wehub-resource-sync
1220 lines
41 KiB
Python
1220 lines
41 KiB
Python
# Copyright (c) 2018 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 gradient_checker
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import numpy as np
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from decorator_helper import prog_scope
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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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get_places,
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is_custom_device,
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skip_check_grad_ci,
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)
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import paddle
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import paddle.distributed as dist
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from paddle import base
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from paddle.pir_utils import IrGuard
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class TestConcatOp(OpTest):
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def setUp(self):
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self.op_type = "concat"
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self.python_api = paddle.concat
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self.public_python_api = paddle.concat
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self.prim_op_type = "prim"
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self.dtype = self.get_dtype()
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self.init_test_data()
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self.if_enable_cinn()
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self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]}
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self.attrs = {'axis': self.axis}
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if self.axis < 0:
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self.actual_axis = self.axis + len(self.x0.shape)
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self.actual_axis = max(0, self.actual_axis)
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else:
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self.actual_axis = self.axis
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self.outputs = {
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'Out': np.concatenate(
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(self.x0, self.x1, self.x2), axis=self.actual_axis
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)
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}
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def get_dtype(self):
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return "float64"
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def test_check_output(self):
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if self.dtype == np.uint16:
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place = get_device_place()
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self.check_output_with_place(place, check_pir=True)
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else:
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self.check_output(check_pir=True)
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def test_check_grad(self):
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if self.dtype == np.uint16:
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place = get_device_place()
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self.check_grad_with_place(
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place,
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['x0'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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self.check_grad_with_place(
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place,
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['x1'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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self.check_grad_with_place(
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place,
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['x2'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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else:
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self.check_grad(
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['x0'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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self.check_grad(
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['x1'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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self.check_grad(
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['x2'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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def init_test_data(self):
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if self.dtype == np.uint16:
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x0 = np.random.random((5, 1, 4, 5)).astype(np.float32)
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self.x0 = convert_float_to_uint16(x0)
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x1 = np.random.random((5, 2, 4, 5)).astype(np.float32)
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self.x1 = convert_float_to_uint16(x1)
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x2 = np.random.random((5, 3, 4, 5)).astype(np.float32)
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self.x2 = convert_float_to_uint16(x2)
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else:
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self.x0 = np.random.random((5, 1, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((5, 2, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((5, 3, 4, 5)).astype(self.dtype)
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self.axis = 1
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def if_enable_cinn(self):
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pass
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class TestConcatOp2(TestConcatOp):
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def init_test_data(self):
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self.x0 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.axis = 1
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@skip_check_grad_ci(
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reason="The function 'check_grad' for large inputs is too slow."
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)
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class TestConcatOp3(TestConcatOp):
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def init_test_data(self):
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self.x0 = np.random.random((1, 256, 170, 256)).astype(self.dtype)
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self.x1 = np.random.random((1, 128, 170, 256)).astype(self.dtype)
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self.x2 = np.random.random((1, 128, 170, 256)).astype(self.dtype)
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self.axis = 1
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def test_check_grad(self):
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pass
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@skip_check_grad_ci(
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reason="This test will meet fetch error when there is a null grad. The detailed information is in PR#17015."
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)
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class TestConcatOp4(TestConcatOp):
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def init_test_data(self):
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self.x0 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((0, 3, 4, 5)).astype(self.dtype)
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self.axis = 0
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def test_check_grad(self):
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pass
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class TestConcatOp5(TestConcatOp):
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def init_test_data(self):
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self.x0 = np.random.random((5, 1, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((5, 2, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((5, 3, 4, 5)).astype(self.dtype)
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self.axis = -3
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class TestConcatOp6(TestConcatOp):
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def setUp(self):
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self.op_type = "concat"
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self.dtype = self.get_dtype()
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self.python_api = paddle.concat
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self.public_python_api = paddle.concat
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self.init_test_data()
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self.if_enable_cinn()
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self.lod = [[20, 80]]
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self.out_lod = [[20, 80, 20, 80, 20, 80]]
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self.inputs = {
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'X': [
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('x0', (self.x0, self.lod)),
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('x1', (self.x1, self.lod)),
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('x2', (self.x2, self.lod)),
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]
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}
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self.attrs = {'axis': self.axis}
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if self.axis < 0:
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self.actual_axis = self.axis + len(self.x0.shape)
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self.actual_axis = max(0, self.actual_axis)
