[Feature] Integrate SM100 DeepGEMM support (#20087)

benchmarks/kernels/benchmark_moe.py

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

tests/kernels/moe/test_block_fp8.py

@@ -15,13 +15,13 @@ from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
 from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_topk, modular_triton_fused_moe)
 from vllm.platforms import current_platform
+from vllm.utils import has_deep_gemm
+from vllm.utils.deep_gemm import is_blackwell_deep_gemm_used
 
-dg_available = False
+dg_available = has_deep_gemm()
-try:
+
-    import deep_gemm
+if dg_available:
-    dg_available = True
+    from deep_gemm import get_m_alignment_for_contiguous_layout
-except ImportError:
-    pass
 
 if current_platform.get_device_capability() < (9, 0):
     pytest.skip("FP8 Triton requires CUDA 9.0 or higher",
@@ -224,6 +224,7 @@ def test_w8a8_block_fp8_fused_moe(M, N, K, E, topk, block_size, dtype, seed,
 @pytest.mark.parametrize("topk", TOP_KS)
 @pytest.mark.parametrize("seed", SEEDS)
 @pytest.mark.skipif(not dg_available, reason="DeepGemm kernels not available.")
+@pytest.mark.skipif(is_blackwell_deep_gemm_used(), reason="Not E8M0 scale MOE")
 @torch.inference_mode()
 def test_w8a8_block_fp8_deep_gemm_fused_moe(M, N, K, E, topk, seed,
                                             monkeypatch):
@@ -238,8 +239,7 @@ def test_w8a8_block_fp8_deep_gemm_fused_moe(M, N, K, E, topk, seed,
     torch.manual_seed(seed)
 
     monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(chunk_size))
-
+    block_m = get_m_alignment_for_contiguous_layout()
-    block_m = deep_gemm.get_m_alignment_for_contiguous_layout()
     block_size = [block_m, block_m]
     dtype = torch.bfloat16
 

tests/kernels/moe/test_deepep_deepgemm_moe.py

@@ -20,6 +20,7 @@ from vllm.model_executor.layers.fused_moe.modular_kernel import (
     FusedMoEModularKernel)
 from vllm.platforms import current_platform
 from vllm.utils import has_deep_ep, has_deep_gemm
+from vllm.utils.deep_gemm import is_blackwell_deep_gemm_used
 
 from .parallel_utils import ProcessGroupInfo, parallel_launch
 from .utils import make_test_weights
@@ -368,6 +369,8 @@ NUM_EXPERTS = [32]
 @pytest.mark.parametrize("world_dp_size", [(2, 1)])
 @requires_deep_ep
 @requires_deep_gemm
+@pytest.mark.skipif(is_blackwell_deep_gemm_used(),
+                    reason="Skipping test for Blackwell DeepGEMM")
 def test_ht_deepep_deepgemm_moe(mnk: tuple[int, int, int], num_experts: int,
                                 topk: int, world_dp_size: tuple[int, int]):
     """
@@ -423,6 +426,8 @@ USE_FP8_DISPATCH = [False]
 @pytest.mark.parametrize("world_dp_size", [(2, 1)])
 @requires_deep_ep
 @requires_deep_gemm
+@pytest.mark.skipif(is_blackwell_deep_gemm_used(),
+                    reason="Skipping test for Blackwell DeepGEMM")
 def test_ll_deepep_deepgemm_moe(
     mnk: tuple[int, int, int],
     num_experts: int,

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 (
+from vllm.utils import has_deep_gemm
-    per_token_group_quant_fp8)
+from vllm.utils.deep_gemm import (calc_diff, per_block_cast_to_fp8,
-from vllm.utils import cdiv
+                                  per_token_group_cast_to_fp8)
 
-has_deep_gemm = importlib.util.find_spec("deep_gemm") is not None
+BLOCK_SIZE = [128, 128]
-
-if has_deep_gemm:
-    import deep_gemm
-    BLOCK_M = deep_gemm.get_m_alignment_for_contiguous_layout()
-    BLOCK_SIZE = [BLOCK_M, BLOCK_M]
 
 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,
     )
-
+    diff = calc_diff(out_deepgemm, out_triton)
-    base = out_triton.abs().mean()
+    assert diff < 0.001, f"Diff exceeded 1%: {diff}"
-    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),
-    )
 
