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decoding.py
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# Copyright (c) 2021 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 collections import defaultdict
from contextlib import contextmanager
import os
import functools
import numpy as np
from functools import partial
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.fluid.layer_helper import LayerHelper
import paddle
import paddlenlp
from paddlenlp.ops.ext_utils import load, LOADED_EXT
from paddlenlp.utils.log import logger
from paddlenlp.transformers.utils import fn_args_to_dict
def infer_transformer_decoding(
enc_output, memory_seq_lens, word_emb, slf_ln_weight, slf_ln_bias,
slf_q_weight, slf_q_bias, slf_k_weight, slf_k_bias, slf_v_weight,
slf_v_bias, slf_out_weight, slf_out_bias, cross_ln_weight,
cross_ln_bias, cross_q_weight, cross_q_bias, cross_k_weight,
cross_k_bias, cross_v_weight, cross_v_bias, cross_out_weight,
cross_out_bias, ffn_ln_weight, ffn_ln_bias, ffn_inter_weight,
ffn_inter_bias, ffn_out_weight, ffn_out_bias, decoder_ln_weight,
decoder_ln_bias, linear_weight, linear_bias, pos_emb,
_decoding_strategy, _beam_size, _topk, _topp, _n_head, _size_per_head,
_n_layer, _bos_id, _eos_id, _max_out_len, _diversity_rate, _rel_len,
_alpha):
helper = LayerHelper('fusion_decoding', **locals())
inputs = {
'Input': enc_output,
'MemSeqLen': memory_seq_lens,
'WordEmbedding': word_emb,
'SelfLayernormWeight@VECTOR': slf_ln_weight,
'SelfLayernormBias@VECTOR': slf_ln_bias,
'SelfQueryWeight@VECTOR': slf_q_weight,
'SelfQueryBias@VECTOR': slf_q_bias,
'SelfKeyWeight@VECTOR': slf_k_weight,
'SelfKeyBias@VECTOR': slf_k_bias,
'SelfValueWeight@VECTOR': slf_v_weight,
'SelfValueBias@VECTOR': slf_v_bias,
'SelfOutWeight@VECTOR': slf_out_weight,
'SelfOutBias@VECTOR': slf_out_bias,
'CrossLayernormWeight@VECTOR': cross_ln_weight,
'CrossLayernormBias@VECTOR': cross_ln_bias,
'CrossQueryWeight@VECTOR': cross_q_weight,
'CrossQueryBias@VECTOR': cross_q_bias,
'CrossKeyWeight@VECTOR': cross_k_weight,
'CrossKeyBias@VECTOR': cross_k_bias,
'CrossValueWeight@VECTOR': cross_v_weight,
'CrossValueBias@VECTOR': cross_v_bias,
'CrossOutWeight@VECTOR': cross_out_weight,
'CrossOutBias@VECTOR': cross_out_bias,
'FFNLayernormWeight@VECTOR': ffn_ln_weight,
'FFNLayernormBias@VECTOR': ffn_ln_bias,
'FFNInterWeight@VECTOR': ffn_inter_weight,
'FFNInterBias@VECTOR': ffn_inter_bias,
'FFNOutWeight@VECTOR': ffn_out_weight,
'FFNOutBias@VECTOR': ffn_out_bias,
'DecoderLayernormWeight': decoder_ln_weight,
'DecoderLayernormBias': decoder_ln_bias,
'EmbWeight': linear_weight,
'EmbBias': linear_bias,
'PositionEncEmb': pos_emb
}
attrs = {
'decoding_strategy': _decoding_strategy,
'beam_size': _beam_size,
'topk': _topk,
'topp': _topp,
'n_head': _n_head,
'size_per_head': _size_per_head,
'num_layer': _n_layer,
'bos_id': _bos_id,
'eos_id': _eos_id,
'max_len': _max_out_len,
'beam_search_diversity_rate': _diversity_rate,
"rel_len": _rel_len,
"alpha": _alpha
}
output_ids = helper.create_variable(dtype="int32")
parent_ids = helper.create_variable(dtype="int32")
sequence_length = helper.create_variable(dtype="int32")
outputs = {
'OutputIds': output_ids,
'ParentIds': parent_ids,
'SequenceLength': sequence_length
}
helper.append_op(type='fusion_decoding',
inputs=inputs,
outputs=outputs,
attrs=attrs)
return output_ids, parent_ids, sequence_length
def infer_force_decoding(
enc_output, memory_seq_lens, word_emb, slf_ln_weight, slf_ln_bias,
slf_q_weight, slf_q_bias, slf_k_weight, slf_k_bias, slf_v_weight,
slf_v_bias, slf_out_weight, slf_out_bias, cross_ln_weight,
cross_ln_bias, cross_q_weight, cross_q_bias, cross_k_weight,
cross_k_bias, cross_v_weight, cross_v_bias, cross_out_weight,
cross_out_bias, ffn_ln_weight, ffn_ln_bias, ffn_inter_weight,
