wehub-resource-sync
319 lines
10 KiB
Python
319 lines
10 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import OpTest, paddle_static_guard
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import paddle
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from paddle import base
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from paddle.base import core
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from paddle.incubate.layers.nn import tdm_sampler
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def create_tdm_travel():
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tree_travel = [
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[1, 3, 7, 14],
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[1, 3, 7, 15],
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[1, 3, 8, 16],
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[1, 3, 8, 17],
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[1, 4, 9, 18],
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[1, 4, 9, 19],
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[1, 4, 10, 20],
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[1, 4, 10, 21],
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[2, 5, 11, 22],
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[2, 5, 11, 23],
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[2, 5, 12, 24],
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[2, 5, 12, 25],
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[2, 6, 13, 0],
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]
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return tree_travel
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def create_tdm_layer():
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tree_layer = [
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[1, 2],
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[3, 4, 5, 6],
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[7, 8, 9, 10, 11, 12, 13],
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[14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25],
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]
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return tree_layer
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type_dict = {
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"int32": int(core.VarDesc.VarType.INT32),
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"int64": int(core.VarDesc.VarType.INT64),
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}
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class TestTDMSamplerOp(OpTest):
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def setUp(self):
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self.__class__.op_type = "tdm_sampler"
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self.config()
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self.tree_travel = create_tdm_travel()
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self.tree_layer = create_tdm_layer()
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output_0 = self.x_shape[0]
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output_1 = len(self.neg_samples_num_list) + np.sum(
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self.neg_samples_num_list
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)
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self.output_shape = (output_0, output_1)
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self.layer_sample_nums = [1 + i for i in self.neg_samples_num_list]
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layer_node_num_list = [len(i) for i in self.tree_layer]
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tree_layer_offset = [0]
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tree_layer_flat = []
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node_nums = 0
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for layer_idx, layer_node in enumerate(layer_node_num_list):
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tree_layer_flat += self.tree_layer[layer_idx]
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node_nums += layer_node
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tree_layer_offset.append(node_nums)
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travel_np = np.array(self.tree_travel).astype(self.tree_dtype)
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layer_np = np.array(tree_layer_flat).astype(self.tree_dtype)
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layer_np = layer_np.reshape([-1, 1])
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self.x_np = np.random.randint(low=0, high=13, size=self.x_shape).astype(
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self.x_type
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)
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out = np.random.random(self.output_shape).astype(self.out_dtype)
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label = np.random.random(self.output_shape).astype(self.out_dtype)
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mask = np.random.random(self.output_shape).astype(self.out_dtype)
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self.attrs = {
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'neg_samples_num_list': self.neg_samples_num_list,
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'output_positive': True,
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'layer_offset': tree_layer_offset,
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'seed': 0,
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'dtype': type_dict[self.out_dtype],
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}
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self.inputs = {'X': self.x_np, 'Travel': travel_np, 'Layer': layer_np}
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self.outputs = {'Out': out, 'Labels': label, 'Mask': mask}
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def config(self):
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"""set test shape & type"""
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self.neg_samples_num_list = [0, 0, 0, 0]
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self.x_shape = (10, 1)
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self.x_type = 'int32'
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self.tree_dtype = 'int32'
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self.out_dtype = 'int32'
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def test_check_output(self):
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places = self._get_places()
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for place in places:
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outs, fetch_list = self._calc_output(place)
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self.out = [np.array(out) for out in outs]
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x_res = self.out[fetch_list.index('Out')]
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label_res = self.out[fetch_list.index('Labels')]
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mask_res = self.out[fetch_list.index('Mask')]
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# check dtype
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if self.out_dtype == 'int32':
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assert x_res.dtype == np.int32
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assert label_res.dtype == np.int32
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assert mask_res.dtype == np.int32
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elif self.out_dtype == 'int64':
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assert x_res.dtype == np.int64
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assert label_res.dtype == np.int64
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assert mask_res.dtype == np.int64
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x_res = x_res.reshape(self.output_shape)
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label_res = label_res.reshape(self.output_shape)
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mask_res = mask_res.reshape(self.output_shape)
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layer_nums = len(self.neg_samples_num_list)
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for batch_ids, x_batch in enumerate(x_res):
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start_offset = 0
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positive_travel = []
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for layer_idx in range(layer_nums):
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end_offset = start_offset + self.layer_sample_nums[layer_idx]
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sampling_res = x_batch[start_offset:end_offset]
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sampling_res_list = sampling_res.tolist()
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positive_travel.append(sampling_res_list[0])
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label_sampling_res = label_res[batch_ids][
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start_offset:end_offset
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]
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mask_sampling_res = mask_res[batch_ids][start_offset:end_offset]
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# check unique
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if sampling_res_list[0] != 0:
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assert len(set(sampling_res_list)) == len(
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sampling_res_list
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), (
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f"len(set(sampling_res_list)): {len(set(sampling_res_list))}, len(sampling_res_list): {len(sampling_res_list)} , sample_res: {sampling_res}, label_res:{label_sampling_res}, mask_res: {mask_sampling_res}"
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)
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# check legal
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layer_node = self.tree_layer[layer_idx]
