_dim
if isinstance(config, Qwen3VLTextConfig):
num_heads = config.num_attention_heads
elif isinstance(config, Qwen3VLVisionConfig):
num_heads = config.num_heads
else:
raise TypeError(f"Unexpected config class {type(config).__name__}.")
return config.hidden_size // num_heads

7
tensorrt_llm/_torch/models/checkpoints/mistral/config_loader.py
View File

@ -103,17 +103,14 @@ def _remap_mistral_yarn_args(config: dict) -> dict:
"apply_scale": "apply_yarn_scaling",
}
yarn_config = config.get("yarn") or {}
config["rope_parameters"] = {
config["rope_scaling"] = {
"rope_type": "yarn",
"mscale_all_dim": 1,
}
if rope_theta := config.pop("rope_theta", None):
config["rope_parameters"]["rope_theta"] = rope_theta
for old_name, new_name in yarn_config_map.items():
if old_name in yarn_config:
config["rope_parameters"][new_name] = yarn_config.pop(old_name)
config["rope_scaling"][new_name] = yarn_config.pop(old_name)
assert len(yarn_config) == 0, f"Unparsed yarn config: {yarn_config}"

64
tensorrt_llm/_torch/models/modeling_mistral.py
View File

@ -46,6 +46,7 @@ from tensorrt_llm.inputs import (BaseMultimodalDummyInputsBuilder,
MultimodalPlaceholderPlacement, TextPrompt,
register_input_processor)
from tensorrt_llm.inputs.multimodal import MultimodalParams
from tensorrt_llm.inputs.utils import encode_base64_image
from tensorrt_llm.llmapi import SamplingParams
from tensorrt_llm.logger import logger
@ -58,16 +59,28 @@ class MistralAttention(Attention):
layer_idx: int | None = None,
):
config = model_config.pretrained_config
rope_params = RopeParams.from_config(config)
rope_params_section = getattr(config, "rope_scaling", None) or getattr(
config, "rope_parameters", None)
rope_type = getattr(rope_params_section, "rope_type", None)
if rope_type == "yarn":
pos_embd_params = PositionalEmbeddingParams(
type=PositionEmbeddingType.yarn,
rope=rope_params,
is_neox=False)
else:
pos_embd_params = PositionalEmbeddingParams(
type=PositionEmbeddingType.rope_gpt_neox,
rope=rope_params,
)
super().__init__(
hidden_size=config.hidden_size,
num_attention_heads=config.num_attention_heads,
num_key_value_heads=config.num_key_value_heads,
max_position_embeddings=config.max_position_embeddings,
bias=False,
pos_embd_params=PositionalEmbeddingParams(
type=PositionEmbeddingType.rope_gpt_neox,
rope=RopeParams.from_config(config),
),
pos_embd_params=pos_embd_params,
layer_idx=layer_idx,
dtype=config.torch_dtype,
config=model_config,
@ -266,20 +279,18 @@ class MistralCommonImageProcessor:
}
def get_num_tokens_per_image(self, image_sizes):
# FIXME avoid double loading with custom loader
h, w = image_sizes
ncols, nrows = self.image_processor._image_to_num_tokens(
Image.new("RGB", (w, h)))
return ncols * nrows + nrows
def __call__(self, text, images, media, **kwargs):
assert media is not None
if isinstance(media, str):
media = [media]
mm_items = [{"type": "image_url", "image_url": url} for url in media]
logger.debug(f"text: {text}")
def __call__(self, text, images, **kwargs):
mm_items = []
if images:
mm_items = [{
"type": "image",
"base64": encode_base64_image(image)
} for image in images]
conversation = [{
"role": "user",
@ -292,19 +303,20 @@ class MistralCommonImageProcessor:
encoded = self.tokenizer.transformers_tokenizer.apply_chat_template(
conversation, tokenize=True, return_dict=True, return_tensors='pt')
logger.debug(
f"encoded.pixel_values.shape: {encoded.pixel_values.shape}, encoded.input_ids: {encoded.input_ids[0][-20:]}"
)
logger.debug(
f"encoded.input_ids list: {self.tokenizer.transformers_tokenizer.apply_chat_template(conversation)}"
)
processed = {
"input_ids": encoded.input_ids,
"pixel_values": encoded.pixel_values.to(self.dtype),
"attention_mask": encoded.attention_mask,
"image_sizes": torch.tensor([encoded.pixel_values.shape[2:]])
}
# text-only mode for VLM
if "pixel_values" in encoded:
processed.update({
"pixel_values":
encoded.pixel_values.to(self.dtype),
"attention_mask":
encoded.attention_mask,
"image_sizes":
torch.tensor([encoded.pixel_values.shape[2:]])
})
return processed
@ -376,7 +388,6 @@ class Mistral3InputProcessor(BaseMultimodalInputProcessor,
self, inputs: TextPrompt, sampling_params: SamplingParams
) -> Tuple[List[int], ExtraProcessedInputs | None]:
images = inputs.get("multi_modal_data", {}).get("image")
mm_processor_kwargs = inputs.get("mm_processor_kwargs", {})
do_rescale = getattr(self.processor.image_processor, "do_rescale",
False)
if images is not None and isinstance(images[0], torch.Tensor):
@ -384,18 +395,15 @@ class Mistral3InputProcessor(BaseMultimodalInputProcessor,
# format is "pt" (pytorch tensors), but not for "pil" (PIL images).
do_rescale = False
if mm_processor_kwargs:
# Currently, we only support image modality in MistralCommonImageProcessor.
if images is not None:
processed = self.processor(
text=inputs["prompt"],
images=images,
do_rescale=do_rescale,
**mm_processor_kwargs,
)
else:
processed = self.text_processor(
text=inputs["prompt"],
images=images,
do_rescale=do_rescale,
)
input_ids = processed.pop("input_ids").tolist()[0]