ffn_inter_bias, ffn_out_weight, ffn_out_bias, decoder_ln_weight,
decoder_ln_bias, linear_weight, linear_bias, pos_emb, trg_word,
_decoding_strategy, _beam_size, _topk, _topp, _n_head, _size_per_head,
_n_layer, _bos_id, _eos_id, _max_out_len, _diversity_rate, _rel_len,
_alpha):
helper = LayerHelper('fusion_force_decoding', **locals())
inputs = {
'Input': enc_output,
'MemSeqLen': memory_seq_lens,
'WordEmbedding': word_emb,
'SelfLayernormWeight@VECTOR': slf_ln_weight,
'SelfLayernormBias@VECTOR': slf_ln_bias,
'SelfQueryWeight@VECTOR': slf_q_weight,
'SelfQueryBias@VECTOR': slf_q_bias,
'SelfKeyWeight@VECTOR': slf_k_weight,
'SelfKeyBias@VECTOR': slf_k_bias,
'SelfValueWeight@VECTOR': slf_v_weight,
'SelfValueBias@VECTOR': slf_v_bias,
'SelfOutWeight@VECTOR': slf_out_weight,
'SelfOutBias@VECTOR': slf_out_bias,
'CrossLayernormWeight@VECTOR': cross_ln_weight,
'CrossLayernormBias@VECTOR': cross_ln_bias,
'CrossQueryWeight@VECTOR': cross_q_weight,
'CrossQueryBias@VECTOR': cross_q_bias,
'CrossKeyWeight@VECTOR': cross_k_weight,
'CrossKeyBias@VECTOR': cross_k_bias,
'CrossValueWeight@VECTOR': cross_v_weight,
'CrossValueBias@VECTOR': cross_v_bias,
'CrossOutWeight@VECTOR': cross_out_weight,
'CrossOutBias@VECTOR': cross_out_bias,
'FFNLayernormWeight@VECTOR': ffn_ln_weight,
'FFNLayernormBias@VECTOR': ffn_ln_bias,
'FFNInterWeight@VECTOR': ffn_inter_weight,
'FFNInterBias@VECTOR': ffn_inter_bias,
'FFNOutWeight@VECTOR': ffn_out_weight,
'FFNOutBias@VECTOR': ffn_out_bias,
'DecoderLayernormWeight': decoder_ln_weight,
'DecoderLayernormBias': decoder_ln_bias,
'EmbWeight': linear_weight,
'EmbBias': linear_bias,
'PositionEncEmb': pos_emb,
# The input of custom op must be given.
# Dispensable() and Intermediate() are not supported.
'TrgWord': trg_word
}
attrs = {
'decoding_strategy': _decoding_strategy,
'beam_size': _beam_size,
'topk': _topk,
'topp': _topp,
'n_head': _n_head,
'size_per_head': _size_per_head,
'num_layer': _n_layer,
'bos_id': _bos_id,
'eos_id': _eos_id,
'max_len': _max_out_len,
'beam_search_diversity_rate': _diversity_rate,
"rel_len": _rel_len,
"alpha": _alpha
}
output_ids = helper.create_variable(dtype="int32")
parent_ids = helper.create_variable(dtype="int32")
sequence_length = helper.create_variable(dtype="int32")
outputs = {
'OutputIds': output_ids,
'ParentIds': parent_ids,
'SequenceLength': sequence_length
}
helper.append_op(type='fusion_force_decoding',
inputs=inputs,
outputs=outputs,
attrs=attrs)
return output_ids, parent_ids, sequence_length
def infer_gpt_decoding(input, attn_mask, mem_seq_len, word_emb, slf_ln_weight,
slf_ln_bias, slf_q_weight, slf_q_bias, slf_k_weight,
slf_k_bias, slf_v_weight, slf_v_bias, slf_out_weight,
slf_out_bias, ffn_ln_weight, ffn_ln_bias,
ffn_inter_weight, ffn_inter_bias, ffn_out_weight,
ffn_out_bias, decoder_ln_weight, decoder_ln_bias,
pos_emb, linear_weight, topk, topp, max_out_len,
head_num, size_per_head, num_layer, bos_id, eos_id,
temperature, use_fp16_decoding):
helper = LayerHelper('fusion_gpt', **locals())
inputs = {
"Input": input,
"AttentionMask": attn_mask,
"StartLength": mem_seq_len,
"WordEmbedding": word_emb,
"SelfLayernormWeight@VECTOR": slf_ln_weight,
"SelfLayernormBias@VECTOR": slf_ln_bias,
"SelfQueryWeight@VECTOR": slf_q_weight,
"SelfQueryBias@VECTOR": slf_q_bias,
"SelfKeyWeight@VECTOR": slf_k_weight,
"SelfKeyBias@VECTOR": slf_k_bias,
"SelfValueWeight@VECTOR": slf_v_weight,
"SelfValueBias@VECTOR": slf_v_bias,
"SelfOutWeight@VECTOR": slf_out_weight,
"SelfOutBias@VECTOR": slf_out_bias,
"FFNLayernormWeight@VECTOR": ffn_ln_weight,
"FFNLayernormBias@VECTOR": ffn_ln_bias,
"FFNInterWeight@VECTOR": ffn_inter_weight,
"FFNInterBias@VECTOR": ffn_inter_bias,
"FFNOutWeight@VECTOR": ffn_out_weight,
"FFNOutBias@VECTOR": ffn_out_bias,
"DecoderLayernormWeight": decoder_ln_weight,
