wehub-resource-sync
1101 lines
36 KiB
Python
1101 lines
36 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import tempfile
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import unittest
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import warnings
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import gradient_checker
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import numpy as np
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from decorator_helper import prog_scope
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from op import Operator
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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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convert_uint16_to_float,
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get_device,
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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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from utils import dygraph_guard, static_guard
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import paddle
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import paddle.inference as paddle_infer
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from paddle import base, enable_static
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from paddle.base import core
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from paddle.base.layer_helper import LayerHelper
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from paddle.framework import in_pir_mode
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def sum_wrapper(X, use_onednn=False):
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res = paddle.full(shape=X[0].shape, fill_value=0.0, dtype=X[0].dtype)
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for x in X:
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res = paddle.add(res, x)
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return res
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class TestSumOp(OpTest):
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def setUp(self):
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self.op_type = "sum"
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self.python_api = paddle.add_n
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self.public_python_api = paddle.add_n
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self.prim_op_type = "comp"
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self.init_kernel_type()
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self.use_onednn = False
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self.init_kernel_type()
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x0 = np.random.random((3, 40)).astype(self.dtype)
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x1 = np.random.random((3, 40)).astype(self.dtype)
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x2 = np.random.random((3, 40)).astype(self.dtype)
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self.inputs = {"X": [("x0", x0), ("x1", x1), ("x2", x2)]}
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y = x0 + x1 + x2
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self.outputs = {'Out': y}
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self.attrs = {'use_onednn': self.use_onednn}
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def init_kernel_type(self):
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self.dtype = np.float64
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def test_check_output(self):
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self.check_output(
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check_prim=True,
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check_cinn=True,
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check_pir=True,
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check_prim_pir=True,
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)
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def test_check_grad(self):
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self.check_grad(
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['x0'],
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'Out',
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check_prim=True,
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check_cinn=True,
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check_pir=True,
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check_prim_pir=True,
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)
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class TestSelectedRowsSumOp(unittest.TestCase):
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def setUp(self):
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self.height = 10
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self.row_numel = 12
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self.rows = [0, 1, 2, 3, 4, 5, 6]
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self.dtype = np.float64
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self.init_kernel_type()
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def check_with_place(self, place, inplace):
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self.check_input_and_output(
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core.Scope(), place, inplace, True, True, True
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)
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self.check_input_and_output(
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core.Scope(), place, inplace, False, True, True
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)
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self.check_input_and_output(
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core.Scope(), place, inplace, False, False, True
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)
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self.check_input_and_output(
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core.Scope(), place, inplace, False, False, False
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)
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def init_kernel_type(self):
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pass
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def _get_array(self, rows, row_numel):
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array = np.ones((len(rows), row_numel)).astype(self.dtype)
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for i in range(len(rows)):
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array[i] *= rows[i]
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return array
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def check_input_and_output(
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self,
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scope,
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place,
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inplace,
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w1_has_data=False,
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w2_has_data=False,
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w3_has_data=False,
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):
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self.create_selected_rows(scope, place, "W1", w1_has_data)
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self.create_selected_rows(scope, place, "W2", w2_has_data)
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self.create_selected_rows(scope, place, "W3", w3_has_data)
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# create Out Variable
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if inplace:
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out_var_name = "W1"
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else:
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out_var_name = "Out"
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out = scope.var(out_var_name).get_selected_rows()
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# create and run sum operator
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sum_op = Operator("sum", X=["W1", "W2", "W3"], Out=out_var_name)
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sum_op.run(scope, place)
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has_data_w_num = 0
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for has_data in [w1_has_data, w2_has_data, w3_has_data]:
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if has_data:
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has_data_w_num += 1
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if has_data_w_num > 0:
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self.assertEqual(len(out.rows()), 7)
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np.testing.assert_array_equal(
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np.array(out.get_tensor()),
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self._get_array(self.rows, self.row_numel) * has_data_w_num,
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)
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else:
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self.assertEqual(len(out.rows()), 0)
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def create_selected_rows(self, scope, place, var_name, has_data):
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# create and initialize W Variable
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if has_data:
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rows = self.rows
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else:
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rows = []
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var = scope.var(var_name)
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w_selected_rows = var.get_selected_rows()
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w_selected_rows.set_height(self.height)
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w_selected_rows.set_rows(rows)
