wehub-resource-sync
645 lines
21 KiB
Python
645 lines
21 KiB
Python
# Copyright (c) 2024 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 core
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def meshgrid_wrapper(x):
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return paddle.tensor.meshgrid(x[0], x[1])
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class TestMeshgridOp(OpTest):
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def setUp(self):
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self.op_type = "meshgrid"
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self.prim_op_type = "comp"
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self.python_api = meshgrid_wrapper
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self.public_python_api = meshgrid_wrapper
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self.init_data_type()
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self.init_inputs_and_outputs()
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self.python_out_sig = ['out0', 'out1']
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self.if_enable_cinn()
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def init_data_type(self):
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self.dtype = np.float64
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def test_check_output(self):
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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self.check_output(check_pir=True)
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else:
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self.check_output(
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check_prim=True, check_pir=True, check_prim_pir=True
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)
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def test_check_grad(self):
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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self.check_grad(
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['x0'],
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['out0', 'out1'],
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check_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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['out0', 'out1'],
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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_inputs_and_outputs(self):
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self.shape = self.get_x_shape()
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ins = []
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outs = []
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for i in range(len(self.shape)):
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ins.append(np.random.random((self.shape[i],)).astype(self.dtype))
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for i in range(len(self.shape)):
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out_reshape = [1] * len(self.shape)
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out_reshape[i] = self.shape[i]
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out_temp = np.reshape(ins[i], out_reshape)
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outs.append(np.broadcast_to(out_temp, self.shape))
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self.inputs = {'X': [(f'x{i}', ins[i]) for i in range(len(ins))]}
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self.outputs = {'Out': [(f'out{i}', outs[i]) for i in range(len(outs))]}
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def get_x_shape(self):
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return [100, 200]
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def if_enable_cinn(self):
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# 拆解tile_grad导致cinn运行超时
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self.enable_cinn = False
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class TestMeshgridOp2(TestMeshgridOp):
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def get_x_shape(self):
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return [100, 300]
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class TestMeshgridOp2Fp16(TestMeshgridOp):
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def get_x_shape(self):
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return [100, 300]
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def init_data_type(self):
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self.dtype = np.float16
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class TestMeshgridOp2Complex64(TestMeshgridOp):
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def get_x_shape(self):
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return [100, 300]
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def init_data_type(self):
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self.dtype = np.complex64
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class TestMeshgridOp2Complex128(TestMeshgridOp):
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def get_x_shape(self):
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return [100, 300]
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def init_data_type(self):
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self.dtype = np.complex128
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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 or not support the bfloat16",
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)
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class TestMeshgridOpBFP16OP(TestMeshgridOp):
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def init_data_type(self):
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self.data_type = np.uint16
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def init_inputs_and_outputs(self):
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self.shape = self.get_x_shape()
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ins = []
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outs = []
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for i in range(len(self.shape)):
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ins.append(np.random.random((self.shape[i],)).astype(self.dtype))
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for i in range(len(self.shape)):
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out_reshape = [1] * len(self.shape)
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out_reshape[i] = self.shape[i]
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out_temp = np.reshape(ins[i], out_reshape)
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outs.append(np.broadcast_to(out_temp, self.shape))
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self.inputs = {
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'X': [
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(f'x{i}', convert_float_to_uint16(ins[i]))