"DecoderLayernormBias": decoder_ln_bias,
"PositionEncEmb": pos_emb,
"EmbWeight": linear_weight
}
tensor_para_size = get_ft_para_conf().tensor_para_size
layer_para_size = get_ft_para_conf().layer_para_size
layer_para_batch_size = get_ft_para_conf().layer_para_batch_size
attrs = {
"topk": topk,
"topp": topp,
"max_len": max_out_len,
"n_head": head_num,
"size_per_head": size_per_head,
"num_layer": num_layer,
"bos_id": bos_id,
"eos_id": eos_id,
"temperature": temperature,
"use_fp16": use_fp16_decoding,
"tensor_para_size": tensor_para_size,
"layer_para_size": layer_para_size,
"layer_para_batch_size": layer_para_batch_size
}
output_ids = helper.create_variable(dtype="int32")
outputs = {'OutputIds': output_ids}
helper.append_op(type='fusion_gpt',
inputs=inputs,
outputs=outputs,
attrs=attrs)
return output_ids
def infer_unified_decoding(
input_ids, attn_mask, memory_seq_lens, type_id, decoder_type_id,
logits_mask, word_emb, slf_ln_weight, slf_ln_bias, slf_q_weight,
slf_q_bias, slf_k_weight, slf_k_bias, slf_v_weight, slf_v_bias,
slf_out_weight, slf_out_bias, ffn_ln_weight, ffn_ln_bias,
ffn_inter_weight, ffn_inter_bias, ffn_out_weight, ffn_out_bias,
decoder_ln_weight, decoder_ln_bias, trans_weight, trans_bias,
lm_ln_weight, lm_ln_bias, linear_weight, linear_bias, pos_emb, type_emb,
role_id, decoder_role_id, role_emb, position_id, decoder_position_id,
_decoding_strategy, _beam_size, _topk, _topp, _n_head, _size_per_head,
_n_layer, _bos_id, _eos_id, _max_out_len, _diversity_rate, _unk_id,
_mask_id, _temperature, _len_penalty, _normalize_before, _pos_bias,
_hidden_act, _rel_len, _early_stopping, _min_length):
helper = LayerHelper('fusion_unified_decoding', **locals())
inputs = {
"InputIds": input_ids,
"AttnMask": attn_mask,
"MemSeqLen": memory_seq_lens,
"TypeIds": type_id,
"DecTypeIds": decoder_type_id,
"LogitsMask": logits_mask,
"WordEmbedding": word_emb,
"SelfLayernormWeight@VECTOR": slf_ln_weight,
"SelfLayernormBias@VECTOR": slf_ln_bias,
"SelfQueryWeight@VECTOR": slf_q_weight,
"SelfQueryBias@VECTOR": slf_q_bias,
"SelfKeyWeight@VECTOR": slf_k_weight,
"SelfKeyBias@VECTOR": slf_k_bias,
"SelfValueWeight@VECTOR": slf_v_weight,
"SelfValueBias@VECTOR": slf_v_bias,
"SelfOutWeight@VECTOR": slf_out_weight,
"SelfOutBias@VECTOR": slf_out_bias,
"FFNLayernormWeight@VECTOR": ffn_ln_weight,
"FFNLayernormBias@VECTOR": ffn_ln_bias,
"FFNInterWeight@VECTOR": ffn_inter_weight,
"FFNInterBias@VECTOR": ffn_inter_bias,
"FFNOutWeight@VECTOR": ffn_out_weight,
"FFNOutBias@VECTOR": ffn_out_bias,
"DecoderLayernormWeight": decoder_ln_weight,
"DecoderLayernormBias": decoder_ln_bias,
"TransWeight": trans_weight,
"TransBias": trans_bias,
"LMLayernormWeight": lm_ln_weight,
"LMLayernormBias": lm_ln_bias,
"EmbWeight": linear_weight,
"EmbBias": linear_bias,
"PositionEncEmb": pos_emb,
"TypeEmb": type_emb,
"RoleIds": role_id,
"DecRoleIds": decoder_role_id,
"RoleEmbedding": role_emb,
"PositionIds": position_id,
"DecPositionIds": decoder_position_id
}
tensor_para_size = get_ft_para_conf().tensor_para_size
layer_para_size = get_ft_para_conf().layer_para_size
layer_para_batch_size = get_ft_para_conf().layer_para_batch_size
attrs = {
"decoding_strategy": _decoding_strategy,
"beam_size": _beam_size,
"topk": _topk,
"topp": _topp,
"n_head": _n_head,
"size_per_head": _size_per_head,
"num_layer": _n_layer,
"bos_id": _bos_id,
"eos_id": _eos_id,
"max_len": _max_out_len,
"beam_search_diversity_rate": _diversity_rate,
"unk_id": _unk_id,
"mask_id": _mask_id,
"temperature": _temperature,
"len_penalty": _len_penalty,
"normalize_before": _normalize_before,
"pos_bias": _pos_bias,
"hidden_act": _hidden_act,
"rel_len": _rel_len,
"early_stopping": _early_stopping,
"min_length": _min_length,
"tensor_para_size": tensor_para_size,
"layer_para_size": layer_para_size,
