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[Bugfix] Fix Qwen3-coder moe tuned config #24072

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Merged
merged 5 commits into from
Sep 7, 2025
Merged

[Bugfix] Fix Qwen3-coder moe tuned config #24072

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a82569e
Done
jeejeelee Sep 2, 2025
cc160c9
Fix
jeejeelee Sep 6, 2025
3f8a157
Merge branch 'main' into fix-benchmark-moe
jeejeelee Sep 6, 2025
f998107
Merge branch 'main' into fix-benchmark-moe
yewentao256 Sep 6, 2025
dc3b000
Merge branch 'main' into fix-benchmark-moe
jeejeelee Sep 7, 2025
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4 changes: 3 additions & 1 deletion benchmarks/kernels/benchmark_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -678,7 +678,9 @@ def _distribute(method: str, inputs: list[Any]) -> list[Any]:
is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16)
search_space = get_configs_compute_bound(is_fp16, block_quant_shape)
print(f"Start tuning over {len(search_space)} configurations...")

if use_deep_gemm:
print("Only supports tuning triton kernels, set use_deep_gemm=False.")
use_deep_gemm = False
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@gemini-code-assist gemini-code-assist bot Sep 2, 2025

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high

While this change correctly prevents the crash by disabling use_deep_gemm during tuning, it might be better to fail explicitly rather than silently changing the behavior. A user might intend to tune for DeepGEMM and miss the print statement in the logs, leading them to unknowingly use a Triton-tuned configuration for DeepGEMM workloads, which could be suboptimal.

Consider raising a ValueError to make the incompatibility explicit and guide the user to the correct usage.

Suggested change
print("Only supports tuning triton kernels, set use_deep_gemm=False.")
use_deep_gemm = False
raise ValueError(
"Tuning with --use-deep-gemm is not supported as it only tunes Triton kernels. Please remove the flag.")

start = time.time()
configs = _distribute(
"tune",
Expand Down
80 changes: 40 additions & 40 deletions ...e/configs/E=20,N=2560,device_name=NVIDIA_H20-3e,dtype=fp8_w8a8,block_shape=[128,128].json
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Original file line number Diff line number Diff line change
Expand Up @@ -18,18 +18,18 @@
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
"num_stages": 3
},
"16": {
"BLOCK_SIZE_M": 16,
Expand Down Expand Up @@ -58,7 +58,7 @@
"48": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
Expand All @@ -74,73 +74,73 @@
"96": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
"num_stages": 4
},
"128": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
"num_stages": 4
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
"num_stages": 4
},
"512": {
"BLOCK_SIZE_M": 256,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_warps": 4,
"num_stages": 4
},
"1024": {
"BLOCK_SIZE_M": 256,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 16,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 4
},
"2048": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 5
"num_warps": 4,
"num_stages": 3
},
"3072": {
"BLOCK_SIZE_M": 128,
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
"num_stages": 3
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
},
"4096": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 5
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
}
}