1011 lines
40 KiB
Python
1011 lines
40 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright 2021-2022 The Alibaba DAMO NLP Team Authors.
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# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
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# Copyright (c) 2019, NVIDIA CORPORATION. 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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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import logging
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import math
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import torch
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import torch.nn.functional as F
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from megatron_util import mpu
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from torch import nn
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from modelscope.utils.nlp.distributed import (normal_init_method,
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scaled_init_method)
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from .configuration import PlugNLGConfig, PlugNLUConfig
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logger = logging.getLogger(__name__)
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def gelu(x):
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"""Implementation of the gelu activation function.
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For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
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0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
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"""
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return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
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def swish(x):
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return x * torch.sigmoid(x)
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ACT2FN = {'gelu': gelu, 'relu': torch.nn.functional.relu, 'swish': swish}
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class BertLayerNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-12):
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"""Construct a layernorm module in the TF style (epsilon inside the square root).
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"""
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super(BertLayerNorm, self).__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.bias = nn.Parameter(torch.zeros(hidden_size))
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self.variance_epsilon = eps
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def forward(self, x):
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u = x.mean(-1, keepdim=True)
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s = (x - u).pow(2).mean(-1, keepdim=True)
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x = (x - u) / torch.sqrt(s + self.variance_epsilon)
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return self.weight * x + self.bias
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class BertEmbeddings(nn.Module):
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"""Construct the embeddings from word, position and token_type embeddings.
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"""
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def __init__(self, config):
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super(BertEmbeddings, self).__init__()
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self.word_embeddings = mpu.VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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init_method=normal_init_method(
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mean=0.0, std=config.initializer_range))
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self.position_embeddings = nn.Embedding(config.max_position_embeddings,
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config.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
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config.hidden_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.fp32_layernorm = config.fp32_layernorm
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self.fp32_embedding = config.fp32_embedding
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self.fp32_tokentypes = config.fp32_tokentypes
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self.LayerNorm = BertLayerNorm(
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config.hidden_size, eps=config.layernorm_epsilon)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, input_ids, token_type_ids=None, position_ids=None):
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seq_length = input_ids.size(1)
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if position_ids is None:
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position_ids = torch.arange(
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seq_length, dtype=torch.long, device=input_ids.device)
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position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
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if token_type_ids is None:
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token_type_ids = torch.zeros_like(input_ids)
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words_embeddings = self.word_embeddings(input_ids)
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position_embeddings = self.position_embeddings(position_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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if not self.fp32_tokentypes:
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embeddings = words_embeddings + position_embeddings + token_type_embeddings
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if self.fp32_embedding and not self.fp32_layernorm:
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embeddings = embeddings.half()
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previous_type = embeddings.type()
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if self.fp32_layernorm:
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embeddings = embeddings.float()
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embeddings = self.LayerNorm(embeddings)
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if self.fp32_layernorm:
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if self.fp32_embedding:
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embeddings = embeddings.half()
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else:
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embeddings = embeddings.type(previous_type)
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else:
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embeddings = words_embeddings.float() + position_embeddings.float(
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) + token_type_embeddings.float()
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if self.fp32_tokentypes and not self.fp32_layernorm:
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embeddings = embeddings.half()
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previous_type = embeddings.type()
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if self.fp32_layernorm:
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embeddings = embeddings.float()
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embeddings = self.LayerNorm(embeddings)
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if self.fp32_layernorm:
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if self.fp32_tokentypes:
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embeddings = embeddings.half()
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else:
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embeddings = embeddings.type(previous_type)
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embeddings = self.dropout(embeddings)
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return embeddings
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class BertSelfOutput(nn.Module):
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def __init__(self, config):
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super(BertSelfOutput, self).__init__()
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if hasattr(config, 'deep_init') and config.deep_init:
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init_method = scaled_init_method(
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mean=0.0,
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std=config.initializer_range,
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num_layers=config.num_hidden_layers)
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else:
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init_method = normal_init_method(
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mean=0.0, std=config.initializer_range)
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self.dense = mpu.RowParallelLinear(
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input_size=config.hidden_size,
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output_size=config.hidden_size,
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bias=True,
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input_is_parallel=True,
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stride=1,
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init_method=init_method)
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self.fp32_layernorm = config.fp32_layernorm
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if not config.pre_ln:
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self.LayerNorm = BertLayerNorm(