"layer_para_batch_size": layer_para_batch_size
}
output_ids = helper.create_variable(dtype="int32")
parent_ids = helper.create_variable(dtype="int32")
sequence_length = helper.create_variable(dtype="int32")
output_scores = helper.create_variable(dtype="float32")
outputs = {
'OutputIds': output_ids,
'ParentIds': parent_ids,
'SequenceLength': sequence_length,
"OutputScores": output_scores
}
helper.append_op(type='fusion_unified_decoding',
inputs=inputs,
outputs=outputs,
attrs=attrs)
return output_ids, parent_ids, sequence_length, output_scores
def infer_bart_decoding(
enc_output, memory_seq_lens, word_emb, slf_ln_weight, slf_ln_bias,
slf_q_weight, slf_q_bias, slf_k_weight, slf_k_bias, slf_v_weight,
slf_v_bias, slf_out_weight, slf_out_bias, cross_ln_weight,
cross_ln_bias, cross_q_weight, cross_q_bias, cross_k_weight,
cross_k_bias, cross_v_weight, cross_v_bias, cross_out_weight,
cross_out_bias, ffn_ln_weight, ffn_ln_bias, ffn_inter_weight,
ffn_inter_bias, ffn_out_weight, ffn_out_bias, decoder_ln_weight,
decoder_ln_bias, linear_weight, linear_bias, pos_emb,
_decoding_strategy, _beam_size, _topk, _topp, _n_head, _size_per_head,
_n_layer, _bos_id, _eos_id, _max_out_len, _diversity_rate, _rel_len,
_alpha, _early_stopping):
helper = LayerHelper('fusion_bart_decoding', **locals())
inputs = {
'Input': enc_output,
'MemSeqLen': memory_seq_lens,
'WordEmbedding': word_emb,
'SelfLayernormWeight@VECTOR': slf_ln_weight,
'SelfLayernormBias@VECTOR': slf_ln_bias,
'SelfQueryWeight@VECTOR': slf_q_weight,
'SelfQueryBias@VECTOR': slf_q_bias,
'SelfKeyWeight@VECTOR': slf_k_weight,
'SelfKeyBias@VECTOR': slf_k_bias,
'SelfValueWeight@VECTOR': slf_v_weight,
'SelfValueBias@VECTOR': slf_v_bias,
'SelfOutWeight@VECTOR': slf_out_weight,
'SelfOutBias@VECTOR': slf_out_bias,
'CrossLayernormWeight@VECTOR': cross_ln_weight,
'CrossLayernormBias@VECTOR': cross_ln_bias,
'CrossQueryWeight@VECTOR': cross_q_weight,
'CrossQueryBias@VECTOR': cross_q_bias,
'CrossKeyWeight@VECTOR': cross_k_weight,
'CrossKeyBias@VECTOR': cross_k_bias,
'CrossValueWeight@VECTOR': cross_v_weight,
'CrossValueBias@VECTOR': cross_v_bias,
'CrossOutWeight@VECTOR': cross_out_weight,
'CrossOutBias@VECTOR': cross_out_bias,
'FFNLayernormWeight@VECTOR': ffn_ln_weight,
'FFNLayernormBias@VECTOR': ffn_ln_bias,
'FFNInterWeight@VECTOR': ffn_inter_weight,
'FFNInterBias@VECTOR': ffn_inter_bias,
'FFNOutWeight@VECTOR': ffn_out_weight,
'FFNOutBias@VECTOR': ffn_out_bias,
'DecoderLayernormWeight': decoder_ln_weight,
'DecoderLayernormBias': decoder_ln_bias,
'EmbWeight': linear_weight,
'EmbBias': linear_bias,
'PositionEncEmb': pos_emb
}
attrs = {
'decoding_strategy': _decoding_strategy,
'beam_size': _beam_size,
'topk': _topk,
'topp': _topp,
'n_head': _n_head,
'size_per_head': _size_per_head,
'num_layer': _n_layer,
'bos_id': _bos_id,
'eos_id': _eos_id,
'max_len': _max_out_len,
'beam_search_diversity_rate': _diversity_rate,
"rel_len": _rel_len,
"alpha": _alpha,
"early_stopping": _early_stopping
}
output_ids = helper.create_variable(dtype="int32")
parent_ids = helper.create_variable(dtype="int32")
sequence_length = helper.create_variable(dtype="int32")
outputs = {
'OutputIds': output_ids,
'ParentIds': parent_ids,
'SequenceLength': sequence_length
}
helper.append_op(type='fusion_bart_decoding',
inputs=inputs,
outputs=outputs,
attrs=attrs)
return output_ids, parent_ids, sequence_length
def infer_mbart_decoding(
enc_output, memory_seq_lens, word_emb, slf_ln_weight, slf_ln_bias,
slf_q_weight, slf_q_bias, slf_k_weight, slf_k_bias, slf_v_weight,
slf_v_bias, slf_out_weight, slf_out_bias, cross_ln_weight,
cross_ln_bias, cross_q_weight, cross_q_bias, cross_k_weight,
cross_k_bias, cross_v_weight, cross_v_bias, cross_out_weight,
cross_out_bias, ffn_ln_weight, ffn_ln_bias, ffn_inter_weight,
ffn_inter_bias, ffn_out_weight, ffn_out_bias, decoder_ln_weight,