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else:
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self.actual_axis = self.axis
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out = np.concatenate((self.x0, self.x1, self.x2), axis=self.actual_axis)
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self.outputs = {'Out': (out, self.out_lod)}
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def if_enable_cinn(self):
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pass
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def test_check_output(self):
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self.check_output(check_pir=False)
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def test_check_grad(self):
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self.check_grad(['x0'], 'Out', check_pir=False)
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self.check_grad(['x1'], 'Out', check_pir=False)
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self.check_grad(['x2'], 'Out', check_pir=False)
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def init_test_data(self):
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self.x0 = np.random.random([100]).astype(self.dtype)
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self.x1 = np.random.random([100]).astype(self.dtype)
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self.x2 = np.random.random([100]).astype(self.dtype)
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self.axis = 0
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class TestConcatOp7(TestConcatOp):
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def setUp(self):
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self.op_type = "concat"
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self.python_api = paddle.concat
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self.public_python_api = paddle.concat
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self.prim_op_type = "prim"
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self.if_enable_cinn()
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self.dtype = self.get_dtype()
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self.init_test_data()
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self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]}
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self.attrs = {'axis': self.axis}
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if self.axis < 0:
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self.actual_axis = self.axis + len(self.x0.shape)
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self.actual_axis = max(0, self.actual_axis)
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else:
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self.actual_axis = self.axis
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self.outputs = {
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'Out': np.concatenate(
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(self.x0, self.x1, self.x2), axis=self.actual_axis
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)
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}
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def if_enable_cinn(self):
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pass
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def get_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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['x0'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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self.check_grad(
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['x1'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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self.check_grad(
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['x2'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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def init_test_data(self):
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if self.dtype == np.uint16:
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x0 = np.random.random((5, 1, 4, 5)).astype(np.float32)
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self.x0 = convert_float_to_uint16(x0)
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x1 = np.random.random((5, 2, 4, 5)).astype(np.float32)
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self.x1 = convert_float_to_uint16(x1)
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x2 = np.random.random((5, 3, 4, 5)).astype(np.float32)
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self.x2 = convert_float_to_uint16(x2)
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else:
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self.x0 = np.random.random((5, 1, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((5, 2, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((5, 3, 4, 5)).astype(self.dtype)
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self.axis = 1
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class TestConcatOp0Size(TestConcatOp):
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def setUp(self):
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self.op_type = "concat"
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self.python_api = paddle.concat
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self.public_python_api = paddle.concat
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self.prim_op_type = "prim"
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self.if_enable_cinn()
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self.dtype = self.get_dtype()
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self.init_test_data()
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self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]}
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self.attrs = {'axis': self.axis}
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if self.axis < 0:
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self.actual_axis = self.axis + len(self.x0.shape)
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self.actual_axis = max(0, self.actual_axis)
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else:
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self.actual_axis = self.axis
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self.outputs = {
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'Out': np.concatenate(
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(self.x0, self.x1, self.x2), axis=self.actual_axis
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)
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}
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def if_enable_cinn(self):
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pass
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def get_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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['x0'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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self.check_grad(
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['x1'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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self.check_grad(
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['x2'],
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'Out',
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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def init_test_data(self):
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if self.dtype == np.uint16:
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x0 = np.random.random((5, 1, 4, 5)).astype(np.float32)
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self.x0 = convert_float_to_uint16(x0)
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x1 = np.random.random((5, 0, 4, 5)).astype(np.float32)
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self.x1 = convert_float_to_uint16(x1)
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x2 = np.random.random((5, 3, 4, 5)).astype(np.float32)
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self.x2 = convert_float_to_uint16(x2)
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else:
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self.x0 = np.random.random((5, 1, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((5, 0, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((5, 3, 4, 5)).astype(self.dtype)
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self.axis = 1
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def create_test_AxisTensor(parent):
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class TestConcatAxisTensor(parent):
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def setUp(self):
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self.op_type = "concat"
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self.python_api = paddle.concat
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self.public_python_api = paddle.concat
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self.dtype = self.get_dtype()
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self.init_test_data()
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self.inputs = {
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'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)],
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'AxisTensor': np.array([self.axis]).astype("int32"),
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}
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self.attrs = {}
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if self.axis < 0:
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self.actual_axis = self.axis + len(self.x0.shape)
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self.actual_axis = max(0, self.actual_axis)
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else:
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self.actual_axis = self.axis
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self.outputs = {
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'Out': np.concatenate(
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(self.x0, self.x1, self.x2), axis=self.actual_axis
