Add fused top-K softmax kernel for MoE (vllm-project#2769)

csrc/moe/moe_ops.cpp

@@ -0,0 +1,7 @@
+#include "moe_ops.h"
+
+#include <torch/extension.h>
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("topk_softmax", &topk_softmax, "Apply topk softmax to the gating outputs.");
+}

csrc/moe/moe_ops.h

@@ -0,0 +1,9 @@
+#pragma once
+
+#include <torch/extension.h>
+
+void topk_softmax(
+  torch::Tensor& topk_weights,
+  torch::Tensor& topk_indices,
+  torch::Tensor& token_expert_indices,
+  torch::Tensor& gating_output);

csrc/pybind.cpp

@@ -48,8 +48,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     &rotary_embedding,
     "Apply GPT-NeoX or GPT-J style rotary embedding to query and key");
 
-#ifndef USE_ROCM
   // Quantization ops
+#ifndef USE_ROCM
   ops.def("awq_gemm", &awq_gemm, "Quantized GEMM for AWQ");
   ops.def("awq_dequantize", &awq_dequantize, "Dequantization for AWQ");
   ops.def("marlin_gemm", &marlin_gemm, "Marlin Optimized Quantized GEMM for GPTQ");

setup.py

@@ -341,6 +341,17 @@ def get_torch_arch_list() -> Set[str]:
     vllm_extension_sources.append(
         "csrc/quantization/marlin/marlin_cuda_kernel.cu")
 
+    # Add MoE kernels.
+    ext_modules.append(
+        CUDAExtension(
+            name="vllm._moe_C",
+            sources=glob("csrc/moe/*.cu") + glob("csrc/moe/*.cpp"),
+            extra_compile_args={
+                "cxx": CXX_FLAGS,
+                "nvcc": NVCC_FLAGS,
+            },
+        ))
+
 if not _is_neuron():
     vllm_extension = CUDAExtension(
         name="vllm._C",

tests/kernels/test_moe.py

@@ -2,10 +2,8 @@
 
 Run `pytest tests/kernels/test_moe.py`.
 """
-
 import pytest
 import torch
-
 from transformers import MixtralConfig
 from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
 
@@ -14,22 +12,21 @@
 from vllm.model_executor.models.mixtral import MixtralMoE
 
 
-def torch_moe(a, w1, w2, topk_weight, topk_ids):
+def torch_moe(a, w1, w2, score, topk):
     B, D = a.shape
-    a = a.view(B, -1, D).repeat(1, topk_ids.shape[1], 1).reshape(-1, D)
-    out = torch.zeros(B * topk_ids.shape[1],
-                      w2.shape[1],
-                      dtype=a.dtype,
-                      device=a.device)
-    topk_ids = topk_ids.view(-1)
+    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)
     for i in range(w1.shape[0]):
         mask = topk_ids == i
         if mask.sum():
             out[mask] = SiluAndMul()(
                 a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(0, 1)
     return (out.view(B, -1, w2.shape[1]) *
-            topk_weight.view(B, -1, 1)).sum(dim=1)
+            topk_weight.view(B, -1, 1).to(out.dtype)).sum(dim=1)
 
 
 @pytest.mark.parametrize("m", [512, 222, 33, 1])
@@ -51,11 +48,8 @@ def test_fused_moe(
     w2 = torch.randn((e, k, n), device='cuda', dtype=dtype) / 10
 
     score = torch.randn((m, e), device='cuda', dtype=dtype)
-    score = torch.softmax(score, dim=-1)
-    topk_weight, topk_ids = torch.topk(score, topk)
-
-    triton_output = fused_moe(a, w1, w2, topk_weight, topk_ids, False)
-    torch_output = torch_moe(a, w1, w2, topk_weight, topk_ids)
+    triton_output = fused_moe(a, w1, w2, score, topk, renormalize=False)
+    torch_output = torch_moe(a, w1, w2, score, topk)
     assert torch.allclose(triton_output, torch_output, atol=1e-2, rtol=0)
 
 
@@ -75,7 +69,7 @@ def test_mixtral_moe(dtype: torch.dtype):
         intermediate_size=config.intermediate_size,
         params_dtype=dtype,
         tp_size=1,
-    )
+    ).cuda()
 
     # Load the weights
     vllm_moe.gate.linear_weights["weight"][:] = hf_moe.gate.weight.data

vllm/model_executor/layers/fused_moe.py

@@ -4,6 +4,7 @@
 import triton.language as tl
 
 from vllm._C import ops
+from vllm.utils import is_hip
 
 
 @triton.jit
@@ -177,7 +178,6 @@ def invoke_fused_moe_kernel(A: torch.Tensor, B: torch.Tensor, C: torch.Tensor,
                             expert_ids: torch.Tensor,
                             num_tokens_post_padded: torch.Tensor,
                             mul_routed_weight: bool, top_k: int, config: dict):
-
     assert topk_weights.stride(1) == 1
     assert sorted_token_ids.stride(0) == 1
 
@@ -210,28 +210,35 @@ def invoke_fused_moe_kernel(A: torch.Tensor, B: torch.Tensor, C: torch.Tensor,
     )
 
 
-def fused_moe(hidden_states: torch.Tensor,
-              w1: torch.Tensor,
-              w2: torch.Tensor,
-              topk_weights: torch.Tensor,
-              topk_ids: torch.Tensor,
-              inplace=False):
+def fused_moe(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    inplace: bool = False,
+) -> torch.Tensor:
     """
     This function computes a Mixture of Experts (MoE) layer using two sets of weights, w1 and w2, and top-k gating mechanism.
     
