wehub-resource-sync
973 lines
33 KiB
Python
973 lines
33 KiB
Python
# Copyright (c) 2022 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 itertools
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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,
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get_device_place,
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is_custom_device,
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skip_check_grad_ci,
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)
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from utils import dygraph_guard
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import paddle
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import paddle.static
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from paddle import base
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from paddle.base import core
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paddle.enable_static()
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def broadcast_wrapper(shape=[1, 10, 12, 1]):
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def div_wrapper(x, y, axis=-1):
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return paddle.divide(x, paddle.reshape(y, shape))
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return div_wrapper
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class ElementwiseDivOp(OpTest):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.python_api = paddle.divide
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self.public_python_api = paddle.divide
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self.prim_op_type = "prim"
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self.init_args()
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self.init_dtype()
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self.init_shape()
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self.if_check_prim()
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self.if_enable_cinn()
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x = self.gen_data(self.x_shape).astype(self.val_dtype)
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y = self.gen_data(self.y_shape).astype(self.val_dtype)
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out = self.compute_output(x, y).astype(self.val_dtype)
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grad_out = np.ones(out.shape).astype(self.val_dtype)
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grad_x = self.compute_gradient_x(grad_out, y).astype(self.val_dtype)
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grad_y = self.compute_gradient_y(grad_out, out, y).astype(
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self.val_dtype
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)
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# Convert np.float32 data to np.uint16 for bfloat16 Paddle OP
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if self.dtype == np.uint16:
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x = convert_float_to_uint16(x)
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y = convert_float_to_uint16(y)
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out = convert_float_to_uint16(out)
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grad_out = convert_float_to_uint16(grad_out)
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grad_x = convert_float_to_uint16(grad_x)
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grad_y = convert_float_to_uint16(grad_y)
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': out}
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self.grad_out = grad_out
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self.grad_x = grad_x
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self.grad_y = grad_y
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def if_enable_cinn(self):
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self.enable_cinn = True
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def init_args(self):
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self.check_pir = True
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self.check_dygraph = True
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self.place = None
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def init_dtype(self):
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self.dtype = np.float64
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self.val_dtype = np.float64
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def init_shape(self):
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self.x_shape = [13, 17]
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self.y_shape = [13, 17]
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def if_check_prim(self):
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self.check_prim = True
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self.check_prim_pir = True
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def gen_data(self, shape):
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return np.random.uniform(0.1, 1, shape)
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def compute_output(self, x, y):
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return x / y
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def compute_gradient_x(self, grad_out, y):
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return grad_out / y
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def compute_gradient_y(self, grad_out, out, y):
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return -1 * grad_out * out / y
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def test_check_output(self):
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if self.place is None:
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self.check_output(
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check_pir=self.check_pir, check_dygraph=self.check_dygraph
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)
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else:
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self.check_output_with_place(
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self.place,
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check_pir=self.check_pir,
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check_dygraph=self.check_dygraph,
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)
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def test_check_gradient(self):
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check_list = []
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check_list.append(
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{
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'grad': ['X', 'Y'],
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'no_grad': None,
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'val_grad': [self.grad_x, self.grad_y],
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}
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)
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check_list.append(
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{'grad': ['Y'], 'no_grad': set('X'), 'val_grad': [self.grad_y]}
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)
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check_list.append(
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{'grad': ['X'], 'no_grad': set('Y'), 'val_grad': [self.grad_x]}
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)
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for check_option in check_list:
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check_args = [check_option['grad'], 'Out']
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check_kwargs = {
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'no_grad_set': check_option['no_grad'],
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'user_defined_grads': check_option['val_grad'],