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)
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}
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def test_check_output(self):
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if self.dtype == np.uint16:
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place = get_device_place()
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self.check_output_with_place(
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place, check_pir=True, check_symbol_infer=False
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)
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else:
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad(self):
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if (
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parent.__name__ == 'TestConcatOp4'
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or parent.__name__ == 'TestConcatOp3'
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):
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return
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if self.dtype == np.uint16:
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place = get_device_place()
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self.check_grad_with_place(place, ['x0'], 'Out', check_pir=True)
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self.check_grad_with_place(place, ['x1'], 'Out', check_pir=True)
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self.check_grad_with_place(place, ['x2'], 'Out', check_pir=True)
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else:
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self.check_grad(['x0'], 'Out', check_pir=True)
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self.check_grad(['x1'], 'Out', check_pir=True)
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self.check_grad(['x2'], 'Out', check_pir=True)
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cls_name = "{}_{}".format(parent.__name__, "AxisTensor")
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TestConcatAxisTensor.__name__ = cls_name
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globals()[cls_name] = TestConcatAxisTensor
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create_test_AxisTensor(TestConcatOp)
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create_test_AxisTensor(TestConcatOp2)
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create_test_AxisTensor(TestConcatOp3)
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create_test_AxisTensor(TestConcatOp4)
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create_test_AxisTensor(TestConcatOp5)
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create_test_AxisTensor(TestConcatOp6)
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# ----------------Concat Fp16----------------
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def create_test_fp16(parent):
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class TestConcatFp16(parent):
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def setUp(self):
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self.op_type = "concat"
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self.prim_op_type = "prim"
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self.python_api = paddle.concat
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self.public_python_api = paddle.concat
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self.enable_cinn = False
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self.dtype = self.get_dtype()
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self.init_test_data()
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self.inputs = {
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'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]
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}
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self.attrs = {'axis': self.axis}
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if self.axis < 0:
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self.actual_axis = self.axis + len(self.x0.shape)
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self.actual_axis = max(0, self.actual_axis)
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else:
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self.actual_axis = self.axis
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self.outputs = {
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'Out': np.concatenate(
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(self.x0, self.x1, self.x2), axis=self.actual_axis
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)
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}
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def test_check_grad(self):
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if (
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parent.__name__ == 'TestConcatOp4'
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or parent.__name__ == 'TestConcatOp3'
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):
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return
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if self.dtype == np.uint16:
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place = get_device_place()
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self.check_grad_with_place(
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place,
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['x0'],
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'Out',
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check_pir=True,
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check_prim=True,
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check_prim_pir=True,
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)
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self.check_grad_with_place(
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place,
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['x1'],
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'Out',
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check_pir=True,
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check_prim=True,
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check_prim_pir=True,
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)
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self.check_grad_with_place(
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place,
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['x2'],
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'Out',
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check_pir=True,
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check_prim=True,
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check_prim_pir=True,
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)
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else:
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self.check_grad(
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['x0'],
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'Out',
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check_pir=True,
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check_prim=True,
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check_prim_pir=True,
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)
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self.check_grad(
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['x1'],
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'Out',
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check_pir=True,
|
|
check_prim=True,
|
|
check_prim_pir=True,
|
|
)
|
|
self.check_grad(
|
|
['x2'],
|
|
'Out',
|
|
check_pir=True,
|
|
check_prim=True,
|
|
check_prim_pir=True,
|
|
)
|
|
|
|
def get_dtype(self):
|
|
return np.float16
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "Fp16")
|
|
TestConcatFp16.__name__ = cls_name
|
|
globals()[cls_name] = TestConcatFp16
|
|
|
|
|
|
create_test_fp16(TestConcatOp)
|
|
create_test_fp16(TestConcatOp2)
|
|
create_test_fp16(TestConcatOp3)
|
|
create_test_fp16(TestConcatOp4)
|
|
|
|
create_test_fp16(TestConcatOp5)
|
|
|
|
create_test_fp16(TestConcatOp6)
|
|
|
|
|
|
# ----------------Concat Bf16----------------
|
|
def create_test_bf16(parent):
|
|
@unittest.skipIf(
|
|
not (paddle.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestConcatBf16(parent):
|
|
def setUp(self):
|
|
self.op_type = "concat"
|
|
self.prim_op_type = "prim"
|
|
self.python_api = paddle.concat
|
|
self.public_python_api = paddle.concat
|
|
self.enable_cinn = False
|
|
self.dtype = self.get_dtype()
|
|
self.init_test_data()
|
|
self.inputs = {
|
|
'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]
|
|
}
|
|
self.attrs = {'axis': self.axis}
|
|
if self.axis < 0:
|
|
self.actual_axis = self.axis + len(self.x0.shape)
|
|
self.actual_axis = max(0, self.actual_axis)
|
|
else:
|
|
self.actual_axis = self.axis
|
|
|
|
self.outputs = {
|
|
'Out': np.concatenate(
|
|
(self.x0, self.x1, self.x2), axis=self.actual_axis
|
|
)
|
|
}
|
|
|
|
def test_check_grad(self):
|
|
if (
|
|
parent.__name__ == 'TestConcatOp4'
|
|
or parent.__name__ == 'TestConcatOp3'
|
|
):
|
|
return
|
|
if self.dtype == np.uint16:
|
|
place = get_device_place()
|
|
self.check_grad_with_place(
|
|
place,
|
|
['x0'],
|
|
'Out',
|
|
check_pir=True,
|
|
check_prim=True,
|
|
check_prim_pir=True,
|
|
)
|
|
self.check_grad_with_place(
|
|
place,
|
|
['x1'],
|
|
'Out',
|
|
check_pir=True,
|
|
check_prim=True,
|
|
check_prim_pir=True,
|
|
)
|
|
self.check_grad_with_place(
|
|
place,
|
|
['x2'],
|
|
'Out',
|
|
check_pir=True,
|
|
check_prim=True,
|
|
check_prim_pir=True,
|
|
)
|
|
else:
|
|
self.check_grad(
|
|
['x0'],
|
|
'Out',
|
|
check_pir=True,
|
|
check_prim=True,
|
|
check_prim_pir=True,
|
|
)
|
|
self.check_grad(
|
|
['x1'],
|
|
'Out',
|
|
check_pir=True,
|
|
check_prim=True,
|
|
check_prim_pir=True,
|
|
)
|
|
self.check_grad(
|
|
['x2'],
|
|
'Out',
|
|
check_pir=True,
|
|
check_prim=True,
|
|
check_prim_pir=True,
|
|
)
|
|
|
|
def get_dtype(self):
|
|
return np.uint16
|
|
|
|
def if_enable_cinn(self):
|
|
self.enable_cinn = False
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "Bf16")
