[Kernels][Bugfix] Use torch op for all kernels in FusedMoE forward. Add...

[Kernels][Bugfix] Use torch op for all kernels in FusedMoE forward.  Add additional testing for cudagraphs. (#19717)
Signed-off-by: Bill Nell <bnell@redhat.com>

tests/kernels/moe/test_cutlass_moe.py

@@ -29,7 +29,10 @@ MNK_FACTORS = [
     (224, 1024, 1536),
     (224, 3072, 1024),
     (224, 3072, 1536),
-    (1024 * 128, 1024, 1024),
+    (32768, 1024, 1024),
+    # These sizes trigger wrong answers.
+    #(7232, 2048, 5120),
+    #(40000, 2048, 5120),
 ]
 
 vllm_config = VllmConfig(parallel_config=ParallelConfig(
@@ -232,8 +235,10 @@ def test_cutlass_moe_8_bit_no_graph(
     topk: int,
     per_act_token: bool,
     per_out_ch: bool,
+    monkeypatch,
 ):
     current_platform.seed_everything(7)
+    monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
     with set_current_vllm_config(vllm_config):
         mt = MOETensors8Bit.make_moe_tensors_8bit(m, k, n, e, per_act_token,
                                                   per_out_ch)
@@ -274,8 +279,10 @@ def test_cutlass_moe_8_bit_cuda_graph(
     topk: int,
     per_act_token: bool,
     per_out_ch: bool,
+    monkeypatch,
 ):
     current_platform.seed_everything(7)
+    monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
     with set_current_vllm_config(vllm_config):
         dtype = torch.half
 
@@ -329,8 +336,10 @@ def test_cutlass_moe_8_bit_EP(
     per_act_token: bool,
     per_out_channel: bool,
     ep_size: int,
+    monkeypatch,
 ):
     current_platform.seed_everything(7)
+    monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
     with set_current_vllm_config(vllm_config):
         mt = MOETensors8Bit.make_moe_tensors_8bit(m, k, n, e, per_act_token,
                                                   per_out_channel)

tests/kernels/moe/test_moe.py

@@ -4,6 +4,9 @@
 
 Run `pytest tests/kernels/test_moe.py`.
 """
+import functools
+from typing import Callable, Optional, Union
+
 import pytest
 import torch
 from torch.nn import Parameter
@@ -14,6 +17,7 @@ from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
 import vllm.model_executor.layers.fused_moe  # noqa
 from tests.kernels.utils import opcheck, stack_and_dev, torch_moe
 from vllm.config import VllmConfig, set_current_vllm_config
+from vllm.forward_context import set_forward_context
 from vllm.model_executor.layers.fused_moe import fused_moe
 from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_topk, modular_triton_fused_moe)
@@ -40,7 +44,76 @@ vllm_config.scheduler_config.max_num_seqs = 128
 vllm_config.scheduler_config.max_model_len = 8192
 
 
-@pytest.mark.parametrize("m", [1, 33, 64, 222, 1024 * 128])
+def run_moe_test(
+    baseline: Union[Callable, torch.Tensor],
+    moe_fn: Callable,
+    a: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    score: torch.Tensor,
+    topk: int,
+    global_num_experts: int = -1,
+    expert_map: Optional[torch.Tensor] = None,
+    padding: bool = False,
+    use_compile: bool = False,
+    use_cudagraph: bool = False,
+    atol: float = 2e-2,
+    rtol: float = 0,
+) -> torch.Tensor:
+    if isinstance(baseline, torch.Tensor):
+        baseline_output = baseline
+    else:
+        baseline_output = baseline(a,
+                                   w1,
+                                   w2,
+                                   score,
+                                   topk,
+                                   global_num_experts=global_num_experts,
+                                   expert_map=expert_map)
+
+    # Pad the weight if moe padding is enabled
+    if padding:
+        w1 = F.pad(w1, (0, 128), "constant", 0)[..., 0:-128]
+        w2 = F.pad(w2, (0, 128), "constant", 0)[..., 0:-128]
+
+    if use_compile:
+        moe_fn = torch.compile(moe_fn, backend="inductor", fullgraph=True)
+        torch._dynamo.mark_dynamic(a, 0)
+        torch._dynamo.mark_dynamic(score, 0)
+
+    test_output = moe_fn(a,
+                         w1,
+                         w2,
+                         score,
+                         topk,
+                         global_num_experts=global_num_experts,
+                         expert_map=expert_map)
+
+    if use_cudagraph:
+        test_output.fill_(0)
+        stream = torch.cuda.Stream()
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph, stream=stream):
+            test_output = moe_fn(a,
+                                 w1,
+                                 w2,
+                                 score,
+                                 topk,
+                                 global_num_experts=global_num_experts,
+                                 expert_map=expert_map)
+        torch.cuda.synchronize()
+        graph.replay()
+        torch.cuda.synchronize()
+
+    torch.testing.assert_close(test_output,
+                               baseline_output,
+                               atol=atol,
+                               rtol=rtol)
+
+    return baseline_output
+
+
+@pytest.mark.parametrize("m", [1, 33, 64, 222, 32768, 40000])
 @pytest.mark.parametrize("n", [128, 1024, 2048])
 @pytest.mark.parametrize("k", [128, 511, 1024])
 @pytest.mark.parametrize("e", NUM_EXPERTS)
@@ -48,6 +121,7 @@ vllm_config.scheduler_config.max_model_len = 8192
 @pytest.mark.parametrize("ep_size", EP_SIZE)
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
 @pytest.mark.parametrize("padding", [True, False])
+@pytest.mark.parametrize("chunk_size", [8192])
 def test_fused_moe(
     m: int,
     n: int,
@@ -57,7 +131,17 @@ def test_fused_moe(
     ep_size: int,
     dtype: torch.dtype,
     padding: bool,
+    chunk_size: int,
+    monkeypatch,
 ):
+    current_platform.seed_everything(7)
+
+    monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(chunk_size))
+
+    #
+    # Setup test data
+    #
+
     a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
     w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
     w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
@@ -77,58 +161,70 @@ def test_fused_moe(
     else:
         e_map = None
 