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config.hidden_size, eps=config.layernorm_epsilon)
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else:
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self.LayerNorm = None
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(
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self,
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hidden_states,
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input_tensor,
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):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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ln_input = hidden_states + input_tensor
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if self.LayerNorm is not None:
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previous_type = ln_input.type()
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if self.fp32_layernorm:
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ln_input = ln_input.float()
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hidden_states = self.LayerNorm(ln_input)
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if self.fp32_layernorm:
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hidden_states = hidden_states.type(previous_type)
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else:
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hidden_states = ln_input
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return hidden_states
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class BertAttention(nn.Module):
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def __init__(self, config):
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super(BertAttention, self).__init__()
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self.fp32_layernorm = config.fp32_layernorm
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if config.pre_ln:
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self.LayerNorm = BertLayerNorm(
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config.hidden_size, eps=config.layernorm_epsilon)
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else:
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self.LayerNorm = None
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self.self = mpu.BertParallelSelfAttention(
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hidden_size=config.hidden_size,
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num_attention_heads=config.num_attention_heads,
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dropout_prob=config.attention_probs_dropout_prob,
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output_parallel=True,
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init_method=normal_init_method(
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mean=0.0, std=config.initializer_range),
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separate=config.attn_separate)
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self.output = BertSelfOutput(config)
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def forward(
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self,
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input_tensor,
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attention_mask,
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):
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if self.LayerNorm is not None:
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ln_input = input_tensor
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previous_type = input_tensor.type()
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if self.fp32_layernorm:
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ln_input = input_tensor.float()
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ln_output = self.LayerNorm(ln_input)
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if self.fp32_layernorm:
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ln_output = ln_output.type(previous_type)
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self_output = self.self(
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ln_output,
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attention_mask,
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)
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else:
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self_output = self.self(
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input_tensor,
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attention_mask,
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)
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attention_output = self.output(
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self_output,
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input_tensor,
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)
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return attention_output
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class BertIntermediate(nn.Module):
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def __init__(self, config):
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super(BertIntermediate, self).__init__()
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self.dense = mpu.ColumnParallelLinear(
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input_size=config.hidden_size,
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output_size=config.intermediate_size,
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bias=True,
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gather_output=False,
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stride=1,
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init_method=normal_init_method(
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mean=0.0, std=config.initializer_range))
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self.intermediate_act_fn = ACT2FN[config.hidden_act] \
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if isinstance(config.hidden_act, str) else config.hidden_act
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def forward(
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self,
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hidden_states,
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):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class BertOutput(nn.Module):
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def __init__(self, config):
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super(BertOutput, self).__init__()
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if hasattr(config, 'deep_init') and config.deep_init:
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init_method = scaled_init_method(
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mean=0.0,
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std=config.initializer_range,
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num_layers=config.num_hidden_layers)
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else:
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init_method = normal_init_method(
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mean=0.0, std=config.initializer_range)
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self.dense = mpu.RowParallelLinear(
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input_size=config.intermediate_size,
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output_size=config.hidden_size,
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bias=True,
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input_is_parallel=True,
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stride=1,
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init_method=init_method)
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self.fp32_layernorm = config.fp32_layernorm
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if not config.pre_ln:
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self.LayerNorm = BertLayerNorm(
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config.hidden_size, eps=config.layernorm_epsilon)
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else:
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self.LayerNorm = None
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(
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self,
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hidden_states,
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input_tensor,
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):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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ln_input = hidden_states + input_tensor
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if self.LayerNorm is not None:
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previous_type = ln_input.type()
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if self.fp32_layernorm:
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ln_input = ln_input.float()
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hidden_states = self.LayerNorm(ln_input)
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if self.fp32_layernorm:
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hidden_states = hidden_states.type(previous_type)
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else:
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hidden_states = ln_input
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return hidden_states
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class BertLayer(nn.Module):
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def __init__(self, config):
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super(BertLayer, self).__init__()
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self.attention = BertAttention(config)
