# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np import paddle from paddle.fluid import core from paddle.fluid.tests.unittests.eager_op_test import ( OpTest, OpTestTool, convert_float_to_uint16, ) @OpTestTool.skip_if_not_cpu_bf16() class TestMulOneDNNOp(OpTest): def setUp(self): self.op_type = "mul" self.attrs = {'use_mkldnn': True} self.init_shapes_and_attrs() self.x_fp32 = np.random.random(self.x_shape).astype(np.float32) self.y_fp32 = np.random.random(self.y_shape).astype(np.float32) self.x = self.x_fp32 self.y = self.y_fp32 self.init_inputs_dtype() self.inputs = {'X': self.x, 'Y': self.y} output = np.dot( np.reshape(self.x_fp32, self.np_x_shape), np.reshape(self.y_fp32, self.np_y_shape), ) self.outputs = {'Out': np.reshape(output, self.out_shape)} def init_shapes_and_attrs(self): self.x_shape = (20, 5) self.y_shape = (5, 21) self.np_x_shape = (20, 5) self.np_y_shape = (5, 21) self.out_shape = (20, 21) def init_inputs_dtype(self): pass def test_check_output(self): self.check_output_with_place(core.CPUPlace()) def test_check_grad(self): self.check_grad_with_place(core.CPUPlace(), ['X', 'Y'], 'Out') def test_check_grad_ingore_x(self): self.check_grad_with_place(core.CPUPlace(), ['Y'], 'Out', set('X')) def test_check_grad_ingore_y(self): self.check_grad_with_place(core.CPUPlace(), ['X'], 'Out', set('Y')) class TestMulXNumColDims2OneDNNOp(TestMulOneDNNOp): def init_shapes_and_attrs(self): self.x_shape = (6, 7, 5) self.y_shape = (5, 21) self.np_x_shape = (42, 5) self.np_y_shape = (5, 21) self.out_shape = (6, 7, 21) self.attrs["x_num_col_dims"] = 2 class TestMulYNumColDims2OneDNNOp(TestMulOneDNNOp): def init_shapes_and_attrs(self): self.x_shape = (20, 6) self.y_shape = (2, 3, 21) self.np_x_shape = (20, 6) self.np_y_shape = (6, 21) self.out_shape = (20, 21) self.attrs["y_num_col_dims"] = 2 class TestMulYAndXNumColDims2OneDNNOp(TestMulOneDNNOp): def init_shapes_and_attrs(self): self.x_shape = (10, 5, 6) self.y_shape = (2, 3, 21) self.np_x_shape = (50, 6) self.np_y_shape = (6, 21) self.out_shape = (10, 5, 21) self.attrs["x_num_col_dims"] = 2 self.attrs["y_num_col_dims"] = 2 class TestMulBF16OneDNNOp(TestMulOneDNNOp): def init_inputs_dtype(self): self.x = convert_float_to_uint16(self.x) self.y = convert_float_to_uint16(self.y) def calculate_grads(self): x_np = np.reshape(self.x_fp32, self.np_x_shape) y_np = np.reshape(self.y_fp32, self.np_y_shape) self.dout = self.outputs['Out'] self.dout_np = np.reshape(self.dout, (x_np.shape[0], y_np.shape[1])) y_np_trans = np.transpose(y_np, (1, 0)) x_np_trans = np.transpose(x_np, (1, 0)) self.dx = np.matmul(self.dout_np, y_np_trans) self.dy = np.matmul(x_np_trans, self.dout_np) def test_check_grad(self): self.calculate_grads() self.check_grad_with_place( core.CPUPlace(), ['X', 'Y'], 'Out', user_defined_grads=[self.dx, self.dy], user_defined_grad_outputs=[convert_float_to_uint16(self.dout)], ) def test_check_grad_ingore_x(self): self.calculate_grads() self.check_grad_with_place( core.CPUPlace(), ['Y'], 'Out', set('X'), user_defined_grads=[self.dy], user_defined_grad_outputs=[convert_float_to_uint16(self.dout)], ) def test_check_grad_ingore_y(self): self.calculate_grads() self.check_grad_with_place( core.CPUPlace(), ['X'], 'Out', set('Y'), user_defined_grads=[self.dx], user_defined_grad_outputs=[convert_float_to_uint16(self.dout)], ) if __name__ == "__main__": paddle.enable_static() unittest.main()