|
|
TestConcatBf16.__name__ = cls_name
|
|
globals()[cls_name] = TestConcatBf16
|
|
|
|
|
|
# add all unit test maybe timeout.
|
|
create_test_bf16(TestConcatOp)
|
|
create_test_bf16(TestConcatOp2)
|
|
# create_test_bf16(TestConcatOp3)
|
|
create_test_bf16(TestConcatOp4)
|
|
# create_test_bf16(TestConcatOp5)
|
|
# create_test_bf16(TestConcatOp6)
|
|
|
|
|
|
class TestConcatOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
paddle.enable_static()
|
|
with paddle.base.program_guard(
|
|
paddle.base.Program(), paddle.base.Program()
|
|
):
|
|
# The input type of concat_op should be list.
|
|
|
|
x1 = paddle.static.data(shape=[-1, 4], dtype='int32', name='x1')
|
|
paddle.concat(x1)
|
|
|
|
# The item in input must be Variable.
|
|
x2 = base.create_lod_tensor(
|
|
np.array([[-1]]), [[1]], base.CPUPlace()
|
|
)
|
|
x3 = base.create_lod_tensor(
|
|
np.array([[-1]]), [[1]], base.CPUPlace()
|
|
)
|
|
self.assertRaises(TypeError, paddle.concat, [x2])
|
|
# The input dtype of concat_op must be float16, float32, float64, int32, int64.
|
|
|
|
x4 = paddle.static.data(shape=[-1, 4], dtype='uint8', name='x4')
|
|
x5 = paddle.static.data(shape=[-1, 4], dtype='uint8', name='x5')
|
|
self.assertRaises(TypeError, paddle.concat, [x4, x5])
|
|
x6 = paddle.static.data(shape=[-1, 4], dtype='float16', name='x6')
|
|
x7 = paddle.static.data(shape=[-1, 4], dtype='float16', name='x7')
|
|
x8 = paddle.static.data(shape=[-1, 4], dtype='float32', name='x8')
|
|
paddle.concat([x6, x7])
|
|
|
|
# The type of axis in concat_op should be int or Variable.
|
|
def test_axis_type():
|
|
paddle.concat([x6, x7], 3.2)
|
|
|
|
self.assertRaises(TypeError, test_axis_type)
|
|
|
|
def test_input_same_dtype():
|
|
paddle.concat([x7, x8])
|
|
|
|
self.assertRaises(TypeError, test_input_same_dtype)
|
|
paddle.disable_static()
|
|
|
|
|
|
class TestConcatAPI(unittest.TestCase):
|
|
def test_base_api(self):
|
|
paddle.enable_static()
|
|
with paddle.base.program_guard(paddle.base.Program()):
|
|
x_1 = paddle.static.data(
|
|
shape=[None, 1, 4, 5], dtype='int32', name='x_1'
|
|
)
|
|
paddle.concat([x_1, x_1], 0)
|
|
|
|
input_2 = np.random.random([2, 1, 4, 5]).astype("int32")
|
|
input_3 = np.random.random([2, 2, 4, 5]).astype("int32")
|
|
x_2 = paddle.static.data(
|
|
shape=[2, 1, 4, 5], dtype='int32', name='x_2'
|
|
)
|
|
x_3 = paddle.static.data(
|
|
shape=[2, 2, 4, 5], dtype='int32', name='x_3'
|
|
)
|
|
positive_1_int32 = paddle.tensor.fill_constant([1], "int32", 1)
|
|
positive_1_int64 = paddle.tensor.fill_constant([1], "int64", 1)
|
|
out_1 = paddle.concat([x_2, x_3], axis=1)
|
|
out_2 = paddle.concat([x_2, x_3], axis=positive_1_int32)
|
|
out_3 = paddle.concat([x_2, x_3], axis=positive_1_int64)
|
|
|
|
exe = base.Executor(place=base.CPUPlace())
|
|
[res_1, res_2, res_3] = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={"x_1": input_2, "x_2": input_2, "x_3": input_3},
|
|
fetch_list=[out_1, out_2, out_3],
|
|
)
|
|
np.testing.assert_array_equal(
|
|
res_1, np.concatenate((input_2, input_3), axis=1)
|
|
)
|
|
np.testing.assert_array_equal(
|
|
res_2, np.concatenate((input_2, input_3), axis=1)
|
|
)
|
|
np.testing.assert_array_equal(
|
|
res_3, np.concatenate((input_2, input_3), axis=1)
|
|
)
|
|
|
|
def test_api(self):
|
|
paddle.enable_static()
|
|
with paddle.base.program_guard(paddle.base.Program()):
|
|
x_1 = paddle.static.data(
|
|