 
 # Note: W1 has shape (E, 2N, K), so N = 512

tests/kernels/quantization/test_block_fp8.py

@@ -8,19 +8,15 @@ import pytest
 import torch
 
 from tests.kernels.quant_utils import (native_per_token_group_quant_fp8,
-                                       native_w8a8_block_matmul,
+                                       native_w8a8_block_matmul)
-                                       per_block_cast_to_fp8)
 from vllm.config import VllmConfig
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    per_token_group_quant_fp8, w8a8_block_fp8_matmul)
+    get_col_major_tma_aligned_tensor, per_token_group_quant_fp8,
+    w8a8_block_fp8_matmul)
 from vllm.platforms import current_platform
-
+from vllm.utils import has_deep_gemm
-dg_available = False
+from vllm.utils.deep_gemm import (fp8_gemm_nt, per_block_cast_to_fp8,
-try:
+                                  per_token_group_cast_to_fp8)
-    import deep_gemm
-    dg_available = True
-except ImportError:
-    pass
 
 if current_platform.get_device_capability() < (9, 0):
     pytest.skip("FP8 Triton requires CUDA 9.0 or higher",
@@ -106,7 +102,8 @@ def test_w8a8_block_fp8_matmul(M, N, K, block_size, out_dtype, seed):
 @pytest.mark.parametrize(
     "M,N,K,block_size,out_dtype,seed",
     itertools.product(M, N, K, BLOCK_SIZE, OUT_DTYPES, SEEDS))
-@pytest.mark.skipif(not dg_available, reason="DeepGemm kernels not available.")
+@pytest.mark.skipif(not has_deep_gemm(),
+                    reason="DeepGemm kernels not available.")
 @torch.inference_mode()
 def test_w8a8_block_fp8_deep_gemm_matmul(M, N, K, block_size, out_dtype, seed):
     # only aligned sizes
@@ -120,9 +117,7 @@ def test_w8a8_block_fp8_deep_gemm_matmul(M, N, K, block_size, out_dtype, seed):
     A_fp32 = (torch.rand(M, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
     B_fp32 = (torch.rand(N, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
 
-    _, block_k = block_size[0], block_size[1]
+    A_fp8, As_fp8 = per_token_group_cast_to_fp8(A_fp32, block_size[1])
-
-    A_fp8, As_fp8 = per_token_group_quant_fp8(A_fp32, block_k)
     B_fp8, Bs_fp8 = per_block_cast_to_fp8(B_fp32)
 
     As = As_fp8.to(torch.float32)
@@ -132,14 +127,14 @@ def test_w8a8_block_fp8_deep_gemm_matmul(M, N, K, block_size, out_dtype, seed):
                                        out_dtype)
 
     # Transpose earlier so that the testing will not trigger transposing kernels
-    As_fp8 = deep_gemm.get_col_major_tma_aligned_tensor(As_fp8)
+    As_fp8 = get_col_major_tma_aligned_tensor(As_fp8)
 
     out = torch.zeros((M, N), device='cuda', dtype=out_dtype)
 
     assert As_fp8.shape == (M, (K + 127) //
                             128), f"{As_fp8.shape} != {(M, (K + 127) // 128)}"
 
-    deep_gemm.gemm_fp8_fp8_bf16_nt((A_fp8, As_fp8), (B_fp8, Bs_fp8), out)
+    fp8_gemm_nt((A_fp8, As_fp8), (B_fp8, Bs_fp8), out)
 
     rel_diff = (torch.mean(
         torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))) /

vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py

@@ -11,6 +11,7 @@ from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceDelegate)
 from vllm.model_executor.layers.fused_moe.utils import _resize_cache
 from vllm.triton_utils import tl, triton
+from vllm.utils.deep_gemm import fp8_m_grouped_gemm_nt_masked
 
 logger = init_logger(__name__)
 
@@ -271,7 +272,6 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         assert expert_tokens_meta is not None
         expert_num_tokens = expert_tokens_meta.expert_num_tokens
 
-        import deep_gemm as dg
         assert hidden_states.ndim == 3
         assert self.block_shape is not None
 
