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, score, topk):
    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)
    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).to(out.dtype)).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)
    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)


@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,
    ).cuda()

    # 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])