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'user_defined_grad_outputs': [self.grad_out],
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'check_dygraph': self.check_dygraph,
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'check_prim': self.check_prim,
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'check_prim_pir': self.check_prim_pir,
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}
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if self.place is None:
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self.check_grad(
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*check_args, **check_kwargs, check_pir=self.check_pir
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)
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else:
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check_args.insert(0, self.place)
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self.check_grad_with_place(
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*check_args, **check_kwargs, check_pir=self.check_pir
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)
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class TestElementwiseDivPrimOpFp32(ElementwiseDivOp):
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def init_dtype(self):
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self.dtype = np.float32
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self.val_dtype = np.float32
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class TestElementwiseDivOp_ZeroDim1(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = []
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self.y_shape = []
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class TestElementwiseDivOp_ZeroDim2(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [13, 17]
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self.y_shape = []
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def compute_output(self, x, y):
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return x / y.reshape([1, 1])
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def compute_gradient_x(self, grad_out, y):
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return grad_out / y.reshape([1, 1])
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def compute_gradient_y(self, grad_out, out, y):
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return np.sum(-1 * grad_out * out / y.reshape([1, 1]))
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class TestElementwiseDivOp_ZeroDim3(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = []
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self.y_shape = [13, 17]
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def compute_output(self, x, y):
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return x.reshape([1, 1]) / y
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def compute_gradient_x(self, grad_out, y):
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return np.sum(grad_out / y)
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def compute_gradient_y(self, grad_out, out, y):
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return -1 * grad_out * out / y
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class TestElementwiseDivOp_ZeroSize1(ElementwiseDivOp):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [3]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [0, 3]).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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def test_check_gradient(self):
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pass
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class TestElementwiseDivOp_ZeroSize2(TestElementwiseDivOp_ZeroSize1):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [1, 3, 4]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [0, 3, 4]).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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class TestElementwiseDivOp_ZeroSize3(TestElementwiseDivOp_ZeroSize1):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [1, 0, 2]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [3, 0, 1]).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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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 TestElementwiseDivOpBF16(ElementwiseDivOp):
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def init_args(self):
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# In due to output data type inconsistency of bfloat16 paddle op, we disable the dygraph check.
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self.check_dygraph = False
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self.place = get_device_place()
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def init_dtype(self):
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self.dtype = np.uint16
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self.val_dtype = np.float32
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def init_shape(self):
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self.x_shape = [12, 13]
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self.y_shape = [12, 13]
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def test_check_gradient(self):
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check_list = []
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check_list.append(
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{
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'grad': ['X', 'Y'],
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'no_grad': None,
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'val_grad': [self.grad_x, self.grad_y],
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}
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)
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check_list.append(
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{'grad': ['Y'], 'no_grad': set('X'), 'val_grad': [self.grad_y]}
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)
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check_list.append(
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{'grad': ['X'], 'no_grad': set('Y'), 'val_grad': [self.grad_x]}
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)
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for check_option in check_list:
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check_args = [check_option['grad'], 'Out']
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check_kwargs = {
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'no_grad_set': check_option['no_grad'],
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'check_dygraph': self.check_dygraph,
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'check_prim': self.check_prim,
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'check_prim_pir': self.check_prim_pir,
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}
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if self.place is None:
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self.check_grad(*check_args, **check_kwargs, check_pir=True)
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else:
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check_args.insert(0, self.place)
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self.check_grad_with_place(
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*check_args, **check_kwargs, check_pir=True
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)
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def if_check_prim(self):
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self.check_prim = True
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self.check_prim_pir = True
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def if_enable_cinn(self):
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self.enable_cinn = False
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@skip_check_grad_ci(
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reason="[skip shape check] Use y_shape(1) to test broadcast."
