[Feature] Integrate SM100 DeepGEMM support (#20087)
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@@ -86,6 +86,9 @@ def benchmark_config(
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(num_experts, 2 * shard_intermediate_size), dtype=torch.float32
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)
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w2_scale = torch.randn((hidden_size, num_experts), dtype=torch.float32)
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if use_deep_gemm:
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# we use the default block shape for deepgemm
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block_quant_shape = [128, 128]
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if use_fp8_w8a8:
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if block_quant_shape:
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block_n, block_k = block_quant_shape[0], block_quant_shape[1]
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