@@ -289,18 +289,15 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         # for the M expectation of each batch, correctly setting this value
         # may lead to better performance.
         expected_m = max_num_tokens
-
+        fp8_m_grouped_gemm_nt_masked((a1q, a1q_scale), (w1, w1_scale),
-        dg.m_grouped_gemm_fp8_fp8_bf16_nt_masked((a1q, a1q_scale),
+                                     out=workspace1,
-                                                 (w1, w1_scale),
+                                     masked_m=expert_num_tokens,
-                                                 out=workspace1,
+                                     expected_m=expected_m)
-                                                 masked_m=expert_num_tokens,
-                                                 expected_m=expected_m)
 
         a2q, a2q_scale = silu_mul_fp8_quant_deep_gemm(workspace1,
                                                       expert_num_tokens)
 
-        dg.m_grouped_gemm_fp8_fp8_bf16_nt_masked((a2q, a2q_scale),
+        fp8_m_grouped_gemm_nt_masked((a2q, a2q_scale), (w2, w2_scale),
-                                                 (w2, w2_scale),
+                                     out=output,
-                                                 out=output,
+                                     masked_m=expert_num_tokens,
-                                                 masked_m=expert_num_tokens,
+                                     expected_m=expected_m)
-                                                 expected_m=expected_m)

vllm/model_executor/layers/fused_moe/deep_gemm_moe.py

@@ -14,9 +14,10 @@ from vllm.model_executor.layers.fused_moe.prepare_finalize import (
     MoEPrepareAndFinalizeNoEP)
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceDelegate)
-from vllm.model_executor.layers.fused_moe.utils import (
+from vllm.model_executor.layers.fused_moe.utils import _resize_cache
-    _resize_cache, per_token_group_quant_fp8)
 from vllm.utils import has_deep_gemm, round_up
+from vllm.utils.deep_gemm import (m_grouped_fp8_gemm_nt_contiguous,
+                                  per_token_group_cast_to_fp8)
 
 logger = init_logger(__name__)
 
@@ -127,7 +128,6 @@ class DeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         workspace2: torch.Tensor,
         expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
     ):
-        import deep_gemm as dg
         assert self.block_shape is not None
 
         a1q = hidden_states
@@ -164,19 +164,19 @@ class DeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
                                   (M_sum, N // 2))
         mm2_out = _resize_cache(workspace2, (M_sum, K))
 
-        dg.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(
+        m_grouped_fp8_gemm_nt_contiguous((a1q, a1q_scale), (w1, w1_scale),
-            (a1q, a1q_scale), (w1, w1_scale), mm1_out, expert_ids)
+                                         mm1_out, expert_ids)
 
         self.activation(activation, act_out, mm1_out.view(-1, N))
 
         a2q_scale: Optional[torch.Tensor] = None
-        a2q, a2q_scale = per_token_group_quant_fp8(act_out,
+        a2q, a2q_scale = per_token_group_cast_to_fp8(act_out,
-                                                   self.block_shape[1],
+                                                     self.block_shape[1],
-                                                   column_major_scales=True,
+                                                     column_major_scales=True,
-                                                   out_q=quant_out)
+                                                     out_q=quant_out)
 
-        dg.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(
+        m_grouped_fp8_gemm_nt_contiguous((a2q, a2q_scale), (w2, w2_scale),
-            (a2q, a2q_scale), (w2, w2_scale), mm2_out, expert_ids)
+                                         mm2_out, expert_ids)
 
         torch.index_select(mm2_out, 0, inv_perm, out=output.view((-1, K)))
 

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -34,6 +34,7 @@ from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils import direct_register_custom_op
+from vllm.utils.deep_gemm import is_blackwell_deep_gemm_used
 
 from .rocm_aiter_fused_moe import is_rocm_aiter_moe_enabled
 
@@ -1171,9 +1172,15 @@ def fused_experts(
         allow_cutlass_block_scaled_grouped_gemm: bool = False) -> torch.Tensor:
     # For now, disable DeepGemm for small N (<= 512) until better
     # permute/unpermute ops are available.
+    # However, on B200, we use DeepGemm for all cases becuase they only support
+    # E8M0 scale, which means we requantize the weight and input to the specific
+    # scale. Fallen back to cutlass or triton for some cases would cause
+    # accuracy issue.
     N = w1.size(1)
-    if (allow_deep_gemm and use_fp8_w8a8 and N > 512
+    should_use_deep_gemm = ((N > 512
-            and _valid_deep_gemm(hidden_states, w1, w2)):
+                             and _valid_deep_gemm(hidden_states, w1, w2))
+                            or is_blackwell_deep_gemm_used())
+    if (allow_deep_gemm and use_fp8_w8a8 and should_use_deep_gemm):
         assert apply_router_weight_on_input is False
         return deep_gemm_moe_fp8(
             hidden_states=hidden_states,
@@ -1363,7 +1370,6 @@ def fused_experts_impl(
 