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self.intermediate = BertIntermediate(config)
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self.output = BertOutput(config)
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self.fp32_layernorm = config.fp32_layernorm
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if config.pre_ln:
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self.LayerNorm = BertLayerNorm(
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config.hidden_size, eps=config.layernorm_epsilon)
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else:
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self.LayerNorm = None
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def forward(self, hidden_states, attention_mask):
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attention_output = self.attention(hidden_states, attention_mask)
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if self.LayerNorm is not None:
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ln_input = attention_output
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previous_type = attention_output.type()
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if self.fp32_layernorm:
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ln_input = attention_output.float()
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ln_output = self.LayerNorm(ln_input)
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if self.fp32_layernorm:
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ln_output = ln_output.type(previous_type)
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intermediate_output = self.intermediate(ln_output)
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else:
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.output(intermediate_output, attention_output)
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return layer_output
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class BertEncoder(nn.Module):
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def __init__(self, config):
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super(BertEncoder, self).__init__()
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self.layer = nn.ModuleList(
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[BertLayer(config) for _ in range(config.num_hidden_layers)])
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self.fp32_layernorm = config.fp32_layernorm
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if config.pre_ln:
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self.LayerNorm = BertLayerNorm(
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config.hidden_size, eps=config.layernorm_epsilon)
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else:
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self.LayerNorm = None
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def forward(
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self,
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hidden_states,
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attention_mask,
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output_all_encoded_layers=True,
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checkpoint_activations=False,
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detach_index=-1,
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):
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all_encoder_layers = []
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def custom(start, end):
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def custom_forward(*inputs):
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layers = self.layer[start:end]
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x_ = inputs[0]
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for layer in layers:
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x_ = layer(x_, inputs[1])
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return x_
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return custom_forward
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if checkpoint_activations:
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layer_idx = 0
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num_layers = len(self.layer)
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chunk_length = 1
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while layer_idx < num_layers:
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hidden_states = mpu.checkpoint(
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custom(layer_idx, layer_idx + chunk_length), hidden_states,
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attention_mask * 1)
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if detach_index == layer_idx:
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hidden_states.detach_()
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layer_idx += chunk_length
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# decoder layers
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else:
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for i, layer_module in enumerate(self.layer):
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hidden_states = layer_module(hidden_states, attention_mask)
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if detach_index == i:
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hidden_states.detach_()
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if i == len(self.layer) - 1 and self.LayerNorm is not None:
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previous_type = hidden_states.type()
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if self.fp32_layernorm:
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hidden_states = hidden_states.float()
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hidden_states = self.LayerNorm(hidden_states)
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if self.fp32_layernorm:
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hidden_states = hidden_states.type(previous_type)
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if output_all_encoded_layers:
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all_encoder_layers.append(hidden_states)
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if not output_all_encoded_layers or checkpoint_activations:
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if self.LayerNorm is not None:
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previous_type = hidden_states.type()
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if self.fp32_layernorm:
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hidden_states = hidden_states.float()
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hidden_states = self.LayerNorm(hidden_states)
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if self.fp32_layernorm:
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hidden_states = hidden_states.type(previous_type)
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all_encoder_layers.append(hidden_states)
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return all_encoder_layers
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class BertPooler(nn.Module):
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def __init__(self, config):
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super(BertPooler, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.activation = nn.Tanh()
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def forward(self, hidden_states):
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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first_token_tensor = hidden_states[:, 0]
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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class BertPredictionHeadTransform(nn.Module):
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def __init__(self, config):
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super(BertPredictionHeadTransform, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.transform_act_fn = ACT2FN[config.hidden_act] \
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if isinstance(config.hidden_act, str) else config.hidden_act
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self.LayerNorm = BertLayerNorm(
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config.hidden_size, eps=config.layernorm_epsilon)
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self.fp32_layernorm = config.fp32_layernorm
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.transform_act_fn(hidden_states)
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previous_type = hidden_states.type()
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if self.fp32_layernorm:
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hidden_states = hidden_states.float()
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hidden_states = self.LayerNorm(hidden_states)
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if self.fp32_layernorm:
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hidden_states = hidden_states.type(previous_type)
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return hidden_states
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|
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class BertLMPredictionHead(nn.Module):
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def __init__(self, config, bert_model_embedding_weights):
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super(BertLMPredictionHead, self).__init__()
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self.transform = BertPredictionHeadTransform(config)
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# The output weights are the same as the input embeddings, but there is
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# an output-only bias for each token.
