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>
* refine shutdown signal of PyExecutor
Signed-off-by: QI JUN <22017000+QiJune@users.noreply.github.com>
* clean
Signed-off-by: QI JUN <22017000+QiJune@users.noreply.github.com>
* fix ci
Signed-off-by: QI JUN <22017000+QiJune@users.noreply.github.com>
* fix ci
Signed-off-by: QI JUN <22017000+QiJune@users.noreply.github.com>
---------
Signed-off-by: QI JUN <22017000+QiJune@users.noreply.github.com>
* Fixed typos in the scaffolding README.MD
Signed-off-by: Anton <44649959+amemov@users.noreply.github.com>
* Fixed links for 'More examples' and 'Contribute Guide'
Signed-off-by: Anton <44649959+amemov@users.noreply.github.com>
---------
Signed-off-by: Anton <44649959+amemov@users.noreply.github.com>
* refactor: CreateNewDecoderRequests
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Consolidate request generation in CreateNewDecoderRequests
- Removed the GenerateRequestOptions class and integrated its functionality into CreateNewDecoderRequests.
- Updated the constructor of CreateNewDecoderRequests to accept parameters for speculative decoding and normalization options.
- Modified the operator() method to handle request generation directly, improving code organization and reducing redundancy.
- Cleaned up associated includes and references throughout the codebase.
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Simplify request handling in CreateNewDecoderRequests
- Removed the generateRequestOptions method and integrated its logic directly into the operator() method.
- Updated the request generation process to improve clarity and reduce redundancy.
- Adjusted the return type to streamline the handling of batch slots, decoder requests, and sampling configurations.
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Enhance createDecoderRequests method in CreateNewDecoderRequests
- Updated the createDecoderRequests method to include additional parameters for decoder state and CUDA streams, improving flexibility in request handling.
- Removed redundant request generation logic from the operator() method, streamlining the process.
- Adjusted the newRequest method to utilize the updated decoder request structure, enhancing clarity and maintainability.
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Use MedusaBuffers instead of RuntimeBuffers in CreateNewDecoderRequests
- Updated references from RuntimeBuffers to MedusaBuffers across the CreateNewDecoderRequests class and its methods, enhancing clarity in buffer management.
- Adjusted method signatures and internal logic to accommodate the new MedusaBuffers type, ensuring compatibility with existing functionality.
- Cleaned up unnecessary includes and improved code organization for better maintainability.
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Update CreateNewDecoderRequests to use DecoderState and CudaStream parameters
- Modified method signatures in CreateNewDecoderRequests to replace GptDecoderBatched with runtime::decoder::DecoderState and added a separate CudaStream for the decoder.
- Adjusted the implementation of the operator() method to accommodate the new parameters, enhancing flexibility in request handling.
- Updated associated bindings in the pybind11 interface to reflect the changes in method signatures, ensuring consistency across the codebase.
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Update TRTLLMSampler to use refactored create_new_decoder_requests
- Updated the sampler.py to reflect changes in the request handling logic, replacing generate_request_options with create_new_decoder_requests for improved clarity and consistency.
- Updated bindings and method signatures for decoder stream handling.
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Update gptDecoderBatchedTest to use CreateNewDecoderRequests::newRequest
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
---------
Signed-off-by: Robin Kobus <19427718+Funatiq@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>
---------
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>
---------
Signed-off-by: bhsueh <11360707+byshiue@users.noreply.github.com>
* added a restricted pcikler and depickler in a sepparate serialization function.
Signed-off-by: coldwaterq@users.noreply.github.com <coldwaterq@users.noreply.github.com>
* updated IPC to remove approved classes, removed the serialization function because it didn't work for all objects that made debugging harder, added tests.
Signed-off-by: coldwaterq@users.noreply.github.com <coldwaterq@users.noreply.github.com>
* removed LLM arg and moved class registration to a serialization module function. Also added missing classes to approved list.
Signed-off-by: coldwaterq <coldwaterq@users.noreply.github.com>
* cleaned up a couple files to reduce conflicts with main.