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w_array = self._get_array(self.rows, self.row_numel)
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w_tensor = w_selected_rows.get_tensor()
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w_tensor.set(w_array, place)
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return var
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def test_w_is_selected_rows(self):
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for place in get_places():
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for inplace in [True, False]:
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self.check_with_place(place, inplace)
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class TestSelectedRowsSumOpInt(TestSelectedRowsSumOp):
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def init_kernel_type(self):
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self.dtype = np.int32
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@unittest.skipIf(
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not core.supports_bfloat16(), 'place does not support BF16 evaluation'
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)
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class TestSelectedRowsSumBF16Op(TestSelectedRowsSumOp):
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def setUp(self):
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self.height = 10
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self.row_numel = 12
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self.rows = [0, 1, 2, 3, 4, 5, 6]
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self.dtype = np.uint16
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self.init_kernel_type()
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np.random.seed(12345)
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self.data = np.random.random((len(self.rows), self.row_numel)).astype(
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np.float32
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)
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def _get_array(self, rows, row_numel):
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if len(rows) > 0:
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return convert_float_to_uint16(self.data)
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else:
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return np.ndarray((0, row_numel), dtype=self.dtype)
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def check_input_and_output(
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self,
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scope,
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place,
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inplace,
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w1_has_data=False,
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w2_has_data=False,
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w3_has_data=False,
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):
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self.create_selected_rows(scope, place, "W1", w1_has_data)
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self.create_selected_rows(scope, place, "W2", w2_has_data)
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self.create_selected_rows(scope, place, "W3", w3_has_data)
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# create Out Variable
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if inplace:
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out_var_name = "W1"
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else:
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out_var_name = "Out"
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out = scope.var(out_var_name).get_selected_rows()
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# create and run sum operator
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sum_op = Operator("sum", X=["W1", "W2", "W3"], Out=out_var_name)
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sum_op.run(scope, place)
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has_data_w_num = 0
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for has_data in [w1_has_data, w2_has_data, w3_has_data]:
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if has_data:
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has_data_w_num += 1
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if has_data_w_num > 0:
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self.assertEqual(len(out.rows()), 7)
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out_bf16 = np.array(out.get_tensor())
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out_fp32 = convert_uint16_to_float(out_bf16)
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ref_fp32 = (
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convert_uint16_to_float(
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self._get_array(self.rows, self.row_numel)
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)
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* has_data_w_num
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)
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np.testing.assert_allclose(out_fp32, ref_fp32, atol=0, rtol=0.95e-2)
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else:
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self.assertEqual(len(out.rows()), 0)
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def test_w_is_selected_rows(self):
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for inplace in [True, False]:
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self.check_with_place(core.CPUPlace(), inplace)
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class TestSelectedRowsSumBF16OpBigRow(TestSelectedRowsSumBF16Op):
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def init_kernel_type(self):
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self.row_numel = 102
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class TestDenseTensorAndSelectedRowsOp(TestSelectedRowsSumOp):
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def setUp(self):
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self.height = 10
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self.row_numel = 12
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self.rows = [0, 1, 2, 2, 4, 5, 6]
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self.dtype = np.float64
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def check_with_place(self, place, inplace):
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scope = core.Scope()
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if inplace:
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self.create_lod_tensor(scope, place, "x1")
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self.create_selected_rows(scope, place, "x2", True)
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out = scope.var("x1").get_tensor()
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out_name = "x1"
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else:
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self.create_selected_rows(scope, place, "x1", True)
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self.create_lod_tensor(scope, place, "x2")
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out = scope.var("out").get_tensor()
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out_name = "out"
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# create and run sum operator
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sum_op = Operator("sum", X=["x1", "x2"], Out=out_name)
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sum_op.run(scope, place)
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result = np.ones((1, self.height)).astype(np.int32).tolist()[0]
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for ele in self.rows:
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result[ele] += 1
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out_t = np.array(out)
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self.assertEqual(out_t.shape[0], self.height)
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np.testing.assert_array_equal(
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out_t,
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self._get_array(list(range(self.height)), self.row_numel)
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* np.tile(np.array(result).reshape(self.height, 1), self.row_numel),
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)
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def create_lod_tensor(self, scope, place, var_name):
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var = scope.var(var_name)
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w_tensor = var.get_tensor()
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w_array = self._get_array(list(range(self.height)), self.row_numel)
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w_tensor.set(w_array, place)
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return var
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# ----------- test fp16 -----------
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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 TestAFP16SumOp(TestSumOp):
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def init_kernel_type(self):
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self.dtype = np.float16
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def test_check_output(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_output_with_place(
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place,
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check_cinn=True,
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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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# FIXME: Because of the precision fp16, max_relative_error
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# should be 0.15 here.