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self.decoder_weight = bert_model_embedding_weights
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self.bias = nn.Parameter(
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torch.zeros(bert_model_embedding_weights.size(0)))
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self.bias.model_parallel = True
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self.fp32_embedding = config.fp32_embedding
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self.fp32_layernorm = config.fp32_layernorm
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def convert_to_type(tensor):
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if self.fp32_embedding:
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return tensor.half()
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else:
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return tensor
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self.type_converter = convert_to_type
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self.converted = False
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def forward(self, hidden_states):
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if not self.converted:
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self.converted = True
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if self.fp32_embedding:
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self.transform.half()
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if self.fp32_layernorm:
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self.transform.LayerNorm.float()
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hidden_states = self.transform(self.type_converter(hidden_states))
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hidden_states = mpu.copy_to_model_parallel_region(hidden_states)
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hidden_states = F.linear(
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self.type_converter(hidden_states),
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self.type_converter(self.decoder_weight),
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self.type_converter(self.bias))
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return hidden_states
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class BertPreTrainingHeads(nn.Module):
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def __init__(self, config, bert_model_embedding_weights):
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super(BertPreTrainingHeads, self).__init__()
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self.predictions = BertLMPredictionHead(config,
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bert_model_embedding_weights)
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self.seq_relationship = nn.Linear(config.hidden_size, 3)
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def forward(self, sequence_output, pooled_output):
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|
prediction_scores = self.predictions(sequence_output)
|
|
for p in self.seq_relationship.parameters():
|
|
if p is None:
|
|
continue
|
|
pooled_output = pooled_output.type_as(p)
|
|
seq_relationship_score = self.seq_relationship(pooled_output)
|
|
return prediction_scores, seq_relationship_score
|
|
|
|
|
|
class PreTrainedBertModel(nn.Module):
|
|
""" An abstract class to handle weights initialization and
|
|
a simple interface for dowloading and loading pretrained models.
|
|
"""
|
|
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super(PreTrainedBertModel, self).__init__()
|
|
if not isinstance(config, PlugNLUConfig) and not isinstance(
|
|
config, PlugNLGConfig):
|
|
raise ValueError(
|
|
'Parameter config in `{}(config)` should be an instance of class `BertConfig`. '
|
|
'To create a model from a Google pretrained model use '
|
|
'`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`'.format(
|
|
self.__class__.__name__, self.__class__.__name__))
|
|
self.config = config
|
|
|
|
def init_bert_weights(self, module):
|
|
""" Initialize the weights.
|
|
"""
|
|
if isinstance(module, (nn.Linear, nn.Embedding)):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(
|
|
mean=0.0, std=self.config.initializer_range)
|
|
elif isinstance(module, BertLayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
if isinstance(module, nn.Linear) and module.bias is not None:
|
|
module.bias.data.zero_()
|
|
|
|
|
|
class BertModel(PreTrainedBertModel):
|
|
"""BERT model ("Bidirectional Embedding Representations from a Transformer").
|
|
|
|
Params:
|
|
config: a BertConfig class instance with the configuration to build a new model
|
|
|
|
Inputs:
|
|
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
|
|
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
|
|
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
|
a `sentence B` token (see BERT paper for more details).
|
|
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
|
|
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
|
input sequence length in the current batch. It's the mask that we typically use for attention when
|
|
a batch has varying length sentences.
|
|
`output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as
|
|
described below. Default: `True`.
|
|
|
|
Outputs: Tuple of (encoded_layers, pooled_output)
|
|
`encoded_layers`: controled by `output_all_encoded_layers` argument:
|
|
- `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
|
|
of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
|
|
encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
|
|
- `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
|
|
to the last attention block of shape [batch_size, sequence_length, hidden_size],
|
|
`pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
|
|
classifier pretrained on top of the hidden state associated to the first character of the
|
|
input (`CLF`) to train on the Next-Sentence task (see BERT's paper).