110
tensorrt_llm/_torch/models/modeling_qwen3vl.py
View File

@ -25,6 +25,7 @@ from ...inputs import (
MultimodalPlaceholderPlacement,
TextPrompt,
register_input_processor,
support_multimodal_disaggregated,
)
from ...inputs.multimodal import MultimodalParams
from ...logger import logger
@ -350,6 +351,85 @@ class Qwen3VLInputProcessorBase(BaseMultimodalInputProcessor, BaseMultimodalDumm
"multimodal_data": multimodal_data,
}
def get_prompt_token_ids(
self, inputs: TextPrompt, mm_handles: List[Dict[str, Any]]
) -> Tuple[List[int], List[int], List[int]]:
"""
Build input token ids with multimodal placeholders expanded to the number of MM tokens.
Args:
inputs: Text prompt input container. Must contain a non-empty prompt string.
mm_handles: List of multimodal embedding handles. Currently only a single handle is supported.
Returns:
Tuple[List[int], List[int], List[int]]:
- expanded_ids: token ids with each image token expanded to a placeholder repeated per MM token
- mm_token_length: per-image MM token lengths
- mm_token_offsets: start offsets (positions) for each image's MM tokens within expanded_ids
"""
# TODO: Move this function to the base input processor class when extending for more models
text_prompt = inputs.get("prompt")
if not text_prompt:
raise ValueError("Text prompt is required but not provided")
if not isinstance(mm_handles, list):
raise TypeError("mm_handles must be a list")
if len(mm_handles) > 1:
# TODO: only support single multimodal item within a request for now
raise NotImplementedError("Only one mm_handle is supported for Qwen3 VL for now")
hidden_size = mm_handles[0]["tensor_size"][1]
num_deepstack_levels = len(self.config.vision_config.deepstack_visual_indexes)
# This is because, unlike previous Qwen VL models, the embeddings are concatenated with
# feature maps from deepstack layers.
expected_size = self.config.text_config.hidden_size * (1 + num_deepstack_levels)
if hidden_size != expected_size:
raise RuntimeError(
f"Expected multimodal embedding to have hidden size {expected_size}, got {hidden_size}."
)
input_ids = self.tokenizer(text_prompt, return_tensors="pt").input_ids[0]
# TODO: what about `video_token_id`?
image_token_index = self.config.image_token_id
image_mask = input_ids == image_token_index
image_positions = torch.where(image_mask)[0]
num_images = len(image_positions)
assert num_images == len(mm_handles), "Number of images must match number of mm_handles"
total_mm_tokens = sum(mm_handle["tensor_size"][0] for mm_handle in mm_handles)
final_length = len(input_ids) - num_images + total_mm_tokens
# Create output tensor
expanded_ids = torch.empty(final_length, dtype=input_ids.dtype)
placeholder_id = self.tllm_multimodal_token_id
# Fill the expanded sequence
write_pos = 0
image_cnt = 0
mm_token_length = []
mm_token_offsets = []
for read_pos in range(len(input_ids)):
if input_ids[read_pos] == image_token_index:
# Replace with placeholder id
mm_token_num = mm_handles[image_cnt]["tensor_size"][0]
expanded_ids[write_pos : write_pos + mm_token_num] = placeholder_id
mm_token_offsets.append(write_pos)
mm_token_length.append(mm_token_num)
write_pos += mm_token_num
image_cnt += 1
else:
# Copy text token as-is
expanded_ids[write_pos] = input_ids[read_pos]
write_pos += 1
assert write_pos == final_length, f"Write position mismatch: {write_pos} != {final_length}"
assert mm_token_length[-1] + mm_token_offsets[-1] <= final_length, (
f"mm_token_length[-1] + mm_token_offsets[-1] ({mm_token_length[-1] + mm_token_offsets[-1]}) should be less "
f"than or equal to final_length ({final_length})"
)
return expanded_ids.to(torch.int32).tolist(), mm_token_length, mm_token_offsets
class Qwen3VLVisionAttention(Qwen2_5_VLVisionAttention):
def __init__(self, model_config, layer_idx):
@ -825,6 +905,7 @@ class Qwen3VLModelBase(PreTrainedModel):
llm_model_config.pretrained_config.architectures = ["Qwen3MoeForCausalLM"]
else:
raise ValueError(f"Unsupported architecture: {self.original_arch}")
# Qwen3ForCausalLM.
self.llm = AutoModelForCausalLM.from_config(llm_model_config)
if not _is_disagg():
@ -953,22 +1034,16 @@ class Qwen3VLModelBase(PreTrainedModel):
# NOTE: Qwen*-VL series has mrope_config even on the text-only prompts,
# so we need to separate the mm_multimodal_params from the text-only prompts.
mm_multimodal_params = [
multimodal_param
for multimodal_param in multimodal_params
if multimodal_param.multimodal_data.get("image", {}).get("pixel_values") is not None
or multimodal_param.multimodal_data.get("video", {}).get("pixel_values_videos")
is not None
]
mm_multimodal_params = self._get_requests_with_mm_data(multimodal_params)
if len(mm_multimodal_params) > 0:
if not _is_disagg():
mm_embeds = get_multimodal_embeddings(
encoder_forward_fn=self.mm_encoder.forward,
multimodal_params=mm_multimodal_params,
)
else:
elif not getattr(self, "support_mm_disagg", False):
raise NotImplementedError(
"Qwen3VLModel does not support disaggregated inference yet. Please unset "
f"{type(self)} does not support disaggregated inference yet. Please unset "
"the TLLM_MULTIMODAL_DISAGGREGATED environment variable, or set it to '0'."
)
mm_embeds = find_input_mm_embeds(mm_embeds, mm_multimodal_params)
@ -1008,7 +1083,24 @@ class Qwen3VLModelBase(PreTrainedModel):
logger.debug(f"output shape: {output_prob.shape}")
return output_prob
def _get_requests_with_mm_data(self, multimodal_params):
mm_multimodal_params = []
for multimodal_param in multimodal_params:
data = multimodal_param.multimodal_data
if (
# The first 2 conditions check whether there is input on which inference should be run.
data.get("image", {}).get("pixel_values") is not None
or data.get("video", {}).get("pixel_values_videos") is not None
# This condition corresponds to when the embeddings are already populated, as is e.g.
# the case in EPD disagg in the prefill worker.
or data.get("multimodal_embedding")
):
mm_multimodal_params.append(multimodal_param)
return mm_multimodal_params
@support_multimodal_disaggregated
@register_vision_encoder(Qwen3VisionModelBase, vlm_base_model=Qwen3VisionModel)
@register_auto_model("Qwen3VLForConditionalGeneration")
@register_input_processor(

31
tensorrt_llm/evaluate/lm_eval.py
View File

@ -52,7 +52,9 @@ class LmEvalWrapper(TemplateLM):
llm: Union[LLM, PyTorchLLM],
sampling_params: Optional[SamplingParams] = None,
streaming: bool = False,
chat_template_kwargs: Optional[dict[str, Any]] = None):
chat_template_kwargs: Optional[dict[str, Any]] = None,
model_type: str | None = None,
is_force_single_image: bool = False):
super().__init__()
self.llm = llm
self.sampling_params = sampling_params
@ -163,7 +165,9 @@ class MultimodalLmEvalWrapper(LmEvalWrapper):
sampling_params: Optional[SamplingParams] = None,
streaming: bool = False,
max_images: int = 999,
chat_template_kwargs: Optional[dict[str, Any]] = None):
chat_template_kwargs: Optional[dict[str, Any]] = None,
model_type: str | None = None,
is_force_single_image: bool = False):
"""
Initialize the multimodal wrapper.
@ -179,7 +183,9 @@ class MultimodalLmEvalWrapper(LmEvalWrapper):
self.MULTIMODAL = True
self.max_images = max_images
self.chat_template_kwargs = chat_template_kwargs
self.model_type = self._get_model_type(llm)
self.model_type = model_type if model_type is not None else self._get_model_type(
llm)
self.is_force_single_image = is_force_single_image
# NOTE: In TRT-LLM, currently we do not support interleaved text and image. Instead, we are adding image placeholders at the end of the text or at the beginning of the text.
# So, until we support interleaved text and image, we set this to False.
@ -287,9 +293,14 @@ class MultimodalLmEvalWrapper(LmEvalWrapper):
prompt = prompt_inputs(prompt)
# NOTE: Convert RGBA format to RGB format
images = [
convert_image_mode(img, "RGB") for img in media_data["visual"]
]
if self.is_force_single_image:
# NOTE: This is a workaround to force single image for models which only support single image.
images = [convert_image_mode(media_data["visual"][0], "RGB")]
else:
images = [
convert_image_mode(img, "RGB")
for img in media_data["visual"]
]
prompt["multi_modal_data"] = {"image": images}
sampling_params = self._get_sampling_params(gen_kwargs)
@ -429,14 +440,18 @@ class LmEvalEvaluator(Evaluator):
llm: Union[LLM, PyTorchLLM],
sampling_params: Optional[SamplingParams] = None,
streaming: bool = False,
scores_filter: str = None) -> float:
scores_filter: str = None,
model_type: str = None,
is_force_single_image: bool = False) -> float:
import lm_eval
lm_cls = MultimodalLmEvalWrapper if self.MULTIMODAL else LmEvalWrapper
results = lm_eval.evaluate(
lm=lm_cls(llm,
sampling_params=sampling_params,
streaming=streaming,
chat_template_kwargs=self.chat_template_kwargs),
chat_template_kwargs=self.chat_template_kwargs,
model_type=model_type,
is_force_single_image=is_force_single_image),
task_dict=self.task_dict,
limit=self.num_samples,
apply_chat_template=self.apply_chat_template,

