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 → tests/kernels/test_moe.py

+"""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):
@@ -48,3 +57,48 @@ def test_fused_moe(
     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)