|
|
|
|
Examples:
|
|
>>> # Already been converted into WordPiece token ids
|
|
>>> input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
>>> input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
>>> token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
>>> config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
|
>>> num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
>>> model = modeling.BertModel(config=config)
|
|
>>> all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super(BertModel, self).__init__(config)
|
|
self.embeddings = BertEmbeddings(config)
|
|
self.encoder = BertEncoder(config)
|
|
self.pooler = BertPooler(config)
|
|
self.apply(self.init_bert_weights)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
token_type_ids=None,
|
|
attention_mask=None,
|
|
output_all_encoded_layers=True,
|
|
checkpoint_activations=False,
|
|
detach_index=-1,
|
|
):
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones_like(input_ids)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros_like(input_ids)
|
|
|
|
# We create a 3D attention mask from a 2D tensor mask.
|
|
# Sizes are [batch_size, 1, 1, to_seq_length]
|
|
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
|
|
# this attention mask is more simple than the triangular masking of causal attention
|
|
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
|
|
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
|
|
|
|
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
|
|
# masked positions, this operation will create a tensor which is 0.0 for
|
|
# positions we want to attend and -10000.0 for masked positions.
|
|
# Since we are adding it to the raw scores before the softmax, this is
|
|
# effectively the same as removing these entirely.
|
|
extended_attention_mask = extended_attention_mask.to(
|
|
dtype=next(self.encoder.parameters()).dtype) # fp16 compatibility
|
|
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
|
|
|
|
embedding_output = self.embeddings(input_ids, token_type_ids)
|
|
encoded_layers = self.encoder(
|
|
embedding_output,
|
|
extended_attention_mask,
|
|
output_all_encoded_layers=output_all_encoded_layers,
|
|
checkpoint_activations=checkpoint_activations,
|
|
detach_index=detach_index)
|
|
sequence_output = encoded_layers[-1]
|
|
for p in self.pooler.parameters():
|
|
if p is None:
|
|
continue
|
|
sequence_output = sequence_output.type_as(p)
|
|
break
|
|
|
|
pooled_output = sequence_output[:, 0]
|
|
if not output_all_encoded_layers or checkpoint_activations:
|
|
encoded_layers = encoded_layers[-1]
|
|
return encoded_layers, pooled_output
|
|
|
|
|
|
class DecodeLayer(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super(DecodeLayer, self).__init__()
|
|
init_method = normal_init_method(
|
|
mean=0.0, std=config.initializer_range)
|
|
output_layer_init_method = scaled_init_method(
|
|
mean=0.0,
|
|
std=config.initializer_range,
|
|
num_layers=config.num_hidden_layers)
|
|
|
|
self.attention = mpu.GPT2ParallelSelfAttention(
|
|
hidden_size=config.hidden_size,
|
|
num_attention_heads=config.num_attention_heads,
|
|
attention_dropout_prob=config.attention_probs_dropout_prob,
|
|
output_dropout_prob=config.hidden_dropout_prob,
|
|
init_method=init_method,
|
|
output_layer_init_method=output_layer_init_method,
|
|
)
|
|
|
|
self.cross_attention = mpu.PalmParallelCrossAttention(
|
|
hidden_size=config.hidden_size,
|
|
num_attention_heads=config.num_attention_heads,
|
|
attention_dropout_prob=config.attention_probs_dropout_prob,
|
|
output_dropout_prob=config.hidden_dropout_prob,
|
|
init_method=init_method,
|
