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