6
tensorrt_llm/inputs/utils.py
View File

@ -774,12 +774,6 @@ def default_multimodal_input_loader(
mm_placeholder_counts=[mm_placeholder_counts])
input = {"prompt": prompt}
# When the tokenizer is a MistralTokenizer, we need to keep the source media to handle in processor later.
from tensorrt_llm._torch.models.checkpoints.mistral.tokenizer import \
MistralTokenizer
if isinstance(tokenizer, MistralTokenizer):
input["mm_processor_kwargs"] = {"media": media}
if mm_placeholder_counts:
if mm_embeddings is not None:
input[

4
tensorrt_llm/llmapi/llm.py
View File

@ -491,8 +491,8 @@ class BaseLLM:
elif 'multi_modal_embeddings' in inputs:
mm_embedding_info = inputs['multi_modal_embeddings']
prompt_token_ids, extra_processed_inputs = cast(
self.input_processor,
BaseMultimodalInputProcessor).attach_multimodal_embeddings(
BaseMultimodalInputProcessor,
self.input_processor).attach_multimodal_embeddings(
inputs, mm_embedding_info, sampling_params)
else:
with nvtx_range_debug("input_processor"):

2
tensorrt_llm/version.py
View File

@ -12,4 +12,4 @@
# 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.
__version__ = "1.2.0rc7"
__version__ = "1.2.0rc8"

2
tests/integration/defs/accuracy/accuracy_core.py
View File

@ -402,6 +402,8 @@ class MMMU(AccuracyTask):
is_multimodal=True,
apply_chat_template=True)
EVALUATE_KWARGS = dict(model_type=None, is_force_single_image=False)
class PassKeyRetrieval64k(AccuracyTask):
DATASET = "passkey_retrieval_64k"

4
tests/integration/defs/accuracy/references/mmlu.yaml
View File

@ -345,9 +345,9 @@ mistralai/Mistral-Nemo-12b-Base:
- quant_algo: FP8
accuracy: 69.66
mistral/Mistral-Large-3-675B:
- accuracy: 87.54
- accuracy: 85.30
- spec_dec_algo: Eagle
accuracy: 87.54
accuracy: 85.30
nvidia/Nemotron-Super-V3:
- accuracy: 81.07
- quant_algo: NVFP4

3
tests/integration/defs/accuracy/references/mmmu.yaml
View File

@ -25,4 +25,5 @@ microsoft/Phi-4-multimodal-instruct:
Qwen/Qwen3-VL-30B-A3B-Instruct:
- accuracy: 55.33
mistral/Mistral-Large-3-675B:
- accuracy: 60.00
# Mistral Large 3 675B only supports single image input, so accuracy is lower.
- accuracy: 47

15
tests/integration/defs/accuracy/test_llm_api_pytorch_multimodal.py
View File

@ -293,8 +293,19 @@ class TestMistralLarge3_675B(LlmapiAccuracyTestHarness):
],
)
def test_nvfp4_4gpus(
self, tp_size, pp_size, ep_size, attention_dp, cuda_graph, overlap_scheduler, moe_backend
self,
tp_size,
pp_size,
ep_size,
attention_dp,
cuda_graph,
overlap_scheduler,
moe_backend,
mocker,
):
mocker.patch.dict(
MMMU.EVALUATE_KWARGS, {"model_type": "mistral_large_3", "is_force_single_image": True}
)
pytorch_config = dict(
disable_overlap_scheduler=not overlap_scheduler,
cuda_graph_config=CudaGraphConfig() if cuda_graph else None,
@ -315,4 +326,4 @@ class TestMistralLarge3_675B(LlmapiAccuracyTestHarness):
kv_cache_config=kv_cache_config,
) as llm:
task = MMMU(self.MODEL_NAME)
task.evaluate(llm, sampling_params=self.sampling_params, model_type="mistral_large_3")
task.evaluate(llm, sampling_params=self.sampling_params)

