Signed-off-by: Hao Lu <14827759+hlu1@users.noreply.github.com@users.noreply.github.com>
Co-authored-by: Hao Lu <14827759+hlu1@users.noreply.github.com@users.noreply.github.com>
Signed-off-by: Hao Lu <14827759+hlu1@users.noreply.github.com@users.noreply.github.com>
Co-authored-by: Hao Lu <14827759+hlu1@users.noreply.github.com@users.noreply.github.com>
* MoE TRTLLM backend for Qwen3
Signed-off-by: Anthony Chang <anchengc@nvidia.com>
* add extra moe_backend to test
Signed-off-by: Anthony Chang <anchengc@nvidia.com>
* address comments
Signed-off-by: Anthony Chang <anchengc@nvidia.com>
* conditionally compile kernels on newer archs
Signed-off-by: Anthony Chang <anchengc@nvidia.com>
* missing positional arg
Signed-off-by: Anthony Chang <anchengc@nvidia.com>
* Update the routing kernels
Signed-off-by: Christina Zhang <christinaz@nvidia.com>
* Revise usage of TLLM_LOG_ERROR
Signed-off-by: Christina Zhang <christinaz@nvidia.com>
* Add unit test for Qwen3 moe (trtllm_gen backend)
Signed-off-by: Christina Zhang <christinaz@nvidia.com>
* improve weight processing speed of moe_backend=TRTLLM; roughly 2x
Signed-off-by: Anthony Chang <anchengc@nvidia.com>
* tidy and minor fix
Signed-off-by: Anthony Chang <anchengc@nvidia.com>
* temporarily disable accuracy test that has known issue
Signed-off-by: Anthony Chang <anchengc@nvidia.com>
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Signed-off-by: Anthony Chang <anchengc@nvidia.com>
Signed-off-by: Christina Zhang <christinaz@nvidia.com>
Co-authored-by: Christina Zhang <christinaz@nvidia.com>
* fix bug of qwen3 fp4 workflow with EP
Signed-off-by: bhsueh <11360707+byshiue@users.noreply.github.com>
* fix bug of qwen3_moe with ep
Signed-off-by: bhsueh <11360707+byshiue@users.noreply.github.com>
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Signed-off-by: bhsueh <11360707+byshiue@users.noreply.github.com>
* add bidirectional support and fix EarlyStopDecoder unsqueeze to be compatible with LogitsStorage
Signed-off-by: Rohan Varma <rohanv@nvidia.com>
* run pre-commit
Signed-off-by: Rohan Varma <rohanv@nvidia.com>
* instead of bidirectional flag use ModelConfig.is_generation
Signed-off-by: Rohan Varma <rohanv@nvidia.com>
* fix unit test to extract logits from correct dim
Signed-off-by: Rohan Varma <rohanv@nvidia.com>
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Signed-off-by: Rohan Varma <rohanv@nvidia.com>
Support DeepSeek-R1 W4A8 on Hopper
Co-authored-by: Barry Kang <43644113+Barry-Delaney@users.noreply.github.com>
Co-authored-by: Jiang Shao <91270701+StudyingShao@users.noreply.github.com>
Signed-off-by: Barry Kang <43644113+Barry-Delaney@users.noreply.github.com>
This issue is found for tp=ep=8 on the multi-node machine due to the inconsistent PP sizes.
* Reform the workspace allocation implementation to avoid the list-out-of-range issues.
* Disable min_latency_mode under the multi-node scenario to avoid the illegal memory access issue.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* feat: Add rename_weights_with_regex function for dynamic weight key renaming
Introduced a new utility function to rename weight keys in a dictionary based on regex pattern matching. This allows for flexible mapping of keys from Hugging Face naming conventions to TRT-LLM naming conventions, enhancing model compatibility and usability.
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
* feat: Implement SiglipVisionModel and related components
Added the SiglipVisionModel along with its associated classes, including SiglipAttention, SiglipEncoderLayer, and SiglipEncoder.
Additionally, a new test suite for the SiglipVisionModel has been created to ensure compatibility with Hugging Face outputs.
Currently SiglipVisionModel support batch size larger than one. Also, inputs and outputs shape are same with the HF for compatibility.
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
* feat: Add CLIPVisionModel and associated components
Introduced the CLIPVisionModel along with its related classes, including CLIPAttention, CLIPEncoderLayer, CLIPEncoder, and CLIPVisionTransformer. This implementation aligns with Hugging Face's CLIP architecture, ensuring compatibility in input and output shapes.
