[Feature] Integrate SM100 DeepGEMM support (#20087)

tests/kernels/moe/test_deepgemm.py

@@ -13,48 +13,18 @@ import torch
 
 # vLLM fused-expert reference (Triton fallback + DeepGEMM option)
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
-from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    per_token_group_quant_fp8)
-from vllm.utils import cdiv
+from vllm.utils import has_deep_gemm
+from vllm.utils.deep_gemm import (calc_diff, per_block_cast_to_fp8,
+                                  per_token_group_cast_to_fp8)
 
-has_deep_gemm = importlib.util.find_spec("deep_gemm") is not None
-
-if has_deep_gemm:
-    import deep_gemm
-    BLOCK_M = deep_gemm.get_m_alignment_for_contiguous_layout()
-    BLOCK_SIZE = [BLOCK_M, BLOCK_M]
+BLOCK_SIZE = [128, 128]
 
 requires_deep_gemm = pytest.mark.skipif(
-    not has_deep_gemm,
+    not has_deep_gemm(),
     reason="Requires deep_gemm kernels",
 )
 
 
-def calc_diff(x: torch.Tensor, y: torch.Tensor):
-    x, y = x.double(), y.double()
-    denominator = (x * x + y * y).sum()
-    sim = 2 * (x * y).sum() / denominator
-    return 1 - sim
-
-
-def per_block_cast_to_fp8(
-        x: torch.Tensor,
-        block_size_n: int = 128) -> tuple[torch.Tensor, torch.Tensor]:
-    assert x.dim() == 2
-    m, n = x.shape
-    x_padded = torch.zeros(
-        (cdiv(m, 128) * 128, cdiv(n, block_size_n) * block_size_n),
-        dtype=x.dtype,
-        device=x.device)
-    x_padded[:m, :n] = x
-    x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, block_size_n)
-    x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
-    x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
-    x_scaled_sub = x_scaled.view_as(x_padded)[:m, :n].contiguous()
-    scales = (x_amax / 448.0).view(x_view.size(0), x_view.size(2))
-    return x_scaled_sub, scales
-
-
 def make_block_quant_fp8_weights(
     e: int,
     n: int,
@@ -111,7 +81,7 @@ def run_single_case(m, n, k, topk, num_experts, block_size):
     """
     tokens_bf16 = torch.randn(
         m, k, device="cuda", dtype=torch.bfloat16).clamp_min_(-1).clamp_max_(1)
-    _, a1_scale = per_token_group_quant_fp8(tokens_bf16, block_size[1])
+    _, a1_scale = per_token_group_cast_to_fp8(tokens_bf16, block_size[1])
 
     # expert weight tensors
     w1, w2, w1_s, w2_s = make_block_quant_fp8_weights(num_experts, n, k,
@@ -155,17 +125,8 @@ def run_single_case(m, n, k, topk, num_experts, block_size):
         block_shape=block_size,
         allow_deep_gemm=True,
     )
-
-    base = out_triton.abs().mean()
-    atol = 0.1 * base.clamp(min=1e-2)  # 10% of mean, but not lower than 1e-3
-    rtol = 0.05
-    # ----- Compare -----
-    torch.testing.assert_close(
-        out_deepgemm.to(torch.float32),
-        out_triton.to(torch.float32),
-        rtol=rtol,
-        atol=float(atol),
-    )
+    diff = calc_diff(out_deepgemm, out_triton)
+    assert diff < 0.001, f"Diff exceeded 1%: {diff}"
 
 
 # Note: W1 has shape (E, 2N, K), so N = 512