-    m_fused_moe = modular_triton_fused_moe(use_fp8_w8a8=False,
-                                           use_int8_w8a8=False,
-                                           use_int8_w8a16=False,
-                                           use_int4_w4a16=False,
-                                           per_channel_quant=False,
-                                           block_shape=None)
-
-    with set_current_vllm_config(vllm_config):
-        torch_output = torch_moe(a, w1, w2, score, topk, e_map)
-        iterative_output = iterative_moe(a,
-                                         w1,
-                                         w2,
-                                         score,
-                                         topk,
-                                         global_num_experts=e,
-                                         expert_map=e_map,
-                                         renormalize=False)
-
-        # Pad the weight if moe padding is enabled
-        if padding:
-            w1 = F.pad(w1, (0, 128), "constant", 0)[..., 0:-128]
-            torch.cuda.empty_cache()
-            w2 = F.pad(w2, (0, 128), "constant", 0)[..., 0:-128]
-            torch.cuda.empty_cache()
+    #
+    # Setup test functions
+    #
+
+    m_fused_moe_fn = modular_triton_fused_moe(use_fp8_w8a8=False,
+                                              use_int8_w8a8=False,
+                                              use_int8_w8a16=False,
+                                              use_int4_w4a16=False,
+                                              per_channel_quant=False,
+                                              block_shape=None)
+
+    def m_fused_moe(
+        a: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        score: torch.Tensor,
+        topk: int,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        topk_weights, topk_ids, _ = fused_topk(a, score, topk, False)
+        return m_fused_moe_fn(a,
+                              w1,
+                              w2,
+                              topk_weights,
+                              topk_ids,
+                              global_num_experts=global_num_experts,
+                              expert_map=expert_map)
+
+    fused_moe_fn = functools.partial(fused_moe, renormalize=False)
+
+    #
+    # Run tests
+    #
+    runner = functools.partial(
+        run_moe_test,
+        a=a,
+        w1=w1,
+        w2=w2,
+        score=score,
+        topk=topk,
+        global_num_experts=e,
+        expert_map=e_map,
+        padding=padding,
+    )
 
-        triton_output = fused_moe(a,
-                                  w1,
-                                  w2,
-                                  score,
-                                  topk,
-                                  global_num_experts=e,
-                                  expert_map=e_map,
-                                  renormalize=False)
+    # Note: for now use_compile will error out if the problem size is
+    # large enough to trigger chunking. I'm leaving the flag and
+    # setup code in case we are able to revisit this later.
+    use_compile = False
 
-        topk_weights, topk_ids, _ = fused_topk(a, score, topk, False)
-        m_triton_output = m_fused_moe(a,
-                                      w1,
-                                      w2,
-                                      topk_weights,
-                                      topk_ids,
-                                      global_num_experts=e,
-                                      expert_map=e_map)
+    use_cudagraph = (n >= 1024 and k >= 1024
+                     and current_platform.is_cuda_alike())
 
-    torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
-    torch.testing.assert_close(m_triton_output,
-                               torch_output,
-                               atol=2e-2,
-                               rtol=0)
-    torch.testing.assert_close(iterative_output,
-                               torch_output,
-                               atol=2e-2,
-                               rtol=0)
+    with set_current_vllm_config(vllm_config):
+        baseline_output = runner(torch_moe, iterative_moe)
+        runner(baseline_output,
+               fused_moe_fn,
+               use_compile=use_compile,
+               use_cudagraph=use_cudagraph)
+        runner(baseline_output,
+               m_fused_moe,
+               use_compile=use_compile,
+               use_cudagraph=use_cudagraph)
 
 
 @pytest.mark.parametrize("m", [1, 32, 222])
@@ -238,7 +334,12 @@ def test_fused_moe_wn16(m: int, n: int, k: int, e: int, topk: int,
                                   w1_zp=w1_qzeros if has_zp else None,
                                   w2_zp=w2_qzeros if has_zp else None,
                                   block_shape=[0, group_size])
-        torch_output = torch_moe(a, w1_ref, w2_ref, score, topk, e_map)
+        torch_output = torch_moe(a,
+                                 w1_ref,
+                                 w2_ref,
+                                 score,
+                                 topk,
+                                 expert_map=e_map)
 
     torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
 
@@ -265,45 +366,51 @@ def test_mixtral_moe(dtype: torch.dtype, padding: bool, use_rocm_aiter: bool,
             pytest.skip("AITER ROCm test skip for float32")
 
     # Instantiate our and huggingface's MoE blocks
-    config = MixtralConfig()
-    hf_moe = MixtralSparseMoeBlock(config).to(dtype).to("cuda")
-    vllm_moe = MixtralMoE(
-        num_experts=config.num_local_experts,
-        top_k=config.num_experts_per_tok,
-        hidden_size=config.hidden_size,
-        intermediate_size=config.intermediate_size,
-        params_dtype=dtype,
-        tp_size=1,
-        dp_size=1,
-    ).cuda()
-
-    # Load the weights
-    vllm_moe.gate.weight.data[:] = hf_moe.gate.weight.data
-    for i in range(config.num_local_experts):
-        weights = (hf_moe.experts[i].w1.weight.data,
-                   hf_moe.experts[i].w3.weight.data)
-        vllm_moe.experts.w13_weight[i][:] = torch.cat(weights, dim=0)
-        vllm_moe.experts.w2_weight[i][:] = hf_moe.experts[i].w2.weight.data
-
-    # Generate input batch of dimensions [batch_size, seq_len, hidden_dim]
-    hf_inputs = torch.randn((1, 64, config.hidden_size)).to(dtype).to("cuda")
-    # vLLM uses 1D query [num_tokens, hidden_dim]
-    vllm_inputs = hf_inputs.flatten(0, 1)
+    vllm_config.compilation_config.static_forward_context = dict()
+    with (set_current_vllm_config(vllm_config),
+          set_forward_context(None, vllm_config)):
+        config = MixtralConfig()
+        hf_moe = MixtralSparseMoeBlock(config).to(dtype).to("cuda")
+        vllm_moe = MixtralMoE(
+            num_experts=config.num_local_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            params_dtype=dtype,
+            tp_size=1,
+            dp_size=1,
+        ).cuda()
+
+        # Load the weights
+        vllm_moe.gate.weight.data[:] = hf_moe.gate.weight.data
+        for i in range(config.num_local_experts):
+            weights = (hf_moe.experts[i].w1.weight.data,
+                       hf_moe.experts[i].w3.weight.data)
+            vllm_moe.experts.w13_weight[i][:] = torch.cat(weights, dim=0)
+            vllm_moe.experts.w2_weight[i][:] = hf_moe.experts[i].w2.weight.data
+
+        # Generate input batch of dimensions [batch_size, seq_len, hidden_dim]
+        hf_inputs = torch.randn(
+            (1, 64, config.hidden_size)).to(dtype).to("cuda")
+        # vLLM uses 1D query [num_tokens, hidden_dim]
+        vllm_inputs = hf_inputs.flatten(0, 1)
 