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)
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class TestElementwiseDivOpScalar(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [20, 3, 4]
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self.y_shape = [1]
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def compute_gradient_y(self, grad_out, out, y):
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return np.array([np.sum(-1 * grad_out * out / y)])
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class TestElementwiseDivOpVector(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [100]
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self.y_shape = [100]
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class TestElementwiseDivOpNoPrim(ElementwiseDivOp):
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def test_check_gradient(self):
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check_list = []
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check_list.append(
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{
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'grad': ['X', 'Y'],
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'no_grad': None,
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'val_grad': [self.grad_x, self.grad_y],
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}
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)
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check_list.append(
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{'grad': ['Y'], 'no_grad': set('X'), 'val_grad': [self.grad_y]}
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)
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check_list.append(
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{'grad': ['X'], 'no_grad': set('Y'), 'val_grad': [self.grad_x]}
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)
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for check_option in check_list:
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check_args = [check_option['grad'], 'Out']
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check_kwargs = {
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'no_grad_set': check_option['no_grad'],
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'user_defined_grads': check_option['val_grad'],
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'user_defined_grad_outputs': [self.grad_out],
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'check_dygraph': self.check_dygraph,
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}
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if self.place is None:
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self.check_grad(*check_args, **check_kwargs, check_pir=True)
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else:
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check_args.insert(0, self.place)
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self.check_grad_with_place(
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*check_args, **check_kwargs, check_pir=True
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)
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class TestElementwiseDivOpBroadcast0(TestElementwiseDivOpNoPrim):
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def init_shape(self):
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self.x_shape = [100, 3, 4]
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self.y_shape = [100]
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self.attrs = {'axis': 0}
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self.python_api = broadcast_wrapper(shape=[100, 1, 1])
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def compute_output(self, x, y):
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return x / y.reshape(100, 1, 1)
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def compute_gradient_x(self, grad_out, y):
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return grad_out / y.reshape(100, 1, 1)
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def compute_gradient_y(self, grad_out, out, y):
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return np.sum(-1 * grad_out * out / y.reshape(100, 1, 1), axis=(1, 2))
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class TestElementwiseDivOpBroadcast1(TestElementwiseDivOpNoPrim):
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def init_shape(self):
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self.x_shape = [2, 100, 4]
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self.y_shape = [100]