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for i in range(len(ins))
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]
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}
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self.outputs = {
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'Out': [
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(f'out{i}', convert_float_to_uint16(outs[i]))
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for i in range(len(outs))
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]
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}
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def if_enable_cinn(self):
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self.enable_cinn = False
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def test_check_output(self):
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self.check_output_with_place(
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place=get_device_place(), check_pir=True, check_prim_pir=True
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)
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def test_check_grad(self):
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self.check_grad_with_place(
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get_device_place(),
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['x0'],
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['out0', 'out1'],
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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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class TestMeshgridOp3(unittest.TestCase):
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def test_api(self):
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input_1 = np.random.randint(
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0,
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100,
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[
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100,
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],
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).astype('int32')
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input_2 = np.random.randint(
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0,
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100,
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[
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200,
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],
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).astype('int32')
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out_1 = np.reshape(input_1, [100, 1])
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out_1 = np.broadcast_to(out_1, [100, 200])
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out_2 = np.reshape(input_2, [1, 200])
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out_2 = np.broadcast_to(out_2, [100, 200])
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(shape=[100], dtype='int32', name='x')
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y = paddle.static.data(shape=[200], dtype='int32', name='y')
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exe = base.Executor(place=base.CPUPlace())
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grid_x, grid_y = paddle.tensor.meshgrid(x, y)
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res_1, res_2 = exe.run(
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paddle.static.default_main_program(),
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feed={'x': input_1, 'y': input_2},
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fetch_list=[grid_x, grid_y],
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)
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np.testing.assert_array_equal(res_1, out_1)
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np.testing.assert_array_equal(res_2, out_2)
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class TestMeshgridOp4(unittest.TestCase):
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def test_list_input(self):
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input_1 = np.random.randint(
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0,
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100,
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[
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100,
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],
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).astype('int32')
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input_2 = np.random.randint(
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0,
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100,
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[
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200,
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],
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).astype('int32')
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out_1 = np.reshape(input_1, [100, 1])
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out_1 = np.broadcast_to(out_1, [100, 200])
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out_2 = np.reshape(input_2, [1, 200])
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out_2 = np.broadcast_to(out_2, [100, 200])
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(shape=[100], dtype='int32', name='x')
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y = paddle.static.data(shape=[200], dtype='int32', name='y')
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exe = base.Executor(place=base.CPUPlace())
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grid_x, grid_y = paddle.tensor.meshgrid([x, y])
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res_1, res_2 = exe.run(
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paddle.static.default_main_program(),
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feed={'x': input_1, 'y': input_2},
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fetch_list=[grid_x, grid_y],
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)
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np.testing.assert_array_equal(res_1, out_1)
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np.testing.assert_array_equal(res_2, out_2)
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class TestMeshgridOp5(unittest.TestCase):
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def test_tuple_input(self):
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input_1 = np.random.randint(
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0,
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100,
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[
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100,
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],
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).astype('int32')