145
tests/integration/defs/perf/disagg/README.md
View File

@ -132,6 +132,141 @@ poetry run pytest --disagg test_disagg.py -s -vv -m accuracy
poetry run pytest --disagg test_disagg.py -s -vv -k "deepseek-r1-fp4_1k1k"
```
## Batch Job Submission
The framework supports automatic batch job submission to maximize parallelism in SLURM cluster environments. Instead of submitting jobs one-by-one, it groups test cases into batches and submits entire batches when needed.
### Quick Start
**Default batch size (5 jobs per batch):**
```bash
# Run all tests with default batching
poetry run pytest --disagg test_disagg.py -s -vv
# Run with test list
poetry run pytest --disagg test_disagg.py -s -vv --disagg-test-list=./testlist/all.txt
```
**Custom batch size:**
```bash
# Set batch size via command line
poetry run pytest --disagg test_disagg.py -s -vv --disagg-batch-size=10
# Set batch size via environment variable
export DISAGG_BATCH_SIZE=20
poetry run pytest --disagg test_disagg.py -s -vv
# Submit all jobs at once (unlimited batch)
poetry run pytest --disagg test_disagg.py -s -vv --disagg-batch-size=0
```
### How Batch Submission Works
```
Pytest Collection Phase:
- Collects all test cases (e.g., 100 tests)
- BatchManager splits them into batches (e.g., 20 batches of 5)
Pytest Execution Phase:
Test 0 runs:
-> Triggers submission of Batch 0 (jobs 0-4)
-> Waits for job 0 to complete
Test 1-4 run:
-> Batch 0 already submitted, directly wait for completion
Test 5 runs:
-> Triggers submission of Batch 1 (jobs 5-9)
-> Waits for job 5 to complete
... and so on
```
### Key Benefits
- **Parallel Execution**: All jobs in a batch run simultaneously on SLURM cluster
- **Reduced Wait Time**: Total time ≈ MAX(job time) instead of SUM(job times)
- **Automatic Management**: No need to manually split test lists
- **Lazy Loading**: Only submits batches when needed
### Configuration Options
**Priority**: Command line option > Environment variable > Default (5)
**Examples:**
```bash
# Small batch for quick testing
poetry run pytest --disagg test_disagg.py -s -vv --disagg-batch-size=3 \
--disagg-test-list=./testlist/debug.txt
# Large batch for production
poetry run pytest --disagg test_disagg.py -s -vv --disagg-batch-size=50 \
--disagg-test-list=./testlist/all.txt
# Submit all at once
poetry run pytest --disagg test_disagg.py -s -vv --disagg-batch-size=0
```
### Timeout Configuration
The default timeout for waiting for job completion is **10 hours (36000 seconds)**, which accounts for:
- SLURM queue wait time
- Job execution time
- Buffer for delays
### Performance Comparison
**Before (Sequential Submission):**
```
Case 1: submit + wait (1.5h) = 1.5h
Case 2: submit + wait (1.5h) = 1.5h
Case 3: submit + wait (1.5h) = 1.5h
...
Total: 50 × 1.5h = 75 hours
```
**After (Batch Submission, batch_size=50):**
```
Batch 0 (50 jobs): submitted in parallel
Case 1: wait (1.5h)
Case 2-50: wait (0s, already done)
Total: ~1.5 hours
```
**Speedup: 50x**
### Troubleshooting
**Check BatchManager initialization:**
```
======================================================================
Batch Manager Initialized
Batch size: 5 jobs per batch
======================================================================
Total test configs: 20
Total batches: 4
```
**Monitor batch submission:**
```
======================================================================
Submitting Batch 0
Range: [0:5] (5 jobs)
======================================================================
[ 1/5] Job 1234 <- test_config_id_1
[ 2/5] Job 1235 <- test_config_id_2
...
```
**If jobs timeout frequently:**
- Check SLURM queue status
- Consider reducing batch size to avoid resource contention
- Verify that timeout (36000s) is sufficient for your workload
## Test Naming Convention
Tests are automatically named using the format:
@ -193,6 +328,7 @@ Test results are saved to:
- `GPU_TYPE`: Current GPU type (default: GB200)
- `OUTPUT_PATH`: Directory for test results and logs
- `WORK_DIR`: Working directory for benchmark execution
- `DISAGG_BATCH_SIZE`: Default batch size for job submission (default: 5)
- `DEBUG_MODE`: Enable debug mode (set to "1" to skip job submission)
- `DEBUG_JOB_ID`: Job ID to use in debug mode
@ -212,10 +348,11 @@ The framework consists of:
1. **ConfigLoader**: Scans and loads YAML configurations
2. **ConfigValidator**: Validates configuration correctness
3. **JobManager**: Handles SLURM job submission and monitoring
4. **LogParser**: Extracts metrics from benchmark logs
5. **TestCaseTracker**: Tracks test execution timing
6. **ResultSaver**: Saves results to CSV
3. **BatchManager**: Manages batch job submission for parallel execution
4. **JobManager**: Handles SLURM job submission and monitoring
5. **LogParser**: Extracts metrics from benchmark logs
6. **TestCaseTracker**: Tracks test execution timing
7. **ResultSaver**: Saves results to CSV
## Benefits

193
tests/integration/defs/perf/disagg/conftest.py
View File

@ -1,9 +1,11 @@
"""Pytest configuration for disagg tests.
Only collects tests in this directory when --disagg parameter is provided.
Can share options like --disagg-test-list defined in this conftest.py.
Provides batch job submission capability to improve parallelism.
"""
import os
import pytest
from utils.logger import logger
@ -23,6 +25,15 @@ def pytest_addoption(parser):
help="Path to a file containing test IDs (one per line) to run. "
"Example: pytest --disagg --disagg-test-list=testlist/testlist_gb200.txt",
)
parser.addoption(
"--disagg-batch-size",
action="store",
type=int,
default=None,
help="Number of jobs to submit per batch. Default: from env DISAGG_BATCH_SIZE or 5. "
"Set to 0 for unlimited (submit all at once). "
"Example: pytest --disagg --disagg-batch-size=10",
)
def pytest_collect_directory(path, parent):
@ -45,7 +56,6 @@ def pytest_collect_directory(path, parent):
return True
# With --disagg parameter, proceed with normal collection
# Can subsequently use --disagg-test-list and other options from main conftest.py for filtering
return None
@ -88,7 +98,7 @@ def pytest_collection_modifyitems(config, items):
for item in items:
# item.nodeid is the full test identifier like:
# "test_disagg_simple.py::TestDisaggBenchmark::test_benchmark[deepseek-r1-fp4:1k1k:...]"
# "test_disagg.py::TestDisaggBenchmark::test_benchmark[deepseek-r1-fp4:1k1k:...]"
if item.nodeid in wanted_tests:
selected.append(item)
else:
@ -112,3 +122,180 @@ def pytest_collection_modifyitems(config, items):
logger.warning(f"Please check that the test IDs in {test_list_file} are correct.")
logger.info(f"{'=' * 70}\n")
class BatchManager:
"""Batch job submission manager for disagg tests.
Automatically splits test cases into batches and submits them on-demand
to maximize parallelism in SLURM cluster environments.
Key features:
- Lazy batch submission: only submits when needed
