From e180d15b5d87d8e694391955b52d4a5ca64617cc Mon Sep 17 00:00:00 2001 From: xixiaoyao Date: Mon, 28 Oct 2019 19:23:57 +0800 Subject: [PATCH] add bert --- build/lib/paddlepalm/__init__.py | 5 - build/lib/paddlepalm/backbone/__init__.py | 0 build/lib/paddlepalm/backbone/bert.py | 156 --- build/lib/paddlepalm/backbone/ernie.py | 175 ---- .../lib/paddlepalm/backbone/utils/__init__.py | 0 .../paddlepalm/backbone/utils/transformer.py | 341 ------ build/lib/paddlepalm/default_settings.py | 42 - build/lib/paddlepalm/interface.py | 173 --- build/lib/paddlepalm/mtl_controller.py | 724 ------------- build/lib/paddlepalm/optimizer/__init__.py | 0 build/lib/paddlepalm/optimizer/adam.py | 103 -- build/lib/paddlepalm/reader/__init__.py | 0 build/lib/paddlepalm/reader/cls4bert.py | 0 build/lib/paddlepalm/reader/match.py | 103 -- build/lib/paddlepalm/reader/match4ernie.py | 103 -- build/lib/paddlepalm/reader/mlm.py | 93 -- build/lib/paddlepalm/reader/mrc4bert.py | 656 ------------ build/lib/paddlepalm/reader/mrc4ernie.py | 119 --- build/lib/paddlepalm/reader/utils/__init__.py | 0 .../paddlepalm/reader/utils/batching4bert.py | 184 ---- .../paddlepalm/reader/utils/batching4ernie.py | 175 ---- .../paddlepalm/reader/utils/mlm_batching.py | 177 ---- .../paddlepalm/reader/utils/mrqa_helper.py | 84 -- .../paddlepalm/reader/utils/reader4ernie.py | 982 ------------------ build/lib/paddlepalm/task_instance.py | 288 ----- .../lib/paddlepalm/task_paradigm/__init__.py | 0 build/lib/paddlepalm/task_paradigm/cls.py | 60 -- build/lib/paddlepalm/task_paradigm/match.py | 70 -- build/lib/paddlepalm/task_paradigm/mlm.py | 115 -- build/lib/paddlepalm/task_paradigm/mrc.py | 486 --------- build/lib/paddlepalm/tokenizer/__init__.py | 0 .../paddlepalm/tokenizer/bert_tokenizer.py | 374 ------- .../paddlepalm/tokenizer/ernie_tokenizer.py | 417 -------- build/lib/paddlepalm/utils/__init__.py | 0 build/lib/paddlepalm/utils/config_helper.py | 311 ------ build/lib/paddlepalm/utils/print_helper.py | 31 - build/lib/paddlepalm/utils/reader_helper.py | 226 ---- build/lib/paddlepalm/utils/saver.py | 80 -- .../paddlepalm/utils/textprocess_helper.py | 19 - dist/paddle_palm-1.2-py2.7.egg | Bin 166800 -> 0 bytes paddle_palm.egg-info/PKG-INFO | 105 -- paddle_palm.egg-info/SOURCES.txt | 47 - paddle_palm.egg-info/dependency_links.txt | 1 - paddle_palm.egg-info/not-zip-safe | 1 - paddle_palm.egg-info/top_level.txt | 1 - 45 files changed, 7027 deletions(-) delete mode 100644 build/lib/paddlepalm/__init__.py delete mode 100644 build/lib/paddlepalm/backbone/__init__.py delete mode 100644 build/lib/paddlepalm/backbone/bert.py delete mode 100644 build/lib/paddlepalm/backbone/ernie.py delete mode 100644 build/lib/paddlepalm/backbone/utils/__init__.py delete mode 100644 build/lib/paddlepalm/backbone/utils/transformer.py delete mode 100644 build/lib/paddlepalm/default_settings.py delete mode 100644 build/lib/paddlepalm/interface.py delete mode 100644 build/lib/paddlepalm/mtl_controller.py delete mode 100644 build/lib/paddlepalm/optimizer/__init__.py delete mode 100644 build/lib/paddlepalm/optimizer/adam.py delete mode 100644 build/lib/paddlepalm/reader/__init__.py delete mode 100644 build/lib/paddlepalm/reader/cls4bert.py delete mode 100644 build/lib/paddlepalm/reader/match.py delete mode 100644 build/lib/paddlepalm/reader/match4ernie.py delete mode 100644 build/lib/paddlepalm/reader/mlm.py delete mode 100644 build/lib/paddlepalm/reader/mrc4bert.py delete mode 100644 build/lib/paddlepalm/reader/mrc4ernie.py delete mode 100644 build/lib/paddlepalm/reader/utils/__init__.py delete mode 100644 build/lib/paddlepalm/reader/utils/batching4bert.py delete mode 100644 build/lib/paddlepalm/reader/utils/batching4ernie.py delete mode 100644 build/lib/paddlepalm/reader/utils/mlm_batching.py delete mode 100644 build/lib/paddlepalm/reader/utils/mrqa_helper.py delete mode 100644 build/lib/paddlepalm/reader/utils/reader4ernie.py delete mode 100644 build/lib/paddlepalm/task_instance.py delete mode 100644 build/lib/paddlepalm/task_paradigm/__init__.py delete mode 100644 build/lib/paddlepalm/task_paradigm/cls.py delete mode 100644 build/lib/paddlepalm/task_paradigm/match.py delete mode 100644 build/lib/paddlepalm/task_paradigm/mlm.py delete mode 100644 build/lib/paddlepalm/task_paradigm/mrc.py delete mode 100644 build/lib/paddlepalm/tokenizer/__init__.py delete mode 100644 build/lib/paddlepalm/tokenizer/bert_tokenizer.py delete mode 100644 build/lib/paddlepalm/tokenizer/ernie_tokenizer.py delete mode 100644 build/lib/paddlepalm/utils/__init__.py delete mode 100644 build/lib/paddlepalm/utils/config_helper.py delete mode 100644 build/lib/paddlepalm/utils/print_helper.py delete mode 100644 build/lib/paddlepalm/utils/reader_helper.py delete mode 100644 build/lib/paddlepalm/utils/saver.py delete mode 100644 build/lib/paddlepalm/utils/textprocess_helper.py delete mode 100644 dist/paddle_palm-1.2-py2.7.egg delete mode 100644 paddle_palm.egg-info/PKG-INFO delete mode 100644 paddle_palm.egg-info/SOURCES.txt delete mode 100644 paddle_palm.egg-info/dependency_links.txt delete mode 100644 paddle_palm.egg-info/not-zip-safe delete mode 100644 paddle_palm.egg-info/top_level.txt diff --git a/build/lib/paddlepalm/__init__.py b/build/lib/paddlepalm/__init__.py deleted file mode 100644 index c7b42da..0000000 --- a/build/lib/paddlepalm/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ - -import sys -from paddlepalm.mtl_controller import Controller -sys.path.append('paddlepalm') - diff --git a/build/lib/paddlepalm/backbone/__init__.py b/build/lib/paddlepalm/backbone/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/build/lib/paddlepalm/backbone/bert.py b/build/lib/paddlepalm/backbone/bert.py deleted file mode 100644 index 7f48109..0000000 --- a/build/lib/paddlepalm/backbone/bert.py +++ /dev/null @@ -1,156 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""v1.1 -BERT model.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from paddle import fluid -from paddle.fluid import layers - -from paddlepalm.backbone.utils.transformer import pre_process_layer, encoder -from paddlepalm.interface import backbone - - -class Model(backbone): - - def __init__(self, config, phase): - - # self._is_training = phase == 'train' # backbone一般不用关心运行阶段，因为outputs在任何阶段基本不会变 - self._emb_size = config["hidden_size"] - self._n_layer = config["num_hidden_layers"] - self._n_head = config["num_attention_heads"] - self._voc_size = config["vocab_size"] - self._max_position_seq_len = config["max_position_embeddings"] - self._sent_types = config["type_vocab_size"] - self._hidden_act = config["hidden_act"] - self._prepostprocess_dropout = config["hidden_dropout_prob"] - self._attention_dropout = config["attention_probs_dropout_prob"] - - self.model_name = model_name - - self._word_emb_name = self.model_name + "word_embedding" - self._pos_emb_name = self.model_name + "pos_embedding" - self._sent_emb_name = self.model_name + "sent_embedding" - - # Initialize all weigths by truncated normal initializer, and all biases - # will be initialized by constant zero by default. - self._param_initializer = fluid.initializer.TruncatedNormal( - scale=config["initializer_range"]) - - @property - def inputs_attr(self): - return {"token_ids": [[-1, -1, 1], 'int64'], - "position_ids": [[-1, -1, 1], 'int64'], - "segment_ids": [[-1, -1, 1], 'int64'], - "input_mask": [[-1, -1, 1], 'float32']} - - @property - def outputs_attr(self): - return {"word_embedding": [[-1, -1, self._emb_size], 'float32'], - "encoder_outputs": [[-1, -1, self._emb_size], 'float32'], - "sentence_embedding": [[-1, self._emb_size], 'float32'], - "sentence_pair_embedding": [[-1, self._emb_size], 'float32']} - - def build(self, inputs): - src_ids = inputs['token_ids'] - pos_ids = inputs['position_ids'] - sent_ids = inputs['segment_ids'] - input_mask = inputs['input_mask'] - # padding id in vocabulary must be set to 0 - emb_out = layers.embedding( - input=src_ids, - size=[self._voc_size, self._emb_size], - dtype="float32", - param_attr=fluid.ParamAttr( - name=self._word_emb_name, initializer=self._param_initializer), - is_sparse=False) - - self.emb_out = emb_out - - position_emb_out = layers.embedding( - input=pos_ids, - size=[self._max_position_seq_len, self._emb_size], - dtype="float32", - param_attr=fluid.ParamAttr( - name=self._pos_emb_name, initializer=self._param_initializer)) - - self.position_emb_out = position_emb_out - - sent_emb_out = layers.embedding( - sent_ids, - size=[self._sent_types, self._emb_size], - dtype="float32" - param_attr=fluid.ParamAttr( - name=self._sent_emb_name, initializer=self._param_initializer)) - - self.sent_emb_out = sent_emb_out - - emb_out = emb_out + position_emb_out + sent_emb_out - - emb_out = pre_process_layer( - emb_out, 'nd', self._prepostprocess_dropout, name='pre_encoder') - - self_attn_mask = layers.matmul( - x = input_mask, y = input_mask, transpose_y = True) - - self_attn_mask = layers.scale( - x = self_attn_mask, scale = 10000.0, bias = -1.0, bias_after_scale = False) - - n_head_self_attn_mask = layers.stack( - x=[self_attn_mask] * self._n_head, axis=1) - - n_head_self_attn_mask.stop_gradient = True - - enc_out = encoder( - enc_input = emb_out, - attn_bias = n_head_self_attn_mask, - n_layer = self._n_layer, - n_head = self._n_head, - d_key = self._emb_size // self._n_head, - d_value = self._emb_size // self._n_head, - d_model = self._emb_size, - d_inner_hid = self._emb_size * 4, - prepostprocess_dropout = self._prepostprocess_dropout, - attention_dropout = self._attention_dropout, - relu_dropout = 0, - hidden_act = self._hidden_act, - preprocess_cmd = "", - postprocess_cmd = "dan", - param_initializer = self._param_initializer, - name = self.model_name + 'encoder') - - next_sent_feat = layers.slice( - input = enc_out, axes = [1], starts = [0], ends = [1]) - next_sent_feat = layers.fc( - input = next_sent_feat, - size = self._emb_size, - act = "tanh", - param_attr = fluid.ParamAttr( - name = self.model_name + "pooled_fc.w_0", - initializer = self._param_initializer), - bias_attr = "pooled_fc.b_0") - - return {'word_embedding': emb_out, - 'encoder_outputs': enc_out, - 'sentence_embedding': next_sent_feat, - 'sentence_pair_embedding': next_sent_feat} - - def postprocess(self, rt_outputs): - pass - - diff --git a/build/lib/paddlepalm/backbone/ernie.py b/build/lib/paddlepalm/backbone/ernie.py deleted file mode 100644 index 1e47153..0000000 --- a/build/lib/paddlepalm/backbone/ernie.py +++ /dev/null @@ -1,175 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Ernie model.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals -from __future__ import absolute_import - -from paddle import fluid -from paddle.fluid import layers - -from paddlepalm.backbone.utils.transformer import pre_process_layer, encoder -from paddlepalm.interface import backbone - - -class Model(backbone): - - def __init__(self, - config, - phase): - - # self._is_training = phase == 'train' # backbone一般不用关心运行阶段，因为outputs在任何阶段基本不会变 - - self._emb_size = config['hidden_size'] - self._n_layer = config['num_hidden_layers'] - self._n_head = config['num_attention_heads'] - self._voc_size = config['vocab_size'] - self._max_position_seq_len = config['max_position_embeddings'] - if config['sent_type_vocab_size']: - self._sent_types = config['sent_type_vocab_size'] - else: - self._sent_types = config['type_vocab_size'] - - self._task_types = config['task_type_vocab_size'] - - self._hidden_act = config['hidden_act'] - self._prepostprocess_dropout = config['hidden_dropout_prob'] - self._attention_dropout = config['attention_probs_dropout_prob'] - - self._word_emb_name = "word_embedding" - self._pos_emb_name = "pos_embedding" - self._sent_emb_name = "sent_embedding" - self._task_emb_name = "task_embedding" - self._emb_dtype = "float32" - - self._param_initializer = fluid.initializer.TruncatedNormal( - scale=config['initializer_range']) - - @property - def inputs_attr(self): - return {"token_ids": [[-1, -1, 1], 'int64'], - "position_ids": [[-1, -1, 1], 'int64'], - "segment_ids": [[-1, -1, 1], 'int64'], - "input_mask": [[-1, -1, 1], 'float32'], - "task_ids": [[-1,-1, 1], 'int64']} - - @property - def outputs_attr(self): - return {"word_embedding": [[-1, -1, self._emb_size], 'float32'], - "embedding_table": [[-1, self._voc_size, self._emb_size], 'float32'], - "encoder_outputs": [[-1, -1, self._emb_size], 'float32'], - "sentence_embedding": [[-1, self._emb_size], 'float32'], - "sentence_pair_embedding": [[-1, self._emb_size], 'float32']} - - def build(self, inputs, scope_name=""): - - src_ids = inputs['token_ids'] - pos_ids = inputs['position_ids'] - sent_ids = inputs['segment_ids'] - input_mask = inputs['input_mask'] - task_ids = inputs['task_ids'] - - # padding id in vocabulary must be set to 0 - emb_out = fluid.layers.embedding( - input=src_ids, - size=[self._voc_size, self._emb_size], - dtype=self._emb_dtype, - param_attr=fluid.ParamAttr( - name=scope_name+self._word_emb_name, initializer=self._param_initializer), - is_sparse=False) - - # fluid.global_scope().find_var('backbone-word_embedding').get_tensor() - embedding_table = fluid.default_main_program().global_block().var(scope_name+self._word_emb_name) - - position_emb_out = fluid.layers.embedding( - input=pos_ids, - size=[self._max_position_seq_len, self._emb_size], - dtype=self._emb_dtype, - param_attr=fluid.ParamAttr( - name=scope_name+self._pos_emb_name, initializer=self._param_initializer)) - - sent_emb_out = fluid.layers.embedding( - sent_ids, - size=[self._sent_types, self._emb_size], - dtype=self._emb_dtype, - param_attr=fluid.ParamAttr( - name=scope_name+self._sent_emb_name, initializer=self._param_initializer)) - - emb_out = emb_out + position_emb_out - emb_out = emb_out + sent_emb_out - - task_emb_out = fluid.layers.embedding( - task_ids, - size=[self._task_types, self._emb_size], - dtype=self._emb_dtype, - param_attr=fluid.ParamAttr( - name=scope_name+self._task_emb_name, - initializer=self._param_initializer)) - - emb_out = emb_out + task_emb_out - - emb_out = pre_process_layer( - emb_out, 'nd', self._prepostprocess_dropout, name=scope_name+'pre_encoder') - - self_attn_mask = fluid.layers.matmul( - x=input_mask, y=input_mask, transpose_y=True) - - self_attn_mask = fluid.layers.scale( - x=self_attn_mask, scale=10000.0, bias=-1.0, bias_after_scale=False) - n_head_self_attn_mask = fluid.layers.stack( - x=[self_attn_mask] * self._n_head, axis=1) - n_head_self_attn_mask.stop_gradient = True - - enc_out = encoder( - enc_input=emb_out, - attn_bias=n_head_self_attn_mask, - n_layer=self._n_layer, - n_head=self._n_head, - d_key=self._emb_size // self._n_head, - d_value=self._emb_size // self._n_head, - d_model=self._emb_size, - d_inner_hid=self._emb_size * 4, - prepostprocess_dropout=self._prepostprocess_dropout, - attention_dropout=self._attention_dropout, - relu_dropout=0, - hidden_act=self._hidden_act, - preprocess_cmd="", - postprocess_cmd="dan", - param_initializer=self._param_initializer, - name=scope_name+'encoder') - - - next_sent_feat = fluid.layers.slice( - input=enc_out, axes=[1], starts=[0], ends=[1]) - next_sent_feat = fluid.layers.reshape(next_sent_feat, [-1, next_sent_feat.shape[-1]]) - next_sent_feat = fluid.layers.fc( - input=next_sent_feat, - size=self._emb_size, - act="tanh", - param_attr=fluid.ParamAttr( - name=scope_name+"pooled_fc.w_0", initializer=self._param_initializer), - bias_attr=scope_name+"pooled_fc.b_0") - - return {'embedding_table': embedding_table, - 'word_embedding': emb_out, - 'encoder_outputs': enc_out, - 'sentence_embedding': next_sent_feat, - 'sentence_pair_embedding': next_sent_feat} - - def postprocess(self, rt_outputs): - pass diff --git a/build/lib/paddlepalm/backbone/utils/__init__.py b/build/lib/paddlepalm/backbone/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/build/lib/paddlepalm/backbone/utils/transformer.py b/build/lib/paddlepalm/backbone/utils/transformer.py deleted file mode 100644 index d5aa5c7..0000000 --- a/build/lib/paddlepalm/backbone/utils/transformer.py +++ /dev/null @@ -1,341 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Transformer encoder.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from functools import partial - -import paddle.fluid as fluid -import paddle.fluid.layers as layers - -def multi_head_attention(queries, - keys, - values, - attn_bias, - d_key, - d_value, - d_model, - n_head=1, - dropout_rate=0., - cache=None, - param_initializer=None, - name='multi_head_att'): - """ - Multi-Head Attention. Note that attn_bias is added to the logit before - computing softmax activiation to mask certain selected positions so that - they will not considered in attention weights. - """ - keys = queries if keys is None else keys - values = keys if values is None else values - - if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3): - raise ValueError( - "Inputs: quries, keys and values should all be 3-D tensors.") - - def __compute_qkv(queries, keys, values, n_head, d_key, d_value): - """ - Add linear projection to queries, keys, and values. - """ - q = layers.fc(input=queries, - size=d_key * n_head, - num_flatten_dims=2, - param_attr=fluid.ParamAttr( - name=name + '_query_fc.w_0', - initializer=param_initializer), - bias_attr=name + '_query_fc.b_0') - k = layers.fc(input=keys, - size=d_key * n_head, - num_flatten_dims=2, - param_attr=fluid.ParamAttr( - name=name + '_key_fc.w_0', - initializer=param_initializer), - bias_attr=name + '_key_fc.b_0') - v = layers.fc(input=values, - size=d_value * n_head, - num_flatten_dims=2, - param_attr=fluid.ParamAttr( - name=name + '_value_fc.w_0', - initializer=param_initializer), - bias_attr=name + '_value_fc.b_0') - return q, k, v - - def __split_heads(x, n_head): - """ - Reshape the last dimension of inpunt tensor x so that it becomes two - dimensions and then transpose. Specifically, input a tensor with shape - [bs, max_sequence_length, n_head * hidden_dim] then output a tensor - with shape [bs, n_head, max_sequence_length, hidden_dim]. - """ - hidden_size = x.shape[-1] - # The value 0 in shape attr means copying the corresponding dimension - # size of the input as the output dimension size. - reshaped = layers.reshape( - x=x, shape=[0, 0, n_head, hidden_size // n_head], inplace=True) - - # permuate the dimensions into: - # [batch_size, n_head, max_sequence_len, hidden_size_per_head] - return layers.transpose(x=reshaped, perm=[0, 2, 1, 3]) - - def __combine_heads(x): - """ - Transpose and then reshape the last two dimensions of inpunt tensor x - so that it becomes one dimension, which is reverse to __split_heads. - """ - if len(x.shape) == 3: return x - if len(x.shape) != 4: - raise ValueError("Input(x) should be a 4-D Tensor.") - - trans_x = layers.transpose(x, perm=[0, 2, 1, 3]) - # The value 0 in shape attr means copying the corresponding dimension - # size of the input as the output dimension size. - return layers.reshape( - x=trans_x, - shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]], - inplace=True) - - def scaled_dot_product_attention(q, k, v, attn_bias, d_key, dropout_rate): - """ - Scaled Dot-Product Attention - """ - scaled_q = layers.scale(x=q, scale=d_key**-0.5) - product = layers.matmul(x=scaled_q, y=k, transpose_y=True) - if attn_bias: - product += attn_bias - weights = layers.softmax(product) - if dropout_rate: - weights = layers.dropout( - weights, - dropout_prob=dropout_rate, - dropout_implementation="upscale_in_train", - is_test=False) - out = layers.matmul(weights, v) - return out - - q, k, v = __compute_qkv(queries, keys, values, n_head, d_key, d_value) - - if cache is not None: # use cache and concat time steps - # Since the inplace reshape in __split_heads changes the shape of k and - # v, which is the cache input for next time step, reshape the cache - # input from the previous time step first. - k = cache["k"] = layers.concat( - [layers.reshape( - cache["k"], shape=[0, 0, d_model]), k], axis=1) - v = cache["v"] = layers.concat( - [layers.reshape( - cache["v"], shape=[0, 0, d_model]), v], axis=1) - - q = __split_heads(q, n_head) - k = __split_heads(k, n_head) - v = __split_heads(v, n_head) - - ctx_multiheads = scaled_dot_product_attention(q, k, v, attn_bias, d_key, - dropout_rate) - - out = __combine_heads(ctx_multiheads) - - # Project back to the model size. - proj_out = layers.fc(input=out, - size=d_model, - num_flatten_dims=2, - param_attr=fluid.ParamAttr( - name=name + '_output_fc.w_0', - initializer=param_initializer), - bias_attr=name + '_output_fc.b_0') - return proj_out - - -def positionwise_feed_forward(x, - d_inner_hid, - d_hid, - dropout_rate, - hidden_act, - param_initializer=None, - name='ffn'): - """ - Position-wise Feed-Forward Networks. - This module consists of two linear transformations with a ReLU activation - in between, which is applied to each position separately and identically. - """ - hidden = layers.fc(input=x, - size=d_inner_hid, - num_flatten_dims=2, - act=hidden_act, - param_attr=fluid.ParamAttr( - name=name + '_fc_0.w_0', - initializer=param_initializer), - bias_attr=name + '_fc_0.b_0') - if dropout_rate: - hidden = layers.dropout( - hidden, - dropout_prob=dropout_rate, - dropout_implementation="upscale_in_train", - is_test=False) - out = layers.fc(input=hidden, - size=d_hid, - num_flatten_dims=2, - param_attr=fluid.ParamAttr( - name=name + '_fc_1.w_0', initializer=param_initializer), - bias_attr=name + '_fc_1.b_0') - return out - - -def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0., - name=''): - """ - Add residual connection, layer normalization and droput to the out tensor - optionally according to the value of process_cmd. - This will be used before or after multi-head attention and position-wise - feed-forward networks. - """ - for cmd in process_cmd: - if cmd == "a": # add residual connection - out = out + prev_out if prev_out else out - elif cmd == "n": # add layer normalization - out_dtype = out.dtype - if out_dtype == fluid.core.VarDesc.VarType.FP16: - out = layers.cast(x=out, dtype="float32") - out = layers.layer_norm( - out, - begin_norm_axis=len(out.shape) - 1, - param_attr=fluid.ParamAttr( - name=name + '_layer_norm_scale', - initializer=fluid.initializer.Constant(1.)), - bias_attr=fluid.ParamAttr( - name=name + '_layer_norm_bias', - initializer=fluid.initializer.Constant(0.))) - if out_dtype == fluid.core.VarDesc.VarType.FP16: - out = layers.cast(x=out, dtype="float16") - elif cmd == "d": # add dropout - if dropout_rate: - out = layers.dropout( - out, - dropout_prob=dropout_rate, - dropout_implementation="upscale_in_train", - is_test=False) - return out - - -pre_process_layer = partial(pre_post_process_layer, None) -post_process_layer = pre_post_process_layer - - -def encoder_layer(enc_input, - attn_bias, - n_head, - d_key, - d_value, - d_model, - d_inner_hid, - prepostprocess_dropout, - attention_dropout, - relu_dropout, - hidden_act, - preprocess_cmd="n", - postprocess_cmd="da", - param_initializer=None, - name=''): - """The encoder layers that can be stacked to form a deep encoder. - This module consits of a multi-head (self) attention followed by - position-wise feed-forward networks and both the two components companied - with the post_process_layer to add residual connection, layer normalization - and droput. - """ - attn_output = multi_head_attention( - pre_process_layer( - enc_input, - preprocess_cmd, - prepostprocess_dropout, - name=name + '_pre_att'), - None, - None, - attn_bias, - d_key, - d_value, - d_model, - n_head, - attention_dropout, - param_initializer=param_initializer, - name=name + '_multi_head_att') - attn_output = post_process_layer( - enc_input, - attn_output, - postprocess_cmd, - prepostprocess_dropout, - name=name + '_post_att') - ffd_output = positionwise_feed_forward( - pre_process_layer( - attn_output, - preprocess_cmd, - prepostprocess_dropout, - name=name + '_pre_ffn'), - d_inner_hid, - d_model, - relu_dropout, - hidden_act, - param_initializer=param_initializer, - name=name + '_ffn') - return post_process_layer( - attn_output, - ffd_output, - postprocess_cmd, - prepostprocess_dropout, - name=name + '_post_ffn') - - -def encoder(enc_input, - attn_bias, - n_layer, - n_head, - d_key, - d_value, - d_model, - d_inner_hid, - prepostprocess_dropout, - attention_dropout, - relu_dropout, - hidden_act, - preprocess_cmd="n", - postprocess_cmd="da", - param_initializer=None, - name=''): - """ - The encoder is composed of a stack of identical layers returned by calling - encoder_layer. - """ - for i in range(n_layer): - enc_output = encoder_layer( - enc_input, - attn_bias, - n_head, - d_key, - d_value, - d_model, - d_inner_hid, - prepostprocess_dropout, - attention_dropout, - relu_dropout, - hidden_act, - preprocess_cmd, - postprocess_cmd, - param_initializer=param_initializer, - name=name + '_layer_' + str(i)) - enc_input = enc_output - enc_output = pre_process_layer( - enc_output, preprocess_cmd, prepostprocess_dropout, name="post_encoder") - - return enc_output diff --git a/build/lib/paddlepalm/default_settings.py b/build/lib/paddlepalm/default_settings.py deleted file mode 100644 index 4f003ea..0000000 --- a/build/lib/paddlepalm/default_settings.py +++ /dev/null @@ -1,42 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -BACKBONE_DIR='paddlepalm.backbone' -TASK_INSTANCE_DIR='paddlepalm.task_instance' -READER_DIR='paddlepalm.reader' -PARADIGM_DIR='paddlepalm.task_paradigm' -OPTIMIZER_DIR='paddlepalm.optimizer' -OPTIMIZE_METHOD='optimize' - -REQUIRED_ARGS={ - 'task_instance': str, - 'backbone': str, - 'optimizer': str, - 'learning_rate': float, - 'batch_size': int - } - -OPTIONAL_ARGS={ - 'mix_ratio': str, - 'target_tag': str, - 'reuse_rag': str - } - -TASK_REQUIRED_ARGS={ - 'paradigm': str, - 'reader': str, - 'train_file': str - } - diff --git a/build/lib/paddlepalm/interface.py b/build/lib/paddlepalm/interface.py deleted file mode 100644 index 06c93ac..0000000 --- a/build/lib/paddlepalm/interface.py +++ /dev/null @@ -1,173 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""v1.1""" - -class reader(object): - """interface of data manager.""" - - def __init__(self, config): - assert isinstance(config, dict) - - # @property - # def inputs_attr(self): - # """描述reader输入对象的属性，包含各个对象的名字、shape以及数据类型。当某个对象为标量数据类型（如str, int, float等）时，shape设置为空列表[]，当某个对象的某个维度长度可变时，shape中的相应维度设置为-1. - # Return: - # dict类型。对各个输入对象的属性描述。例如， - # 对于文本分类任务，可能需要包含输入文本和所属标签的id - # {"text": ([], 'str'), - # "label": ([], 'int')} - # 对于标注任务，可能需要输入词序列和对应的标签 - # {"tokens", ([-1], 'str'), - # "tags", ([-1], 'str')} - # 对于机器阅读理解任务，可能需要包含上下文、问题、回答、答案区域的起止位置等 - # {"paragraph", ([], 'str'), - # "question", ([], 'str'), - # "start_position", ([], 'int') - # """ - # raise NotImplementedError() - - @property - def outputs_attr(self): - """描述reader输出对象（被yield出的对象）的属性，包含各个对象的名字、shape以及数据类型。当某个对象为标量数据类型（如str, int, float等）时，shape设置为空列表[]，当某个对象的某个维度长度可变时，shape中的相应维度设置为-1。 - 注意：当使用mini-batch梯度下降学习策略时，，应为常规的输入对象设置batch_size维度（一般为-1） - Return: - dict类型。对各个输入对象的属性描述。