[Unit Test] Add unit test for deep gemm (#20090)

Signed-off-by: yewentao256 <zhyanwentao@126.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

tests/kernels/moe/test_deepgemm.py

@@ -0,0 +1,225 @@
+# 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']}"