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self.attrs = {'axis': 1}
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self.python_api = broadcast_wrapper(shape=[1, 100, 1])
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def compute_output(self, x, y):
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return x / y.reshape(1, 100, 1)
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def compute_gradient_x(self, grad_out, y):
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return grad_out / y.reshape(1, 100, 1)
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def compute_gradient_y(self, grad_out, out, y):
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return np.sum(-1 * grad_out * out / y.reshape(1, 100, 1), axis=(0, 2))
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class TestElementwiseDivOpBroadcast2(TestElementwiseDivOpNoPrim):
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def init_shape(self):
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self.x_shape = [2, 3, 100]
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self.y_shape = [100]
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self.python_api = broadcast_wrapper(shape=[1, 1, 100])
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def compute_output(self, x, y):
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return x / y.reshape(1, 1, 100)
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def compute_gradient_x(self, grad_out, y):
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return grad_out / y.reshape(1, 1, 100)
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def compute_gradient_y(self, grad_out, out, y):
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return np.sum(-1 * grad_out * out / y.reshape(1, 1, 100), axis=(0, 1))
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class TestElementwiseDivOpBroadcast3(TestElementwiseDivOpNoPrim):
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def init_shape(self):
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self.x_shape = [2, 10, 12, 5]
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self.y_shape = [10, 12]
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self.attrs = {'axis': 1}
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self.python_api = broadcast_wrapper(shape=[1, 10, 12, 1])
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def compute_output(self, x, y):
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return x / y.reshape(1, 10, 12, 1)
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def compute_gradient_x(self, grad_out, y):
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return grad_out / y.reshape(1, 10, 12, 1)
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def compute_gradient_y(self, grad_out, out, y):
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return np.sum(
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-1 * grad_out * out / y.reshape(1, 10, 12, 1), axis=(0, 3)
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)
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class TestElementwiseDivOpBroadcast4(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [2, 3, 50]
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self.y_shape = [2, 1, 50]
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def compute_gradient_y(self, grad_out, out, y):
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return np.sum(-1 * grad_out * out / y, axis=(1)).reshape(2, 1, 50)
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class TestElementwiseDivOpBroadcast5(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [2, 3, 4, 20]
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self.y_shape = [2, 3, 1, 20]
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def compute_gradient_y(self, grad_out, out, y):
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return np.sum(-1 * grad_out * out / y, axis=(2)).reshape(2, 3, 1, 20)
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class TestElementwiseDivOpCommonuse1(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [2, 3, 100]
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self.y_shape = [1, 1, 100]
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def compute_gradient_y(self, grad_out, out, y):
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return np.sum(-1 * grad_out * out / y, axis=(0, 1)).reshape(1, 1, 100)
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class TestElementwiseDivOpCommonuse2(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [30, 3, 1, 5]