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def test_check_grad(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_grad(
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['x0'],
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'Out',
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check_cinn=True,
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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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def create_test_sum_fp16_class(parent):
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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 TestSumFp16Case(parent):
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def init_kernel_type(self):
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self.dtype = np.float16
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def test_w_is_selected_rows(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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for inplace in [True, False]:
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self.check_with_place(place, inplace)
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cls_name = "{}_{}".format(parent.__name__, "SumFp16Test")
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TestSumFp16Case.__name__ = cls_name
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globals()[cls_name] = TestSumFp16Case
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# ----------- test bf16 -----------
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class TestSumBF16Op(OpTest):
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def setUp(self):
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self.op_type = "sum"
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self.prim_op_type = "prim"
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self.python_api = paddle.add_n
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self.public_python_api = paddle.add_n
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self.init_kernel_type()
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x0 = np.random.random((3, 40)).astype(np.float32)
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x1 = np.random.random((3, 40)).astype(np.float32)
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x2 = np.random.random((3, 40)).astype(np.float32)
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y = x0 + x1 + x2
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self.inputs = {
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"X": [
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("x0", convert_float_to_uint16(x0)),
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("x1", convert_float_to_uint16(x1)),
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("x2", convert_float_to_uint16(x2)),
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]
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}
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self.outputs = {'Out': convert_float_to_uint16(y)}
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def init_kernel_type(self):
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self.dtype = np.uint16
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def test_check_output(self):
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# new dynamic graph mode does not support unit16 type
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self.check_output(
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check_dygraph=False,
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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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def test_check_grad(self):
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# new dynamic graph mode does not support unit16 type
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self.check_grad(
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['x0'],
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'Out',
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check_dygraph=False,
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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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class TestSumOpDtypeAsPaddleDtype(unittest.TestCase):
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def setUp(self):
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self.shape = [2, 3, 4]
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self.axis = 0