         curr_topk_ids = topk_ids[begin_chunk_idx:end_chunk_idx]
         curr_topk_weights = topk_weights[begin_chunk_idx:end_chunk_idx]
-
         qcurr_hidden_states, a1q_scale = moe_kernel_quantize_input(
             A=curr_hidden_states,
             A_scale=a1_scale,

vllm/model_executor/layers/fused_moe/prepare_finalize.py

@@ -48,7 +48,6 @@ class MoEPrepareAndFinalizeNoEP(mk.FusedMoEPrepareAndFinalize):
             assert topk == 1, \
                 "apply_router_weight_on_input is only implemented for topk=1"
             a1.mul_(topk_weights.to(a1.dtype))
-
         a1q, a1q_scale = moe_kernel_quantize_input(
             a1, a1_scale, quant_config.quant_dtype,
             quant_config.per_act_token_quant, quant_config.block_shape)

vllm/model_executor/layers/fused_moe/triton_deep_gemm_moe.py

@@ -9,6 +9,7 @@ from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
 from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
     DeepGemmExperts, _valid_deep_gemm, _valid_deep_gemm_shape)
 from vllm.model_executor.layers.fused_moe.fused_moe import TritonExperts
+from vllm.utils.deep_gemm import is_blackwell_deep_gemm_used
 
 
 class TritonOrDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
@@ -102,7 +103,8 @@ class TritonOrDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         # Note: the deep gemm workspaces are strictly larger than the triton
         # workspaces so we can be pessimistic here and allocate for DeepGemm
         # even if we fall back to triton later, e.g. if expert maps are set.
-        if self.allow_deep_gemm and _valid_deep_gemm_shape(M, N, K):
+        if self.allow_deep_gemm and (_valid_deep_gemm_shape(M, N, K)
+                                     or is_blackwell_deep_gemm_used()):
             assert self.deep_gemm_expert is not None
             return self.deep_gemm_expert.workspace_shapes(
                 a, aq, M, N, K, topk, global_num_experts, local_num_experts)
@@ -132,7 +134,8 @@ class TritonOrDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
     ):
         use_deep_gemm = (self.allow_deep_gemm
-                         and _valid_deep_gemm(hidden_states, w1, w2))
+                         and (_valid_deep_gemm(hidden_states, w1, w2)
+                              or is_blackwell_deep_gemm_used()))
 
         experts = self.deep_gemm_expert if use_deep_gemm else self.triton_expert
         assert experts is not None

vllm/model_executor/layers/fused_moe/utils.py

@@ -15,6 +15,8 @@ from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils import cdiv
+from vllm.utils.deep_gemm import (is_blackwell_deep_gemm_used,
+                                  per_token_group_cast_to_fp8)
 
 
 @triton.jit
@@ -115,7 +117,10 @@ def _fp8_quantize(
         assert not per_act_token
         assert len(block_shape) == 2
         _, block_k = block_shape[0], block_shape[1]
-        A, A_scale = per_token_group_quant_fp8(A, block_k)
+        if is_blackwell_deep_gemm_used():
+            A, A_scale = per_token_group_cast_to_fp8(A, block_k)
+        else:
+            A, A_scale = per_token_group_quant_fp8(A, block_k)
         assert cdiv(A.size(-1), block_k) == A_scale.size(-1)
 
     return A, A_scale

vllm/model_executor/layers/mamba/ops/causal_conv1d.py

@@ -8,10 +8,9 @@ from typing import Optional, Union
 
 import numpy as np
 import torch
-import triton
-import triton.language as tl
 
 from vllm.attention.backends.utils import PAD_SLOT_ID
+from vllm.triton_utils import tl, triton
 