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layer_node.append(0)
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for sample in sampling_res_list:
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assert sample in layer_node, (
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f"sample: {sample}, layer_node: {layer_node} , sample_res: {sampling_res}, label_res: {label_sampling_res}, mask_res:{mask_sampling_res}"
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)
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# check label
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label_flag = 1
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if sampling_res[0] == 0:
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label_flag = 0
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assert label_sampling_res[0] == label_flag
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# check mask
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padding_index = np.where(sampling_res == 0)
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assert not np.sum(mask_sampling_res[padding_index]), (
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f"np.sum(mask_sampling_res[padding_index]): {np.sum(mask_sampling_res[padding_index])} "
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)
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start_offset = end_offset
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# check travel legal
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x_id = int(np.asarray(self.x_np[batch_ids]).item())
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assert self.tree_travel[x_id] == positive_travel
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class TestCase1(TestTDMSamplerOp):
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def config(self):
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"""test input int64"""
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self.neg_samples_num_list = [0, 0, 0, 0]
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self.x_shape = (10, 1)
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self.x_type = 'int64'
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self.tree_dtype = 'int64'
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self.out_dtype = 'int32'
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class TestCase2(TestTDMSamplerOp):
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def config(self):
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"""test dtype int64"""
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self.neg_samples_num_list = [0, 0, 0, 0]
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self.x_shape = (10, 1)
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self.x_type = 'int32'
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self.tree_dtype = 'int32'
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self.out_dtype = 'int64'
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class TestCase3(TestTDMSamplerOp):
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def config(self):
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"""test all dtype int64"""
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self.neg_samples_num_list = [0, 0, 0, 0]
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self.x_shape = (10, 1)
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self.x_type = 'int64'
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self.tree_dtype = 'int64'
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self.out_dtype = 'int64'
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class TestCase4(TestTDMSamplerOp):
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def config(self):
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"""test one neg"""
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self.neg_samples_num_list = [1, 1, 1, 1]
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self.x_shape = (10, 1)
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self.x_type = 'int64'
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self.tree_dtype = 'int32'
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self.out_dtype = 'int64'
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class TestCase5(TestTDMSamplerOp):
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def config(self):
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"""test normal neg"""
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self.neg_samples_num_list = [1, 2, 3, 4]
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self.x_shape = (10, 1)
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self.x_type = 'int64'
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self.tree_dtype = 'int32'
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self.out_dtype = 'int64'
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class TestCase6(TestTDMSamplerOp):
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def config(self):
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"""test huge batchsize"""
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self.neg_samples_num_list = [1, 2, 3, 4]
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self.x_shape = (100, 1)
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self.x_type = 'int64'
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self.tree_dtype = 'int32'
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self.out_dtype = 'int64'
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class TestCase7(TestTDMSamplerOp):
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def config(self):
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"""test full neg"""
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self.neg_samples_num_list = [1, 3, 6, 11]
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self.x_shape = (10, 1)
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self.x_type = 'int64'
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self.tree_dtype = 'int32'
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self.out_dtype = 'int64'
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class TestTDMSamplerShape(unittest.TestCase):
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def test_shape(self):
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with paddle_static_guard():
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x = paddle.static.data(name='x', shape=[-1, 1], dtype='int32')
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tdm_tree_travel = create_tdm_travel()
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tdm_tree_layer = create_tdm_layer()
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layer_node_num_list = [len(i) for i in tdm_tree_layer]
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tree_layer_flat = []
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for layer_idx, layer_node in enumerate(layer_node_num_list):
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tree_layer_flat += tdm_tree_layer[layer_idx]
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travel_array = np.array(tdm_tree_travel).astype('int32')
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layer_array = np.array(tree_layer_flat).astype('int32')
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neg_samples_num_list = [1, 2, 3, 4]
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leaf_node_num = 13
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sample, label, mask = tdm_sampler(
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x,
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neg_samples_num_list,
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layer_node_num_list,
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leaf_node_num,
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tree_travel_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Assign(travel_array)
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),
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tree_layer_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Assign(layer_array)
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),
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output_positive=True,
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output_list=True,
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seed=0,
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tree_dtype='int32',
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dtype='int32',
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)
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place = base.CPUPlace()
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exe = base.Executor(place=place)
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exe.run(base.default_startup_program())
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feed = {
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'x': np.array(
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[
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[0],
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[1],
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[2],
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[3],
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[4],
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[5],
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[6],
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[7],
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[8],
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[9],
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[10],
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[11],
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[12],
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]
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).astype('int32')
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}
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exe.run(feed=feed)
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if __name__ == "__main__":
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unittest.main()
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