# Copyright (c) 2020 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 from op_test import ( OpTest, convert_float_to_uint16, get_device_place, is_custom_device, ) import paddle import paddle.base.dygraph as dg from paddle import base from paddle.base import core class TestKronOp(OpTest): def setUp(self): self.op_type = "kron" self.prim_op_type = "prim" self.python_api = paddle.kron self.public_python_api = paddle.kron self.dtype = self._init_dtype() x = np.random.uniform(size=(10, 10)).astype(self.dtype) y = np.random.uniform(size=(10, 10)).astype(self.dtype) out_ref = np.kron(x, y) self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': out_ref} def _init_dtype(self): return "float64" def test_check_output(self): self.check_output(check_pir=True) def test_check_grad(self): self.check_grad( ['X', 'Y'], 'Out', check_pir=True, check_prim_pir=True, ) def test_check_grad_ignore_x(self): self.check_grad( ['Y'], 'Out', no_grad_set=set('X'), check_pir=True, check_prim_pir=True, ) def test_check_grad_ignore_y(self): self.check_grad( ['X'], 'Out', no_grad_set=set('Y'), check_pir=True, check_prim_pir=True, ) class TestKronOp2(TestKronOp): def setUp(self): self.op_type = "kron" self.prim_op_type = "prim" self.python_api = paddle.kron self.public_python_api = paddle.kron self.dtype = self._init_dtype() x = np.random.uniform(size=(5, 5, 4)).astype(self.dtype) y = np.random.uniform(size=(100)).astype(self.dtype) out_ref = np.kron(x, y) self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': out_ref} class TestKronOp3(TestKronOp): def setUp(self): self.op_type = "kron" self.prim_op_type = "prim" self.python_api = paddle.kron self.public_python_api = paddle.kron self.dtype = self._init_dtype() x = np.random.uniform(size=(10, 10)).astype(self.dtype) y = np.random.uniform(size=(5, 5, 4)).astype(self.dtype) out_ref = np.kron(x, y) self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': out_ref} class TestKronOp4(TestKronOp): def setUp(self): self.op_type = "kron" self.prim_op_type = "prim" self.python_api = paddle.kron self.public_python_api = paddle.kron self.dtype = self._init_dtype() x = np.random.uniform(size=(5, 5, 5)).astype(self.dtype) y = np.random.uniform(size=(2, 3, 4, 2, 2, 3)).astype(self.dtype) out_ref = np.kron(x, y) self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': out_ref} class TestKronOp5(TestKronOp): def setUp(self): self.op_type = "kron" self.prim_op_type = "prim" self.python_api = paddle.kron self.public_python_api = paddle.kron self.dtype = self._init_dtype() x = np.random.uniform(size=(2, 3, 4, 3, 2)).astype(self.dtype) y = np.random.uniform(size=(10, 10)).astype(self.dtype) out_ref = np.kron(x, y) self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': out_ref} class TestKronOp6(TestKronOp): def setUp(self): self.op_type = "kron" self.prim_op_type = "prim" self.python_api = paddle.kron self.public_python_api = paddle.kron self.dtype = self._init_dtype() x = np.random.uniform(size=(10, 10, 2)).astype(self.dtype) y = np.random.uniform(size=(2, 3, 4, 3, 2)).astype(self.dtype) out_ref = np.kron(x, y) self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': out_ref} class TestKronFP16Op(TestKronOp): def _init_dtype(self): return "float16" class TestKronOp_ZeroSize(OpTest): def setUp(self): self.op_type = "kron" self.prim_op_type = "prim" self.python_api = paddle.kron self.public_python_api = paddle.kron self.dtype = self._init_dtype() x = np.random.uniform(size=(10, 0, 2)).astype(self.dtype) y = np.random.uniform(size=(2, 3, 4, 3, 2)).astype(self.dtype) out_ref = np.kron(x, y) self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': out_ref} def _init_dtype(self): return "float64" def test_check_output(self): self.check_output(check_pir=True) def