wehub-resource-sync
326 lines
11 KiB
Python
326 lines
11 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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from paddle import base
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from paddle.base import Program, program_guard
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np.random.seed(1024)
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class TestIndexSelectOp(OpTest):
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def setUp(self):
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self.python_api = paddle.index_select
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self.public_python_api = paddle.index_select
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self.op_type = "index_select"
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self.prim_op_type = "comp"
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self.init_dtype_type()
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index_np = np.random.randint(
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low=-self.x_shape[self.dim],
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high=self.x_shape[self.dim],
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size=self.index_size,
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)
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x_np = np.random.random(self.x_shape).astype(self.x_type)
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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x_np = (
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np.random.random(self.x_shape)
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+ 1j * np.random.random(self.x_shape)
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).astype(self.x_type)
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self.inputs = {'X': x_np, 'Index': index_np}
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self.attrs = {'dim': self.dim}
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outer_loop = np.prod(self.x_shape[: self.dim])
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x_reshape = [outer_loop, *self.x_shape[self.dim :]]
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x_np_reshape = np.reshape(x_np, tuple(x_reshape))
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out_list = []
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for i in range(outer_loop):
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for j in range(self.index_size):
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out_list.append(x_np_reshape[i, index_np[j]])
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self.out_shape = list(self.x_shape)
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self.out_shape[self.dim] = self.index_size
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self.out_shape = tuple(self.out_shape)
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out = np.reshape(out_list, self.out_shape)
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self.outputs = {'Out': out}
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def init_dtype_type(self):
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self.dim = 1
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self.x_type = np.float64
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self.index_type = np.int64
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self.x_shape = (100, 4, 5)
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self.index_size = 100
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def test_check_output(self):
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if self.x_type == np.complex64 or self.x_type == np.complex128:
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self.check_output(check_pir=True, check_prim_pir=False)
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else:
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self.check_output(check_pir=True, check_prim_pir=True)
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def test_check_grad_normal(self):
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if self.x_type == np.complex64 or self.x_type == np.complex128:
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self.check_grad(['X'], 'Out', check_pir=True)
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else:
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self.check_grad(['X'], 'Out', check_pir=True)
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class TestIndexSelectOpCase2(TestIndexSelectOp):
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def init_dtype_type(self):
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self.x_type = np.float32
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self.index_type = np.int32
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self.dim = -2
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self.x_shape = (10, 10, 4, 10)
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self.index_size = 10
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class TestIndexSelectOp_ZeroSize(OpTest):
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def setUp(self):
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self.python_api = paddle.index_select
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self.public_python_api = paddle.index_select
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self.op_type = "index_select"
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self.init_dtype_type()
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index_np = np.random.randint(
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low=-self.x_shape[self.dim],
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high=self.x_shape[self.dim],
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size=self.index_size,
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)
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x_np = np.random.random(self.x_shape).astype(self.x_type)
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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x_np = (
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np.random.random(self.x_shape)
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+ 1j * np.random.random(self.x_shape)
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).astype(self.x_type)
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self.inputs = {'X': x_np, 'Index': index_np}
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self.attrs = {'dim': self.dim}
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outer_loop = np.prod(self.x_shape[: self.dim])
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x_reshape = [outer_loop, *self.x_shape[self.dim :]]
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x_np_reshape = np.reshape(x_np, tuple(x_reshape))
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out_list = []
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for i in range(outer_loop):
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for j in range(self.index_size):
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out_list.append(x_np_reshape[i, index_np[j]])
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self.out_shape = list(self.x_shape)
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self.out_shape[self.dim] = self.index_size
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self.out_shape = tuple(self.out_shape)
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out = np.reshape(out_list, self.out_shape)
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self.outputs = {'Out': out}
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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(['X'], 'Out', check_pir=True)
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def init_dtype_type(self):
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self.x_type = np.float64
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self.index_type = np.int64
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self.dim = 1
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# shape[dim] can not be 0.