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input_2 = np.random.randint(
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0,
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100,
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[
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200,
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],
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).astype('int32')
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out_1 = np.reshape(input_1, [100, 1])
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out_1 = np.broadcast_to(out_1, [100, 200])
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out_2 = np.reshape(input_2, [1, 200])
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out_2 = np.broadcast_to(out_2, [100, 200])
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(shape=[100], dtype='int32', name='x')
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y = paddle.static.data(shape=[200], dtype='int32', name='y')
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exe = base.Executor(place=base.CPUPlace())
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grid_x, grid_y = paddle.tensor.meshgrid((x, y))
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res_1, res_2 = exe.run(
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paddle.static.default_main_program(),
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feed={'x': input_1, 'y': input_2},
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fetch_list=[grid_x, grid_y],
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)
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np.testing.assert_array_equal(res_1, out_1)
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np.testing.assert_array_equal(res_2, out_2)
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class TestMeshgridOp6(unittest.TestCase):
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def test_api_with_dygraph(self):
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input_3 = np.random.randint(
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0,
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100,
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[
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100,
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],
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).astype('int32')
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input_4 = np.random.randint(
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0,
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100,
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[
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200,
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],
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).astype('int32')
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with base.dygraph.guard():
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tensor_3 = paddle.to_tensor(input_3)
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tensor_4 = paddle.to_tensor(input_4)
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res_3, res_4 = paddle.tensor.meshgrid(tensor_3, tensor_4)
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np.testing.assert_array_equal(res_3.shape, [100, 200])
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np.testing.assert_array_equal(res_4.shape, [100, 200])
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class TestMeshgridOpIndexing(unittest.TestCase):
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def setUp(self):
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self.input_3 = np.random.randint(0, 100, [100]).astype('int32')
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self.input_4 = np.random.randint(0, 100, [200]).astype('int32')
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def test_api_with_dygraph_indexing_xy(self):
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np_res_3, np_res_4 = np.meshgrid(
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self.input_3, self.input_4, indexing='xy'
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)
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with base.dygraph.guard():
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tensor_3 = paddle.to_tensor(self.input_3)
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tensor_4 = paddle.to_tensor(self.input_4)
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res_3, res_4 = paddle.tensor.meshgrid(
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tensor_3, tensor_4, indexing='xy'
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)
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np.testing.assert_array_equal(res_3.shape, np_res_3.shape)
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np.testing.assert_array_equal(res_4.shape, np_res_4.shape)
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np.testing.assert_array_equal(res_3.numpy(), np_res_3)
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np.testing.assert_array_equal(res_3.numpy(), np_res_3)
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np.testing.assert_array_equal(res_4.numpy(), np_res_4)
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def test_api_with_dygraph_indexing_ij(self):
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np_res_3, np_res_4 = np.meshgrid(
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self.input_3, self.input_4, indexing='ij'
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)
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with base.dygraph.guard():
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tensor_3 = paddle.to_tensor(self.input_3)
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tensor_4 = paddle.to_tensor(self.input_4)
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res_3, res_4 = paddle.tensor.meshgrid(
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tensor_3, tensor_4, indexing='ij'
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)
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np.testing.assert_array_equal(res_3.shape, np_res_3.shape)
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np.testing.assert_array_equal(res_4.shape, np_res_4.shape)
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np.testing.assert_array_equal(res_3.numpy(), np_res_3)
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np.testing.assert_array_equal(res_4.numpy(), np_res_4)
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def test_indexing_default(self):
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np_res_3, np_res_4 = np.meshgrid(
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self.input_3, self.input_4, indexing='ij'
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)
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with base.dygraph.guard():
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tensor_3 = paddle.to_tensor(self.input_3)