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self.input_dtype = 'float32'
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self.test_dtypes = [
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paddle.int32,
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paddle.int64,
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paddle.float64,
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paddle.bool,
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]
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def test_dygraph(self):
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with dygraph_guard():
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x_paddle = paddle.ones(shape=self.shape, dtype=self.input_dtype)
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for dtype_input in self.test_dtypes:
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paddle_result = paddle.sum(
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x_paddle, axis=self.axis, dtype=dtype_input
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)
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self.assertEqual(paddle_result.dtype, dtype_input)
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def test_static(self):
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with static_guard():
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for dtype_input in self.test_dtypes:
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(
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name='x', shape=self.shape, dtype=self.input_dtype
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)
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result = paddle.sum(x, axis=self.axis, dtype=dtype_input)
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self.assertEqual(result.dtype, dtype_input)
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class API_Test_Add_n(unittest.TestCase):
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def test_api(self):
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with base.program_guard(base.Program(), base.Program()):
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input0 = paddle.tensor.fill_constant(
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shape=[2, 3], dtype='int64', value=5
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)
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input1 = paddle.tensor.fill_constant(
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shape=[2, 3], dtype='int64', value=3
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)
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expected_result = np.empty((2, 3))
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expected_result.fill(8)
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sum_value = paddle.add_n([input0, input1])
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exe = base.Executor(base.CPUPlace())
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result = exe.run(fetch_list=[sum_value])
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self.assertEqual((result == expected_result).all(), True)
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with base.dygraph.guard():
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input0 = paddle.ones(shape=[2, 3], dtype='float32')
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expected_result = np.empty((2, 3))
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expected_result.fill(2)
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sum_value = paddle.add_n([input0, input0])
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self.assertEqual((sum_value.numpy() == expected_result).all(), True)
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def test_dygraph_api(self):
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with base.dygraph.guard():
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input0 = paddle.ones(shape=[2, 3], dtype='float32')
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input1 = paddle.ones(shape=[2, 3], dtype='float32')
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input0.stop_gradient = False
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input1.stop_gradient = False
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expected_result = np.empty((2, 3))
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expected_result.fill(2)
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sum_value = paddle.add_n([input0, input1])
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self.assertEqual((sum_value.numpy() == expected_result).all(), True)