-    # Pad the weight if moe padding is enabled
-    if padding:
-        vllm_moe.experts.w13_weight = Parameter(F.pad(
-            vllm_moe.experts.w13_weight, (0, 128), "constant", 0)[..., 0:-128],
-                                                requires_grad=False)
-        torch.cuda.empty_cache()
-        vllm_moe.experts.w2_weight = Parameter(F.pad(
-            vllm_moe.experts.w2_weight, (0, 128), "constant", 0)[..., 0:-128],
-                                               requires_grad=False)
-        torch.cuda.empty_cache()
-
-    # Run forward passes for both MoE blocks
-    hf_states, _ = hf_moe.forward(hf_inputs)
-    vllm_states = vllm_moe.forward(vllm_inputs)
+        # Pad the weight if moe padding is enabled
+        if padding:
+            vllm_moe.experts.w13_weight = Parameter(F.pad(
+                vllm_moe.experts.w13_weight, (0, 128), "constant", 0)[...,
+                                                                      0:-128],
+                                                    requires_grad=False)
+            torch.cuda.empty_cache()
+            vllm_moe.experts.w2_weight = Parameter(F.pad(
+                vllm_moe.experts.w2_weight, (0, 128), "constant", 0)[...,
+                                                                     0:-128],
+                                                   requires_grad=False)
+            torch.cuda.empty_cache()
+
+        # Run forward passes for both MoE blocks
+        hf_states, _ = hf_moe.forward(hf_inputs)
+        vllm_states = vllm_moe.forward(vllm_inputs)
 
     mixtral_moe_tol = {
         torch.float32: 1e-3,
@@ -546,7 +653,12 @@ def test_fused_marlin_moe(
     topk_weights, topk_ids, _ = fused_topk(a, score, topk, False)
 
     with set_current_vllm_config(vllm_config):
-        torch_output = torch_moe(a, w_ref1, w_ref2, score, topk, e_map)
+        torch_output = torch_moe(a,
+                                 w_ref1,
+                                 w_ref2,
+                                 score,
+                                 topk,
+                                 expert_map=e_map)
 
     marlin_output = torch.ops.vllm.fused_marlin_moe(
         a,

tests/kernels/moe/test_nvfp4_moe.py

@@ -136,7 +136,7 @@ def test_cutlass_fp4_moe_no_graph(m: int, n: int, k: int, e: int, topk: int,
                                                   device=w2.device,
                                                   block_size=quant_blocksize)
 
-        torch_output = torch_moe(a_in_dtype, w1_d, w2_d, score, topk, None)
+        torch_output = torch_moe(a_in_dtype, w1_d, w2_d, score, topk)
 
         torch.testing.assert_close(torch_output,
                                    cutlass_output,

tests/kernels/moe/test_pplx_cutlass_moe.py

@@ -6,9 +6,9 @@ from typing import Optional
 import pytest
 import torch
 
+from tests.kernels.utils import torch_experts
 from vllm import _custom_ops as ops
 from vllm.config import VllmConfig, set_current_vllm_config
-from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.modular_kernel import (
@@ -164,22 +164,6 @@ vllm_config.scheduler_config.max_num_seqs = 128
 vllm_config.scheduler_config.max_model_len = 8192
 
 
-def torch_moe2(a, w1, w2, topk_weight, topk_ids):
-    M, K = a.shape
-    topk = topk_ids.shape[1]
-    a = a.view(M, -1, K).repeat(1, topk, 1).reshape(-1, K)
-    out = torch.zeros(M * topk, w2.shape[1], dtype=a.dtype, device=a.device)
-    num_experts = w1.shape[0]
-    for i in range(num_experts):
-        mask = (topk_ids == i).view(-1)
-        if mask.sum():
-            out[mask] = SiluAndMul()(
-                a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(0, 1)
-
-    return (out.view(M, -1, w2.shape[1]) *
-            topk_weight.view(M, -1, 1).to(out.dtype)).sum(dim=1)
-
-
 def _pplx_moe(
     pgi: ProcessGroupInfo,
     dp_size: int,
@@ -210,8 +194,8 @@ def _pplx_moe(
         group_name = cpu_group.group_name
 
     with set_current_vllm_config(vllm_config):
-        torch_output = torch_moe2(a_full, w1_full, w2_full, topk_weights,
-                                  topk_ids)
+        torch_output = torch_experts(a_full, w1_full, w2_full, topk_weights,
+                                     topk_ids)
         pplx_output = pplx_cutlass_moe(pgi, dp_size, a, w1, w2, w1_scale,
                                        w2_scale, topk_weights, topk_ids,
                                        a1_scale, out_dtype, per_act_token,

tests/kernels/moe/test_pplx_moe.py

@@ -18,8 +18,8 @@ try:
 except ImportError:
     has_pplx = False
 
+from tests.kernels.utils import torch_experts
 from vllm.config import VllmConfig, set_current_vllm_config
-from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import override_config
 from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
     BatchedExperts, BatchedPrepareAndFinalize, BatchedTritonExperts)
@@ -163,29 +163,6 @@ def batched_moe(
     return fused_experts(a, w1, w2, topk_weight, topk_ids, num_experts)
 