shape=[None, 1, 4, 5], dtype='int32', name='x_1'
|
|
)
|
|
paddle.concat([x_1, x_1], 0)
|
|
|
|
input_2 = np.random.random([2, 1, 4, 5]).astype("int32")
|
|
input_3 = np.random.random([2, 2, 4, 5]).astype("int32")
|
|
x_2 = paddle.static.data(
|
|
shape=[2, 1, 4, 5], dtype='int32', name='x_2'
|
|
)
|
|
x_3 = paddle.static.data(
|
|
shape=[2, 2, 4, 5], dtype='int32', name='x_3'
|
|
)
|
|
positive_1_int32 = paddle.tensor.fill_constant([1], "int32", 1)
|
|
positive_1_int64 = paddle.tensor.fill_constant([1], "int64", 1)
|
|
negative_int64 = paddle.tensor.fill_constant([1], "int64", -3)
|
|
out_1 = paddle.concat(x=[x_2, x_3], axis=1)
|
|
out_2 = paddle.concat(x=[x_2, x_3], axis=positive_1_int32)
|
|
out_3 = paddle.concat(x=[x_2, x_3], axis=positive_1_int64)
|
|
out_4 = paddle.concat(x=[x_2, x_3], axis=negative_int64)
|
|
|
|
exe = paddle.static.Executor(place=paddle.CPUPlace())
|
|
[res_1, res_2, res_3, res_4] = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={"x_1": input_2, "x_2": input_2, "x_3": input_3},
|
|
fetch_list=[out_1, out_2, out_3, out_4],
|
|
)
|
|
np.testing.assert_array_equal(
|
|
res_1, np.concatenate((input_2, input_3), axis=1)
|
|
)
|
|
np.testing.assert_array_equal(
|
|
res_2, np.concatenate((input_2, input_3), axis=1)
|
|
)
|
|
np.testing.assert_array_equal(
|
|
res_3, np.concatenate((input_2, input_3), axis=1)
|
|
)
|
|
np.testing.assert_array_equal(
|
|
res_4, np.concatenate((input_2, input_3), axis=1)
|
|
)
|
|
|
|
def test_imperative(self):
|
|
in1 = np.array([[1, 2, 3], [4, 5, 6]])
|
|
in2 = np.array([[11, 12, 13], [14, 15, 16]])
|
|
in3 = np.array([[21, 22], [23, 24]])
|
|
paddle.disable_static()
|
|
x1 = paddle.to_tensor(in1)
|
|
x2 = paddle.to_tensor(in2)
|
|
x3 = paddle.to_tensor(in3)
|
|
out1 = paddle.concat([x1, x2, x3], axis=-1)
|
|
out2 = paddle.concat(x=[x1, x2], axis=0)
|
|
np_out1 = np.concatenate([in1, in2, in3], axis=-1)
|
|
np_out2 = np.concatenate([in1, in2], axis=0)
|
|
paddle.enable_static()
|
|
self.assertEqual((out1.numpy() == np_out1).all(), True)
|
|
self.assertEqual((out2.numpy() == np_out2).all(), True)
|
|
|
|
def test_errors(self):
|
|
with paddle.base.program_guard(
|
|
paddle.base.Program(), paddle.base.Program()
|
|
):
|
|
# The item in input must be Variable.
|
|
x2 = base.create_lod_tensor(
|
|
np.array([[-1]]), [[1]], base.CPUPlace()
|
|
)
|
|
x3 = base.create_lod_tensor(
|
|
np.array([[-1]]), [[1]], base.CPUPlace()
|
|
)
|
|
self.assertRaises(TypeError, paddle.concat, [x2])
|
|
# The input dtype of concat_op must be float16, float32, float64, int32, int64.
|
|
x4 = paddle.static.data(shape=[4], dtype='uint8', name='x4')
|
|
x5 = paddle.static.data(shape=[4], dtype='uint8', name='x5')
|
|
self.assertRaises(TypeError, paddle.concat, [x4, x5])
|
|
|
|
# The type of axis in concat_op should be int or Variable.
|
|
x6 = paddle.static.data(shape=[-1, 4], dtype='float16', name='x6')
|
|
x7 = paddle.static.data(shape=[-1, 4], dtype='float16', name='x7')
|
|
x8 = paddle.static.data(shape=[-1, 4], dtype='float32', name='x8')
|
|
|
|
def test_axis_type():
|
|
paddle.concat([x6, x7], 3.2)
|
|
|
|
self.assertRaises(TypeError, test_axis_type)
|
|
|
|
def test_input_same_dtype():
|
|
paddle.concat([x7, x8])
|
|
|
|
self.assertRaises(TypeError, test_input_same_dtype)
|
|
|
|
|
|
class TestConcatAPIWithDenseTensorArray(unittest.TestCase):
|
|
"""
|
|
Test concat api when the input(x) is a DenseTensorArray.
