wehub-resource-sync
1243 lines
42 KiB
Python
1243 lines
42 KiB
Python
# Copyright (c) 2021 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 random
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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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get_places,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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np.random.seed(0)
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def cumprod_wrapper(x, dim=-1, exclusive=False, reverse=False):
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return paddle._C_ops.cumprod(x, dim, exclusive, reverse)
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# define cumprod grad function.
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def cumprod_grad(x, y, dy, dx, shape, dim, exclusive=False, reverse=False):
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if dim < 0:
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dim += len(shape)
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mid_dim = shape[dim]
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outer_dim = 1
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inner_dim = 1
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for i in range(0, dim):
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outer_dim *= shape[i]
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for i in range(dim + 1, len(shape)):
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inner_dim *= shape[i]
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if not reverse:
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for i in range(outer_dim):
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for k in range(inner_dim):
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for j in range(mid_dim):
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index = i * mid_dim * inner_dim + j * inner_dim + k
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for n in range(mid_dim):
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pos = i * mid_dim * inner_dim + n * inner_dim + k
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elem = 0
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if exclusive:
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if pos > index:
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elem = dy[pos] * y[index]
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for m in range(
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index + inner_dim, pos, inner_dim
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):
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elem *= x[m]
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else:
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elem = 0
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else:
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if j == 0:
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elem = dy[pos]
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else:
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elem = dy[pos] * y[index - inner_dim]
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if pos > index:
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for m in range(
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index + inner_dim,
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pos + inner_dim,
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inner_dim,
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):
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elem *= x[m]
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elif pos < index:
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elem = 0
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dx[index] += elem
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else:
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for i in range(outer_dim):
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for k in range(inner_dim):
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for j in range(mid_dim - 1, -1, -1):
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index = i * mid_dim * inner_dim + j * inner_dim + k
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for n in range(mid_dim - 1, -1, -1):
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pos = i * mid_dim * inner_dim + n * inner_dim + k
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elem = 0
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if exclusive:
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if pos < index:
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elem = dy[pos] * y[index]
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for m in range(
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index - inner_dim, pos, -inner_dim
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):
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elem *= x[m]
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else:
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if j == mid_dim - 1:
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elem = dy[pos]
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else:
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elem = dy[pos] * y[index + inner_dim]
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if pos < index:
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for m in range(
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index - inner_dim,
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pos - inner_dim,
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-inner_dim,
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):
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elem *= x[m]
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elif pos > index:
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elem = 0
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dx[index] += elem
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# test function.
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class TestCumprod(OpTest):
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def init_params(self):
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self.shape = (2, 3, 4, 5)
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self.zero_nums = [0, 10, 20, 30, int(np.prod(self.shape))]
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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 setUp(self):
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paddle.enable_static()
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self.init_params()
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self.init_dtype()
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self.op_type = "cumprod"
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self.python_api = cumprod_wrapper
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self.inputs = {'X': None}
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self.outputs = {'Out': None}
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self.attrs = {'dim': None}
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def prepare_inputs_outputs_attrs(self, dim, zero_num):
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self.x = (
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np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
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# np.ones(self.shape).astype(self.val_dtype)
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)
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if zero_num > 0:
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zero_num = min(zero_num, self.x.size)
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shape = self.x.shape
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self.x = self.x.flatten()
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indices = random.sample(range(self.x.size), zero_num)
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for i in indices:
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self.x[i] = 0
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self.x = np.reshape(self.x, self.shape)
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self.out = np.cumprod(self.x, axis=dim)
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if self.dtype == np.uint16:
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self.inputs = {'X': convert_float_to_uint16(self.x)}
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self.outputs = {'Out': convert_float_to_uint16(self.out)}
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else:
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self.inputs = {'X': self.x}
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self.outputs = {'Out': self.out}
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self.attrs = {'dim': dim}
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def init_grad_input_output(self, dim):
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reshape_x = self.x.reshape(self.x.size)
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self.grad_out = np.ones(self.x.size, self.val_dtype)
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self.grad_x = np.zeros(self.x.size, self.val_dtype)
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out_data = self.out.reshape(self.x.size)
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if self.dtype == np.complex128 or self.dtype == np.complex64:
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reshape_x = np.conj(reshape_x)
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out_data = np.conj(out_data)
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cumprod_grad(
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reshape_x, out_data, self.grad_out, self.grad_x, self.shape, dim
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)
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if self.dtype == np.uint16:
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self.grad_x = convert_float_to_uint16(
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self.grad_x.reshape(self.shape)
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)
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self.grad_out = convert_float_to_uint16(
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self.grad_out.reshape(self.shape)
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)
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else:
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self.grad_x = self.grad_x.reshape(self.shape)
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self.grad_out = self.grad_out.reshape(self.shape)
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# test forward.
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def test_check_output(self):
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for dim in range(-len(self.shape), len(self.shape)):
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for zero_num in self.zero_nums:
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self.prepare_inputs_outputs_attrs(dim, zero_num)
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self.check_output(check_pir=True)
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# test backward.