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tensor_4 = paddle.to_tensor(self.input_4)
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res_3, res_4 = paddle.tensor.meshgrid(tensor_3, tensor_4)
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res_3_n, res_4_n = paddle.tensor.meshgrid(
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tensor_3, tensor_4, indexing=None
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)
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np.testing.assert_array_equal(res_3.numpy(), np_res_3)
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np.testing.assert_array_equal(res_4.numpy(), np_res_4)
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np.testing.assert_array_equal(res_3_n.numpy(), np_res_3)
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np.testing.assert_array_equal(res_4_n.numpy(), np_res_4)
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def test_indexing_invalid_value(self):
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with base.dygraph.guard():
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tensor_3 = paddle.to_tensor(self.input_3)
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tensor_4 = paddle.to_tensor(self.input_4)
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invalid_indexing = "ab"
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with self.assertRaises(ValueError) as cm:
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res_3, res_4 = paddle.tensor.meshgrid(
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tensor_3, tensor_4, indexing=invalid_indexing
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)
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class TestMeshgridOp7(unittest.TestCase):
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def test_api_with_dygraph_list_input(self):
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input_3 = np.random.randint(
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0,
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100,
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[
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100,
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],
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).astype('int32')
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input_4 = np.random.randint(
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0,
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100,
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[
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200,
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],
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).astype('int32')
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with base.dygraph.guard():
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tensor_3 = paddle.to_tensor(input_3)
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tensor_4 = paddle.to_tensor(input_4)
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res_3, res_4 = paddle.tensor.meshgrid([tensor_3, tensor_4])
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np.testing.assert_array_equal(res_3.shape, [100, 200])
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np.testing.assert_array_equal(res_4.shape, [100, 200])
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class TestMeshgridOp8(unittest.TestCase):
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def test_api_with_dygraph_tuple_input(self):
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input_3 = np.random.randint(
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0,
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100,
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[
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100,
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],
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).astype('int32')
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input_4 = np.random.randint(
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0,
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100,
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[
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200,
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],
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).astype('int32')
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with base.dygraph.guard():
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tensor_3 = paddle.to_tensor(input_3)
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tensor_4 = paddle.to_tensor(input_4)
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res_3, res_4 = paddle.tensor.meshgrid((tensor_3, tensor_4))
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np.testing.assert_array_equal(res_3.shape, [100, 200])
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np.testing.assert_array_equal(res_4.shape, [100, 200])
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class TestMeshgridOpComplexStatic(unittest.TestCase):
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def test_tuple_input(self):
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input_1 = np.random.randint(
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0,
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100,
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[
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100,
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],
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).astype('complex64')
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input_2 = np.random.randint(
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0,
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100,
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[
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200,
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],
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).astype('complex64')
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out_1 = np.reshape(input_1, [100, 1])
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out_1 = np.broadcast_to(out_1, [100, 200])
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out_2 = np.reshape(input_2, [1, 200])
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out_2 = np.broadcast_to(out_2, [100, 200])
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(shape=[100], dtype='complex64', name='x')
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y = paddle.static.data(shape=[200], dtype='complex64', name='y')
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exe = base.Executor(place=base.CPUPlace())
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grid_x, grid_y = paddle.tensor.meshgrid((x, y))
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res_1, res_2 = exe.run(
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paddle.static.default_main_program(),