Signed-off-by: coldwaterq <coldwaterq@users.noreply.github.com>
* fix unit tests
Signed-off-by: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com>
* reorder BASE_ZMQ_CLASSES list alphabetically
Signed-off-by: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com>
* fix tests and move LogitsProcessor registration to base class
Signed-off-by: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com>
* revert changes to import log of tensorrt_llm._torch.models
Signed-off-by: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com>
* added comments to explain why BASE_ZMQ_CLASSES has to be passed into spawned child processes
Signed-off-by: coldwaterq <coldwaterq@users.noreply.github.com>
* fix tests and move LogitsProcessor registration to base class
Signed-off-by: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com>
* additional comments for multiprocess approved list sync
Signed-off-by: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com>
* add dataclass from tests
Signed-off-by: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com>
---------
Signed-off-by: coldwaterq@users.noreply.github.com <coldwaterq@users.noreply.github.com>
Signed-off-by: coldwaterq <coldwaterq@users.noreply.github.com>
Signed-off-by: Yibin Li <109242046+yibinl-nvidia@users.noreply.github.com>
Co-authored-by: Yibin Li <109242046+yibinl-nvidia@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>
---------
Signed-off-by: Rohan Varma <rohanv@nvidia.com>
* Fix TRTLLMSampler.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* Added type hint.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
---------
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* first commit of cpp moe loadbalance code
Signed-off-by: Dongxu Yang <78518666+dongxuy04@users.noreply.github.com>
* add python bindings for moe load balance
Signed-off-by: Dongxu Yang <78518666+dongxuy04@users.noreply.github.com>
* add python wrapper, ut and bug fixes
Signed-off-by: Dongxu Yang <78518666+dongxuy04@users.noreply.github.com>
* add binding for layerId and update binding test
Signed-off-by: Dongxu Yang <78518666+dongxuy04@users.noreply.github.com>
* add host tensor sharing and ut
Signed-off-by: Dongxu Yang <78518666+dongxuy04@users.noreply.github.com>
---------
Signed-off-by: Dongxu Yang <78518666+dongxuy04@users.noreply.github.com>
* [AutoDeploy] HF factory improvements
Signed-off-by: Lucas Liebenwein <11156568+lucaslie@users.noreply.github.com>
* improve monkey-patches and add unit tests
Signed-off-by: Lucas Liebenwein <11156568+lucaslie@users.noreply.github.com>
---------
Signed-off-by: Lucas Liebenwein <11156568+lucaslie@users.noreply.github.com>
This PR adds a customized allreduce to TensorRT-LLM. The new allreduce is used for communication on PCIe-based GPUs via low-precision quantization, which can accelerate the PCIe allreduce process.
Signed-off-by: Hui Kang <hkang@nvidia.com>
Co-authored-by: Hui Kang <hkang@nvidia.com>
* Add llama4 disagg accuracy tests
Signed-off-by: Iman Tabrizian <10105175+tabrizian@users.noreply.github.com>
* Make it async and add GSM8K benchmark
Signed-off-by: Iman Tabrizian <10105175+tabrizian@users.noreply.github.com>
---------
Signed-off-by: Iman Tabrizian <10105175+tabrizian@users.noreply.github.com>
* Deduce default max_tokens for trtllm-serve
Signed-off-by: Pengyun Lin <81065165+LinPoly@users.noreply.github.com>
* Improve executor_config.max_seq_len assignment in TRT workflow
Signed-off-by: Pengyun Lin <81065165+LinPoly@users.noreply.github.com>
* Enhance error message
Signed-off-by: Pengyun Lin <81065165+LinPoly@users.noreply.github.com>
* Add deduced max_tokens test
Signed-off-by: Pengyun Lin <81065165+LinPoly@users.noreply.github.com>
---------
Signed-off-by: Pengyun Lin <81065165+LinPoly@users.noreply.github.com>
chore: restore symmetry of worker start/shutdown
chore: fix return type of cal_max_tokens
chore: type some more return values
fix: free resources before re-claiming
Signed-off-by: ixlmar <206748156+ixlmar@users.noreply.github.com>
* refactor: Copy sequence lengths once in decoder setup
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Update DecoderInputBuffers to remove duplicated buffers
- Renamed and reorganized buffer variables in decoderBuffers.h and decoderBuffers.cpp for better readability.
- Adjusted references in generateRequestOptions.cpp to align with the new buffer structure.
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Move getEmbeddingBias to anonymous namespace
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Filter context requests
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: GenerateRequestOptions using more fine-grained functions
- Added a new method `createDecoderRequests` to encapsulate the logic for creating decoder requests from finished context requests.
- Updated the `operator()` method to utilize the new method, improving code clarity and maintainability.
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Update TRTLLMDecoder
- Updated the `generate_request_options` call.
- Updated the `make_decoding_batch_input_output` call.
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Remove const where we modify input buffers
- Changed `DecoderInputBuffers` parameters from const references to non-const references in multiple functions to allow modifications.
- Updated related function calls to ensure compatibility with the new parameter types.
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* fixup! refactor: Copy sequence lengths once in decoder setup
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
---------
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* add docstring to summarize current rope support
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* minor: replace call_method, adjust inserting order of cos_sin_cache calculation node
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* add unit test for triton rope and ds rope
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* update rope matcher to match DS RoPE, add custom op for reference, add unit test case
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* cache cos[pos_idx].unsqueeze and sin[pos_idxs].unsqueeze
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* minor doc update
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* separate pattern matching and optimization for explicit and complex rope + minor updates
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* clean rope impl in repo
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* replace fused_flattened_mla_with_cache's rope impl with torch_apply_rope_with_qk_interleaving, update unit test
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* minor
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* separate layout infer and transpose to a new transformation
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* update rope_with_explicit_freqs and rope_with_input_interleaved to expose unsqueeze_dim and support match_rope_layout, add unit tests
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* solve merge conflict in transform.py, need to fix optimize_rope with cuda graph capture
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* minor clean up after rebase
Signed-off-by: Ubuntu <201670829+Fridah-nv@users.noreply.github.com>
* fix pre-commit
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* support map to bnsd layout and infer unsqueeze_dim from op
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* fix cos/sin not the same across prompts in the same batch issue when mapping to flashinfer op
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* fix for unit test
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* fix custom op input/output node ordering issue for DeepSeek V3 rope
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* clean code
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* minor
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* move flattening of cos_sin_cache to the graph, update flashinfer op docstring and test
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* debug transform unit test failure
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
---------
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
Signed-off-by: Ubuntu <201670829+Fridah-nv@users.noreply.github.com>
Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>
* update matcher to match expert compute first, then extract other args with LCA
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* support 3D and 2D input in torch.ops.moe.trtllm_fused_moe
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* update custom ops to support 3D and 2D inputs
Signed-off-by: Ubuntu <201670829+Fridah-nv@users.noreply.github.com>
* update deepseek patch
Signed-off-by: Ubuntu <201670829+Fridah-nv@users.noreply.github.com>
---------
Signed-off-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com>
* Add test case for kv memory estimation
* Dump running log into file and parse kv cache memory size from file
* Set bigger peak memory size for mixed percision case and test_ptp_quickstart_advanced_eagle3 case
* Revert change to usage of fraction
* use context manager to guard temp files
Signed-off-by: Hui Gao <huig@nvidia.com>
This PR adds a customized allreduce to TensorRT-LLM. The new allreduce is used for communication on PCIe-based GPUs via low-precision quantization, which can accelerate the PCIe allreduce process.
Signed-off-by: Hui Kang <hkang@nvidia.com>
Co-authored-by: Hui Kang <hkang@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>
* Add chunking to PyT heuristic.
Signed-off-by: Frank Di Natale <3429989+FrankD412@users.noreply.github.com>
* Cast tokens and batch size to ints.
Signed-off-by: Frank Di Natale <3429989+FrankD412@users.noreply.github.com>
---------
Signed-off-by: Frank Di Natale <3429989+FrankD412@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>
Prefetching safetensors files so that they are stored in the system file
cache. This significantly speeds up the model weight loading for the
very first run after entering the docker container.
This is beneficial because model weight loading is done layer-by-layer,
which means reading from the safetensors chunk-by-chunk, and that cannot
utilize the internet bandwidth very well, assuming that these files are
stored in some network drives. Instead, loading the whole files in bulk
can achieve higher internet bandwidth utilization.
When running with world_size>1, all ranks collaboratedly prefetch these
files.
In theory, we should add heuristics to decide whether to prefetch the
files or not, but that is beyond the scope of this commit.
For example, when the CPU memory is small, doing prefetching may result
in file cache thrashing, resulting in slower weight loading time.
Signed-off-by: Po-Han Huang <pohanh@nvidia.com>
* feat: Add heuristic for GroupRMSNorm kernel selection.
Implements a logistic regression model to dynamically select between:
- GroupRMSNormBaseKernel: Allocates warps proportional to sum of dimensions
(better SM occupancy in most cases)
- GroupRMSNormLargeBatch: Allocates warps proportional to max dimension
(better block scheduling in large batch scenarios)
Selection heuristic considers batch size, allocated warps, and scheduling
efficiency on the current GPU architecture. Models for Compute Capability
9.x and 10.x are trained base on nsys kernel runtime data.
The default kernel selection is the base kernel.
The python operator group_rms_norm will use the heuristic by default.
User can pick to use the base or large batch kernels as well.
Signed-off-by: Simeng Liu <simengl@nvidia.com>
* Address the comments.
Signed-off-by: Simeng Liu <simengl@nvidia.com>
---------
Signed-off-by: Simeng Liu <simengl@nvidia.com>
* Set cuda graph max batch size.
Signed-off-by: Frank Di Natale <3429989+FrankD412@users.noreply.github.com>
* Set padding.