|
attn_separate=False,
|
|
output_layer_init_method=output_layer_init_method,
|
|
)
|
|
|
|
self.input_layernorm = BertLayerNorm(
|
|
config.hidden_size, eps=config.layernorm_epsilon)
|
|
self.post_attention_layernorm = BertLayerNorm(
|
|
config.hidden_size, eps=config.layernorm_epsilon)
|
|
self.post_cross_attention_layernorm = BertLayerNorm(
|
|
config.hidden_size, eps=config.layernorm_epsilon)
|
|
|
|
self.intermediate = mpu.ColumnParallelLinear(
|
|
config.hidden_size,
|
|
config.intermediate_size,
|
|
gather_output=False,
|
|
init_method=init_method,
|
|
)
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act] \
|
|
if isinstance(config.hidden_act, str) else config.hidden_act
|
|
self.output = mpu.RowParallelLinear(
|
|
config.intermediate_size,
|
|
config.hidden_size,
|
|
input_is_parallel=True,
|
|
init_method=output_layer_init_method,
|
|
)
|
|
|
|
self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)
|
|
self.fp32_layernorm = config.fp32_layernorm
|
|
|
|
def convert_to_type(tensor):
|
|
if self.fp32_layernorm:
|
|
return tensor.float()
|
|
else:
|
|
return tensor
|
|
|
|
self.type_converter = convert_to_type
|
|
|
|
# def forward(self, hidden_states, enc_attn_mask, dec_attn_mask):
|
|
def forward(self,
|
|
hidden_states,
|
|
enc_hidden_states,
|
|
enc_attn_mask,
|
|
dec_attn_mask,
|
|
is_infer=False):
|
|
residual = hidden_states
|
|
previous_type = hidden_states.type()
|
|
hidden_states = self.input_layernorm(
|
|
self.type_converter(hidden_states))
|
|
if self.fp32_layernorm:
|
|
hidden_states = hidden_states.type(previous_type)
|
|
hidden_states = self.attention(
|
|
hidden_states, dec_attn_mask, is_infer=is_infer)
|
|
|
|
hidden_states = residual + hidden_states
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(
|
|
self.type_converter(hidden_states))
|
|
if self.fp32_layernorm:
|
|
hidden_states = hidden_states.type(previous_type)
|
|
hidden_states = self.cross_attention(hidden_states, enc_hidden_states,
|
|
enc_attn_mask)
|
|
hidden_states = residual + hidden_states
|
|
residual = hidden_states
|
|
hidden_states = self.post_cross_attention_layernorm(
|
|
self.type_converter(hidden_states))
|
|
if self.fp32_layernorm:
|
|
hidden_states = hidden_states.type(previous_type)
|
|
hidden_states = self.intermediate(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
|
|
hidden_states = self.output(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
return hidden_states
|
|
|
|
|
|
class BertDecoder(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super(BertDecoder, self).__init__()
|
|
self.layer = nn.ModuleList(
|
|
[DecodeLayer(config) for _ in range(config.dec_hidden_layers)])
|
|
|
|
self.final_layernorm = BertLayerNorm(
|
|
config.hidden_size, eps=config.layernorm_epsilon)
|
|
self.fp32_layernorm = config.fp32_layernorm
|
|
|
|
def forward(self,
|
|
hidden_states,
|
|
enc_hidden_states,
|
|
enc_attn_mask,
|
|
dec_attn_mask,
|
|
checkpoint_activations=False,
|
|
output_all_encoded_layers=False,
|
|
is_infer=False):
|
|
|
|
def custom(start, end):
|
|
|
|
def custom_forward(*inputs):
|
|
layers = self.layer[start:end]
|
|
x_ = inputs[0]
|
|
for layer in layers:
|
|
x_ = layer(
|
|
x_,
|
|
inputs[1],
|
|
inputs[2],
|
|
dec_attn_mask * 1,
|
|
is_infer=is_infer)
|
|
return x_
|
|
|
|
return custom_forward
|
|
|
|
pre_enc_hidden = enc_hidden_states.data
|
|
if checkpoint_activations:
|
|
layer_idx = 0
|
|
num_layers = len(self.layer)
|
|
chunk_length = 1
|
|
while layer_idx < num_layers:
|
|
hidden_states = mpu.checkpoint(
|
|
custom(layer_idx, layer_idx + chunk_length), hidden_states,
|
|
