Merge tag 'v0.8.5' into v0.8.5-dev

tests/kernels/test_block_fp8.py → tests/kernels/quantization/test_block_fp8.py

@@ -6,6 +6,7 @@ import itertools
 import pytest
 import torch
 
+from tests.kernels.quant_utils import native_w8a8_block_matmul
 from vllm.config import VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import fused_moe
@@ -18,8 +19,6 @@ from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     per_token_group_quant_fp8, w8a8_block_fp8_matmul)
 from vllm.platforms import current_platform
 
-from .utils_block import native_w8a8_block_matmul
-
 dg_available = False
 try:
     import deep_gemm

tests/kernels/test_block_int8.py → tests/kernels/quantization/test_block_int8.py

@@ -6,6 +6,7 @@ import itertools
 import pytest
 import torch
 
+from tests.kernels.quant_utils import native_w8a8_block_matmul
 from vllm.config import VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import fused_moe
@@ -13,8 +14,6 @@ from vllm.model_executor.layers.quantization.utils.int8_utils import (
     w8a8_block_int8_matmul)
 from vllm.platforms import current_platform
 
-from .utils_block import native_w8a8_block_matmul
-
 if current_platform.get_device_capability() < (7, 0):
     pytest.skip("INT8 Triton requires CUDA 7.0 or higher",
                 allow_module_level=True)

tests/kernels/test_cutlass_2of4_sparse.py → tests/kernels/quantization/test_cutlass_2of4_sparse.py

@@ -7,13 +7,12 @@ Run `pytest tests/kernels/test_semi_structured.py`.
 import pytest
 import torch
 
+from tests.kernels.utils import baseline_scaled_mm, to_fp8, to_int8
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     sparse_cutlass_supported)
 from vllm.platforms import current_platform
 
-from .utils import baseline_scaled_mm, to_fp8, to_int8
-
 CUDA_DEVICES = [
     f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
 ]

tests/kernels/test_cutlass.py → tests/kernels/quantization/test_cutlass_scaled_mm.py

@@ -8,13 +8,11 @@ import random
 import pytest
 import torch
 
-from tests.kernels.utils import opcheck
+from tests.kernels.utils import baseline_scaled_mm, opcheck, to_fp8, to_int8
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
 from vllm.utils import cdiv
 
-from .utils import baseline_scaled_mm, to_fp8, to_int8
-
 MNK_FACTORS = [
     (1, 256, 128),
     (1, 16384, 1024),

tests/kernels/quantization/test_rocm_skinny_gemms.py

+# SPDX-License-Identifier: Apache-2.0
+import pytest
+import torch
+
+import vllm._custom_ops as ops
+from tests.kernels.quant_utils import ref_dynamic_per_tensor_fp8_quant
+from vllm.platforms import current_platform
+
+DTYPES = [torch.bfloat16, torch.float16]
+M = [16, 32, 64, 128, 256, 512, 1024, 4096, 8192]
+K = [8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]  # k % 8 == 0
+N = [1, 2, 3, 4]
+SEEDS = [0]
+
+
+@pytest.mark.parametrize("n", [1])  # only test for batch size 1
+@pytest.mark.parametrize("k", K)
+@pytest.mark.parametrize("m", M)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("rows_per_block", [2, 4, 8, 16])
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.skipif(not current_platform.is_rocm(),
+                    reason="only test for rocm")
+@torch.inference_mode()
+def test_rocm_llmm1_kernel(n, k, m, dtype, rows_per_block, seed):
+    torch.manual_seed(seed)
+    A = torch.rand(n, k, dtype=dtype, device="cuda")
+    B = torch.rand(m, k, dtype=dtype, device="cuda")
+
+    ref_out = torch.matmul(A, B.t())
+    out = ops.LLMM1(B, A, rows_per_block)
+
+    assert torch.allclose(out, ref_out, rtol=0.01)
+
+
+@pytest.mark.parametrize("n", N)  # only test for batch size <= 4
+@pytest.mark.parametrize("k", K + [9216, 10240, 16384])
+@pytest.mark.parametrize("m", [8] + M)  # m >= 8
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.skipif(not current_platform.is_rocm(),
+                    reason="only test for rocm")
+def test_rocm_wvsplitk_kernel(n, k, m, dtype, seed):
+    torch.manual_seed(seed)
+    cu_count = current_platform.get_cu_count()
+
+    A = torch.rand(n, k, dtype=dtype, device="cuda")
+    B = torch.rand(m, k, dtype=dtype, device="cuda")
+
+    ref_out = torch.matmul(A, B.t())
+    out = ops.wvSplitK(B, A, cu_count)
+
+    assert torch.allclose(out, ref_out, rtol=0.01)
+
+
+@pytest.mark.parametrize("n", N)  # only test for batch size <= 4
+@pytest.mark.parametrize("k", K[1:] + [14336, 24576, 32768])  # k % 16 == 0
+@pytest.mark.parametrize("m", M + [28672])  # m >= 16
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.skipif(not current_platform.is_rocm(),
+                    reason="only test for rocm")
+def test_rocm_wvsplitk_fp8_kernel(n, k, m, dtype, seed):
+    torch.manual_seed(seed)
+
+    A = torch.rand(n, k, device="cuda")
+    B = torch.rand(m, k, device="cuda")
+
+    A, scale_a = ref_dynamic_per_tensor_fp8_quant(A)
+    B, scale_b = ref_dynamic_per_tensor_fp8_quant(B)
+
+    ref_out = torch._scaled_mm(A,
+                               B.t(),
+                               out_dtype=dtype,
+                               scale_a=scale_a,
+                               scale_b=scale_b)
+    out = ops.wvSplitKQ(B, A, dtype, scale_a, scale_b,
+                        current_platform.get_cu_count())
+
+    assert torch.allclose(out, ref_out, rtol=0.01)