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expected_grad_result = np.empty((2, 3))
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expected_grad_result.fill(1)
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sum_value.backward()
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self.assertEqual(
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(input0.grad.numpy() == expected_grad_result).all(), True
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)
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self.assertEqual(
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(input1.grad.numpy() == expected_grad_result).all(), True
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)
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def test_add_n_and_add_and_grad(self):
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with base.dygraph.guard():
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np_x = np.array([[1, 2, 3], [4, 5, 6]])
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np_y = [[7, 8, 9], [10, 11, 12]]
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np_z = [[1, 1, 1], [1, 1, 1]]
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x = paddle.to_tensor(np_x, dtype='float32', stop_gradient=False)
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y = paddle.to_tensor(np_y, dtype='float32', stop_gradient=False)
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z = paddle.to_tensor(np_z, dtype='float32')
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out1 = x + z
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out2 = y + z
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out = paddle.add_n([out1, out2])
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dx, dy = paddle.grad([out], [x, y], create_graph=True)
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expected_out = np.array([[10.0, 12.0, 14.0], [16.0, 18.0, 20.0]])
|
|
expected_dx = np.array([[1, 1, 1], [1, 1, 1]])
|
|
expected_dy = np.array([[1, 1, 1], [1, 1, 1]])
|
|
|
|
np.testing.assert_allclose(out, expected_out, rtol=1e-05)
|
|
np.testing.assert_allclose(dx, expected_dx, rtol=1e-05)
|
|
np.testing.assert_allclose(dy, expected_dy, rtol=1e-05)
|
|
|
|
|
|
class TestRaiseSumError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
|
|
def test_type():
|
|
paddle.add_n([11, 22])
|
|
|
|
self.assertRaises(TypeError, test_type)
|
|
|
|
def test_dtype():
|
|
data1 = paddle.static.data(
|
|
name="input1", shape=[10], dtype="int8"
|
|
)
|
|
data2 = paddle.static.data(
|
|
name="input2", shape=[10], dtype="int8"
|
|
)
|
|
paddle.add_n([data1, data2])
|
|
|
|
self.assertRaises(TypeError, test_dtype)
|
|
|
|
def test_dtype1():
|
|
data1 = paddle.static.data(
|
|
name="input1", shape=[10], dtype="int8"
|
|
)
|
|
paddle.add_n(data1)
|
|
|
|
self.assertRaises(TypeError, test_dtype1)
|
|
|
|
|
|
class TestRaiseSumsError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
|
|
def test_type():
|
|
paddle.add_n([11, 22])
|
|
|
|
self.assertRaises(TypeError, test_type)
|
|
|
|
def test_dtype():
|
|
data1 = paddle.static.data(
|
|
name="input1", shape=[10], dtype="int8"
|
|
)
|
|
data2 = paddle.static.data(
|
|
name="input2", shape=[10], dtype="int8"
|
|
)
|
|
paddle.add_n([data1, data2])
|
|
|
|
self.assertRaises(TypeError, test_dtype)
|
|
|
|
def test_dtype1():
|
|
data1 = paddle.static.data(
|
|
name="input3", shape=[10], dtype="int8"
|
|
)
|
|
paddle.add_n(data1)
|
|
|
|
self.assertRaises(TypeError, test_dtype1)
|
|
|
|
|
|
class TestSumOpDtype(unittest.TestCase):
|
|
def setUp(self):
|
|
self.shape = [0, 1, 1]
|
|
self.axis = 0
|
|
self.input_dtype = 'int32'
|
|
self.output_dtype = 'int32'
|
|
self.paddle_output_dtype = paddle.int32
|
|
|
|
def test_dygraph(self):
|
|
with dygraph_guard():
|
|
x_paddle = paddle.zeros(shape=self.shape, dtype=self.input_dtype)
|
|
paddle_result = x_paddle.sum(
|
|
axis=self.axis, dtype=self.output_dtype
|
|
)
|
|
|
|
self.assertEqual(paddle_result.dtype, self.paddle_output_dtype)
|
|
|
|
def test_static(self):
|
|
with (
|
|
static_guard(),
|
|
paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
),
|
|
):
|
|
x = paddle.static.data(
|
|
name='x', shape=self.shape, dtype=self.input_dtype
|
|
)
|
|
result = paddle.sum(x, axis=self.axis, dtype=self.output_dtype)
|
|
self.assertEqual(result[0].dtype, self.paddle_output_dtype)
|
|
|
|
|
|
class TestSumOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
def test_empty_list_input():
|
|
with base.dygraph.guard():