decoder_ln_bias, mbart_ln_weight, mbart_ln_bias, linear_weight,
linear_bias, pos_emb, trg_word, _decoding_strategy, _beam_size, _topk,
_topp, _n_head, _size_per_head, _n_layer, _bos_id, _eos_id,
_max_out_len, _diversity_rate, _rel_len, _alpha, _temperature,
_early_stopping, _hidden_act):
helper = LayerHelper('fusion_mbart_decoding', **locals())
inputs = {
'Input': enc_output,
'MemSeqLen': memory_seq_lens,
'WordEmbedding': word_emb,
'SelfLayernormWeight@VECTOR': slf_ln_weight,
'SelfLayernormBias@VECTOR': slf_ln_bias,
'SelfQueryWeight@VECTOR': slf_q_weight,
'SelfQueryBias@VECTOR': slf_q_bias,
'SelfKeyWeight@VECTOR': slf_k_weight,
'SelfKeyBias@VECTOR': slf_k_bias,
'SelfValueWeight@VECTOR': slf_v_weight,
'SelfValueBias@VECTOR': slf_v_bias,
'SelfOutWeight@VECTOR': slf_out_weight,
'SelfOutBias@VECTOR': slf_out_bias,
'CrossLayernormWeight@VECTOR': cross_ln_weight,
'CrossLayernormBias@VECTOR': cross_ln_bias,
'CrossQueryWeight@VECTOR': cross_q_weight,
'CrossQueryBias@VECTOR': cross_q_bias,
'CrossKeyWeight@VECTOR': cross_k_weight,
'CrossKeyBias@VECTOR': cross_k_bias,
'CrossValueWeight@VECTOR': cross_v_weight,
'CrossValueBias@VECTOR': cross_v_bias,
'CrossOutWeight@VECTOR': cross_out_weight,
'CrossOutBias@VECTOR': cross_out_bias,
'FFNLayernormWeight@VECTOR': ffn_ln_weight,
'FFNLayernormBias@VECTOR': ffn_ln_bias,
'FFNInterWeight@VECTOR': ffn_inter_weight,
'FFNInterBias@VECTOR': ffn_inter_bias,
'FFNOutWeight@VECTOR': ffn_out_weight,
'FFNOutBias@VECTOR': ffn_out_bias,
'DecoderLayernormWeight': decoder_ln_weight,
'DecoderLayernormBias': decoder_ln_bias,
'MBARTLayernormWeight': mbart_ln_weight,
'MBARTLayernormBias': mbart_ln_bias,
'EmbWeight': linear_weight,
'EmbBias': linear_bias,
'PositionEncEmb': pos_emb,
# The input of custom op must be given.
# Dispensable() and Intermediate() are not supported.
'TrgWord': trg_word
}
attrs = {
'decoding_strategy': _decoding_strategy,
'beam_size': _beam_size,
'topk': _topk,
'topp': _topp,
'n_head': _n_head,
'size_per_head': _size_per_head,
'num_layer': _n_layer,
'bos_id': _bos_id,
'eos_id': _eos_id,
'max_len': _max_out_len,
'beam_search_diversity_rate': _diversity_rate,
"rel_len": _rel_len,
"alpha": _alpha,
"temperature": _temperature,
"early_stopping": _early_stopping,
"hidden_act": _hidden_act
}
output_ids = helper.create_variable(dtype="int32")
parent_ids = helper.create_variable(dtype="int32")
sequence_length = helper.create_variable(dtype="int32")
outputs = {
'OutputIds': output_ids,
'ParentIds': parent_ids,
'SequenceLength': sequence_length
}
helper.append_op(type='fusion_mbart_decoding',
inputs=inputs,
outputs=outputs,
attrs=attrs)
return output_ids, parent_ids, sequence_length
def finalize(beam_size,
output_ids,
parent_ids,
out_seq_lens,
forced_eos_token_id=None,
max_seq_len=None,
decoding_strategy="beam_search"):
if max_seq_len is None:
max_seq_len = paddle.max(out_seq_lens)
ids = paddle.slice(output_ids, [0], [0], [max_seq_len])
if decoding_strategy.startswith("beam_search"):
parent_ids = paddle.slice(parent_ids, [0], [0], [max_seq_len]) % (
beam_size * 2 if decoding_strategy.endswith("_v2")
or decoding_strategy.endswith("_v3") else beam_size)
ids = paddle.nn.functional.gather_tree(ids, parent_ids)
if forced_eos_token_id is not None:
ids[-1, :, :] = forced_eos_token_id
else:
if forced_eos_token_id is not None:
ids[-1, :] = forced_eos_token_id
return ids
def transfer_param(p, is_bias=False, dtype="float16", restore_data=False):
param_shape = p.shape
# Allow CPU/GPU and float16/float32 transfer
# NOTE: str(p.place) differs between paddle develop and 2.2
if str(p.dtype)[-len(dtype):] == dtype and ("gpu" in str(p.place).lower() or
"cuda" in str(p.place).lower()):
return p
if restore_data:
if paddle.in_dynamic_mode():
param_data = p.numpy()
