Because deep_gemm.gp8_gemm_nt will trigger many JIT processes during the inference phase, we need to sweep these shapes ahead of time. Apply the AutoTuner framework to achieve this and retain the potential capability to tune the swap_ab flag.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
Signed-off-by: Barry Kang <43644113+Barry-Delaney@users.noreply.github.com>
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
Signed-off-by: Yuxian Qiu <142763828+yuxianq@users.noreply.github.com>
Signed-off-by: Zongfei Jing <20381269+zongfeijing@users.noreply.github.com>
Co-authored-by: Barry Kang <43644113+Barry-Delaney@users.noreply.github.com>
Co-authored-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
Co-authored-by: Yuxian Qiu <142763828+yuxianq@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>
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>
* 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>
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>
* Instead of allocating UserBuffers at beginning of runtime, UB buffers
are now managed with global allocator. The allocator will dynamically
assign free UB buffer or allocate new buffer for torch tensor. It makes
userbuffers easier to use.
* In common usecase, the Userbuffers will be allocated correctly during
warm up stage. There is no dynamic allocation during inference.
* UB fusion pattern is rewroten using the new UB Allocator. It contains
following passes:
1. Fuse Quant with allreduce, replace with UB impl, and insert a
copy_to_userbuffers. Currently the normal allreduce still does not
support FP8 quant. So this need to be done in UB pass
2. Convert all supported allreduce with UB and insert copy_to_userbuffers.
3. Fuse op before ar with the copy_to_userbuffers. So the op directly
writes to the userbuffer
4. Remove userbuffers finalize if the output is connect to another UB
allreduce.
Signed-off-by: Jin Li <59594262+liji-nv@users.noreply.github.com>
* Several optimizations and fixings on the Autotuner.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Apply the new Python side Autotuner on current linear for nvFP4 data type.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Apply the new Python side Autotuner on MoE op
* Remove routers from cache key to improve inference perf
* Prevent unnecessary code profiling. Use do_preparation keyword to select which part should be executed during before evaluating any tactic.
* Remove try-catch inside moe profiling process.
* Move default tactic -1 to 0 transforms in cpp runner.
* Revise relavant tests.
* Predefined the bucketizing strategy for fused_moe
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Add specific_profile support for AutoTuner to bypass the standard cache search process for perf optimization
* Add specific_profile for moe
* Add specific profile for linear
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Fixing and revising according to reviewer's suggestions.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Use lru_cache for inference pref optimization.
* Revert gen_custom_cache_key feature
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Replace runner with runner id to achieve a serializable cache.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Code clean up and minor fixings.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Move all tunable runners and custom ops into torch_custom_ops.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Treat min_latency_mode as a independent dynamic tensor. Modify get_valid_tactics to suit for it.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
---------
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
