mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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129bf19980
* 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> |
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| .. | ||
| _torch | ||
| auto_parallel | ||
| bench | ||
| commands | ||
| evaluate | ||
| executor | ||
| inputs | ||
| layers | ||
| llmapi | ||
| models | ||
| plugin | ||
| quantization | ||
| runtime | ||
| scaffolding | ||
| serve | ||
| tools | ||
| __init__.py | ||
| _common.py | ||
| _dlpack_utils.py | ||
| _ipc_utils.py | ||
| _mnnvl_utils.py | ||
| _utils.py | ||
| builder.py | ||
| disaggregated_params.py | ||
| functional.py | ||
| graph_rewriting.py | ||
| logger.py | ||
| lora_manager.py | ||
| mapping.py | ||
| module.py | ||
| network.py | ||
| parameter.py | ||
| profiler.py | ||
| prompt_adapter_manager.py | ||
| python_plugin.py | ||
| sampling_params.py | ||
| top_model_mixin.py | ||
| version.py | ||