mirror of
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104 lines
3.1 KiB
Python
104 lines
3.1 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 fp8_int4_gemm kernel"""
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import unittest
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import numpy as np
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import paddle
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from fastdeploy.model_executor.ops.gpu import (
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scaled_gemm_f8_i4_f16,
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scaled_gemm_f8_i4_f16_weight_quantize,
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)
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class Test(unittest.TestCase):
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def setUp(self):
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"""
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Initialize.
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"""
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paddle.seed(2024)
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print(paddle.device.cuda.get_device_properties())
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print(paddle.__git_commit__)
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def quant_fp8_pertensor(self, tensor):
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"""
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quant_fp8_pertensor
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"""
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scale = paddle.max(paddle.abs(tensor))
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tensor = paddle.cast((tensor * 448 / scale).clip(-448, 448), "float8_e4m3fn").astype(tensor.dtype)
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return tensor, scale
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def dequant_fp8_pertensor(self, tensor, scale):
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"""
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dequant_fp8_pertensor
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"""
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tensor = (tensor / 448 * scale).astype(tensor.dtype)
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return tensor
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def quant_int4_fp8_matmul(self, A, B, dtype):
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"""
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quant_int4_fp8_matmul
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"""
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A_fp8, A_fp8_scale = self.quant_fp8_pertensor(A)
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B_fp8, B_fp8_scale = self.quant_fp8_pertensor(B)
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processed_B, w_scale = scaled_gemm_f8_i4_f16_weight_quantize(B_fp8, groupsize=-1, scale_dtype="float16")
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w_scale = paddle.view(w_scale, dtype)
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out_scale = (A_fp8_scale / 448) * (B_fp8_scale / 448)
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out = scaled_gemm_f8_i4_f16(
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x=paddle.cast(A_fp8, "float8_e4m3fn").cuda(),
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y=processed_B.cuda(),
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scale=w_scale.cuda(),
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zero_points=None,
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bias=None,
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out_scale=out_scale,
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groupsize=0,
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out_dtype=dtype,
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)
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return out
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def test_fp16(self):
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"""
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Check fp16.
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"""
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A_fp32 = paddle.ones((4, 128)).clip(-448, 448)
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B_fp32 = paddle.ones((128, 512)).clip(-448, 448)
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C_fp32 = paddle.matmul(A_fp32, B_fp32)
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out = self.quant_int4_fp8_matmul(A_fp32, B_fp32, "float16")
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out = paddle.cast(out, "float32")
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np.testing.assert_allclose(C_fp32.numpy(), out.numpy(), rtol=1e-04, atol=1e-04)
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def test_bf16(self):
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"""
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Check bf16.
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"""
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A_fp32 = paddle.ones((4, 128)).clip(-448, 448)
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B_fp32 = paddle.ones((128, 512)).clip(-448, 448)
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C_fp32 = paddle.matmul(A_fp32, B_fp32)
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out = self.quant_int4_fp8_matmul(A_fp32, B_fp32, "bfloat16")
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out = paddle.cast(out, "float32")
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np.testing.assert_allclose(C_fp32.numpy(), out.numpy(), rtol=1e-04, atol=1e-04)
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if __name__ == "__main__":
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unittest.main()
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