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self.y_shape = [30, 1, 4, 1]
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def compute_gradient_x(self, grad_out, y):
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return np.sum(grad_out / y, axis=(2)).reshape(30, 3, 1, 5)
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def compute_gradient_y(self, grad_out, out, y):
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return np.sum(-1 * grad_out * out / y, axis=(1, 3)).reshape(30, 1, 4, 1)
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class TestElementwiseDivOpXsizeLessThanYsize(ElementwiseDivOp):
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def init_shape(self):
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self.x_shape = [10, 12]
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self.y_shape = [2, 3, 10, 12]
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self.attrs = {'axis': 2}
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def compute_gradient_x(self, grad_out, y):
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return np.sum(grad_out / y, axis=(0, 1))
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class TestElementwiseDivOpInt(ElementwiseDivOp):
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def init_args(self):
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self.check_pir = False
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self.check_dygraph = False
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self.place = None
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def if_check_prim(self):
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self.check_prim = False
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self.check_prim_pir = False
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def init_dtype(self):
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self.dtype = np.int32
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self.val_dtype = np.int32
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def gen_data(self, shape):
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return np.random.randint(1, 5, size=shape)
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def compute_output(self, x, y):
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return x / y
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def create_test_fp16_class(parent, max_relative_error=2e-3):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestElementwiseDivFP16Op(parent):
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def init_dtype(self):
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self.dtype = np.float16
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self.val_dtype = np.float16
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def if_enable_cinn(self):
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self.enable_cinn = True
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def test_check_gradient(self):
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check_list = []
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check_list.append(
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{
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'grad': ['X', 'Y'],
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'no_grad': None,
|
|
'val_grad': [self.grad_x, self.grad_y],
|
|
}
|
|
)
|
|
check_list.append(
|
|
{'grad': ['Y'], 'no_grad': set('X'), 'val_grad': [self.grad_y]}
|
|
)
|
|
check_list.append(
|
|
{'grad': ['X'], 'no_grad': set('Y'), 'val_grad': [self.grad_x]}
|
|
)
|
|
for check_option in check_list:
|
|
check_args = [check_option['grad'], 'Out']
|
|
check_kwargs = {
|
|
'no_grad_set': check_option['no_grad'],
|
|
'user_defined_grads': check_option['val_grad'],
|
|
'user_defined_grad_outputs': [self.grad_out],
|
|
'check_dygraph': self.check_dygraph,
|
|
'max_relative_error': max_relative_error,
|
|
}
|
|
if self.place is None:
|
|
self.check_grad(*check_args, **check_kwargs, check_pir=True)
|
|
else:
|
|
check_args.insert(0, self.place)
|
|
self.check_grad_with_place(
|
|
*check_args,
|
|
**check_kwargs,
|
|
check_pir=True,
|
|
check_prim=True,
|
|
check_prim_pir=True,
|
|
)
|
|
|
|
cls_name = "{}_{}".format(parent.__name__, "Fp16")
|
|
TestElementwiseDivFP16Op.__name__ = cls_name
|
|
globals()[cls_name] = TestElementwiseDivFP16Op
|
|
|
|
|
|
create_test_fp16_class(ElementwiseDivOp)
|
|
create_test_fp16_class(TestElementwiseDivOp_ZeroDim1)
|
|
create_test_fp16_class(TestElementwiseDivOp_ZeroDim2)
|
|
create_test_fp16_class(TestElementwiseDivOp_ZeroDim3)
|
|
create_test_fp16_class(TestElementwiseDivOpScalar)
|
|