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def test_check_grad(self):
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for dim in range(-len(self.shape), len(self.shape)):
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for zero_num in self.zero_nums:
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self.prepare_inputs_outputs_attrs(dim, zero_num)
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self.init_grad_input_output(dim)
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if self.dtype == np.float64:
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self.check_grad(['X'], 'Out', check_pir=True)
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else:
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self.check_grad(
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['X'],
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'Out',
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user_defined_grads=[self.grad_x],
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user_defined_grad_outputs=[self.grad_out],
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check_pir=True,
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)
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# test float32 case.
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class TestCumprodFP32Op(TestCumprod):
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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 TestCumprodFP16Op(TestCumprod):
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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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@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 TestCumprodBF16Op(TestCumprod):
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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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# test forward.
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def test_check_output(self):
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for dim in range(-len(self.shape), len(self.shape)):
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for zero_num in self.zero_nums:
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self.prepare_inputs_outputs_attrs(dim, zero_num)
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self.check_output_with_place(get_device_place())
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# test backward.
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def test_check_grad(self):
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for dim in range(-len(self.shape), len(self.shape)):
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for zero_num in self.zero_nums:
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self.prepare_inputs_outputs_attrs(dim, zero_num)
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self.init_grad_input_output(dim)
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self.check_grad_with_place(
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get_device_place(),
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['X'],
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'Out',
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user_defined_grads=[self.grad_x],
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user_defined_grad_outputs=[self.grad_out],
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)
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# test complex64 case.
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class TestCumprodComplex64Op(TestCumprod):
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def init_dtype(self):
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self.dtype = np.complex64
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self.val_dtype = np.complex64
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# test complex128 case.
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class TestCumprodComplex128Op(TestCumprod):
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def init_dtype(self):
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self.dtype = np.complex128
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self.val_dtype = np.complex128
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# test api.
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class TestCumprodAPI(unittest.TestCase):
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def init_dtype(self):
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self.dtype = 'float64'
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self.shape = [2, 3, 10, 10]
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def setUp(self):
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paddle.enable_static()
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self.init_dtype()
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self.x = (np.random.rand(2, 3, 10, 10) + 0.5).astype(self.dtype)
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self.place = get_places()
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# test static graph api.
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def test_static_api(self):
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paddle.enable_static()
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def run(place):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('X', self.shape, dtype=self.dtype)
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out = paddle.cumprod(x, -2)
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exe = paddle.static.Executor(place)
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res = exe.run(feed={'X': self.x}, fetch_list=[out])
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out_ref = np.cumprod(self.x, -2)
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for r in res:
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np.testing.assert_allclose(out_ref, r, rtol=1e-05)
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for place in self.place:
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run(place)
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# test dynamic graph api.
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def test_dygraph_api(self):
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def run(place):
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paddle.disable_static(place)
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x = paddle.to_tensor(self.x)
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out = paddle.cumprod(x, 1)
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out_ref = np.cumprod(self.x, 1)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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paddle.enable_static()
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for place in self.place:
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run(place)
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# test function.
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class TestCumprodReverse(TestCumprod):
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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 prepare_inputs_outputs_attrs(self, dim, zero_num):
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self.x = (
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np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
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)
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if zero_num > 0:
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zero_num = min(zero_num, self.x.size)
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shape = self.x.shape
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self.x = self.x.flatten()
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indices = random.sample(range(self.x.size), zero_num)
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for i in indices:
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self.x[i] = 0
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self.x = np.reshape(self.x, self.shape)
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self.out = np.flip(
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np.flip(self.x, axis=dim).cumprod(axis=dim), axis=dim
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)
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if self.dtype == np.uint16:
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self.inputs = {'X': convert_float_to_uint16(self.x)}
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self.outputs = {'Out': convert_float_to_uint16(self.out)}
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else:
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self.inputs = {'X': self.x}
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self.outputs = {'Out': self.out}
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self.attrs = {'dim': dim, 'reverse': True}
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def init_grad_input_output(self, dim):
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reshape_x = self.x.reshape(self.x.size)
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self.grad_out = np.ones(self.x.size, self.val_dtype)
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self.grad_x = np.zeros(self.x.size, self.val_dtype)
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out_data = self.out.reshape(self.x.size)
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if self.dtype == np.complex128 or self.dtype == np.complex64:
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reshape_x = np.conj(reshape_x)
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out_data = np.conj(out_data)
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cumprod_grad(
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reshape_x,
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out_data,
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self.grad_out,
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self.grad_x,
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self.shape,
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dim,
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exclusive=False,
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reverse=True,
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)
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if self.dtype == np.uint16:
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self.grad_x = convert_float_to_uint16(
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self.grad_x.reshape(self.shape)
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)
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self.grad_out = convert_float_to_uint16(
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self.grad_out.reshape(self.shape)
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)
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else:
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self.grad_x = self.grad_x.reshape(self.shape)
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self.grad_out = self.grad_out.reshape(self.shape)
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# test function.
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class TestCumprodReverseCase1(TestCumprod):
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def init_params(self):
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self.shape = (120,)
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self.zero_nums = [0, 1, 10]
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# test backward.