enc_hidden_states, enc_attn_mask * 1)
|
|
enc_hidden_states.data = pre_enc_hidden
|
|
layer_idx += chunk_length
|
|
else:
|
|
for i, layer_module in enumerate(self.layer):
|
|
hidden_states = layer_module(
|
|
hidden_states,
|
|
enc_hidden_states,
|
|
enc_attn_mask,
|
|
dec_attn_mask,
|
|
is_infer=is_infer)
|
|
|
|
previous_type = hidden_states.type()
|
|
if self.fp32_layernorm:
|
|
hidden_states = hidden_states.float()
|
|
hidden_states = self.final_layernorm(hidden_states)
|
|
if self.fp32_layernorm:
|
|
hidden_states = hidden_states.type(previous_type)
|
|
|
|
return [hidden_states]
|
|
|
|
|
|
class DecodeModel(PreTrainedBertModel):
|
|
|
|
def __init__(self, config):
|
|
super(DecodeModel, self).__init__(config)
|
|
self.decoder = BertDecoder(config)
|
|
self.apply(self.init_bert_weights)
|
|
|
|
def forward(self,
|
|
embeddings,
|
|
sequence_output,
|
|
decode_input_ids,
|
|
position_ids=None,
|
|
enc_attn_mask=None,
|
|
dec_attn_mask=None,
|
|
checkpoint_activations=False,
|
|
is_infer=False):
|
|
extended_attention_mask = enc_attn_mask.unsqueeze(1).unsqueeze(2)
|
|
extended_attention_mask = extended_attention_mask.to(
|
|
dtype=next(self.decoder.parameters()).dtype) # fp16 compatibility
|
|
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
|
|
|
|
embedding_output = embeddings(decode_input_ids)
|
|
sequence_output = self.decoder(
|
|
embedding_output,
|
|
sequence_output,
|
|
extended_attention_mask,
|
|
dec_attn_mask,
|
|
checkpoint_activations=False,
|
|
is_infer=is_infer)
|
|
return sequence_output[-1]
|
|
|
|
|
|
class PalmForPreTraining(PreTrainedBertModel):
|
|
|
|
def __init__(self, config):
|
|
super(PalmForPreTraining, self).__init__(config)
|
|
self.bert = BertModel(config)
|
|
self.cls = BertPreTrainingHeads(
|
|
config, self.bert.embeddings.word_embeddings.weight)
|
|
self.decoder = DecodeModel(config)
|
|
self.apply(self.init_bert_weights)
|
|
|
|
def forward(self,
|
|
input_ids,
|
|
token_type_ids=None,
|
|
attention_mask=None,
|
|
decode_input_ids=None,
|
|
position_ids=None,
|
|
decode_attention_mask=None,
|
|
lm_labels=None,
|
|
checkpoint_activations=False,
|
|
is_infer=False,
|
|
sequence_output=None,
|
|
parallel_output=True):
|
|
if sequence_output is None:
|
|
sequence_output, pooled_output = self.bert(
|
|
input_ids,
|
|
token_type_ids,
|
|
attention_mask,
|
|
output_all_encoded_layers=False,
|
|
checkpoint_activations=checkpoint_activations)
|
|
prediction_scores, seq_relationship_score = self.cls(
|
|
sequence_output, pooled_output)
|
|
else:
|
|
prediction_scores = None
|
|
sequence_output = sequence_output.to(
|
|
dtype=next(self.decoder.parameters()).dtype)
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones_like(input_ids)
|
|
decode_output = self.decoder(
|
|
self.bert.embeddings,
|
|
sequence_output,
|
|
decode_input_ids,
|
|
position_ids,
|
|
attention_mask,
|
|
decode_attention_mask,
|
|
checkpoint_activations=checkpoint_activations,
|
|
is_infer=is_infer)
|
|
|
|
transformer_output_parallel = mpu.copy_to_model_parallel_region(
|
|
decode_output)
|
|
|
|
logits_parallel = F.linear(transformer_output_parallel,
|
|
self.bert.embeddings.word_embeddings.weight)
|
|
|
|
if parallel_output:
|
|
return prediction_scores, logits_parallel
|
|
if is_infer:
|
|
return prediction_scores, mpu.gather_from_model_parallel_region(
|
|
logits_parallel), sequence_output
|
|
return prediction_scores, mpu.gather_from_model_parallel_region(
|
|
logits_parallel)
|
|
|
|
|
|
class PlugModel(torch.nn.Module):
|
|
"""
|
|
The bare Plug Model transformer outputting raw hidden-states without any specific head on top.
