vllm/tests/kernels/moe/test_deepgemm.py

226 lines
6.5 KiB
Python

# SPDX-License-Identifier: Apache-2.0
"""
Unit-test DeepGEMM FP8 kernels (no DeepEP).
Compare DeepGEMM path against the Triton fallback inside vLLM's fused_experts.
"""
import importlib
import math
import pytest
import torch
# vLLM fused-expert reference (Triton fallback + DeepGEMM option)
from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
per_token_group_quant_fp8)
from vllm.utils import cdiv
has_deep_gemm = importlib.util.find_spec("deep_gemm") is not None
if has_deep_gemm:
import deep_gemm
BLOCK_M = deep_gemm.get_m_alignment_for_contiguous_layout()
BLOCK_SIZE = [BLOCK_M, BLOCK_M]
requires_deep_gemm = pytest.mark.skipif(
not has_deep_gemm,
reason="Requires deep_gemm kernels",
)
def calc_diff(x: torch.Tensor, y: torch.Tensor):
x, y = x.double(), y.double()
denominator = (x * x + y * y).sum()
sim = 2 * (x * y).sum() / denominator
return 1 - sim
def per_block_cast_to_fp8(
x: torch.Tensor,
block_size_n: int = 128) -> tuple[torch.Tensor, torch.Tensor]:
assert x.dim() == 2
m, n = x.shape
x_padded = torch.zeros(
(cdiv(m, 128) * 128, cdiv(n, block_size_n) * block_size_n),
dtype=x.dtype,
device=x.device)
x_padded[:m, :n] = x
x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, block_size_n)
x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
x_scaled_sub = x_scaled.view_as(x_padded)[:m, :n].contiguous()
scales = (x_amax / 448.0).view(x_view.size(0), x_view.size(2))
return x_scaled_sub, scales
def make_block_quant_fp8_weights(
e: int,
n: int,
k: int,
block_size: list[int],
):
"""
Generate (w1, w2) expert weights and their per-block scale tensors
in FP8 block-quantized format.
w1 shape: (E, 2N, K)
w2 shape: (E, K, N)
"""
dtype = torch.bfloat16
fp8_max, fp8_min = torch.finfo(torch.float8_e4m3fn).max, torch.finfo(
torch.float8_e4m3fn).min
# bf16 reference weights
w1_bf16 = torch.randn(e, 2 * n, k, device="cuda", dtype=dtype) / 10
w2_bf16 = torch.randn(e, k, n, device="cuda", dtype=dtype) / 10
w1_bf16.clamp_(fp8_min, fp8_max)
w2_bf16.clamp_(fp8_min, fp8_max)
block_n, block_k = block_size
n_tiles_w1 = math.ceil((2 * n) / block_n)
k_tiles_w1 = math.ceil(k / block_k)
n_tiles_w2 = math.ceil(k / block_n)
k_tiles_w2 = math.ceil(n / block_k)
w1 = torch.empty_like(w1_bf16, dtype=torch.float8_e4m3fn)
w2 = torch.empty_like(w2_bf16, dtype=torch.float8_e4m3fn)
w1_s = torch.empty(e,
n_tiles_w1,
k_tiles_w1,
device="cuda",
dtype=torch.float32)
w2_s = torch.empty(e,
n_tiles_w2,
k_tiles_w2,
device="cuda",
dtype=torch.float32)
for i in range(e):
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])
return w1, w2, w1_s, w2_s
def run_single_case(m, n, k, topk, num_experts, block_size):
"""
Run one (M,N,K) configuration on a single GPU and assert DeepGEMM ==
Triton baseline within tolerance.
"""
tokens_bf16 = torch.randn(
m, k, device="cuda", dtype=torch.bfloat16).clamp_min_(-1).clamp_max_(1)
_, a1_scale = per_token_group_quant_fp8(tokens_bf16, block_size[1])
# expert weight tensors
w1, w2, w1_s, w2_s = make_block_quant_fp8_weights(num_experts, n, k,
block_size)
router_logits = torch.randn(m,
num_experts,
device="cuda",
dtype=torch.float32)
topk_weights, topk_ids = torch.topk(router_logits, k=topk, dim=-1)
topk_weights = torch.nn.functional.softmax(topk_weights, dim=-1)
# triton referrence
out_triton = fused_experts(
hidden_states=tokens_bf16,
w1=w1,
w2=w2,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=False,
use_fp8_w8a8=True,
w1_scale=w1_s,
w2_scale=w2_s,
a1_scale=a1_scale,
block_shape=block_size,
allow_deep_gemm=False,
)
# DeepGemm
out_deepgemm = fused_experts(
hidden_states=tokens_bf16,
w1=w1,
w2=w2,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=False,
use_fp8_w8a8=True,
w1_scale=w1_s,
w2_scale=w2_s,
a1_scale=a1_scale,
block_shape=block_size,
allow_deep_gemm=True,
)
base = out_triton.abs().mean()
atol = 0.1 * base.clamp(min=1e-2) # 10% of mean, but not lower than 1e-3
rtol = 0.05
# ----- Compare -----
torch.testing.assert_close(
out_deepgemm.to(torch.float32),
out_triton.to(torch.float32),
rtol=rtol,
atol=float(atol),
)
# Note: W1 has shape (E, 2N, K), so N = 512
# can trigger the deepgemm path.
MNKs = [
(1024, 512, 128),
(1024, 512, 512),
(2048, 512, 512),
(512, 1024, 1024),
(512, 2048, 2048),
(4096, 4096, 1024),
]
TOPKS = [2, 6]
NUM_EXPERTS = [32]
@pytest.mark.parametrize("mnk", MNKs)
@pytest.mark.parametrize("topk", TOPKS)
@pytest.mark.parametrize("num_experts", NUM_EXPERTS)
@requires_deep_gemm
def test_deepgemm_vs_triton(mnk, topk, num_experts, monkeypatch):
with monkeypatch.context() as m:
m.setenv("VLLM_USE_DEEP_GEMM", "1")
_fused_moe_mod = importlib.import_module(
"vllm.model_executor.layers.fused_moe.fused_moe")
call_counter = {"cnt": 0}
orig_fn = _fused_moe_mod.deep_gemm_moe_fp8
def _spy_deep_gemm_moe_fp8(*args, **kwargs):
call_counter["cnt"] += 1
return orig_fn(*args, **kwargs)
monkeypatch.setattr(_fused_moe_mod, "deep_gemm_moe_fp8",
_spy_deep_gemm_moe_fp8)
m, n, k = mnk
if topk > num_experts:
pytest.skip(f"topk={topk} > num_experts={num_experts}")
run_single_case(
m=m,
n=n,
k=k,
topk=topk,
num_experts=num_experts,
block_size=BLOCK_SIZE,
)
# ensure that the DeepGEMM path was indeed taken.
assert call_counter["cnt"] == 1, \
f"DeepGEMM path was not executed during the test. " \
f"Call counter: {call_counter['cnt']}"