Signed-off-by: Frank Di Natale <3429989+FrankD412@users.noreply.github.com>
---------
Signed-off-by: Frank Di Natale <3429989+FrankD412@users.noreply.github.com>
* fix add_dummy_requests.
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
* add max_seq_len to eagle3 test and fix add_dummy_requests.
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
* fix prompt_len in add_dummy_requests.
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
* add prepare_resource condition in add_dummy_requests.
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
* add some description of token_nums to add_dummy_requests and fix token_nums in torch compile warmup.
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
* fix available_tokens.
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
---------
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
* fix relaxed acceptance to support enable this feature in context phase.
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
* fix sample_and_accept_draft_tokens unit test.
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
---------
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
* Fallback to NCCL for various patterns when input size is large.
Move the previous implementation to cpp side.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Revising.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
---------
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Properly get decoding mode according to same logic as cpp.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* Cross reference getDecodingMode implementations in pytorch - cpp.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* Better bindings for DecodingMode.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* Revert to version in main.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* Fix.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* Revert configuration.py.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
---------
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* disable overlap in encoder
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* feat: invokeGatherBatch
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* feat: overlap same batch
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* chore: add enableTrtOverlap to ExecutorConfig
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* disable overlap for beam search and spec decode
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* skip overlap tests with beam search or speculative decoding
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* moveFinishedContextRequestsToGeneration and skip unfinished requests in updateRequests
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* enable overlap in GptChunkedLongContextTests
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* feat: Enable overlap in gptManagerBenchmark
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* feat: Improve early exit
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* refactor: Use OptionalRef for newOutputTokens tensor
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* feat: Add overlap scheduling support to TRTLLMDecoder
- Updated TRTLLMDecoder to accept an `enable_overlap_scheduler` parameter.
- Modified the decoder's internal logic to utilize the overlap scheduling feature.
- Adjusted the sequence lengths handling to ensure compatibility with the new scheduling approach.
- Enhanced unit tests to include cases for the overlap scheduler with the TRTLLMDecoder.
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* fix: allNewTokens in PP
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
---------
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
* Instantiate decoder early to have better mem estimation.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* Improve mem estimation by instantiating decoder earlier.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
---------
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* Fix AllReduce kernel hang issue when both tp and pp are enabled.
Allocate one workspace for each pp rank to avoid potential race.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* update waive list
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
---------
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>
---------
Signed-off-by: qixiang-99 <203170375+qixiang-99@users.noreply.github.com>
Co-authored-by: Haohang Huang <31998628+symphonylyh@users.noreply.github.com>
* Move all casters to customCasters.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* Use customCasters in all bindings.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* Added customCasters to userbuffers.
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
---------
Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com>
* feat: Add group_rms_norm kernel to normalize multiple inputs in a single operator.
Previously, the RMSNorm implementation only supported a single input tensor. With group_rms_norm, multiple tensors can be normalized together:
```python
input_a, input_b, ... = group_rms_norm([input_a, input_b, ...])
```
All input tensors must share the same batch dimension. The kernel partitions work by dynamically assigning warp groups proportional to the last dimension of each input, improving launch efficiency and reducing overhead.
This MR provides two implementations:
GroupRMSNormKernel: Optimized for small-to-medium batch sizes
GroupRMSNormKernelLargeBatch: Contains additional optimizations for large batch sizes
Both kernels are currently exposed as custom PyTorch ops. A future MR will implement heuristic-based kernel selection and expose a unified interface.
Signed-off-by: Simeng Liu <simengl@nvidia.com>
* Resolve comments and fix typo with IS_FLASHINFER_AVAILABLE
Signed-off-by: Simeng Liu <simengl@nvidia.com>
---------
Signed-off-by: Simeng Liu <simengl@nvidia.com>
When input size is larger than the max workspace size, we shall fallback to NCCL + corresponding pre/post function to ensure the functionality of AllReduce.
Signed-off-by: Yukun He <23156053+hyukn@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>
---------
Signed-off-by: Erin Ho <14718778+hchings@users.noreply.github.com>
* support return logprob in llmapi
Signed-off-by: Erin Ho <14718778+hchings@users.noreply.github.com>
update and add test
Signed-off-by: Erin Ho <14718778+hchings@users.noreply.github.com>
stability test
Signed-off-by: Erin Ho <14718778+hchings@users.noreply.github.com>
* revert removal of old flag
Signed-off-by: Erin Ho <erinh@nvidia.com>
Signed-off-by: Erin Ho <14718778+hchings@users.noreply.github.com>
---------
Signed-off-by: Erin Ho <14718778+hchings@users.noreply.github.com>
Signed-off-by: Erin Ho <erinh@nvidia.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>
---------
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>