test_check_grad(self): self.check_grad( ['X', 'Y'], 'Out', check_pir=True, ) @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and not support the bfloat16", ) class TestKronBF16Op(TestKronOp): def setUp(self): self.op_type = "kron" self.prim_op_type = "prim" self.python_api = paddle.kron self.public_python_api = paddle.kron self.dtype = np.uint16 self.np_dtype = "float32" x = np.random.uniform(size=(10, 10)).astype(self.np_dtype) y = np.random.uniform(size=(10, 10)).astype(self.np_dtype) out_ref = np.kron(x, y) self.inputs = { 'X': convert_float_to_uint16(x), 'Y': convert_float_to_uint16(y), } self.outputs = {'Out': convert_float_to_uint16(out_ref)} # bfloat16 requires using place self.place = get_device_place() def test_check_output(self): self.check_output_with_place(self.place, check_pir=True) def test_check_grad(self): self.check_grad_with_place( self.place, ['X', 'Y'], 'Out', check_pir=True, check_prim_pir=True, ) def test_check_grad_ignore_x(self): self.check_grad_with_place( self.place, ['Y'], 'Out', no_grad_set=set('X'), check_pir=True, check_prim_pir=True, ) def test_check_grad_ignore_y(self): self.check_grad_with_place( self.place, ['X'], 'Out', no_grad_set=set('Y'), check_pir=True, check_prim_pir=True, ) class TestKronLayer(unittest.TestCase): def test_case(self): a = np.random.randn(10, 10).astype(np.float64) b = np.random.randn(10, 10).astype(np.float64) place = base.CPUPlace() with dg.guard(place): a_var = paddle.to_tensor(a) b_var = paddle.to_tensor(b) c_var = paddle.kron(a_var, b_var) np.testing.assert_allclose(c_var.numpy(), np.kron(a, b)) def test_case_with_output(self): place = base.CPUPlace() a = np.random.randn(10, 10).astype(np.float64) b = np.random.randn(10, 10).astype(np.float64) out_np = np.kron(a, b) paddle.enable_static() prog = paddle.static.Program() with ( base.unique_name.guard(), paddle.static.program_guard(prog, prog), ): a_var = paddle.static.data("a", [-1, -1], dtype="float64") b_var = paddle.static.data("b", [-1, -1], dtype="float64") out_var = paddle.kron(a_var, b_var) exe = paddle.static.Executor(place=place) (res,) = exe.run(prog, feed={'a': a, 'b': b}, fetch_list=[out_var]) np.testing.assert_allclose(res, out_np) class TestComplexKronOp(OpTest): def setUp(self): self.op_type = "kron" self.python_api = paddle.kron self.x_shape = np.array([10, 10]) self.y_shape = np.array([3, 35]) self.out_shape = self.x_shape * self.y_shape self.init_base_dtype() self.init_input_output() self.inputs = { 'X': OpTest.np_dtype_to_base_dtype(self.x), 'Y': OpTest.np_dtype_to_base_dtype(self.y), } self.attrs = {'axis': -1, 'use_onednn': False} self.outputs = {'Out': self.out} def init_base_dtype(self): self.dtype = np.complex128 def init_input_output(self): self.x = np.random.random(self.x_shape).astype( self.dtype ) + 1j * np.random.random(self.x_shape).astype(self.dtype) self.y = np.random.random(self.y_shape).astype( self.dtype ) + 1j * np.random.random(self.y_shape).astype(self.dtype) self.out = np.kron(self.x, self.y) def test_check_output(self): self.check_output(check_pir=True) def test_check_grad_normal(self): self.check_grad( ['X', 'Y'], 'Out', check_pir=True, ) def test_check_grad_ignore_x(self): self.check_grad( ['Y'], 'Out', no_grad_set=set("X"), check_pir=True, ) def test_check_grad_ignore_y(self): self.check_grad( ['X'], 'Out', no_grad_set=set('Y'), check_pir=True, ) class TestKronOpTypePromotion(TestComplexKronOp): def init_input_output(self): self.x = np.random.random(self.x_shape).astype(self.dtype) self.y = np.random.random(self.y_shape).astype( self.dtype ) + 1j * np.random.random(self.y_shape).astype(self.dtype) self.out = np.kron(self.x, self.y) if __name__ == '__main__': paddle.enable_static() unittest.main()