- Configurable batch size via CLI or environment variable
- Maintains job_id mapping for all submitted jobs
"""
def __init__(self, batch_size=5):
"""Initialize batch manager.
Args:
batch_size: Number of jobs per batch. None or 0 means unlimited (submit all at once).
Default is 5 if not specified.
"""
# Normalize batch_size: None, 0, or negative means unlimited
if batch_size is None or batch_size <= 0:
self.batch_size = None
else:
self.batch_size = batch_size
self.submitted_batches = set() # Track which batch numbers have been submitted
self.job_mapping = {} # Map test_id -> SLURM job_id
self.all_configs = [] # Ordered list of all test configs
logger.info(f"\n{'=' * 70}")
logger.info("Batch Manager Initialized")
if self.batch_size:
logger.info(f"Batch size: {self.batch_size} jobs per batch")
else:
logger.info("Batch size: unlimited (submit all at once)")
logger.info(f"{'=' * 70}\n")
def add_config(self, test_config):
"""Add a test configuration to the manager.
Called during initialization to build the ordered list of configs.
Args:
test_config: TestConfig object to add
"""
self.all_configs.append(test_config)
def get_job_id(self, test_config):
"""Get SLURM job ID for a test config, submitting batch if needed.
This is the main entry point. It:
1. Determines which batch the test belongs to
2. Submits the entire batch if not already submitted
3. Returns the job_id for this specific test
Args:
test_config: TestConfig object to get job_id for
Returns:
str: SLURM job ID, or None if submission failed
"""
# Find the index of this config in the ordered list
try:
idx = next(
i for i, c in enumerate(self.all_configs) if c.test_id == test_config.test_id
)
except StopIteration:
logger.error(f"Config not found in manager: {test_config.test_id}")
return None
# Calculate which batch this test belongs to
if self.batch_size:
batch_num = idx // self.batch_size
else:
batch_num = 0 # All tests in one batch
# Submit the batch if not already submitted
if batch_num not in self.submitted_batches:
self._submit_batch(batch_num)
# Return the cached job_id
return self.job_mapping.get(test_config.test_id)
def _submit_batch(self, batch_num):
"""Submit all jobs in a specific batch.
Args:
batch_num: Batch number to submit (0-indexed)
"""
from execution.executor import JobManager
# Calculate batch range
if self.batch_size:
start_idx = batch_num * self.batch_size
end_idx = min(start_idx + self.batch_size, len(self.all_configs))
else:
start_idx = 0
end_idx = len(self.all_configs)
batch_configs = self.all_configs[start_idx:end_idx]
logger.info(f"\n{'=' * 70}")
logger.info(f"Submitting Batch {batch_num}")
logger.info(f"Range: [{start_idx}:{end_idx}] ({len(batch_configs)} jobs)")
logger.info(f"{'=' * 70}\n")
# Submit all jobs in this batch
success_count = 0
for i, config in enumerate(batch_configs, 1):
try:
success, job_id = JobManager.submit_test_job(config)
if success and job_id:
self.job_mapping[config.test_id] = job_id
success_count += 1
# Truncate test_id for display
display_id = (
config.test_id[:60] + "..." if len(config.test_id) > 60 else config.test_id
)
logger.success(f" [{i:3d}/{len(batch_configs)}] Job {job_id} <- {display_id}")
else:
self.job_mapping[config.test_id] = None
logger.error(f" [{i:3d}/{len(batch_configs)}] Failed: {config.test_id[:50]}")
except Exception as e:
self.job_mapping[config.test_id] = None
logger.error(f" [{i:3d}/{len(batch_configs)}] Error: {e}")
# Mark batch as submitted
self.submitted_batches.add(batch_num)
logger.info(f"\n{'=' * 70}")
logger.success(
f"Batch {batch_num} Complete: {success_count}/{len(batch_configs)} succeeded"
)
logger.info(f"{'=' * 70}\n")
@pytest.fixture(scope="session")
def batch_manager(request):
"""Provide batch manager fixture for test methods.
This session-scoped fixture creates and initializes the BatchManager
with all collected test configs.
Returns:
BatchManager: Initialized batch manager instance
"""
# Get batch size from CLI option or environment variable
batch_size = request.config.getoption("--disagg-batch-size")
if batch_size is None:
env_batch_size = os.getenv("DISAGG_BATCH_SIZE")
if env_batch_size:
try:
batch_size = int(env_batch_size)
except ValueError:
logger.warning(f"Invalid DISAGG_BATCH_SIZE: {env_batch_size}, using default 5")
batch_size = 5
else:
batch_size = 5 # Default batch size
# Create batch manager
manager = BatchManager(batch_size=batch_size)
# Extract all test configs from collected items
for item in request.session.items:
if hasattr(item, "callspec") and "test_config" in item.callspec.params:
manager.add_config(item.callspec.params["test_config"])
# Log statistics
logger.info(f"Total test configs: {len(manager.all_configs)}")
if manager.batch_size:
total_batches = (len(manager.all_configs) + manager.batch_size - 1) // manager.batch_size
logger.info(f"Total batches: {total_batches}")
logger.info("")
return manager

4
tests/integration/defs/perf/disagg/execution/executor.py
View File

@ -114,7 +114,9 @@ class JobManager:
logger.debug(f"Script: {script_path}")
logger.debug(f"Log file: {output_log_file}")
output = exec_cmd_with_output(sbatch_args, timeout=60)
# Use check=False to allow submission even with Kerberos warnings
# (mimics submit.py behavior)
output = exec_cmd_with_output(sbatch_args, timeout=60, check=False)
job_id = output.strip()
# Parse job ID (--parsable returns just the job ID)

19
tests/integration/defs/perf/disagg/execution/subprocess_utils.py
View File

@ -33,19 +33,22 @@ def exec_cmd(*popenargs, timeout: Optional[float] = None, **kwargs) -> int:
return result.returncode
def exec_cmd_with_output(*popenargs, timeout: Optional[float] = None, **kwargs) -> str:
def exec_cmd_with_output(
*popenargs, timeout: Optional[float] = None, check: bool = True, **kwargs
) -> str:
"""Execute command and return output as string.
Args:
*popenargs: Command and arguments
timeout: Timeout in seconds
check: If True, raise CalledProcessError on non-zero exit code (default: True)
**kwargs: Additional subprocess arguments
Returns:
stdout as string (decoded from bytes)
Raises:
subprocess.CalledProcessError: If command returns non-zero exit code
subprocess.CalledProcessError: If check=True and command returns non-zero exit code
subprocess.TimeoutExpired: If timeout is reached
"""
result = subprocess.run(
@ -53,11 +56,15 @@ def exec_cmd_with_output(*popenargs, timeout: Optional[float] = None, **kwargs)
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
timeout=timeout,
check=True,
check=check,
**kwargs,
)
# Log stderr if it exists
# Log stderr if it exists (as warning if check=False, as error if check=True)
if result.stderr:
stderr_output = result.stderr.decode()
logger.error(f"Command stderr: {stderr_output}")
stderr_output = result.stderr.decode().strip()
if stderr_output:
if check:
logger.error(f"Command stderr: {stderr_output}")
else:
logger.warning(f"Command stderr: {stderr_output}")