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feed={'x': input_1, 'y': input_2},
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fetch_list=[grid_x, grid_y],
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)
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np.testing.assert_array_equal(res_1, out_1)
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np.testing.assert_array_equal(res_2, out_2)
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class TestMeshgridOpComplexDygraph(unittest.TestCase):
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def test_api_with_dygraph_tuple_input(self):
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input_3 = np.random.randint(
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0,
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100,
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[
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100,
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],
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).astype('complex64')
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input_4 = np.random.randint(
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0,
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100,
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[
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200,
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],
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).astype('complex64')
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with base.dygraph.guard():
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tensor_3 = paddle.to_tensor(input_3)
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tensor_4 = paddle.to_tensor(input_4)
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res_3, res_4 = paddle.tensor.meshgrid((tensor_3, tensor_4))
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np.testing.assert_array_equal(res_3.shape, [100, 200])
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np.testing.assert_array_equal(res_4.shape, [100, 200])
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class TestMeshGrid_ZeroDim(TestMeshgridOp):
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def init_inputs_and_outputs(self):
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self.shape = self.get_x_shape()
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ins = []
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outs = []
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ins.append(np.random.random([]).astype(self.dtype))
|
|
ins.append(np.random.random([2]).astype(self.dtype))
|
|
ins.append(np.random.random([3]).astype(self.dtype))
|
|
for i in range(len(self.shape)):
|
|
out_reshape = [1] * len(self.shape)
|
|
out_reshape[i] = self.shape[i]
|
|
out_temp = np.reshape(ins[i], out_reshape)
|
|
outs.append(np.broadcast_to(out_temp, self.shape))
|
|
self.inputs = {'X': [(f'x{i}', ins[i]) for i in range(len(ins))]}
|
|
self.outputs = {'Out': [(f'out{i}', outs[i]) for i in range(len(outs))]}
|
|
|
|
def get_x_shape(self):
|
|
return [1, 2, 3]
|
|
|
|
def if_enable_cinn(self):
|
|
self.enable_cinn = False
|
|
|
|
|
|
class TestMeshgridEager(unittest.TestCase):
|
|
def test_dygraph_api(self):
|
|
input_1 = np.random.randint(
|
|
0,
|
|
100,
|
|
[
|
|
100,
|
|
],
|
|
).astype('int32')
|
|
input_2 = np.random.randint(
|
|
0,
|
|
100,
|
|
[
|
|
200,
|
|
],
|
|
).astype('int32')
|
|
|
|
with base.dygraph.guard():
|
|
tensor_1 = paddle.to_tensor(input_1)
|
|
tensor_2 = paddle.to_tensor(input_2)
|
|
tensor_1.stop_gradient = False
|
|
tensor_2.stop_gradient = False
|
|
res_1, res_2 = paddle.tensor.meshgrid((tensor_1, tensor_2))
|
|
sum = paddle.add_n([res_1, res_2])
|
|
sum.backward()
|
|
tensor_eager_1 = paddle.to_tensor(input_1)
|
|
tensor_eager_2 = paddle.to_tensor(input_2)
|
|
tensor_eager_1.stop_gradient = False
|
|
tensor_eager_2.stop_gradient = False
|
|
res_eager_1, res_eager_2 = paddle.tensor.meshgrid(
|
|
(tensor_eager_1, tensor_eager_2)
|
|
)
|
|
sum_eager = paddle.add_n([res_eager_1, res_eager_2])
|
|
sum_eager.backward()
|
|
self.assertEqual(
|
|
(tensor_1.grad.numpy() == tensor_eager_1.grad.numpy()).all(),
|
|
True,
|
|
)
|
|
self.assertEqual(
|
|
(tensor_2.grad.numpy() == tensor_eager_2.grad.numpy()).all(),
|
|
True,
|
|
)
|
|
|
|
|
|
class TestMeshgridEmptyTensor(unittest.TestCase):
|
|
def _get_places(self):
|
|
places = [base.CPUPlace()]
|
|
if paddle.is_compiled_with_cuda() or is_custom_device():
|
|
places.append(get_device_place())
|
|
return places
|
|
|
|
def _generate_inputs(self, shapes):
|
|
return [np.random.random(shape).astype('float64') for shape in shapes]
|
|
|
|
def _test_with_shapes(self, shapes, expected_shapes, place=None):
|
|
inputs = self._generate_inputs(shapes)
|
|
|
|
if place is None: # Dygraph mode
|
|
with base.dygraph.guard():
|
|
tensors = [paddle.to_tensor(inp) for inp in inputs]
|
|
results = paddle.meshgrid(tensors)
|
|
else: # Static mode
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
data_tensors = [
|
|
paddle.static.data(
|
|
shape=shape, dtype='float64', name=f'x{i}'
|
|
)
|
|
for i, shape in enumerate(shapes)
|
|
]
|
|
exe = base.Executor(place=place)
|
|
grid_results = paddle.tensor.meshgrid(data_tensors)
|
|
feed_dict = {f'x{i}': inp for i, inp in enumerate(inputs)}
|
|
results = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed=feed_dict,
|
|
fetch_list=grid_results,
|
|
)
|
|
|
|
for result, expected_shape in zip(results, expected_shapes):
|
|
np.testing.assert_array_equal(result.shape, expected_shape)
|
|
|
|
def test_api_with_dygraph_empty_tensor_input(self):
|
|
self._test_with_shapes([(100,), (0,)], [[100, 0], [100, 0]])
|
|
|
|
def _test_api_with_static_empty_tensor_input(self, place):
|
|
self._test_with_shapes([(100,), (0,)], [[100, 0], [100, 0]], place)
|
|
|
|
def test_api_with_static_empty_tensor_input(self):
|
|
for place in self._get_places():
|
|
self._test_api_with_static_empty_tensor_input(place)
|
|
|
|
|
|
class TestMeshgridEmptyTensor2(TestMeshgridEmptyTensor):
|
|
def test_api_with_dygraph_empty_tensor_input(self):
|
|
self._test_with_shapes(
|
|
[(0,), (0,), (0,)], [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
|
|
)
|
|
|
|
def _test_api_with_static_empty_tensor_input(self, place):
|
|
self._test_with_shapes(
|
|
[(0,), (0,), (0,)], [[0, 0, 0], [0, 0, 0], [0, 0, 0]], place
|
|
)
|
|
|
|
|
|
class TestMeshgridZeroSizeGrad(unittest.TestCase):
|
|
def test_zero_size_grad_dynamic(self):
|
|
with base.dygraph.guard():
|
|
x = paddle.to_tensor(
|
|
np.ones([3]), dtype="float32", stop_gradient=False
|
|
)
|
|
y = paddle.to_tensor(
|
|
np.ones([0]), dtype="float32", stop_gradient=False
|
|
)
|
|
x_grid, y_grid = paddle.meshgrid(x, y)
|
|
z = paddle.sum(x_grid + y_grid)
|
|
z.backward()
|
|
x_grad_expacted = np.zeros([3])
|
|
y_grad_expacted = np.zeros([0])
|
|
np.testing.assert_array_equal(x.grad, x_grad_expacted)
|
|
np.testing.assert_array_equal(y.grad, y_grad_expacted)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|