例如， - 对于文本分类和匹配任务，yield的输出内容可能包含如下的对象（下游backbone和task可按需访问其中的对象） - {"token_ids": ([-1, max_len], 'int64'), - "input_ids": ([-1, max_len], 'int64'), - "segment_ids": ([-1, max_len], 'int64'), - "input_mask": ([-1, max_len], 'float32'), - "label": ([-1], 'int')} - """ - raise NotImplementedError() - - # def parse_line(self): - # """框架内部使用字典描述每个样本，字典的key为inputs_attr，value为每个input对应的符合attr描述的值。 - # 该函数负责将文本行解析成符合inputs_attr描述的字典类型的样本。默认的parse_line方法会读取json格式的数据集文件，数据集的每一行为json格式描述的样本。 - # 用户可通过对该方法的继承改写来适配不同格式的数据集，例如csv格式甚至tfrecord文件。 - # """ - # raise NotImplementedError() - # - # def tokenize(self, line): - # """框架中内置了word piece tokenizer等分词器，用户可通过修改tokenizer超参数来制定使用的分词器，若内置的分词器均无法满足需求，用户可通过对该方法的继承改写来自定义分词器。 - # Args: - # - line: a unicode string. - # Return: - # a list of tokens - # """ - # raise NotImplementedError() - - def iterator(self): - """数据集遍历接口，注意，当数据集遍历到尾部时该接口应自动完成指针重置，即重新从数据集头部开始新的遍历。 - Yield: - (dict) elements that meet the requirements in output_templete - """ - raise NotImplementedError() - - @property - def num_examples(self): - """数据集中的样本数量，即每个epoch中iterator所生成的样本数。注意，使用滑动窗口等可能导致数据集样本数发生变化的策略时，该接口应返回runtime阶段的实际样本数。""" - raise NotImplementedError() - - - -class backbone(object): - """interface of backbone model.""" - - def __init__(self, config, phase): - """ - Args: - config: dict类型。描述了 多任务配置文件+预训练模型配置文件 中定义超参数 - phase: str类型。运行阶段，目前支持train和predict - """ - assert isinstance(config, dict) - - @property - def inputs_attr(self): - """描述backbone从reader处需要得到的输入对象的属性，包含各个对象的名字、shape以及数据类型。当某个对象为标量数据类型（如str, int, float等）时，shape设置为空列表[]，当某个对象的某个维度长度可变时，shape中的相应维度设置为-1。 - Return: - dict类型。对各个输入对象的属性描述。例如， - 对于文本分类和匹配任务，bert backbone依赖的reader对象主要包含如下的对象 - {"token_ids": ([-1, max_len], 'int64'), - "input_ids": ([-1, max_len], 'int64'), - "segment_ids": ([-1, max_len], 'int64'), - "input_mask": ([-1, max_len], 'float32')}""" - raise NotImplementedError() - - @property - def outputs_attr(self): - """描述backbone输出对象的属性，包含各个对象的名字、shape以及数据类型。当某个对象为标量数据类型（如str, int, float等）时，shape设置为空列表[]，当某个对象的某个维度长度可变时，shape中的相应维度设置为-1。 - Return: - dict类型。对各个输出对象的属性描述。例如， - 对于文本分类和匹配任务，bert backbone的输出内容可能包含如下的对象 - {"word_emb": ([-1, max_seqlen, word_emb_size], 'float32'), - "sentence_emb": ([-1, hidden_size], 'float32'), - "sim_vec": ([-1, hidden_size], 'float32')}""" - raise NotImplementedError() - - def build(self, inputs): - """建立backbone的计算图。将符合inputs_attr描述的静态图Variable输入映射成符合outputs_attr描述的静态图Variable输出。 - Args: - inputs: dict类型。字典中包含inputs_attr中的对象名到计算图Variable的映射，inputs中至少会包含inputs_attr中定义的对象 - Return: - 需要输出的计算图变量，输出对象会被加入到fetch_list中，从而在每个训练/推理step时得到runtime的计算结果，该计算结果会被传入postprocess方法中供用户处理。 - """ - raise NotImplementedError() - - - - -class task_paradigm(object): - - def __init__(self, config, phase, backbone_config): - """ - config: dict类型。描述了 任务实例(task instance)+多任务配置文件 中定义超参数 - phase: str类型。运行阶段，目前支持train和predict - """ - - @property - def inputs_attrs(self): - """描述task_layer需要从reader, backbone等输入对象集合所读取到的输入对象的属性，第一级key为对象集和的名字，如backbone，reader等（后续会支持更灵活的输入），第二级key为对象集和中各对象的属性，包括对象的名字，shape和dtype。当某个对象为标量数据类型（如str, int, float等）时，shape设置为空列表[]，当某个对象的某个维度长度可变时，shape中的相应维度设置为-1。 - Return: - dict类型。对各个对象集及其输入对象的属性描述。""" - raise NotImplementedError() - - @property - def outputs_attr(self): - """描述task输出对象的属性，包括对象的名字，shape和dtype。输出对象会被加入到fetch_list中，从而在每个训练/推理step时得到runtime的计算结果，该计算结果会被传入postprocess方法中供用户处理。 - 当某个对象为标量数据类型（如str, int, float等）时，shape设置为空列表[]，当某个对象的某个维度长度可变时，shape中的相应维度设置为-1。 - Return: - dict类型。对各个输入对象的属性描述。注意，训练阶段必须包含名为loss的输出对象。 - """ - - raise NotImplementedError() - - def build(self, inputs): - """建立task_layer的计算图。将符合inputs_attrs描述的来自各个对象集的静态图Variables映射成符合outputs_attr描述的静态图Variable输出。 - Args: - inputs: dict类型。字典中包含inputs_attrs中的对象名到计算图Variable的映射，inputs中至少会包含inputs_attr中定义的对象 - Return: - 需要输出的计算图变量，输出对象会被加入到fetch_list中，从而在每个训练/推理step时得到runtime的计算结果，该计算结果会被传入postprocess方法中供用户处理。 - - """ - raise NotImplementedError() - - def postprocess(self, rt_outputs): - """每个训练或推理step后针对当前batch的task_layer的runtime计算结果进行相关后处理。注意，rt_outputs除了包含build方法，还自动包含了loss的计算结果。""" - pass - - def post_postprocess(self, global_buffer): - pass - diff --git a/build/lib/paddlepalm/mtl_controller.py b/build/lib/paddlepalm/mtl_controller.py deleted file mode 100644 index b4716d5..0000000 --- a/build/lib/paddlepalm/mtl_controller.py +++ /dev/null @@ -1,724 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import sys -import importlib -import multiprocessing -from paddle import fluid -from paddle.fluid import layers -import yaml -import json -import logging -import time -import numpy as np - -from paddlepalm.utils.saver import init_pretraining_params, init_checkpoint -from paddlepalm.utils.config_helper import PDConfig -from paddlepalm.utils.print_helper import print_dict -from paddlepalm.utils.reader_helper import create_net_inputs, create_iterator_fn, create_joint_iterator_fn, merge_input_attrs - -from paddlepalm.default_settings import * -from paddlepalm.task_instance import TaskInstance, check_instances - -DEBUG=False -VERBOSE=0 - -def _get_basename(f): - return os.path.splitext(f)[0] - - -def _get_suffix(f): - return os.path.splitext(f)[-1] - - -def _parse_yaml(f, asdict=True, support_cmd_line=False): - assert os.path.exists(f), "file {} not found.".format(f) - if support_cmd_line: - args = PDConfig(yaml_file=f, fuse_args=True) - args.build() - return args.asdict() if asdict else args - else: - if asdict: - with open(f, "r") as fin: - yaml_config = yaml.load(fin, Loader=yaml.SafeLoader) - return yaml_config - else: - raise NotImplementedError() - - -def _parse_json(f, asdict=True, support_cmd_line=False): - assert os.path.exists(f), "file {} not found.".format(f) - if support_cmd_line: - args = PDConfig(json_file=f, fuse_args=support_cmd_line) - args.build() - return args.asdict() if asdict else args - else: - if asdict: - with open(f, "r") as fin: - config = json.load(fin) - return config - else: - raise NotImplementedError() - - -def _parse_list(string, astype=str): - assert isinstance(string, str), "{} is not a string.".format(string) - if ',' not in string: - return [astype(string)] - string = string.replace(',', ' ') - return [astype(i) for i in string.split()] - - -def _try_float(s): - try: - float(s) - return(float(s)) - except: - return s - - -def _check_conf(conf, checklist=None): - assert isinstance(conf, dict), "{} is not a dict.".format(conf) - ret = {} - for k,v in conf.items(): - if isinstance(v, str): - v = _try_float(v) - ret[k] = v - if checklist is not None: - for k, t in checklist: - assert k in ret, "required argument {} is NOT exist in config file.".format(k) - assert isintance(ret[k], t), "value type of argument {} should be {}".format(k, t) - return ret - - -# TODO: 增加None机制，允许hidden size、batch size和seqlen设置为None -def _check_io(in_attr, out_attr, strict=False, in_name="left", out_name="right"): - for name, attr in in_attr.items(): - assert name in out_attr, in_name+': '+name+' not found in '+out_name - if attr != out_attr[name]: - if strict: - raise ValueError(name+': shape or dtype not consistent!') - else: - logging.warning('{}: shape or dtype not consistent!\n{}:\n{}\n{}:\n{}'.format(name, in_name, attr, out_name, out_attr[name])) - - -def _merge_conf(conf1, conf2, conf1_first=True, strict=False): - assert isinstance(conf1, dict), "{} is not a dict.".format(conf1) - assert isinstance(conf2, dict), "{} is not a dict.".format(conf2) - base_conf = conf2 if conf1_first else conf1 - base_conf = base_conf.copy() - new_conf = conf1 if conf1_first else conf2 - - for k, v in new_conf.items(): - if k in base_conf: - if base_conf[k] != v: - raise Warning("value of argument {} has been updated to {}.".format(k, v)) - else: - if strict: - continue - - base_conf[k] = v - return base_conf - - -def _encode_inputs(inputs, scope_name, sep='/', cand_set=None): - outputs = {} - for k, v in inputs.items(): - if cand_set is not None: - if k in cand_set: - outputs[k] = v - if scope_name+sep+k in cand_set: - outputs[scope_name+sep+k] = v - else: - outputs[scope_name+sep+k] = v - return outputs - - -def _decode_inputs(inputs, scope_name, sep='/', keep_unk_keys=True): - outputs = {} - for name, value in inputs.items(): - # var for backbone are also available to tasks - if keep_unk_keys and sep not in name: - outputs[name] = value - # var for this inst - if name.startswith(scope_name+'/'): - outputs[name[len(scope_name+'/'):]] = value - return outputs - - -def _init_env(use_gpu): - if use_gpu: - place = fluid.CUDAPlace(0) - dev_count = fluid.core.get_cuda_device_count() - else: - place = fluid.CPUPlace() - dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) - return fluid.Executor(place), dev_count - - -def _fit_attr(conf, fit_attr, strict=False): - for i, attr in fit_attr.items(): - if i not in conf: - if strict: - raise Exception('Argument {} is required to create a controller.'.format(i)) - else: - continue - conf[i] = attr(conf[i]) - return conf - - -class Controller(object): - - def __init__(self, config=None, task_dir='.', for_train=True): - """ - Args: - config: (str|dict) 字符串类型时，给出yaml格式的config配置文件路径； - """ - - self._for_train = for_train - # default mtl_conf - # if config is None and config_path is None: - # raise ValueError('For config and config_path, at least one of them should be set.') - - if isinstance(config, str): - mtl_conf = _parse_yaml(config, support_cmd_line=True) - # if config is not None: - # mtl_conf = _merge_conf(config, mtl_conf) - else: - mtl_conf = config - - mtl_conf = _check_conf(mtl_conf) - mtl_conf = _fit_attr(mtl_conf, REQUIRED_ARGS, strict=True) - mtl_conf = _fit_attr(mtl_conf, OPTIONAL_ARGS, strict=False) - - exe, dev_count = _init_env(use_gpu=mtl_conf.get('use_gpu', True)) - self.exe = exe - self.dev_count = dev_count - - print_dict(mtl_conf, title='main configuration') - - # parse task instances and target tags - instnames = _parse_list(mtl_conf['task_instance']) - assert len(instnames) == len(set(instnames)), "repeated task_instance is NOT supported." - num_instances = len(instnames) - self.num_instances = num_instances - - instname_to_conf = {} - instname_to_id = {} - for id, instname in enumerate(instnames): - instpath = os.path.join(task_dir, instname+'.yaml') - conf = _parse_yaml(instpath, support_cmd_line=False) - # conf = _check_conf(conf, TASK_INSTANCE_REQUIRED_ARGS) - conf = _check_conf(conf) - temp_conf = _merge_conf(mtl_conf, conf, strict=True) - print_dict(temp_conf, title='{} configuration'.format(instname)) - conf = _merge_conf(mtl_conf, conf) - - instname_to_conf[instname] = conf - instname_to_id[instname] = id - - # create task instances - instances = [] - for name in instnames: - instances.append(TaskInstance(name, instname_to_id[name], instname_to_conf[name])) - - check_instances(instances) - - # parse target_tag - if 'target_tag' in mtl_conf: - target_tag = str(mtl_conf['target_tag']) - tags = _parse_list(target_tag, astype=int) - assert len(tags) == len(instnames), "number of target_tag is NOT consistent with that in task_instance." - for tag, inst in zip(tags, instances): - inst.is_target = tag - else: - tags = [i.is_target for i in instances] - num_targets = sum(tags) - num_auxes = num_instances - num_targets - - # parse mix ratios - if 'mix_ratio' in mtl_conf: - mix_ratio = str(mtl_conf['mix_ratio']) - mrs = _parse_list(mix_ratio, astype=float) - assert len(mrs) == num_instances, "number of mix_ratios is NOT consistent with num_instances." - else: - # TODO: 增加joint training模式，让num_epochs平等的作用于每个instance - mrs = [1.0] * num_instances - - for mr, inst in zip(mrs, instances): - inst.mix_ratio = mr - - # parse task layer reuse tags - instname_to_reusehost = {i:i for i in instnames} - if 'task_reuse_tag' in mtl_conf: - tags = _parse_list(mtl_conf['task_reuse_tag'], astype=int) - assert len(tags) == num_targets, 'number of reuse_tags is NOT consistent with number of instances.' - else: - tags = [] - mapper = {} - for inst in instances: - # 有环则tag_id + 1，否则被mapper shutdown - history = set() - history.add(inst.name) - cur_inst = inst - while True: - # 发现有环 - if cur_inst.task_reuse_scope in history: - mapper[inst.name] = len(tags) - break - # 发现在mapper中 - elif cur_inst.task_reuse_scope in mapper: - mapper[inst.name] = mapper[cur_inst.task_reuse_scope] - break - else: - cur_inst = name_to_instance[cur_inst.task_reuse_scope] - history.add(cur_inst.name) - - tags.append(mapper[inst.name]) - # 注意，上面这段需要做单元测试 - - for i in range(1, num_instances): - for j in range(i): - if tags[i] == tags[j]: - # check paradigm of reused tasks - assert instances[i].task_paradigm == \ - instances[j].task_paradigm, \ - "paradigm of reuse tasks should be consistent" - instances[i].task_reuse_scope = instances[j].name - break - - # parse Reader and Paradigm for each instance - for inst in instances: - reader_name = inst.config['reader'] - reader_mod = importlib.import_module(READER_DIR + '.' + reader_name) - Reader = getattr(reader_mod, 'Reader') - - parad_name = inst.config['paradigm'] - parad_mod = importlib.import_module(PARADIGM_DIR + '.' + parad_name) - Paradigm = getattr(parad_mod, 'TaskParadigm') - - inst.Reader = Reader - inst.Paradigm = Paradigm - - # prepare backbone - if 'backbone_config_path' in mtl_conf: - bb_conf = _parse_json(mtl_conf['backbone_config_path']) - bb_conf = _merge_conf(mtl_conf, bb_conf) - else: - bb_conf = mtl_conf - print_dict(bb_conf, title='backbone configuration'.format(instname)) - - bb_name = mtl_conf['backbone'] - bb_mod = importlib.import_module(BACKBONE_DIR + '.' + bb_name) - Backbone = getattr(bb_mod, 'Model') - - self.instances = instances - self.mrs = mrs - self.Backbone = Backbone - self.bb_conf = bb_conf - self.bb_name = bb_name - - self.has_init_train = False - self.has_init_pred = False - - if self._for_train: - print("initialing for training...") - self._init_train() - self.has_init_train = True - - def _init_train(self): - - instances = self.instances - Backbone = self.Backbone - bb_conf = self.bb_conf - bb_name = self.bb_name - dev_count = self.dev_count - num_instances = len(instances) - mrs = self.mrs - - # set first_target/main task instance - main_inst = None - for inst in instances: - if inst.is_target: - main_inst = inst - inst.is_first_target = True - break - main_conf = main_inst.config - if not os.path.exists(main_conf['save_path']): - os.makedirs(main_conf['save_path']) - - # prepare backbone - train_backbone = Backbone(bb_conf, phase='train') - pred_backbone = Backbone(bb_conf, phase='pred') - - # create reader, task - # then check i/o across reader, backbone and task_layer - task_attrs = [] - pred_task_attrs = [] - for inst in instances: - - train_reader = inst.Reader(inst.config, phase='train') - inst.reader['train'] = train_reader - train_parad = inst.Paradigm(inst.config, phase='train', backbone_config=bb_conf) - inst.task_layer['train'] = train_parad - task_attr_from_reader = _encode_inputs(train_parad.inputs_attrs['reader'], inst.name) - task_attrs.append(task_attr_from_reader) - - _check_io(train_backbone.inputs_attr, train_reader.outputs_attr, in_name=bb_name+'_backbone', out_name='reader.train') - _check_io(train_parad.inputs_attrs['reader'], train_reader.outputs_attr, in_name='task_paradigm.train.reader', out_name='reader.train') - _check_io(train_parad.inputs_attrs['backbone'], train_backbone.outputs_attr, in_name='task_paradigm.train.backbone', out_name=bb_name+'_backbone') - - if inst.is_target: - if 'pred_file' not in inst.config: - inst.config['pred_file'] = '' - pred_reader = inst.Reader(inst.config, phase='pred') - pred_parad = inst.Paradigm(inst.config, phase='pred', backbone_config=bb_conf) - # inst.reader['pred'] = pred_reader # 这里创建的reader是个假reader，只是为了读取output_attr而已，所以不做保存 - inst.task_layer['pred'] = pred_parad - # 框架有巨坑，先这样写吧 - task_attr_from_reader = _encode_inputs(pred_parad.inputs_attrs['reader'], inst.name) - pred_task_attrs.append(task_attr_from_reader) - # task_attr = pred_parad.inputs_attrs['reader'] - _check_io(pred_backbone.inputs_attr, pred_reader.outputs_attr, in_name=bb_name+'_backbone', out_name='reader.pred') - _check_io(pred_parad.inputs_attrs['reader'], pred_reader.outputs_attr, in_name='task_paradigm.pred.reader', out_name='reader.pred') - _check_io(pred_parad.inputs_attrs['backbone'], pred_backbone.outputs_attr, in_name='task_paradigm.pred.backbone', out_name=bb_name+'_backbone') - - # merge reader input attrs from backbone and task_instances - joint_input_names, joint_shape_and_dtypes, name_to_position = merge_input_attrs(train_backbone.inputs_attr, task_attrs) - pred_joint_input_names, pred_joint_shape_and_dtypes, _ = merge_input_attrs(pred_backbone.inputs_attr, pred_task_attrs, insert_taskid=False) - # shapes: [task_id, shapes_of_backbone, shapes_of_inst1, ..., shapes_of_instN] - - if DEBUG: - print('----- for debug -----') - print('joint input names:') - print(joint_input_names) - print('joint input shape and dtypes:') - print(joint_shape_and_dtypes) - - # load data - for inst in instances: - print(inst.name+": preparing data...") - inst.reader['train'].load_data() - - # merge dataset iterators and create net input vars - iterators = [] - prefixes = [] - mrs = [] - for inst in instances: - iterators.append(inst.reader['train'].iterator()) - prefixes.append(inst.name) - mrs.append(inst.mix_ratio) - - joint_iterator_fn = create_joint_iterator_fn(iterators, prefixes, joint_shape_and_dtypes, mrs, name_to_position, dev_count=dev_count, verbose=VERBOSE) - - input_attrs = [[i, j, k] for i, (j,k) in zip(joint_input_names, joint_shape_and_dtypes)] - pred_input_attrs = [[i, j, k] for i, (j,k) in zip(pred_joint_input_names, pred_joint_shape_and_dtypes)] - net_inputs = create_net_inputs(input_attrs, async=True, iterator_fn=joint_iterator_fn, dev_count=dev_count, n_prefetch=3) - - # build backbone and task layers - # 不指定scope名字会挂，框架有坑 - train_prog = fluid.default_main_program() - train_init_prog = fluid.default_startup_program() - # 别用unique_name.guard了，没用的，无法作用到param_attr里的name上 - with fluid.unique_name.guard("backbone-"): - bb_output_vars = train_backbone.build(net_inputs, scope_name='__paddlepalm_') - assert sorted(bb_output_vars.keys()) == sorted(train_backbone.outputs_attr.keys()) - #for var in train_init_prog.blocks[0].vars: - # print(var) - - # 会挂 - # 这里是否有必要新建一个program？是的，被坑死了 - pred_prog = fluid.Program() - pred_init_prog = fluid.Program() - - with fluid.program_guard(main_program = pred_prog, startup_program = pred_init_prog): - pred_net_inputs = create_net_inputs(pred_input_attrs) - # 别用unique_name.guard了，没用的，无法作用到param_attr里的name上 - # with fluid.unique_name.guard("backbone-"): - pred_bb_output_vars = pred_backbone.build(pred_net_inputs, scope_name='__paddlepalm_') - - fluid.framework.switch_main_program(train_prog) - fluid.framework.switch_startup_program(train_init_prog) - - # pred_backbone = train_backbone - # pred_bb_output_vars = bb_output_vars - - task_output_vars = {} - for inst in instances: - task_inputs = {'backbone': bb_output_vars} - task_inputs_from_reader = _decode_inputs(net_inputs, inst.name) - task_inputs['reader'] = task_inputs_from_reader - - scope = inst.task_reuse_scope + '/' - with fluid.unique_name.guard(scope): - output_vars = inst.build_task_layer(task_inputs, phase='train') - output_vars = {inst.name+'/'+key: val for key, val in output_vars.items()} - old = len(task_output_vars) # for debug - task_output_vars.update(output_vars) - assert len(task_output_vars) - old == len(output_vars) # for debug - - # # prepare predict vars for saving inference model - if inst.is_target: - - # task_attr = inst.task_layer['pred'].inputs_attrs['reader'] - # _input_names, _shape_and_dtypes, _ = merge_input_attrs(pred_backbone.inputs_attr, task_attr, insert_taskid=False) - # pred_input_attrs = [[i, j, k] for i, (j,k) in zip(_input_names, _shape_and_dtypes)] - - with fluid.program_guard(pred_prog, pred_init_prog): - # pred_net_inputs = create_net_inputs(pred_input_attrs) - # 这里同时建立了pred阶段的backbone计算图，不知道是否会造成额外的显存开销（paddle不会计算运行路径） - cur_inputs = _decode_inputs(pred_net_inputs, inst.name) - inst.pred_input = cur_inputs - pred_task_inputs = {'backbone': pred_bb_output_vars, 'reader': cur_inputs} - scope = inst.task_reuse_scope + '/' - with fluid.unique_name.guard(scope): - inst.build_task_layer(pred_task_inputs, phase='pred') - - - bb_fetches = {k: v.name for k,v in bb_output_vars.items()} - task_fetches = {k: v.name for k,v in task_output_vars.items()} - # fetches = bb_fetches.copy() # 注意！框架在多卡时无法fetch变长维度的tensor，这里加入bb的out后会挂 - # fetches.update(task_fetches) - fetches = task_fetches - fetches['__task_id'] = net_inputs['__task_id'].name - - # compute loss - task_id_var = net_inputs['__task_id'] - task_id_vec = layers.one_hot(task_id_var, num_instances) - losses = fluid.layers.concat([task_output_vars[inst.name+'/loss'] for inst in instances], axis=0) - loss = layers.reduce_sum(task_id_vec * losses) - - main_reader = main_inst.reader['train'] - - num_examples = main_reader.num_examples - for inst in instances: - max_train_steps = int(main_conf['num_epochs']* inst.mix_ratio * (num_examples // main_conf['batch_size'] // dev_count)) - if inst.is_target: - print('{}: expected train steps {}.'.format(inst.name, max_train_steps)) - inst.steps_pur_epoch = inst.reader['train'].num_examples // main_conf['batch_size'] // dev_count - inst.expected_train_steps = max_train_steps - - global_max_train_steps = int(main_conf['num_epochs'] * sum(mrs) * (num_examples // main_conf['batch_size'] // dev_count)) - print('Estimated overall train steps {}.'.format(global_max_train_steps)) - - if 'warmup_proportion' in main_conf and main_conf['warmup_proportion'] > 0: - warmup_steps = int(global_max_train_steps * main_conf['warmup_proportion']) - print('Warmup steps: '+str(warmup_steps)) - else: - warmup_steps = 0 - # steps_pur_epoch = num_examples // main_conf['batch_size'] // dev_count - - # build optimizer - # 其实也完全可以支持每个任务用它自己的optimizer - if 'optimizer' in main_conf: - optim_mod = importlib.import_module(OPTIMIZER_DIR + '.' + main_conf['optimizer']) - optimize = getattr(optim_mod, OPTIMIZE_METHOD) - optimize(loss, main_conf, max_train_steps, warmup_steps, fluid.default_main_program()) - - loss.persistable = True - if main_conf.get('use_ema', False): - assert 'ema_decay' in main_conf, "ema_decay should be set when use_ema is enabled." - ema = fluid.optimizer.ExponentialMovingAverage(main_conf['ema_decay']) - ema.update() - - # prepare for train - self.train_backbone = train_backbone - self.train_program = fluid.CompiledProgram(fluid.default_main_program()).with_data_parallel(loss_name=loss.name) - self.saver_program = fluid.default_main_program() - - self.main_inst = main_inst - self.fetches = fetches - self.has_init_train = True - self.has_init_pred = True - # self.max_train_steps = max_train_steps - # self.steps_pur_epoch = steps_pur_epoch - - self.exe.run(fluid.default_startup_program()) - print("\nRandomly initialize parameters...\n") - - def _init_pred(self, instance, infer_model_path): - inst = instance - - pred_backbone = self.Backbone(self.bb_conf, phase='pred') - pred_parad = inst.Paradigm(inst.config, phase='pred', backbone_config=self.bb_conf) - inst.task_layer['pred'] = pred_parad - pred_joint_input_names, pred_joint_shape_and_dtypes, name_to_position = merge_input_attrs( - pred_backbone.inputs_attr, inst.task_layer['pred'].inputs_attrs['reader'], - insert_taskid=False) - - pred_prog = inst.load(infer_model_path) - # pred_prog = fluid.CompiledProgram(pred_prog).with_data_parallel() - if inst.reader['pred'] is None: - pred_reader = inst.Reader(inst.config, phase='pred') - inst.reader['pred'] = pred_reader - return pred_prog - - def load_pretrain(self, pretrain_model_path=None): - # load pretrain model (or ckpt) - if pretrain_model_path is None: - assert 'pretrain_model_path' in self.main_conf, "pretrain_model_path NOT set." - pretrain_model_path = self.main_conf['pretrain_model_path'] - - init_pretraining_params( - self.exe, - pretrain_model_path, - main_program=fluid.default_startup_program()) - - - def train(self): - # TODO: 备份各种配置文件，以便用户断点重新训练以及支持将来的预测 - - if not self.has_init_train: - self._init_train() - self.has_init_train = True - - instances = self.instances - num_instances = self.num_instances - main_inst = self.main_inst - main_conf = main_inst.config - - backbone = self.train_backbone - train_program = self.train_program - saver_program = self.saver_program - fetches = self.fetches - - # max_train_steps = self.max_train_steps - # steps_pur_epoch = self.steps_pur_epoch - - finish = [] - for inst in instances: - if inst.is_target: - finish.append(False) - - def train_finish(): - for inst in instances: - if inst.is_target: - if not inst.train_finish: - return False - return True - - # do training - # loss_fetches = {inst.name+'/loss': inst.task_layer['train'].loss for inst in instances} - # old = len(fetches) # for debug - # fetches.update(loss_fetches) - # assert len(fetches) == old + len(loss_fetches) # for debug and avoid user-caused bug - # assert 'task_id' not in fetches # for debug and avoid user-caused bug - # fetches['task_id'] = task_id_var - fetch_names, fetch_list = zip(*fetches.items()) - - main_step = 0 # only count for main task - global_step = 0 # count for all tasks - epoch = 0 - time_begin = time.time() - backbone_buffer = [] - while not train_finish(): - rt_outputs = self.exe.run(train_program, fetch_list=fetch_list) - rt_outputs = {k:v for k,v in zip(fetch_names, rt_outputs)} - rt_task_id = np.squeeze(rt_outputs['__task_id']).tolist() - # 注意注释掉这一行之后，训练日志实际是错误的 - # assert (not isinstance(rt_task_id, list)) or len(set(rt_task_id)) == 1, rt_task_id - rt_task_id = rt_task_id[0] if isinstance(rt_task_id, list) else rt_task_id - cur_task = instances[rt_task_id] - - backbone_rt_outputs = {k:v for k,v in rt_outputs.items() if '/' not in k} - backbone_buffer.append(backbone.postprocess(backbone_rt_outputs)) - - task_rt_outputs = {k[len(cur_task.name+'/'):]: v for k,v in rt_outputs.items() if k.startswith(cur_task.name+'/')} - instances[rt_task_id].task_layer['train'].postprocess(task_rt_outputs) - - global_step += 1 - # if cur_task.is_target: - cur_task.cur_train_step += 1 - - if global_step % main_conf.get('print_every_n_steps', 5) == 0: - loss = rt_outputs[cur_task.name+'/loss'] - loss = np.mean(np.squeeze(loss)).tolist() - - time_end = time.time() - time_cost = time_end - time_begin - - print("Global step: {}. Task: {}, step {}/{} (epoch {}), loss: {:.3f}, speed: {:.2f} steps/s".format( - global_step, cur_task.name, cur_task.cur_train_step, cur_task.steps_pur_epoch, cur_task.cur_train_epoch, - loss, main_conf.get('print_every_n_steps', 5) / time_cost)) - time_begin = time.time() - - if cur_task.train_finish and cur_task.cur_train_step + cur_task.cur_train_epoch * cur_task.steps_pur_epoch == cur_task.expected_train_steps: - print(cur_task.name+': train finished!') - cur_task.save() - - if 'save_every_n_steps' in main_conf and global_step % main_conf['save_every_n_steps'] == 0: - save_path = os.path.join(main_conf['save_path'], - "step_" + str(global_step)) - fluid.io.save_persistables(self.exe, save_path, saver_program) - - save_path = os.path.join(main_conf['save_path'], - "step_" + str(global_step) + "_final") - fluid.io.save_persistables(self.exe, save_path, saver_program) - - def pred(self, task_instance, inference_model_dir=None): - if self._for_train: - raise Exception('This controller is a trainer. Please build a new controller with for_train=False for predicting.') - - assert isinstance(task_instance, str) - if isinstance(inference_model_dir, str): - assert os.path.exists(inference_model_dir), inference_model_dir+" not found." - if not self.has_init_pred and inference_model_dir is None: - raise ValueError('infer_model_path is required for prediction.') - - instance = None - for inst in self.instances: - if inst.name == task_instance: - instance = inst - break - - if instance is None: - raise ValueError(task_instance + ' is not a valid task_instance.') - - pred_prog = self._init_pred(instance, inference_model_dir) - - inst = instance - inst.reader['pred'].load_data() - fetch_names, fetch_vars = inst.pred_fetch_list - - # iterator = create_iterator_fn(inst.reader['pred'].iterator, inst.name, pred_joint_shape_and_dtypes, name_to_position) - mapper = {k:v for k,v in inst.pred_input} - buf = [] - for feed in inst.reader['pred'].iterator(): - feed = _encode_inputs(feed, inst.name, cand_set=mapper) - feed = {mapper[k]: v for k,v in feed.items()} - - rt_outputs = self.exe.run(pred_prog, feed, fetch_vars) - rt_outputs = {k:v for k,v in zip(fetch_names, rt_outputs)} - inst.postprocess(rt_outputs, phase='pred') - reader_outputs = inst.reader['pred'].get_epoch_outputs() - inst.epoch_postprocess({'reader':reader_outputs}, phase='pred') - - - - -if __name__ == '__main__': - assert len(sys.argv) == 2, "Usage: python mtl_controller.py " - conf_path = sys.argv[1] - del sys.argv[1] - controller = Controller(conf_path) - if controller.main_conf['do_train']: - controller.train() - - - - - - diff --git a/build/lib/paddlepalm/optimizer/__init__.py b/build/lib/paddlepalm/optimizer/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/build/lib/paddlepalm/optimizer/adam.py b/build/lib/paddlepalm/optimizer/adam.py deleted file mode 100644 index e08e45b..0000000 --- a/build/lib/paddlepalm/optimizer/adam.py +++ /dev/null @@ -1,103 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Optimization and learning rate scheduling.