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45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from unittest.mock import patch
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import pytest
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import torch
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from vllm.model_executor.layers.quantization.utils import fp8_utils
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@pytest.mark.parametrize("shape", [(32, 128), (64, 256), (16, 512)])
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@pytest.mark.parametrize("column_major", [False, True])
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@pytest.mark.parametrize("scale_ue8m0", [False, True])
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@pytest.mark.parametrize("group_size", [64, 128])
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_per_token_group_quant_fp8(shape, column_major: bool,
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scale_ue8m0: bool, group_size: int):
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device = "cuda"
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torch.manual_seed(42)
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num_tokens, hidden_dim = shape
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x = (torch.randn(
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(num_tokens, hidden_dim), device=device, dtype=torch.bfloat16) * 8)
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# cuda path
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out_q, scale = fp8_utils.per_token_group_quant_fp8(
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x,
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group_size,
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column_major_scales=column_major,
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use_ue8m0=scale_ue8m0,
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)
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# triton ref
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with patch("vllm.platforms.current_platform.is_cuda", return_value=False):
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ref_q, ref_s = fp8_utils.per_token_group_quant_fp8(
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x,
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group_size,
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column_major_scales=column_major,
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use_ue8m0=scale_ue8m0,
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)
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assert torch.allclose(out_q.float(), ref_q.float(), atol=0.15, rtol=0.15)
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assert torch.allclose(scale, ref_s, atol=0.01, rtol=0.01)
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