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
* feat: Enhance CLIPVisionModel with attention metadata preparation and unit tests
Updated the CLIPVisionModel to include a method for preparing attention metadata, simplifying the model's usage. Additionally, added a comprehensive unit test suite for the CLIPVisionModel, ensuring compatibility with Hugging Face outputs and validating model performance across various scenarios.
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
* feat: Refactor SiglipVisionModel with attention metadata preparation and update unit tests
Enhanced the SiglipVisionModel by adding a method to prepare attention metadata, streamlining its usage. Updated unit tests to validate model performance and compatibility with Hugging Face outputs, including adjustments to the configuration and test scenarios.
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
* refactor: Remove unused rotary_emb parameter from CLIP and Siglip attention classes
Eliminated the rotary_emb parameter from the CLIPAttention and SiglipAttention classes to streamline the code. Updated unit tests to reflect changes in the model configurations, including clarifications in the default configurations sourced from Hugging Face.
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
* feat: Integrate CLIPVisionModel into LlavaNextInputProcessor and enhance weight loading
Added CLIPVisionModel to the LlavaNextInputProcessor for improved vision processing. Updated the model loading mechanism to ensure compatibility with the new vision model and added attention metadata preparation. Removed debug print statements from weight renaming function for cleaner code.
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
* refactor: Remove unused max_position_embeddings from CLIPAttention and update Siglip classes to use CLIP components
Removed the unused max_position_embeddings variable from the CLIPAttention class. Updated the Siglip classes to utilize CLIP components, specifically replacing SiglipEncoder and SiglipAttention with their CLIP counterparts, streamlining the codebase and enhancing consistency across models.
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
* refactor: Consolidate weight loading logic into a shared implementation
Refactored the weight loading process across CLIP and Siglip models by using a new utility function, _load_weights_impl, to streamline the loading mechanism. This change enhances code maintainability and reduces redundancy in weight handling, ensuring consistent behavior across different model architectures.
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
* refactor: Simplify output handling in CLIP and Siglip models by removing output_hidden_states parameter
Removed the output_hidden_states parameter from the CLIPEncoder and SiglipVisionTransformer classes, streamlining the output handling process. Updated the corresponding unit tests to reflect these changes and ensure compatibility with the new output structure.
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
* feat: Enhance LlavaNextInputProcessor with dynamic model loading and memory optimization
Updated the LlavaNextInputProcessor to support dynamic model loading from local paths or Hugging Face, improving memory efficiency by partially loading the model components. Integrated the LlavaNextMultiModalProjector and adjusted weight loading to ensure compatibility with the new architecture.
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
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Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
Co-authored-by: Haohang Huang <31998628+symphonylyh@users.noreply.github.com>
* support lp in pytorch backend
Signed-off-by: Erin Ho <14718778+hchings@users.noreply.github.com>
* fix tp
Signed-off-by: Erin Ho <14718778+hchings@users.noreply.github.com>
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Signed-off-by: Erin Ho <14718778+hchings@users.noreply.github.com>
* add qwen3 dense model pytorch backend support, initial commit
solve the results error issue
add qwen3 moe model pytorch backend support
reformat the code
* perf - use flash_infer rmsnorm for qwen3
* feat - support qwen3 moe rmsnorm
* Put the computation of Q and K norm (in attn) into a single CUDA stream, and get a 5% - 8% throughput improvement on Qwen3 4B and Qwen3 - moe 30B - A3B.
* Put the computation of Q and K norm (in attn) into a single CUDA stream, and get a 5% - 8% throughput improvement on Qwen3 4B and Qwen3 - moe 30B - A3B. -- Forgot to update all modifications.
* fix bugs of running qwen3 public models and fp8 models
Signed-off-by: bhsueh <11360707+byshiue@users.noreply.github.com>
* fix bugs due to rebase
Signed-off-by: bhsueh <11360707+byshiue@users.noreply.github.com>
* fix bugs captured by pre-commi
Signed-off-by: bhsueh <11360707+byshiue@users.noreply.github.com>
* fix bug of attention
Signed-off-by: bhsueh <11360707+byshiue@users.noreply.github.com>
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Signed-off-by: bhsueh <11360707+byshiue@users.noreply.github.com>
Co-authored-by: Keddy Jin <jin.gq@aliyun.com>
Co-authored-by: Jiying Dong <87510204+dongjiyingdjy@users.noreply.github.com>
Co-authored-by: shao <shao@nvidia.com>
