add qwen3

vllm-v0.6.2/tests/kernels/test_triton_scaled_mm.py

@@ -0,0 +1,106 @@
+"""Tests for the triton_scaled_mm kernel
+
+Run `pytest tests/kernels/test_triton_scaled_mm.py`.
+"""
+import importlib
+from typing import Optional, Type
+
+import pytest
+import torch
+
+from vllm.platforms import current_platform
+
+device = "cuda"
+
+
+def scaled_mm_torch(a: torch.Tensor,
+                    b: torch.Tensor,
+                    scale_a: torch.Tensor,
+                    scale_b: torch.Tensor,
+                    out_dtype: Type[torch.dtype],
+                    bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    out = torch.mm(a.to(torch.float32), b.to(torch.float32))
+    out = scale_a * out
+    out = scale_b.T * out
+    out = out.to(out_dtype)
+    if bias is not None:
+        out = out + bias
+
+    return out
+
+
+def get_8bit_types():
+    types = [torch.int8]
+    supports_fp8 = current_platform.has_device_capability(89)
+    if current_platform.is_rocm() and supports_fp8:
+        types.append(torch.float8_e4m3fnuz)
+    elif current_platform.is_cuda() and supports_fp8:
+        types.append(torch.float8_e4m3fn)
+    return types
+
+
+@pytest.mark.parametrize("M", [1, 33, 64, 512])
+@pytest.mark.parametrize("N", [256, 971, 20486])
+@pytest.mark.parametrize("K", [128, 496, 1024])
+@pytest.mark.parametrize("out_dtype", [torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("in_dtype", get_8bit_types())
+@pytest.mark.parametrize("use_scalar_scale_a", [True, False])
+@pytest.mark.parametrize("use_scalar_scale_b", [True, False])
+@pytest.mark.parametrize("use_bias", [True, False])
+def test_scaled_mm(M, N, K, in_dtype, out_dtype, use_scalar_scale_a,
+                   use_scalar_scale_b, use_bias):
+    is_floating_point_type = lambda t: torch.tensor([1, 1], dtype=t
+                                                    ).is_floating_point()
+
+    current_platform.seed_everything(0)
+
+    # NOTE: There are cases, where if the matrix is large enough, an output
+    # like 65504.4 can be produced, and can easily turn into inf when
+    # multiplied when using float16/bfloat16.  This means one function, e.g.,
+    # testing function, and another function, e.g. golden function, can
+    # produce a non-inf value while the other produces an inf value, and
+    # will cause assert_close/allclose to fail, even though if overflow
+    # wouldn't have occurred, the values would have been "close."
+    #
+    # So, the values here are kept small enough to avoid this situation.
+    if is_floating_point_type(in_dtype):
+        a = (0.25 * torch.rand(
+            (M, K), dtype=torch.float32, device=device)).to(in_dtype)
+        b = (0.25 * torch.rand(
+            (K, N), dtype=torch.float32, device=device)).to(in_dtype)
+    else:
+        a = torch.randint(-32, 32, (M, K), dtype=in_dtype, device=device)
+        b = torch.randint(-32, 32, (K, N), dtype=in_dtype, device=device)
+
+    if use_scalar_scale_a:
+        scale_a = torch.rand((1, 1), device=device)
+    else:
+        scale_a = 0.25 * torch.rand((M, 1), device=device)
+
+    if use_scalar_scale_b:
+        scale_b = torch.rand((1, 1), device=device)
+    else:
+        scale_b = 0.25 * torch.rand((N, 1), device=device)
+
+    bias = None
+    if use_bias:
+        bias = torch.rand((N, ), device=device, dtype=out_dtype)
+
+    triton_scaled_mm_module = importlib.import_module(
+        "vllm.model_executor.layers.quantization.compressed_tensors."
+        "triton_scaled_mm")
+    triton_scaled_mm = triton_scaled_mm_module.triton_scaled_mm
+
+    c_check = triton_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
+
+    a_cpu = a.cpu()
+    b_cpu = b.cpu()
+    scale_a_cpu = scale_a.cpu()
+    scale_b_cpu = scale_b.cpu()
+    bias_cpu = None if bias is None else bias.cpu()
+
+    c_actual = scaled_mm_torch(a_cpu, b_cpu, scale_a_cpu, scale_b_cpu,
+                               out_dtype, bias_cpu)
+
+    c_check_cpu = c_check.cpu()
+    torch.testing.assert_close(c_check_cpu, c_actual, rtol=1e-1, atol=1e-1)