|
|
"""
|
|
|
|
def setUp(self):
|
|
self.axis = 1
|
|
self.python = paddle.concat
|
|
self.iter_num = 3
|
|
self.input_shape = [2, 3]
|
|
self.x = np.random.random(self.input_shape).astype("float32")
|
|
self.place = (
|
|
get_device_place()
|
|
if (base.is_compiled_with_cuda() or is_custom_device())
|
|
else base.CPUPlace()
|
|
)
|
|
|
|
def set_program(self, use_base_api):
|
|
paddle.enable_static()
|
|
if use_base_api:
|
|
self.program = paddle.base.Program()
|
|
with paddle.base.program_guard(self.program):
|
|
input = paddle.assign(self.x)
|
|
tensor_array = paddle.tensor.create_array(dtype='float32')
|
|
zero = paddle.tensor.fill_constant(
|
|
shape=[1], value=0, dtype="int64"
|
|
)
|
|
|
|
for i in range(self.iter_num):
|
|
paddle.tensor.array_write(input, zero + i, tensor_array)
|
|
|
|
self.out_var = paddle.concat(tensor_array, axis=self.axis)
|
|
else:
|
|
self.program = paddle.base.Program()
|
|
with paddle.base.program_guard(self.program):
|
|
input = paddle.assign(self.x)
|
|
tensor_array = paddle.tensor.create_array(
|
|
dtype='float32'
|
|
) # Api create_array is not supported in paddle 2.0 yet.
|
|
zero = paddle.zeros(shape=[1], dtype="int64")
|
|
|
|
for i in range(self.iter_num):
|
|
# Api array_write is not supported in paddle 2.0 yet.
|
|
paddle.tensor.array_write(input, zero + i, tensor_array)
|
|
|
|
self.out_var = paddle.concat(tensor_array, axis=self.axis)
|
|
|
|
def test_base_api(self):
|
|
self._run_static_mode(use_base_api=True)
|
|
|
|
def test_paddle_api(self):
|
|
self._run_static_mode(use_base_api=False)
|
|
|
|
def _run_static_mode(self, use_base_api):
|
|
self.set_program(use_base_api)
|
|
self.assertTrue(self.out_var.shape[self.axis] == -1)
|
|
exe = base.Executor(self.place)
|
|
res = exe.run(self.program, fetch_list=self.out_var)
|
|
np.testing.assert_array_equal(
|
|
res[0], np.concatenate([self.x] * self.iter_num, axis=self.axis)
|
|
)
|
|
|
|
|
|
class TestConcatDoubleGradCheck(unittest.TestCase):
|
|
def concat_wrapper(self, x):
|
|
return paddle.concat(x)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data1 = paddle.static.data('data1', [2, 3], dtype)
|
|
data1.persistable = True
|
|
data1.stop_gradient = False
|
|
data2 = paddle.static.data('data2', [2, 3], dtype)
|
|
data2.persistable = True
|
|
data2.stop_gradient = False
|
|
out = paddle.concat([data1, data2])
|
|
data1_arr = np.random.uniform(-1, 1, data1.shape).astype(dtype)
|
|
data2_arr = np.random.uniform(-1, 1, data2.shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[data1, data2],
|
|
out,
|
|
x_init=[data1_arr, data2_arr],
|
|
place=place,
|
|
eps=eps,
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.concat_wrapper,
|
|
[data1, data2],
|
|
out,
|
|
x_init=[data1_arr, data2_arr],
|
|
place=place,
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestConcatTripleGradCheck(unittest.TestCase):
|
|
def concat_wrapper(self, x):
|
|
return paddle.concat(x, 1)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data1 = paddle.static.data('data1', [2, 3, 4], dtype)
|
|
data1.persistable = True
|
|
data1.stop_gradient = False
|
|
data2 = paddle.static.data('data2', [2, 3, 4], dtype)
|
|
data2.persistable = True
|
|
data2.stop_gradient = False
|
|
out = paddle.concat([data1, data2], 1)
|
|
data1_arr = np.random.uniform(-1, 1, data1.shape).astype(dtype)
|
|
data2_arr = np.random.uniform(-1, 1, data2.shape).astype(dtype)
|
|
gradient_checker.triple_grad_check(
|
|
[data1, data2],
|
|
out,
|
|
x_init=[data1_arr, data2_arr],
|
|
place=place,
|
|
eps=eps,
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.concat_wrapper,
|
|
[data1, data2],
|
|
out,
|
|
x_init=[data1_arr, data2_arr],
|
|
place=place,
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestConcatOpAutoParallel(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "concat"
|
|
self.python_api = paddle.concat
|
|
self.public_python_api = paddle.concat