# Creating parameters with Assign initializer is too slow. Maybe we
# can cast to fp16 directly and get a tensor, while we do it more
# elaborately to get a ParamBase. Also note `VarBase.set_value`
# enforce the same dtype and can not be used directly.
new_p = type(p)(shape=param_shape, dtype=dtype, is_bias=is_bias)
new_p.value().get_tensor().set(
param_data.astype(dtype),
paddle.fluid.framework._current_expected_place())
return new_p
else:
param_data = np.array(paddle.static.global_scope().find_var(
p.name).get_tensor())
return paddle.create_parameter(
shape=param_shape,
dtype=dtype,
is_bias=is_bias,
default_initializer=paddle.nn.initializer.Assign(param_data)
if restore_data else None)
def _convert_qkv(q_proj,
k_proj,
v_proj,
attr="weight",
use_numpy=True,
del_param=False,
dummy_tensor=None):
ft_para_conf = get_ft_para_conf()
# TODO(guosheng): maybe static graph need this
# p = faster_model.create_parameter(
# shape=[q.shape[0], q.shape[1] + k.shape[1] + v.shape[1]],
# dtype=q.dtype,
# is_bias=is_bias)
q = getattr(q_proj, attr)
k = getattr(k_proj, attr)
v = getattr(v_proj, attr)
if use_numpy:
q = q.numpy()
if del_param:
if attr == "weight":
del q_proj.weight
else:
del q_proj.bias
k = k.numpy()
if del_param:
if attr == "weight":
del k_proj.weight
else:
del k_proj.bias
v = v.numpy()
if del_param:
if attr == "weight":
del v_proj.weight
else:
del v_proj.bias
else:
if del_param:
for i in [q_proj, k_proj, v_proj]:
if attr == "weight":
del i.weight
else:
del i.bias
q = ft_para_conf.slice_weight(q, 1)
k = ft_para_conf.slice_weight(k, 1)
v = ft_para_conf.slice_weight(v, 1)
if del_param:
# NOTE: dygraph_to_static/convert_call_func.py would log the converted
# function. For linear layer, if we delete the params, log would fail.
# And the log requires weight to be a 2D tensor.
# NOTE: Assignment to parameter 'weight' should be of type
# Parameter or None, thus delete before in case of tensor.
setattr(q_proj, attr, dummy_tensor)
setattr(k_proj, attr, dummy_tensor)
setattr(v_proj, attr, dummy_tensor)
if use_numpy:
p = paddle.to_tensor(np.concatenate([q, k, v], axis=-1))
else:
p = paddle.concat([q, k, v], axis=-1)
return p
def convert_params(faster_model,
model,
fuse_qkv=1,
use_fp16=False,
restore_data=False):
r"""
Convert parameters included in Transformer layer (`nn.TransformerEncoder`
and `gpt.modeling.TransformerDecoder`) from original models to the format
of faster models.
Args:
faster_model (Layer): The faster model object.
model (Layer): The Transformer layer. It can be an instance of
`nn.TransformerEncoder` or `gpt.modeling.TransformerDecoder`
currently, and `nn.TransformerDecoder` would be supported soon.
fuse_qkv (int): 0 for nofuse, 1 for fuse, 2 for fuse and delete the
unfused parameters. If environment variable `PPFG_QKV_MEM_OPT` is
set and the weights of q/k/v is fused, it will try to delete the
original unfused weights. Note the rollback to original model would
not be guarantee anymore when the faster model failed if the original
weights are deleted. Default to 1.
use_fp16 (bool): Whether to use float16. Maybe we should use the default
dtype as the highest priority later. Default to `False`.
restore_data (bool): If `False`, need to reload the weight values. It
should be `True` for weight loaded models. Default to `False`.
Returns:
defaultdict: Each value is a list including converted parameters in all
layers. For other parameters not included in Transformer module to
be converted, such as embeddings, you can achieve it by using the
returned dict `params` though `params['word_emb'].append()` directly
which would do CPU/GPU and fp32/fp16 transfer automatically.
"""
if fuse_qkv == 1:
fuse_qkv = 2 if os.getenv("PPFG_QKV_MEM_OPT", "0") == "1" else 1
ft_para_conf = get_ft_para_conf()
class _list(list):
def append(self, item):
if isinstance(item[0], nn.Layer):
# Axis is used for tensor slice in tensor parallel.
# Use None to make no slice on the tensor.
if len(item) == 2:
layer, attr = item
axis = None
else:
layer, attr, axis = item
param = getattr(layer, attr)
if axis is not None and isinstance(layer, nn.Linear):
param = ft_para_conf.slice_weight(param, axis)
param = transfer_param(
param,
is_bias=attr.endswith("bias"),
dtype="float16" if use_fp16 else "float32",
restore_data=restore_data)