|
|
paddle._legacy_C_ops.sum([])
|
|
|
|
def test_list_of_none_input():
|
|
with base.dygraph.guard():
|
|
paddle._legacy_C_ops.sum([None])
|
|
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"sum\(\): argument 'X' \(position 0\) must be list of Tensors",
|
|
test_empty_list_input,
|
|
)
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"sum\(\): argument 'X' \(position 0\) must be list of Tensors",
|
|
test_list_of_none_input,
|
|
)
|
|
|
|
|
|
create_test_sum_fp16_class(TestSelectedRowsSumOp)
|
|
create_test_sum_fp16_class(TestDenseTensorAndSelectedRowsOp)
|
|
|
|
|
|
class TestReduceOPTensorAxisBase(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
paddle.seed(2022)
|
|
self.temp_dir = tempfile.TemporaryDirectory()
|
|
self.save_path = os.path.join(self.temp_dir.name, 'reduce_tensor_axis')
|
|
self.place = (
|
|
get_device_place()
|
|
if (paddle.is_compiled_with_cuda() or is_custom_device())
|
|
else paddle.CPUPlace()
|
|
)
|
|
self.keepdim = False
|
|
self.init_data()
|
|
|
|
def tearDwon(self):
|
|
self.temp_dir.cleanup()
|
|
|
|
def init_data(self):
|
|
self.pd_api = paddle.sum
|
|
self.np_api = np.sum
|
|
self.x = paddle.randn([10, 5, 9, 9], dtype='float64')
|
|
self.np_axis = np.array((1, 2), dtype='int64')
|
|
self.tensor_axis = paddle.to_tensor(self.np_axis, dtype='int64')
|
|
|
|
def test_dygraph(self):
|
|
self.x.stop_gradient = False
|
|
pd_out = self.pd_api(self.x, self.tensor_axis)
|
|
np_out = self.np_api(self.x.numpy(), tuple(self.np_axis))
|
|
np.testing.assert_allclose(
|
|
pd_out.numpy() if pd_out.size > 1 else pd_out.item(), np_out
|
|
)
|
|
pd_out.backward()
|
|
self.assertEqual(self.x.gradient().shape, tuple(self.x.shape))
|
|
|
|
def test_static_and_infer(self):
|
|
paddle.enable_static()
|
|
main_prog = paddle.static.Program()
|
|
startup_prog = paddle.static.Program()
|
|
with paddle.static.program_guard(main_prog, startup_prog):
|
|
# run static
|
|
x = paddle.static.data(
|
|
shape=self.x.shape, name='x', dtype='float32'
|
|
)
|
|
if isinstance(self.tensor_axis, paddle.Tensor):
|
|
axis = paddle.assign(self.np_axis)
|
|
else:
|
|
axis = []
|
|
for i, item in enumerate(self.tensor_axis):
|
|
if isinstance(item, int):
|
|
axis.append(item)
|
|
else:
|
|
axis.append(paddle.full([1], self.np_axis[i], 'int64'))
|
|
linear = paddle.nn.Linear(x.shape[-1], 5)
|
|
linear_out = linear(x)
|
|
out = self.pd_api(linear_out, axis, keepdim=self.keepdim)
|
|
|
|
sgd = paddle.optimizer.SGD(learning_rate=0.0)
|
|
sgd.minimize(paddle.mean(out))
|
|
exe = paddle.static.Executor(self.place)
|
|
exe.run(startup_prog)
|
|
static_out = exe.run(
|
|
feed={'x': self.x.numpy().astype('float32')}, fetch_list=[out]
|
|
)
|
|
|
|
# run infer
|
|
paddle.static.save_inference_model(self.save_path, [x], [out], exe)
|
|
if in_pir_mode():
|
|
config = paddle_infer.Config(
|
|
self.save_path + '.json', self.save_path + '.pdiparams'
|
|
)
|
|
config.enable_new_ir()
|
|
config.enable_new_executor()
|
|
else:
|
|
config = paddle_infer.Config(
|
|
self.save_path + '.pdmodel', self.save_path + '.pdiparams'
|
|
)
|
|
if paddle.is_compiled_with_cuda():
|
|
config.enable_use_gpu(100, 0)
|
|
elif is_custom_device():
|
|
config.enable_custom_device(get_device(), 0)
|
|
else:
|
|
config.disable_gpu()
|
|
predictor = paddle_infer.create_predictor(config)
|
|
input_names = predictor.get_input_names()
|
|
input_handle = predictor.get_input_handle(input_names[0])
|
|
fake_input = self.x.numpy().astype('float32')
|
|
input_handle.reshape(self.x.shape)
|
|
input_handle.copy_from_cpu(fake_input)
|
|
predictor.run()
|
|
output_names = predictor.get_output_names()
|
|
output_handle = predictor.get_output_handle(output_names[0])
|
|
infer_out = output_handle.copy_to_cpu()
|
|
np.testing.assert_allclose(static_out[0], infer_out)
|
|
|
|
|
|
class TestSumWithTensorAxis1(TestReduceOPTensorAxisBase):
|
|
def init_data(self):
|
|
self.pd_api = paddle.sum
|
|
self.np_api = np.sum
|
|
self.x = paddle.randn([10, 5, 9, 9], dtype='float64')
|
|
self.np_axis = np.array([0, 1, 2], dtype='int64')
|
|
self.tensor_axis = [
|
|
0,
|
|
paddle.to_tensor([1], 'int64'),
|
|
paddle.to_tensor([2], 'int64'),
|
|
]
|
|
|
|
|
|
class TestAddNDoubleGradCheck(unittest.TestCase):
|
|
def add_n_wrapper(self, x):
|
|
return paddle.add_n(x)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data1 = paddle.static.data('data1', [3, 4, 5], dtype)
|
|
data1.persistable = True
|
|
data1.stop_gradient = False
|
|
data2 = paddle.static.data('data2', [3, 4, 5], dtype)
|
|
data2.persistable = True
|
|
data2.stop_gradient = False