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def test_check_grad(self):
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for dim in range(-len(self.shape), len(self.shape)):
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for zero_num in self.zero_nums:
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self.prepare_inputs_outputs_attrs(dim, zero_num)
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self.init_grad_input_output(dim)
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if self.dtype == np.float64:
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self.check_grad(
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['X'], 'Out', check_pir=True, max_relative_error=2e-7
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)
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else:
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self.check_grad(
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['X'],
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'Out',
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user_defined_grads=[self.grad_x],
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user_defined_grad_outputs=[self.grad_out],
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check_pir=True,
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)
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# test function.
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class TestCumprodReverseCase2(TestCumprod):
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def init_params(self):
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self.shape = (12, 10)
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self.zero_nums = [0, 1, 10]
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# test function.
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class TestCumprodReverseCase3(TestCumprod):
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def init_params(self):
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self.shape = (3, 4, 10)
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self.zero_nums = [0, 1, 10]
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# test function.
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class TestCumprodReverseCase4(TestCumprod):
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def init_params(self):
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self.shape = (2, 3, 4, 5, 2)
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self.zero_nums = [0, 1, 10]
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# test backward.
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def test_check_grad(self):
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for dim in range(-len(self.shape), len(self.shape)):
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for zero_num in self.zero_nums:
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self.prepare_inputs_outputs_attrs(dim, zero_num)
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self.init_grad_input_output(dim)
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if self.dtype == np.float64:
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self.check_grad(
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['X'], 'Out', check_pir=True, max_relative_error=3e-7
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)
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else:
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self.check_grad(
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['X'],
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'Out',
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user_defined_grads=[self.grad_x],
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user_defined_grad_outputs=[self.grad_out],
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check_pir=True,
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)
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# test function.
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class TestCumprodExclusive(TestCumprod):
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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 prepare_inputs_outputs_attrs(self, dim, zero_num):
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self.x = (
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np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
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)
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if zero_num > 0:
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zero_num = min(zero_num, self.x.size)
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shape = self.x.shape
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self.x = self.x.flatten()
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indices = random.sample(range(self.x.size), zero_num)
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for i in indices:
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self.x[i] = 0
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self.x = np.reshape(self.x, self.shape)
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ones_shape = list(self.shape)
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ones_shape[dim] = 1
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if dim == -4 or dim == 0:
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x_temp = self.x[:-1, :, :, :]
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elif dim == -3 or dim == 1:
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x_temp = self.x[:, :-1, :, :]
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elif dim == -2 or dim == 2:
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x_temp = self.x[:, :, :-1, :]
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elif dim == -1 or dim == 3:
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x_temp = self.x[:, :, :, :-1]
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self.out = np.concatenate(
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(
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np.ones(ones_shape, dtype=self.dtype),
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x_temp.cumprod(axis=dim),
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),
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axis=dim,
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)
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if self.dtype == np.uint16:
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self.inputs = {'X': convert_float_to_uint16(self.x)}
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self.outputs = {'Out': convert_float_to_uint16(self.out)}
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else:
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self.inputs = {'X': self.x}
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self.outputs = {'Out': self.out}
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self.attrs = {'dim': dim, 'exclusive': True}
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def init_grad_input_output(self, dim):
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reshape_x = self.x.reshape(self.x.size)
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self.grad_out = np.ones(self.x.size, self.val_dtype)
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self.grad_x = np.zeros(self.x.size, self.val_dtype)
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out_data = self.out.reshape(self.x.size)
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if self.dtype == np.complex128 or self.dtype == np.complex64:
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reshape_x = np.conj(reshape_x)
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out_data = np.conj(out_data)
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cumprod_grad(
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reshape_x,
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out_data,
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self.grad_out,
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self.grad_x,
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self.shape,
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dim,
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exclusive=True,
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reverse=False,
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)
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if self.dtype == np.uint16:
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self.grad_x = convert_float_to_uint16(
|
|
self.grad_x.reshape(self.shape)
|
|
)
|
|
self.grad_out = convert_float_to_uint16(
|
|
self.grad_out.reshape(self.shape)
|
|
)
|
|
else:
|
|
self.grad_x = self.grad_x.reshape(self.shape)
|
|
self.grad_out = self.grad_out.reshape(self.shape)
|
|
|
|
|
|
# test function.
|
|
class TestCumprodExclusiveCase1(TestCumprodExclusive):
|
|
def init_params(self):
|
|
self.shape = (120,)
|
|
self.zero_nums = [0, 1, 10]
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
self.val_dtype = np.float64
|
|
|
|
def prepare_inputs_outputs_attrs(self, dim, zero_num):
|
|
self.x = (
|
|
np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
|
|
)
|
|
if zero_num > 0:
|
|
zero_num = min(zero_num, self.x.size)
|
|
shape = self.x.shape
|
|
self.x = self.x.flatten()
|
|
indices = random.sample(range(self.x.size), zero_num)
|
|
for i in indices:
|
|
self.x[i] = 0
|
|
self.x = np.reshape(self.x, self.shape)
|
|
ones_shape = (1,)
|
|
x_temp = self.x[:-1]
|
|
self.out = np.concatenate(
|
|
(
|
|
np.ones(ones_shape, dtype=self.dtype),
|
|
x_temp.cumprod(axis=dim),
|
|
),
|
|
axis=dim,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.inputs = {'X': convert_float_to_uint16(self.x)}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.out)}
|
|
else:
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out}
|
|
self.attrs = {'dim': dim, 'exclusive': True}
|
|
|
|
# test backward.
|
|
def test_check_grad(self):
|
|
for dim in range(-len(self.shape), len(self.shape)):
|
|
for zero_num in self.zero_nums:
|
|
self.prepare_inputs_outputs_attrs(dim, zero_num)
|
|
self.init_grad_input_output(dim)
|
|
if self.dtype == np.float64:
|
|
self.check_grad(
|
|
['X'], 'Out', check_pir=True, max_relative_error=2e-7
|
|
)
|
|
else:
|
|
self.check_grad(
|
|
['X'],
|
|
'Out',
|
|
user_defined_grads=[self.grad_x],
|
|
user_defined_grad_outputs=[self.grad_out],
|
|
check_pir=True,
|
|
)
|
|
|
|
|
|
# test function.
|
|
class TestCumprodExclusiveCase2(TestCumprodExclusive):
|
|
def init_params(self):
|
|
self.shape = (12, 10)
|
|
self.zero_nums = [0, 1, 10]
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
self.val_dtype = np.float64
|
|
|
|
def prepare_inputs_outputs_attrs(self, dim, zero_num):
|
|
self.x = (
|
|
np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
|
|
)
|
|
if zero_num > 0:
|
|
zero_num = min(zero_num, self.x.size)
|
|
shape = self.x.shape
|
|
self.x = self.x.flatten()
|
|
indices = random.sample(range(self.x.size), zero_num)
|
|
for i in indices:
|
|
self.x[i] = 0
|
|
self.x = np.reshape(self.x, self.shape)
|
|
ones_shape = list(self.shape)
|
|
ones_shape[dim] = 1
|
|
if dim == -2 or dim == 0:
|
|
x_temp = self.x[:-1, :]
|
|
elif dim == -1 or dim == 1:
|
|
x_temp = self.x[:, :-1]
|
|
self.out = np.concatenate(
|
|
(
|
|
np.ones(ones_shape, dtype=self.dtype),
|
|
x_temp.cumprod(axis=dim),
|
|
),
|
|
axis=dim,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.inputs = {'X': convert_float_to_uint16(self.x)}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.out)}
|
|
else:
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out}
|
|
self.attrs = {'dim': dim, 'exclusive': True}
|
|
|
|
|
|
# test function.
|
|
class TestCumprodExclusiveCase3(TestCumprodExclusive):
|
|
def init_params(self):
|
|
self.shape = (3, 4, 10)
|
|
self.zero_nums = [0, 1, 10]
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
self.val_dtype = np.float64
|
|
|
|
def prepare_inputs_outputs_attrs(self, dim, zero_num):
|
|
self.x = (
|
|
np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
|
|
)
|
|
if zero_num > 0:
|
|
zero_num = min(zero_num, self.x.size)
|
|
shape = self.x.shape
|
|
self.x = self.x.flatten()
|
|
indices = random.sample(range(self.x.size), zero_num)
|
|
for i in indices:
|
|
self.x[i] = 0
|
|
self.x = np.reshape(self.x, self.shape)
|
|
ones_shape = list(self.shape)
|
|
ones_shape[dim] = 1
|
|
if dim == -3 or dim == 0:
|
|
x_temp = self.x[:-1, :, :]
|
|
elif dim == -2 or dim == 1:
|
|
x_temp = self.x[:, :-1, :]
|
|
elif dim == -1 or dim == 2:
|
|
x_temp = self.x[:, :, :-1]
|
|
self.out = np.concatenate(
|
|
(
|
|
np.ones(ones_shape, dtype=self.dtype),
|
|
x_temp.cumprod(axis=dim),
|
|
),
|
|
axis=dim,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.inputs = {'X': convert_float_to_uint16(self.x)}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.out)}
|
|
else:
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out}
|
|
self.attrs = {'dim': dim, 'exclusive': True}
|
|
|
|
|
|
# test function.
|
|
class TestCumprodExclusiveCase4(TestCumprodExclusive):
|
|
def init_params(self):
|
|
self.shape = (2, 3, 4, 5, 2)
|
|
self.zero_nums = [0, 1, 10]
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
self.val_dtype = np.float64
|
|
|
|
def prepare_inputs_outputs_attrs(self, dim, zero_num):
|
|
self.x = (
|
|
np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
|
|
)
|
|
if zero_num > 0:
|
|
zero_num = min(zero_num, self.x.size)
|
|
shape = self.x.shape
|
|
self.x = self.x.flatten()
|
|
indices = random.sample(range(self.x.size), zero_num)
|
|
for i in indices:
|
|
self.x[i] = 0
|
|
self.x = np.reshape(self.x, self.shape)
|
|
ones_shape = list(self.shape)
|
|
ones_shape[dim] = 1
|
|
if dim == -5 or dim == 0:
|
|
x_temp = self.x[:-1, :, :, :, :]
|
|
elif dim == -4 or dim == 1:
|
|
x_temp = self.x[:, :-1, :, :, :]
|
|
elif dim == -3 or dim == 2:
|
|
x_temp = self.x[:, :, :-1, :, :]
|
|
elif dim == -2 or dim == 3:
|
|
x_temp = self.x[:, :, :, :-1, :]
|
|
elif dim == -1 or dim == 4:
|
|
x_temp = self.x[:, :, :, :, :-1]
|
|
self.out = np.concatenate(
|
|
(
|
|
np.ones(ones_shape, dtype=self.dtype),
|
|
x_temp.cumprod(axis=dim),
|
|
),
|
|
axis=dim,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.inputs = {'X': convert_float_to_uint16(self.x)}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.out)}
|
|
else:
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out}
|
|
self.attrs = {'dim': dim, 'exclusive': True}
|
|
|
|
# test backward.
|
|
def test_check_grad(self):
|
|
for dim in range(-len(self.shape), len(self.shape)):
|
|
for zero_num in self.zero_nums:
|
|
self.prepare_inputs_outputs_attrs(dim, zero_num)
|
|
self.init_grad_input_output(dim)
|
|
if self.dtype == np.float64:
|
|
self.check_grad(
|
|
['X'], 'Out', check_pir=True, max_relative_error=2e-7
|
|
)
|
|
else:
|
|
self.check_grad(
|
|
['X'],
|
|
'Out',
|
|
user_defined_grads=[self.grad_x],
|
|
user_defined_grad_outputs=[self.grad_out],
|
|
check_pir=True,
|
|
)
|
|
|
|
|
|
# # test function.
|
|
class TestCumprodExclusiveAndReverse(TestCumprod):
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
self.val_dtype = np.float64
|
|
|
|
def prepare_inputs_outputs_attrs(self, dim, zero_num):
|
|
self.x = (
|
|
np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
|
|
)
|
|
if zero_num > 0:
|
|
zero_num = min(zero_num, self.x.size)
|
|
shape = self.x.shape
|
|
self.x = self.x.flatten()
|
|
indices = random.sample(range(self.x.size), zero_num)
|
|
for i in indices:
|
|
self.x[i] = 0
|
|
self.x = np.reshape(self.x, self.shape)
|
|
ones_shape = list(self.shape)
|
|
ones_shape[dim] = 1
|
|
if dim == -4 or dim == 0:
|
|
x_temp = self.x[1:, :, :, :]
|
|
elif dim == -3 or dim == 1:
|
|
x_temp = self.x[:, 1:, :, :]
|
|
elif dim == -2 or dim == 2:
|
|
x_temp = self.x[:, :, 1:, :]
|
|
elif dim == -1 or dim == 3:
|
|
x_temp = self.x[:, :, :, 1:]
|
|
self.out = np.flip(
|
|
np.concatenate(
|
|
(
|
|
np.ones(ones_shape, dtype=self.dtype),
|
|
np.flip(x_temp, axis=dim).cumprod(axis=dim),
|
|
),
|
|
axis=dim,
|
|
),
|
|
axis=dim,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.inputs = {'X': convert_float_to_uint16(self.x)}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.out)}
|
|
else:
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out}
|
|
self.attrs = {'dim': dim, 'exclusive': True, 'reverse': True}
|
|
|
|
def init_grad_input_output(self, dim):
|
|
reshape_x = self.x.reshape(self.x.size)
|
|
self.grad_out = np.ones(self.x.size, self.val_dtype)
|
|
self.grad_x = np.zeros(self.x.size, self.val_dtype)
|
|
out_data = self.out.reshape(self.x.size)
|
|
if self.dtype == np.complex128 or self.dtype == np.complex64:
|
|
reshape_x = np.conj(reshape_x)
|
|
out_data = np.conj(out_data)
|
|
cumprod_grad(
|
|
reshape_x,
|
|
out_data,
|
|
self.grad_out,
|
|
self.grad_x,
|
|
self.shape,
|
|
dim,
|
|
exclusive=True,
|
|
reverse=True,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.grad_x = convert_float_to_uint16(
|
|
self.grad_x.reshape(self.shape)
|
|
)
|
|
self.grad_out = convert_float_to_uint16(
|
|
self.grad_out.reshape(self.shape)
|
|
)
|
|
else:
|
|
self.grad_x = self.grad_x.reshape(self.shape)
|
|
self.grad_out = self.grad_out.reshape(self.shape)
|
|
|
|
|
|
class TestCumprodExclusiveAndReverseCase1(TestCumprodExclusiveAndReverse):
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
self.val_dtype = np.float64
|
|
|
|
def init_params(self):
|
|
self.shape = (120,)
|
|
self.zero_nums = [0, 1, 10]
|
|
|
|
def prepare_inputs_outputs_attrs(self, dim, zero_num):
|
|
self.x = (
|
|
np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
|
|
)
|
|
if zero_num > 0:
|
|
zero_num = min(zero_num, self.x.size)
|
|
shape = self.x.shape
|
|
self.x = self.x.flatten()
|
|
indices = random.sample(range(self.x.size), zero_num)
|
|
for i in indices:
|
|
self.x[i] = 0
|
|
self.x = np.reshape(self.x, self.shape)
|
|
ones_shape = list(self.shape)
|
|
ones_shape[dim] = 1
|
|
if dim == -1 or dim == 0:
|
|
x_temp = self.x[1:]
|
|
self.out = np.flip(
|
|
np.concatenate(
|
|
(
|
|
np.ones(ones_shape, dtype=self.dtype),
|
|
np.flip(x_temp, axis=dim).cumprod(axis=dim),
|
|
),
|
|
axis=dim,
|
|
),
|
|
axis=dim,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.inputs = {'X': convert_float_to_uint16(self.x)}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.out)}
|
|
else:
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out}
|
|
self.attrs = {'dim': dim, 'exclusive': True, 'reverse': True}
|
|
|
|
|
|
class TestCumprodExclusiveAndReverseCase2(TestCumprodExclusiveAndReverse):
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
self.val_dtype = np.float64
|
|
|
|
def init_params(self):
|
|
self.shape = (12, 10)
|
|
self.zero_nums = [0, 1, 10]
|
|
|
|
def prepare_inputs_outputs_attrs(self, dim, zero_num):
|
|
self.x = (
|
|
np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
|
|
)
|
|
if zero_num > 0:
|
|
zero_num = min(zero_num, self.x.size)
|
|
shape = self.x.shape
|
|
self.x = self.x.flatten()
|
|
indices = random.sample(range(self.x.size), zero_num)
|
|
for i in indices:
|
|
self.x[i] = 0
|
|
self.x = np.reshape(self.x, self.shape)
|
|
ones_shape = list(self.shape)
|
|
ones_shape[dim] = 1
|
|
if dim == -2 or dim == 0:
|
|
x_temp = self.x[1:, :]
|
|
elif dim == -1 or dim == 1:
|
|
x_temp = self.x[:, 1:]
|
|
self.out = np.flip(
|
|
np.concatenate(
|
|
(
|
|
np.ones(ones_shape, dtype=self.dtype),
|
|
np.flip(x_temp, axis=dim).cumprod(axis=dim),
|
|
),
|
|
axis=dim,
|
|
),
|
|
axis=dim,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.inputs = {'X': convert_float_to_uint16(self.x)}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.out)}
|
|
else:
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out}
|
|
self.attrs = {'dim': dim, 'exclusive': True, 'reverse': True}
|
|
|
|
|
|
class TestCumprodExclusiveAndReverseCase3(TestCumprodExclusiveAndReverse):
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
self.val_dtype = np.float64
|
|
|
|
def init_params(self):
|
|
self.shape = (3, 4, 10)
|
|
self.zero_nums = [0, 1, 10]
|
|
|
|
def prepare_inputs_outputs_attrs(self, dim, zero_num):
|
|
self.x = (
|
|
np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
|
|
)
|
|
if zero_num > 0:
|
|
zero_num = min(zero_num, self.x.size)
|
|
shape = self.x.shape
|
|
self.x = self.x.flatten()
|
|
indices = random.sample(range(self.x.size), zero_num)
|
|
for i in indices:
|
|
self.x[i] = 0
|
|
self.x = np.reshape(self.x, self.shape)
|
|
ones_shape = list(self.shape)
|
|
ones_shape[dim] = 1
|
|
if dim == -3 or dim == 0:
|
|
x_temp = self.x[1:, :, :]
|
|
elif dim == -2 or dim == 1:
|
|
x_temp = self.x[:, 1:, :]
|
|
elif dim == -1 or dim == 2:
|
|
x_temp = self.x[:, :, 1:]
|
|
self.out = np.flip(
|
|
np.concatenate(
|
|
(
|
|
np.ones(ones_shape, dtype=self.dtype),
|
|
np.flip(x_temp, axis=dim).cumprod(axis=dim),
|
|
),
|
|
axis=dim,
|
|
),
|
|
axis=dim,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.inputs = {'X': convert_float_to_uint16(self.x)}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.out)}
|
|
else:
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out}
|
|
self.attrs = {'dim': dim, 'exclusive': True, 'reverse': True}
|
|
|
|
|
|
class TestCumprodExclusiveAndReverseCase4(TestCumprodExclusiveAndReverse):
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
self.val_dtype = np.float64
|
|
|
|
def init_params(self):
|
|
self.shape = (2, 3, 4, 5, 2)
|
|
self.zero_nums = [0, 1, 10]
|
|
|
|
def prepare_inputs_outputs_attrs(self, dim, zero_num):
|
|
self.x = (
|
|
np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
|
|
)
|
|
if zero_num > 0:
|
|
zero_num = min(zero_num, self.x.size)
|
|
shape = self.x.shape
|
|
self.x = self.x.flatten()
|
|
indices = random.sample(range(self.x.size), zero_num)
|
|
for i in indices:
|
|
self.x[i] = 0
|
|
self.x = np.reshape(self.x, self.shape)
|
|
ones_shape = list(self.shape)
|
|
ones_shape[dim] = 1
|
|
if dim == -5 or dim == 0:
|
|
x_temp = self.x[1:, :, :, :, :]
|
|
elif dim == -4 or dim == 1:
|
|
x_temp = self.x[:, 1:, :, :, :]
|
|
elif dim == -3 or dim == 2:
|
|
x_temp = self.x[:, :, 1:, :, :]
|
|
elif dim == -2 or dim == 3:
|
|
x_temp = self.x[:, :, :, 1:, :]
|
|
elif dim == -1 or dim == 4:
|
|
x_temp = self.x[:, :, :, :, 1:]
|
|
self.out = np.flip(
|
|
np.concatenate(
|
|
(
|
|
np.ones(ones_shape, dtype=self.dtype),
|
|
np.flip(x_temp, axis=dim).cumprod(axis=dim),
|
|
),
|
|
axis=dim,
|
|
),
|
|
axis=dim,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.inputs = {'X': convert_float_to_uint16(self.x)}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.out)}
|
|
else:
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out}
|
|
self.attrs = {'dim': dim, 'exclusive': True, 'reverse': True}
|
|
|
|
|
|
# # test function.
|
|
class TestCumprodOuter1AndInner1(OpTest): # used to pass ci-coverage
|
|
def init_params(self):
|
|
self.shape = (1, 100, 1)
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
self.val_dtype = np.float64
|
|
|
|
def setUp(self):
|
|
paddle.enable_static()
|
|
self.init_params()
|
|
self.init_dtype()
|
|
self.op_type = "cumprod"
|
|
self.python_api = cumprod_wrapper
|
|
self.inputs = {'X': None}
|
|
self.outputs = {'Out': None}
|
|
self.attrs = {'dim': None}
|
|
|
|
def prepare_inputs_outputs_attrs(self, reverse):
|
|
self.x = (
|
|
np.random.uniform(0.0, 0.5, self.shape).astype(self.val_dtype) + 0.5
|
|
)
|
|
if reverse:
|
|
self.out = np.flip(
|
|
np.concatenate(
|
|
(
|
|
np.ones((1, 1, 1), dtype=self.dtype),
|
|
np.flip(self.x, axis=1)[:, :-1, :].cumprod(axis=1),
|
|
),
|
|
axis=1,
|
|
),
|
|
axis=1,
|
|
)
|
|
else:
|
|
self.out = np.concatenate(
|
|
(
|
|
np.ones((1, 1, 1), dtype=self.dtype),
|
|
self.x[:, :-1, :].cumprod(axis=1),
|
|
),
|
|
axis=1,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.inputs = {'X': convert_float_to_uint16(self.x)}
|
|
self.outputs = {'Out': convert_float_to_uint16(self.out)}
|
|
else:
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out}
|
|
self.attrs = {'dim': 1, 'exclusive': True, 'reverse': reverse}
|
|
|
|
def init_grad_input_output(self, reverse):
|
|
reshape_x = self.x.reshape(self.x.size)
|
|
self.grad_out = np.ones(self.x.size, self.val_dtype)
|
|
self.grad_x = np.zeros(self.x.size, self.val_dtype)
|
|
out_data = self.out.reshape(self.x.size)
|
|
if self.dtype == np.complex128 or self.dtype == np.complex64:
|
|
reshape_x = np.conj(reshape_x)
|
|
out_data = np.conj(out_data)
|
|
cumprod_grad(
|
|
reshape_x,
|
|
out_data,
|
|
self.grad_out,
|
|
self.grad_x,
|
|
self.shape,
|
|
1,
|
|
exclusive=True,
|
|
reverse=reverse,
|
|
)
|
|
if self.dtype == np.uint16:
|
|
self.grad_x = convert_float_to_uint16(
|
|
self.grad_x.reshape(self.shape)
|
|
)
|
|
self.grad_out = convert_float_to_uint16(
|
|
self.grad_out.reshape(self.shape)
|
|
)
|
|
else:
|
|
self.grad_x = self.grad_x.reshape(self.shape)
|
|
self.grad_out = self.grad_out.reshape(self.shape)
|
|
|
|
# test forward.
|
|
def test_check_output(self):
|
|
self.prepare_inputs_outputs_attrs(reverse=True)
|
|
self.check_output(check_pir=True)
|
|
self.prepare_inputs_outputs_attrs(reverse=False)
|
|
self.check_output(check_pir=True)
|
|
|
|
# test backward.
|
|
def test_check_grad(self):
|
|
for reverse in [True, False]:
|
|
self.prepare_inputs_outputs_attrs(reverse)
|
|
self.init_grad_input_output(reverse)
|
|
if self.dtype == np.float64:
|
|
self.check_grad(['X'], 'Out', check_pir=True)
|
|
else:
|
|
self.check_grad(
|
|
['X'],
|
|
'Out',
|
|
user_defined_grads=[self.grad_x],
|
|
user_defined_grad_outputs=[self.grad_out],
|
|
check_pir=True,
|
|
)
|
|
|
|
|
|
class TestCumprodAPI_ZeroSize(unittest.TestCase):
|
|
def init_dtype(self):
|
|
self.dtype = 'float64'
|
|
self.shape = [0, 3, 10, 10]
|
|
|
|
def setUp(self):
|
|
self.init_dtype()
|
|
self.x = (np.random.rand(0, 3, 10, 10) + 0.5).astype(self.dtype)
|
|
self.place = get_places()
|
|
|
|
# test dynamic graph api.
|
|
def test_dygraph_api(self):
|
|
def run(place):
|
|
paddle.disable_static(place)
|
|
x = paddle.to_tensor(self.x)
|
|
x.stop_gradient = False
|
|
out = paddle.cumprod(x, 1)
|
|
out_ref = np.cumprod(self.x, 1)
|
|
np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
|
|
paddle.sum(out).backward()
|
|
np.testing.assert_allclose(x.grad.shape, x.shape)
|
|
paddle.enable_static()
|
|
|
|
for place in self.place:
|
|
run(place)
|
|
|
|
|
|
class TestCumprodAPI_WithFlatten(unittest.TestCase):
|
|
def init_dtype(self):
|
|
self.dtype = 'float64'
|
|
self.shape = [3, 10, 10]
|
|
|
|
def setUp(self):
|
|
self.init_dtype()
|
|
self.x = (np.random.rand(3, 10, 10) + 0.5).astype(self.dtype)
|
|
self.place = get_places()
|
|
|
|
# test dynamic graph api.
|
|
def test_dygraph_api(self):
|
|
def run(place):
|
|
paddle.disable_static(place)
|
|
x = paddle.to_tensor(self.x)
|
|
x.stop_gradient = False
|
|
out = paddle.cumprod(x, None)
|
|
out_ref = np.cumprod(self.x, None)
|
|
np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
|
|
|
|
out_grad_ref = np.ones_like(out_ref)
|
|
out_grad = paddle.to_tensor(out_grad_ref)
|
|
x_grad_ref = np.zeros_like(self.x).flatten()
|
|
(x_grad,) = paddle.grad(out, [x], [out_grad])
|
|
cumprod_grad(
|
|
self.x.flatten(),
|
|
out_ref,
|
|
out_grad_ref,
|
|
x_grad_ref,
|
|
[np.prod(self.shape)],
|
|
-1,
|
|
exclusive=False,
|
|
reverse=False,
|
|
)
|
|
x_grad_ref = x_grad_ref.reshape(self.shape)
|
|
np.testing.assert_allclose(x_grad_ref, x_grad.numpy(), rtol=1e-05)
|
|
paddle.enable_static()
|
|
|
|
for place in self.place:
|
|
run(place)
|
|
|
|
def test_static_api(self):
|
|
def run(place):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data('X', self.shape, dtype=self.dtype)
|
|
out = paddle.cumprod(x, None)
|
|
exe = paddle.static.Executor(place)
|
|
(out,) = exe.run(feed={'X': self.x}, fetch_list=[out])
|
|
out_ref = np.cumprod(self.x, None)
|
|
np.testing.assert_allclose(out_ref, out, rtol=1e-05)
|
|
|
|
for place in self.place:
|
|
run(place)
|
|
|
|
|
|
class TestCumprodAPI_Compatibility(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(2025)
|
|
self.places = ['cpu', get_device_place()]
|
|
self.shape = [2, 3, 4]
|
|
self.dtype = "float32"
|
|
self.init_data()
|
|
|
|
def init_data(self):
|
|
self.np_x = np.random.rand(*self.shape).astype(self.dtype)
|
|
self.dim = 1
|
|
|
|
def test_dygraph_Compatibility(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(self.np_x)
|
|
paddle_dygraph_out = []
|
|
# Position args (args)
|
|
out1 = paddle.cumprod(x, self.dim)
|
|
paddle_dygraph_out.append(out1)
|
|
# Keywords args (kwargs) for paddle
|
|
out2 = paddle.cumprod(x=x, dim=self.dim)
|
|
paddle_dygraph_out.append(out2)
|
|
# Keywords args for torch compatibility
|
|
out3 = paddle.cumprod(input=x, dim=self.dim)
|
|
paddle_dygraph_out.append(out3)
|
|
# Tensor method args
|
|
out4 = x.cumprod(dim=self.dim)
|
|
paddle_dygraph_out.append(out4)
|
|
# Test 'out' parameter for torch compatibility
|
|
out5 = paddle.empty_like(x)
|
|
paddle.cumprod(x, dim=self.dim, out=out5)
|
|
paddle_dygraph_out.append(out5)
|
|
# Numpy reference output
|
|
ref_out = np.cumprod(self.np_x, axis=self.dim)
|
|
|
|
for out in paddle_dygraph_out:
|
|
np.testing.assert_allclose(ref_out, out.numpy(), rtol=1e-05)
|
|
paddle.enable_static()
|
|
|
|
def test_static_Compatibility(self):
|
|
paddle.enable_static()
|
|
main = paddle.static.Program()
|
|
startup = paddle.static.Program()
|
|
with paddle.base.program_guard(main, startup):
|
|
x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
|
|
# Position args (args)
|
|
out1 = paddle.cumprod(x, self.dim)
|
|
# Keywords args (kwargs) for paddle
|
|
out2 = paddle.cumprod(x=x, dim=self.dim)
|
|
# Keywords args for torch compatibility
|
|
out3 = paddle.cumprod(input=x, dim=self.dim)
|
|
# Tensor method args
|
|
out4 = x.cumprod(dim=self.dim)
|
|
# Numpy reference output
|
|
ref_out = np.cumprod(self.np_x, axis=self.dim)
|
|
|
|
fetch_list = [out1, out2, out3, out4]
|
|
for place in self.places:
|
|
exe = paddle.base.Executor(place)
|
|
fetches = exe.run(
|
|
main,
|
|
feed={"x": self.np_x},
|
|
fetch_list=fetch_list,
|
|
)
|
|
for out in fetches:
|
|
np.testing.assert_allclose(out, ref_out, rtol=1e-05)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|