|
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
Parameters:
|
|
config ([`PlugNLGConfig`]): Model configuration class with all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the [`~DistributedPlug.initialize_model`] method to load the model weights.
|
|
Examples:
|
|
|
|
>>> # The PLUG model has 27B parameters and usually need to run on multiple GPUs. The example given
|
|
>>> # here only initializes a slice of the model on a single GPU.
|
|
>>> # Check out the [`~DistributedPipeline.__init__`] method to initialize entire PLUG model.
|
|
>>> from modelscope.models.nlp.plug import PlugNLGConfig, PlugModel
|
|
|
|
>>> # Initializing a Plug configuration
|
|
>>> configuration = PlugNLGConfig()
|
|
|
|
>>> # Initializing a model from the configuration
|
|
>>> model = PlugModel(configuration)
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super(PlugModel, self).__init__()
|
|
self.config = config
|
|
self.model = PalmForPreTraining(self.config)
|
|
|
|
def forward(self,
|
|
input_tokens,
|
|
token_type_ids=None,
|
|
attention_mask=None,
|
|
target_tokens=None,
|
|
position_ids=None,
|
|
decode_attention_mask=None,
|
|
checkpoint_activations=False,
|
|
is_infer=False,
|
|
sequence_output=None,
|
|
parallel_output=True):
|
|
"""
|
|
Parameters:
|
|
input_tokens (`torch.LongTensor` of shape `(batch_size, input_tokens_length)`):
|
|
`input_tokens_length` = `sequence_length`. Indices of input sequence tokens in the vocabulary.
|
|
Indices can be obtained using transformers [`BertTokenizer`]. See
|
|
[`TextGenerationPreprocessor.__call__`] for details.
|
|
token_type_ids (`torch.LongTensor` of shape `(batch_size, input_tokens_length)`, *optional*, defaults to
|
|
None):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
|
|
1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*, defaults to None):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
target_tokens (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*, defaults to None):
|
|
Target token ids(labels) for language modeling. Note that the labels **are shifted** inside the model,
|
|
i.e. you can set `target_tokens = input_tokens` Indices are selected in
|
|
`[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only
|
|
computed for labels in `[0, ..., config.vocab_size]`
|
|
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*, defaults to None):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
|
|
`[0, config.max_position_embeddings - 1]`.
|
|
|
|
decode_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*, defaults
|
|
to None):
|
|
Mask to avoid performing attention on padding token indices of target tokens. Mask values selected in
|
|
`[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
checkpoint_activations (`boolean`, *optional*, defaults to `False`):
|
|
Whether gradient checkpointing is activated for this model or not.
|
|
is_infer (`boolean`, *optional*, defaults to `False`):
|
|
Whether or not to perform single inference.
|
|
sequence_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*,
|
|
defaults to None):
|
|
Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the
|
|
model. A single forward() call can produce one single token. To generate the current token, the
|
|
sequence_output generated by the `forward()` of the previous token is required.
|
|
parallel_output (`boolean`, *optional*, defaults to `True`):
|
|
To parallel return output, or gather it before return.
|
|
|
|
|
|
"""
|
|
return self.model(
|
|
input_tokens,
|
|
token_type_ids,
|
|
attention_mask,
|
|
target_tokens,
|
|
position_ids,
|
|
decode_attention_mask,
|
|
checkpoint_activations=checkpoint_activations,
|
|
is_infer=is_infer,
|
|
sequence_output=sequence_output,
|
|
parallel_output=parallel_output)
|
|
|
|
def state_dict(self, destination=None, prefix='', keep_vars=False):
|
|
return self.model.state_dict(
|
|
destination=destination, prefix=prefix, keep_vars=keep_vars)
|
|
|
|
def load_state_dict(self, state_dict, strict=True):
|
|
return self.model.load_state_dict(state_dict, strict=strict)
|