|
|
out = paddle.add_n([data1, data2])
|
|
data1_arr = np.random.uniform(-1, 1, data1.shape).astype(dtype)
|
|
data2_arr = np.random.uniform(-1, 1, data1.shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[data1, data2],
|
|
out,
|
|
x_init=[data1_arr, data2_arr],
|
|
place=place,
|
|
eps=eps,
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.add_n_wrapper,
|
|
[data1, data2],
|
|
out,
|
|
x_init=[data1_arr, data2_arr],
|
|
place=place,
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestAddNTripleGradCheck(unittest.TestCase):
|
|
def add_n_wrapper(self, x):
|
|
return paddle.add_n(x)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data1 = paddle.static.data('data1', [3, 4, 5], dtype)
|
|
data1.persistable = True
|
|
data1.stop_gradient = False
|
|
data2 = paddle.static.data('data2', [3, 4, 5], dtype)
|
|
data2.persistable = True
|
|
data2.stop_gradient = False
|
|
out = paddle.add_n([data1, data2])
|
|
data1_arr = np.random.uniform(-1, 1, data1.shape).astype(dtype)
|
|
data2_arr = np.random.uniform(-1, 1, data1.shape).astype(dtype)
|
|
|
|
gradient_checker.triple_grad_check(
|
|
[data1, data2],
|
|
out,
|
|
x_init=[data1_arr, data2_arr],
|
|
place=place,
|
|
eps=eps,
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.add_n_wrapper,
|
|
[data1, data2],
|
|
out,
|
|
x_init=[data1_arr, data2_arr],
|
|
place=place,
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestSumDoubleGradCheck(unittest.TestCase):
|
|
def sum_wrapper(self, x):
|
|
return paddle.sum(x[0], axis=1, keepdim=True)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data = paddle.static.data('data', [2, 4], dtype)
|
|
data.persistable = True
|
|
out = paddle.sum(data, axis=1, keepdim=True)
|
|
data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
|
|
|
|
gradient_checker.double_grad_check(
|
|
[data], out, x_init=[data_arr], place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.sum_wrapper, [data], out, x_init=[data_arr], place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestSumTripleGradCheck(unittest.TestCase):
|
|
def sum_wrapper(self, x):
|
|
return paddle.sum(x[0], axis=1, keepdim=True)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data = paddle.static.data('data', [2, 4], dtype)
|
|
data.persistable = True
|
|
out = paddle.sum(data, axis=1, keepdim=True)
|
|
data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
|
|
|
|
gradient_checker.triple_grad_check(
|
|
[data], out, x_init=[data_arr], place=place, eps=eps
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.sum_wrapper, [data], out, x_init=[data_arr], place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestSumAPIWarnings(unittest.TestCase):
|
|
def test_warnings(self):
|
|
with (
|
|
paddle.pir_utils.OldIrGuard(),
|
|
warnings.catch_warnings(record=True) as context,
|
|
):
|
|
warnings.simplefilter("always")
|
|
paddle.enable_static()
|
|
helper = LayerHelper("sum")
|
|
data = paddle.static.data(
|
|
name='data', shape=[32, 32], dtype='float32'
|
|
)
|
|
out = helper.create_variable_for_type_inference(dtype=data.dtype)
|
|
attrs = {'dim': [1], 'keep_dim': True, 'reduce_all': True}
|
|
os.environ["FLAGS_print_extra_attrs"] = '1'
|
|
helper.append_op(
|
|
type="reduce_sum",
|
|
inputs={'X': data},
|
|
outputs={'Out': out},
|
|
attrs=attrs,
|
|
)
|
|
self.assertTrue(
|
|
"op reduce_sum's attr reduce_all = True is not the default value: False"
|
|
in str(context[-1].message)
|
|
)
|
|
os.environ["FLAGS_print_extra_attrs"] = '0'
|
|
|
|
|
|
class TestSum_BoolToInt64_ZeroSize(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(123)
|
|
self.shape = [3, 0, 2]
|
|
self.places = get_places()
|
|
|
|
def check_result(
|
|
self, dygraph_result, expected_result, axis, keepdim, dtype, place
|
|
):
|
|
self.assertTrue(
|
|
(dygraph_result == expected_result).all(),
|
|
f"Shape: {self.shape}, Axis: {axis}, Keepdim: {keepdim}, Dtype: {dtype}, Place: {place}",
|
|
)
|
|
|
|
def _test_dygraph(self, place, axis, keepdim, dtype):
|
|
with dygraph_guard():
|
|
x_np = np.random.random(self.shape).astype(dtype)
|
|
x = paddle.to_tensor(x_np)
|
|
x.stop_gradient = False
|
|
dygraph_result = paddle.sum(x, axis=axis, keepdim=keepdim)
|
|
expected_result = np.sum(x_np, axis=axis, keepdims=keepdim)
|
|
self.check_result(
|
|
dygraph_result.numpy(),
|
|
expected_result,
|
|
axis,
|
|
keepdim,
|
|
dtype,
|
|
place,
|
|
)
|
|
paddle.sum(dygraph_result).backward()
|
|
np.testing.assert_allclose(x.grad.shape, x.shape)
|
|
|
|
def test_zero_size(self):
|
|
keepdims_options = [True, False]
|
|
for place in self.places:
|
|
for keepdim in keepdims_options:
|
|
self._test_dygraph(place, None, keepdim, "bool")
|
|
self._test_dygraph(place, None, keepdim, "int32")
|
|
|
|
|
|
class TestSumOp_Compatibility(unittest.TestCase):
|
|
def setUp(self):
|
|
self.shape = [2, 3, 4]
|
|
self.axis = 0
|
|
self.input_dtype = 'float32'
|
|
self.test_dtypes = [
|
|
np.int32,
|
|
np.int64,
|
|
np.float64,
|
|
np.bool,
|
|
]
|
|
|
|
def test_dygraph(self):
|
|
with dygraph_guard():
|
|
x_paddle = paddle.ones(shape=self.shape, dtype=self.input_dtype)
|
|
for dtype_input in self.test_dtypes:
|
|
numpy_result = np.sum(
|
|
x_paddle.numpy(),
|
|
axis=self.axis,
|
|
dtype=np.dtype(dtype_input),
|
|
keepdims=False,
|
|
)
|
|
|
|
# paddle test case
|
|
paddle_result0 = paddle.sum(x_paddle, self.axis, dtype_input)
|
|
np.testing.assert_allclose(paddle_result0, numpy_result)
|
|
|
|
paddle_result1 = paddle.sum(
|
|
x_paddle, self.axis, dtype_input, False
|
|
)
|
|
np.testing.assert_allclose(paddle_result1, numpy_result)
|
|
|
|
paddle_result2 = paddle.sum(
|
|
x=x_paddle, axis=self.axis, dtype=dtype_input, keepdim=False
|
|
)
|
|
np.testing.assert_allclose(paddle_result2, numpy_result)
|
|
|
|
# torch test case
|
|
paddle_result3 = paddle.sum(
|
|
input=x_paddle, dim=self.axis, keepdim=False
|
|
)
|
|
self.assertEqual(paddle_result3.dtype, paddle.float32)
|
|
|
|
paddle_result4 = paddle.sum(
|
|
input=x_paddle,
|
|
dim=self.axis,
|
|
keepdim=False,
|
|
dtype=dtype_input,
|
|
)
|
|
np.testing.assert_allclose(paddle_result4, numpy_result)
|
|
|
|
paddle_result5 = paddle.sum(
|
|
x_paddle, self.axis, keepdim=False, dtype=dtype_input
|
|
)
|
|
np.testing.assert_allclose(paddle_result5, numpy_result)
|
|
|
|
paddle_result6 = paddle.sum(
|
|
x_paddle, self.axis, False, dtype=dtype_input
|
|
)
|
|
np.testing.assert_allclose(paddle_result6, numpy_result)
|
|
|
|
paddle_result7 = paddle.sum(
|
|
x_paddle, self.axis, False, dtype_input
|
|
)
|
|
np.testing.assert_allclose(paddle_result7, numpy_result)
|
|
|
|
paddle_result8 = paddle.sum(
|
|
x_paddle, self.axis, dtype_input, False
|
|
)
|
|
np.testing.assert_allclose(paddle_result8, numpy_result)
|
|
|
|
paddle_result9 = paddle.sum(x_paddle, self.axis, False)
|
|
self.assertEqual(paddle_result9.dtype, paddle.float32)
|
|
|
|
paddle_result10 = paddle.sum(x_paddle, self.axis, dtype_input)
|
|
np.testing.assert_allclose(paddle_result10, numpy_result)
|
|
|
|
def test_static(self):
|
|
self.test_dtypes = [
|
|
paddle.int32,
|
|
paddle.int64,
|
|
paddle.float64,
|
|
paddle.bool,
|
|
]
|
|
with static_guard():
|
|
for dtype_input in self.test_dtypes:
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x_paddle = paddle.static.data(
|
|
name='x', shape=self.shape, dtype=self.input_dtype
|
|
)
|
|
|
|
# paddle test case
|
|
paddle_result0 = paddle.sum(
|
|
x_paddle, axis=self.axis, dtype=dtype_input
|
|
)
|
|
self.assertEqual(paddle_result0.dtype, dtype_input)
|
|
|
|
paddle_result1 = paddle.sum(
|
|
x_paddle,
|
|
axis=self.axis,
|
|
dtype=dtype_input,
|
|
keepdim=False,
|
|
)
|
|
self.assertEqual(paddle_result1.dtype, dtype_input)
|
|
|
|
paddle_result2 = paddle.sum(
|
|
x=x_paddle,
|
|
axis=self.axis,
|
|
dtype=dtype_input,
|
|
keepdim=False,
|
|
)
|
|
self.assertEqual(paddle_result2.dtype, dtype_input)
|
|
|
|
# torch test case
|
|
paddle_result3 = paddle.sum(
|
|
input=x_paddle, dim=self.axis, keepdim=False
|
|
)
|
|
self.assertEqual(paddle_result3.dtype, paddle.float32)
|
|
|
|
paddle_result4 = paddle.sum(
|
|
input=x_paddle,
|
|
dim=self.axis,
|
|
keepdim=False,
|
|
dtype=dtype_input,
|
|
)
|
|
self.assertEqual(paddle_result4.dtype, dtype_input)
|
|
|
|
paddle_result5 = paddle.sum(
|
|
x_paddle, self.axis, keepdim=False, dtype=dtype_input
|
|
)
|
|
self.assertEqual(paddle_result5.dtype, dtype_input)
|
|
|
|
paddle_result6 = paddle.sum(
|
|
x_paddle, self.axis, False, dtype=dtype_input
|
|
)
|
|
self.assertEqual(paddle_result6.dtype, dtype_input)
|
|
|
|
paddle_result7 = paddle.sum(
|
|
x_paddle, self.axis, False, dtype_input
|
|
)
|
|
self.assertEqual(paddle_result7.dtype, dtype_input)
|
|
|
|
paddle_result8 = paddle.sum(
|
|
x_paddle, self.axis, dtype_input, False
|
|
)
|
|
self.assertEqual(paddle_result8.dtype, dtype_input)
|
|
|
|
paddle_result9 = paddle.sum(x_paddle, self.axis, False)
|
|
self.assertEqual(paddle_result9.dtype, paddle.float32)
|
|
|
|
paddle_result10 = paddle.sum(
|
|
x_paddle, self.axis, dtype_input
|
|
)
|
|
self.assertEqual(paddle_result10.dtype, dtype_input)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
enable_static()
|
|
unittest.main()
|