     Parameters:
     - hidden_states (torch.Tensor): The input tensor to the MoE layer.
     - w1 (torch.Tensor): The first set of expert weights.
     - w2 (torch.Tensor): The second set of expert weights.
-    - topk_weights (torch.Tensor): The weights for the top-k selected experts.
-    - topk_ids (torch.Tensor): The indices of the top-k selected experts.
+    - gating_output (torch.Tensor): The output of the gating operation (before softmax).
+    - topk (int): The number of top-k experts to select.
+    - renormalize (bool): If True, renormalize the top-k weights to sum to 1.
     - inplace (bool): If True, perform the operation in-place. Defaults to False.
     
     Returns:
     - torch.Tensor: The output tensor after applying the MoE layer.
     """
     # Check constraints.
-    assert hidden_states.shape[1] == w1.shape[2], "Incompatible dimensions"
+    assert hidden_states.shape[0] == gating_output.shape[0], (
+        "Number of tokens mismatch")
+    assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
+    assert gating_output.shape[1] == w1.shape[0], "Number of experts mismatch"
     assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
     assert w1.is_contiguous(), "Expert weights1 must be contiguous"
     assert w2.is_contiguous(), "Expert weights2 must be contiguous"
@@ -241,6 +248,37 @@ def fused_moe(hidden_states: torch.Tensor,
     M, _ = hidden_states.shape
     E, N, _ = w1.shape
 
+    if is_hip():
+        # The MoE kernels are not yet supported on ROCm.
+        routing_weights = torch.softmax(gating_output,
+                                        dim=-1,
+                                        dtype=torch.float32)
+        topk_weights, topk_ids = torch.topk(routing_weights, topk, dim=-1)
+    else:
+        import vllm._moe_C as moe_kernels
+
+        topk_weights = torch.empty(M,
+                                   topk,
+                                   dtype=torch.float32,
+                                   device=hidden_states.device)
+        topk_ids = torch.empty(M,
+                               topk,
+                               dtype=torch.int32,
+                               device=hidden_states.device)
+        token_expert_indicies = torch.empty(M,
+                                            topk,
+                                            dtype=torch.int32,
+                                            device=hidden_states.device)
+        moe_kernels.topk_softmax(
+            topk_weights,
+            topk_ids,
+            token_expert_indicies,
+            gating_output.float(),  # TODO(woosuk): Optimize this.
+        )
+        del token_expert_indicies  # Not used. Will be used in the future.
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
     config = {
         'BLOCK_SIZE_M': 64,
         'BLOCK_SIZE_N': 64,

vllm/model_executor/models/deepseek.py

@@ -25,7 +25,6 @@
 
 import torch
 from torch import nn
-import torch.nn.functional as F
 from transformers import PretrainedConfig
 
 from vllm.model_executor.input_metadata import InputMetadata
@@ -155,20 +154,12 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             shared_output = self.shared_experts(hidden_states)
         # router_logits: (batch * sequence_length, n_experts)
         router_logits, _ = self.gate(hidden_states)
-
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        routing_weights, selected_experts = torch.topk(routing_weights,
-                                                       self.top_k,
-                                                       dim=-1)
-
-        if self.config.norm_topk_prob:
-            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-
         final_hidden_states = fused_moe(hidden_states,
                                         self.w1,
                                         self.w2,
-                                        routing_weights,
-                                        selected_experts,
+                                        router_logits,
+                                        self.top_k,
+                                        renormalize=self.config.norm_topk_prob,
                                         inplace=True)
 
         if self.config.n_shared_experts is not None:

vllm/model_executor/models/mixtral.py

@@ -24,8 +24,6 @@
 from typing import List, Optional, Tuple
 
 import torch
-import torch.nn.functional as F
-
 from torch import nn
 from transformers import MixtralConfig
 
@@ -128,18 +126,12 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states = hidden_states.view(-1, self.hidden_size)
         # router_logits: (batch * sequence_length, n_experts)
         router_logits, _ = self.gate(hidden_states)
-
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        routing_weights, selected_experts = torch.topk(routing_weights,
-                                                       self.top_k,
-                                                       dim=-1)
-        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-
         final_hidden_states = fused_moe(hidden_states,
                                         self.ws,
                                         self.w2s,
-                                        routing_weights,
-                                        selected_experts,
+                                        router_logits,
+                                        self.top_k,
+                                        renormalize=True,
                                         inplace=True)
 
         if self.tp_size > 1: