wehub-resource-sync
1117 lines
35 KiB
Python
1117 lines
35 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from legacy_test.test_dist_base import (
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TestParallelDyGraphRunnerBase,
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runtime_main,
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)
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import paddle
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import paddle.nn.functional as F
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from paddle import base
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from paddle.nn import Layer
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from paddle.optimizer.lr import NoamDecay
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"""
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Note(chenweihang): To compare loss of single-card and multi-card
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in our dist test framework, two parameters need to be adjusted:
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1. set the dropout rate to 0.
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2. set the weights for Transformer.forward to constant.
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3. to test sparse optimize, set weight_sharing to be False
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"""
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class TrainTaskConfig:
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"""
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TrainTaskConfig
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"""
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# the epoch number to train.
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pass_num = 20
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# the number of sequences contained in a mini-batch.
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# deprecated, set batch_size in args.
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batch_size = 32
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# the hyper parameters for Adam optimizer.
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# This static learning_rate will be multiplied to the LearningRateScheduler
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# derived learning rate the to get the final learning rate.
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learning_rate = 2.0
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beta1 = 0.9
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beta2 = 0.997
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eps = 1e-9
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# the parameters for learning rate scheduling.
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warmup_steps = 8000
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# the weight used to mix up the ground-truth distribution and the fixed
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# uniform distribution in label smoothing when training.
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# Set this as zero if label smoothing is not wanted.
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label_smooth_eps = 0.1
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class ModelHyperParams:
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# These following five vocabularies related configurations will be set
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# automatically according to the passed vocabulary path and special tokens.
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# size of source word dictionary.
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src_vocab_size = 10000
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# size of target word dictionary
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trg_vocab_size = 10000
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# index for <bos> token
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bos_idx = 0
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# index for <eos> token
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eos_idx = 1
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# index for <unk> token
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unk_idx = 2
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# max length of sequences deciding the size of position encoding table.
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max_length = 4
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# the dimension for word embeddings, which is also the last dimension of
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# the input and output of multi-head attention, position-wise feed-forward
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# networks, encoder and decoder.
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d_model = 512
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# size of the hidden layer in position-wise feed-forward networks.
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d_inner_hid = 2048
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# the dimension that keys are projected to for dot-product attention.
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d_key = 64
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# the dimension that values are projected to for dot-product attention.
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d_value = 64
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# number of head used in multi-head attention.
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n_head = 8
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# number of sub-layers to be stacked in the encoder and decoder.
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n_layer = 6
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# dropout rates of different modules.
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prepostprocess_dropout = 0
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attention_dropout = 0
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relu_dropout = 0
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# to process before each sub-layer
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preprocess_cmd = "n" # layer normalization
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# to process after each sub-layer
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postprocess_cmd = "da" # dropout + residual connection
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# random seed used in dropout for CE.
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dropout_seed = None
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# the flag indicating whether to share embedding and softmax weights.
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# vocabularies in source and target should be same for weight sharing.
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weight_sharing = False
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# The placeholder for batch_size in compile time. Must be -1 currently to be
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# consistent with some ops' infer-shape output in compile time, such as the
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# sequence_expand op used in beamsearch decoder.
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batch_size = -1
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# The placeholder for sequence length in compile time.
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seq_len = ModelHyperParams.max_length
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# Here list the data shapes and data types of all inputs.
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# The shapes here act as placeholder and are set to pass the infer-shape in
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# compile time.
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input_descs = {
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# The actual data shape of src_word is:
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# [batch_size, max_src_len_in_batch, 1]
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"src_word": [(batch_size, seq_len, 1), "int64", 2],
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# The actual data shape of src_pos is:
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# [batch_size, max_src_len_in_batch, 1]
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"src_pos": [(batch_size, seq_len, 1), "int64"],
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# This input is used to remove attention weights on paddings in the
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# encoder.
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# The actual data shape of src_slf_attn_bias is:
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# [batch_size, n_head, max_src_len_in_batch, max_src_len_in_batch]
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"src_slf_attn_bias": [
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(batch_size, ModelHyperParams.n_head, seq_len, seq_len),
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"float32",
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],
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# The actual data shape of trg_word is:
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# [batch_size, max_trg_len_in_batch, 1]
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"trg_word": [
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(batch_size, seq_len, 1),
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"int64",
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2,
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], # lod_level is only used in fast decoder.
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# The actual data shape of trg_pos is:
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# [batch_size, max_trg_len_in_batch, 1]
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"trg_pos": [(batch_size, seq_len, 1), "int64"],
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# This input is used to remove attention weights on paddings and
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# subsequent words in the decoder.
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# The actual data shape of trg_slf_attn_bias is:
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# [batch_size, n_head, max_trg_len_in_batch, max_trg_len_in_batch]
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"trg_slf_attn_bias": [
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(batch_size, ModelHyperParams.n_head, seq_len, seq_len),
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"float32",
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],
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# This input is used to remove attention weights on paddings of the source
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# input in the encoder-decoder attention.
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# The actual data shape of trg_src_attn_bias is:
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# [batch_size, n_head, max_trg_len_in_batch, max_src_len_in_batch]
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"trg_src_attn_bias": [
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(batch_size, ModelHyperParams.n_head, seq_len, seq_len),
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"float32",
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],
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# This input is used in independent decoder program for inference.
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# The actual data shape of enc_output is:
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# [batch_size, max_src_len_in_batch, d_model]
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"enc_output": [(batch_size, seq_len, ModelHyperParams.d_model), "float32"],
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# The actual data shape of label_word is:
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# [batch_size * max_trg_len_in_batch, 1]
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"lbl_word": [(batch_size * seq_len, 1), "int64"],
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# This input is used to mask out the loss of padding tokens.
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# The actual data shape of label_weight is:
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# [batch_size * max_trg_len_in_batch, 1]
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"lbl_weight": [(batch_size * seq_len, 1), "float32"],
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# This input is used in beam-search decoder.
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"init_score": [(batch_size, 1), "float32", 2],
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# This input is used in beam-search decoder for the first gather
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# (cell states updation)
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"init_idx": [(batch_size,), "int32"],
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}
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# Names of word embedding table which might be reused for weight sharing.
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word_emb_param_names = (
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"src_word_emb_table",
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"trg_word_emb_table",
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)
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# Names of position encoding table which will be initialized externally.
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pos_enc_param_names = (
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"src_pos_enc_table",
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"trg_pos_enc_table",
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)
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# separated inputs for different usages.
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encoder_data_input_fields = (
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"src_word",
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"src_pos",
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"src_slf_attn_bias",
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)
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decoder_data_input_fields = (
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"trg_word",
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"trg_pos",
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"trg_slf_attn_bias",
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"trg_src_attn_bias",
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"enc_output",
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)
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label_data_input_fields = (
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"lbl_word",
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"lbl_weight",
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)
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# In fast decoder, trg_pos (only containing the current time step) is generated
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# by ops and trg_slf_attn_bias is not needed.
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fast_decoder_data_input_fields = (
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"trg_word",
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# "init_score",
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# "init_idx",
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"trg_src_attn_bias",
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)
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def position_encoding_init(n_position, d_pos_vec):
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"""
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Generate the initial values for the sinusoid position encoding table.
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"""
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channels = d_pos_vec
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position = np.arange(n_position)
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num_timescales = channels // 2
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log_timescale_increment = np.log(1e4 / float(1)) / (num_timescales - 1)
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inv_timescales = (
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np.exp(np.arange(num_timescales)) * -log_timescale_increment
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)
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scaled_time = np.expand_dims(position, 1) * np.expand_dims(
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inv_timescales, 0
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)
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signal = np.concatenate([np.sin(scaled_time), np.cos(scaled_time)], axis=1)
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signal = np.pad(signal, [[0, 0], [0, np.mod(channels, 2)]], 'constant')
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position_enc = signal
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return position_enc.astype("float32")
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pos_inp1 = position_encoding_init(
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ModelHyperParams.max_length, ModelHyperParams.d_model
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)
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pos_inp2 = position_encoding_init(
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ModelHyperParams.max_length, ModelHyperParams.d_model
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)
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class PrePostProcessLayer(Layer):
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def __init__(self, d_model, process_cmd, shape_len=None):
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super().__init__()
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for cmd in process_cmd:
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if cmd == "n":
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self._layer_norm = paddle.nn.LayerNorm(
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normalized_shape=d_model,
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weight_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(1.0)
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),
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bias_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(0.0)
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),
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)
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def forward(self, prev_out, out, process_cmd, dropout_rate=0.0):
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for cmd in process_cmd:
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if cmd == "a": # add residual connection
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out = out + prev_out if prev_out is not None else out
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elif cmd == "n": # add layer normalization
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out = self._layer_norm(out)
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elif cmd == "d": # add dropout
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if dropout_rate:
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out = paddle.nn.functional.dropout(
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out,
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p=dropout_rate,
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)
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return out
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class PositionwiseFeedForwardLayer(Layer):
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def __init__(self, d_inner_hid, d_hid, dropout_rate):
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super().__init__()
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self._i2h = paddle.nn.Linear(d_hid, d_inner_hid)
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self._h2o = paddle.nn.Linear(d_inner_hid, d_hid)
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self._dropout_rate = dropout_rate
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def forward(self, x):
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hidden = self._i2h(x)
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if self._dropout_rate:
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hidden = paddle.nn.functional.dropout(
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hidden,
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p=self._dropout_rate,
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)
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out = self._h2o(hidden)
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return out
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class MultiHeadAttentionLayer(Layer):
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def __init__(
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self,
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d_key,
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d_value,
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d_model,
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n_head=1,
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dropout_rate=0.0,
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cache=None,
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gather_idx=None,
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static_kv=False,
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):
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super().__init__()
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self._n_head = n_head
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self._d_key = d_key
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self._d_value = d_value
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self._d_model = d_model
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self._dropout_rate = dropout_rate
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self._q_fc = paddle.nn.Linear(
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self._d_model, d_key * n_head, bias_attr=False
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)
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self._k_fc = paddle.nn.Linear(
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self._d_model, d_key * n_head, bias_attr=False
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)
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self._v_fc = paddle.nn.Linear(
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self._d_model, d_value * n_head, bias_attr=False
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)
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self._proj_fc = paddle.nn.Linear(
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d_value * n_head, self._d_model, bias_attr=False
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)
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def forward(self, queries, keys, values, attn_bias):
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# compute q ,k ,v
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keys = queries if keys is None else keys
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values = keys if values is None else values
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q = self._q_fc(queries)
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k = self._k_fc(keys)
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v = self._v_fc(values)
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# split head
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reshaped_q = paddle.reshape(
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x=q, shape=[0, 0, self._n_head, self._d_key]
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)
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transpose_q = paddle.transpose(x=reshaped_q, perm=[0, 2, 1, 3])
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reshaped_k = paddle.reshape(
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x=k, shape=[0, 0, self._n_head, self._d_key]
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)
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transpose_k = paddle.transpose(x=reshaped_k, perm=[0, 2, 1, 3])
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reshaped_v = paddle.reshape(
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x=v, shape=[0, 0, self._n_head, self._d_value]
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)
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transpose_v = paddle.transpose(x=reshaped_v, perm=[0, 2, 1, 3])
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# scale dot product attention
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product = paddle.matmul(
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x=transpose_q,
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y=transpose_k,
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transpose_y=True,
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)
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product = paddle.scale(product, scale=self._d_model**-0.5)
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if attn_bias is not None:
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product += attn_bias
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weights = paddle.nn.functional.softmax(product)
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if self._dropout_rate:
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weights_dropped = paddle.nn.functional.dropout(
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weights,
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p=self._dropout_rate,
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)
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out = paddle.matmul(weights_dropped, transpose_v)
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else:
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out = paddle.matmul(weights, transpose_v)
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# combine heads
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if len(out.shape) != 4:
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raise ValueError("Input(x) should be a 4-D Tensor.")
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trans_x = paddle.transpose(out, perm=[0, 2, 1, 3])
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final_out = paddle.reshape(
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x=trans_x,
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shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]],
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)
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# fc to output
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proj_out = self._proj_fc(final_out)
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return proj_out
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class EncoderSubLayer(Layer):
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def __init__(
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self,
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n_head,
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d_key,
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d_value,
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d_model,
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d_inner_hid,
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prepostprocess_dropout,
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attention_dropout,
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relu_dropout,
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preprocess_cmd="n",
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postprocess_cmd="da",
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):
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super().__init__()
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self._preprocess_cmd = preprocess_cmd
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self._postprocess_cmd = postprocess_cmd
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self._prepostprocess_dropout = prepostprocess_dropout
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self._preprocess_layer = PrePostProcessLayer(
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d_model, self._preprocess_cmd, 3
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)
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self._multihead_attention_layer = MultiHeadAttentionLayer(
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d_key, d_value, d_model, n_head, attention_dropout
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)
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self._postprocess_layer = PrePostProcessLayer(
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d_model, self._postprocess_cmd, None
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)
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self._preprocess_layer2 = PrePostProcessLayer(
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d_model, self._preprocess_cmd, 3
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)
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self._positionwise_feed_forward = PositionwiseFeedForwardLayer(
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d_inner_hid, d_model, relu_dropout
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)
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self._postprocess_layer2 = PrePostProcessLayer(
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d_model, self._postprocess_cmd, None
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)
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def forward(self, enc_input, attn_bias):
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pre_process_multihead = self._preprocess_layer(
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None, enc_input, self._preprocess_cmd, self._prepostprocess_dropout
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)
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attn_output = self._multihead_attention_layer(
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pre_process_multihead, None, None, attn_bias
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)
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attn_output = self._postprocess_layer(
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enc_input,
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attn_output,
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self._postprocess_cmd,
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self._prepostprocess_dropout,
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)
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pre_process2_output = self._preprocess_layer2(
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None,
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attn_output,
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self._preprocess_cmd,
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self._prepostprocess_dropout,
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)
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ffd_output = self._positionwise_feed_forward(pre_process2_output)
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return self._postprocess_layer2(
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attn_output,
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ffd_output,
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self._postprocess_cmd,
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self._prepostprocess_dropout,
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)
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|
|
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class EncoderLayer(Layer):
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|
def __init__(
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self,
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n_layer,
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n_head,
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d_key,
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d_value,
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d_model,
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d_inner_hid,
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prepostprocess_dropout,
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attention_dropout,
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relu_dropout,
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preprocess_cmd="n",
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postprocess_cmd="da",
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):
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super().__init__()
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self._preprocess_cmd = preprocess_cmd
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self._encoder_sublayers = []
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self._prepostprocess_dropout = prepostprocess_dropout
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self._n_layer = n_layer
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self._preprocess_layer = PrePostProcessLayer(
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d_model, self._preprocess_cmd, 3
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)
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for i in range(n_layer):
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self._encoder_sublayers.append(
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self.add_sublayer(
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f'esl_{i}',
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EncoderSubLayer(
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n_head,
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d_key,
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d_value,
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d_model,
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d_inner_hid,
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prepostprocess_dropout,
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attention_dropout,
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relu_dropout,
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preprocess_cmd,
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postprocess_cmd,
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),
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)
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)
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def forward(self, enc_input, attn_bias):
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for i in range(self._n_layer):
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enc_output = self._encoder_sublayers[i](enc_input, attn_bias)
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enc_input = enc_output
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return self._preprocess_layer(
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None, enc_output, self._preprocess_cmd, self._prepostprocess_dropout
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)
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|
|
|
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class PrepareEncoderDecoderLayer(Layer):
|
|
def __init__(
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self,
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|
src_vocab_size,
|
|
src_emb_dim,
|
|
src_max_len,
|
|
dropout_rate,
|
|
is_sparse=False,
|
|
word_emb_param_name=None,
|
|
pos_enc_param_name=None,
|
|
):
|
|
super().__init__()
|
|
self._src_max_len = src_max_len
|
|
self._src_emb_dim = src_emb_dim
|
|
self._src_vocab_size = src_vocab_size
|
|
self._dropout_rate = dropout_rate
|
|
self._input_emb = paddle.nn.Embedding(
|
|
src_vocab_size,
|
|
src_emb_dim,
|
|
sparse=is_sparse,
|
|
weight_attr=base.ParamAttr(
|
|
name=word_emb_param_name,
|
|
initializer=paddle.nn.initializer.Normal(
|
|
0.0, src_emb_dim**-0.5
|
|
),
|
|
),
|
|
)
|
|
|
|
if pos_enc_param_name is pos_enc_param_names[0]:
|
|
pos_inp = pos_inp1
|
|
else:
|
|
pos_inp = pos_inp2
|
|
self._pos_emb = paddle.nn.Embedding(
|
|
self._src_max_len,
|
|
src_emb_dim,
|
|
sparse=is_sparse,
|
|
weight_attr=base.ParamAttr(
|
|
name=pos_enc_param_name,
|
|
initializer=paddle.nn.initializer.Assign(pos_inp),
|
|
trainable=False,
|
|
),
|
|
)
|
|
|
|
def forward(self, src_word, src_pos):
|
|
src_word_emb = self._input_emb(src_word)
|
|
src_word_emb = paddle.scale(
|
|
x=src_word_emb, scale=self._src_emb_dim**0.5
|
|
)
|
|
# # TODO change this to fit dynamic length input
|
|
src_pos_emb = self._pos_emb(src_pos)
|
|
src_pos_emb.stop_gradient = True
|
|
enc_input = src_word_emb + src_pos_emb
|
|
return (
|
|
paddle.nn.functional.dropout(
|
|
enc_input,
|
|
p=self._dropout_rate,
|
|
)
|
|
if self._dropout_rate
|
|
else enc_input
|
|
)
|
|
|
|
|
|
class WrapEncoderLayer(Layer):
|
|
def __init__(
|
|
self,
|
|
src_vocab_size,
|
|
max_length,
|
|
n_layer,
|
|
n_head,
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
d_inner_hid,
|
|
prepostprocess_dropout,
|
|
attention_dropout,
|
|
relu_dropout,
|
|
preprocess_cmd,
|
|
postprocess_cmd,
|
|
weight_sharing,
|
|
is_sparse=False,
|
|
):
|
|
"""
|
|
The wrapper assembles together all needed layers for the encoder.
|
|
"""
|
|
super().__init__()
|
|
|
|
self._prepare_encoder_layer = PrepareEncoderDecoderLayer(
|
|
src_vocab_size,
|
|
d_model,
|
|
max_length,
|
|
prepostprocess_dropout,
|
|
is_sparse=is_sparse,
|
|
word_emb_param_name=word_emb_param_names[0],
|
|
pos_enc_param_name=pos_enc_param_names[0],
|
|
)
|
|
self._encoder = EncoderLayer(
|
|
n_layer,
|
|
n_head,
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
d_inner_hid,
|
|
prepostprocess_dropout,
|
|
attention_dropout,
|
|
relu_dropout,
|
|
preprocess_cmd,
|
|
postprocess_cmd,
|
|
)
|
|
|
|
def forward(self, enc_inputs):
|
|
src_word, src_pos, src_slf_attn_bias = enc_inputs
|
|
enc_input = self._prepare_encoder_layer(src_word, src_pos)
|
|
enc_output = self._encoder(enc_input, src_slf_attn_bias)
|
|
return enc_output
|
|
|
|
|
|
class DecoderSubLayer(Layer):
|
|
def __init__(
|
|
self,
|
|
n_head,
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
d_inner_hid,
|
|
prepostprocess_dropout,
|
|
attention_dropout,
|
|
relu_dropout,
|
|
preprocess_cmd,
|
|
postprocess_cmd,
|
|
cache=None,
|
|
gather_idx=None,
|
|
):
|
|
super().__init__()
|
|
self._postprocess_cmd = postprocess_cmd
|
|
self._preprocess_cmd = preprocess_cmd
|
|
self._prepostprocess_dropout = prepostprocess_dropout
|
|
self._pre_process_layer = PrePostProcessLayer(
|
|
d_model, preprocess_cmd, 3
|
|
)
|
|
self._multihead_attention_layer = MultiHeadAttentionLayer(
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
n_head,
|
|
attention_dropout,
|
|
cache=cache,
|
|
gather_idx=gather_idx,
|
|
)
|
|
self._post_process_layer = PrePostProcessLayer(
|
|
d_model, postprocess_cmd, None
|
|
)
|
|
self._pre_process_layer2 = PrePostProcessLayer(
|
|
d_model, preprocess_cmd, 3
|
|
)
|
|
self._multihead_attention_layer2 = MultiHeadAttentionLayer(
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
n_head,
|
|
attention_dropout,
|
|
cache=cache,
|
|
gather_idx=gather_idx,
|
|
static_kv=True,
|
|
)
|
|
self._post_process_layer2 = PrePostProcessLayer(
|
|
d_model, postprocess_cmd, None
|
|
)
|
|
self._pre_process_layer3 = PrePostProcessLayer(
|
|
d_model, preprocess_cmd, 3
|
|
)
|
|
self._positionwise_feed_forward_layer = PositionwiseFeedForwardLayer(
|
|
d_inner_hid, d_model, relu_dropout
|
|
)
|
|
self._post_process_layer3 = PrePostProcessLayer(
|
|
d_model, postprocess_cmd, None
|
|
)
|
|
|
|
def forward(self, dec_input, enc_output, slf_attn_bias, dec_enc_attn_bias):
|
|
pre_process_rlt = self._pre_process_layer(
|
|
None, dec_input, self._preprocess_cmd, self._prepostprocess_dropout
|
|
)
|
|
slf_attn_output = self._multihead_attention_layer(
|
|
pre_process_rlt, None, None, slf_attn_bias
|
|
)
|
|
slf_attn_output_pp = self._post_process_layer(
|
|
dec_input,
|
|
slf_attn_output,
|
|
self._postprocess_cmd,
|
|
self._prepostprocess_dropout,
|
|
)
|
|
pre_process_rlt2 = self._pre_process_layer2(
|
|
None,
|
|
slf_attn_output_pp,
|
|
self._preprocess_cmd,
|
|
self._prepostprocess_dropout,
|
|
)
|
|
enc_attn_output_pp = self._multihead_attention_layer2(
|
|
pre_process_rlt2, enc_output, enc_output, dec_enc_attn_bias
|
|
)
|
|
enc_attn_output = self._post_process_layer2(
|
|
slf_attn_output_pp,
|
|
enc_attn_output_pp,
|
|
self._postprocess_cmd,
|
|
self._prepostprocess_dropout,
|
|
)
|
|
pre_process_rlt3 = self._pre_process_layer3(
|
|
None,
|
|
enc_attn_output,
|
|
self._preprocess_cmd,
|
|
self._prepostprocess_dropout,
|
|
)
|
|
ffd_output = self._positionwise_feed_forward_layer(pre_process_rlt3)
|
|
dec_output = self._post_process_layer3(
|
|
enc_attn_output,
|
|
ffd_output,
|
|
self._postprocess_cmd,
|
|
self._prepostprocess_dropout,
|
|
)
|
|
return dec_output
|
|
|
|
|
|
class DecoderLayer(Layer):
|
|
def __init__(
|
|
self,
|
|
n_layer,
|
|
n_head,
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
d_inner_hid,
|
|
prepostprocess_dropout,
|
|
attention_dropout,
|
|
relu_dropout,
|
|
preprocess_cmd,
|
|
postprocess_cmd,
|
|
caches=None,
|
|
gather_idx=None,
|
|
):
|
|
super().__init__()
|
|
self._pre_process_layer = PrePostProcessLayer(
|
|
d_model, preprocess_cmd, 3
|
|
)
|
|
self._decoder_sub_layers = []
|
|
self._n_layer = n_layer
|
|
self._preprocess_cmd = preprocess_cmd
|
|
self._prepostprocess_dropout = prepostprocess_dropout
|
|
for i in range(n_layer):
|
|
self._decoder_sub_layers.append(
|
|
self.add_sublayer(
|
|
f'dsl_{i}',
|
|
DecoderSubLayer(
|
|
n_head,
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
d_inner_hid,
|
|
prepostprocess_dropout,
|
|
attention_dropout,
|
|
relu_dropout,
|
|
preprocess_cmd,
|
|
postprocess_cmd,
|
|
cache=None if caches is None else caches[i],
|
|
gather_idx=gather_idx,
|
|
),
|
|
)
|
|
)
|
|
|
|
def forward(
|
|
self, dec_input, enc_output, dec_slf_attn_bias, dec_enc_attn_bias
|
|
):
|
|
for i in range(self._n_layer):
|
|
tmp_dec_output = self._decoder_sub_layers[i](
|
|
dec_input, enc_output, dec_slf_attn_bias, dec_enc_attn_bias
|
|
)
|
|
dec_input = tmp_dec_output
|
|
|
|
dec_output = self._pre_process_layer(
|
|
None,
|
|
tmp_dec_output,
|
|
self._preprocess_cmd,
|
|
self._prepostprocess_dropout,
|
|
)
|
|
return dec_output
|
|
|
|
|
|
class WrapDecoderLayer(Layer):
|
|
def __init__(
|
|
self,
|
|
trg_vocab_size,
|
|
max_length,
|
|
n_layer,
|
|
n_head,
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
d_inner_hid,
|
|
prepostprocess_dropout,
|
|
attention_dropout,
|
|
relu_dropout,
|
|
preprocess_cmd,
|
|
postprocess_cmd,
|
|
weight_sharing,
|
|
caches=None,
|
|
gather_idx=None,
|
|
is_sparse=False,
|
|
):
|
|
"""
|
|
The wrapper assembles together all needed layers for the encoder.
|
|
"""
|
|
super().__init__()
|
|
|
|
self._prepare_decoder_layer = PrepareEncoderDecoderLayer(
|
|
trg_vocab_size,
|
|
d_model,
|
|
max_length,
|
|
prepostprocess_dropout,
|
|
is_sparse=is_sparse,
|
|
word_emb_param_name=word_emb_param_names[1],
|
|
pos_enc_param_name=pos_enc_param_names[1],
|
|
)
|
|
self._decoder_layer = DecoderLayer(
|
|
n_layer,
|
|
n_head,
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
d_inner_hid,
|
|
prepostprocess_dropout,
|
|
attention_dropout,
|
|
relu_dropout,
|
|
preprocess_cmd,
|
|
postprocess_cmd,
|
|
caches=caches,
|
|
gather_idx=gather_idx,
|
|
)
|
|
self._weight_sharing = weight_sharing
|
|
if not weight_sharing:
|
|
self._fc = paddle.nn.Linear(
|
|
d_model, trg_vocab_size, bias_attr=False
|
|
)
|
|
|
|
def forward(self, dec_inputs=None, enc_output=None):
|
|
trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias = dec_inputs
|
|
dec_input = self._prepare_decoder_layer(trg_word, trg_pos)
|
|
dec_output = self._decoder_layer(
|
|
dec_input, enc_output, trg_slf_attn_bias, trg_src_attn_bias
|
|
)
|
|
|
|
dec_output_reshape = paddle.reshape(
|
|
dec_output, shape=[-1, dec_output.shape[-1]]
|
|
)
|
|
|
|
if self._weight_sharing:
|
|
predict = paddle.matmul(
|
|
x=dec_output_reshape,
|
|
y=self._prepare_decoder_layer._input_emb.weight,
|
|
transpose_y=True,
|
|
)
|
|
else:
|
|
predict = self._fc(dec_output_reshape)
|
|
|
|
if dec_inputs is None:
|
|
# Return probs for independent decoder program.
|
|
predict_out = paddle.nn.functional.softmax(predict)
|
|
return predict_out
|
|
return predict
|
|
|
|
|
|
class TransFormer(Layer):
|
|
def __init__(
|
|
self,
|
|
src_vocab_size,
|
|
trg_vocab_size,
|
|
max_length,
|
|
n_layer,
|
|
n_head,
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
d_inner_hid,
|
|
prepostprocess_dropout,
|
|
attention_dropout,
|
|
relu_dropout,
|
|
preprocess_cmd,
|
|
postprocess_cmd,
|
|
weight_sharing,
|
|
label_smooth_eps,
|
|
use_py_reader=False,
|
|
is_test=False,
|
|
is_sparse=False,
|
|
):
|
|
super().__init__()
|
|
self._label_smooth_eps = label_smooth_eps
|
|
self._trg_vocab_size = trg_vocab_size
|
|
if weight_sharing:
|
|
assert src_vocab_size == trg_vocab_size, (
|
|
"Vocabularies in source and target should be same for weight sharing."
|
|
)
|
|
self._wrap_encoder_layer = WrapEncoderLayer(
|
|
src_vocab_size,
|
|
max_length,
|
|
n_layer,
|
|
n_head,
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
d_inner_hid,
|
|
prepostprocess_dropout,
|
|
attention_dropout,
|
|
relu_dropout,
|
|
preprocess_cmd,
|
|
postprocess_cmd,
|
|
weight_sharing,
|
|
is_sparse=is_sparse,
|
|
)
|
|
self._wrap_decoder_layer = WrapDecoderLayer(
|
|
trg_vocab_size,
|
|
max_length,
|
|
n_layer,
|
|
n_head,
|
|
d_key,
|
|
d_value,
|
|
d_model,
|
|
d_inner_hid,
|
|
prepostprocess_dropout,
|
|
attention_dropout,
|
|
relu_dropout,
|
|
preprocess_cmd,
|
|
postprocess_cmd,
|
|
weight_sharing,
|
|
is_sparse=is_sparse,
|
|
)
|
|
|
|
if weight_sharing:
|
|
self._wrap_decoder_layer._prepare_decoder_layer._input_emb.weight = self._wrap_encoder_layer._prepare_encoder_layer._input_emb.weight
|
|
|
|
def forward(self, enc_inputs, dec_inputs, label, weights):
|
|
enc_output = self._wrap_encoder_layer(enc_inputs)
|
|
predict = self._wrap_decoder_layer(dec_inputs, enc_output)
|
|
if self._label_smooth_eps:
|
|
label_out = F.label_smooth(
|
|
label=paddle.squeeze(
|
|
paddle.nn.functional.one_hot(label, self._trg_vocab_size)
|
|
),
|
|
epsilon=self._label_smooth_eps,
|
|
)
|
|
|
|
cost = paddle.nn.functional.softmax_with_cross_entropy(
|
|
logits=predict,
|
|
label=label_out,
|
|
soft_label=True if self._label_smooth_eps else False,
|
|
)
|
|
weighted_cost = cost * weights
|
|
sum_cost = paddle.sum(weighted_cost)
|
|
token_num = paddle.sum(weights)
|
|
token_num.stop_gradient = True
|
|
avg_cost = sum_cost / token_num
|
|
return sum_cost, avg_cost, predict, token_num
|
|
|
|
|
|
# how many batches we use
|
|
batch_num = 5
|
|
|
|
|
|
def fake_data_reader():
|
|
def __reader__():
|
|
iteration = TrainTaskConfig.batch_size * batch_num
|
|
for _ in range(iteration):
|
|
# random data
|
|
np.random.seed = 90
|
|
src_word_np = (
|
|
np.arange(1, seq_len + 1).reshape([seq_len]).astype('int64')
|
|
)
|
|
src_pos_np = np.random.randint(
|
|
1, seq_len, size=(seq_len), dtype='int64'
|
|
)
|
|
src_slf_attn_bias_np = np.random.randn(
|
|
ModelHyperParams.n_head, seq_len, seq_len
|
|
).astype('float32')
|
|
|
|
trg_word_np = (
|
|
np.arange(1, seq_len + 1).reshape([seq_len]).astype('int64')
|
|
)
|
|
trg_pos_np = np.random.randint(
|
|
1, seq_len, size=(seq_len), dtype='int64'
|
|
)
|
|
trg_slf_attn_bias_np = np.random.randn(
|
|
ModelHyperParams.n_head, seq_len, seq_len
|
|
).astype('float32')
|
|
trg_src_attn_bias_np = np.random.randn(
|
|
ModelHyperParams.n_head, seq_len, seq_len
|
|
).astype('float32')
|
|
|
|
lbl_word_np = np.random.randint(
|
|
1,
|
|
ModelHyperParams.src_vocab_size - 1,
|
|
size=(seq_len, 1),
|
|
dtype='int64',
|
|
)
|
|
|
|
# Note(chenweihang): weight will introduce diff, so use constant here
|
|
lbl_weight_np = np.ones((seq_len, 1)).astype('int64')
|
|
|
|
data_inputs = [
|
|
src_word_np,
|
|
src_pos_np,
|
|
src_slf_attn_bias_np,
|
|
trg_word_np,
|
|
trg_pos_np,
|
|
trg_slf_attn_bias_np,
|
|
trg_src_attn_bias_np,
|
|
lbl_word_np,
|
|
lbl_weight_np,
|
|
]
|
|
|
|
yield data_inputs
|
|
|
|
return __reader__
|
|
|
|
|
|
def np_to_variable(data):
|
|
batch_size = len(data)
|
|
src_word_np = np.array([x[0] for x in data]).astype('int64')
|
|
src_pos_np = np.array([x[1] for x in data]).astype('int64')
|
|
src_slf_attn_bias_np = np.array([x[2] for x in data]).astype('float32')
|
|
trg_word_np = np.array([x[3] for x in data]).astype('int64')
|
|
trg_pos_np = np.array([x[4] for x in data]).astype('int64')
|
|
trg_slf_attn_bias_np = np.array([x[5] for x in data]).astype('float32')
|
|
trg_src_attn_bias_np = np.array([x[6] for x in data]).astype('float32')
|
|
lbl_word_np = np.array([x[7] for x in data]).astype('int64')
|
|
lbl_weight_np = np.array([x[8] for x in data]).astype('float32')
|
|
|
|
lbl_word_np = lbl_word_np.reshape(batch_size * seq_len, 1)
|
|
lbl_weight_np = lbl_weight_np.reshape(batch_size * seq_len, 1)
|
|
|
|
data_inputs = [
|
|
src_word_np,
|
|
src_pos_np,
|
|
src_slf_attn_bias_np,
|
|
trg_word_np,
|
|
trg_pos_np,
|
|
trg_slf_attn_bias_np,
|
|
trg_src_attn_bias_np,
|
|
lbl_word_np,
|
|
lbl_weight_np,
|
|
]
|
|
|
|
var_inputs = []
|
|
for i, field in enumerate(
|
|
encoder_data_input_fields
|
|
+ decoder_data_input_fields[:-1]
|
|
+ label_data_input_fields
|
|
):
|
|
var_inputs.append(paddle.to_tensor(data_inputs[i]))
|
|
|
|
enc_inputs = var_inputs[0 : len(encoder_data_input_fields)]
|
|
dec_inputs = var_inputs[
|
|
len(encoder_data_input_fields) : len(encoder_data_input_fields)
|
|
+ len(decoder_data_input_fields[:-1])
|
|
]
|
|
label = var_inputs[-2]
|
|
weights = var_inputs[-1]
|
|
|
|
return enc_inputs, dec_inputs, label, weights
|
|
|
|
|
|
naive_optimize = True
|
|
|
|
|
|
class TestTransformer(TestParallelDyGraphRunnerBase):
|
|
def get_model(self):
|
|
model = TransFormer(
|
|
ModelHyperParams.src_vocab_size,
|
|
ModelHyperParams.trg_vocab_size,
|
|
ModelHyperParams.max_length + 1,
|
|
ModelHyperParams.n_layer,
|
|
ModelHyperParams.n_head,
|
|
ModelHyperParams.d_key,
|
|
ModelHyperParams.d_value,
|
|
ModelHyperParams.d_model,
|
|
ModelHyperParams.d_inner_hid,
|
|
ModelHyperParams.prepostprocess_dropout,
|
|
ModelHyperParams.attention_dropout,
|
|
ModelHyperParams.relu_dropout,
|
|
ModelHyperParams.preprocess_cmd,
|
|
ModelHyperParams.postprocess_cmd,
|
|
ModelHyperParams.weight_sharing,
|
|
TrainTaskConfig.label_smooth_eps,
|
|
is_sparse=True,
|
|
)
|
|
train_reader = paddle.batch(
|
|
fake_data_reader(), TrainTaskConfig.batch_size
|
|
)
|
|
if naive_optimize:
|
|
optimizer = paddle.optimizer.SGD(
|
|
learning_rate=0.001, parameters=model.parameters()
|
|
)
|
|
else:
|
|
optimizer = paddle.optimizer.Adam(
|
|
learning_rate=NoamDecay(
|
|
ModelHyperParams.d_model,
|
|
TrainTaskConfig.warmup_steps,
|
|
TrainTaskConfig.learning_rate,
|
|
),
|
|
beta1=TrainTaskConfig.beta1,
|
|
beta2=TrainTaskConfig.beta2,
|
|
epsilon=TrainTaskConfig.eps,
|
|
parameters=model.parameters(),
|
|
)
|
|
|
|
return model, train_reader, optimizer
|
|
|
|
def run_one_loop(self, model, optimizer, batch):
|
|
enc_inputs, dec_inputs, label, weights = np_to_variable(batch)
|
|
|
|
dy_sum_cost, dy_avg_cost, dy_predict, dy_token_num = model(
|
|
enc_inputs, dec_inputs, label, weights
|
|
)
|
|
|
|
return dy_avg_cost
|
|
|
|
|
|
if __name__ == "__main__":
|
|
runtime_main(TestTransformer)
|