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89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""UT for per_channel_fp8_fp8_half_gemm_fused kernel"""
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import os
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import unittest
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from itertools import product
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import numpy as np
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import paddle
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class Test(unittest.TestCase):
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def setUp(self):
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"""
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Initialize the test environment,
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including setting random seeds and environment variables.
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"""
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paddle.seed(2003)
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os.environ["FLAGS_use_cutlass_device_best_config_path"] = "default"
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def testcase1(self):
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"""
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Check if the per_channel_fp8_fp8_half_gemm_fused function works properly.
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"""
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prop = paddle.device.cuda.get_device_properties()
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cc = prop.major * 10 + prop.minor
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if cc < 89:
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self.skipTest("per_channel_fp8_fp8_half_gemm_fused only support sm89+")
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from fastdeploy.model_executor.ops.gpu import (
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per_channel_fp8_fp8_half_gemm_fused,
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)
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nks = [[2048, 2048], [2048, 5504], [6144, 2048]]
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nks = nks + [[4096, 4096], [4096, 12800], [6144, 4096]]
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nks = nks + [[5120, 5120], [5120, 13824], [15360, 5120]]
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m = [1, 32, 64, 128, 256, 512, 1024, 2048]
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combinations = list(product(m, nks))
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for m, (n, k) in combinations:
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A_bf16 = paddle.rand(shape=[m, k], dtype="bfloat16")
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A_fp8 = paddle.cast(A_bf16, "float8_e4m3fn")
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B_bf16 = paddle.rand(shape=[n, k], dtype="bfloat16")
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B_fp8 = B_bf16.astype("float8_e4m3fn")
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scalar_scale = paddle.full([1], 0.5, dtype="float32")
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channel_scale = paddle.rand(shape=[n], dtype="float32")
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bias = paddle.rand(shape=[n], dtype="bfloat16")
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result_bf16 = paddle.matmul(A_bf16, B_bf16, transpose_y=True) * scalar_scale * channel_scale + bias
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result_fp8 = per_channel_fp8_fp8_half_gemm_fused(
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A_fp8,
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B_fp8,
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bias=bias,
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scalar_scale=scalar_scale,
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channel_scale=channel_scale,
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transpose_x=False,
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transpose_y=True,
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output_dtype="bfloat16",
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)
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# absolute_error = paddle.abs(result_bf16 - result_fp8)
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# mean_absolute_error = paddle.mean(absolute_error)
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relative_error = paddle.abs(result_bf16 - result_fp8) / (paddle.abs(result_bf16))
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mean_relative_error = paddle.mean(relative_error)
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np.testing.assert_allclose(
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mean_relative_error.numpy(),
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np.array([0.001]),
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rtol=0.001,
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atol=0.25,
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)
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if __name__ == "__main__":
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unittest.main()
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