|
|
self.prim_op_type = "prim"
|
|
self.dtype = self.get_dtype()
|
|
self.init_test_data()
|
|
self.if_enable_cinn()
|
|
self.init_inputs()
|
|
self.attrs = {'axis': self.axis}
|
|
if self.axis < 0:
|
|
self.actual_axis = self.axis + len(self.x0.shape)
|
|
self.actual_axis = max(0, self.actual_axis)
|
|
else:
|
|
self.actual_axis = self.axis
|
|
|
|
self.outputs = {
|
|
'Out': np.concatenate(
|
|
(self.x0, self.x1, self.x2), axis=self.actual_axis
|
|
)
|
|
}
|
|
|
|
def get_dtype(self):
|
|
return "float64"
|
|
|
|
def init_inputs(self):
|
|
self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]}
|
|
self.placements = {
|
|
'X': [
|
|
('x0', [dist.Shard(2)]),
|
|
('x1', [dist.Shard(2)]),
|
|
('x2', [dist.Shard(2)]),
|
|
]
|
|
}
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(
|
|
['x0'],
|
|
'Out',
|
|
check_auto_parallel=True,
|
|
)
|
|
self.check_grad(
|
|
['x0', 'x1', 'x2'],
|
|
'Out',
|
|
check_auto_parallel=True,
|
|
)
|
|
|
|
def init_test_data(self):
|
|
if self.dtype == np.uint16:
|
|
x0 = np.random.random((16, 4, 4)).astype(np.float32)
|
|
self.x0 = convert_float_to_uint16(x0)
|
|
x1 = np.random.random((64, 4, 4)).astype(np.float32)
|
|
self.x1 = convert_float_to_uint16(x1)
|
|
x2 = np.random.random((16, 4, 4)).astype(np.float32)
|
|
self.x2 = convert_float_to_uint16(x2)
|
|
else:
|
|
self.x0 = np.random.random((16, 4, 4)).astype(self.dtype)
|
|
self.x1 = np.random.random((64, 4, 4)).astype(self.dtype)
|
|
self.x2 = np.random.random((16, 4, 4)).astype(self.dtype)
|
|
self.axis = 0
|
|
|
|
def if_enable_cinn(self):
|
|
pass
|
|
|
|
|
|
class TestConcatOpErrorWithPir(unittest.TestCase):
|
|
def test_errors_with_pir(self):
|
|
paddle.enable_static()
|
|
with paddle.base.program_guard(
|
|
paddle.base.Program(), paddle.base.Program()
|
|
):
|
|
# The type of axis in concat_op should be int or Variable.
|
|
x6 = paddle.static.data(shape=[-1, 4], dtype='float32', name='x6')
|
|
x7 = paddle.static.data(shape=[-1, 4], dtype='float32', name='x7')
|
|
x8 = paddle.static.data(shape=[-1, 4], dtype='float64', name='x8')
|
|
|
|
def test_axis_type():
|
|
paddle.concat([x6, x7], 3.2)
|
|
|
|
self.assertRaises(TypeError, test_axis_type)
|
|
|
|
# The input dtype must be same.
|
|
def test_input_same_dtype():
|
|
paddle.concat([x7, x8])
|
|
|
|
self.assertRaises(TypeError, test_input_same_dtype)
|
|
|
|
def test_empty_inputs_dygraph(self):
|
|
paddle.disable_static()
|
|
with self.assertRaisesRegex(ValueError, "but got empty list"):
|
|
paddle.concat([])
|
|
|
|
def test_empty_inputs_static(self):
|
|
with (
|
|
IrGuard(),
|
|
paddle.base.program_guard(
|
|
paddle.base.Program(), paddle.base.Program()
|
|
),
|
|
self.assertRaisesRegex(ValueError, "but got empty list"),
|
|
):
|
|
paddle.concat([], axis=0)
|
|
|
|
|
|
class TestConcatOpZeroSize1(TestConcatOp):
|
|
def init_test_data(self):
|
|
self.x0 = np.random.random((2, 0, 4, 5)).astype(self.dtype)
|
|
self.x1 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
|
|
self.x2 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
|
|
self.axis = 1
|
|
|
|
|
|
class TestConcatOpZeroSize2(TestConcatOp):
|
|
def init_test_data(self):
|
|
self.x0 = np.random.random((2, 0, 1, 5)).astype(self.dtype)
|
|
self.x1 = np.random.random((2, 0, 2, 5)).astype(self.dtype)
|
|
self.x2 = np.random.random((2, 0, 4, 5)).astype(self.dtype)
|
|
self.axis = 2
|
|
|
|
|
|
class TestConcatOpZeroSize3(TestConcatOp):
|
|
def init_test_data(self):
|
|
self.x0 = np.random.random((0, 0, 0, 0)).astype(self.dtype)
|
|
self.x1 = np.random.random((0, 0, 0, 0)).astype(self.dtype)
|
|
self.x2 = np.random.random((0, 0, 0, 0)).astype(self.dtype)
|
|
self.axis = 2
|
|
|
|
|
|
class TestConcatOpZeroSize4(TestConcatOp):
|
|
def init_test_data(self):
|
|
self.x0 = np.random.random((0, 1, 2, 3)).astype(self.dtype)
|
|
self.x1 = np.random.random((0, 1, 2, 3)).astype(self.dtype)
|
|