create_test_fp16_class(TestElementwiseDivOpVector)
|
|
create_test_fp16_class(TestElementwiseDivOpBroadcast0)
|
|
create_test_fp16_class(TestElementwiseDivOpBroadcast1)
|
|
create_test_fp16_class(TestElementwiseDivOpBroadcast2)
|
|
create_test_fp16_class(TestElementwiseDivOpBroadcast3)
|
|
create_test_fp16_class(TestElementwiseDivOpBroadcast4)
|
|
create_test_fp16_class(TestElementwiseDivOpBroadcast5)
|
|
create_test_fp16_class(TestElementwiseDivOpCommonuse1)
|
|
create_test_fp16_class(TestElementwiseDivOpCommonuse2)
|
|
create_test_fp16_class(TestElementwiseDivOpXsizeLessThanYsize)
|
|
|
|
|
|
class TestElementwiseDivBroadcast(unittest.TestCase):
|
|
def test_shape_with_batch_sizes(self):
|
|
main_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program):
|
|
x_var = paddle.static.data(
|
|
name='x', dtype='float32', shape=[None, 3, None, None]
|
|
)
|
|
one = 2.0
|
|
out = one / x_var
|
|
exe = base.Executor(base.CPUPlace())
|
|
x = np.random.uniform(0.1, 0.6, (1, 3, 32, 32)).astype("float32")
|
|
(out_result,) = exe.run(feed={'x': x}, fetch_list=[out])
|
|
self.assertEqual((out_result == (2 / x)).all(), True)
|
|
|
|
|
|
class TestDivideOp(unittest.TestCase):
|
|
def test_name(self):
|
|
with paddle.pir_utils.OldIrGuard():
|
|
main_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program):
|
|
x = paddle.static.data(name="x", shape=[2, 3], dtype="float32")
|
|
y = paddle.static.data(name='y', shape=[2, 3], dtype='float32')
|
|
|
|
y_1 = paddle.divide(x, y, name='div_res')
|
|
|
|
self.assertEqual(('div_res' in y_1.name), True)
|
|
|
|
def test_dygraph(self):
|
|
with base.dygraph.guard():
|
|
np_x = np.array([2, 3, 4]).astype('float64')
|
|
np_y = np.array([1, 5, 2]).astype('float64')
|
|
x = paddle.to_tensor(np_x)
|
|
y = paddle.to_tensor(np_y)
|
|
z = paddle.divide(x, y)
|
|
np_z = z.numpy(False)
|
|
z_expected = np.array([2.0, 0.6, 2.0])
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
|
|
# new ir doesn't support complex right now, skip new ir op test
|
|
class TestComplexElementwiseDivOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_div"
|
|
self.python_api = paddle.divide
|
|
self.init_base_dtype()
|
|
self.init_input_output()
|
|
|
|
self.inputs = {
|
|
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
|
'Y': OpTest.np_dtype_to_base_dtype(self.y),
|
|
}
|
|
self.attrs = {'axis': -1, 'use_onednn': False}
|
|
self.outputs = {'Out': self.out}
|
|
|
|
def init_base_dtype(self):
|
|
self.dtype = np.complex128
|
|
|
|
def init_input_output(self):
|
|
self.x = np.random.random((2, 3, 4, 5)).astype(
|
|
self.dtype
|
|
) + 1j * np.random.random((2, 3, 4, 5)).astype(self.dtype)
|
|
self.y = np.random.random((2, 3, 4, 5)).astype(
|
|
self.dtype
|
|
) + 1j * np.random.random((2, 3, 4, 5)).astype(self.dtype)
|
|
self.out = self.x / self.y
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True)
|
|
|
|
def test_check_grad_normal(self):
|
|
self.check_grad(
|
|
['X', 'Y'],
|
|
'Out',
|
|
numeric_grad_delta=1e-5,
|
|
max_relative_error=1e-6,
|
|
check_pir=True,
|
|
)
|
|
|
|
def test_check_grad_ignore_x(self):
|
|
self.check_grad(
|
|
['Y'],
|
|
'Out',
|
|
no_grad_set=set("X"),
|
|
numeric_grad_delta=1e-5,
|
|
max_relative_error=1e-6,
|
|
check_pir=True,
|
|
)
|
|
|
|
def test_check_grad_ignore_y(self):
|
|
self.check_grad(
|
|
['X'],
|
|
'Out',
|
|
no_grad_set=set('Y'),
|
|
numeric_grad_delta=1e-5,
|
|
max_relative_error=1e-6,
|
|
check_pir=True,
|
|
)
|
|
|
|
|
|
class TestRealComplexElementwiseDivOp(TestComplexElementwiseDivOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.random((2, 3, 4, 5)).astype(self.dtype)
|
|
self.y = np.random.random((2, 3, 4, 5)).astype(
|
|
self.dtype
|
|
) + 1j * np.random.random((2, 3, 4, 5)).astype(self.dtype)
|
|
self.out = self.x / self.y
|
|
|
|
def init_grad_input_output(self):
|
|
self.grad_out = np.ones((2, 3, 4, 5), self.dtype) + 1j * np.ones(
|
|
(2, 3, 4, 5), self.dtype
|
|
)
|
|
self.grad_x = np.real(self.grad_out / np.conj(self.y))
|
|
self.grad_y = -self.grad_out * np.conj(self.x / self.y / self.y)
|
|
|
|
|
|
class TestElementwiseDivop(unittest.TestCase):
|
|
def test_dygraph_div(self):
|
|
paddle.disable_static()
|
|
|
|
np_a = np.random.random((2, 3, 4)).astype(np.float32)
|
|
np_b = np.random.random((2, 3, 4)).astype(np.float32)
|
|
np_a[np.abs(np_a) < 0.0005] = 0.002
|
|
np_b[np.abs(np_b) < 0.0005] = 0.002
|
|
|
|
tensor_a = paddle.to_tensor(np_a, dtype="float32")
|
|
tensor_b = paddle.to_tensor(np_b, dtype="float32")
|
|
|
|
# normal case: nparray / tenor
|
|
expect_out = np_a / np_b
|
|
actual_out = np_a / tensor_b
|
|
np.testing.assert_allclose(actual_out, expect_out)
|
|
|
|
