Sync from v0.13

tests/kernels/quantization/test_block_int8.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from https://github.com/sgl-project/sglang/blob/main/test/srt/test_block_int8.py
+import itertools
+
+import pytest
+import torch
+
+from tests.kernels.quant_utils import native_w8a8_block_matmul
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.quantization.utils.int8_utils import (
+    w8a8_block_int8_matmul,
+)
+from vllm.platforms import current_platform
+
+if current_platform.get_device_capability() < (7, 0):
+    pytest.skip("INT8 Triton requires CUDA 7.0 or higher", allow_module_level=True)
+
+vllm_config = VllmConfig()
+
+DTYPES = [torch.half, torch.bfloat16]
+M = [1, 33, 64, 222]
+N = [128, 1024]
+K = [256, 4096]
+# BLOCK_SIZE = [[64, 64], [64, 128], [128, 64], [128, 128]]
+BLOCK_SIZE = [[128, 128]]
+SEEDS = [0]
+
+
+@pytest.fixture(autouse=True, scope="module")
+def setup_cuda():
+    """Sets the default CUDA device for all tests in this module."""
+    torch.set_default_device("cuda")
+
+
+@pytest.mark.parametrize(
+    "M,N,K,block_size,out_dtype,seed",
+    itertools.product(M, N, K, BLOCK_SIZE, DTYPES, SEEDS),
+)
+@torch.inference_mode()
+def test_w8a8_block_int8_matmul(M, N, K, block_size, out_dtype, seed):
+    torch.manual_seed(seed)
+    factor_for_scale = 1e-2
+    int8_info = torch.iinfo(torch.int8)
+    int8_max, int8_min = int8_info.max, int8_info.min
+
+    A_fp32 = (torch.rand(M, K, dtype=torch.float32) - 0.5) * 2 * int8_max
+    A_fp8 = A_fp32.clamp(min=int8_min, max=int8_max).to(torch.float8_e4m3fn)
+
+    B_fp32 = (torch.rand(N, K, dtype=torch.float32) - 0.5) * 2 * int8_max
+    B_fp8 = B_fp32.clamp(min=int8_min, max=int8_max).to(torch.float8_e4m3fn)
+
+    block_n, block_k = block_size[0], block_size[1]
+    n_tiles = (N + block_n - 1) // block_n
+    k_tiles = (K + block_k - 1) // block_k
+
+    As = torch.rand(M, k_tiles, dtype=torch.float32) * factor_for_scale
+    Bs = torch.rand(n_tiles, k_tiles, dtype=torch.float32) * factor_for_scale
+
+    ref_out = native_w8a8_block_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
+    out = w8a8_block_int8_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
+
+    rel_diff = torch.mean(
+        torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))
+    ) / torch.mean(torch.abs(ref_out.to(torch.float32)))
+    assert rel_diff < 0.001