self.x2 = np.random.random((0, 1, 2, 3)).astype(self.dtype)
|
|
self.axis = 2
|
|
|
|
|
|
class TestConcatOpZeroSize5(TestConcatOp):
|
|
def init_test_data(self):
|
|
self.x0 = np.random.random((0, 1, 2, 3)).astype(self.dtype)
|
|
self.x1 = np.random.random((0, 1, 2, 3)).astype(self.dtype)
|
|
self.x2 = np.random.random((0, 1, 2, 3)).astype(self.dtype)
|
|
self.axis = 2
|
|
|
|
|
|
class TestConcatOutAndParaDecorator(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
self.apis = [
|
|
paddle.concat,
|
|
paddle.cat,
|
|
paddle.concatenate,
|
|
]
|
|
self.test_types = [
|
|
"decorator1",
|
|
"decorator2",
|
|
"out",
|
|
"out_decorator",
|
|
]
|
|
|
|
def do_test(self, api, test_type):
|
|
single_shape = [2, 3, 4]
|
|
out_shape = [2, 3, 12]
|
|
x = paddle.arange(np.prod(single_shape), dtype="float32").reshape(
|
|
single_shape
|
|
)
|
|
y = paddle.arange(np.prod(single_shape), dtype="float32").reshape(
|
|
single_shape
|
|
)
|
|
z = paddle.arange(np.prod(single_shape), dtype="float32").reshape(
|
|
single_shape
|
|
)
|
|
x.stop_gradient = y.stop_gradient = z.stop_gradient = False
|
|
inputs = [x, y, z]
|
|
axis = -1
|
|
out = paddle.randn(out_shape, dtype="float32")
|
|
out.stop_gradient = False
|
|
if test_type == "raw":
|
|
res = api(inputs, axis)
|
|
loss = res.mean()
|
|
loss.backward()
|
|
x_grad, y_grad, z_grad = x.grad, y.grad, z.grad
|
|
return res, x_grad, y_grad, z_grad
|
|
elif test_type == "decorator1":
|
|
res = api(inputs, axis, out=out)
|
|
loss = res.mean()
|
|
loss.backward()
|
|
x_grad, y_grad, z_grad = x.grad, y.grad, z.grad
|
|
return res, x_grad, y_grad, z_grad
|
|
elif test_type == "decorator2":
|
|
res = api(inputs, dim=axis)
|
|
loss = res.mean()
|
|
loss.backward()
|
|
x_grad, y_grad, z_grad = x.grad, y.grad, z.grad
|
|
return res, x_grad, y_grad, z_grad
|
|
elif test_type == "out":
|
|
res = api(inputs, axis, out=out)
|
|
loss = out.mean()
|
|
loss.backward()
|
|
x_grad, y_grad, z_grad = x.grad, y.grad, z.grad
|
|
return out, x_grad, y_grad, z_grad
|
|
elif test_type == "out_decorator":
|
|
res = api(inputs, dim=axis, out=out)
|
|
loss = out.mean()
|
|
loss.backward()
|
|
x_grad, y_grad, z_grad = x.grad, y.grad, z.grad
|
|
return out, x_grad, y_grad, z_grad
|
|
else:
|
|
raise NotImplementedError(
|
|
f"Test type {test_type} is not implemented."
|
|
)
|
|
|
|
def test_concat_out_and_para_decorator(self):
|
|
res_std, x_grad_std, y_grad_std, z_grad_std = self.do_test(
|
|
paddle.concat, "raw"
|
|
)
|
|
for api in self.apis:
|
|
for test_type in self.test_types:
|
|
res, x_grad, y_grad, z_grad = self.do_test(api, test_type)
|
|
np.testing.assert_allclose(
|
|
res_std.numpy(), res.numpy(), rtol=1e-20, atol=1e-20
|
|
)
|
|
np.testing.assert_allclose(
|
|
x_grad_std.numpy(), x_grad.numpy(), rtol=1e-20, atol=1e-20
|
|
)
|
|
np.testing.assert_allclose(
|
|
y_grad_std.numpy(), y_grad.numpy(), rtol=1e-20, atol=1e-20
|
|
)
|
|
np.testing.assert_allclose(
|
|
z_grad_std.numpy(), z_grad.numpy(), rtol=1e-20, atol=1e-20
|
|
)
|
|
|
|
|
|
class TestConcatOpAlias(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
|
|
def test_check_output(self):
|
|
"""
|
|
Test the alias of concat function.
|
|
``concat(tensors=x, dim=axis)`` is equivalent to ``concat(x=x, axis=axis)``
|
|
"""
|
|
shape_cases = [
|
|
[2],
|
|
[2, 4],
|
|
[2, 4, 8],
|
|
]
|
|
axis_cases = [0, -1]
|
|
|
|
for shape in shape_cases:
|
|
for axis in axis_cases:
|
|
x1 = paddle.rand(shape)
|
|
x2 = paddle.rand(shape)
|
|
combinations = [
|
|
{"x": [x1, x2], "axis": axis},
|
|
{"x": [x1, x2], "dim": axis},
|
|
{"tensors": [x1, x2], "axis": axis},
|
|
{"tensors": [x1, x2], "dim": axis},
|
|
]
|
|
# Get baseline result
|
|
baseline = paddle.concat(x=[x1, x2], axis=axis)
|
|
expected = baseline.numpy()
|
|
for params in combinations:
|
|
out = paddle.concat(**params)
|
|
np.testing.assert_array_equal(out.numpy(), expected)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|