# normal case: tensor / nparray
|
|
actual_out = tensor_a / np_b
|
|
np.testing.assert_allclose(actual_out, expect_out)
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
# The new ir and dynamic graphs are not consistent with the int division of the old ir.
|
|
class TestElementwiseDivopInt(unittest.TestCase):
|
|
def test_dygraph_div(self):
|
|
paddle.disable_static()
|
|
|
|
np_a = np.random.randint(1, 5, size=(2, 3, 4)).astype(np.int32)
|
|
np_b = np.random.randint(1, 5, size=(2, 3, 4)).astype(np.int32)
|
|
expect_res = np_a / np_b
|
|
expect_a_grad = (1 / np_b).astype(np.int32)
|
|
expect_b_grad = (-np_a / np_b**2).astype(np.int32)
|
|
|
|
paddle_a = paddle.to_tensor(np_a, stop_gradient=False)
|
|
paddle_b = paddle.to_tensor(np_b, stop_gradient=False)
|
|
actual_res = paddle_a / paddle_b
|
|
actual_res.backward()
|
|
actual_a_grad = paddle_a.grad
|
|
actual_b_grad = paddle_b.grad
|
|
np.testing.assert_allclose(actual_res, expect_res)
|
|
np.testing.assert_allclose(expect_a_grad, actual_a_grad)
|
|
np.testing.assert_allclose(expect_b_grad, actual_b_grad)
|
|
paddle.enable_static()
|
|
|
|
def test_pir_div(self):
|
|
with paddle.pir_utils.IrGuard():
|
|
exe = paddle.static.Executor()
|
|
main_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
np_a = np.random.randint(1, 5, size=(2, 3, 4)).astype(np.int32)
|
|
np_b = np.random.randint(1, 5, size=(2, 3, 4)).astype(np.int32)
|
|
expect_res = np_a / np_b
|
|
expect_a_grad = (1 / np_b).astype(np.int32)
|
|
expect_b_grad = (-np_a / np_b**2).astype(np.int32)
|
|
|
|
paddle_a = paddle.to_tensor(np_a, stop_gradient=False)
|
|
paddle_b = paddle.to_tensor(np_b, stop_gradient=False)
|
|
out = paddle_a / paddle_b
|
|
actual_grad = paddle.static.gradients(out, [paddle_a, paddle_b])
|
|
actual_res = exe.run(
|
|
main_program, fetch_list=[out, actual_grad]
|
|
)
|
|
np.testing.assert_allclose(actual_res[0], expect_res)
|
|
np.testing.assert_allclose(actual_res[1], expect_a_grad)
|
|
np.testing.assert_allclose(actual_res[2], expect_b_grad)
|
|
|
|
|
|
class TestDivApiZeroSize(unittest.TestCase):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(1, 3, 4).astype('float32')
|
|
self.y_numpy = np.random.rand(0, 3, 4).astype('float32')
|
|
|
|
def _executed_api(self, x, y, name=None):
|
|
return paddle.divide(x, y, name)
|
|
|
|
def test_declarative(self):
|
|
self.init_data()
|
|
with base.program_guard(base.Program()):
|
|
x = paddle.static.data(
|
|
name="x", shape=self.x_numpy.shape, dtype=self.x_numpy.dtype
|
|
)
|
|
y = paddle.static.data(
|
|
name="y", shape=self.y_numpy.shape, dtype=self.y_numpy.dtype
|
|
)
|
|
z = self._executed_api(x, y)
|
|
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
z_value = exe.run(
|
|
feed={"x": self.x_numpy, "y": self.y_numpy}, fetch_list=[z]
|
|
)
|
|
np_z = np.divide(self.x_numpy, self.y_numpy)
|
|
np.testing.assert_allclose(z_value[0], np_z, rtol=1e-05, atol=1e-05)
|
|
|
|
def test_dygraph(self):
|
|
self.init_data()
|
|
places = (
|
|
[paddle.CPUPlace(), get_device_place()]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [paddle.CPUPlace()]
|
|
)
|
|
for place in places:
|
|
with base.dygraph.guard(place):
|
|
x = paddle.to_tensor(self.x_numpy)
|
|
y = paddle.to_tensor(self.y_numpy)
|
|
z = self._executed_api(x, y)
|
|
np_z = np.divide(self.x_numpy, self.y_numpy)
|
|
np.testing.assert_allclose(z, np_z, rtol=1e-05, atol=1e-05)
|
|
|
|
|
|
class TestDivApiZeroSize2(TestDivApiZeroSize):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(3).astype('float32')
|
|
self.y_numpy = np.random.rand(0, 3).astype('float32')
|
|
|
|
|
|
class TestDivApiZeroSize3(TestDivApiZeroSize):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(2, 0).astype('float32')
|
|
self.y_numpy = np.random.rand(1, 0).astype('float32')
|
|
|
|
|
|
class TestDivApiZeroSize4(TestDivApiZeroSize):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(1, 0, 2).astype('float32')
|
|
self.y_numpy = np.random.rand(3, 0, 1).astype('float32')
|
|
|
|
|
|
class TestDivComplexDtype(unittest.TestCase):
|
|
def test(self):
|
|
with dygraph_guard():
|
|
places = ['cpu']
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
places.append(get_device())
|
|
shapes = [[], [1], [1, 1]]
|
|
values = [
|
|
-paddle.inf,
|
|
paddle.inf,
|
|
paddle.nan,
|
|
-np.zeros([]),
|
|
+np.zeros([]),
|
|
paddle.nan,
|
|
-paddle.nan,
|
|
1e-23,
|
|
-1e-23,
|
|
]
|
|
dtypes = ["float32", "float64", "complex64", "complex128"]
|
|
|
|
for place in places:
|
|
with base.device_guard(place):
|
|
for (
|
|
shape_x,
|
|
shape_y,
|
|
x,
|
|
y,
|
|
dtype_x,
|
|
dtype_y,
|
|
) in itertools.product(
|
|
shapes, shapes, values, values, dtypes, dtypes