return result.stdout.decode()

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep16_bs64_eplb288_mtp3_ccb-NIXL.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: 512 1075
concurrency_list: 512 1024
input_length: 1024
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep16_bs64_eplb288_mtp3_ccb-UCX.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: 512 1075
concurrency_list: 512 1024
input_length: 1024
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp1_ccb-NIXL.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '2150'
concurrency_list: '2048'
input_length: 1024
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp1_ccb-UCX.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '2150'
concurrency_list: '2048'
input_length: 1024
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-NIXL.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '1075'
concurrency_list: '1024'
input_length: 1024
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-UCX.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '1075'
concurrency_list: '1024'
input_length: 1024
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '2150'
concurrency_list: '2048'
input_length: 1024
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-UCX.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '2150'
concurrency_list: '2048'
input_length: 1024
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-NIXL.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '1075'
concurrency_list: '1024'
input_length: 8192
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-UCX.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '1075'
concurrency_list: '1024'
input_length: 8192
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '538'
concurrency_list: '512'
input_length: 8192
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-UCX.yaml
View File

@ -22,7 +22,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '538'
concurrency_list: '512'
input_length: 8192
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-NIXL.yaml
View File

@ -23,7 +23,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '1075'
concurrency_list: '1024'
input_length: 1024
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml
View File

@ -23,7 +23,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '2150'
concurrency_list: '2048'
input_length: 1024
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-NIXL.yaml
View File

@ -23,7 +23,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '1075'
concurrency_list: '1024'
input_length: 8192
output_length: 1024
dataset_file: <dataset_file>

2
tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml
View File

@ -23,7 +23,7 @@ benchmark:
multi_round: 8
benchmark_ratio: 0.8
streaming: true
concurrency_list: '538'
concurrency_list: '512'
input_length: 8192
output_length: 1024
dataset_file: <dataset_file>

39
tests/integration/defs/perf/disagg/test_disagg.py
View File

@ -62,7 +62,7 @@ class TestDisaggBenchmark:
@pytest.mark.perf
@pytest.mark.parametrize("test_config", PERF_TEST_CASES)
def test_benchmark(self, request, test_config: TestConfig):
def test_benchmark(self, request, batch_manager, test_config: TestConfig):
"""Performance benchmark test for YAML configurations."""
full_test_name = request.node.name
@ -101,15 +101,14 @@ class TestDisaggBenchmark:
)
job_id = EnvManager.get_debug_job_id()
else:
# Submit job using JobManager
success, job_id = JobManager.submit_test_job(test_config)
# Get job_id from batch manager (auto-submits batch if needed)
job_id = batch_manager.get_job_id(test_config)
# Validate submission result
assert success, f"Job submission failed: {test_config.test_id}"
assert job_id, "Unable to get job ID"
assert job_id, f"Failed to get job_id for {test_config.test_id}"
# Wait for completion (timeout/early failure handled inside)
JobManager.wait_for_completion(job_id, 7200, test_config, check_early_failure=True)
# Wait for completion (timeout: 10 hours = 36000 seconds)
JobManager.wait_for_completion(job_id, 36000, test_config, check_early_failure=True)
# End tracking test case
test_tracker.end_test_case()
@ -136,7 +135,7 @@ class TestDisaggBenchmark:
@pytest.mark.accuracy
@pytest.mark.parametrize("test_config", ACCURACY_TEST_CASES)
def test_accuracy(self, request, test_config: TestConfig):
def test_accuracy(self, request, batch_manager, test_config: TestConfig):
"""Accuracy test for YAML configurations."""
full_test_name = request.node.name
@ -179,15 +178,14 @@ class TestDisaggBenchmark:
)
job_id = EnvManager.get_debug_job_id()
else:
# Submit job using JobManager
success, job_id = JobManager.submit_test_job(test_config)
# Get job_id from batch manager (auto-submits batch if needed)
job_id = batch_manager.get_job_id(test_config)
# Validate submission result
assert success, f"Job submission failed: {test_config.test_id}"
assert job_id, "Unable to get job ID"
assert job_id, f"Failed to get job_id for {test_config.test_id}"
# Wait for completion (timeout/early failure handled inside)
JobManager.wait_for_completion(job_id, 10800, test_config, check_early_failure=True)
# Wait for completion (timeout: 10 hours = 36000 seconds)
JobManager.wait_for_completion(job_id, 36000, test_config, check_early_failure=True)
# End tracking test case
test_tracker.end_test_case()
@ -216,7 +214,7 @@ class TestDisaggBenchmark:
@pytest.mark.stress
@pytest.mark.parametrize("test_config", STRESS_TEST_CASES)
def test_stress(self, request, test_config: TestConfig):
def test_stress(self, request, batch_manager, test_config: TestConfig):
"""Stress test combining performance benchmarks and accuracy validation.
This test type is designed for stress testing scenarios where both
@ -265,15 +263,14 @@ class TestDisaggBenchmark:
)
job_id = EnvManager.get_debug_job_id()
else:
# Submit job using JobManager
success, job_id = JobManager.submit_test_job(test_config)
# Get job_id from batch manager (auto-submits batch if needed)
job_id = batch_manager.get_job_id(test_config)
# Validate submission result
assert success, f"Job submission failed: {test_config.test_id}"
assert job_id, "Unable to get job ID"
assert job_id, f"Failed to get job_id for {test_config.test_id}"
# Wait for completion (longer timeout for stress tests: 4 hours)
JobManager.wait_for_completion(job_id, 10800, test_config, check_early_failure=True)
# Wait for completion (timeout: 10 hours = 36000 seconds)
JobManager.wait_for_completion(job_id, 36000, test_config, check_early_failure=True)
# End tracking test case
test_tracker.end_test_case()

4
tests/integration/defs/test_unittests.py
View File

@ -126,8 +126,8 @@ def test_unittests_v2(llm_root, llm_venv, case: str, output_dir, request):
f'results-sub-unittests-{case_fn}.xml')
command = [
'-m', 'pytest', ignore_opt, "-v", "--tb=short", "-rF", "--timeout=2400",
"--timeout-method=thread"
'-m', 'pytest', ignore_opt, "-vv", "--tb=short", "-rF",