 
-# Note: same as torch_moe but with fused_topk factored out.
-def torch_moe2(
-    a: torch.Tensor,
-    w1: torch.Tensor,
-    w2: torch.Tensor,
-    topk_weight: torch.Tensor,
-    topk_ids: torch.Tensor,
-) -> torch.Tensor:
-    M, K = a.shape
-    topk = topk_ids.shape[1]
-    a = a.view(M, -1, K).repeat(1, topk, 1).reshape(-1, K)
-    out = torch.zeros(M * topk, w2.shape[1], dtype=a.dtype, device=a.device)
-    num_experts = w1.shape[0]
-    for i in range(num_experts):
-        mask = (topk_ids == i).view(-1)
-        if mask.sum():
-            out[mask] = SiluAndMul()(
-                a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(0, 1)
-
-    return (out.view(M, -1, w2.shape[1]) *
-            topk_weight.view(M, -1, 1).to(out.dtype)).sum(dim=1)
-
-
 @pytest.mark.parametrize("m", [1, 33, 64, 222])
 @pytest.mark.parametrize("n", [128, 1024, 2048])
 @pytest.mark.parametrize("k", [128, 512, 1024])
@@ -209,7 +186,7 @@ def test_fused_moe_batched_experts(
 
     with set_current_vllm_config(vllm_config):
         topk_weight, topk_ids, _ = fused_topk(a, score, topk, False)
-        baseline_output = torch_moe2(a, w1, w2, topk_weight, topk_ids)
+        baseline_output = torch_experts(a, w1, w2, topk_weight, topk_ids)
         torch_output = torch_batched_moe(a, w1, w2, topk_weight, topk_ids)
         batched_output = batched_moe(a, w1, w2, topk_weight, topk_ids)
 
@@ -409,7 +386,7 @@ def pplx_moe(
     w2: torch.Tensor,
     topk_weight: torch.Tensor,
     topk_ids: torch.Tensor,
-    use_compile: bool = True,
+    use_compile: bool = False,
     use_cudagraphs: bool = True,
 ) -> torch.Tensor:
     from vllm.model_executor.layers.fused_moe.pplx_prepare_finalize import (
@@ -470,10 +447,16 @@ def pplx_moe(
     w1_chunk = chunk_by_rank(w1, rank, world_size).to(device)
     w2_chunk = chunk_by_rank(w2, rank, world_size).to(device)
 
+    # Note: for now use_compile will error out if the problem size is
+    # large enough to trigger chunking. I'm leaving the flag and
+    # setup code in case we are able to revisit this later.
     if use_compile:
         _fused_experts = torch.compile(fused_experts,
                                        backend='inductor',
                                        fullgraph=True)
+        torch._dynamo.mark_dynamic(a_chunk, 0)
+        torch._dynamo.mark_dynamic(chunk_topk_weight, 0)
+        torch._dynamo.mark_dynamic(chunk_topk_ids, 0)
     else:
         _fused_experts = fused_experts
 
@@ -576,7 +559,7 @@ def _pplx_moe(
 
     with set_current_vllm_config(vllm_config), override_config(moe_config):
         topk_weight, topk_ids, _ = fused_topk(a, score, topk, False)
-        torch_output = torch_moe2(a, w1, w2, topk_weight, topk_ids)
+        torch_output = torch_experts(a, w1, w2, topk_weight, topk_ids)
         pplx_output = pplx_moe(group_name, pgi.rank, pgi.world_size, dp_size,
                                a, w1, w2, topk_weight, topk_ids)
         # TODO (bnell): fix + re-enable

tests/kernels/quantization/test_block_fp8.py

@@ -403,19 +403,24 @@ def deep_gemm_w8a8_block_fp8_moe(M, K, a, w1, w2, w1_s, w2_s, score, topk,
     itertools.product(M_moe_dg, N_moe, K_moe, E, TOP_KS, SEEDS))
 @pytest.mark.skipif(not dg_available, reason="DeepGemm kernels not available.")
 @torch.inference_mode()
-def test_w8a8_block_fp8_deep_gemm_fused_moe(M, N, K, E, topk, seed):
-
-    block_m = deep_gemm.get_m_alignment_for_contiguous_layout()
-    block_size = [block_m, block_m]
-    dtype = torch.bfloat16
-
+def test_w8a8_block_fp8_deep_gemm_fused_moe(M, N, K, E, topk, seed,
+                                            monkeypatch):
     if topk > E:
         pytest.skip(f"Skipping test: topk={topk} > E={E}")
 
     if not _valid_deep_gemm_shape(M, N, K):
         pytest.skip(f"Skipping test: invalid size m={M}, n={N}, k={K}")
 
+    chunk_size = 1024
+
     torch.manual_seed(seed)
+
+    monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(chunk_size))
+
+    block_m = deep_gemm.get_m_alignment_for_contiguous_layout()
+    block_size = [block_m, block_m]
+    dtype = torch.bfloat16
+
     fp8_info = torch.finfo(torch.float8_e4m3fn)
     fp8_max, fp8_min = fp8_info.max, fp8_info.min
 
@@ -451,6 +456,14 @@ def test_w8a8_block_fp8_deep_gemm_fused_moe(M, N, K, E, topk, seed):
         w1[i], w1_s[i] = per_block_cast_to_fp8(w1_bf16[i])
         w2[i], w2_s[i] = per_block_cast_to_fp8(w2_bf16[i])
 
+    # Note: for now use_compile will error out if the problem size is
+    # large enough to trigger chunking. I'm leaving the flag and
+    # setup code in case we are able to revisit this later.
+    use_compile = False
+
+    use_cudagraph = (chunk_size < M and N >= 1024 and K >= 1024
+                     and current_platform.is_cuda_alike())
+
     # Set the context to avoid lots of warning spam.
     with set_current_vllm_config(vllm_config):
         if M >= 128:
@@ -463,7 +476,29 @@ def test_w8a8_block_fp8_deep_gemm_fused_moe(M, N, K, E, topk, seed):
         topk_weights, topk_ids, token_expert_indices = fused_topk(
             a, score.float(), topk, False)
 