 
 @triton.jit()

vllm/model_executor/layers/quantization/deepgemm.py

@@ -6,10 +6,8 @@ import torch
 
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
-from vllm.utils import direct_register_custom_op, has_deep_gemm
+from vllm.utils import direct_register_custom_op
-
+from vllm.utils.deep_gemm import fp8_gemm_nt
-if has_deep_gemm():
-    import deep_gemm
 
 logger = logging.getLogger(__name__)
 
@@ -57,7 +55,7 @@ def w8a8_block_fp8_matmul_deepgemm(
                                                  output_dtype)
     # Deepgemm only supports output tensor type as bfloat16
     assert C.dtype == torch.bfloat16
-    deep_gemm.gemm_fp8_fp8_bf16_nt((A, As), (B, Bs), C)
+    fp8_gemm_nt((A, As), (B, Bs), C)
     return C
 
 

vllm/model_executor/layers/quantization/fp8.py

@@ -23,6 +23,8 @@ from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    get_col_major_tma_aligned_tensor, requant_weight_ue8m0_inplace)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin,
     prepare_moe_fp8_layer_for_marlin)
@@ -40,6 +42,7 @@ from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 from vllm.utils import has_deep_gemm
+from vllm.utils.deep_gemm import is_blackwell_deep_gemm_used
 
 if TYPE_CHECKING:
     from vllm.model_executor.models.utils import WeightsMapper
@@ -393,6 +396,19 @@ class Fp8LinearMethod(LinearMethodBase):
             # Activations not quantized for marlin.
             del layer.input_scale
 
+        # On B200, DeepGemm only support E8M0 scale, which means we need to
+        # requantize the weight and input to the specific scale
+        # at the same time.
+        if is_blackwell_deep_gemm_used():
+            assert layer.weight_block_size is not None
+            block_sz = tuple(layer.weight_block_size)
+            requant_weight_ue8m0_inplace(
+                layer.weight.data,
+                layer.weight_scale_inv.data if hasattr(
+                    layer, "weight_scale_inv") else layer.weight_scale.data,
+                block_sz,
+            )
+
     def apply(self,
               layer: torch.nn.Module,
               x: torch.Tensor,
@@ -670,15 +686,14 @@ class Fp8MoEMethod(FusedMoEMethodBase):
 
             # DeepGemm scales need to be transposed and aligned.  We try to do
             # it ahead of time for performance reasons.
-            if self.allow_deep_gemm:
+            if self.allow_deep_gemm and not is_blackwell_deep_gemm_used():
                 # Lazy import to avoid CUDA initialization problems.
-                import deep_gemm as dg
                 if _is_col_major(layer.w13_weight_scale_inv):
                     layer.w13_weight_scale_inv = \
-                        dg.get_col_major_tma_aligned_tensor(layer.w13_weight_scale_inv).contiguous()
+                        get_col_major_tma_aligned_tensor(layer.w13_weight_scale_inv).contiguous()
                 if _is_col_major(layer.w2_weight_scale_inv):
                     layer.w2_weight_scale_inv = \
-                        dg.get_col_major_tma_aligned_tensor(layer.w2_weight_scale_inv).contiguous()
+                        get_col_major_tma_aligned_tensor(layer.w2_weight_scale_inv).contiguous()
 
         # If checkpoint is fp16, quantize in place.
         elif not self.quant_config.is_checkpoint_fp8_serialized:
@@ -797,6 +812,29 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             del layer.w13_input_scale
             del layer.w2_input_scale
 
+        if is_blackwell_deep_gemm_used():
+            assert layer.weight_block_size is not None
+            # Re-quantise the expert weights so their scales are UE8M0.
+            block_sz = tuple(layer.weight_block_size)
+            requant_weight_ue8m0_inplace(
+                layer.w13_weight.data,
+                layer.w13_weight_scale_inv.data,
+                block_sz,
+            )
+            requant_weight_ue8m0_inplace(
+                layer.w2_weight.data,
+                layer.w2_weight_scale_inv.data,
+                block_sz,
+            )
+
+            # Ensure column-major TMA alignment expected by DeepGEMM.
+            if _is_col_major(layer.w13_weight_scale_inv):
+                layer.w13_weight_scale_inv = get_col_major_tma_aligned_tensor(
+                    layer.w13_weight_scale_inv).contiguous()
+            if _is_col_major(layer.w2_weight_scale_inv):
+                layer.w2_weight_scale_inv = get_col_major_tma_aligned_tensor(
+                    layer.w2_weight_scale_inv).contiguous()
+
     def select_gemm_impl(
         self,
         prepare_finalize: FusedMoEPrepareAndFinalize,

