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99 lines
3.7 KiB
Python
99 lines
3.7 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 air_topp_sampling kernel"""
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import subprocess
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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.layers.quantization.ops import (
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cutlass_scaled_mm,
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scaled_fp8_quant,
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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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np.random.seed(42)
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self.prop = paddle.device.cuda.get_device_properties()
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self.sm_version = self.prop.major * 10 + self.prop.minor
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print(self.prop)
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print(paddle.__git_commit__)
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nvcc_output = subprocess.check_output(["nvcc", "--version"], universal_newlines=True)
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output = nvcc_output.split()
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release_idx = output.index("release") + 1
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self.nvcc_cuda_version = float(output[release_idx].split(",")[0])
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def test_cutlass_scaled_mm_fp8(self):
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"""
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Check cutlass_scaled_mm output.
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"""
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if self.sm_version < 89:
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self.skipTest("cutlass_scaled_mm with fp8 input only support sm89+")
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M = 32
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N = 1024
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K = 1024
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a = paddle.rand([M, K], dtype=paddle.bfloat16)
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b = paddle.rand([N, K], dtype=paddle.bfloat16)
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b_q, b_scales = scaled_fp8_quant(b, use_per_token_if_dynamic=False)
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a_q, a_scales = scaled_fp8_quant(a, use_per_token_if_dynamic=True)
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# Ensure quantized tensors and scales are valid
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assert a_q.numel() > 0, "Quantized tensor 'a_q' must not be empty"
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assert b_q.numel() > 0, "Quantized tensor 'b_q' must not be empty"
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assert a_scales.numel() > 0, "Scale tensor 'a_scales' must not be empty"
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assert b_scales.numel() > 0, "Scale tensor 'b_scales' must not be empty"
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bias = paddle.rand([N], dtype=paddle.bfloat16)
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baseline = paddle.matmul(a, b, transpose_x=False, transpose_y=True)
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if bias is not None:
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baseline = paddle.add(baseline, bias)
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out_type = a.dtype
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c = cutlass_scaled_mm(a_q, b_q, a_scales, b_scales, out_type, bias)
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euqal = np.allclose(baseline.numpy(), c.numpy(), rtol=1e-2, atol=1e-2)
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print(euqal) #
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def test_cutlass_scaled_mm_int8(self):
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"""
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Check cutlass_scaled_mm output.
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"""
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M = 32
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N = 1024
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K = 512
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a = paddle.rand([M, K], dtype=paddle.bfloat16)
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b = paddle.rand([N, K], dtype=paddle.bfloat16)
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a_scales = (a.cast(paddle.float32).abs().max(axis=-1) / 127)[:, None]
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a_q = paddle.clip(a / a_scales, -127, 127).cast(paddle.int8)
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b_scales = (b.cast(paddle.float32).abs().max(axis=-1) / 127)[:, None]
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b_q = paddle.clip(b / b_scales, -127, 127).cast(paddle.int8)
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bias = paddle.rand([N], dtype=paddle.bfloat16)
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baseline = paddle.matmul(a, b, transpose_x=False, transpose_y=True)
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if bias is not None:
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baseline = paddle.add(baseline, bias)
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out_type = a.dtype
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c = cutlass_scaled_mm(a_q, b_q, a_scales, b_scales, out_type, bias)
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euqal = np.allclose(baseline.numpy(), c.numpy(), rtol=1e-2, atol=1e-2)
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print(euqal) #
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if __name__ == "__main__":
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unittest.main()
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