-        out = deep_gemm_moe_fp8(a, w1, w2, w1_s, w2_s, topk_weights, topk_ids)
+        if use_compile:
+            deep_gemm_moe_fp8_fn = torch.compile(deep_gemm_moe_fp8,
+                                                 backend="inductor",
+                                                 fullgraph=True)
+            torch._dynamo.mark_dynamic(a, 0)
+            torch._dynamo.mark_dynamic(topk_weights, 0)
+            torch._dynamo.mark_dynamic(topk_ids, 0)
+        else:
+            deep_gemm_moe_fp8_fn = deep_gemm_moe_fp8
+
+        out = deep_gemm_moe_fp8_fn(a, w1, w2, w1_s, w2_s, topk_weights,
+                                   topk_ids)
+
+        if use_cudagraph:
+            out.fill_(0)
+            stream = torch.cuda.Stream()
+            graph = torch.cuda.CUDAGraph()
+            with torch.cuda.graph(graph, stream=stream):
+                out = deep_gemm_moe_fp8_fn(a, w1, w2, w1_s, w2_s, topk_weights,
+                                           topk_ids)
+            torch.cuda.synchronize()
+            graph.replay()
+            torch.cuda.synchronize()
 
     #print(f"{out.sum()=}")
     #print(f"{ref_out.sum()=}")

tests/kernels/utils.py

@@ -1054,12 +1054,21 @@ def compute_max_diff(output, output_ref):
         torch.abs(output_ref))
 
 
-def torch_moe(a, w1, w2, score, topk, expert_map):
+def torch_experts(a: torch.Tensor,
+                  w1: torch.Tensor,
+                  w2: torch.Tensor,
+                  topk_weight: torch.Tensor,
+                  topk_ids: torch.Tensor,
+                  global_num_experts: int = -1,
+                  expert_map: Optional[torch.Tensor] = None) -> torch.Tensor:
+    assert (global_num_experts == -1
+            or (global_num_experts == w1.shape[0] and expert_map is None)
+            or (expert_map is not None
+                and global_num_experts == expert_map.shape[0]))
+    topk = topk_ids.shape[1]
     B, D = a.shape
     a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
     out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
-    score = torch.softmax(score, dim=-1, dtype=torch.float32)
-    topk_weight, topk_ids = torch.topk(score, topk)
     topk_weight = topk_weight.view(-1)
     topk_ids = topk_ids.view(-1)
     if expert_map is not None:
@@ -1073,6 +1082,19 @@ def torch_moe(a, w1, w2, score, topk, expert_map):
             topk_weight.view(B, -1, 1).to(out.dtype)).sum(dim=1)
 
 
+def torch_moe(a: torch.Tensor,
+              w1: torch.Tensor,
+              w2: torch.Tensor,
+              score: torch.Tensor,
+              topk: int,
+              global_num_experts: int = -1,
+              expert_map: Optional[torch.Tensor] = None) -> torch.Tensor:
+    score = torch.softmax(score, dim=-1, dtype=torch.float32)
+    topk_weight, topk_ids = torch.topk(score, topk)
+    return torch_experts(a, w1, w2, topk_weight, topk_ids, global_num_experts,
+                         expert_map)
+
+
 def torch_moe_single(a, w, score, topk):
     B, D = a.shape
     a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)

vllm/envs.py

@@ -981,6 +981,7 @@ def compute_hash() -> str:
         "VLLM_DP_RANK",
         "VLLM_DP_SIZE",
         "VLLM_USE_STANDALONE_COMPILE",
+        "VLLM_FUSED_MOE_CHUNK_SIZE",
     ]
     for key in environment_variables_to_hash:
         if key in environment_variables:

vllm/model_executor/layers/fused_moe/cutlass_moe.py

@@ -41,24 +41,24 @@ def run_cutlass_moe_fp8(
     assert w1.dtype == torch.float8_e4m3fn
     assert w2.dtype == torch.float8_e4m3fn
     if expert_num_tokens is None:
-        assert a1q.shape[1] == w1.shape[2], "Hidden size mismatch w1"
+        assert a1q.size(1) == w1.size(2), "Hidden size mismatch w1"
     else:
-        assert a1q.shape[2] == w1.shape[2], "Hidden size mismatch w1"
-    assert w1.shape[1] == w2.shape[2] * 2, "Hidden size mismatch w2"
-    assert w1_scale.dim() == 1 or w1_scale.shape[1] == 1 or w1_scale.shape[
-        1] == w1.shape[1], "W1 scale shape mismatch"
-    assert w2_scale.dim() == 1 or w2_scale.shape[1] == 1 or w2_scale.shape[
-        1] == w2.shape[1], "W2 scale shape mismatch"
-    assert w1.shape[0] == w2.shape[0], "Expert number mismatch"
-    assert a1q_scale is None or a1q_scale.dim(
-    ) == 0 or a1q_scale.shape[0] == 1 or a1q_scale.shape[0] == a1q.shape[
-        0], "Input scale shape mismatch"
-    assert w1.shape[0] == w2.shape[0], "Weights expert number mismatch"
-    assert w1.shape[0] == w1_scale.shape[0], "w1 scales expert number mismatch"
-    assert w1.shape[0] == w2_scale.shape[0], "w2 scales expert number mismatch"
-    assert a2_scale is None or a2_scale.dim(
-    ) == 0 or a2_scale.shape[0] == 1 or a2_scale.shape[0] == a1q.shape[
-        0], "Intermediate scale shape mismatch"
+        assert a1q.size(2) == w1.size(2), "Hidden size mismatch w1"
+    assert w1.size(1) == w2.size(2) * 2, "Hidden size mismatch w2"
+    assert w1_scale.dim() == 1 or w1_scale.size(
+        1) == 1 or w1_scale.shape[1] == w1.size(1), "W1 scale shape mismatch"
+    assert w2_scale.dim() == 1 or w2_scale.size(
+        1) == 1 or w2_scale.shape[1] == w2.size(1), "W2 scale shape mismatch"
+    assert w1.size(0) == w2.size(0), "Expert number mismatch"
+    assert a1q_scale is None or a1q_scale.dim() == 0 or a1q_scale.size(
+        0) == 1 or a1q_scale.size(
+            0) == a1q.shape[0], "Input scale shape mismatch"
+    assert w1.size(0) == w2.size(0), "Weights expert number mismatch"
+    assert w1.size(0) == w1_scale.size(0), "w1 scales expert number mismatch"
+    assert w1.size(0) == w2_scale.size(0), "w2 scales expert number mismatch"
+    assert a2_scale is None or a2_scale.dim() == 0 or a2_scale.size(
+        0) == 1 or a2_scale.size(
+            0) == a1q.shape[0], "Intermediate scale shape mismatch"
     assert out_dtype in [torch.half, torch.bfloat16], "Invalid output dtype"
     if expert_map is not None:
         assert expert_num_tokens is None
@@ -75,12 +75,12 @@ def run_cutlass_moe_fp8(
     # their tokens are already contiguous for each expert as a result of
     # the dispatch function.
 