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self.x_shape = (0, 10, 0, 0)
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self.index_size = 10
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class TestIndexSelectOpCaseSingleThread(TestIndexSelectOp):
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def init_dtype_type(self):
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if base.is_compiled_with_cuda() or is_custom_device():
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base.set_flags({'FLAGS_cudnn_deterministic': True})
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self.x_type = np.float32
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self.index_type = np.int32
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self.dim = -2
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self.x_shape = (10, 10, 4, 10)
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self.index_size = 10
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class TestIndexSelectFP16OP(TestIndexSelectOp):
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def init_dtype_type(self):
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self.dim = 1
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self.x_type = np.float16
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self.index_type = np.int64
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self.x_shape = (100, 4, 5)
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self.index_size = 100
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class TestIndexSelectBoolOP(TestIndexSelectOp):
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def init_dtype_type(self):
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self.dim = 1
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self.x_type = bool
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self.index_type = np.int64
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self.x_shape = (100, 4, 5)
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self.index_size = 100
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def test_check_grad_normal(self):
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pass
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# no scatter op (the backward op of index_select/gather) for bf16
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@unittest.skipIf(
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not (paddle.is_compiled_with_cuda() or is_custom_device()),
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"paddle is not compiled with cuda",
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)
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class TestIndexSelectBF16Op(OpTest):
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def setUp(self):
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self.python_api = paddle.index_select
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self.public_python_api = paddle.index_select
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self.prim_op_type = "comp"
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self.op_type = "index_select"
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self.init_dtype_type()
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self.if_skip_cinn()
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index_np = np.random.randint(
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low=-self.x_shape[self.dim],
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high=self.x_shape[self.dim],
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size=self.index_size,
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)
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x_np = np.random.random(self.x_shape).astype(np.float32)
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self.inputs = {'X': convert_float_to_uint16(x_np), 'Index': index_np}
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self.attrs = {'dim': self.dim}
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outer_loop = np.prod(self.x_shape[: self.dim])
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x_reshape = [outer_loop, *self.x_shape[self.dim :]]
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x_np_reshape = np.reshape(x_np, tuple(x_reshape))
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out_list = []
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for i in range(outer_loop):
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for j in range(self.index_size):
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out_list.append(x_np_reshape[i, index_np[j]])
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self.out_shape = list(self.x_shape)
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self.out_shape[self.dim] = self.index_size
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self.out_shape = tuple(self.out_shape)
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out = np.reshape(out_list, self.out_shape)
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self.outputs = {'Out': convert_float_to_uint16(out)}
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def if_skip_cinn(self):
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self.enable_cinn = False
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def init_dtype_type(self):
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self.dim = 1
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self.x_type = np.uint16
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self.index_type = np.int64
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self.x_shape = (20, 4, 5)
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self.index_size = 100
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(place, check_pir=True, check_prim_pir=True)
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def test_check_grad_normal(self):
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place = get_device_place()
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self.check_grad_with_place(place, ['X'], 'Out', check_pir=True)
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class TestIndexSelectComplex64(TestIndexSelectOp):
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def init_dtype_type(self):
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self.x_type = np.complex64
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self.index_type = np.int32
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self.dim = -2
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self.x_shape = (10, 10, 4, 10)
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self.index_size = 10
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class TestIndexSelectComplex128(TestIndexSelectOp):
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def init_dtype_type(self):
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self.x_type = np.complex128
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self.index_type = np.int32
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self.dim = -2
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self.x_shape = (10, 10, 4, 10)
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self.index_size = 10
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class TestIndexSelectAPI(unittest.TestCase):
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def input_data(self):
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self.data_x = np.array(
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[
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[1.0, 2.0, 3.0, 4.0],
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[5.0, 6.0, 7.0, 8.0],
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[9.0, 10.0, 11.0, 12.0],
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]
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).astype("float32")
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self.data_index = np.array([0, 1, 1]).astype('int32')
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def test_index_select_api(self):
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paddle.enable_static()
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self.input_data()
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# case 1:
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with program_guard(Program(), Program()):
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x = paddle.static.data(name='x', shape=[-1, 4])
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index = paddle.static.data(name='index', shape=[3], dtype='int32')
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z = paddle.index_select(x, index, axis=1)
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exe = base.Executor(base.CPUPlace())
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(res,) = exe.run(
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feed={'x': self.data_x, 'index': self.data_index},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.array(
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[[1.0, 2.0, 2.0], [5.0, 6.0, 6.0], [9.0, 10.0, 10.0]]
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)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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# case 2:
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[-1, 4])
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index = paddle.static.data(name='index', shape=[3], dtype='int32')
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z = paddle.index_select(x, index)
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exe = base.Executor(base.CPUPlace())
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(res,) = exe.run(
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feed={'x': self.data_x, 'index': self.data_index},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.array(
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[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [5.0, 6.0, 7.0, 8.0]]
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)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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def test_dygraph_api(self):
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paddle.disable_static()
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self.input_data()
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# case 1:
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_x)
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index = paddle.to_tensor(self.data_index)
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z = paddle.index_select(x, index)
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np_z = z.numpy()
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expect_out = np.array(
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[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [5.0, 6.0, 7.0, 8.0]]
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)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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# case 2:
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_x)
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index = paddle.to_tensor(self.data_index)
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z = paddle.index_select(x, index, axis=1)
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np_z = z.numpy()
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expect_out = np.array(
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[[1.0, 2.0, 2.0], [5.0, 6.0, 6.0], [9.0, 10.0, 10.0]]
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)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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if __name__ == '__main__':
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unittest.main()
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