Add unit test for Mixtral MoE layer (#2677)

Dockerfile

@@ -7,6 +7,12 @@ FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
 RUN apt-get update -y \
     && apt-get install -y python3-pip git
 
+# Workaround for https://github.com/openai/triton/issues/2507 and
+# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
+# this won't be needed for future versions of this docker image
+# or future versions of triton.
+RUN ldconfig /usr/local/cuda-12.1/compat/
+
 WORKDIR /workspace
 
 # install build and runtime dependencies

tests/kernels/test_fused_moe.py

@@ -1,50 +0,0 @@
-import pytest
-import torch
-
-from vllm.model_executor.layers.fused_moe import fused_moe
-from vllm.model_executor.layers.activation import SiluAndMul
-
-
-def torch_moe(a, w1, w2, topk_weight, topk_ids):
-    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)
-    topk_weight = topk_weight.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)
-
-
-@pytest.mark.parametrize("m", [512, 222, 33, 1])
-@pytest.mark.parametrize("n", [2048, 256, 1024])
-@pytest.mark.parametrize("k", [128, 511, 1024])
-@pytest.mark.parametrize("e", [8, 64])
-@pytest.mark.parametrize("topk", [2, 6])
-@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
-def test_fused_moe(
-    m: int,
-    n: int,
-    k: int,
-    e: int,
-    topk: int,
-    dtype: torch.dtype,
-):
-    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
-
-    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)
-    assert torch.allclose(triton_output, torch_output, atol=1e-2, rtol=0)

tests/kernels/test_moe.py

@@ -0,0 +1,104 @@
+"""Tests for the MOE layers.
+
+Run `pytest tests/kernels/test_moe.py`.
+"""
+
+import pytest
+import torch
+
+from transformers import MixtralConfig
+from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
+
+from vllm.model_executor.layers.fused_moe import fused_moe
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.models.mixtral import MixtralMoE
+
+
+def torch_moe(a, w1, w2, topk_weight, topk_ids):
+    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)
+    topk_weight = topk_weight.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)
+
+
+@pytest.mark.parametrize("m", [512, 222, 33, 1])
+@pytest.mark.parametrize("n", [2048, 256, 1024])
+@pytest.mark.parametrize("k", [128, 511, 1024])
+@pytest.mark.parametrize("e", [8, 64])
+@pytest.mark.parametrize("topk", [2, 6])
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+def test_fused_moe(
+    m: int,
+    n: int,
+    k: int,
+    e: int,
+    topk: int,
+    dtype: torch.dtype,
+):
+    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
+
+    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)
+    assert torch.allclose(triton_output, torch_output, atol=1e-2, rtol=0)
+
+
+@pytest.mark.parametrize("dtype",
+                         [torch.float32, torch.float16, torch.bfloat16])
+@torch.inference_mode()
+def test_mixtral_moe(dtype: torch.dtype):
+    "Make sure our Mixtral MoE implementation agrees with the one from huggingface."
+
+    # 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,
+    )
+
+    # Load the weights
+    vllm_moe.gate.linear_weights["weight"][:] = 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.ws[i][:] = torch.cat(weights, dim=0)
+        vllm_moe.w2s[i][:] = hf_moe.experts[i].w2.weight.data
+
+    # Generate input batch of dimensions [batch_size, seq_len, hidden_dim]
+    inputs = torch.randn((1, 64, config.hidden_size)).to(dtype).to("cuda")
+
+    # Run forward passes for both MoE blocks
+    hf_states, _ = hf_moe.forward(inputs)
+    vllm_states = vllm_moe.forward(inputs)
+
+    mixtral_moe_tol = {
+        torch.float32: 1e-3,
+        torch.float16: 1e-3,
+        torch.bfloat16: 1e-2,
+    }
+
+    assert torch.allclose(hf_states,
+                          vllm_states,
+                          rtol=mixtral_moe_tol[dtype],
+                          atol=mixtral_moe_tol[dtype])

vllm/model_executor/layers/fused_moe.py

@@ -235,7 +235,9 @@ def fused_moe(hidden_states: torch.Tensor,
     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"
-    assert hidden_states.dtype in [torch.float16, torch.bfloat16]
+    assert hidden_states.dtype in [
+        torch.float32, torch.float16, torch.bfloat16
+    ]
     M, _ = hidden_states.shape
     E, N, _ = w1.shape
 

vllm/model_executor/models/mixtral.py

@@ -70,13 +70,14 @@ class MixtralMoE(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         params_dtype: Optional[torch.dtype] = None,
+        tp_size: Optional[int] = None,
     ):
         super().__init__()
-        tp_size = get_tensor_model_parallel_world_size()
+        self.tp_size = tp_size or get_tensor_model_parallel_world_size()
         self.num_total_experts = num_experts
         self.top_k = top_k
         self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size // tp_size
+        self.intermediate_size = intermediate_size // self.tp_size
 
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
@@ -141,8 +142,9 @@ class MixtralMoE(nn.Module):
                                         selected_experts,
                                         inplace=True)
 
-        final_hidden_states = tensor_model_parallel_all_reduce(
-            final_hidden_states)
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(
+                final_hidden_states)
 
         return final_hidden_states.view(batch_size, sequence_length,
                                         hidden_size)