-    M = a1q.shape[0]  # non batched expert M
-    padded_M = a1q.shape[1]  # batched expert M
+    M = a1q.size(0)  # non batched expert M
+    padded_M = a1q.size(1)  # batched expert M
     _, K, N = w2.shape
     device = a1q.device
 
-    assert w1.shape[2] == K
+    assert w1.size(2) == K
     assert global_num_experts != -1
     assert a1q_scale is not None
 
@@ -91,8 +91,8 @@ def run_cutlass_moe_fp8(
     else:
         local_topk_ids = topk_ids
 
-    topk = local_topk_ids.shape[1]
-    local_E = w1.shape[0]
+    topk = local_topk_ids.size(1)
+    local_E = w1.size(0)
 
     if use_batched_format:
         assert expert_num_tokens is not None
@@ -111,10 +111,10 @@ def run_cutlass_moe_fp8(
                                          problem_sizes2, expert_num_tokens,
                                          local_E, padded_M, N, K)
 
-        w1_scale = w1_scale.reshape(w1_scale.shape[0], -1)
-        w2_scale = w2_scale.reshape(w2_scale.shape[0], -1)
-        a1q = a1q.reshape(-1, a1q.shape[2])
-        a1q_scale = a1q_scale.reshape(-1, a1q_scale.shape[2]).contiguous()
+        w1_scale = w1_scale.reshape(w1_scale.size(0), -1)
+        w2_scale = w2_scale.reshape(w2_scale.size(0), -1)
+        a1q = a1q.reshape(-1, a1q.size(2))
+        a1q_scale = a1q_scale.reshape(-1, a1q_scale.size(2)).contiguous()
 
     else:
         expert_offsets = torch.empty((global_num_experts + 1),
@@ -151,19 +151,19 @@ def run_cutlass_moe_fp8(
         a1q_scale = a1q_scale[a_map] if per_act_token else a1q_scale
         expert_offsets = expert_offsets[:-1]
 
-    ab_strides1 = torch.full((w1.shape[0], ),
+    ab_strides1 = torch.full((w1.size(0), ),
                              K,
                              device=device,
                              dtype=torch.int64)
-    c_strides1 = torch.full((w1.shape[0], ),
+    c_strides1 = torch.full((w1.size(0), ),
                             2 * N,
                             device=device,
                             dtype=torch.int64)
-    ab_strides2 = torch.full((w1.shape[0], ),
+    ab_strides2 = torch.full((w1.size(0), ),
                              N,
                              device=device,
                              dtype=torch.int64)
-    c_strides2 = torch.full((w1.shape[0], ),
+    c_strides2 = torch.full((w1.size(0), ),
                             K,
                             device=device,
                             dtype=torch.int64)
@@ -237,7 +237,7 @@ class CutlassExpertsFp8(mk.FusedMoEPermuteExpertsUnpermute):
         workspace2: tuple[int, ...] = ()
         output: tuple[int, ...] = ()
         if self.use_batched_format:
-            padded_M = aq.shape[1]
+            padded_M = aq.size(1)
             workspace1 = (self.max_experts_per_worker, padded_M, max(N, K))
             workspace2 = (self.max_experts_per_worker, padded_M, (N // 2))
             output = (self.max_experts_per_worker, padded_M, K)
@@ -332,7 +332,7 @@ def cutlass_moe_fp8(
     """
     per_act_token = a1_scale.numel() != 1 if a1_scale is not None else (
         a2_scale.numel() != 1 if a2_scale is not None else False)
-    per_out_ch = w1_scale.numel() != w1_q.shape[0]
+    per_out_ch = w1_scale.numel() != w1_q.size(0)
 
     out_dtype = a.dtype
 
@@ -425,11 +425,11 @@ def cutlass_moe_fp4(a: torch.Tensor, a1_gscale: torch.Tensor,
     assert (m == m_a), "input shape mismatch"
     assert 2 * half_k_w1 == k_w2, "Hidden size mismatch w2 and w1"
     assert a.dtype in [torch.half, torch.bfloat16], "Invalid input dtype"
-    assert (topk_weights.shape[0] == m and topk_ids.shape[0]
+    assert (topk_weights.size(0) == m and topk_ids.size(0)
             == m), ("topk must be provided for each row of a")
 
     out_dtype = a.dtype
-    num_topk = topk_ids.shape[1]
+    num_topk = topk_ids.size(1)
 
     expert_offsets = torch.empty((e + 1), dtype=torch.int32, device=device)
     blockscale_offsets = torch.empty((e + 1), dtype=torch.int32, device=device)
@@ -463,7 +463,7 @@ def cutlass_moe_fp4(a: torch.Tensor, a1_gscale: torch.Tensor,
                                 out_dtype, device)
     del rep_a_fp4, rep_a_blockscale
     # hidden size dimension is split to one halfpytho sized tensor.
-    intermediate = torch.empty((m * num_topk, w1_fp4.shape[1] // 2),
+    intermediate = torch.empty((m * num_topk, w1_fp4.size(1) // 2),
                                device=device,
                                dtype=out_dtype)
 

vllm/model_executor/layers/fused_moe/deep_gemm_moe.py

@@ -48,7 +48,7 @@ def _valid_deep_gemm(hidden_states: torch.Tensor, w1: torch.Tensor,
     M = hidden_states.size(0)
     _, K, N = w2.size()
     if not _valid_deep_gemm_shape(M, N, K):
-        logger.debug("DeepGemm disabled: unalinged problem size.")
+        logger.debug("DeepGemm disabled: unaligned problem size.")
         return False
 
     if (w1.dtype != torch.float8_e4m3fn or w2.dtype != torch.float8_e4m3fn):

vllm/model_executor/layers/fused_moe/deepep_ll_prepare_finalize.py

@@ -25,7 +25,7 @@ def dequant_fp8(expert_x_fp8: torch.Tensor,
     expert_x_fp32 = expert_x_fp8.to(torch.float32).view(
         num_experts, -1, DEEPEP_QUANT_BLOCK_SIZE)
     expert_x_scales = expert_x_scales.view(num_experts, -1, 1)
-    return (expert_x_fp32 * expert_x_scales).view(expert_x_fp8.shape)
+    return (expert_x_fp32 * expert_x_scales).view(expert_x_fp8.size())
 
 
 class DeepEPLLPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -488,10 +488,10 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
 
     if use_fp8_w8a8 or use_int8_w8a8:
         assert B_scale is not None
-        assert (block_shape is None or triton.cdiv(B.shape[-2], block_shape[0])
-                == B_scale.shape[-2])
-        assert (block_shape is None or triton.cdiv(B.shape[-1], block_shape[1])
-                == B_scale.shape[-1])
+        assert (block_shape is None
+                or triton.cdiv(B.size(-2), block_shape[0]) == B_scale.size(-2))
+        assert (block_shape is None
+                or triton.cdiv(B.size(-1), block_shape[1]) == B_scale.size(-1))
 
     elif use_int8_w8a16 or use_int4_w4a16:
         assert B_scale is not None
@@ -500,19 +500,19 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
         assert A_scale is None
         assert B_scale is None
 
-    M = A.shape[0]
+    M = A.size(0)
     num_tokens = M * top_k
 
-    EM = sorted_token_ids.shape[0]
-    if A.shape[0] < config["BLOCK_SIZE_M"]:
+    EM = sorted_token_ids.size(0)
+    if A.size(0) < config["BLOCK_SIZE_M"]:
         # optimize for small batch_size.
         # We assume that top_ids of each token is unique, so
         # so num_valid_experts <= batch_size <= BLOCK_SIZE_M,
         # and we can skip some invalid blocks.
-        EM = min(sorted_token_ids.shape[0],
-                 A.shape[0] * top_k * config['BLOCK_SIZE_M'])
+        EM = min(sorted_token_ids.size(0),
+                 A.size(0) * top_k * config['BLOCK_SIZE_M'])
     grid = lambda META: (triton.cdiv(EM, META['BLOCK_SIZE_M']) * triton.cdiv(
-        B.shape[1], META['BLOCK_SIZE_N']), )
+        B.size(1), META['BLOCK_SIZE_N']), )
 
     if (use_int8_w8a16 or use_int4_w4a16) and \
             block_shape is not None and block_shape[1] > 0:
@@ -522,16 +522,16 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
         use_moe_wna16_cuda = should_moe_wna16_use_cuda(
             num_valid_tokens=num_tokens,
             group_size=block_shape[1],
-            num_experts=B.shape[0],
+            num_experts=B.size(0),
             bit=4 if use_int4_w4a16 else 8)
         config = config.copy()
         config.update(
             get_moe_wna16_block_config(config=config,
                                        use_moe_wna16_cuda=use_moe_wna16_cuda,
                                        num_valid_tokens=num_tokens,
-                                       size_k=A.shape[1],
-                                       size_n=B.shape[1],
-                                       num_experts=B.shape[1],
+                                       size_k=A.size(1),
+                                       size_n=B.size(1),
+                                       num_experts=B.size(1),
                                        group_size=block_shape[1],
                                        real_top_k=top_k,
                                        block_size_m=config["BLOCK_SIZE_M"]))
@@ -556,8 +556,8 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
             sorted_token_ids,
             expert_ids,
             num_tokens_post_padded,
-            B.shape[1],
-            A.shape[1],
+            B.size(1),
+            A.size(1),
             EM,
             num_tokens,
             A.stride(0),
@@ -573,7 +573,7 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
             B_zp.stride(0) if B_zp is not None else 0,
             B_zp.stride(2) if B_zp is not None else 0,
             B_zp.stride(1) if B_zp is not None else 0,
-            block_k_diviable=A.shape[1] % config["BLOCK_SIZE_K"] == 0,
+            block_k_diviable=A.size(1) % config["BLOCK_SIZE_K"] == 0,
             group_size=block_shape[1],
             MUL_ROUTED_WEIGHT=mul_routed_weight,
             top_k=top_k,
@@ -599,8 +599,8 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
             sorted_token_ids,
             expert_ids,
             num_tokens_post_padded,
-            B.shape[1],
-            B.shape[2],
+            B.size(1),
+            B.size(2),
             EM,
             num_tokens,
             A.stride(0),
@@ -818,7 +818,7 @@ def try_get_optimal_moe_config(
     M: int,
     is_marlin: bool = False,
     block_shape: Optional[list[int]] = None,
-):
+) -> dict[str, int]:
     from vllm.model_executor.layers.fused_moe import get_config
     override_config = get_config()
     if override_config:
@@ -873,10 +873,10 @@ def fused_topk(
     renormalize: bool,
     indices_type: Optional[torch.dtype] = None,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    assert hidden_states.shape[0] == gating_output.shape[0], (
+    assert hidden_states.size(0) == gating_output.size(0), (
         "Number of tokens mismatch")
 
-    M, _ = hidden_states.shape
+    M, _ = hidden_states.size()
 
     topk_weights = torch.empty(M,
                                topk,
@@ -915,7 +915,7 @@ def grouped_topk(
     e_score_correction_bias: Optional[torch.Tensor] = None
 ) -> tuple[torch.Tensor, torch.Tensor]:
 
-    assert hidden_states.shape[0] == gating_output.shape[0], (
+    assert hidden_states.size(0) == gating_output.size(0), (
         "Number of tokens mismatch")
 
     if scoring_func == "softmax":
@@ -925,7 +925,7 @@ def grouped_topk(
     else:
         raise ValueError(f"Unsupported scoring function: {scoring_func}")
 
-    num_token = scores.shape[0]
+    num_token = scores.size(0)
     if e_score_correction_bias is not None:
         # Store original scores before applying correction bias. We use biased
         # scores for expert selection but original scores for routing weights
@@ -942,7 +942,7 @@ def grouped_topk(
     group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
     score_mask = group_mask.unsqueeze(-1).expand(
         num_token, num_expert_group,
-        scores.shape[-1] // num_expert_group).reshape(num_token, -1)  # [n, e]
+        scores.size(-1) // num_expert_group).reshape(num_token, -1)  # [n, e]
     tmp_scores = scores.masked_fill(~score_mask.bool(),
                                     float("-inf"))  # [n, e]
 