vllm/model_executor/layers/quantization/utils/fp8_utils.py

@@ -5,6 +5,7 @@
 import functools
 import json
 import os
+from collections.abc import Sequence
 from typing import Any, Callable, Optional, Union
 
 import torch
@@ -13,7 +14,7 @@ import vllm.envs as envs
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    scaled_dequantize)
+    group_broadcast)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     CUTLASS_BLOCK_FP8_SUPPORTED)
 from vllm.platforms import current_platform
@@ -235,7 +236,7 @@ def block_quant_to_tensor_quant(
     The outputs are tensor-wise quantization tensor and tensor-wise
     quantization scale. Note only float8 is supported for now.
     """
-    x_dq_block = scaled_dequantize(x_q_block, x_s)
+    x_dq_block = group_broadcast(x_q_block, x_s)
     x_q_tensor, scale = input_to_float8(x_dq_block, dtype=x_q_block.dtype)
     return x_q_tensor, scale
 
@@ -651,3 +652,124 @@ def w8a8_block_fp8_matmul(
     )
 
     return C
+
+
+# Taken from https://github.com/deepseek-ai/DeepGEMM/blob/0c88cd01392c1073c7049a97d6328c7bba9b3947
+# TODO(wentao): remove this function when DeepGEMM exposes this function
+def get_tma_aligned_size(x: int, element_size: int) -> int:
+    """
+    Global memory address of TMA must be 16-byte aligned.
+    Since we use column-major layout for the LHS scaling tensor,
+        the M-axis of the LHS scaling tensor needs to be padded to a multiple of
+        16 bytes.
+
+    Arguments:
+        x: original M-axis shape of the LHS scaling tensor.
+        element_size: element size of the LHS scaling tensor.
+
+    Returns:
+        M-axis shape of the LHS scaling tensor after padding.
+    """
+    tma_alignment_bytes = 16
+    assert tma_alignment_bytes % element_size == 0
+    alignment = tma_alignment_bytes // element_size
+    return cdiv(x, alignment) * alignment
+
+
+# Taken from https://github.com/deepseek-ai/DeepGEMM/blob/0c88cd01392c1073c7049a97d6328c7bba9b3947
+# TODO(wentao): remove this function when DeepGEMM exposes this function
+def get_col_major_tma_aligned_tensor(x: torch.Tensor) -> torch.Tensor:
+    """
+    Returns TMA-aligned transposed format of the input tensor. `torch.transpose`
+        will be called if necessary.
+    If the input tensor is already column-major layout and 16-byte aligned along
+        the M axis (thus meets the requirement of LHS scaling tensor in
+        DeepGEMM), this function will do nothing.
+
+    Arguments:
+        x: usually the LHS scaling tensor in GEMM.
+
+    Returns:
+        The LHS scaling tensor of TMA-aligned transposed format.
+    """
+    # NOTES: for the extreme performance, you may rewrite/fuse this function in
+    # CUDA
+    assert x.dim() in (2, 3)
+    remove_dim = False
+    m, n = x.shape[-2], x.shape[-1]
+    aligned_m = get_tma_aligned_size(m, x.element_size())
+    if x.dim() == 2:
+        if x.stride(0) == 1 and x.stride(1) == aligned_m:
+            return x
+        x, remove_dim = x.unsqueeze(0), True
+
+    b = x.shape[0]
+
+    # The last kernel gives a column-major TMA aligned layout
+    if x.stride(0) == aligned_m * n and x.stride(1) == 1 and x.stride(
+            2) == aligned_m:
+        return x.squeeze(0) if remove_dim else x
+
+    # Normal layout requires transposing
+    aligned_x = torch.transpose(
+        torch.empty((b, n, aligned_m), device=x.device, dtype=x.dtype), 1, 2)
+    aligned_x[:, :m, :] = x
+    aligned_x = aligned_x[:, :m, :]
+    return aligned_x.squeeze(0) if remove_dim else aligned_x
+
+
+def requant_weight_ue8m0_inplace(
+        weight: torch.Tensor,
+        weight_scale: torch.Tensor,
+        block_size: Sequence[int] = (128, 128),
+) -> None:
+    """Re-quantise *weight* so that its per-block scaling factors are in the
+    UE8M0 (power-of-two) format expected by the new DeepGEMM kernels inplace.
+
+    Args:
+        weight: Block-quantised weight tensor stored in ``torch.float8_e4m3fn``.
+            Expected shape ``(..., M, K)``.
+        weight_scale: Corresponding per-block scale tensor (``torch.float32``)
+            with shape ``(..., M // block_size[0], K // block_size[1])``.
+        block_size: 2-element iterable ``[block_m, block_k]`` describing the
+            block quantisation granularity.
+    """
+    if weight.numel() == 0:
+        return
+
+    if weight.dtype != torch.float8_e4m3fn:
+        raise ValueError("Expected *weight* to be torch.float8_e4m3fn, got "
+                         f"{weight.dtype} instead.")
+
+    from vllm.utils.deep_gemm import per_block_cast_to_fp8
+
+    block_m, block_k = int(block_size[0]), int(block_size[1])
+
+    # Flatten leading dimensions so we can iterate over the last two dims.
+    leading_shape = weight.shape[:-2]
+    if len(leading_shape) == 0:
+        w_view = weight.unsqueeze(0)
+        s_view = weight_scale.unsqueeze(0)
+    else:
+        w_view = weight.reshape(-1, weight.shape[-2], weight.shape[-1])
+        s_view = weight_scale.reshape(-1, *weight_scale.shape[-2:])
+
+    num_mats = w_view.size(0)
+    for idx in range(num_mats):
+        w_q = w_view[idx]
+        s_old = s_view[idx]
+
+        # De-quantise with the *old* scaling factors (float32).
+        m_cur, k_cur = w_q.shape
+        s_float = s_old.to(torch.float32)
+        # Expand scales along rows and cols by block size, then crop.
+        s_exp_r = torch.repeat_interleave(s_float, block_m, dim=0)
+        s_exp = torch.repeat_interleave(s_exp_r, block_k, dim=1)
+        s_exp = s_exp[:m_cur, :k_cur]
+        w_dq = w_q.to(torch.float32) * s_exp
+        # Re-quantise using power-of-two scaling (UE8M0).
+        w_requant, s_requant = per_block_cast_to_fp8(w_dq, [block_m, block_k])
+
+        # Write back the results in-place.
+        w_q.copy_(w_requant)
+        s_old.copy_(s_requant)

vllm/utils/deep_gemm.py

@@ -0,0 +1,152 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Compatibility wrapper for DeepGEMM API changes.
+
+Users of vLLM should always import **only** these wrappers.
+"""
+from __future__ import annotations
+
+import functools
+import importlib
+from typing import Any, Callable, NoReturn
+
+import torch
+
+import vllm.envs as envs
+from vllm.utils import cuda_get_device_properties, has_deep_gemm
+
+
+@functools.cache
+def is_blackwell_deep_gemm_used() -> bool:
+    """Return ``True`` if vLLM is configured to use DeepGEMM on a
+    Blackwell-class GPU.
+    """
+
+    if not (envs.VLLM_USE_DEEP_GEMM and has_deep_gemm()
+            and _per_block_cast_impl is not None):
+        return False
+
+    return cuda_get_device_properties(0, ("major", ))[0] == 10
+
+
+def _missing(*_: Any, **__: Any) -> NoReturn:
+    """Placeholder for unavailable DeepGEMM backend."""
+    raise RuntimeError(
+        "DeepGEMM backend is not available. Please install the `deep_gemm` "
+        "package to enable FP8 kernels.")
+
+
+def _resolve_symbol(module, new: str, old: str) -> Callable[..., Any] | None:
+    """Return the *new* symbol if it exists, otherwise the *old* one."""
+    if hasattr(module, new):
+        return getattr(module, new)
+    if hasattr(module, old):
+        return getattr(module, old)
+    return None
+
+
+if not has_deep_gemm():
+    _fp8_gemm_nt_impl: Callable[..., Any] | None = None
+    _grouped_impl: Callable[..., Any] | None = None
+    _grouped_masked_impl: Callable[..., Any] | None = None
+    _per_token_cast_impl: Callable[..., Any] | None = None
+    _per_block_cast_impl: Callable[..., Any] | None = None
+else:
+    _dg = importlib.import_module("deep_gemm")  # type: ignore
+
+    _fp8_gemm_nt_impl = _resolve_symbol(
+        _dg,
+        "fp8_gemm_nt",
+        "gemm_fp8_fp8_bf16_nt",
+    )
+    _grouped_impl = _resolve_symbol(
+        _dg,
+        "m_grouped_fp8_gemm_nt_contiguous",
+        "m_grouped_gemm_fp8_fp8_bf16_nt_contiguous",
+    )
+    _grouped_masked_impl = _resolve_symbol(
+        _dg,
+        "fp8_m_grouped_gemm_nt_masked",
+        "m_grouped_gemm_fp8_fp8_bf16_nt_masked",
+    )
+
+    # Try to get per_token_cast_to_fp8 from DeepGEMM math utils.
+    try:
+        _math_mod = importlib.import_module(
+            "deep_gemm.utils.math")  # type: ignore
+        _per_token_cast_impl = getattr(_math_mod, "per_token_cast_to_fp8",
+                                       None)
+        _per_block_cast_impl = getattr(_math_mod, "per_block_cast_to_fp8",
+                                       None)
+    except ModuleNotFoundError:
+        _per_token_cast_impl = None
+        _per_block_cast_impl = None
+
+
+def fp8_gemm_nt(*args, **kwargs):
+    if _fp8_gemm_nt_impl is None:
+        return _missing(*args, **kwargs)
+    return _fp8_gemm_nt_impl(*args, **kwargs)
+
+
+def m_grouped_fp8_gemm_nt_contiguous(*args, **kwargs):
+    if _grouped_impl is None:
+        return _missing(*args, **kwargs)
+    return _grouped_impl(*args, **kwargs)
+
+
+def fp8_m_grouped_gemm_nt_masked(*args, **kwargs):
+    if _grouped_masked_impl is None:
+        return _missing(*args, **kwargs)
+    return _grouped_masked_impl(*args, **kwargs)
+
+
+def per_token_group_cast_to_fp8(x, group_size, *args, **kwargs):
+    """Wrapper for token-wise FP8 quantisation.
+
+    • If DeepGEMM provides ``per_token_cast_to_fp8`` (new API), use it.
+    • Otherwise, fall back to vLLM's ``per_token_group_quant_fp8``
+    """
+
+    if _per_token_cast_impl is not None and is_blackwell_deep_gemm_used():
+        assert group_size == 128, "group_size must be 128 for deepgemm"
+        return _per_token_cast_impl(x)
+
+    from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+        per_token_group_quant_fp8 as _ptg)
+    return _ptg(x, group_size, *args, **kwargs)
+
+
+def per_block_cast_to_fp8(x, *args, **kwargs):
+    if _per_block_cast_impl is not None and is_blackwell_deep_gemm_used():
+        return _per_block_cast_impl(x)
+    # TODO: refactor the `per_block_cast_to_fp8` from tests to vllm utils
+    from tests.kernels.quant_utils import per_block_cast_to_fp8 as _pbcf
+    return _pbcf(x, *args, **kwargs)
+
+
+def calc_diff(x: torch.Tensor, y: torch.Tensor):
+    """Return a global difference metric for unit tests.
+
+    DeepGEMM kernels on Blackwell/B200 currently exhibit noticeable per-element
+    error, causing ``torch.testing.assert_close`` to fail.  Instead of checking
+    every element, we compute a cosine-style similarity over the whole tensor
+    and report ``1 - sim``.  Once kernel accuracy improves this helper can be
+    removed.
+    """
+
+    x, y = x.double(), y.double()
+    denominator = (x * x + y * y).sum()
+    sim = 2 * (x * y).sum() / denominator
+    return 1 - sim
+
+
+__all__ = [
+    "calc_diff",
+    "fp8_gemm_nt",
+    "m_grouped_fp8_gemm_nt_contiguous",
+    "fp8_m_grouped_gemm_nt_masked",
+    "per_token_group_cast_to_fp8",
+    "per_block_cast_to_fp8",
+    "is_blackwell_deep_gemm_used",
+]