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np -import paddle.fluid as fluid - -def linear_warmup_decay(learning_rate, warmup_steps, num_train_steps): - """ Applies linear warmup of learning rate from 0 and decay to 0.""" - with fluid.default_main_program()._lr_schedule_guard(): - lr = fluid.layers.tensor.create_global_var( - shape=[1], - value=0.0, - dtype='float32', - persistable=True, - name="scheduled_learning_rate") - - global_step = fluid.layers.learning_rate_scheduler._decay_step_counter() - - with fluid.layers.control_flow.Switch() as switch: - with switch.case(global_step < warmup_steps): - warmup_lr = learning_rate * (global_step / warmup_steps) - fluid.layers.tensor.assign(warmup_lr, lr) - with switch.default(): - decayed_lr = fluid.layers.learning_rate_scheduler.polynomial_decay( - learning_rate=learning_rate, - decay_steps=num_train_steps, - end_learning_rate=0.0, - power=1.0, - cycle=False) - fluid.layers.tensor.assign(decayed_lr, lr) - - return lr - - -def optimize(loss, config, max_train_steps=None, warmup_steps=0, train_program=None): - if warmup_steps > 0: - decay_strategy = config.get('lr_scheduler', 'linear_warmup_decay') - if decay_strategy == 'noam_decay': - scheduled_lr = fluid.layers.learning_rate_scheduler\ - .noam_decay(1/(warmup_steps *(config['learning_rate'] ** 2)), - warmup_steps) - elif decay_strategy == 'linear_warmup_decay': - scheduled_lr = linear_warmup_decay(config['learning_rate'], warmup_steps, - max_train_steps) - else: - raise ValueError("Unkown lr_scheduler, should be " - "'noam_decay' or 'linear_warmup_decay'") - optimizer = fluid.optimizer.Adam(learning_rate=scheduled_lr) - else: - optimizer = fluid.optimizer.Adam(learning_rate=config['learning_rate']) - scheduled_lr = config['learning_rate'] - - clip_norm_thres = 1.0 - # When using mixed precision training, scale the gradient clip threshold - # by loss_scaling - fluid.clip.set_gradient_clip( - clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=clip_norm_thres)) - - def exclude_from_weight_decay(name): - if name.find("layer_norm") > -1: - return True - bias_suffix = ["_bias", "_b", ".b_0"] - for suffix in bias_suffix: - if name.endswith(suffix): - return True - return False - - param_list = dict() - - for param in train_program.global_block().all_parameters(): - param_list[param.name] = param * 1.0 - param_list[param.name].stop_gradient = True - - _, param_grads = optimizer.minimize(loss) - - - if config.get('weight_decay', 0) > 0: - for param, grad in param_grads: - if exclude_from_weight_decay(param.name): - continue - with param.block.program._optimized_guard( - [param, grad]), fluid.framework.name_scope("weight_decay"): - updated_param = param - param_list[ - param.name] * config['weight_decay'] * scheduled_lr - fluid.layers.assign(output=param, input=updated_param) - diff --git a/build/lib/paddlepalm/reader/__init__.py b/build/lib/paddlepalm/reader/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/build/lib/paddlepalm/reader/cls4bert.py b/build/lib/paddlepalm/reader/cls4bert.py deleted file mode 100644 index e69de29..0000000 diff --git a/build/lib/paddlepalm/reader/match.py b/build/lib/paddlepalm/reader/match.py deleted file mode 100644 index 0ef8fde..0000000 --- a/build/lib/paddlepalm/reader/match.py +++ /dev/null @@ -1,103 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from paddlepalm.interface import reader -from paddlepalm.reader.utils.reader4ernie import ClassifyReader - -class Reader(reader): - - def __init__(self, config, phase='train', dev_count=1, print_prefix=''): - """ - Args: - phase: train, eval, pred - """ - - self._is_training = phase == 'train' - - reader = ClassifyReader(config['vocab_path'], - max_seq_len=config['max_seq_len'], - do_lower_case=config.get('do_lower_case', False), - for_cn=config.get('for_cn', False), - random_seed=config.get('seed', None)) - self._reader = reader - self._dev_count = dev_count - - self._batch_size = config['batch_size'] - self._max_seq_len = config['max_seq_len'] - if phase == 'train': - self._input_file = config['train_file'] - self._num_epochs = None # 防止iteartor终止 - self._shuffle = config.get('shuffle', False) - self._shuffle_buffer = config.get('shuffle_buffer', 5000) - elif phase == 'eval': - self._input_file = config['dev_file'] - self._num_epochs = 1 - self._shuffle = False - self._batch_size = config.get('pred_batch_size', self._batch_size) - elif phase == 'pred': - self._input_file = config['pred_file'] - self._num_epochs = 1 - self._shuffle = False - self._batch_size = config.get('pred_batch_size', self._batch_size) - - self._phase = phase - # self._batch_size = - self._print_first_n = config.get('print_first_n', 1) - - - @property - def outputs_attr(self): - if self._is_training: - return {"token_ids": [[-1, -1, 1], 'int64'], - "position_ids": [[-1, -1, 1], 'int64'], - "segment_ids": [[-1, -1, 1], 'int64'], - "input_mask": [[-1, -1, 1], 'float32'], - "label_ids": [[-1,1], 'int64'], - "task_ids": [[-1, -1, 1], 'int64'] - } - else: - return {"token_ids": [[-1, -1, 1], 'int64'], - "position_ids": [[-1, -1, 1], 'int64'], - "segment_ids": [[-1, -1, 1], 'int64'], - "task_ids": [[-1, -1, 1], 'int64'], - "input_mask": [[-1, -1, 1], 'float32'] - } - - - def load_data(self): - self._data_generator = self._reader.data_generator(self._input_file, self._batch_size, self._num_epochs, dev_count=self._dev_count, shuffle=self._shuffle, phase=self._phase) - - def iterator(self): - - def list_to_dict(x): - names = ['token_ids', 'segment_ids', 'position_ids', 'task_ids', 'input_mask', - 'label_ids', 'unique_ids'] - outputs = {n: i for n,i in zip(names, x)} - del outputs['unique_ids'] - if not self._is_training: - del outputs['label_ids'] - return outputs - - for batch in self._data_generator(): - yield list_to_dict(batch) - - def get_epoch_outputs(self): - return {'examples': self._reader.get_examples(self._phase), - 'features': self._reader.get_features(self._phase)} - - @property - def num_examples(self): - return self._reader.get_num_examples(phase=self._phase) - diff --git a/build/lib/paddlepalm/reader/match4ernie.py b/build/lib/paddlepalm/reader/match4ernie.py deleted file mode 100644 index 0ef8fde..0000000 --- a/build/lib/paddlepalm/reader/match4ernie.py +++ /dev/null @@ -1,103 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from paddlepalm.interface import reader -from paddlepalm.reader.utils.reader4ernie import ClassifyReader - -class Reader(reader): - - def __init__(self, config, phase='train', dev_count=1, print_prefix=''): - """ - Args: - phase: train, eval, pred - """ - - self._is_training = phase == 'train' - - reader = ClassifyReader(config['vocab_path'], - max_seq_len=config['max_seq_len'], - do_lower_case=config.get('do_lower_case', False), - for_cn=config.get('for_cn', False), - random_seed=config.get('seed', None)) - self._reader = reader - self._dev_count = dev_count - - self._batch_size = config['batch_size'] - self._max_seq_len = config['max_seq_len'] - if phase == 'train': - self._input_file = config['train_file'] - self._num_epochs = None # 防止iteartor终止 - self._shuffle = config.get('shuffle', False) - self._shuffle_buffer = config.get('shuffle_buffer', 5000) - elif phase == 'eval': - self._input_file = config['dev_file'] - self._num_epochs = 1 - self._shuffle = False - self._batch_size = config.get('pred_batch_size', self._batch_size) - elif phase == 'pred': - self._input_file = config['pred_file'] - self._num_epochs = 1 - self._shuffle = False - self._batch_size = config.get('pred_batch_size', self._batch_size) - - self._phase = phase - # self._batch_size = - self._print_first_n = config.get('print_first_n', 1) - - - @property - def outputs_attr(self): - if self._is_training: - return {"token_ids": [[-1, -1, 1], 'int64'], - "position_ids": [[-1, -1, 1], 'int64'], - "segment_ids": [[-1, -1, 1], 'int64'], - "input_mask": [[-1, -1, 1], 'float32'], - "label_ids": [[-1,1], 'int64'], - "task_ids": [[-1, -1, 1], 'int64'] - } - else: - return {"token_ids": [[-1, -1, 1], 'int64'], - "position_ids": [[-1, -1, 1], 'int64'], - "segment_ids": [[-1, -1, 1], 'int64'], - "task_ids": [[-1, -1, 1], 'int64'], - "input_mask": [[-1, -1, 1], 'float32'] - } - - - def load_data(self): - self._data_generator = self._reader.data_generator(self._input_file, self._batch_size, self._num_epochs, dev_count=self._dev_count, shuffle=self._shuffle, phase=self._phase) - - def iterator(self): - - def list_to_dict(x): - names = ['token_ids', 'segment_ids', 'position_ids', 'task_ids', 'input_mask', - 'label_ids', 'unique_ids'] - outputs = {n: i for n,i in zip(names, x)} - del outputs['unique_ids'] - if not self._is_training: - del outputs['label_ids'] - return outputs - - for batch in self._data_generator(): - yield list_to_dict(batch) - - def get_epoch_outputs(self): - return {'examples': self._reader.get_examples(self._phase), - 'features': self._reader.get_features(self._phase)} - - @property - def num_examples(self): - return self._reader.get_num_examples(phase=self._phase) - diff --git a/build/lib/paddlepalm/reader/mlm.py b/build/lib/paddlepalm/reader/mlm.py deleted file mode 100644 index 338fcf2..0000000 --- a/build/lib/paddlepalm/reader/mlm.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from paddlepalm.interface import reader -from paddlepalm.reader.utils.reader4ernie import MaskLMReader - -class Reader(reader): - - def __init__(self, config, phase='train', dev_count=1, print_prefix=''): - """ - Args: - phase: train, eval, pred - """ - - self._is_training = phase == 'train' - - reader = MaskLMReader(config['vocab_path'], - max_seq_len=config['max_seq_len'], - do_lower_case=config.get('do_lower_case', False), - for_cn=config.get('for_cn', False), - random_seed=config.get('seed', None)) - self._reader = reader - self._dev_count = dev_count - - self._batch_size = config['batch_size'] - self._max_seq_len = config['max_seq_len'] - if phase == 'train': - self._input_file = config['train_file'] - self._num_epochs = None # 防止iteartor终止 - self._shuffle = config.get('shuffle', False) - self._shuffle_buffer = config.get('shuffle_buffer', 5000) - elif phase == 'eval': - self._input_file = config['dev_file'] - self._num_epochs = 1 - self._shuffle = False - self._batch_size = config.get('pred_batch_size', self._batch_size) - elif phase == 'pred': - self._input_file = config['pred_file'] - self._num_epochs = 1 - self._shuffle = False - self._batch_size = config.get('pred_batch_size', self._batch_size) - - self._phase = phase - # self._batch_size = - self._print_first_n = config.get('print_first_n', 1) - - - @property - def outputs_attr(self): - return {"token_ids": [[-1, -1, 1], 'int64'], - "position_ids": [[-1, -1, 1], 'int64'], - "segment_ids": [[-1, -1, 1], 'int64'], - "input_mask": [[-1, -1, 1], 'float32'], - "task_ids": [[-1, -1, 1], 'int64'], - "mask_label": [[-1, 1], 'int64'], - "mask_pos": [[-1, 1], 'int64'] - } - - - def load_data(self): - self._data_generator = self._reader.data_generator(self._input_file, self._batch_size, self._num_epochs, dev_count=self._dev_count, shuffle=self._shuffle, phase=self._phase) - - def iterator(self): - - def list_to_dict(x): - names = ['token_ids', 'position_ids', 'segment_ids', 'input_mask', - 'task_ids', 'mask_label', 'mask_pos'] - outputs = {n: i for n,i in zip(names, x)} - return outputs - - for batch in self._data_generator(): - yield list_to_dict(batch) - - def get_epoch_outputs(self): - return {'examples': self._reader.get_examples(self._phase), - 'features': self._reader.get_features(self._phase)} - - @property - def num_examples(self): - return self._reader.get_num_examples(phase=self._phase) - diff --git a/build/lib/paddlepalm/reader/mrc4bert.py b/build/lib/paddlepalm/reader/mrc4bert.py deleted file mode 100644 index ebf9aed..0000000 --- a/build/lib/paddlepalm/reader/mrc4bert.py +++ /dev/null @@ -1,656 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import absolute_import -from paddlepalm.interface import reader -from paddlepalm.utils.textprocess_helper import is_whitespace -from paddlepalm.reader.utils.mrqa_helper import MRQAExample, MRQAFeature -import paddlepalm.tokenizer.bert_tokenizer as tokenization - -class Reader(reader): - - def __init__(self, config, phase='train', dev_count=1, print_prefix=''): - """ - Args: - phase: train, eval, pred - """ - - self._is_training = phase == 'train' - - self._tokenizer = tokenization.FullTokenizer( - vocab_file=config['vocab_path'], do_lower_case=config.get('do_lower_case', False)) - self._max_seq_length = config['max_seq_len'] - self._doc_stride = config['doc_stride'] - self._max_query_length = config['max_query_len'] - - if phase == 'train': - self._input_file = config['train_file'] - self._num_epochs = config['num_epochs'] - self._shuffle = config.get('shuffle', False) - self._shuffle_buffer = config.get('shuffle_buffer', 5000) - if phase == 'eval': - self._input_file = config['dev_file'] - self._num_epochs = 1 - self._shuffle = False - elif phase == 'pred': - self._input_file = config['predict_file'] - self._num_epochs = 1 - self._shuffle = False - - # self._batch_size = - self._batch_size = config['batch_size'] - self._pred_batch_size = config.get('pred_batch_size', self._batch_size) - self._print_first_n = config.get('print_first_n', 1) - self._with_negative = config.get('with_negative', False) - self._sample_rate = config.get('sample_rate', 0.02) - - # TODO: without slide window version - self._with_slide_window = config.get('with_slide_window', False) - - self.vocab = self._tokenizer.vocab - self.vocab_size = len(self.vocab) - self.pad_id = self.vocab["[PAD]"] - self.cls_id = self.vocab["[CLS]"] - self.sep_id = self.vocab["[SEP]"] - self.mask_id = self.vocab["[MASK]"] - - self.current_train_example = -1 - self.num_train_examples = -1 - self.current_train_epoch = -1 - - self.n_examples = None - - print(print_prefix + 'reading raw data...') - with open(input_file, "r") as reader: - self.raw_data = json.load(reader)["data"] - print(print_prfix + 'done!') - - @property - def outputs_attr(self): - if self._is_training: - return {"token_ids": [[-1, self.max_seq_len, 1], 'int64'], - "position_ids": [[-1, self.max_seq_len, 1], 'int64'], - "segment_ids": [[-1, self.max_seq_len, 1], 'int64'], - "input_mask": [[-1, self.max_seq_len, 1], 'float32'], - "start_positions": [[-1, self.max_seq_len, 1], 'int64'], - "end_positions": [[-1, self.max_seq_len, 1], 'int64'] - } - else: - return {"token_ids": [[-1, self.max_seq_len, 1], 'int64'], - "position_ids": [[-1, self.max_seq_len, 1], 'int64'], - "segment_ids": [[-1, self.max_seq_len, 1], 'int64'], - "input_mask": [[-1, self.max_seq_len, 1], 'float32'], - "unique_ids": [[-1, 1], 'int64'] - } - - def iterator(self): - - features = [] - for i in self._num_epochs: - if self._is_training: - print(self.print_prefix + '{} epoch {} {}'.format('-'*16, i, '-'*16)) - example_id = 0 - feature_id = 1000000000 - for line in self.train_file: - raw = self.parse_line(line) - - examples = _raw_to_examples(raw['context'], raw['qa_list'], is_training=self._is_training) - for example in examples: - features.extend(_example_to_features(example, example_id, self._tokenizer, \ - self._max_seq_length, self._doc_stride, self._max_query_length, \ - id_offset=1000000000+len(features), is_training=self._is_training)) - if len(features) >= self._batch_size * self._dev_count: - for batch, total_token_num in _features_to_batches( \ - features[:self._batch_size * self._dev_count], \ - batch_size, in_tokens=self._in_tokens): - temp = prepare_batch_data(batch, total_token_num, \ - max_len=self._max_seq_length, voc_size=-1, \ - pad_id=self.pad_id, cls_id=self.cls_id, sep_id=self.sep_id, mask_id=-1, \ - return_input_mask=True, return_max_len=False, return_num_token=False) - if self._is_training: - tok_ids, pos_ids, seg_ids, input_mask, start_positions, end_positions = temp - yield {"token_ids": tok_ids, "position_ids": pos_ids, "segment_ids": seg_ids, "input_mask": input_mask, "start_positions": start_positions, 'end_positions': end_positions} - else: - tok_ids, pos_ids, seg_ids, input_mask, unique_ids = temp - yield {"token_ids": tok_ids, "position_ids": pos_ids, "segment_ids": seg_ids, "input_mask": input_mask, "unique_ids": unique_ids} - - features = features[self._batch_size * self._dev_count:] - example_id += 1 - - # The last batch may be discarded when running with distributed prediction, so we build some fake batches for the last prediction step. - if self._is_training and len(features) > 0: - pred_batches = [] - for batch, total_token_num in _features_to_batches( \ - features[:self._batch_size * self._dev_count], \ - batch_size, in_tokens=self._in_tokens): - pred_batches.append(prepare_batch_data(batch, total_token_num, max_len=self._max_seq_length, voc_size=-1, - pad_id=self.pad_id, cls_id=self.cls_id, sep_id=self.sep_id, mask_id=-1, \ - return_input_mask=True, return_max_len=False, return_num_token=False)) - - fake_batch = pred_batches[-1] - fake_batch = fake_batch[:-1] + [np.array([-1]*len(fake_batch[0]))] - pred_batches = pred_batches + [fake_batch] * (dev_count - len(pred_batches)) - for batch in pred_batches: - yield batch - - @property - def num_examples(self): - if self.n_examples is None: - self.n_examples = _estimate_runtime_examples(self.raw_data, self._sample_rate, self._tokenizer, \ - self._max_seq_length, self._doc_stride, self._max_query_length, \ - remove_impossible_questions=True, filter_invalid_spans=True) - return self.n_examples - # return math.ceil(n_examples * self._num_epochs / float(self._batch_size * self._dev_count)) - - - -def _raw_to_examples(context, qa_list, is_training=True, remove_impossible_questions=True, filter_invalid_spans=True): - """ - Args: - context: (str) the paragraph that provide information for QA - qa_list: (list) nested dict. Each element in qa_list should contain at least 'id' and 'question'. And the .... - """ - examples = [] - doc_tokens = [] - char_to_word_offset = [] - prev_is_whitespace = True - for c in context: - if is_whitespace(c): - prev_is_whitespace = True - else: - if prev_is_whitespace: - doc_tokens.append(c) - else: - doc_tokens[-1] += c - prev_is_whitespace = False - char_to_word_offset.append(len(doc_tokens) - 1) - - for qa in qa_list: - qas_id = qa["id"] - question_text = qa["question"] - start_position = None - end_position = None - orig_answer_text = None - is_impossible = False - if is_training: - - assert len(qa["answers"]) == 1, "For training, each question should have exactly 1 answer." - - if ('is_impossible' in qa) and (qa["is_impossible"]): - if remove_impossible_questions or filter_invalid_spans: - continue - else: - start_position = -1 - end_position = -1 - orig_answer_text = "" - is_impossible = True - else: - answer = qa["answers"][0] - orig_answer_text = answer["text"] - answer_offset = answer["answer_start"] - answer_length = len(orig_answer_text) - start_position = char_to_word_offset[answer_offset] - end_position = char_to_word_offset[answer_offset + - answer_length - 1] - - # remove corrupt samples - actual_text = " ".join(doc_tokens[start_position:( - end_position + 1)]) - cleaned_answer_text = " ".join( - tokenization.whitespace_tokenize(orig_answer_text)) - if actual_text.find(cleaned_answer_text) == -1: - print(self.print_prefix + "Could not find answer: '%s' vs. '%s'", - actual_text, cleaned_answer_text) - continue - - examples.append(MRQAExample( - qas_id=qas_id, - question_text=question_text, - doc_tokens=doc_tokens, - orig_answer_text=orig_answer_text, - start_position=start_position, - end_position=end_position, - is_impossible=is_impossible)) - - return examples - - - - -def _example_to_features(example, example_id, tokenizer, max_seq_length, doc_stride, max_query_length, id_offset, is_training): - - query_tokens = tokenizer.tokenize(example.question_text) - - if len(query_tokens) > max_query_length: - query_tokens = query_tokens[0:max_query_length] - - tok_to_orig_index = [] - orig_to_tok_index = [] - all_doc_tokens = [] - for (i, token) in enumerate(example.doc_tokens): - orig_to_tok_index.append(len(all_doc_tokens)) - sub_tokens = tokenizer.tokenize(token) - for sub_token in sub_tokens: - tok_to_orig_index.append(i) - all_doc_tokens.append(sub_token) - - tok_start_position = None - tok_end_position = None - if is_training and example.is_impossible: - tok_start_position = -1 - tok_end_position = -1 - if is_training and not example.is_impossible: - tok_start_position = orig_to_tok_index[example.start_position] - if example.end_position < len(example.doc_tokens) - 1: - tok_end_position = orig_to_tok_index[example.end_position + - 1] - 1 - else: - tok_end_position = len(all_doc_tokens) - 1 - (tok_start_position, tok_end_position) = _improve_answer_span( - all_doc_tokens, tok_start_position, tok_end_position, tokenizer, - example.orig_answer_text) - - # The -3 accounts for [CLS], [SEP] and [SEP] - max_tokens_for_doc = max_seq_length - len(query_tokens) - 3 - - # We can have documents that are longer than the maximum sequence length. - # To deal with this we do a sliding window approach, where we take chunks - # of the up to our max length with a stride of `doc_stride`. - _DocSpan = collections.namedtuple( # pylint: disable=invalid-name - "DocSpan", ["start", "length"]) - doc_spans = [] - start_offset = 0 - while start_offset < len(all_doc_tokens): - length = len(all_doc_tokens) - start_offset - if length > max_tokens_for_doc: - length = max_tokens_for_doc - doc_spans.append(_DocSpan(start=start_offset, length=length)) - if start_offset + length == len(all_doc_tokens): - break - start_offset += min(length, doc_stride) - - for (doc_span_index, doc_span) in enumerate(doc_spans): - tokens = [] - token_to_orig_map = {} - token_is_max_context = {} - segment_ids = [] - tokens.append("[CLS]") - segment_ids.append(0) - for token in query_tokens: - tokens.append(token) - segment_ids.append(0) - tokens.append("[SEP]") - segment_ids.append(0) - - for i in range(doc_span.length): - split_token_index = doc_span.start + i - token_to_orig_map[len(tokens)] = tok_to_orig_index[ - split_token_index] - - is_max_context = _check_is_max_context( - doc_spans, doc_span_index, split_token_index) - token_is_max_context[len(tokens)] = is_max_context - tokens.append(all_doc_tokens[split_token_index]) - segment_ids.append(1) - tokens.append("[SEP]") - segment_ids.append(1) - - input_ids = tokenizer.convert_tokens_to_ids(tokens) - - # The mask has 1 for real tokens and 0 for padding tokens. Only real - # tokens are attended to. - input_mask = [1] * len(input_ids) - - # Zero-pad up to the sequence length. - #while len(input_ids) < max_seq_length: - # input_ids.append(0) - # input_mask.append(0) - # segment_ids.append(0) - - #assert len(input_ids) == max_seq_length - #assert len(input_mask) == max_seq_length - #assert len(segment_ids) == max_seq_length - - start_position = None - end_position = None - if is_training and not example.is_impossible: - # For training, if our document chunk does not contain an annotation - # we throw it out, since there is nothing to predict. - doc_start = doc_span.start - doc_end = doc_span.start + doc_span.length - 1 - out_of_span = False - if not (tok_start_position >= doc_start and - tok_end_position <= doc_end): - out_of_span = True - if out_of_span: - start_position = 0 - end_position = 0 - continue - else: - doc_offset = len(query_tokens) + 2 - start_position = tok_start_position - doc_start + doc_offset - end_position = tok_end_position - doc_start + doc_offset - - if is_training and example.is_impossible: - start_position = 0 - end_position = 0 - - def format_print(): - print("*** Example ***") - print("unique_id: %s" % (unique_id)) - print("example_index: %s" % (example_index)) - print("doc_span_index: %s" % (doc_span_index)) - print("tokens: %s" % " ".join( - [tokenization.printable_text(x) for x in tokens])) - print("token_to_orig_map: %s" % " ".join([ - "%d:%d" % (x, y) - for (x, y) in six.iteritems(token_to_orig_map) - ])) - print("token_is_max_context: %s" % " ".join([ - "%d:%s" % (x, y) - for (x, y) in six.iteritems(token_is_max_context) - ])) - print("input_ids: %s" % " ".join([str(x) for x in input_ids])) - print("input_mask: %s" % " ".join([str(x) for x in input_mask])) - print("segment_ids: %s" % - " ".join([str(x) for x in segment_ids])) - if is_training and example.is_impossible: - print("impossible example") - if is_training and not example.is_impossible: - answer_text = " ".join(tokens[start_position:(end_position + - 1)]) - print("start_position: %d" % (start_position)) - print("end_position: %d" % (end_position)) - print("answer: %s" % - (tokenization.printable_text(answer_text))) - - if self._print_first_n > 0: - format_print() - self._print_first_n -= 1 - - features.append(MRQAFeature( - unique_id=id_offset, - example_index=example_id, - doc_span_index=doc_span_index, - tokens=tokens, - token_to_orig_map=token_to_orig_map, - token_is_max_context=token_is_max_context, - input_ids=input_ids, - input_mask=input_mask, - segment_ids=segment_ids, - start_position=start_position, - end_position=end_position, - is_impossible=example.is_impossible)) - - id_offset += 1 - - return features - - -def _features_to_batches(features, batch_size, in_tokens): - batch, total_token_num, max_len = [], 0, 0 - for (index, feature) in enumerate(features): - if phase == 'train': - self.current_train_example = index + 1 - seq_len = len(feature.input_ids) - labels = [feature.unique_id - ] if feature.start_position is None else [ - feature.start_position, feature.end_position - ] - example = [ - feature.input_ids, feature.segment_ids, range(seq_len) - ] + labels - max_len = max(max_len, seq_len) - - if in_tokens: - to_append = (len(batch) + 1) * max_len <= batch_size - else: - to_append = len(batch) < batch_size - - if to_append: - batch.append(example) - total_token_num += seq_len - else: - yield batch, total_token_num - batch, total_token_num, max_len = [example - ], seq_len, seq_len - if len(batch) > 0: - yield batch, total_token_num - - -def _estimate_runtime_examples(data, sample_rate, tokenizer, \ - max_seq_length, doc_stride, max_query_length, \ - remove_impossible_questions=True, filter_invalid_spans=True): - """Count runtime examples which may differ from number of raw samples due to sliding window operation and etc.. - This is useful to get correct warmup steps for training.""" - - assert sample_rate > 0.0 and sample_rate <= 1.0, "sample_rate must be set between 0.0~1.0" - - num_raw_examples = 0 - for entry in data: - for paragraph in entry["paragraphs"]: - paragraph_text = paragraph["context"] - for qa in paragraph["qas"]: - num_raw_examples += 1 - # print("num raw examples:{}".format(num_raw_examples)) - - def is_whitespace(c): - if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F: - return True - return False - - sampled_examples = [] - first_samp = True - for entry in data: - for paragraph in entry["paragraphs"]: - doc_tokens = None - for qa in paragraph["qas"]: - if not first_samp and random.random() > sample_rate and sample_rate < 1.0: - continue - - if doc_tokens is None: - paragraph_text = paragraph["context"] - doc_tokens = [] - char_to_word_offset = [] - prev_is_whitespace = True - for c in paragraph_text: - if is_whitespace(c): - prev_is_whitespace = True - else: - if prev_is_whitespace: - doc_tokens.append(c) - else: - doc_tokens[-1] += c - prev_is_whitespace = False - char_to_word_offset.append(len(doc_tokens) - 1) - - assert len(qa["answers"]) == 1, "For training, each question should have exactly 1 answer." - - qas_id = qa["id"] - question_text = qa["question"] - start_position = None - end_position = None - orig_answer_text = None - is_impossible = False - - if ('is_impossible' in qa) and (qa["is_impossible"]): - if remove_impossible_questions or filter_invalid_spans: - continue - else: - start_position = -1 - end_position = -1 - orig_answer_text = "" - is_impossible = True - else: - answer = qa["answers"][0] - orig_answer_text = answer["text"] - answer_offset = answer["answer_start"] - answer_length = len(orig_answer_text) - start_position = char_to_word_offset[answer_offset] - end_position = char_to_word_offset[answer_offset + - answer_length - 1] - - # remove corrupt samples - actual_text = " ".join(doc_tokens[start_position:( - end_position + 1)]) - cleaned_answer_text = " ".join( - tokenization.whitespace_tokenize(orig_answer_text)) - if actual_text.find(cleaned_answer_text) == -1: - continue - - example = MRQAExample( - qas_id=qas_id, - question_text=question_text, - doc_tokens=doc_tokens, - orig_answer_text=orig_answer_text, - start_position=start_position, - end_position=end_position, - is_impossible=is_impossible) - - sampled_examples.append(example) - first_samp = False - - - runtime_sample_rate = len(sampled_examples) / float(num_raw_examples) - - runtime_samp_cnt = 0 - - for example in sampled_examples: - query_tokens = tokenizer.tokenize(example.question_text) - - if len(query_tokens) > max_query_length: - query_tokens = query_tokens[0:max_query_length] - - tok_to_orig_index = [] - orig_to_tok_index = [] - all_doc_tokens = [] - for (i, token) in enumerate(example.doc_tokens): - orig_to_tok_index.append(len(all_doc_tokens)) - sub_tokens = tokenizer.tokenize(token) - for sub_token in sub_tokens: - tok_to_orig_index.append(i) - all_doc_tokens.append(sub_token) - - tok_start_position = None - tok_end_position = None - - tok_start_position = orig_to_tok_index[example.start_position] - if example.end_position < len(example.doc_tokens) - 1: - tok_end_position = orig_to_tok_index[example.end_position + 1] - 1 - else: - tok_end_position = len(all_doc_tokens) - 1 - (tok_start_position, tok_end_position) = _improve_answer_span( - all_doc_tokens, tok_start_position, tok_end_position, tokenizer, - example.orig_answer_text) - - # The -3 accounts for [CLS], [SEP] and [SEP] - max_tokens_for_doc = max_seq_length - len(query_tokens) - 3 - - _DocSpan = collections.namedtuple( # pylint: disable=invalid-name - "DocSpan", ["start", "length"]) - doc_spans = [] - start_offset = 0 - while start_offset < len(all_doc_tokens): - length = len(all_doc_tokens) - start_offset - if length > max_tokens_for_doc: - length = max_tokens_for_doc - doc_spans.append(_DocSpan(start=start_offset, length=length)) - if start_offset + length == len(all_doc_tokens): - break - start_offset += min(length, doc_stride) - - for (doc_span_index, doc_span) in enumerate(doc_spans): - doc_start = doc_span.start - doc_end = doc_span.start + doc_span.length - 1 - if filter_invalid_spans and not (tok_start_position >= doc_start and tok_end_position <= doc_end): - continue - runtime_samp_cnt += 1 - return int(runtime_samp_cnt/runtime_sample_rate) - - -def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, - orig_answer_text): - """Returns tokenized answer spans that better match the annotated answer.""" - - # The MRQA annotations are character based. We first project them to - # whitespace-tokenized words. But then after WordPiece tokenization, we can - # often find a "better match". For example: - # - # Question: What year was John Smith born? - # Context: The leader was John Smith (1895-1943). - # Answer: 1895 - # - # The original whitespace-tokenized answer will be "(1895-1943).". However - # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match - # the exact answer, 1895. - # - # However, this is not always possible. Consider the following: - # - # Question: What country is the top exporter of electornics? - # Context: The Japanese electronics industry is the lagest in the world. - # Answer: Japan - # - # In this case, the annotator chose "Japan" as a character sub-span of - # the word "Japanese". Since our WordPiece tokenizer does not split - # "Japanese", we just use "Japanese" as the annotation. This is fairly rare - # in MRQA, but does happen. - tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text)) - - for new_start in range(input_start, input_end + 1): - for new_end in range(input_end, new_start - 1, -1): - text_span = " ".join(doc_tokens[new_start:(new_end + 1)]) - if text_span == tok_answer_text: - return (new_start, new_end) - - return (input_start, input_end) - - -def _check_is_max_context(doc_spans, cur_span_index, position): - """Check if this is the 'max context' doc span for the token.""" - - # Because of the sliding window approach taken to scoring documents, a single - # token can appear in multiple documents. E.g. - # Doc: the man went to the store and bought a gallon of milk - # Span A: the man went to the - # Span B: to the store and bought - # Span C: and bought a gallon of - # ... - # - # Now the word 'bought' will have two scores from spans B and C. We only - # want to consider the score with "maximum context", which we define as - # the *minimum* of its left and right context (the *sum* of left and - # right context will always be the same, of course). - # - # In the example the maximum context for 'bought' would be span C since - # it has 1 left context and 3 right context, while span B has 4 left context - # and 0 right context. - best_score = None - best_span_index = None - for (span_index, doc_span) in enumerate(doc_spans): - end = doc_span.start + doc_span.length - 1 - if position < doc_span.start: - continue - if position > end: - continue - num_left_context = position - doc_span.start - num_right_context = end - position - score = min(num_left_context, - num_right_context) + 0.01 * doc_span.length - if best_score is None or score > best_score: - best_score = score - best_span_index = span_index - - return cur_span_index == best_span_index - diff --git a/build/lib/paddlepalm/reader/mrc4ernie.py b/build/lib/paddlepalm/reader/mrc4ernie.py deleted file mode 100644 index 0c550df..0000000 --- a/build/lib/paddlepalm/reader/mrc4ernie.py +++ /dev/null @@ -1,119 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from paddlepalm.interface import reader -from paddlepalm.reader.utils.reader4ernie import MRCReader - -class Reader(reader): - - def __init__(self, config, phase='train', dev_count=1, print_prefix=''): - """ - Args: - phase: train, eval, pred - """ - - self._is_training = phase == 'train' - - reader = MRCReader(config['vocab_path'], - max_seq_len=config['max_seq_len'], - do_lower_case=config.get('do_lower_case', False), - tokenizer='FullTokenizer', - for_cn=config.get('for_cn', False), - doc_stride=config['doc_stride'], - max_query_length=config['max_query_len'], - random_seed=config.get('seed', None)) - self._reader = reader - self._dev_count = dev_count - - self._batch_size = config['batch_size'] - self._max_seq_len = config['max_seq_len'] - if phase == 'train': - self._input_file = config['train_file'] - # self._num_epochs = config['num_epochs'] - self._num_epochs = None # 防止iteartor终止 - self._shuffle = config.get('shuffle', False) - self._shuffle_buffer = config.get('shuffle_buffer', 5000) - if phase == 'eval': - self._input_file = config['dev_file'] - self._num_epochs = 1 - self._shuffle = False - self._batch_size = config.get('pred_batch_size', self._batch_size) - elif phase == 'pred': - self._input_file = config['pred_file'] - self._num_epochs = 1 - self._shuffle = False - self._batch_size = config.get('pred_batch_size', self._batch_size) - - self._phase = phase - # self._batch_size = - self._print_first_n = config.get('print_first_n', 1) - - # TODO: without slide window version - self._with_slide_window = config.get('with_slide_window', False) - - - @property - def outputs_attr(self): - if self._is_training: - return {"token_ids": [[-1, -1, 1], 'int64'], - "position_ids": [[-1, -1, 1], 'int64'], - "segment_ids": [[-1, -1, 1], 'int64'], - "input_mask": [[-1, -1, 1], 'float32'], - "start_positions": [[-1, 1], 'int64'], - "end_positions": [[-1, 1], 'int64'], - "task_ids": [[-1, -1, 1], 'int64'] - } - else: - return {"token_ids": [[-1, -1, 1], 'int64'], - "position_ids": [[-1, -1, 1], 'int64'], - "segment_ids": [[-1, -1, 1], 'int64'], - "task_ids": [[-1, -1, 1], 'int64'], - "input_mask": [[-1, -1, 1], 'float32'], - "unique_ids": [[-1, 1], 'int64'] - } - - @property - def epoch_outputs_attr(self): - if not self._is_training: - return {"examples": None, - "features": None} - - def load_data(self): - self._data_generator = self._reader.data_generator(self._input_file, self._batch_size, self._num_epochs, dev_count=self._dev_count, shuffle=self._shuffle, phase=self._phase) - - def iterator(self): - - def list_to_dict(x): - names = ['token_ids', 'segment_ids', 'position_ids', 'task_ids', 'input_mask', - 'start_positions', 'end_positions', 'unique_ids'] - outputs = {n: i for n,i in zip(names, x)} - if self._is_training: - del outputs['unique_ids'] - else: - del outputs['start_positions'] - del outputs['end_positions'] - return outputs - - for batch in self._data_generator(): - yield list_to_dict(batch) - - def get_epoch_outputs(self): - return {'examples': self._reader.get_examples(self._phase), - 'features': self._reader.get_features(self._phase)} - - @property - def num_examples(self): - return self._reader.get_num_examples(phase=self._phase) - diff --git a/build/lib/paddlepalm/reader/utils/__init__.py b/build/lib/paddlepalm/reader/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/build/lib/paddlepalm/reader/utils/batching4bert.py b/build/lib/paddlepalm/reader/utils/batching4bert.py deleted file mode 100644 index daeb25a..0000000 --- a/build/lib/paddlepalm/reader/utils/batching4bert.py +++ /dev/null @@ -1,184 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Mask, padding and batching.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -import numpy as np - - -def mask(batch_tokens, total_token_num, vocab_size, CLS=1, SEP=2, MASK=3): - """ - Add mask for batch_tokens, return out, mask_label, mask_pos; - Note: mask_pos responding the batch_tokens after padded; - """ - max_len = max([len(sent) for sent in batch_tokens]) - mask_label = [] - mask_pos = [] - prob_mask = np.random.rand(total_token_num) - # Note: the first token is [CLS], so [low=1] - replace_ids = np.random.randint(1, high=vocab_size, size=total_token_num) - pre_sent_len = 0 - prob_index = 0 - for sent_index, sent in enumerate(batch_tokens): - mask_flag = False - prob_index += pre_sent_len - for token_index, token in enumerate(sent): - prob = prob_mask[prob_index + token_index] - if prob > 0.15: - continue - elif 0.03 < prob <= 0.15: - # mask - if token != SEP and token != CLS: - mask_label.append(sent[token_index]) - sent[token_index] = MASK - mask_flag = True - mask_pos.append(sent_index * max_len + token_index) - elif 0.015 < prob <= 0.03: - # random replace - if token != SEP and token != CLS: - mask_label.append(sent[token_index]) - sent[token_index] = replace_ids[prob_index + token_index] - mask_flag = True - mask_pos.append(sent_index * max_len + token_index) - else: - # keep the original token - if token != SEP and token != CLS: - mask_label.append(sent[token_index]) - mask_pos.append(sent_index * max_len + token_index) - pre_sent_len = len(sent) - # ensure at least mask one word in a sentence - while not mask_flag: - token_index = int(np.random.randint(1, high=len(sent) - 1, size=1)) - if sent[token_index] != SEP and sent[token_index] != CLS: - mask_label.append(sent[token_index]) - sent[token_index] = MASK - mask_flag = True - mask_pos.append(sent_index * max_len + token_index) - mask_label = np.array(mask_label).astype("int64").reshape([-1, 1]) - mask_pos = np.array(mask_pos).astype("int64").reshape([-1, 1]) - return batch_tokens, mask_label, mask_pos - - -def prepare_batch_data(insts, - total_token_num, - max_len=None, - voc_size=0, - pad_id=None, - cls_id=None, - sep_id=None, - mask_id=None, - return_input_mask=True, - return_max_len=True, - return_num_token=False): - """ - 1. generate Tensor of data - 2. generate Tensor of position - 3. generate self attention mask, [shape: batch_size * max_len * max_len] - """ - batch_src_ids = [inst[0] for inst in insts] - batch_sent_ids = [inst[1] for inst in insts] - batch_pos_ids = [inst[2] for inst in insts] - labels_list = [] - # compatible with mrqa, whose example includes start/end positions, - # or unique id - for i in range(3, len(insts[0]), 1): - labels = [inst[i] for inst in insts] - labels = np.array(labels).astype("int64").reshape([-1, 1]) - labels_list.append(labels) - # First step: do mask without padding - if mask_id >= 0: - out, mask_label, mask_pos = mask( - batch_src_ids, - total_token_num, - vocab_size=voc_size, - CLS=cls_id, - SEP=sep_id, - MASK=mask_id) - else: - out = batch_src_ids - # Second step: padding - src_id, self_input_mask = pad_batch_data( - out, - max_len=max_len, - pad_idx=pad_id, return_input_mask=True) - pos_id = pad_batch_data( - batch_pos_ids, - max_len=max_len, - pad_idx=pad_id, - return_pos=False, - return_input_mask=False) - sent_id = pad_batch_data( - batch_sent_ids, - max_len=max_len, - pad_idx=pad_id, - return_pos=False, - return_input_mask=False) - if mask_id >= 0: - return_list = [ - src_id, pos_id, sent_id, self_input_mask, mask_label, mask_pos - ] + labels_list - else: - return_list = [src_id, pos_id, sent_id, self_input_mask] + labels_list - return return_list if len(return_list) > 1 else return_list[0] - - -def pad_batch_data(insts, - max_len=None, - pad_idx=0, - return_pos=False, - return_input_mask=False, - return_max_len=False, - return_num_token=False): - """ - Pad the instances to the max sequence length in batch, and generate the - corresponding position data and input mask. - """ - return_list = [] - if max_len is None: - max_len = max(len(inst) for inst in insts) - # Any token included in dict can be used to pad, since the paddings' loss - # will be masked out by weights and make no effect on parameter gradients. - inst_data = np.array([ - list(inst) + list([pad_idx] * (max_len - len(inst))) for inst in insts - ]) - return_list += [inst_data.astype("int64").reshape([-1, max_len, 1])] - # position data - if return_pos: - inst_pos = np.array([ - list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst)) - for inst in insts - ]) - return_list += [inst_pos.astype("int64").reshape([-1, max_len, 1])] - if return_input_mask: - # This is used to avoid attention on paddings. - input_mask_data = np.array([[1] * len(inst) + [0] * - (max_len - len(inst)) for inst in insts]) - input_mask_data = np.expand_dims(input_mask_data, axis=-1) - return_list += [input_mask_data.astype("float32")] - if return_max_len: - return_list += [max_len] - if return_num_token: - num_token = 0 - for inst in insts: - num_token += len(inst) - return_list += [num_token] - return return_list if len(return_list) > 1 else return_list[0] - - -if __name__ == "__main__": - pass - - diff --git a/build/lib/paddlepalm/reader/utils/batching4ernie.py b/build/lib/paddlepalm/reader/utils/batching4ernie.py deleted file mode 100644 index d3d1357..0000000 --- a/build/lib/paddlepalm/reader/utils/batching4ernie.py +++ /dev/null @@ -1,175 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Mask, padding and batching.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from six.moves import xrange - - -def mask(batch_tokens, - seg_labels, - mask_word_tags, - total_token_num, - vocab_size, - CLS=1, - SEP=2, - MASK=3): - """ - Add mask for batch_tokens, return out, mask_label, mask_pos; - Note: mask_pos responding the batch_tokens after padded; - """ - max_len = max([len(sent) for sent in batch_tokens]) - mask_label = [] - mask_pos = [] - prob_mask = np.random.rand(total_token_num) - # Note: the first token is [CLS], so [low=1] - replace_ids = np.random.randint(1, high=vocab_size, size=total_token_num) - pre_sent_len = 0 - prob_index = 0 - for sent_index, sent in enumerate(batch_tokens): - mask_flag = False - mask_word = mask_word_tags[sent_index] - prob_index += pre_sent_len - if mask_word: - beg = 0 - for token_index, token in enumerate(sent): - seg_label = seg_labels[sent_index][token_index] - if seg_label == 1: - continue - if beg == 0: - if seg_label != -1: - beg = token_index - continue - - prob = prob_mask[prob_index + beg] - if prob > 0.15: - pass - else: - for index in xrange(beg, token_index): - prob = prob_mask[prob_index + index] - base_prob = 1.0 - if index == beg: - base_prob = 0.15 - if base_prob * 0.2 < prob <= base_prob: - mask_label.append(sent[index]) - sent[index] = MASK - mask_flag = True - mask_pos.append(sent_index * max_len + index) - elif base_prob * 0.1 < prob <= base_prob * 0.2: - mask_label.append(sent[index]) - sent[index] = replace_ids[prob_index + index] - mask_flag = True - mask_pos.append(sent_index * max_len + index) - else: - mask_label.append(sent[index]) - mask_pos.append(sent_index * max_len + index) - - if seg_label == -1: - beg = 0 - else: - beg = token_index - else: - for token_index, token in enumerate(sent): - prob = prob_mask[prob_index + token_index] - if prob > 0.15: - continue - elif 0.03 < prob <= 0.15: - # mask - if token != SEP and token != CLS: - mask_label.append(sent[token_index]) - sent[token_index] = MASK - mask_flag = True - mask_pos.append(sent_index * max_len + token_index) - elif 0.015 < prob <= 0.03: - # random replace - if token != SEP and token != CLS: - mask_label.append(sent[token_index]) - sent[token_index] = replace_ids[prob_index + - token_index] - mask_flag = True - mask_pos.append(sent_index * max_len + token_index) - else: - # keep the original token - if token != SEP and token != CLS: - mask_label.append(sent[token_index]) - mask_pos.append(sent_index * max_len + token_index) - - pre_sent_len = len(sent) - - mask_label = np.array(mask_label).astype("int64").reshape([-1, 1]) - mask_pos = np.array(mask_pos).astype("int64").reshape([-1, 1]) - return batch_tokens, mask_label, mask_pos - - -def pad_batch_data(insts, - pad_idx=0, - return_pos=False, - return_input_mask=False, - return_max_len=False, - return_num_token=False, - return_seq_lens=False): - """ - Pad the instances to the max sequence length in batch, and generate the - corresponding position data and attention bias. - """ - return_list = [] - max_len = max(len(inst) for inst in insts) - # Any token included in dict can be used to pad, since the paddings' loss - # will be masked out by weights and make no effect on parameter gradients. - - inst_data = np.array( - [inst + list([pad_idx] * (max_len - len(inst))) for inst in insts]) - return_list += [inst_data.astype("int64").reshape([-1, max_len, 1])] - - # position data - if return_pos: - inst_pos = np.array([ - list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst)) - for inst in insts - ]) - - return_list += [inst_pos.astype("int64").reshape([-1, max_len, 1])] - - if return_input_mask: - # This is used to avoid attention on paddings. - input_mask_data = np.array([[1] * len(inst) + [0] * - (max_len - len(inst)) for inst in insts]) - input_mask_data = np.expand_dims(input_mask_data, axis=-1) - return_list += [input_mask_data.astype("float32")] - - if return_max_len: - return_list += [max_len] - - if return_num_token: - num_token = 0 - for inst in insts: - num_token += len(inst) - return_list += [num_token] - - if return_seq_lens: - seq_lens = np.array([len(inst) for inst in insts]) - return_list += [seq_lens.astype("int64").reshape([-1, 1])] - - return return_list if len(return_list) > 1 else return_list[0] - - -if __name__ == "__main__": - - pass diff --git a/build/lib/paddlepalm/reader/utils/mlm_batching.py b/build/lib/paddlepalm/reader/utils/mlm_batching.py deleted file mode 100644 index 991d02d..0000000 --- a/build/lib/paddlepalm/reader/utils/mlm_batching.py +++ /dev/null @@ -1,177 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Mask, padding and batching.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -import numpy as np - - -def mask(batch_tokens, total_token_num, vocab_size, CLS=1, SEP=2, MASK=3): - """ - Add mask for batch_tokens, return out, mask_label, mask_pos; - Note: mask_pos responding the batch_tokens after padded; - """ - max_len = max([len(sent) for sent in batch_tokens]) - mask_label = [] - mask_pos = [] - prob_mask = np.random.rand(total_token_num) - # Note: the first token is [CLS], so [low=1] - replace_ids = np.random.randint(1, high=vocab_size, size=total_token_num) - pre_sent_len = 0 - prob_index = 0 - for sent_index, sent in enumerate(batch_tokens): - mask_flag = False - prob_index += pre_sent_len - for token_index, token in enumerate(sent): - prob = prob_mask[prob_index + token_index] - if prob > 0.15: - continue - elif 0.03 < prob <= 0.15: - # mask - if token != SEP and token != CLS: - mask_label.append(sent[token_index]) - sent[token_index] = MASK - mask_flag = True - mask_pos.append(sent_index * max_len + token_index) - elif 0.015 < prob <= 0.03: - # random replace - if token != SEP and token != CLS: - mask_label.append(sent[token_index]) - sent[token_index] = replace_ids[prob_index + token_index] - mask_flag = True - mask_pos.append(sent_index * max_len + token_index) - else: - # keep the original token - if token != SEP and token != CLS: - mask_label.append(sent[token_index]) - mask_pos.append(sent_index * max_len + token_index) - pre_sent_len = len(sent) - # ensure at least mask one word in a sentence - while not mask_flag: - token_index = int(np.random.randint(1, high=len(sent) - 1, size=1)) - if sent[token_index] != SEP and sent[token_index] != CLS: - mask_label.append(sent[token_index]) - sent[token_index] = MASK - mask_flag = True - mask_pos.append(sent_index * max_len + token_index) - mask_label = np.array(mask_label).astype("int64").reshape([-1, 1]) - mask_pos = np.array(mask_pos).astype("int64").reshape([-1, 1]) - return batch_tokens, mask_label, mask_pos - - -def prepare_batch_data(insts, - total_token_num, - max_len=None, - voc_size=0, - pad_id=None, - cls_id=None, - sep_id=None, - mask_id=None, - task_id=0, - return_input_mask=True, - return_max_len=True, - return_num_token=False): - """ - 1. generate Tensor of data - 2. generate Tensor of position - 3. generate self attention mask, [shape: batch_size * max_len * max_len] - """ - batch_src_ids = [inst[0] for inst in insts] - batch_sent_ids = [inst[1] for inst in insts] - batch_pos_ids = [inst[2] for inst in insts] - - # 这里是否应该反过来？？？否则在task layer里展开后的word embedding是padding后的，这时候word的index是跟没有padding时的index对不上的？ - # First step: do mask without padding - out, mask_label, mask_pos = mask( - batch_src_ids, - total_token_num, - vocab_size=voc_size, - CLS=cls_id, - SEP=sep_id, - MASK=mask_id) - # Second step: padding - src_id, self_input_mask = pad_batch_data( - out, - max_len=max_len, - pad_idx=pad_id, return_input_mask=True) - - pos_id = pad_batch_data( - batch_pos_ids, - max_len=max_len, - pad_idx=pad_id, - return_pos=False, - return_input_mask=False) - sent_id = pad_batch_data( - batch_sent_ids, - max_len=max_len, - pad_idx=pad_id, - return_pos=False, - return_input_mask=False) - task_ids = np.ones_like( - src_id, dtype="int64") * task_id - return_list = [ - src_id, pos_id, sent_id, self_input_mask, task_ids, mask_label, mask_pos - ] - return return_list if len(return_list) > 1 else return_list[0] - - -def pad_batch_data(insts, - max_len=None, - pad_idx=0, - return_pos=False, - return_input_mask=False, - return_max_len=False, - return_num_token=False): - """ - Pad the instances to the max sequence length in batch, and generate the - corresponding position data and input mask. - """ - return_list = [] - if max_len is None: - max_len = max(len(inst) for inst in insts) - # Any token included in dict can be used to pad, since the paddings' loss - # will be masked out by weights and make no effect on parameter gradients. - inst_data = np.array([ - list(inst) + list([pad_idx] * (max_len - len(inst))) for inst in insts - ]) - return_list += [inst_data.astype("int64").reshape([-1, max_len, 1])] - # position data - if return_pos: - inst_pos = np.array([ - list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst)) - for inst in insts - ]) - return_list += [inst_pos.astype("int64").reshape([-1, max_len, 1])] - if return_input_mask: - # This is used to avoid attention on paddings. - input_mask_data = np.array([[1] * len(inst) + [0] * - (max_len - len(inst)) for inst in insts]) - input_mask_data = np.expand_dims(input_mask_data, axis=-1) - return_list += [input_mask_data.astype("float32")] - if return_max_len: - return_list += [max_len] - if return_num_token: - num_token = 0 - for inst in insts: - num_token += len(inst) - return_list += [num_token] - return return_list if len(return_list) > 1 else return_list[0] - - -if __name__ == "__main__": - pass - - diff --git a/build/lib/paddlepalm/reader/utils/mrqa_helper.py b/build/lib/paddlepalm/reader/utils/mrqa_helper.py deleted file mode 100644 index e4f8bf5..0000000 --- a/build/lib/paddlepalm/reader/utils/mrqa_helper.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -class MRQAExample(object): - """A single training/test example for simple sequence classification. - - For examples without an answer, the start and end position are -1. - """ - - def __init__(self, - qas_id, - question_text, - doc_tokens, - orig_answer_text=None, - start_position=None, - end_position=None, - is_impossible=False): - self.qas_id = qas_id - self.question_text = question_text - self.doc_tokens = doc_tokens - self.orig_answer_text = orig_answer_text - self.start_position = start_position - self.end_position = end_position - self.is_impossible = is_impossible - - def __str__(self): - return self.__repr__() - - def __repr__(self): - s = "" - s += "qas_id: %s" % (tokenization.printable_text(self.qas_id)) - s += ", question_text: %s" % ( - tokenization.printable_text(self.question_text)) - s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens)) - if self.start_position: - s += ", start_position: %d" % (self.start_position) - if self.start_position: - s += ", end_position: %d" % (self.end_position) - if self.start_position: - s += ", is_impossible: %r" % (self.is_impossible) - return s - - -class MRQAFeature(object): - """A single set of features of data.""" - - def __init__(self, - unique_id, - example_index, - doc_span_index, - tokens, - token_to_orig_map, - token_is_max_context, - input_ids, - input_mask, - segment_ids, - start_position=None, - end_position=None, - is_impossible=None): - self.unique_id = unique_id - self.example_index = example_index - self.doc_span_index = doc_span_index - self.tokens = tokens - self.token_to_orig_map = token_to_orig_map - self.token_is_max_context = token_is_max_context - self.input_ids = input_ids - self.input_mask = input_mask - self.segment_ids = segment_ids - self.start_position = start_position - self.end_position = end_position - self.is_impossible = is_impossible - diff --git a/build/lib/paddlepalm/reader/utils/reader4ernie.py b/build/lib/paddlepalm/reader/utils/reader4ernie.py deleted file mode 100644 index fcf25e7..0000000 --- a/build/lib/paddlepalm/reader/utils/reader4ernie.py +++ /dev/null @@ -1,982 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals -from __future__ import absolute_import - -import sys -import os -import json -import random -import logging -import numpy as np -import six -from io import open -from collections import namedtuple - -import paddlepalm.tokenizer.ernie_tokenizer as tokenization -from paddlepalm.reader.utils.batching4ernie import pad_batch_data -from paddlepalm.reader.utils.mlm_batching import prepare_batch_data - - -log = logging.getLogger(__name__) - -if six.PY3: - import io - sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8') - sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8') - - -def csv_reader(fd, delimiter='\t'): - def gen(): - for i in fd: - yield i.rstrip('\n').split(delimiter) - return gen() - - -class BaseReader(object): - def __init__(self, - vocab_path, - label_map_config=None, - max_seq_len=512, - do_lower_case=True, - in_tokens=False, - is_inference=False, - random_seed=None, - tokenizer="FullTokenizer", - is_classify=True, - is_regression=False, - for_cn=True, - task_id=0): - self.max_seq_len = max_seq_len - self.tokenizer = tokenization.FullTokenizer( - vocab_file=vocab_path, do_lower_case=do_lower_case) - self.vocab = self.tokenizer.vocab - self.pad_id = self.vocab["[PAD]"] - self.cls_id = self.vocab["[CLS]"] - self.sep_id = self.vocab["[SEP]"] - self.mask_id = self.vocab["[MASK]"] - self.in_tokens = in_tokens - self.is_inference = is_inference - self.for_cn = for_cn - self.task_id = task_id - - np.random.seed(random_seed) - - self.is_classify = is_classify - self.is_regression = is_regression - self.current_example = 0 - self.current_epoch = 0 - self.num_examples = 0 - - self.examples = {} - - if label_map_config: - with open(label_map_config, encoding='utf8') as f: - self.label_map = json.load(f) - else: - self.label_map = None - - def get_train_progress(self): - """Gets progress for training phase.""" - return self.current_example, self.current_epoch - - def _read_tsv(self, input_file, quotechar=None): - """Reads a tab separated value file.""" - with open(input_file, 'r', encoding='utf8') as f: - reader = csv_reader(f) - headers = next(reader) - Example = namedtuple('Example', headers) - - examples = [] - for line in reader: - example = Example(*line) - examples.append(example) - return examples - - def _truncate_seq_pair(self, tokens_a, tokens_b, max_length): - """Truncates a sequence pair in place to the maximum length.""" - - # This is a simple heuristic which will always truncate the longer sequence - # one token at a time. This makes more sense than truncating an equal percent - # of tokens from each, since if one sequence is very short then each token - # that's truncated likely contains more information than a longer sequence. - while True: - total_length = len(tokens_a) + len(tokens_b) - if total_length <= max_length: - break - if len(tokens_a) > len(tokens_b): - tokens_a.pop() - else: - tokens_b.pop() - - def _convert_example_to_record(self, example, max_seq_length, tokenizer): - """Converts a single `Example` into a single `Record`.""" - - text_a = tokenization.convert_to_unicode(example.text_a) - tokens_a = tokenizer.tokenize(text_a) - tokens_b = None - - has_text_b = False - if isinstance(example, dict): - has_text_b = "text_b" in example.keys() - else: - has_text_b = "text_b" in example._fields - - if has_text_b: - text_b = tokenization.convert_to_unicode(example.text_b) - tokens_b = tokenizer.tokenize(text_b) - - if tokens_b: - # Modifies `tokens_a` and `tokens_b` in place so that the total - # length is less than the specified length. - # Account for [CLS], [SEP], [SEP] with "- 3" - self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) - else: - # Account for [CLS] and [SEP] with "- 2" - if len(tokens_a) > max_seq_length - 2: - tokens_a = tokens_a[0:(max_seq_length - 2)] - - # The convention in BERT/ERNIE is: - # (a) For sequence pairs: - # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] - # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 - # (b) For single sequences: - # tokens: [CLS] the dog is hairy . [SEP] - # type_ids: 0 0 0 0 0 0 0 - # - # Where "type_ids" are used to indicate whether this is the first - # sequence or the second sequence. The embedding vectors for `type=0` and - # `type=1` were learned during pre-training and are added to the wordpiece - # embedding vector (and position vector). This is not *strictly* necessary - # since the [SEP] token unambiguously separates the sequences, but it makes - # it easier for the model to learn the concept of sequences. - # - # For classification tasks, the first vector (corresponding to [CLS]) is - # used as as the "sentence vector". Note that this only makes sense because - # the entire model is fine-tuned. - tokens = [] - text_type_ids = [] - tokens.append("[CLS]") - text_type_ids.append(0) - for token in tokens_a: - tokens.append(token) - text_type_ids.append(0) - tokens.append("[SEP]") - text_type_ids.append(0) - - if tokens_b: - for token in tokens_b: - tokens.append(token) - text_type_ids.append(1) - tokens.append("[SEP]") - text_type_ids.append(1) - - token_ids = tokenizer.convert_tokens_to_ids(tokens) - position_ids = list(range(len(token_ids))) - - if self.is_inference: - Record = namedtuple('Record', - ['token_ids', 'text_type_ids', 'position_ids']) - record = Record( - token_ids=token_ids, - text_type_ids=text_type_ids, - position_ids=position_ids) - else: - if self.label_map: - label_id = self.label_map[example.label] - else: - label_id = example.label - - Record = namedtuple('Record', [ - 'token_ids', 'text_type_ids', 'position_ids', 'label_id', 'qid' - ]) - - qid = None - if "qid" in example._fields: - qid = example.qid - - record = Record( - token_ids=token_ids, - text_type_ids=text_type_ids, - position_ids=position_ids, - label_id=label_id, - qid=qid) - return record - - def _prepare_batch_data(self, examples, batch_size, phase=None): - """generate batch records""" - batch_records, max_len = [], 0 - for index, example in enumerate(examples): - if phase == "train": - self.current_example = index - record = self._convert_example_to_record(example, self.max_seq_len, - self.tokenizer) - max_len = max(max_len, len(record.token_ids)) - if self.in_tokens: - to_append = (len(batch_records) + 1) * max_len <= batch_size - else: - to_append = len(batch_records) < batch_size - if to_append: - batch_records.append(record) - else: - yield self._pad_batch_records(batch_records) - batch_records, max_len = [record], len(record.token_ids) - - if phase == 'pred' and batch_records: - yield self._pad_batch_records(batch_records) - - def get_num_examples(self, input_file=None, phase=None): - if self.examples is not None: - if phase is None: - phase = 'all' - return len(self.examples[phase]) - else: - assert input_file is not None, "Argument input_file should be given or the data_generator should be created when this func is called." - examples = self._read_tsv(input_file) - return len(examples) - - def data_generator(self, - input_file, - batch_size, - epoch, - dev_count=1, - shuffle=True, - phase=None): - examples = self._read_tsv(input_file) - if phase is None: - phase = 'all' - self.examples[phase] = examples - - def wrapper(): - all_dev_batches = [] - if epoch is None: - num_epochs = 99999999 - else: - num_epochs = epoch - for epoch_index in range(num_epochs): - if phase == "train": - self.current_example = 0 - self.current_epoch = epoch_index - if shuffle: - np.random.shuffle(examples) - - for batch_data in self._prepare_batch_data( - examples, batch_size, phase=phase): - if len(all_dev_batches) < dev_count: - all_dev_batches.append(batch_data) - if len(all_dev_batches) == dev_count: - for batch in all_dev_batches: - yield batch - all_dev_batches = [] - def f(): - for i in wrapper(): - yield i - - # def f(): - # try: - # for i in wrapper(): - # yield i - # except Exception as e: - # import traceback - # traceback.print_exc() - - return f - - -class MaskLMReader(BaseReader): - - def _convert_example_to_record(self, example, max_seq_length, tokenizer): - """Converts a single `Example` into a single `Record`.""" - - text_a = tokenization.convert_to_unicode(example.text_a) - tokens_a = tokenizer.tokenize(text_a) - tokens_b = None - - - has_text_b = False - if isinstance(example, dict): - has_text_b = "text_b" in example.keys() - else: - has_text_b = "text_b" in example._fields - - if has_text_b: - text_b = tokenization.convert_to_unicode(example.text_b) - tokens_b = tokenizer.tokenize(text_b) - - if tokens_b: - # Modifies `tokens_a` and `tokens_b` in place so that the total - # length is less than the specified length. - # Account for [CLS], [SEP], [SEP] with "- 3" - self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) - else: - # Account for [CLS] and [SEP] with "- 2" - if len(tokens_a) > max_seq_length - 2: - tokens_a = tokens_a[0:(max_seq_length - 2)] - - # The convention in BERT/ERNIE is: - # (a) For sequence pairs: - # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] - # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 - # (b) For single sequences: - # tokens: [CLS] the dog is hairy . [SEP] - # type_ids: 0 0 0 0 0 0 0 - # - # Where "type_ids" are used to indicate whether this is the first - # sequence or the second sequence. The embedding vectors for `type=0` and - # `type=1` were learned during pre-training and are added to the wordpiece - # embedding vector (and position vector). This is not *strictly* necessary - # since the [SEP] token unambiguously separates the sequences, but it makes - # it easier for the model to learn the concept of sequences. - # - # For classification tasks, the first vector (corresponding to [CLS]) is - # used as as the "sentence vector". Note that this only makes sense because - # the entire model is fine-tuned. - tokens = [] - text_type_ids = [] - tokens.append("[CLS]") - text_type_ids.append(0) - for token in tokens_a: - tokens.append(token) - text_type_ids.append(0) - tokens.append("[SEP]") - text_type_ids.append(0) - - if tokens_b: - for token in tokens_b: - tokens.append(token) - text_type_ids.append(1) - tokens.append("[SEP]") - text_type_ids.append(1) - - token_ids = tokenizer.convert_tokens_to_ids(tokens) - position_ids = list(range(len(token_ids))) - - # Record = namedtuple('Record', - # ['token_ids', 'text_type_ids', 'position_ids']) - # record = Record( - # token_ids=token_ids, - # text_type_ids=text_type_ids, - # position_ids=position_ids) - - return [token_ids, text_type_ids, position_ids] - - def batch_reader(self, examples, batch_size, in_tokens, phase): - batch = [] - total_token_num = 0 - for e in examples: - parsed_line = self._convert_example_to_record(e, self.max_seq_len, self.tokenizer) - to_append = len(batch) < batch_size - if to_append: - batch.append(parsed_line) - total_token_num += len(parsed_line[0]) - else: - yield batch, total_token_num - batch = [parsed_line] - total_token_num = len(parsed_line[0]) - - if len(batch) > 0 and phase == 'pred': - yield batch, total_token_num - - def data_generator(self, - input_file, - batch_size, - epoch, - dev_count=1, - shuffle=True, - phase=None): - examples = self._read_tsv(input_file) - if phase is None: - phase = 'all' - self.examples[phase] = examples - - def wrapper(): - all_dev_batches = [] - if epoch is None: - num_epochs = 99999999 - else: - num_epochs = epoch - for epoch_index in range(num_epochs): - if phase == "train": - self.current_example = 0 - self.current_epoch = epoch_index - if shuffle: - np.random.shuffle(examples) - - all_dev_batches = [] - for batch_data, num_tokens in self.batch_reader(examples, - batch_size, self.in_tokens, phase=phase): - batch_data = prepare_batch_data( - batch_data, - num_tokens, - voc_size=len(self.vocab), - pad_id=self.pad_id, - cls_id=self.cls_id, - sep_id=self.sep_id, - mask_id=self.mask_id, - # max_len=self.max_seq_len, # 注意，如果padding到最大长度，会导致mask_pos与实际位置不对应。因为mask pos是基于batch内最大长度来计算的。 - return_input_mask=True, - return_max_len=False, - return_num_token=False) - - if len(all_dev_batches) < dev_count: - all_dev_batches.append(batch_data) - if len(all_dev_batches) == dev_count: - for batch in all_dev_batches: - yield batch - all_dev_batches = [] - - return wrapper - - -class ClassifyReader(BaseReader): - def _read_tsv(self, input_file, quotechar=None): - """Reads a tab separated value file.""" - with open(input_file, 'r', encoding='utf8') as f: - reader = csv_reader(f) - headers = next(reader) - text_indices = [ - index for index, h in enumerate(headers) if h != "label" - ] - Example = namedtuple('Example', headers) - - examples = [] - for line in reader: - for index, text in enumerate(line): - if index in text_indices: - if self.for_cn: - line[index] = text.replace(' ', '') - else: - line[index] = text - example = Example(*line) - examples.append(example) - return examples - - def _pad_batch_records(self, batch_records): - batch_token_ids = [record.token_ids for record in batch_records] - batch_text_type_ids = [record.text_type_ids for record in batch_records] - batch_position_ids = [record.position_ids for record in batch_records] - - if not self.is_inference: - batch_labels = [record.label_id for record in batch_records] - if self.is_classify: - batch_labels = np.array(batch_labels).astype("int64").reshape( - [-1, 1]) - elif self.is_regression: - batch_labels = np.array(batch_labels).astype("float32").reshape( - [-1, 1]) - - if batch_records[0].qid: - batch_qids = [record.qid for record in batch_records] - batch_qids = np.array(batch_qids).astype("int64").reshape( - [-1, 1]) - else: - batch_qids = np.array([]).astype("int64").reshape([-1, 1]) - - # padding - padded_token_ids, input_mask = pad_batch_data( - batch_token_ids, pad_idx=self.pad_id, return_input_mask=True) - padded_text_type_ids = pad_batch_data( - batch_text_type_ids, pad_idx=self.pad_id) - padded_position_ids = pad_batch_data( - batch_position_ids, pad_idx=self.pad_id) - padded_task_ids = np.ones_like( - padded_token_ids, dtype="int64") * self.task_id - - return_list = [ - padded_token_ids, padded_text_type_ids, padded_position_ids, - padded_task_ids, input_mask - ] - if not self.is_inference: - return_list += [batch_labels, batch_qids] - - return return_list - - -class SequenceLabelReader(BaseReader): - def _pad_batch_records(self, batch_records): - batch_token_ids = [record.token_ids for record in batch_records] - batch_text_type_ids = [record.text_type_ids for record in batch_records] - batch_position_ids = [record.position_ids for record in batch_records] - batch_label_ids = [record.label_ids for record in batch_records] - - # padding - padded_token_ids, input_mask, batch_seq_lens = pad_batch_data( - batch_token_ids, - pad_idx=self.pad_id, - return_input_mask=True, - return_seq_lens=True) - padded_text_type_ids = pad_batch_data( - batch_text_type_ids, pad_idx=self.pad_id) - padded_position_ids = pad_batch_data( - batch_position_ids, pad_idx=self.pad_id) - padded_label_ids = pad_batch_data( - batch_label_ids, pad_idx=len(self.label_map) - 1) - padded_task_ids = np.ones_like( - padded_token_ids, dtype="int64") * self.task_id - - return_list = [ - padded_token_ids, padded_text_type_ids, padded_position_ids, - padded_task_ids, input_mask, padded_label_ids, batch_seq_lens - ] - return return_list - - def _reseg_token_label(self, tokens, labels, tokenizer): - assert len(tokens) == len(labels) - ret_tokens = [] - ret_labels = [] - for token, label in zip(tokens, labels): - sub_token = tokenizer.tokenize(token) - if len(sub_token) == 0: - continue - ret_tokens.extend(sub_token) - if len(sub_token) == 1: - ret_labels.append(label) - continue - - if label == "O" or label.startswith("I-"): - ret_labels.extend([label] * len(sub_token)) - elif label.startswith("B-"): - i_label = "I-" + label[2:] - ret_labels.extend([label] + [i_label] * (len(sub_token) - 1)) - elif label.startswith("S-"): - b_laebl = "B-" + label[2:] - e_label = "E-" + label[2:] - i_label = "I-" + label[2:] - ret_labels.extend([b_laebl] + [i_label] * (len(sub_token) - 2) + [e_label]) - elif label.startswith("E-"): - i_label = "I-" + label[2:] - ret_labels.extend([i_label] * (len(sub_token) - 1) + [label]) - - assert len(ret_tokens) == len(ret_labels) - return ret_tokens, ret_labels - - def _convert_example_to_record(self, example, max_seq_length, tokenizer): - tokens = tokenization.convert_to_unicode(example.text_a).split(u"") - labels = tokenization.convert_to_unicode(example.label).split(u"") - tokens, labels = self._reseg_token_label(tokens, labels, tokenizer) - - if len(tokens) > max_seq_length - 2: - tokens = tokens[0:(max_seq_length - 2)] - labels = labels[0:(max_seq_length - 2)] - - tokens = ["[CLS]"] + tokens + ["[SEP]"] - token_ids = tokenizer.convert_tokens_to_ids(tokens) - position_ids = list(range(len(token_ids))) - text_type_ids = [0] * len(token_ids) - no_entity_id = len(self.label_map) - 1 - label_ids = [no_entity_id] + [ - self.label_map[label] for label in labels - ] + [no_entity_id] - - Record = namedtuple( - 'Record', - ['token_ids', 'text_type_ids', 'position_ids', 'label_ids']) - record = Record( - token_ids=token_ids, - text_type_ids=text_type_ids, - position_ids=position_ids, - label_ids=label_ids) - return record - - -class ExtractEmbeddingReader(BaseReader): - def _pad_batch_records(self, batch_records): - batch_token_ids = [record.token_ids for record in batch_records] - batch_text_type_ids = [record.text_type_ids for record in batch_records] - batch_position_ids = [record.position_ids for record in batch_records] - - # padding - padded_token_ids, input_mask, seq_lens = pad_batch_data( - batch_token_ids, - pad_idx=self.pad_id, - return_input_mask=True, - return_seq_lens=True) - padded_text_type_ids = pad_batch_data( - batch_text_type_ids, pad_idx=self.pad_id) - padded_position_ids = pad_batch_data( - batch_position_ids, pad_idx=self.pad_id) - padded_task_ids = np.ones_like( - padded_token_ids, dtype="int64") * self.task_id - - return_list = [ - padded_token_ids, padded_text_type_ids, padded_position_ids, - padded_task_ids, input_mask, seq_lens - ] - - return return_list - - -class MRCReader(BaseReader): - def __init__(self, - vocab_path, - label_map_config=None, - max_seq_len=512, - do_lower_case=True, - in_tokens=False, - random_seed=None, - tokenizer="FullTokenizer", - is_classify=True, - is_regression=False, - for_cn=True, - task_id=0, - doc_stride=128, - max_query_length=64): - self.max_seq_len = max_seq_len - self.tokenizer = tokenization.FullTokenizer( - vocab_file=vocab_path, do_lower_case=do_lower_case) - self.vocab = self.tokenizer.vocab - self.pad_id = self.vocab["[PAD]"] - self.cls_id = self.vocab["[CLS]"] - self.sep_id = self.vocab["[SEP]"] - self.in_tokens = in_tokens - self.for_cn = for_cn - self.task_id = task_id - self.doc_stride = doc_stride - self.max_query_length = max_query_length - self.examples = {} - self.features = {} - - if random_seed is not None: - np.random.seed(random_seed) - - self.current_example = 0 - self.current_epoch = 0 - self.num_examples = 0 - - self.Example = namedtuple('Example', - ['qas_id', 'question_text', 'doc_tokens', 'orig_answer_text', - 'start_position', 'end_position']) - self.Feature = namedtuple("Feature", ["unique_id", "example_index", "doc_span_index", - "tokens", "token_to_orig_map", "token_is_max_context", - "token_ids", "position_ids", "text_type_ids", - "start_position", "end_position"]) - self.DocSpan = namedtuple("DocSpan", ["start", "length"]) - - def _read_json(self, input_file, is_training): - examples = [] - with open(input_file, "r", encoding='utf8') as f: - input_data = json.load(f)["data"] - for entry in input_data: - for paragraph in entry["paragraphs"]: - paragraph_text = paragraph["context"] - for qa in paragraph["qas"]: - qas_id = qa["id"] - question_text = qa["question"] - start_pos = None - end_pos = None - orig_answer_text = None - - if is_training: - if len(qa["answers"]) != 1: - raise ValueError( - "For training, each question should have exactly 1 answer." - ) - - answer = qa["answers"][0] - orig_answer_text = answer["text"] - answer_offset = answer["answer_start"] - answer_length = len(orig_answer_text) - doc_tokens = [ - paragraph_text[:answer_offset], - paragraph_text[answer_offset:answer_offset + - answer_length], - paragraph_text[answer_offset + answer_length:] - ] - - start_pos = 1 - end_pos = 1 - - actual_text = " ".join(doc_tokens[start_pos:(end_pos - + 1)]) - if actual_text.find(orig_answer_text) == -1: - log.info("Could not find answer: '%s' vs. '%s'", - actual_text, orig_answer_text) - continue - else: - doc_tokens = tokenization.tokenize_chinese_chars( - paragraph_text) - - example = self.Example( - qas_id=qas_id, - question_text=question_text, - doc_tokens=doc_tokens, - orig_answer_text=orig_answer_text, - start_position=start_pos, - end_position=end_pos) - examples.append(example) - - return examples - - def _improve_answer_span(self, doc_tokens, input_start, input_end, - tokenizer, orig_answer_text): - tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text)) - - for new_start in range(input_start, input_end + 1): - for new_end in range(input_end, new_start - 1, -1): - text_span = " ".join(doc_tokens[new_start:(new_end + 1)]) - if text_span == tok_answer_text: - return (new_start, new_end) - - return (input_start, input_end) - - def _check_is_max_context(self, doc_spans, cur_span_index, position): - best_score = None - best_span_index = None - for (span_index, doc_span) in enumerate(doc_spans): - end = doc_span.start + doc_span.length - 1 - if position < doc_span.start: - continue - if position > end: - continue - num_left_context = position - doc_span.start - num_right_context = end - position - score = min(num_left_context, - num_right_context) + 0.01 * doc_span.length - if best_score is None or score > best_score: - best_score = score - best_span_index = span_index - - return cur_span_index == best_span_index - - def _convert_example_to_feature(self, examples, max_seq_length, tokenizer, - is_training): - features = [] - unique_id = 1000000000 - - for (example_index, example) in enumerate(examples): - query_tokens = tokenizer.tokenize(example.question_text) - if len(query_tokens) > self.max_query_length: - query_tokens = query_tokens[0:self.max_query_length] - tok_to_orig_index = [] - orig_to_tok_index = [] - all_doc_tokens = [] - for (i, token) in enumerate(example.doc_tokens): - orig_to_tok_index.append(len(all_doc_tokens)) - sub_tokens = tokenizer.tokenize(token) - for sub_token in sub_tokens: - tok_to_orig_index.append(i) - all_doc_tokens.append(sub_token) - - tok_start_position = None - tok_end_position = None - if is_training: - tok_start_position = orig_to_tok_index[example.start_position] - if example.end_position < len(example.doc_tokens) - 1: - tok_end_position = orig_to_tok_index[example.end_position + - 1] - 1 - else: - tok_end_position = len(all_doc_tokens) - 1 - (tok_start_position, - tok_end_position) = self._improve_answer_span( - all_doc_tokens, tok_start_position, tok_end_position, - tokenizer, example.orig_answer_text) - - max_tokens_for_doc = max_seq_length - len(query_tokens) - 3 - doc_spans = [] - start_offset = 0 - while start_offset < len(all_doc_tokens): - length = len(all_doc_tokens) - start_offset - if length > max_tokens_for_doc: - length = max_tokens_for_doc - doc_spans.append(self.DocSpan(start=start_offset, length=length)) - if start_offset + length == len(all_doc_tokens): - break - start_offset += min(length, self.doc_stride) - - for (doc_span_index, doc_span) in enumerate(doc_spans): - tokens = [] - token_to_orig_map = {} - token_is_max_context = {} - text_type_ids = [] - tokens.append("[CLS]") - text_type_ids.append(0) - for token in query_tokens: - tokens.append(token) - text_type_ids.append(0) - tokens.append("[SEP]") - text_type_ids.append(0) - - for i in range(doc_span.length): - split_token_index = doc_span.start + i - token_to_orig_map[len(tokens)] = tok_to_orig_index[ - split_token_index] - - is_max_context = self._check_is_max_context( - doc_spans, doc_span_index, split_token_index) - token_is_max_context[len(tokens)] = is_max_context - tokens.append(all_doc_tokens[split_token_index]) - text_type_ids.append(1) - tokens.append("[SEP]") - text_type_ids.append(1) - - token_ids = tokenizer.convert_tokens_to_ids(tokens) - position_ids = list(range(len(token_ids))) - start_position = None - end_position = None - if is_training: - doc_start = doc_span.start - doc_end = doc_span.start + doc_span.length - 1 - out_of_span = False - if not (tok_start_position >= doc_start and - tok_end_position <= doc_end): - out_of_span = True - if out_of_span: - start_position = 0 - end_position = 0 - else: - doc_offset = len(query_tokens) + 2 - start_position = tok_start_position - doc_start + doc_offset - end_position = tok_end_position - doc_start + doc_offset - - feature = self.Feature( - unique_id=unique_id, - example_index=example_index, - doc_span_index=doc_span_index, - tokens=tokens, - token_to_orig_map=token_to_orig_map, - token_is_max_context=token_is_max_context, - token_ids=token_ids, - position_ids=position_ids, - text_type_ids=text_type_ids, - start_position=start_position, - end_position=end_position) - features.append(feature) - - unique_id += 1 - - return features - - def _prepare_batch_data(self, records, batch_size, phase=None): - """generate batch records""" - batch_records, max_len = [], 0 - - for index, record in enumerate(records): - if phase == "train": - self.current_example = index - max_len = max(max_len, len(record.token_ids)) - if self.in_tokens: - to_append = (len(batch_records) + 1) * max_len <= batch_size - else: - to_append = len(batch_records) < batch_size - if to_append: - batch_records.append(record) - else: - yield self._pad_batch_records(batch_records, phase == "train") - batch_records, max_len = [record], len(record.token_ids) - - if phase == 'pred' and batch_records: - yield self._pad_batch_records(batch_records, phase == "train") - - def _pad_batch_records(self, batch_records, is_training): - batch_token_ids = [record.token_ids for record in batch_records] - batch_text_type_ids = [record.text_type_ids for record in batch_records] - batch_position_ids = [record.position_ids for record in batch_records] - if is_training: - batch_start_position = [ - record.start_position for record in batch_records - ] - batch_end_position = [ - record.end_position for record in batch_records - ] - batch_start_position = np.array(batch_start_position).astype( - "int64").reshape([-1, 1]) - batch_end_position = np.array(batch_end_position).astype( - "int64").reshape([-1, 1]) - - else: - batch_size = len(batch_token_ids) - batch_start_position = np.zeros( - shape=[batch_size, 1], dtype="int64") - batch_end_position = np.zeros(shape=[batch_size, 1], dtype="int64") - - batch_unique_ids = [record.unique_id for record in batch_records] - batch_unique_ids = np.array(batch_unique_ids).astype("int64").reshape( - [-1, 1]) - - # padding - padded_token_ids, input_mask = pad_batch_data( - batch_token_ids, pad_idx=self.pad_id, return_input_mask=True) - padded_text_type_ids = pad_batch_data( - batch_text_type_ids, pad_idx=self.pad_id) - padded_position_ids = pad_batch_data( - batch_position_ids, pad_idx=self.pad_id) - padded_task_ids = np.ones_like( - padded_token_ids, dtype="int64") * self.task_id - - return_list = [ - padded_token_ids, padded_text_type_ids, padded_position_ids, - padded_task_ids, input_mask, batch_start_position, - batch_end_position, batch_unique_ids - ] - - return return_list - - def get_num_examples(self, phase): - return len(self.features[phase]) - - def get_features(self, phase): - return self.features[phase] - - def get_examples(self, phase): - return self.examples[phase] - - def data_generator(self, - input_file, - batch_size, - epoch, - dev_count=1, - shuffle=True, - phase=None): - - examples = self.examples.get(phase, None) - features = self.features.get(phase, None) - if not examples: - examples = self._read_json(input_file, phase == "train") - features = self._convert_example_to_feature( - examples, self.max_seq_len, self.tokenizer, phase == "train") - self.examples[phase] = examples - self.features[phase] = features - - def wrapper(): - all_dev_batches = [] - if epoch is None: - num_epochs = 99999999 - else: - num_epochs = epoch - for epoch_index in range(num_epochs): - if phase == "train": - self.current_example = 0 - self.current_epoch = epoch_index - if phase == "train" and shuffle: - np.random.shuffle(features) - - for batch_data in self._prepare_batch_data( - features, batch_size, phase=phase): - if len(all_dev_batches) < dev_count: - all_dev_batches.append(batch_data) - if len(all_dev_batches) == dev_count: - for batch in all_dev_batches: - yield batch - all_dev_batches = [] - - return wrapper - - -if __name__ == '__main__': - pass diff --git a/build/lib/paddlepalm/task_instance.py b/build/lib/paddlepalm/task_instance.py deleted file mode 100644 index 36ad848..0000000 --- a/build/lib/paddlepalm/task_instance.py +++ /dev/null @@ -1,288 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from paddlepalm.interface import reader as base_reader -from paddlepalm.interface import task_paradigm as base_paradigm -import os -import json -from paddle import fluid - -class TaskInstance(object): - - def __init__(self, name, id, config={}, verbose=True): - self._name = name - self._config = config - self._verbose = verbose - - self._save_infermodel_path = os.path.join(self._config['save_path'], 'infer_model') - self._save_ckpt_path = os.path.join(self._config['save_path'], 'ckpt') - - # following flags can be fetch from instance config file - self._is_target = config.get('is_target', True) - self._first_target = config.get('is_first_target', False) - self._task_reuse_scope = config.get('task_reuse_scope', name) - - self._feeded_var_names = None - self._target_vars = None - - # training process management - self._mix_ratio = None - self._expected_train_steps = None - self._expected_train_epochs = None - self._steps_pur_epoch = None - self._cur_train_epoch = 0 - self._cur_train_step = 0 - self._train_finish = False - - # 存放不同运行阶段（train，eval，pred）的数据集reader，key为phase，value为Reader实例 - self._reader = {'train': None, 'eval': None, 'pred': None} - self._input_layer = None - self._inputname_to_varname = {} - self._task_layer = {'train': None, 'eval': None, 'pred': None} - self._pred_input_name_list = [] - self._pred_input_varname_list = [] - self._pred_fetch_name_list = [] - self._pred_fetch_var_list = [] - - self._Reader = None - self._Paradigm = None - - self._exe = fluid.Executor(fluid.CPUPlace()) - - self._save_protocol = { - 'input_names': 'self._pred_input_name_list', - 'input_varnames': 'self._pred_input_varname_list', - 'fetch_list': 'self._pred_fetch_name_list'} - - - def build_task_layer(self, net_inputs, phase): - output_vars = self._task_layer[phase].build(net_inputs) - if phase == 'pred': - if output_vars is not None: - self._pred_fetch_name_list, self._pred_fetch_var_list = zip(*output_vars.items()) - else: - self._pred_fetch_name_list = [] - self._pred_fetch_var_list = [] - return output_vars - - def postprocess(self, rt_outputs, phase): - return self._task_layer[phase].postprocess(rt_outputs) - - def epoch_postprocess(self, epoch_inputs, phase): - return self._task_layer[phase].epoch_postprocess(epoch_inputs) - - def save(self, suffix=''): - dirpath = self._save_infermodel_path + suffix - self._pred_input_varname_list = [str(i) for i in self._pred_input_varname_list] - - fluid.io.save_inference_model(dirpath, self._pred_input_varname_list, self._pred_fetch_var_list, self._exe) - # fluid.io.save_inference_model(dirpath, self._pred_input_varname_list, self._pred_fetch_var_list, self._exe, params_filename='__params__') - print(self._name + ': inference model saved at ' + dirpath) - - conf = {} - for k, strv in self._save_protocol.items(): - exec('v={}'.format(strv)) - conf[k] = v - with open(os.path.join(dirpath, '__conf__'), 'w') as writer: - writer.write(json.dumps(conf, indent=1)) - - def load(self, infer_model_path=None): - if infer_model_path is None: - infer_model_path = self._save_infermodel_path - for k,v in json.load(open(os.path.join(infer_model_path, '__conf__'))).items(): - strv = self._save_protocol[k] - exec('{}=v'.format(strv)) - pred_prog, self._pred_input_varname_list, self._pred_fetch_var_list = \ - fluid.io.load_inference_model(infer_model_path, self._exe) - # pred_prog, self._pred_input_varname_list, self._pred_fetch_var_list = \ - # fluid.io.load_inference_model(infer_model_path, self._exe, params_filename='__params__') - print(self._name+': inference model loaded from ' + infer_model_path) - return pred_prog - - @property - def name(self): - return self._name - - @property - def Reader(self): - return self._Reader - - @Reader.setter - def Reader(self, cls): - assert base_reader.__name__ == cls.__bases__[-1].__name__, \ - "expect: {}, receive: {}.".format(base_reader.__name__, \ - cls.__bases__[-1].__name__) - self._Reader = cls - - @property - def Paradigm(self): - return self._Paradigm - - @Paradigm.setter - def Paradigm(self, cls): - assert base_paradigm.__name__ == cls.__bases__[-1].__name__, \ - "expect: {}, receive: {}.".format(base_paradigm.__name__, \ - cls.__bases__[-1].__name__) - self._Paradigm = cls - - @property - def config(self): - return self._config - - @property - def reader(self): - return self._reader - - @property - def pred_input(self): - return zip(*[self._pred_input_name_list, self._pred_input_varname_list]) - - @pred_input.setter - def pred_input(self, val): - assert isinstance(val, dict) - self._pred_input_name_list, self._pred_input_varname_list = \ - zip(*[[k, v.name] for k,v in val.items()]) - # print(self._pred_input_name_list) - - @property - def pred_fetch_list(self): - return [self._pred_fetch_name_list, self._pred_fetch_var_list] - - @property - def task_layer(self): - return self._task_layer - - @property - def is_first_target(self): - return self._is_first_target - - @is_first_target.setter - def is_first_target(self, value): - self._is_first_target = bool(value) - if self._is_first_target: - assert self._is_target, "ERROR: only target task could be set as main task." - if self._verbose and self._is_first_target: - print("{}: set as main task".format(self._name)) - - @property - def is_target(self): - if self._is_target is not None: - return self._is_target - else: - raise ValueError("{}: is_target is None".format(self._name)) - - @is_target.setter - def is_target(self, value): - self._is_target = bool(value) - if self._verbose: - if self._is_target: - print('{}: set as target task.'.format(self._name)) - else: - print('{}: set as aux task.'.format(self._name)) - - @property - def mix_ratio(self): - if self._mix_ratio is not None: - return self._mix_ratio - else: - raise ValueError("{}: mix_ratio is None".format(self._name)) - - @mix_ratio.setter - def mix_ratio(self, value): - self._mix_ratio = float(value) - if self._verbose: - print('{}: mix_ratio is set to {}'.format(self._name, self._mix_ratio)) - - @property - def expected_train_steps(self): - return self._expected_train_steps - - @expected_train_steps.setter - def expected_train_steps(self, value): - self._expected_train_steps = value - self._expected_train_epochs = value / float(self._steps_pur_epoch) - - @property - def expected_train_epochs(self): - return self._expected_train_epochs - - @property - def cur_train_epoch(self): - return self._cur_train_epoch - - @cur_train_epoch.setter - def cur_train_epoch(self, value): - self._cur_train_epoch = value - - @property - def cur_train_step(self): - return self._cur_train_step - - @cur_train_step.setter - def cur_train_step(self, value): - self._cur_train_step = value - if self._cur_train_step > self._steps_pur_epoch: - self._cur_train_epoch += 1 - self._cur_train_step = 1 - if self._is_target and self._cur_train_step + self._cur_train_epoch * self._steps_pur_epoch >= self._expected_train_steps: - self._train_finish = True - # fluid.io.save_inference_model(self._save_infermodel_path, ) - - @property - def steps_pur_epoch(self): - return self._steps_pur_epoch - - @steps_pur_epoch.setter - def steps_pur_epoch(self, value): - self._steps_pur_epoch = value - - @property - def train_finish(self): - return self._train_finish - - @property - def task_reuse_scope(self): - if self._task_reuse_scope is not None: - return self._task_reuse_scope - else: - raise ValueError("{}: task_reuse_scope is None".format(self._name)) - - @task_reuse_scope.setter - def task_reuse_scope(self, scope_name): - self._task_reuse_scope = str(scope_name) - if self._verbose: - print('{}: task_reuse_scope is set to {}'.format(self._name, self._task_reuse_scope)) - - - - - - - - - - -def check_instances(insts): - """to check ids, first_target""" - pass - -def _check_ids(): - pass - -def _check_targets(): - pass - -def _check_reuse_scopes(): - pass diff --git a/build/lib/paddlepalm/task_paradigm/__init__.py b/build/lib/paddlepalm/task_paradigm/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/build/lib/paddlepalm/task_paradigm/cls.py b/build/lib/paddlepalm/task_paradigm/cls.py deleted file mode 100644 index f69b817..0000000 --- a/build/lib/paddlepalm/task_paradigm/cls.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import paddle.fluid as fluid -from paddlepalm.interface import task_paradigm -from paddle.fluid import layers - -class TaskParadigm(task_paradigm): - ''' - classification - ''' - def __init___(self, config, phase): - self._is_training = phase == 'train' - self.sent_emb_size = config['hidden_size'] - self.num_classes = config['n_classes'] - - @property - def inputs_attrs(self): - return {'bakcbone': {"sentence_emb": [-1, self.sent_emb_size], 'float32']}, - 'reader': {"label_ids": [[-1, 1], 'int64']}} - - @property - def outputs_attrs(self): - if self._is_training: - return {'loss': [[1], 'float32']} - else: - return {'logits': [-1, self.num_classes], 'float32'} - - def build(self, **inputs): - sent_emb = inputs['backbone']['sentence_emb'] - label_ids = inputs['reader']['label_ids'] - - logits = fluid.layers.fc( - input=ent_emb - size=self.num_classes, - param_attr=fluid.ParamAttr( - name="cls_out_w", - initializer=fluid.initializer.TruncatedNormal(scale=0.1)), - bias_attr=fluid.ParamAttr( - name="cls_out_b", initializer=fluid.initializer.Constant(0.))) - - loss = fluid.layers.softmax_with_cross_entropy( - logits=logits, label=label_ids) - loss = layers.mean(loss) - if self._is_training: - return {"loss": loss} - else: - return {"logits":logits} diff --git a/build/lib/paddlepalm/task_paradigm/match.py b/build/lib/paddlepalm/task_paradigm/match.py deleted file mode 100644 index 58bbf35..0000000 --- a/build/lib/paddlepalm/task_paradigm/match.py +++ /dev/null @@ -1,70 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import paddle.fluid as fluid -from paddlepalm.interface import task_paradigm -from paddle.fluid import layers - -class TaskParadigm(task_paradigm): - ''' - matching - ''' - def __init__(self, config, phase, backbone_config=None): - self._is_training = phase == 'train' - self._hidden_size = backbone_config['hidden_size'] - - @property - def inputs_attrs(self): - if self._is_training: - reader = {"label_ids": [[-1, 1], 'int64']} - else: - reader = {} - bb = {"sentence_pair_embedding": [[-1, self._hidden_size], 'float32']} - return {'reader': reader, 'backbone': bb} - - @property - def outputs_attrs(self): - if self._is_training: - return {"loss": [[1], 'float32']} - else: - return {"logits": [[-1, 1], 'float32']} - - def build(self, inputs): - if self._is_training: - labels = inputs["reader"]["label_ids"] - cls_feats = inputs["backbone"]["sentence_pair_embedding"] - - cls_feats = fluid.layers.dropout( - x=cls_feats, - dropout_prob=0.1, - dropout_implementation="upscale_in_train") - logits = fluid.layers.fc( - input=cls_feats, - size=2, - param_attr=fluid.ParamAttr( - name="cls_out_w", - initializer=fluid.initializer.TruncatedNormal(scale=0.02)), - bias_attr=fluid.ParamAttr( - name="cls_out_b", - initializer=fluid.initializer.Constant(0.))) - - if self._is_training: - ce_loss, probs = fluid.layers.softmax_with_cross_entropy( - logits=logits, label=labels, return_softmax=True) - loss = fluid.layers.mean(x=ce_loss) - return {'loss': loss} - else: - return {'logits': logits} - diff --git a/build/lib/paddlepalm/task_paradigm/mlm.py b/build/lib/paddlepalm/task_paradigm/mlm.py deleted file mode 100644 index 53c2866..0000000 --- a/build/lib/paddlepalm/task_paradigm/mlm.py +++ /dev/null @@ -1,115 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import paddle.fluid as fluid -from paddlepalm.interface import task_paradigm -from paddle.fluid import layers -from paddlepalm.backbone.utils.transformer import pre_process_layer - -class TaskParadigm(task_paradigm): - ''' - matching - ''' - def __init__(self, config, phase, backbone_config=None): - self._is_training = phase == 'train' - self._emb_size = backbone_config['hidden_size'] - self._hidden_size = backbone_config['hidden_size'] - self._vocab_size = backbone_config['vocab_size'] - self._hidden_act = backbone_config['hidden_act'] - self._initializer_range = backbone_config['initializer_range'] - - @property - def inputs_attrs(self): - reader = { - "mask_label": [[-1, 1], 'int64'], - "mask_pos": [[-1, 1], 'int64']} - if not self._is_training: - del reader['mask_label'] - bb = { - "encoder_outputs": [[-1, -1, self._hidden_size], 'float32'], - "embedding_table": [[-1, self._vocab_size, self._emb_size], 'float32']} - return {'reader': reader, 'backbone': bb} - - @property - def outputs_attrs(self): - if self._is_training: - return {"loss": [[1], 'float32']} - else: - return {"logits": [[-1], 'float32']} - - def build(self, inputs): - if self._is_training: - mask_label = inputs["reader"]["mask_label"] - mask_pos = inputs["reader"]["mask_pos"] - word_emb = inputs["backbone"]["embedding_table"] - enc_out = inputs["backbone"]["encoder_outputs"] - - emb_size = word_emb.shape[-1] - - _param_initializer = fluid.initializer.TruncatedNormal( - scale=self._initializer_range) - - mask_pos = fluid.layers.cast(x=mask_pos, dtype='int32') - - reshaped_emb_out = fluid.layers.reshape( - x=enc_out, shape=[-1, emb_size]) - - # extract masked tokens' feature - mask_feat = fluid.layers.gather(input=reshaped_emb_out, index=mask_pos) - - # transform: fc - mask_trans_feat = fluid.layers.fc( - input=mask_feat, - size=emb_size, - act=self._hidden_act, - param_attr=fluid.ParamAttr( - name='mask_lm_trans_fc.w_0', - initializer=_param_initializer), - bias_attr=fluid.ParamAttr(name='mask_lm_trans_fc.b_0')) - # transform: layer norm - mask_trans_feat = pre_process_layer( - mask_trans_feat, 'n', name='mask_lm_trans') - - mask_lm_out_bias_attr = fluid.ParamAttr( - name="mask_lm_out_fc.b_0", - initializer=fluid.initializer.Constant(value=0.0)) - - # print fluid.default_main_program().global_block() - - # fc_out = fluid.layers.matmul( - # x=mask_trans_feat, - # y=fluid.default_main_program().global_block().var( - # _word_emb_name), - # transpose_y=True) - - fc_out = fluid.layers.matmul( - x=mask_trans_feat, - y=word_emb, - transpose_y=True) - fc_out += fluid.layers.create_parameter( - shape=[self._vocab_size], - dtype='float32', - attr=mask_lm_out_bias_attr, - is_bias=True) - - if self._is_training: - mask_lm_loss = fluid.layers.softmax_with_cross_entropy( - logits=fc_out, label=mask_label) - loss = fluid.layers.mean(mask_lm_loss) - return {'loss': loss} - else: - return {'logits': fc_out} - - diff --git a/build/lib/paddlepalm/task_paradigm/mrc.py b/build/lib/paddlepalm/task_paradigm/mrc.py deleted file mode 100644 index 1d3642a..0000000 --- a/build/lib/paddlepalm/task_paradigm/mrc.py +++ /dev/null @@ -1,486 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import paddle.fluid as fluid -from paddlepalm.interface import task_paradigm -import collections -import numpy as np -import os -import math -import six -import paddlepalm.tokenizer.ernie_tokenizer as tokenization -import json - -RawResult = collections.namedtuple("RawResult", - ["unique_id", "start_logits", "end_logits"]) - -class TaskParadigm(task_paradigm): - """""" - - def __init__(self, config, phase, backbone_config=None): - - self._is_training = phase == 'train' - self._max_sequence_length = config['max_seq_len'] - self._hidden_size = backbone_config['hidden_size'] - self._pred_results = [] - - if phase == 'pred': - self._max_answer_length = config.get('max_answer_len', None) - self._null_score_diff_threshold = config.get('null_score_diff_threshold', 0.0) - self._n_best_size = config.get('n_best_size', 20) - self._pred_output_path = config.get('pred_output_path', None) - self._verbose = config.get('verbose', False) - self._with_negative = config.get('with_negative', False) - self._do_lower_case = config.get('do_lower_case', False) - - - @property - def inputs_attrs(self): - if self._is_training: - reader = {"start_positions": [[-1, 1], 'int64'], - "end_positions": [[-1, 1], 'int64']} - else: - reader = {'unique_ids': [[-1, 1], 'int64']} - bb = {"encoder_outputs": [[-1, -1, self._hidden_size], 'float32']} - return {'reader': reader, 'backbone': bb} - - @property - def epoch_inputs_attrs(self): - if not self._is_training: - from_reader = {'examples': None, 'features': None} - return {'reader': from_reader} - - @property - def outputs_attr(self): - if self._is_training: - return {'loss': [[1], 'float32']} - else: - return {'start_logits': [[-1, -1, 1], 'float32'], - 'end_logits': [[-1, -1, 1], 'float32'], - 'unique_ids': [[-1, 1], 'int64']} - - - def build(self, inputs): - if self._is_training: - start_positions = inputs['reader']['start_positions'] - end_positions = inputs['reader']['end_positions'] - else: - unique_id = inputs['reader']['unique_ids'] - - enc_out = inputs['backbone']['encoder_outputs'] - logits = fluid.layers.fc( - input=enc_out, - size=2, - num_flatten_dims=2, - param_attr=fluid.ParamAttr( - name="cls_squad_out_w", - initializer=fluid.initializer.TruncatedNormal(scale=0.02)), - bias_attr=fluid.ParamAttr( - name="cls_squad_out_b", initializer=fluid.initializer.Constant(0.))) - - logits = fluid.layers.transpose(x=logits, perm=[2, 0, 1]) - start_logits, end_logits = fluid.layers.unstack(x=logits, axis=0) - - def _compute_single_loss(logits, positions): - """Compute start/end loss for mrc model""" - loss = fluid.layers.softmax_with_cross_entropy( - logits=logits, label=positions) - loss = fluid.layers.mean(x=loss) - return loss - - if self._is_training: - start_loss = _compute_single_loss(start_logits, start_positions) - end_loss = _compute_single_loss(end_logits, end_positions) - total_loss = (start_loss + end_loss) / 2.0 - return {'loss': total_loss} - else: - return {'start_logits': start_logits, - 'end_logits': end_logits, - 'unique_ids': unique_id} - - - def postprocess(self, rt_outputs): - """this func will be called after each step(batch) of training/evaluating/predicting process.""" - if not self._is_training: - unique_ids = np.squeeze(rt_outputs['unique_ids'], -1) - start_logits = rt_outputs['start_logits'] - end_logits = rt_outputs['end_logits'] - for idx in range(len(unique_ids)): - - if unique_ids[idx] < 0: - continue - if len(self._pred_results) % 1000 == 0: - print("Predicting example: {}".format(len(self._pred_results))) - uid = int(unique_ids[idx]) - - s = [float(x) for x in start_logits[idx].flat] - e = [float(x) for x in end_logits[idx].flat] - self._pred_results.append( - RawResult( - unique_id=uid, - start_logits=s, - end_logits=e)) - - def epoch_postprocess(self, post_inputs): - """(optional interface) this func will be called after evaluation/predicting process and each epoch during training process.""" - - if not self._is_training: - if self._pred_output_path is None: - raise ValueError('argument pred_output_path not found in config. Please add it into config dict/file.') - examples = post_inputs['reader']['examples'] - features = post_inputs['reader']['features'] - if not os.path.exists(self._pred_output_path): - os.makedirs(self._pred_output_path) - output_prediction_file = os.path.join(self._pred_output_path, "predictions.json") - output_nbest_file = os.path.join(self._pred_output_path, "nbest_predictions.json") - output_null_log_odds_file = os.path.join(self._pred_output_path, "null_odds.json") - _write_predictions(examples, features, self._pred_results, - self._n_best_size, self._max_answer_length, - self._do_lower_case, output_prediction_file, - output_nbest_file, output_null_log_odds_file, - self._with_negative, - self._null_score_diff_threshold, self._verbose) - - -def _write_predictions(all_examples, all_features, all_results, n_best_size, - max_answer_length, do_lower_case, output_prediction_file, - output_nbest_file, output_null_log_odds_file, - with_negative, null_score_diff_threshold, - verbose): - """Write final predictions to the json file and log-odds of null if needed.""" - print("Writing predictions to: %s" % (output_prediction_file)) - print("Writing nbest to: %s" % (output_nbest_file)) - - example_index_to_features = collections.defaultdict(list) - for feature in all_features: - example_index_to_features[feature.example_index].append(feature) - - unique_id_to_result = {} - for result in all_results: - unique_id_to_result[result.unique_id] = result - - _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name - "PrelimPrediction", [ - "feature_index", "start_index", "end_index", "start_logit", - "end_logit" - ]) - - all_predictions = collections.OrderedDict() - all_nbest_json = collections.OrderedDict() - scores_diff_json = collections.OrderedDict() - - for (example_index, example) in enumerate(all_examples): - features = example_index_to_features[example_index] - - prelim_predictions = [] - # keep track of the minimum score of null start+end of position 0 - score_null = 1000000 # large and positive - min_null_feature_index = 0 # the paragraph slice with min mull score - null_start_logit = 0 # the start logit at the slice with min null score - null_end_logit = 0 # the end logit at the slice with min null score - for (feature_index, feature) in enumerate(features): - result = unique_id_to_result[feature.unique_id] - start_indexes = _get_best_indexes(result.start_logits, n_best_size) - end_indexes = _get_best_indexes(result.end_logits, n_best_size) - # if we could have irrelevant answers, get the min score of irrelevant - if with_negative: - feature_null_score = result.start_logits[0] + result.end_logits[ - 0] - if feature_null_score < score_null: - score_null = feature_null_score - min_null_feature_index = feature_index - null_start_logit = result.start_logits[0] - null_end_logit = result.end_logits[0] - for start_index in start_indexes: - for end_index in end_indexes: - # We could hypothetically create invalid predictions, e.g., predict - # that the start of the span is in the question. We throw out all - # invalid predictions. - if start_index >= len(feature.tokens): - continue - if end_index >= len(feature.tokens): - continue - if start_index not in feature.token_to_orig_map: - continue - if end_index not in feature.token_to_orig_map: - continue - if not feature.token_is_max_context.get(start_index, False): - continue - if end_index < start_index: - continue - length = end_index - start_index + 1 - if length > max_answer_length: - continue - prelim_predictions.append( - _PrelimPrediction( - feature_index=feature_index, - start_index=start_index, - end_index=end_index, - start_logit=result.start_logits[start_index], - end_logit=result.end_logits[end_index])) - - if with_negative: - prelim_predictions.append( - _PrelimPrediction( - feature_index=min_null_feature_index, - start_index=0, - end_index=0, - start_logit=null_start_logit, - end_logit=null_end_logit)) - prelim_predictions = sorted( - prelim_predictions, - key=lambda x: (x.start_logit + x.end_logit), - reverse=True) - - _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name - "NbestPrediction", ["text", "start_logit", "end_logit"]) - - seen_predictions = {} - nbest = [] - for pred in prelim_predictions: - if len(nbest) >= n_best_size: - break - feature = features[pred.feature_index] - if pred.start_index > 0: # this is a non-null prediction - tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1 - )] - orig_doc_start = feature.token_to_orig_map[pred.start_index] - orig_doc_end = feature.token_to_orig_map[pred.end_index] - orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + - 1)] - tok_text = " ".join(tok_tokens) - - # De-tokenize WordPieces that have been split off. - tok_text = tok_text.replace(" ##", "") - tok_text = tok_text.replace("##", "") - - # Clean whitespace - tok_text = tok_text.strip() - tok_text = " ".join(tok_text.split()) - orig_text = " ".join(orig_tokens) - - final_text = _get_final_text(tok_text, orig_text, do_lower_case, - verbose) - if final_text in seen_predictions: - continue - - seen_predictions[final_text] = True - else: - final_text = "" - seen_predictions[final_text] = True - - nbest.append( - _NbestPrediction( - text=final_text, - start_logit=pred.start_logit, - end_logit=pred.end_logit)) - - # if we didn't inlude the empty option in the n-best, inlcude it - if with_negative: - if "" not in seen_predictions: - nbest.append( - _NbestPrediction( - text="", - start_logit=null_start_logit, - end_logit=null_end_logit)) - # In very rare edge cases we could have no valid predictions. So we - # just create a nonce prediction in this case to avoid failure. - if not nbest: - nbest.append( - _NbestPrediction( - text="empty", start_logit=0.0, end_logit=0.0)) - - assert len(nbest) >= 1 - - total_scores = [] - best_non_null_entry = None - for entry in nbest: - total_scores.append(entry.start_logit + entry.end_logit) - if not best_non_null_entry: - if entry.text: - best_non_null_entry = entry - # debug - if best_non_null_entry is None: - print("Emmm..., sth wrong") - - probs = _compute_softmax(total_scores) - - nbest_json = [] - for (i, entry) in enumerate(nbest): - output = collections.OrderedDict() - output["text"] = entry.text - output["probability"] = probs[i] - output["start_logit"] = entry.start_logit - output["end_logit"] = entry.end_logit - nbest_json.append(output) - - assert len(nbest_json) >= 1 - - if not with_negative: - all_predictions[example.qas_id] = nbest_json[0]["text"] - else: - # predict "" iff the null score - the score of best non-null > threshold - score_diff = score_null - best_non_null_entry.start_logit - ( - best_non_null_entry.end_logit) - scores_diff_json[example.qas_id] = score_diff - if score_diff > null_score_diff_threshold: - all_predictions[example.qas_id] = "" - else: - all_predictions[example.qas_id] = best_non_null_entry.text - - all_nbest_json[example.qas_id] = nbest_json - - with open(output_prediction_file, "w") as writer: - writer.write(json.dumps(all_predictions, indent=4) + "\n") - - with open(output_nbest_file, "w") as writer: - writer.write(json.dumps(all_nbest_json, indent=4) + "\n") - - if with_negative: - with open(output_null_log_odds_file, "w") as writer: - writer.write(json.dumps(scores_diff_json, indent=4) + "\n") - - -def _get_final_text(pred_text, orig_text, do_lower_case, verbose): - """Project the tokenized prediction back to the original text.""" - - # When we created the data, we kept track of the alignment between original - # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So - # now `orig_text` contains the span of our original text corresponding to the - # span that we predicted. - # - # However, `orig_text` may contain extra characters that we don't want in - # our prediction. - # - # For example, let's say: - # pred_text = steve smith - # orig_text = Steve Smith's - # - # We don't want to return `orig_text` because it contains the extra "'s". - # - # We don't want to return `pred_text` because it's already been normalized - # (the MRQA eval script also does punctuation stripping/lower casing but - # our tokenizer does additional normalization like stripping accent - # characters). - # - # What we really want to return is "Steve Smith". - # - # Therefore, we have to apply a semi-complicated alignment heruistic between - # `pred_text` and `orig_text` to get a character-to-charcter alignment. This - # can fail in certain cases in which case we just return `orig_text`. - - def _strip_spaces(text): - ns_chars = [] - ns_to_s_map = collections.OrderedDict() - for (i, c) in enumerate(text): - if c == " ": - continue - ns_to_s_map[len(ns_chars)] = i - ns_chars.append(c) - ns_text = "".join(ns_chars) - return (ns_text, ns_to_s_map) - - # We first tokenize `orig_text`, strip whitespace from the result - # and `pred_text`, and check if they are the same length. If they are - # NOT the same length, the heuristic has failed. If they are the same - # length, we assume the characters are one-to-one aligned. - tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case) - - tok_text = " ".join(tokenizer.tokenize(orig_text)) - - start_position = tok_text.find(pred_text) - if start_position == -1: - if verbose: - print("Unable to find text: '%s' in '%s'" % (pred_text, orig_text)) - return orig_text - end_position = start_position + len(pred_text) - 1 - - (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text) - (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text) - - if len(orig_ns_text) != len(tok_ns_text): - if verbose: - print("Length not equal after stripping spaces: '%s' vs '%s'", - orig_ns_text, tok_ns_text) - return orig_text - - # We then project the characters in `pred_text` back to `orig_text` using - # the character-to-character alignment. - tok_s_to_ns_map = {} - for (i, tok_index) in six.iteritems(tok_ns_to_s_map): - tok_s_to_ns_map[tok_index] = i - - orig_start_position = None - if start_position in tok_s_to_ns_map: - ns_start_position = tok_s_to_ns_map[start_position] - if ns_start_position in orig_ns_to_s_map: - orig_start_position = orig_ns_to_s_map[ns_start_position] - - if orig_start_position is None: - if verbose: - print("Couldn't map start position") - return orig_text - - orig_end_position = None - if end_position in tok_s_to_ns_map: - ns_end_position = tok_s_to_ns_map[end_position] - if ns_end_position in orig_ns_to_s_map: - orig_end_position = orig_ns_to_s_map[ns_end_position] - - if orig_end_position is None: - if verbose: - print("Couldn't map end position") - return orig_text - - output_text = orig_text[orig_start_position:(orig_end_position + 1)] - return output_text - - -def _get_best_indexes(logits, n_best_size): - """Get the n-best logits from a list.""" - index_and_score = sorted( - enumerate(logits), key=lambda x: x[1], reverse=True) - - best_indexes = [] - for i in range(len(index_and_score)): - if i >= n_best_size: - break - best_indexes.append(index_and_score[i][0]) - return best_indexes - - -def _compute_softmax(scores): - """Compute softmax probability over raw logits.""" - if not scores: - return [] - - max_score = None - for score in scores: - if max_score is None or score > max_score: - max_score = score - - exp_scores = [] - total_sum = 0.0 - for score in scores: - x = math.exp(score - max_score) - exp_scores.append(x) - total_sum += x - - probs = [] - for score in exp_scores: - probs.append(score / total_sum) - return probs - - diff --git a/build/lib/paddlepalm/tokenizer/__init__.py b/build/lib/paddlepalm/tokenizer/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/build/lib/paddlepalm/tokenizer/bert_tokenizer.py b/build/lib/paddlepalm/tokenizer/bert_tokenizer.py deleted file mode 100644 index f4cefd0..0000000 --- a/build/lib/paddlepalm/tokenizer/bert_tokenizer.py +++ /dev/null @@ -1,374 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Tokenization classes.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import unicodedata -import six - - -def convert_to_unicode(text): - """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" - if six.PY3: - if isinstance(text, str): - return text - elif isinstance(text, bytes): - return text.decode("utf-8", "ignore") - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - elif six.PY2: - if isinstance(text, str): - return text.decode("utf-8", "ignore") - elif isinstance(text, unicode): - return text - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - else: - raise ValueError("Not running on Python2 or Python 3?") - - -def printable_text(text): - """Returns text encoded in a way suitable for print or `tf.logging`.""" - - # These functions want `str` for both Python2 and Python3, but in one case - # it's a Unicode string and in the other it's a byte string. - if six.PY3: - if isinstance(text, str): - return text - elif isinstance(text, bytes): - return text.decode("utf-8", "ignore") - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - elif six.PY2: - if isinstance(text, str): - return text - elif isinstance(text, unicode): - return text.encode("utf-8") - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - else: - raise ValueError("Not running on Python2 or Python 3?") - - -def load_vocab(vocab_file): - """Loads a vocabulary file into a dictionary.""" - vocab = collections.OrderedDict() - fin = open(vocab_file) - for num, line in enumerate(fin): - items = convert_to_unicode(line.strip()).split("\t") - if len(items) > 2: - break - token = items[0] - index = items[1] if len(items) == 2 else num - token = token.strip() - vocab[token] = int(index) - return vocab - - -def convert_by_vocab(vocab, items): - """Converts a sequence of [tokens|ids] using the vocab.""" - output = [] - for item in items: - output.append(vocab[item]) - return output - - -def convert_tokens_to_ids(vocab, tokens): - return convert_by_vocab(vocab, tokens) - - -def convert_ids_to_tokens(inv_vocab, ids): - return convert_by_vocab(inv_vocab, ids) - - -def whitespace_tokenize(text): - """Runs basic whitespace cleaning and splitting on a peice of text.""" - text = text.strip() - if not text: - return [] - tokens = text.split() - return tokens - - -class FullTokenizer(object): - """Runs end-to-end tokenziation.""" - - def __init__(self, vocab_file, do_lower_case=True): - self.vocab = load_vocab(vocab_file) - self.inv_vocab = {v: k for k, v in self.vocab.items()} - self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) - self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) - - def tokenize(self, text): - split_tokens = [] - for token in self.basic_tokenizer.tokenize(text): - for sub_token in self.wordpiece_tokenizer.tokenize(token): - split_tokens.append(sub_token) - - return split_tokens - - def convert_tokens_to_ids(self, tokens): - return convert_by_vocab(self.vocab, tokens) - - def convert_ids_to_tokens(self, ids): - return convert_by_vocab(self.inv_vocab, ids) - - -class CharTokenizer(object): - """Runs end-to-end tokenziation.""" - - def __init__(self, vocab_file, do_lower_case=True): - self.vocab = load_vocab(vocab_file) - self.inv_vocab = {v: k for k, v in self.vocab.items()} - self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) - - def tokenize(self, text): - split_tokens = [] - for token in text.lower().split(" "): - for sub_token in self.wordpiece_tokenizer.tokenize(token): - split_tokens.append(sub_token) - - return split_tokens - - def convert_tokens_to_ids(self, tokens): - return convert_by_vocab(self.vocab, tokens) - - def convert_ids_to_tokens(self, ids): - return convert_by_vocab(self.inv_vocab, ids) - - -class BasicTokenizer(object): - """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" - - def __init__(self, do_lower_case=True): - """Constructs a BasicTokenizer. - - Args: - do_lower_case: Whether to lower case the input. - """ - self.do_lower_case = do_lower_case - self._never_lowercase = ['[UNK]', '[SEP]', '[PAD]', '[CLS]', '[MASK]'] - - def tokenize(self, text): - """Tokenizes a piece of text.""" - text = convert_to_unicode(text) - text = self._clean_text(text) - - # This was added on November 1st, 2018 for the multilingual and Chinese - # models. This is also applied to the English models now, but it doesn't - # matter since the English models were not trained on any Chinese data - # and generally don't have any Chinese data in them (there are Chinese - # characters in the vocabulary because Wikipedia does have some Chinese - # words in the English Wikipedia.). - text = self._tokenize_chinese_chars(text) - - orig_tokens = whitespace_tokenize(text) - split_tokens = [] - for token in orig_tokens: - if self.do_lower_case and token not in self._never_lowercase: - token = token.lower() - token = self._run_strip_accents(token) - if token in self._never_lowercase: - split_tokens.extend([token]) - else: - split_tokens.extend(self._run_split_on_punc(token)) - - output_tokens = whitespace_tokenize(" ".join(split_tokens)) - return output_tokens - - def _run_strip_accents(self, text): - """Strips accents from a piece of text.""" - text = unicodedata.normalize("NFD", text) - output = [] - for char in text: - cat = unicodedata.category(char) - if cat == "Mn": - continue - output.append(char) - return "".join(output) - - def _run_split_on_punc(self, text): - """Splits punctuation on a piece of text.""" - chars = list(text) - i = 0 - start_new_word = True - output = [] - while i < len(chars): - char = chars[i] - if _is_punctuation(char): - output.append([char]) - start_new_word = True - else: - if start_new_word: - output.append([]) - start_new_word = False - output[-1].append(char) - i += 1 - - return ["".join(x) for x in output] - - def _tokenize_chinese_chars(self, text): - """Adds whitespace around any CJK character.""" - output = [] - for char in text: - cp = ord(char) - if self._is_chinese_char(cp): - output.append(" ") - output.append(char) - output.append(" ") - else: - output.append(char) - return "".join(output) - - def _is_chinese_char(self, cp): - """Checks whether CP is the codepoint of a CJK character.""" - # This defines a "chinese character" as anything in the CJK Unicode block: - # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) - # - # Note that the CJK Unicode block is NOT all Japanese and Korean characters, - # despite its name. The modern Korean Hangul alphabet is a different block, - # as is Japanese Hiragana and Katakana. Those alphabets are used to write - # space-separated words, so they are not treated specially and handled - # like the all of the other languages. - if ((cp >= 0x4E00 and cp <= 0x9FFF) or # - (cp >= 0x3400 and cp <= 0x4DBF) or # - (cp >= 0x20000 and cp <= 0x2A6DF) or # - (cp >= 0x2A700 and cp <= 0x2B73F) or # - (cp >= 0x2B740 and cp <= 0x2B81F) or # - (cp >= 0x2B820 and cp <= 0x2CEAF) or - (cp >= 0xF900 and cp <= 0xFAFF) or # - (cp >= 0x2F800 and cp <= 0x2FA1F)): # - return True - - return False - - def _clean_text(self, text): - """Performs invalid character removal and whitespace cleanup on text.""" - output = [] - for char in text: - cp = ord(char) - if cp == 0 or cp == 0xfffd or _is_control(char): - continue - if _is_whitespace(char): - output.append(" ") - else: - output.append(char) - return "".join(output) - - -class WordpieceTokenizer(object): - """Runs WordPiece tokenziation.""" - - def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100): - self.vocab = vocab - self.unk_token = unk_token - self.max_input_chars_per_word = max_input_chars_per_word - - def tokenize(self, text): - """Tokenizes a piece of text into its word pieces. - - This uses a greedy longest-match-first algorithm to perform tokenization - using the given vocabulary. - - For example: - input = "unaffable" - output = ["un", "##aff", "##able"] - - Args: - text: A single token or whitespace separated tokens. This should have - already been passed through `BasicTokenizer. - - Returns: - A list of wordpiece tokens. - """ - - text = convert_to_unicode(text) - - output_tokens = [] - for token in whitespace_tokenize(text): - chars = list(token) - if len(chars) > self.max_input_chars_per_word: - output_tokens.append(self.unk_token) - continue - - is_bad = False - start = 0 - sub_tokens = [] - while start < len(chars): - end = len(chars) - cur_substr = None - while start < end: - substr = "".join(chars[start:end]) - if start > 0: - substr = "##" + substr - if substr in self.vocab: - cur_substr = substr - break - end -= 1 - if cur_substr is None: - is_bad = True - break - sub_tokens.append(cur_substr) - start = end - - if is_bad: - output_tokens.append(self.unk_token) - else: - output_tokens.extend(sub_tokens) - return output_tokens - - -def _is_whitespace(char): - """Checks whether `chars` is a whitespace character.""" - # \t, \n, and \r are technically contorl characters but we treat them - # as whitespace since they are generally considered as such. - if char == " " or char == "\t" or char == "\n" or char == "\r": - return True - cat = unicodedata.category(char) - if cat == "Zs": - return True - return False - - -def _is_control(char): - """Checks whether `chars` is a control character.""" - # These are technically control characters but we count them as whitespace - # characters. - if char == "\t" or char == "\n" or char == "\r": - return False - cat = unicodedata.category(char) - if cat.startswith("C"): - return True - return False - - -def _is_punctuation(char): - """Checks whether `chars` is a punctuation character.""" - cp = ord(char) - # We treat all non-letter/number ASCII as punctuation. - # Characters such as "^", "$", and "`" are not in the Unicode - # Punctuation class but we treat them as punctuation anyways, for - # consistency. - if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or - (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): - return True - cat = unicodedata.category(char) - if cat.startswith("P"): - return True - return False diff --git a/build/lib/paddlepalm/tokenizer/ernie_tokenizer.py b/build/lib/paddlepalm/tokenizer/ernie_tokenizer.py deleted file mode 100644 index 2e6b044..0000000 --- a/build/lib/paddlepalm/tokenizer/ernie_tokenizer.py +++ /dev/null @@ -1,417 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Tokenization classes.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals -from __future__ import absolute_import - -from io import open - -import collections -import unicodedata -import six - - -def convert_to_unicode(text): - """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" - if six.PY3: - if isinstance(text, str): - return text - elif isinstance(text, bytes): - return text.decode("utf-8", "ignore") - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - elif six.PY2: - if isinstance(text, str): - return text.decode("utf-8", "ignore") - elif isinstance(text, unicode): - return text - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - else: - raise ValueError("Not running on Python2 or Python 3?") - - -def printable_text(text): - """Returns text encoded in a way suitable for print or `tf.logging`.""" - - # These functions want `str` for both Python2 and Python3, but in one case - # it's a Unicode string and in the other it's a byte string. - if six.PY3: - if isinstance(text, str): - return text - elif isinstance(text, bytes): - return text.decode("utf-8", "ignore") - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - elif six.PY2: - if isinstance(text, str): - return text - elif isinstance(text, unicode): - return text.encode("utf-8") - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - else: - raise ValueError("Not running on Python2 or Python 3?") - - -def load_vocab(vocab_file): - """Loads a vocabulary file into a dictionary.""" - vocab = collections.OrderedDict() - with open(vocab_file, encoding='utf8') as fin: - for num, line in enumerate(fin): - items = convert_to_unicode(line.strip()).split("\t") - if len(items) > 2: - break - token = items[0] - index = items[1] if len(items) == 2 else num - token = token.strip() - vocab[token] = int(index) - return vocab - - -def convert_by_vocab(vocab, items): - """Converts a sequence of [tokens|ids] using the vocab.""" - output = [] - for item in items: - output.append(vocab[item]) - return output - - -def convert_tokens_to_ids(vocab, tokens): - return convert_by_vocab(vocab, tokens) - - -def convert_ids_to_tokens(inv_vocab, ids): - return convert_by_vocab(inv_vocab, ids) - - -def whitespace_tokenize(text): - """Runs basic whitespace cleaning and splitting on a peice of text.""" - text = text.strip() - if not text: - return [] - tokens = text.split() - return tokens - - -class FullTokenizer(object): - """Runs end-to-end tokenziation.""" - - def __init__(self, vocab_file, do_lower_case=True): - self.vocab = load_vocab(vocab_file) - self.inv_vocab = {v: k for k, v in self.vocab.items()} - self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) - self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) - - def tokenize(self, text): - split_tokens = [] - for token in self.basic_tokenizer.tokenize(text): - for sub_token in self.wordpiece_tokenizer.tokenize(token): - split_tokens.append(sub_token) - - return split_tokens - - def convert_tokens_to_ids(self, tokens): - return convert_by_vocab(self.vocab, tokens) - - def convert_ids_to_tokens(self, ids): - return convert_by_vocab(self.inv_vocab, ids) - - -class CharTokenizer(object): - """Runs end-to-end tokenziation.""" - - def __init__(self, vocab_file, do_lower_case=True): - self.vocab = load_vocab(vocab_file) - self.inv_vocab = {v: k for k, v in self.vocab.items()} - self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) - - def tokenize(self, text): - split_tokens = [] - for token in text.lower().split(" "): - for sub_token in self.wordpiece_tokenizer.tokenize(token): - split_tokens.append(sub_token) - - return split_tokens - - def convert_tokens_to_ids(self, tokens): - return convert_by_vocab(self.vocab, tokens) - - def convert_ids_to_tokens(self, ids): - return convert_by_vocab(self.inv_vocab, ids) - - -class BasicTokenizer(object): - """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" - - def __init__(self, do_lower_case=True): - """Constructs a BasicTokenizer. - Args: - do_lower_case: Whether to lower case the input. - """ - self.do_lower_case = do_lower_case - - def tokenize(self, text): - """Tokenizes a piece of text.""" - text = convert_to_unicode(text) - text = self._clean_text(text) - - # This was added on November 1st, 2018 for the multilingual and Chinese - # models. This is also applied to the English models now, but it doesn't - # matter since the English models were not trained on any Chinese data - # and generally don't have any Chinese data in them (there are Chinese - # characters in the vocabulary because Wikipedia does have some Chinese - # words in the English Wikipedia.). - text = self._tokenize_chinese_chars(text) - - orig_tokens = whitespace_tokenize(text) - split_tokens = [] - for token in orig_tokens: - if self.do_lower_case: - token = token.lower() - token = self._run_strip_accents(token) - split_tokens.extend(self._run_split_on_punc(token)) - - output_tokens = whitespace_tokenize(" ".join(split_tokens)) - return output_tokens - - def _run_strip_accents(self, text): - """Strips accents from a piece of text.""" - text = unicodedata.normalize("NFD", text) - output = [] - for char in text: - cat = unicodedata.category(char) - if cat == "Mn": - continue - output.append(char) - return "".join(output) - - def _run_split_on_punc(self, text): - """Splits punctuation on a piece of text.""" - chars = list(text) - i = 0 - start_new_word = True - output = [] - while i < len(chars): - char = chars[i] - if _is_punctuation(char): - output.append([char]) - start_new_word = True - else: - if start_new_word: - output.append([]) - start_new_word = False - output[-1].append(char) - i += 1 - - return ["".join(x) for x in output] - - def _tokenize_chinese_chars(self, text): - """Adds whitespace around any CJK character.""" - output = [] - for char in text: - cp = ord(char) - if self._is_chinese_char(cp): - output.append(" ") - output.append(char) - output.append(" ") - else: - output.append(char) - return "".join(output) - - def _is_chinese_char(self, cp): - """Checks whether CP is the codepoint of a CJK character.""" - # This defines a "chinese character" as anything in the CJK Unicode block: - # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) - # - # Note that the CJK Unicode block is NOT all Japanese and Korean characters, - # despite its name. The modern Korean Hangul alphabet is a different block, - # as is Japanese Hiragana and Katakana. Those alphabets are used to write - # space-separated words, so they are not treated specially and handled - # like the all of the other languages. - if ((cp >= 0x4E00 and cp <= 0x9FFF) or # - (cp >= 0x3400 and cp <= 0x4DBF) or # - (cp >= 0x20000 and cp <= 0x2A6DF) or # - (cp >= 0x2A700 and cp <= 0x2B73F) or # - (cp >= 0x2B740 and cp <= 0x2B81F) or # - (cp >= 0x2B820 and cp <= 0x2CEAF) or - (cp >= 0xF900 and cp <= 0xFAFF) or # - (cp >= 0x2F800 and cp <= 0x2FA1F)): # - return True - - return False - - def _clean_text(self, text): - """Performs invalid character removal and whitespace cleanup on text.""" - output = [] - for char in text: - cp = ord(char) - if cp == 0 or cp == 0xfffd or _is_control(char): - continue - if _is_whitespace(char): - output.append(" ") - else: - output.append(char) - return "".join(output) - - -class WordpieceTokenizer(object): - """Runs WordPiece tokenziation.""" - - def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100): - self.vocab = vocab - self.unk_token = unk_token - self.max_input_chars_per_word = max_input_chars_per_word - - def tokenize(self, text): - """Tokenizes a piece of text into its word pieces. - This uses a greedy longest-match-first algorithm to perform tokenization - using the given vocabulary. - For example: - input = "unaffable" - output = ["un", "##aff", "##able"] - Args: - text: A single token or whitespace separated tokens. This should have - already been passed through `BasicTokenizer. - Returns: - A list of wordpiece tokens. - """ - - text = convert_to_unicode(text) - - output_tokens = [] - for token in whitespace_tokenize(text): - chars = list(token) - if len(chars) > self.max_input_chars_per_word: - output_tokens.append(self.unk_token) - continue - - is_bad = False - start = 0 - sub_tokens = [] - while start < len(chars): - end = len(chars) - cur_substr = None - while start < end: - substr = "".join(chars[start:end]) - if start > 0: - substr = "##" + substr - if substr in self.vocab: - cur_substr = substr - break - end -= 1 - if cur_substr is None: - is_bad = True - break - sub_tokens.append(cur_substr) - start = end - - if is_bad: - output_tokens.append(self.unk_token) - else: - output_tokens.extend(sub_tokens) - return output_tokens - - -def _is_whitespace(char): - """Checks whether `chars` is a whitespace character.""" - # \t, \n, and \r are technically contorl characters but we treat them - # as whitespace since they are generally considered as such. - if char == " " or char == "\t" or char == "\n" or char == "\r": - return True - cat = unicodedata.category(char) - if cat == "Zs": - return True - return False - - -def _is_control(char): - """Checks whether `chars` is a control character.""" - # These are technically control characters but we count them as whitespace - # characters. - if char == "\t" or char == "\n" or char == "\r": - return False - cat = unicodedata.category(char) - if cat.startswith("C"): - return True - return False - - -def _is_punctuation(char): - """Checks whether `chars` is a punctuation character.""" - cp = ord(char) - # We treat all non-letter/number ASCII as punctuation. - # Characters such as "^", "$", and "`" are not in the Unicode - # Punctuation class but we treat them as punctuation anyways, for - # consistency. - if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or - (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): - return True - cat = unicodedata.category(char) - if cat.startswith("P"): - return True - return False - - -def tokenize_chinese_chars(text): - """Adds whitespace around any CJK character.""" - - def _is_chinese_char(cp): - """Checks whether CP is the codepoint of a CJK character.""" - # This defines a "chinese character" as anything in the CJK Unicode block: - # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) - # - # Note that the CJK Unicode block is NOT all Japanese and Korean characters, - # despite its name. The modern Korean Hangul alphabet is a different block, - # as is Japanese Hiragana and Katakana. Those alphabets are used to write - # space-separated words, so they are not treated specially and handled - # like the all of the other languages. - if ((cp >= 0x4E00 and cp <= 0x9FFF) or # - (cp >= 0x3400 and cp <= 0x4DBF) or # - (cp >= 0x20000 and cp <= 0x2A6DF) or # - (cp >= 0x2A700 and cp <= 0x2B73F) or # - (cp >= 0x2B740 and cp <= 0x2B81F) or # - (cp >= 0x2B820 and cp <= 0x2CEAF) or - (cp >= 0xF900 and cp <= 0xFAFF) or # - (cp >= 0x2F800 and cp <= 0x2FA1F)): # - return True - - return False - - def _is_whitespace(c): - if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F: - return True - return False - - output = [] - buff = "" - for char in text: - cp = ord(char) - if _is_chinese_char(cp) or _is_whitespace(char): - if buff != "": - output.append(buff) - buff = "" - output.append(char) - else: - buff += char - - if buff != "": - output.append(buff) - - return output diff --git a/build/lib/paddlepalm/utils/__init__.py b/build/lib/paddlepalm/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/build/lib/paddlepalm/utils/config_helper.py b/build/lib/paddlepalm/utils/config_helper.py deleted file mode 100644 index e00f473..0000000 --- a/build/lib/paddlepalm/utils/config_helper.py +++ /dev/null @@ -1,311 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import sys -import argparse -import json -import yaml -import six -import logging - -logging_only_message = "%(message)s" -logging_details = "%(asctime)s.