Signed-off-by: qqiao <qqiao@nvidia.com>
Signed-off-by: Bo Li <22713281+bobboli@users.noreply.github.com>
Signed-off-by: Emma Qiao <qqiao@nvidia.com>
Signed-off-by: xxi <xxi@nvidia.com>
Signed-off-by: xxi <95731198+xxi-nv@users.noreply.github.com>
Co-authored-by: Bo Li <22713281+bobboli@users.noreply.github.com>
Co-authored-by: xxi <xxi@nvidia.com>
Co-authored-by: xxi <95731198+xxi-nv@users.noreply.github.com>
Co-authored-by: Yanchao Lu <yanchaol@nvidia.com>
Four distinct strategies are implemented to accommodate different distributed tuning scenarios, including BROADCAST, INDEPENDENT, MERGE, PARALLEL.
* Distributed tuning is disabled by default, with the INDEPENDENT strategy as the fallback. This conservative approach prevents unexpected behavior in standard use cases.
* Only operations with significant tuning time overhead have been assigned the PARALLEL strategy, which allows the same tensor parallelism (TP) rank to tune tactics concurrently across different ranks. This targeted approach balances performance gains with stability.
* Operations with nested tuning structures, such as NVFP4GemmUnifiedRunner, currently support only the INDEPENDENT strategy. This restriction exists because the synchronization mechanism is optimized only for leaf operations and doesn't yet handle nested hierarchies.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
Signed-off-by: Shijie Wang <jaywan@nvidia.com>
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
Signed-off-by: Shijie <jaywan@nvidia.com>
Co-authored-by: Yukun He <23156053+hyukn@users.noreply.github.com>
The performance results of some kernels could be easily affected by the warm/cold L2 cache status. To achieve more precise profiling results, the L2 cache is cleared for every execution by the circular buffer method for better benchmarking during autotuning.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Skip the shape profile generating process if the profile has already been found in the cache under tuning mode. This is a prerequisite for nested autotuning because host overhead might be included during the profiling of the high-level op.
* Enable the profiling with CUDA graph as the default profiling method.
* Apply a heuristic method to cut off the number of repeat times of profiling according to a few-run time measurement.
Update TRTLLM Cutlass MoE kernels with ReLU2 activation.
Nemotron-6 requires ReLU2 (i.e. squared ReLU) MoE activation function.
The PR adds this and adds an API to set the activation function, in general.
The ReLU2 changes are based on this FlashInfer PR: https://github.com/flashinfer-ai/flashinfer/pull/1954.
The PR also updates the Auto Deploy MoE backend for 16-bit and FP8 from
Triton (`torch.ops.auto_deploy.triton_moe_fused`, `torch.ops.auto_deploy.triton_quant_fp8_moe`) to TRTLLM/Cutlass (`torch.ops.auto_deploy.trtllm_moe_fused`, `torch.ops.auto_deploy.trtllm_quant_fp8_moe_fused`).
Signed-off-by: Neta Zmora <96238833+nzmora-nvidia@users.noreply.github.com>
Signed-off-by: Chenghao Zhang <211069071+nvchenghaoz@users.noreply.github.com>
Co-authored-by: Chenghao Zhang <211069071+nvchenghaoz@users.noreply.github.com>
