Dom Brown 44fb3c1673 [TRTLLM-5770] feat: Integrate TRT-LLM Gen FP8 block scale MoE with Pytorch workflow kernel autotuner (#5207) ... - Adds a new Python custom op (fp8_block_scale_moe_runner) and a FP8BlockScaleMoERunner class for autotuning. - Updates C++ MoE and batched GEMM kernels to accept a configIndex for workspace sizing and execution. - Extends the unit test to run both autotuned and non-autotuned code paths. Signed-off-by: Dom Brown <3886319+DomBrown@users.noreply.github.com>		2025-06-17 21:01:56 +08:00
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__init__.py	[TRTLLM-5770] feat: Integrate TRT-LLM Gen FP8 block scale MoE with Pytorch workflow kernel autotuner (#5207)	2025-06-17 21:01:56 +08:00
cpp_custom_ops.py	Feat/ds r1 min latency opt round3, add router gemm, fused a gemm, PDL (#4560)	2025-06-14 17:36:22 +08:00
flashinfer_custom_ops.py	Cherry pick feat/llama4 to main (#4739)	2025-05-30 05:28:40 +08:00
torch_custom_ops.py	[TRTLLM-5330] perf: Optimize MoE supplementary kernels for large-scale EP (#5215)	2025-06-17 15:23:24 +08:00
trtllm_gen_custom_ops.py	[TRTLLM-5770] feat: Integrate TRT-LLM Gen FP8 block scale MoE with Pytorch workflow kernel autotuner (#5207)	2025-06-17 21:01:56 +08:00
userbuffers_custom_ops.py	feat: Introduce UB allocator for pytorch flow (#3257)	2025-04-08 18:39:49 +08:00