"--timeout=2400", "--timeout-method=thread"
]
if test_prefix:
command += [f"--test-prefix={test_prefix}"]

4
tests/integration/test_lists/test-db/l0_gb200_multi_gpus.yml
View File

@ -72,7 +72,7 @@ l0_gb200_multi_gpus:
- accuracy/test_llm_api_pytorch.py::TestQwen3NextInstruct::test_nvfp4[tp4ep4-cutlass]
- accuracy/test_llm_api_pytorch.py::TestQwen3NextInstruct::test_nvfp4[no_cuda_graph_overlap-cutlass]
- accuracy/test_llm_api_pytorch.py::TestQwen3NextInstruct::test_nvfp4[tp4ep4-trtllm]
- accuracy/test_llm_api_pytorch.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm_eagle] TIMEOUT (90)
- accuracy/test_llm_api_pytorch_multimodal.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm] TIMEOUT (90)
- condition:
ranges:
system_gpu_count:
@ -105,4 +105,4 @@ l0_gb200_multi_gpus:
- accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus_online_eplb[enable_configurable_moe-fp8]
- accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_nvfp4_4gpus[latency_moe_trtllm_eagle3] TIMEOUT (90)
- accuracy/test_llm_api_pytorch.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm] TIMEOUT (90)
- accuracy/test_llm_api_pytorch_multimodal.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm] TIMEOUT (90)
- accuracy/test_llm_api_pytorch.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm_eagle] TIMEOUT (90)

13
tests/integration/test_lists/waives.txt
View File

@ -369,7 +369,6 @@ accuracy/test_llm_api_pytorch_multimodal.py::TestQwen2_5_VL_7B::test_auto_dtype
accuracy/test_llm_api_pytorch_multimodal.py::TestLlava_V1_6_Mistral_7B::test_auto_dtype SKIP (https://nvbugs/5707087)
accuracy/test_llm_api_pytorch_multimodal.py::TestPhi4MMFusedVisionLora::test_auto_dtype SKIP (https://nvbugs/5707087)
disaggregated/test_disaggregated.py::test_disaggregated_ctxtp2pp2_gentp2pp2[TinyLlama-1.1B-Chat-v1.0] SKIP (https://nvbugs/5705199)
unittest/_torch/modules/test_fused_moe.py::test_fused_moe_fp8_blockwise_wide_ep[NotEnabled] SKIP (https://nvbugs/5707392)
accuracy/test_llm_api_pytorch.py::TestLlama3_3NemotronSuper49Bv1::test_auto_dtype_tp2 SKIP (https://nvbugs/5707145)
accuracy/test_llm_api_pytorch.py::TestLlama3_3NemotronSuper49Bv1::test_fp8_prequantized_tp2 SKIP (https://nvbugs/5707145)
accuracy/test_llm_api_pytorch.py::TestNemotronH_56B_Base::test_auto_dtype[tp8-cuda_graph=True] SKIP (https://nvbugs/5640697)
@ -381,8 +380,6 @@ accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4[moe_backend=CUT
accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_2gpus[cutlass-two_model-overlap_scheduler] SKIP (https://nvbugs/5702826)
accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_4gpus[cutlass-two_model-overlap_scheduler] SKIP (https://nvbugs/5702826)
unittest/llmapi/test_llm_pytorch.py::test_llm_reward_model SKIP (https://nvbugs/5670458)
accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_nvfp4[latency_moe_cutlass] SKIP (https://nvbugs/5740377)
accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[dep4_latency_moe_trtllm-torch_compile=False] SKIP (https://nvbugs/5740377)
accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[llguidance-mtp_nextn=2] SKIP (https://nvbugs/5740075)
accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[xgrammar-mtp_nextn=2] SKIP (https://nvbugs/5740075)
unittest/_torch/modeling/test_modeling_out_of_tree.py::TestOutOfTree::test_llm_api[False] SKIP (https://nvbugs/5739981)
@ -394,16 +391,12 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_nvfp4[latency_moe_tr
accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_4gpus[tp4-fp8kv=True-attn_backend=FLASHINFER-torch_compile=True] SKIP (https://nvbugs/5741304)
unittest/_torch/multi_gpu/test_allreduce.py::test_allreduce_fusion_patterns[2-residual_rms_norm_out_quant_fp8-hidden:7168-seqlen:8192] SKIP (https://nvbugs/5741392)
unittest/executor/test_rpc.py::TestRpcCorrectness::test_incremental_task_async SKIP (https://nvbugs/5741476)
accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_4gpus[pp4-fp8kv=True-attn_backend=TRTLLM-torch_compile=False] SKIP (https://nvbugs/5740377)
accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[dep4_latency_moe_cutlass-torch_compile=False] SKIP (https://nvbugs/5740377)
examples/test_phi.py::test_phi_fp8_with_bf16_lora[phi-2] SKIP (https://nvbugs/5744293)
examples/test_phi.py::test_llm_phi_1node_2gpus_summary[Phi-3.5-MoE-instruct-nb:1] SKIP (https://nvbugs/5744293)
examples/test_phi.py::test_llm_phi_quantization_1gpu[phi-2-fp8-bfloat16] SKIP (https://nvbugs/5744293)
test_e2e.py::test_trtllm_bench_llmapi_launch[pytorch_backend-llama-v3-llama3-8b] SKIP (https://nvbugs/5744432)
test_e2e.py::test_trtllm_serve_multimodal_example SKIP (https://nvbugs/5747920)
test_e2e.py::test_trtllm_serve_example SKIP (https://nvbugs/5747938)
unittest/_torch/auto_deploy/unit/singlegpu/test_ad_build_small_single.py::test_build_ad[meta-llama/Llama-4-Scout-17B-16E-Instruct-llm_extra_args8] SKIP (https://nvbugs/5747878)
unittest/_torch/auto_deploy/unit/singlegpu/test_ad_build_small_single.py::test_build_ad[meta-llama/Llama-4-Scout-17B-16E-Instruct-llm_extra_args9] SKIP (https://nvbugs/5747878)
triton_server/test_triton.py::test_opt[opt] SKIP (https://nvbugs/5739981)
unittest/llmapi/test_llm_pytorch.py::test_tinyllama_logits_processor[False] SKIP (https://nvbugs/5771838)
unittest/llmapi/test_llm_pytorch.py::test_tinyllama_logits_processor[True] SKIP (https://nvbugs/5771838)
@ -414,8 +407,6 @@ accuracy/test_cli_flow.py::TestPhi3Mini128kInstruct::test_auto_dtype SKIP (https
accuracy/test_cli_flow.py::TestPhi3Small8kInstruct::test_auto_dtype SKIP (https://nvbugs/5744293)
accuracy/test_cli_flow.py::TestPhi3Small128kInstruct::test_auto_dtype SKIP (https://nvbugs/5744293)
accuracy/test_cli_flow.py::TestPhi3_5MiniInstruct::test_auto_dtype SKIP (https://nvbugs/5744293)
unittest/_torch/auto_deploy/unit/singlegpu/models/test_llama4_vlm_patch.py::test_build_run_llama4_vlm SKIP (https://nvbugs/5747878)
accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus_online_eplb[fp8kv=True-moe_backend=TRTLLM] SKIP (https://nvbugs/5740377)
cpp/test_multi_gpu.py::TestDisagg::test_symmetric_executor[gpt-2proc-mpi_kvcache-90] SKIP (https://nvbugs/5755941)
examples/test_bert.py::test_llm_bert_general[compare_hf-enable_remove_input_padding-use_attention_plugin-enable_context_fmha-tp:1-pp:1-float16-BertForQuestionAnswering-bert/bert-base-cased-squad2] SKIP (https://nvbugs/5608979)
examples/test_bert.py::test_llm_bert_general[compare_hf-enable_remove_input_padding-use_attention_plugin-enable_context_fmha-tp:1-pp:1-float16-RobertaForQuestionAnswering-bert/roberta-base-squad2] SKIP (https://nvbugs/5608979)
@ -449,8 +440,6 @@ test_e2e.py::test_ptp_quickstart_advanced_2gpus_sm120[Nemotron-Super-49B-v1-BF16
unittest/_torch/multi_gpu/test_mnnvl_allreduce.py::test_row_linear_residual_norm_fusion[no_fusion-strategy:8-dtype:bfloat16-hidden:8192-seqlen:[15]] SKIP (https://nvbugs/5761364)