# NOTE: Assignment to parameter 'weight' should be of type
# Parameter or None, thus delete first in case of param is
# a tensor.
# TODO(guosheng): Make slice_weight use `output_param=True`
# and remove delattr. Currently, if `param` is Tensor rather
# than Parameter, it would not be in state_dict.
delattr(layer, attr)
setattr(layer, attr, param)
else:
# NOTE: Compared with if branch, there is no layer attribute
# refered to the transfered param, thus we should set it as
# the layer attribute to be able to convert to static graph.
# Additionally, we suppose no need to process tensor parallel
# here since the param passed in might have been processed.
if len(item) == 2:
param, is_bias = item
attr_handle = lambda x: x
else:
param, is_bias, attr_handle = item
param = transfer_param(
param,
is_bias=is_bias,
dtype="float16" if use_fp16 else "float32",
restore_data=restore_data)
attr_handle(param)
return super().append(param)
params = defaultdict(_list)
def _convert(module):
if isinstance(
module,
(
nn.TransformerEncoder, # nn.TransformerDecoder,
paddlenlp.transformers.gpt.modeling.TransformerDecoder)):
num_layer = len(module.layers)
for i, layer in enumerate(module.layers):
if not ft_para_conf.is_load(i, num_layer):
continue
# fuse_qkv: 0 for nofuse, 1 for fuse,
# 2 for fuse and delete the unfused
if fuse_qkv == 0:
params["slf_q_weight"].append(
(layer.self_attn.q_proj, "weight", 1))
params["slf_q_bias"].append(
(layer.self_attn.q_proj, "bias", 1))
params["slf_k_weight"].append(
(layer.self_attn.k_proj, "weight", 1))
params["slf_k_bias"].append(
(layer.self_attn.k_proj, "bias", 1))
params["slf_v_weight"].append(
(layer.self_attn.v_proj, "weight", 1))
params["slf_v_bias"].append(
(layer.self_attn.v_proj, "bias", 1))
else:
# TODO(guosheng): Tensor with size 0 might be failed in
# paddle develop, thus use tensor with size 1 instead
# temporarily. Besides, we use 2D tensor since jit log
# requires that on linear weight. While size 0 seems all
# right in jit.to_static/jit.save.
dummy_tensor = paddle.zeros([1, 1])
w = _convert_qkv(layer.self_attn.q_proj,
layer.self_attn.k_proj,
layer.self_attn.v_proj,
attr="weight",
use_numpy=fuse_qkv == 2,
del_param=fuse_qkv == 2,
dummy_tensor=dummy_tensor)
b = _convert_qkv(layer.self_attn.q_proj,
layer.self_attn.k_proj,
layer.self_attn.v_proj,
attr="bias",
use_numpy=fuse_qkv == 2,
del_param=fuse_qkv == 2,
dummy_tensor=dummy_tensor)
params["slf_q_weight"].append((w, False))
params["slf_q_bias"].append((b, True))
# NOTE: Use `params["slf_q_weight"][-1]` rather than `w`,
# since the appended tensor might be a new transfered tensor.
# Besides, to allow convert_params be called more than once,
# we find a attr name not existing to avoid overwriting the
# existing attr.
attr = "slf_q_weight_" + str(i)
while hasattr(faster_model, attr):
attr += "_"
setattr(faster_model, attr, params["slf_q_weight"][-1])
attr = "slf_q_bias_" + str(i)
while hasattr(faster_model, attr):
attr += "_"
setattr(faster_model, attr, params["slf_q_bias"][-1])
for key in [
f"slf_{m}_{n}" for m in ("k", "v")
for n in ("weight", "bias")
]:
params[key].append(
(dummy_tensor,
True if key.endswith("bias") else False))
attr = key + "_" + str(i)
while hasattr(faster_model, attr):
attr += "_"
setattr(faster_model, attr, params[key][-1])
params["slf_out_weight"].append(
(layer.self_attn.out_proj, "weight", 0))
params["slf_out_bias"].append(
(layer.self_attn.out_proj, "bias"))
params["slf_ln_weight"].append((layer.norm1, "weight"))
params["slf_ln_bias"].append((layer.norm1, "bias"))
# Slice tensor when append according to axis(1 or 0) if parallel
# is enable.
params["ffn_inter_weight"].append((layer.linear1, "weight", 1))
params["ffn_inter_bias"].append((layer.linear1, "bias", 1))
params["ffn_out_weight"].append((layer.linear2, "weight", 0))
params["ffn_out_bias"].append((layer.linear2, "bias"))
params["ffn_ln_weight"].append((layer.norm2, "weight"))
params["ffn_ln_bias"].append((layer.norm2, "bias"))
if isinstance(module, nn.TransformerDecoder):
# TODO(guosheng): support nn.TransformerDecoder
pass
if module.norm is not None:
params["decoder_ln_weight"].append((module.norm, "weight"))
params["decoder_ln_bias"].append((module.norm, "bias"))
model.apply(_convert)
return params
class InferTransformerDecoding(nn.Layer):
def __init__(self,
decoder,
word_embedding,
positional_embedding,
linear,
num_decoder_layers,
n_head,
d_model,
bos_id=0,
eos_id=1,
decoding_strategy="beam_search",
beam_size=4,
topk=1,
topp=0.0,
max_out_len=256,
diversity_rate=0.0,
decoding_lib=None,
use_fp16_decoding=False,
rel_len=False,
alpha=0.6):
# if decoding_lib is None:
# raise ValueError(
# "The args decoding_lib must be set to use FasterTransformer. ")
# elif not os.path.exists(decoding_lib):
# raise ValueError("The path to decoding lib is not exist.")
if decoding_lib is not None and os.path.isfile(decoding_lib):
# Maybe it has been loadad by `ext_utils.load`
if "FasterTransformer" not in LOADED_EXT.keys():
ops = paddle.utils.cpp_extension.load_op_meta_info_and_register_op(
decoding_lib)
LOADED_EXT["FasterTransformer"] = ops
else:
if decoding_lib is not None:
logger.warning(
"The specified decoding_lib does not exist, and it will be built automatically."
)
load("FasterTransformer", verbose=True)
size_per_head = d_model / n_head
# fuse_qkv can only support size_per_head of [32, 64, 128].
if size_per_head in [32, 64, 128]:
self._fuse_qkv = True
else:
self._fuse_qkv = False
super(InferTransformerDecoding, self).__init__()
for arg, value in locals().items():
if arg not in [
"self", "decoder", "word_embedding", "positional_embedding",
"linear"
]:
setattr(self, "_" + arg, value)
# process weights
if use_fp16_decoding:
for mod in decoder.layers:
mod.norm1.weight = transfer_param(mod.norm1.weight)
mod.norm1.bias = transfer_param(mod.norm1.bias, is_bias=True)
mod.self_attn.q_proj.weight = transfer_param(
mod.self_attn.q_proj.weight)
mod.self_attn.q_proj.bias = transfer_param(
mod.self_attn.q_proj.bias, is_bias=True)
mod.self_attn.k_proj.weight = transfer_param(
mod.self_attn.k_proj.weight)
mod.self_attn.k_proj.bias = transfer_param(
mod.self_attn.k_proj.bias, is_bias=True)
mod.self_attn.v_proj.weight = transfer_param(
mod.self_attn.v_proj.weight)
mod.self_attn.v_proj.bias = transfer_param(
mod.self_attn.v_proj.bias, is_bias=True)
mod.self_attn.out_proj.weight = transfer_param(
mod.self_attn.out_proj.weight)
mod.self_attn.out_proj.bias = transfer_param(
mod.self_attn.out_proj.bias, is_bias=True)
mod.norm2.weight = transfer_param(mod.norm2.weight)
mod.norm2.bias = transfer_param(mod.norm2.bias, is_bias=True)
mod.cross_attn.q_proj.weight = transfer_param(
mod.cross_attn.q_proj.weight)
mod.cross_attn.q_proj.bias = transfer_param(
mod.cross_attn.q_proj.bias, is_bias=True)
mod.cross_attn.k_proj.weight = transfer_param(
mod.cross_attn.k_proj.weight)
mod.cross_attn.k_proj.bias = transfer_param(
mod.cross_attn.k_proj.bias, is_bias=True)
mod.cross_attn.v_proj.weight = transfer_param(
mod.cross_attn.v_proj.weight)
mod.cross_attn.v_proj.bias = transfer_param(
mod.cross_attn.v_proj.bias, is_bias=True)
mod.cross_attn.out_proj.weight = transfer_param(
mod.cross_attn.out_proj.weight)
mod.cross_attn.out_proj.bias = transfer_param(
mod.cross_attn.out_proj.bias, is_bias=True)
mod.norm3.weight = transfer_param(mod.norm3.weight)
mod.norm3.bias = transfer_param(mod.norm3.bias, is_bias=True)
mod.linear1.weight = transfer_param(mod.linear1.weight)
mod.linear1.bias = transfer_param(mod.linear1.bias,
is_bias=True)
mod.linear2.weight = transfer_param(mod.linear2.weight)
mod.linear2.bias = transfer_param(mod.linear2.bias,
is_bias=True)
decoder.norm.weight = transfer_param(decoder.norm.weight)
decoder.norm.bias = transfer_param(decoder.norm.bias, is_bias=True)
linear.weight = transfer_param(linear.weight)
linear.bias = transfer_param(linear.bias, is_bias=True)
positional_embedding.weight = transfer_param(
positional_embedding.weight)
word_embedding.weight = transfer_param(word_embedding.weight)
self.slf_ln_weight = []
self.slf_ln_bias = []
self.slf_q_weight = []
self.slf_q_bias = []
self.slf_k_weight = []
self.slf_k_bias = []
self.slf_v_weight = []
self.slf_v_bias = []
self.slf_out_weight = []
self.slf_out_bias = []
self.cross_ln_weight = []
self.cross_ln_bias = []
self.cross_q_weight = []
self.cross_q_bias = []
self.cross_k_weight = []
self.cross_k_bias = []
self.cross_v_weight = []
self.cross_v_bias = []
self.cross_out_weight = []
self.cross_out_bias = []
self.ffn_ln_weight = []
self.ffn_ln_bias = []
self.ffn_inter_weight = []
self.ffn_inter_bias = []
self.ffn_out_weight = []
self.ffn_out_bias = []