[DNNL][INT8][FP32] MatMul (#23395)
* Initial FP32 DNNL MatMul Implementation * Implement int8 DNNL MatMul * Unify in-kernel-naming, clean UTs * MatmuL: Introduce op caching * Final adjustments test=develop * Remove dy_graph disablement test=develop * Change dnnl header name to new one test=develop * Contrain multi head check to prevent fails test=develop * Resolve dnnl header problems on MAC CI * Variable namings to kernel and skip_grad_ci added test=develop * Prevent MAC CI from failing * Prevent windows build from failing test=develop * Modify UTs to conform to the rules * Modify MatMul aux functions namings test=developrevert-23830-2.0-beta
Michał Gallus
6 years ago
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# Copyright (c) 2020 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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from __future__ import print_function
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import unittest, os
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import numpy as np
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from paddle.fluid.tests.unittests.op_test import OpTest, skip_check_grad_ci
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@skip_check_grad_ci(reason="DNNL's MatMul doesn't implemend grad kernel.")
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class TestDnnlMatMulOp(OpTest):
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def generate_data(self):
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self.x = np.random.random((25, 2, 2)).astype("float32")
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self.y = np.random.random((25, 2, 2)).astype("float32")
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self.alpha = 1.0
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self.out = self.alpha * np.matmul(self.x, self.y)
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def set_attributes(self):
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self.alpha = self.alpha if hasattr(self, 'alpha') else 1.0
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self.attrs = {'alpha': self.alpha}
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def setUp(self):
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# Set max isa, otherwise fails on SKX and earlier
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os.environ["DNNL_MAX_CPU_ISA"] = "AVX"
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self.op_type = "matmul"
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self._cpu_only = True
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self.use_mkldnn = True
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self.generate_data()
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self.set_attributes()
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self.attrs['use_mkldnn'] = True
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self.inputs = {'X': self.x, 'Y': self.y}
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self.outputs = {'Out': self.out}
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def test_check_output(self):
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self.check_output()
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class TestDnnlMatMulOpAlpha(TestDnnlMatMulOp):
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def generate_data(self):
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self.x = np.random.random((17, 2, 3)).astype("float32")
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self.y = np.random.random((17, 3, 2)).astype("float32")
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self.alpha = 2.0
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self.out = self.alpha * np.matmul(self.x, self.y)
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class TestDnnlMatMulOp2D(TestDnnlMatMulOp):
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def print_tensor(self, name, tensor):
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print(name)
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print(tensor)
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def generate_data(self):
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self.x = np.random.random((12, 9)).astype("float32")
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self.y = np.random.random((9, 12)).astype("float32")
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self.out = np.matmul(self.x, self.y)
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class TestDnnlMatMulOpTransposeX(TestDnnlMatMulOp):
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def generate_data(self):
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self.x = np.random.random((12, 9)).astype("float32")
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self.y = np.random.random((12, 9)).astype("float32")
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self.out = np.matmul(np.transpose(self.x), self.y)
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def set_attributes(self):
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self.attrs = {'transpose_X': True}
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class TestDnnlMatMulOpTransposeY(TestDnnlMatMulOp):
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def generate_data(self):
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self.x = np.random.random((12, 9)).astype("float32")
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self.y = np.random.random((12, 9)).astype("float32")
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self.out = np.matmul(self.x, np.transpose(self.y))
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def set_attributes(self):
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self.attrs = {'transpose_Y': True}
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class TestDnnlMatMulOpTransposeY3D(TestDnnlMatMulOp):
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def generate_data(self):
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self.x = np.random.random((17, 3, 2)).astype("float32")
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self.y = np.random.random((17, 3, 2)).astype("float32")
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self.out = np.matmul(self.x, np.transpose(self.y, (0, 2, 1)))
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def set_attributes(self):
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self.attrs = {'transpose_Y': True}
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class TestDnnlMatMulOpInt8NoScales(TestDnnlMatMulOp):
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def generate_data(self):
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self.x = np.random.random((12, 9)).astype("int8")
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self.y = np.random.random((9, 12)).astype("int8")
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self.out = np.matmul(self.x, self.y)
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class TestDnnlMatMulOpInt8(TestDnnlMatMulOp):
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def quantize(self, tensor):
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scale = 127. / np.abs(np.amax(tensor))
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quantized = np.round(scale * tensor).astype("int8")
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return scale, quantized
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def generate_data(self):
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x_float = np.random.random((12, 9)).astype("float32")
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self.x_scale, self.x = self.quantize(x_float)
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y_float = np.random.random((9, 12)).astype("float32")
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self.y_scale, self.y = self.quantize(y_float)
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out_float = np.matmul(x_float, y_float)
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self.out_scale, self.out = self.quantize(out_float)
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def set_attributes(self):
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self.attrs = {
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'Scale_x': self.x_scale,
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'Scale_y': self.y_scale,
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'Scale_out': self.out_scale,
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}
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def test_check_output(self):
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int_atol = 1
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self.check_output(atol=int_atol)
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class TestDnnlMatMulOpInt8ForceFP32(TestDnnlMatMulOpInt8):
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def generate_data(self):
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x_float = np.random.random((12, 9)).astype("float32")
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self.x_scale, self.x = self.quantize(x_float)
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y_float = np.random.random((9, 12)).astype("float32")
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self.y_scale, self.y = self.quantize(y_float)
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out_float = np.matmul(x_float, y_float)
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self.out = out_float
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def set_attributes(self):
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self.attrs = {
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'Scale_x': self.x_scale,
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'Scale_y': self.y_scale,
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'force_fp32_output': True
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}
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class TestDnnlMatMulOpInt8ForceFP32BasicScales(TestDnnlMatMulOp):
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def generate_data(self):
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self.x = np.random.randint(0, 3, (12, 9)).astype("int8")
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self.y = np.random.randint(0, 3, (9, 12)).astype("int8")
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self.out = np.matmul(self.x, self.y).astype("float32")
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def set_attributes(self):
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self.attrs = {'force_fp32_output': True}
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if __name__ == "__main__":
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unittest.main()
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