triton_server/test_triton.py::test_gpt_speculative_decoding[gpt-speculative-decoding] SKIP (https://nvbugs/5762854)
accuracy/test_llm_api_pytorch.py::TestLlama3_1_8B_Instruct_RocketKV::test_auto_dtype SKIP (https://nvbugs/5762822)
accuracy/test_llm_api_pytorch.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm] SKIP (https://nvbugs/5762852)
accuracy/test_llm_api_pytorch.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm_eagle] SKIP (https://nvbugs/5762852)
unittest/_torch/sampler/test_return_logits.py SKIP (https://nvbugs/5764627)
examples/serve/test_serve.py::test_config_file_loading[--config] SKIP (https://nvbugs/5754977)
full:RTXPro6000D/accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-tp2pp2-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False] SKIP (https://nvbugs/5740075)
@ -464,7 +453,6 @@ full:sm89/accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_
accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_ngram SKIP (https://nvbugs/5768068)
test_e2e.py::test_eagle3_output_consistency_4gpus[Qwen3/saved_models_Qwen3-235B-A22B_fp8_hf-Qwen3/qwen3-235B-eagle3] SKIP (https://nvbugs/5685010)
examples/test_mistral.py::test_mistral_with_bf16_lora_torch[mistral-7b-v0.1] SKIP (https://nvbugs/5769855)
accuracy/test_llm_api_pytorch_multimodal.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm] SKIP (TBD)
accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[dp4-trtllm-fp8] SKIP (https://nvbugs/5772396)
full:sm100/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[dp4-trtllm-auto] SKIP (https://nvbugs/5772396)
accuracy/test_llm_api_pytorch.py::TestGLM4_6::test_nvfp4_2_model_mtp[2model_trtllm] SKIP (https://nvbugs/5772360)
@ -485,7 +473,6 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_sof
accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention[target_sparsity_0.9] SKIP (https://nvbugs/5774869)
triton_server/test_triton.py::test_llava_onevision[llava_onevision] SKIP (https://nvbugs/5775205)
triton_server/test_triton.py::test_gpt_ib_lad[gpt-ib-lad] SKIP (https://nvbugs/5775223)
accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[dep4_latency_moe_trtllm-torch_compile=True] SKIP (https://nvbugs/5740377)
unittest/_torch/modules/test_fused_moe.py::test_fused_moe_fp8_blockwise_cute_dsl_multi_gpu[MoEWeightLoadingMode.FUSED_GATE_UP_PROJ-DefaultMoeRoutingMethod-1] SKIP (https://nvbugs/5775256)
unittest/_torch/auto_deploy/unit/multigpu/transformations/library/test_ep_sharding.py::test_ep_shard[3-2] SKIP (https://nvbugs/5777041)
unittest/_torch/auto_deploy/unit/multigpu/transformations/library/test_ep_sharding.py::test_ep_shard[8-2] SKIP (https://nvbugs/5777041)

6
tests/unittest/_torch/auto_deploy/_utils_test/_model_test_utils.py
View File

@ -278,8 +278,8 @@ class FakeFP8Linear(nn.Linear):
def generate_dynamic_shapes(max_batch_size, max_seq_len):
dynamic_shapes = (
{
0: Dim("batch_size", max=max_batch_size),
1: Dim("seq_len", max=max_seq_len),
0: Dim.DYNAMIC,
1: Dim.DYNAMIC,
},
)
return dynamic_shapes
@ -393,7 +393,7 @@ _SMALL_MODEL_CONFIGS = {
},
},
"meta-llama/Llama-4-Scout-17B-16E-Instruct": {
"llm_models_subdir": "Llama-4-Scout-17B-16E-Instruct",
"llm_models_subdir": "llama4-models/Llama-4-Scout-17B-16E-Instruct",
"model_factory": "AutoModelForImageTextToText",
"model_kwargs": {
"text_config": {

9
tests/unittest/_torch/auto_deploy/unit/singlegpu/models/test_hybrid_patches.py
View File

@ -72,12 +72,11 @@ def test_bamba_patches(
position_ids = torch.arange(input_ids.shape[1], device=input_ids.device).repeat(
input_ids.shape[0], 1
)
batch_size_dynamic = Dim.DYNAMIC
seq_len_dynamic = Dim.DYNAMIC
dynamic_shapes = (
{0: Dim("batch_size", min=0, max=8), 1: Dim("seq_len", min=0, max=512)},
{
0: Dim("batch_size", min=0, max=8),
1: Dim("seq_len", min=0, max=512),
},
{0: batch_size_dynamic, 1: seq_len_dynamic},
{0: batch_size_dynamic, 1: seq_len_dynamic},
)
def _run_torch_export_to_gm():

9
tests/unittest/_torch/auto_deploy/unit/singlegpu/models/test_modeling_nemotron_h.py
View File

@ -184,12 +184,11 @@ def test_custom_model_implementation_can_be_exported(
position_ids = torch.arange(input_ids.shape[1], device=input_ids.device).repeat(
input_ids.shape[0], 1
)
batch_size_dynamic = Dim.DYNAMIC
seq_len_dynamic = Dim.DYNAMIC
dynamic_shapes = (
{0: Dim("batch_size", min=0, max=8), 1: Dim("seq_len", min=0, max=512)},
{
0: Dim("batch_size", min=0, max=8),
1: Dim("seq_len", min=0, max=512),
},
{0: batch_size_dynamic, 1: seq_len_dynamic},
{0: batch_size_dynamic, 1: seq_len_dynamic},
)
def _run_torch_export_to_gm():

16
tests/unittest/_torch/auto_deploy/unit/singlegpu/transformations/library/test_attention_matcher.py
View File

@ -84,7 +84,7 @@ class RepeatKVModel(torch.nn.Module):
return output
def get_dynamic_shapes(self):
return {0: Dim("batch_size", max=8), 1: Dim("seq_len", min=4, max=16)}
return {0: Dim.DYNAMIC, 1: Dim.DYNAMIC}
class RepeatKVModel2(RepeatKVModel):
@ -185,7 +185,7 @@ class EagerAttentionModel(torch.nn.Module):
return output
def get_dynamic_shapes(self):
return {0: Dim("batch_size", max=8), 1: Dim("seq_len", min=4, max=16)}
return {0: Dim.DYNAMIC, 1: Dim.DYNAMIC}
class ComplexEagerAttentionModel(torch.nn.Module):
@ -274,7 +274,7 @@ class ComplexEagerAttentionModel(torch.nn.Module):
return output
def get_dynamic_shapes(self):
return {0: Dim("batch_size", max=8), 1: Dim("seq_len", min=4, max=16)}
return {0: Dim.DYNAMIC, 1: Dim.DYNAMIC}
class CounterExampleModel(torch.nn.Module):
@ -329,7 +329,7 @@ class CounterExampleModel(torch.nn.Module):
return features_case1
def get_dynamic_shapes(self):
return {0: Dim("batch_size", max=8), 1: Dim("seq_len", min=4, max=16)}
return {0: Dim.DYNAMIC, 1: Dim.DYNAMIC}
class GroupedAttentionModel(torch.nn.Module):
@ -403,7 +403,7 @@ class GroupedAttentionModel(torch.nn.Module):
return output
def get_dynamic_shapes(self):
return {0: Dim("batch_size", max=8), 1: Dim("seq_len", min=4, max=16)}
return {0: Dim.DYNAMIC, 1: Dim.DYNAMIC}
def _get_match_repeat_kv_optimizer() -> Callable:
@ -907,7 +907,7 @@ class CausalAttentionModel(torch.nn.Module):
return output
def get_dynamic_shapes(self):
return {0: Dim("batch_size", max=8), 1: Dim("seq_len", min=4, max=16)}
return {0: Dim.DYNAMIC, 1: Dim.DYNAMIC}
class Llama3CausalAttentionModel(torch.nn.Module):
@ -1013,7 +1013,7 @@ class Llama3CausalAttentionModel(torch.nn.Module):
return output
def get_dynamic_shapes(self):
return {0: Dim("batch_size", max=8), 1: Dim("seq_len", min=4, max=16)}
return {0: Dim.DYNAMIC, 1: Dim.DYNAMIC}
class AttentionLayoutModel(torch.nn.Module):
@ -1102,7 +1102,7 @@ class AttentionLayoutModel(torch.nn.Module):
return output
def get_dynamic_shapes(self):
return {0: Dim("batch_size", max=8), 1: Dim("seq_len", min=4, max=16)}
return {0: Dim.DYNAMIC, 1: Dim.DYNAMIC}
class BsndAttentionModel(AttentionLayoutModel):

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