%(msecs)03d %(levelname)s %(module)s - %(funcName)s: %(message)s" - - -class JsonConfig(object): - """ - A high-level api for handling json configure file. - """ - - def __init__(self, config_path): - self._config_dict = self._parse(config_path) - - def _parse(self, config_path): - try: - with open(config_path) as json_file: - config_dict = json.load(json_file) - assert isinstance(config_dict, dict), "Object in {} is NOT a dict.".format(config_path) - except: - raise IOError("Error in parsing bert model config file '%s'" % - config_path) - else: - return config_dict - - def __getitem__(self, key): - return self._config_dict[key] - - def asdict(self): - return self._config_dict - - def print_config(self): - for arg, value in sorted(six.iteritems(self._config_dict)): - print('%s: %s' % (arg, value)) - print('------------------------------------------------') - - -class ArgumentGroup(object): - def __init__(self, parser, title, des): - self._group = parser.add_argument_group(title=title, description=des) - - def add_arg(self, name, type, default, help, **kwargs): - type = str2bool if type == bool else type - self._group.add_argument( - "--" + name, - default=default, - type=type, - help=help + ' Default: %(default)s.', - **kwargs) - - -class ArgConfig(object): - """ - A high-level api for handling argument configs. - """ - - def __init__(self): - parser = argparse.ArgumentParser() - - train_g = ArgumentGroup(parser, "training", "training options.") - train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.") - train_g.add_arg("learning_rate", float, 5e-5, - "Learning rate used to train with warmup.") - train_g.add_arg( - "lr_scheduler", - str, - "linear_warmup_decay", - "scheduler of learning rate.", - choices=['linear_warmup_decay', 'noam_decay']) - train_g.add_arg("weight_decay", float, 0.01, - "Weight decay rate for L2 regularizer.") - train_g.add_arg( - "warmup_proportion", float, 0.1, - "Proportion of training steps to perform linear learning rate warmup for." - ) - train_g.add_arg("save_steps", int, 1000, - "The steps interval to save checkpoints.") - train_g.add_arg( - "loss_scaling", float, 1.0, - "Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled." - ) - train_g.add_arg("pred_dir", str, None, - "Path to save the prediction results") - - log_g = ArgumentGroup(parser, "logging", "logging related.") - log_g.add_arg("skip_steps", int, 10, - "The steps interval to print loss.") - log_g.add_arg("verbose", bool, False, "Whether to output verbose log.") - - run_type_g = ArgumentGroup(parser, "run_type", "running type options.") - run_type_g.add_arg("use_cuda", bool, True, - "If set, use GPU for training.") - run_type_g.add_arg( - "use_fast_executor", bool, False, - "If set, use fast parallel executor (in experiment).") - run_type_g.add_arg( - "num_iteration_per_drop_scope", int, 1, - "Ihe iteration intervals to clean up temporary variables.") - run_type_g.add_arg("do_train", bool, True, - "Whether to perform training.") - run_type_g.add_arg("do_predict", bool, True, - "Whether to perform prediction.") - - custom_g = ArgumentGroup(parser, "customize", "customized options.") - - self.custom_g = custom_g - - self.parser = parser - - def add_arg(self, name, dtype, default, descrip): - self.custom_g.add_arg(name, dtype, default, descrip) - - def build_conf(self): - return self.parser.parse_args() - - -def str2bool(v): - # because argparse does not support to parse "true, False" as python - # boolean directly - return v.lower() in ("true", "t", "1") - - -def print_arguments(args, log=None): - if not log: - print('----------- Configuration Arguments -----------') - for arg, value in sorted(six.iteritems(vars(args))): - print('%s: %s' % (arg, value)) - print('------------------------------------------------') - else: - log.info('----------- Configuration Arguments -----------') - for arg, value in sorted(six.iteritems(vars(args))): - log.info('%s: %s' % (arg, value)) - log.info('------------------------------------------------') - - -class PDConfig(object): - """ - A high-level API for managing configuration files in PaddlePaddle. - Can jointly work with command-line-arugment, json files and yaml files. - """ - - def __init__(self, json_file=None, yaml_file=None, fuse_args=True): - """ - Init funciton for PDConfig. - json_file: the path to the json configure file. - yaml_file: the path to the yaml configure file. - fuse_args: if fuse the json/yaml configs with argparse. - """ - - if json_file is not None and yaml_file is not None: - raise Warning( - "json_file and yaml_file can not co-exist for now. please only use one configure file type." - ) - return - - self.args = None - self.arg_config = {} - self.json_config = {} - self.yaml_config = {} - - parser = argparse.ArgumentParser() - - self.yaml_g = ArgumentGroup(parser, "yaml", "options from yaml.") - self.json_g = ArgumentGroup(parser, "json", "options from json.") - self.com_g = ArgumentGroup(parser, "custom", "customized options.") - - self.parser = parser - - if json_file is not None: - assert isinstance(json_file, str) - self.load_json(json_file, fuse_args=fuse_args) - - if yaml_file is not None: - assert isinstance(yaml_file, str) or isinstance(yaml_file, list) - self.load_yaml(yaml_file, fuse_args=fuse_args) - - def load_json(self, file_path, fuse_args=True): - - if not os.path.exists(file_path): - raise Warning("the json file %s does not exist." % file_path) - return - - with open(file_path, "r") as fin: - self.json_config = json.loads(fin.read()) - fin.close() - - if fuse_args: - for name in self.json_config: - if not isinstance(self.json_config[name], int) \ - and not isinstance(self.json_config[name], float) \ - and not isinstance(self.json_config[name], str) \ - and not isinstance(self.json_config[name], bool): - - continue - - self.json_g.add_arg(name, - type(self.json_config[name]), - self.json_config[name], - "This is from %s" % file_path) - - def load_yaml(self, file_path_list, fuse_args=True): - - if isinstance(file_path_list, str): - file_path_list = [file_path_list] - for file_path in file_path_list: - if not os.path.exists(file_path): - raise Warning("the yaml file %s does not exist." % file_path) - return - - with open(file_path, "r") as fin: - self.yaml_config = yaml.load(fin, Loader=yaml.SafeLoader) - if fuse_args: - for name in self.yaml_config: - if not isinstance(self.yaml_config[name], int) \ - and not isinstance(self.yaml_config[name], float) \ - and not isinstance(self.yaml_config[name], str) \ - and not isinstance(self.yaml_config[name], bool): - - continue - - self.yaml_g.add_arg(name, - type(self.yaml_config[name]), - self.yaml_config[name], - "This is from %s" % file_path) - - def build(self): - self.args = self.parser.parse_args() - self.arg_config = vars(self.args) - - def asdict(self): - return self.arg_config - - def __add__(self, new_arg): - assert isinstance(new_arg, list) or isinstance(new_arg, tuple) - assert len(new_arg) >= 3 - assert self.args is None - - name = new_arg[0] - dtype = new_arg[1] - dvalue = new_arg[2] - desc = new_arg[3] if len( - new_arg) == 4 else "Description is not provided." - - self.com_g.add_arg(name, dtype, dvalue, desc) - - return self - - def __getattr__(self, name): - if name in self.arg_config: - return self.arg_config[name] - - if name in self.json_config: - return self.json_config[name] - - if name in self.yaml_config: - return self.yaml_config[name] - - raise Warning("The argument %s is not defined." % name) - - def Print(self): - - print("-" * 70) - for name in self.arg_config: - print("{: <25}\t{}".format(str(name), str(self.arg_config[name]))) - - for name in self.json_config: - if name not in self.arg_config: - print("{: <25}\t{}" % - (str(name), str(self.json_config[name]))) - - for name in self.yaml_config: - if name not in self.arg_config: - print("{: <25}\t{}" % - (str(name), str(self.yaml_config[name]))) - - print("-" * 70) - - -if __name__ == "__main__": - pd_config = PDConfig(yaml_file="./test/bert_config.yaml") - pd_config += ("my_age", int, 18, "I am forever 18.") - pd_config.build() - - print(pd_config.do_train) - print(pd_config.hidden_size) - print(pd_config.my_age) diff --git a/build/lib/paddlepalm/utils/print_helper.py b/build/lib/paddlepalm/utils/print_helper.py deleted file mode 100644 index 842e779..0000000 --- a/build/lib/paddlepalm/utils/print_helper.py +++ /dev/null @@ -1,31 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -MAXLEN = 70 -def print_dict(dic, title=""): - - if title: - title = ' ' + title + ' ' - left_len = (MAXLEN - len(title)) // 2 - title = '-' * left_len + title - right_len = MAXLEN - len(title) - title = title + '-' * right_len - else: - title = '-' * MAXLEN - print(title) - for name in dic: - print("{: <25}\t{}".format(str(name), str(dic[name]))) - print("") - # print("-" * MAXLEN + '\n') diff --git a/build/lib/paddlepalm/utils/reader_helper.py b/build/lib/paddlepalm/utils/reader_helper.py deleted file mode 100644 index c9e5f88..0000000 --- a/build/lib/paddlepalm/utils/reader_helper.py +++ /dev/null @@ -1,226 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import sys -import random -import numpy as np -import paddle -from paddle import fluid -from paddle.fluid import layers - - -def _check_and_adapt_shape_dtype(rt_val, attr, message=""): - if not isinstance(rt_val, np.ndarray): - rt_val = np.array(rt_val) - assert rt_val.dtype != np.dtype('O'), "yielded data is not a valid tensor(number of elements on some dimension may differ)." - if rt_val.dtype == np.dtype('float64'): - rt_val = rt_val.astype('float32') - - shape, dtype = attr - assert rt_val.dtype == np.dtype(dtype), message+"yielded data type not consistent with attr settings. Expect: {}, receive: {}.".format(rt_val.dtype, np.dtype(dtype)) - assert len(shape) == rt_val.ndim, message+"yielded data rank(ndim) not consistent with attr settings. Expect: {}, receive: {}.".format(len(shape), rt_val.ndim) - for rt, exp in zip(rt_val.shape, shape): - if exp is None or exp < 0: - continue - assert rt == exp, "yielded data shape is not consistent with attr settings.Expected:{}Actual:{}".format(exp, rt) - return rt_val - - -def _zero_batch(attrs): - pos_attrs = [] - for shape, dtype in attrs: - pos_shape = [size if size and size > 0 else 1 for size in shape] - pos_attrs.append([pos_shape, dtype]) - - return [np.zeros(shape=shape, dtype=dtype) for shape, dtype in pos_attrs] - - -def _zero_batch_x(attrs, batch_size): - pos_attrs = [] - for shape, dtype in attrs: - # pos_shape = [size if size and size > 0 else 5 for size in shape] - pos_shape = [size for size in shape] - if pos_shape[0] == -1: - pos_shape[0] = batch_size - if pos_shape[1] == -1: - pos_shape[1] = 512 # max seq len - pos_attrs.append([pos_shape, dtype]) - - return [np.zeros(shape=shape, dtype=dtype) for shape, dtype in pos_attrs] - - -def create_net_inputs(input_attrs, async=False, iterator_fn=None, dev_count=1, n_prefetch=1): - inputs = [] - ret = {} - for name, shape, dtype in input_attrs: - p = layers.data(name, shape=shape, dtype=dtype) - ret[name] = p - inputs.append(p) - - if async: - assert iterator_fn is not None, "iterator_fn is needed for building async input layer." - # reader = fluid.io.PyReader(inputs, capacity=dev_count*n_prefetch, iterable=False) - reader = fluid.io.PyReader(inputs, capacity=dev_count, iterable=False) - reader.decorate_batch_generator(iterator_fn) - reader.start() - - return ret - - -def create_iterator_fn(iterator, iterator_prefix, shape_and_dtypes, outname_to_pos, verbose=0): - - def iterator(): - v = verbose - while True: - results = _zero_batch(shape_and_dtypes) - - outputs = next(iterator) # dict type - prefix = iterator_prefixe - for outname, val in outputs.items(): - task_outname = prefix + '/' + outname - - if outname in outname_to_pos: - idx = outname_to_pos[outname] - val = _check_and_adapt_shape_dtype(val, joint_shape_and_dtypes[idx]) - results[idx] = val - - if task_outname in outname_to_pos: - idx = outname_to_pos[task_outname] - val = _check_and_adapt_shape_dtype(val, joint_shape_and_dtypes[idx]) - results[idx] = val - - yield results - - return iterator - - -def create_joint_iterator_fn(iterators, iterator_prefixes, joint_shape_and_dtypes, mrs, outname_to_pos, dev_count=1, keep_one_task=True, verbose=0): - """ - joint_shape_and_dtypes: 本质上是根据bb和parad的attr设定的，并且由reader中的attr自动填充-1（可变）维度得到，因此通过与iterator的校验可以完成runtime的batch正确性检查 - """ - - task_ids = range(len(iterators)) - weights = [mr / float(sum(mrs)) for mr in mrs] - if not keep_one_task: - dev_count = 1 - - # build fake batch - # 注意这种方法会导致一个问题，用户将某任务的mix ratio设置成0后，并不能避免从该任务上读数据，若用户将数据集删掉则会导致崩溃；不过相比之前的zero batch方法，这种方法不必作出只能有一个size=-1的维度且第0维的-1必须是batch size的假设 - results = _zero_batch(joint_shape_and_dtypes) - outbuf = {} - for id in task_ids: - outputs = next(iterators[id]) # dict type - outbuf[id] = outputs - prefix = iterator_prefixes[id] - for outname, val in outputs.items(): - task_outname = prefix + '/' + outname - - if outname in outname_to_pos: - idx = outname_to_pos[outname] - val = _check_and_adapt_shape_dtype(val, joint_shape_and_dtypes[idx], message=outname+': ') - results[idx] = val - - if task_outname in outname_to_pos: - idx = outname_to_pos[task_outname] - val = _check_and_adapt_shape_dtype(val, joint_shape_and_dtypes[idx], message=task_outname+': ') - results[idx] = val - - fake_batch = results - dev_count_bak = dev_count - - def iterator(): - v = verbose - while True: - id = np.random.choice(task_ids, p=weights) - results = fake_batch - if v > 0: - print('----- debug joint iterator -----') - print('sampled task id: '+str(id)) - task_id_tensor = np.array([[id]]).astype("int64") - results[0] = task_id_tensor - - for i in range(dev_count): - results[0] = task_id_tensor - if id in outbuf: - outputs = outbuf[id] - del outbuf[id] - else: - outputs = next(iterators[id]) # dict type - - prefix = iterator_prefixes[id] - for outname, val in outputs.items(): - if v > 0: - print('reader generate: '+outname) - task_outname = prefix + '/' + outname - - if outname in outname_to_pos: - idx = outname_to_pos[outname] - if v > 0: - print(outname + ' is insert in idx ' + str(idx)) - val = _check_and_adapt_shape_dtype(val, joint_shape_and_dtypes[idx], message=outname+': ') - results[idx] = val - - if task_outname in outname_to_pos: - idx = outname_to_pos[task_outname] - if v > 0: - print(task_outname + ' is insert in idx ' + str(idx)) - val = _check_and_adapt_shape_dtype(val, joint_shape_and_dtypes[idx], message=task_outname+': ') - results[idx] = val - - if v > 0: - print('yielded batch len and shapes:') - print(len(results)) - for i in results: - print(np.shape(i)) - print('') - v -= 1 - yield results - - return iterator - - -def merge_input_attrs(backbone_attr, task_attrs, insert_taskid=True): - """ - Args: - task_attrs(list[dict]|dict): task input attributes, key=attr_name, val=[shape, dtype], support single task and nested tasks - """ - if isinstance(task_attrs, dict): - task_attrs = [task_attrs] - - if insert_taskid: - ret = [([1,1], 'int64')] - names = ['__task_id'] - start = 1 - else: - ret = [] - names = [] - start = 0 - - names += sorted(backbone_attr.keys()) - ret.extend([backbone_attr[k] for k in names[start:]]) - name_to_position = {} - # pos=0 is for task_id, thus we start from 1 - for pos, k in enumerate(names): - name_to_position[k] = pos - for task_attr in task_attrs: - task_names = sorted(task_attr.keys()) - names.extend(task_names) - ret.extend([task_attr[k] for k in task_names]) - for pos, k in enumerate(task_names, start=len(name_to_position)): - name_to_position[k] = pos - return names, ret, name_to_position - - diff --git a/build/lib/paddlepalm/utils/saver.py b/build/lib/paddlepalm/utils/saver.py deleted file mode 100644 index 9277f0d..0000000 --- a/build/lib/paddlepalm/utils/saver.py +++ /dev/null @@ -1,80 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import print_function - -import os -import six -import ast -import copy -import tarfile -import shutil - -import numpy as np -import paddle.fluid as fluid - -def init_checkpoint(exe, init_checkpoint_path, main_program, skip_list = []): - assert os.path.exists( - init_checkpoint_path), "[%s] cann't be found." % init_checkpoint_path - - def existed_persitables(var): - if not fluid.io.is_persistable(var): - return False - if var.name in skip_list: - return False - return os.path.exists(os.path.join(init_checkpoint_path, var.name)) - - fluid.io.load_vars( - exe, - init_checkpoint_path, - main_program=main_program, - predicate=existed_persitables) - print("Load model from {}".format(init_checkpoint_path)) - - -def init_pretraining_params(exe, - pretraining_params_path, - main_program): - assert os.path.exists(pretraining_params_path - ), "[%s] cann't be found." % pretraining_params_path - - - assert os.path.exists(os.path.join(pretraining_params_path, '__palmmodel__')), "__palmmodel__ not found." - print("Loading pretraining parameters from {}...".format( - pretraining_params_path)) - - with tarfile.open(os.path.join(pretraining_params_path, '__palmmodel__'), 'r:') as f: - f.extractall(os.path.join(pretraining_params_path, '.temp')) - - log_path = os.path.join(pretraining_params_path, '__palmmodel__') - pretraining_params_path = os.path.join(pretraining_params_path, '.temp') - - def existed_params(var): - if not isinstance(var, fluid.framework.Parameter): - return False - if not os.path.exists(os.path.join(pretraining_params_path, var.name)): - print('Warning: {} not found in {}.'.format(var.name, log_path)) - return os.path.exists(os.path.join(pretraining_params_path, var.name)) - - fluid.io.load_vars( - exe, - pretraining_params_path, - main_program=main_program, - predicate=existed_params) - - shutil.rmtree(pretraining_params_path) - print('') - - diff --git a/build/lib/paddlepalm/utils/textprocess_helper.py b/build/lib/paddlepalm/utils/textprocess_helper.py deleted file mode 100644 index 35607ec..0000000 --- a/build/lib/paddlepalm/utils/textprocess_helper.py +++ /dev/null @@ -1,19 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -def is_whitespace(c): - if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F: - return True - return False diff --git a/dist/paddle_palm-1.2-py2.7.egg b/dist/paddle_palm-1.2-py2.7.egg deleted file mode 100644 index c634ca9ab7285f0615617436dfc085c18fc32610..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 166800 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train_file: 训练集文件路径 - - reader: 数据集载入与处理工具名，框架预置reader列表见[这里](https://www.baidu.com/) - - backbone: 骨架模型名，框架预置reader列表见[这里](https://www.baidu.com/) - - paradigm: 任务范式(类型)名，框架预置paradigm列表见[这里](https://www.baidu.com/) - - ### 2. 完成训练配置 - - 使用yaml格式完成配置多任务学习中的相关参数，如指定任务实例及其相关的主辅关系、参数复用关系、采样权重等 - - ### 3. 开始训练 - - ```python - - import paddlepalm as palm - - if __name__ == '__main__': - controller = palm.Controller('config.yaml', task_dir='task_instance') - controller.load_pretrain('pretrain_model/ernie/params') - controller.train() - ``` - - ### 4. 预测 - - 用户可在训练结束后直接调用pred接口对某个目标任务进行预测 - - 示例： - ```python - import paddlepalm as palm - - if __name__ == '__main__': - controller = palm.Controller(config_path='config.yaml', task_dir='task_instance') - controller.load_pretrain('pretrain_model/ernie/params') - controller.train() - controller.pred('mrqa') - ``` - - 也可新建controller直接预测 - - ```python - import paddlepalm as palm - - if __name__ == '__main__': - controller = palm.Controller(config_path='config.yaml', task_dir='task_instance') - controller.pred('mrqa', infermodel_path='output_model/firstrun2/infer_model') - ``` - - - # 运行机制 - - ### 多任务学习机制 - pass - - ### 训练终止机制 - - - 默认的设置： - - **所有target任务达到目标训练步数后多任务学习停止** - - 未设置成target任务的任务（即辅助任务）不会影响训练终止与否，只是担任”陪训“的角色 - - 注：默认所有的任务都是target任务，用户可以通过`target_tag`来标记目标/辅助任务 - - 每个目标任务的目标训练步数由num_epochs和mix_ratio计算得到 - - ### 保存机制 - - - 默认的设置： - - 训练过程中，保存下来的模型分为checkpoint (ckpt)和inference model (infermodel)两种： - - ckpt保存的是包含所有任务的总计算图（即整个多任务学习计算图），用于训练中断恢复 - - infermodel保存的是某个目标任务的推理计算图和推理依赖的相关配置 - - 对于每个target任务，训练到预期的步数后自动保存inference model，之后不再保存。（注：保存inference model不影响ckpt的保存） - - 用户可改配置 - - 使用`save_ckpt_every_steps`来控制保存ckpt的频率，默认不保存 - - 每个task instance均可使用`save_infermodel_every_steps`来控制该task保存infermodel的频率，默认为-1，即只在达到目标训练步数时保存一下 - - - - -Keywords: paddlepaddle,paddle,multi-task-learning -Platform: any -Classifier: License :: OSI Approved :: Apache Software License -Classifier: Programming Language :: Python -Classifier: Programming Language :: Python :: 2 -Classifier: Programming Language :: Python :: 2.7 -Classifier: Programming Language :: Python :: 3 -Classifier: Programming Language :: Python :: 3.5 -Classifier: Programming Language :: Python :: 3.6 -Classifier: Programming Language :: Python :: 3.7 diff --git a/paddle_palm.egg-info/SOURCES.txt b/paddle_palm.egg-info/SOURCES.txt deleted file mode 100644 index 4f6561e..0000000 --- a/paddle_palm.egg-info/SOURCES.txt +++ /dev/null @@ -1,47 +0,0 @@ -README.md -setup.cfg -setup.py -./paddlepalm/__init__.py -./paddlepalm/default_settings.py -./paddlepalm/interface.py -./paddlepalm/mtl_controller.py -./paddlepalm/task_instance.py -./paddlepalm/backbone/__init__.py -./paddlepalm/backbone/bert.py -./paddlepalm/backbone/ernie.py -./paddlepalm/backbone/utils/__init__.py -./paddlepalm/backbone/utils/transformer.py -./paddlepalm/optimizer/__init__.py -./paddlepalm/optimizer/adam.py -./paddlepalm/reader/__init__.py -./paddlepalm/reader/cls4bert.py -./paddlepalm/reader/match.py -./paddlepalm/reader/match4ernie.py -./paddlepalm/reader/mlm.py -./paddlepalm/reader/mrc4bert.py -./paddlepalm/reader/mrc4ernie.py -./paddlepalm/reader/utils/__init__.py -./paddlepalm/reader/utils/batching4bert.py -./paddlepalm/reader/utils/batching4ernie.py -./paddlepalm/reader/utils/mlm_batching.py -./paddlepalm/reader/utils/mrqa_helper.py -./paddlepalm/reader/utils/reader4ernie.py -./paddlepalm/task_paradigm/__init__.py -./paddlepalm/task_paradigm/cls.py -./paddlepalm/task_paradigm/match.py -./paddlepalm/task_paradigm/mlm.py -./paddlepalm/task_paradigm/mrc.py -./paddlepalm/tokenizer/__init__.py -./paddlepalm/tokenizer/bert_tokenizer.py -./paddlepalm/tokenizer/ernie_tokenizer.py -./paddlepalm/utils/__init__.py -./paddlepalm/utils/config_helper.py -./paddlepalm/utils/print_helper.py -./paddlepalm/utils/reader_helper.py -./paddlepalm/utils/saver.py -./paddlepalm/utils/textprocess_helper.py -paddle_palm.egg-info/PKG-INFO -paddle_palm.egg-info/SOURCES.txt -paddle_palm.egg-info/dependency_links.txt -paddle_palm.egg-info/not-zip-safe -paddle_palm.egg-info/top_level.txt \ No newline at end of file diff --git a/paddle_palm.egg-info/dependency_links.txt b/paddle_palm.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/paddle_palm.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/paddle_palm.egg-info/not-zip-safe b/paddle_palm.egg-info/not-zip-safe deleted file mode 100644 index 8b13789..0000000 --- a/paddle_palm.egg-info/not-zip-safe +++ /dev/null @@ -1 +0,0 @@ - diff --git a/paddle_palm.egg-info/top_level.txt b/paddle_palm.egg-info/top_level.txt deleted file mode 100644 index b136828..0000000 --- a/paddle_palm.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -paddlepalm -- GitLab