|
|
):
|
|
pd_x = paddle.to_tensor(x, dtype=dtype_x).reshape(
|
|
shape_x
|
|
)
|
|
pd_y = paddle.to_tensor(y, dtype=dtype_y).reshape(
|
|
shape_y
|
|
)
|
|
pd_z = paddle.divide(pd_x, pd_y)
|
|
|
|
np_x = np.asarray(x, dtype=dtype_x).reshape(shape_x)
|
|
np_y = np.asarray(y, dtype=dtype_y).reshape(shape_y)
|
|
np_z = np.divide(np_x, np_y)
|
|
|
|
err_msg = (
|
|
f"\n❌ Mismatch detected!\n"
|
|
f"Place: {place}\n"
|
|
f"x={x}, y={y}, dtype_x={dtype_x}, dtype_y={dtype_y}\n"
|
|
f"Shape_x: {shape_x}, Shape_y: {shape_y}\n"
|
|
f"np_x={np_x.item()}, np_y={np_y.item()}, np_z={np_z.item()}\n"
|
|
f"pd_x={pd_x.item()}, pd_y={pd_y.item()}, pd_z={pd_z.item()}"
|
|
)
|
|
np.testing.assert_allclose(
|
|
pd_z.item(), np_z, 0.0, 0.0, err_msg=err_msg
|
|
)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestElementwiseDivOp_Stride(OpTest):
|
|
no_need_check_grad = True
|
|
|
|
def setUp(self):
|
|
self.op_type = "elementwise_div"
|
|
self.python_api = paddle.divide
|
|
self.public_python_api = paddle.divide
|
|
self.transpose_api = paddle.transpose
|
|
self.as_stride_api = paddle.as_strided
|
|
self.init_dtype()
|
|
self.init_input_output()
|
|
|
|
self.inputs_stride = {
|
|
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
|
'Y': OpTest.np_dtype_to_base_dtype(self.y_trans),
|
|
}
|
|
|
|
self.inputs = {
|
|
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
|
'Y': OpTest.np_dtype_to_base_dtype(self.y),
|
|
}
|
|
|
|
self.outputs = {'Out': self.out}
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
self.val_dtype = np.float64
|
|
|
|
def test_check_output(self):
|
|
place = get_device_place()
|
|
self.check_strided_forward = True
|
|
self.check_output(
|
|
place,
|
|
)
|
|
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
|
self.out = self.x / self.y
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
def test_check_gradient(self):
|
|
pass
|
|
|
|
|
|
class TestElementwiseDivOp_Stride1(TestElementwiseDivOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.out = self.x / self.y
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseDivOp_Stride2(TestElementwiseDivOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.out = self.x / self.y
|
|
self.perm = [0, 2, 1, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseDivOp_Stride3(TestElementwiseDivOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
|
|
self.out = self.x / self.y
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseDivOp_Stride4(TestElementwiseDivOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [1, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
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self.out = self.x / self.y
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self.perm = [1, 0, 2, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseDivOp_Stride5(TestElementwiseDivOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "as_stride"
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self.x = np.random.uniform(0.1, 1, [23, 10, 1, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [23, 2, 13, 20]).astype(self.dtype)
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self.y_trans = self.y
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self.y = self.y[:, 0:1, :, 0:1]
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self.out = self.x / self.y
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self.shape_param = [23, 1, 13, 1]
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self.stride_param = [520, 260, 20, 1]
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class TestElementwiseDivOp_Stride_ZeroDim1(TestElementwiseDivOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, []).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = self.x / self.y
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseDivOp_Stride_ZeroSize1(TestElementwiseDivOp_Stride):
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def init_data(self):
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self.strided_input_type = "transpose"
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self.x = np.random.rand(1, 0, 2).astype('float32')
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self.y = np.random.rand(3, 0, 1).astype('float32')
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self.out = self.x / self.y
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self.perm = [2, 1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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if __name__ == '__main__':
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unittest.main()
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