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[ROCm] Allow bf16/bf16/fp32 in nvte_multi_tensor_gemm dispatcher #573

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[ROCm] Allow bf16/bf16/fp32 in nvte_multi_tensor_gemm dispatcher #573

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10 changes: 8 additions & 2 deletions transformer_engine/common/gemm/cublaslt_gemm.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -1163,12 +1163,18 @@ void nvte_multi_tensor_gemm(const NVTETensor *A, const NVTETensor *B, NVTETensor
auto A_dt = inputA->data.dtype;
auto B_dt = inputB->data.dtype;
auto D_dt = OutputD->data.dtype;
// CK FP16/BF16 grouped GEMM dispatcher (ck_tile_grouped_gemm_fp16_dispatch)
// already supports independent D dtype via TRANSFORMER_ENGINE_TYPE_SWITCH_NON_FP8ONLY
// (fp32, fp16, bf16). The previous check required A==B==D, which incorrectly
// rejected the common bf16/bf16/fp32 case (training with fp32 gradient
// accumulation), forcing a fallback to the per-expert hipblaslt loop.
// Relaxed to require A==B in fp16/bf16 and D in {fp32, fp16, bf16}.
return (
(is_fp8_dtype(A_dt) && is_fp8_dtype(B_dt))
) ||
(
(A_dt == B_dt) && (A_dt == D_dt) &&
(is_fp16_dtype(A_dt))
(A_dt == B_dt) && is_fp16_dtype(A_dt) &&
(is_fp16_dtype(D_dt) || D_dt == transformer_engine::DType::kFloat32)
);
#else
auto A_type = get_cuda_dtype(inputA->data.dtype);
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