@@ -1162,7 +1162,7 @@ def fused_experts(hidden_states: torch.Tensor,
                   allow_deep_gemm: bool = False) -> torch.Tensor:
     # For now, disable DeepGemm for small N (<= 512) until better
     # permute/unpermute ops are available.
-    N = w1.shape[1]
+    N = w1.size(1)
     if (allow_deep_gemm and use_fp8_w8a8 and N > 512
             and _valid_deep_gemm(hidden_states, w1, w2)):
         assert apply_router_weight_on_input is False
@@ -1233,13 +1233,13 @@ def fused_experts_impl(
 ) -> torch.Tensor:
     # Check constraints.
     if use_int4_w4a16:
-        assert hidden_states.shape[1] // 2 == w1.shape[
-            2], "Hidden size mismatch"
+        assert hidden_states.size(1) // 2 == w1.size(2), (
+            "Hidden size mismatch")
     else:
-        assert hidden_states.shape[1] == w1.shape[2], (
-            f"Hidden size mismatch {hidden_states.shape[1]} != {w1.shape[2]}")
+        assert hidden_states.size(1) == w1.size(2), (
+            f"Hidden size mismatch {hidden_states.size(1)} != {w1.size(2)}")
 
-    assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
+    assert topk_weights.size() == topk_ids.size(), "topk shape mismatch"
     assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
     assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
     assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
@@ -1247,12 +1247,12 @@ def fused_experts_impl(
         torch.float32, torch.float16, torch.bfloat16
     ]
 
-    num_tokens = hidden_states.shape[0]
-    E, N, _ = w1.shape
-    K = w2.shape[1]
+    num_tokens = hidden_states.size(0)
+    E, N, _ = w1.size()
+    K = w2.size(1)
     if global_num_experts == -1:
         global_num_experts = E
-    top_k_num = topk_ids.shape[1]
+    top_k_num = topk_ids.size(1)
     # We execute the fused_moe kernel in chunks to circumvent this issue:
     # https://github.com/vllm-project/vllm/issues/5938
     CHUNK_SIZE = envs.VLLM_FUSED_MOE_CHUNK_SIZE
@@ -1269,8 +1269,8 @@ def fused_experts_impl(
 
     get_config_func = functools.partial(
         try_get_optimal_moe_config,
-        w1.shape,
-        w2.shape,
+        w1.size(),
+        w2.size(),
         top_k_num,
         config_dtype,
         block_shape=block_shape,
@@ -1310,7 +1310,7 @@ def fused_experts_impl(
                                           min((chunk + 1) * CHUNK_SIZE,
                                               num_tokens))
         curr_hidden_states = hidden_states[begin_chunk_idx:end_chunk_idx]
-        tokens_in_chunk, _ = curr_hidden_states.shape
+        tokens_in_chunk, _ = curr_hidden_states.size()
 
         if tokens_in_chunk == 0:
             break
@@ -1322,7 +1322,7 @@ def fused_experts_impl(
             # do not need to be adjusted.
             intermediate_cache1 = intermediate_cache1[:tokens_in_chunk]
             intermediate_cache2 = intermediate_cache2[:tokens_in_chunk *
-                                                      topk_ids.shape[1]]
+                                                      topk_ids.size(1)]
             intermediate_cache3 = intermediate_cache3[:tokens_in_chunk]
             config = get_config_func(tokens_in_chunk)
 
@@ -1398,7 +1398,7 @@ def fused_experts_impl(
                                 per_channel_quant=per_channel_quant,
                                 block_shape=block_shape)
 
-        ops.moe_sum(intermediate_cache3.view(*intermediate_cache3.shape),
+        ops.moe_sum(intermediate_cache3.view(*intermediate_cache3.size()),
                     out_hidden_states[begin_chunk_idx:end_chunk_idx])
 
     return out_hidden_states
@@ -1611,8 +1611,8 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
                                             dtype=hidden_states.dtype)
 
         config = try_get_optimal_moe_config(
-            w1.shape,
-            w2.shape,
+            w1.size(),
+            w2.size(),
             top_k_num,
             config_dtype,
             num_tokens,

vllm/model_executor/layers/fused_moe/layer.py

@@ -861,13 +861,11 @@ class FusedMoE(torch.nn.Module):
         self.global_num_experts = num_experts
 
         # For smuggling this layer into the fused moe custom op
-        self.use_direct_call = self.dp_size == 1
-        if not self.use_direct_call:
-            compilation_config = vllm_config.compilation_config
-            if prefix in compilation_config.static_forward_context:
-                raise ValueError("Duplicate layer name: {}".format(prefix))
-            compilation_config.static_forward_context[prefix] = self
-            self.layer_name = prefix
+        compilation_config = vllm_config.compilation_config
+        if prefix in compilation_config.static_forward_context:
+            raise ValueError("Duplicate layer name: {}".format(prefix))
+        compilation_config.static_forward_context[prefix] = self
+        self.layer_name = prefix
 
         # Determine expert maps
         if self.use_ep:
@@ -1361,11 +1359,8 @@ class FusedMoE(torch.nn.Module):
 
     def forward(self, hidden_states: torch.Tensor,
                 router_logits: torch.Tensor):
-        if self.use_direct_call:
-            return self.forward_impl(hidden_states, router_logits)
-        else:
-            return torch.ops.vllm.moe_forward(hidden_states, router_logits,
-                                              self.layer_name)
+        return torch.ops.vllm.moe_forward(hidden_states, router_logits,
+                                          self.layer_name)
 
     def forward_impl_chunked(self, full_hidden_states: torch.Tensor,
                              full_router_logits: torch.Tensor):

vllm/model_executor/layers/fused_moe/pplx_prepare_finalize.py

@@ -69,7 +69,7 @@ class PplxPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
             a1 = a1 * rank_topk_weights.to(a1.dtype)
 
         repeat_cols = 4
-        repeat_rows = 1 if self.per_act_token else a1.shape[0]
+        repeat_rows = 1 if self.per_act_token else a1.size(0)
         a1q, a1q_scale = moe_kernel_quantize_input(
             a1, (None if self.per_act_token else a1_scale), self.quant_dtype,
             self.per_act_token, self.block_shape)