megatron/model/glm_model.py

# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION.  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
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"""BERT model."""

import torch
import torch.nn.functional as F

from megatron import get_args
from megatron import mpu
from megatron.model.enums import AttnMaskType
from megatron.model.language_model import parallel_lm_logits
from megatron.model.language_model import get_language_model
from megatron.model import LayerNorm
from megatron.model.utils import openai_gelu, erf_gelu
from megatron.model.utils import get_linear_layer
from megatron.model.utils import init_method_normal
from megatron.model.utils import scaled_init_method_normal
from .module import MegatronModule

def bert_extended_attention_mask(attention_mask):
    # We create a 3D attention mask from a 2D tensor mask.
    # [b, 1, s]
    attention_mask_b1s = attention_mask.unsqueeze(1)
    # [b, s, 1]
    attention_mask_bs1 = attention_mask.unsqueeze(2)
    # [b, s, s]
    attention_mask_bss = attention_mask_b1s * attention_mask_bs1
    # [b, 1, s, s]
    extended_attention_mask = attention_mask_bss.unsqueeze(1)

    # Convert attention mask to binary:
    extended_attention_mask = (extended_attention_mask < 0.5)

    return extended_attention_mask

def bert_position_ids(token_ids):
    # Create position ids
    seq_length = token_ids.size(1)
    position_ids = torch.arange(seq_length, dtype=torch.long,
                                device=token_ids.device)
    position_ids = position_ids.unsqueeze(0).expand_as(token_ids)

    return position_ids


class BertLMHead(MegatronModule):
    """Masked LM head for Bert

    Arguments:
        mpu_vocab_size: model parallel size of vocabulary.
        hidden_size: hidden size
        init_method: init method for weight initialization
        layernorm_epsilon: tolerance for layer norm divisions
        parallel_output: whether output logits being distributed or not.
    """

    def __init__(self, mpu_vocab_size, hidden_size, init_method,
                 layernorm_epsilon, parallel_output):

        super(BertLMHead, self).__init__()

        args = get_args()

        self.bias = torch.nn.Parameter(torch.zeros(mpu_vocab_size))
        mpu.set_tensor_model_parallel_attributes(self.bias, True, 0, 1)
        self.parallel_output = parallel_output

        self.dense = get_linear_layer(hidden_size, hidden_size, init_method)
        setattr(self.dense.weight, 'sequence_parallel', args.sequence_parallel)
        setattr(self.dense.bias, 'sequence_parallel', args.sequence_parallel)

        self.layernorm = LayerNorm(hidden_size,
                                   eps=layernorm_epsilon,
                                   sequence_parallel=args.sequence_parallel)
        self.gelu = torch.nn.functional.gelu
        if args.openai_gelu:
            self.gelu = openai_gelu
        elif args.onnx_safe:
            self.gelu = erf_gelu

    def forward(self, hidden_states, word_embeddings_weight):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.gelu(hidden_states)
        hidden_states = self.layernorm(hidden_states)
        output = parallel_lm_logits(hidden_states,
                                    word_embeddings_weight,
                                    self.parallel_output,
                                    bias=self.bias)
        return output

class GlmModel(MegatronModule):
    """Bert Language model."""

    def __init__(self,
                 num_tokentypes=2,
                 add_binary_head=True,
                 parallel_output=True,
                 pre_process=True,
                 post_process=True,
                 add_encoder=True,
                 add_decoder=True):
        super(GlmModel, self).__init__()
        args = get_args()

        self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy
        self.add_binary_head = add_binary_head
        self.parallel_output = parallel_output
        self.pre_process = pre_process
        self.post_process = post_process
        self.add_encoder = add_encoder
        self.add_decoder = add_decoder

        self.cal_encoder_loss = True
        self.cal_decoder_loss = True
        self.cal_sent_loss = True
        
        init_method = init_method_normal(args.init_method_std)
        scaled_init_method = scaled_init_method_normal(args.init_method_std,
                                                       args.num_layers)
        
        self.language_model, self._language_model_key = get_language_model(
            num_tokentypes=num_tokentypes,
            add_pooler=self.add_binary_head,
            add_encoder=self.add_encoder,
            add_decoder=self.add_decoder,
            encoder_attn_mask_type=AttnMaskType.padding,
            init_method=init_method,
            scaled_init_method=scaled_init_method,
            pre_process=self.pre_process,
            post_process=self.post_process)

        self.initialize_word_embeddings(init_method_normal)
        if self.post_process:
            self.lm_head = BertLMHead(
                self.word_embeddings_weight().size(0),
                args.hidden_size, init_method, args.layernorm_epsilon, parallel_output)
            self._lm_head_key = 'lm_head'
            self.binary_head = None
            if self.add_binary_head:
                self.binary_head = get_linear_layer(args.hidden_size, 2,
                                                    init_method)
                self._binary_head_key = 'binary_head'

    def set_input_tensor(self, input_tensor):
        """See megatron.model.transformer.set_input_tensor()"""
        self.language_model.set_input_tensor(input_tensor)

    def forward(self, 
                encoder_input_ids, 
                encoder_labels,
                decoder_input_ids, 
                decoder_labels,
                encoder_attn_mask,
                decoder_attn_mask, 
                encoder_decoder_attn_mask,
                tokentype_ids=None, 
                lm_labels=None, 
                sentence_labels=None,
                enc_hidden_states=None,
                ):

        encoder_position_ids = bert_position_ids(encoder_input_ids)
        decoder_position_ids = bert_position_ids(decoder_input_ids)
        extended_encoder_attn_mask = bert_extended_attention_mask(encoder_attn_mask)
        extended_decoder_attn_mask = bert_extended_attention_mask(decoder_attn_mask)
        extended_encoder_decoder_attn_mask = bert_extended_attention_mask(encoder_decoder_attn_mask)

        lm_output = self.language_model(encoder_input_ids,
                                        encoder_position_ids,
                                        extended_encoder_attn_mask,
                                        decoder_input_ids,
                                        decoder_position_ids,
                                        extended_decoder_attn_mask,
                                        extended_encoder_decoder_attn_mask,
                                        tokentype_ids=tokentype_ids,
                                        enc_hidden_states=enc_hidden_states)

        encoder_outputs, decoder_outputs, pooled_outputs = lm_output

        return self.post_language_model_processing(encoder_input_ids, 
                                              encoder_outputs,
                                              encoder_labels,
                                              pooled_outputs,
                                              self.lm_head, 
                                              self.binary_head,
                                              self.word_embeddings_weight(),
                                              self.fp16_lm_cross_entropy,
                                              decoder_input_ids,
                                              decoder_outputs,
                                              decoder_labels,
                                              decoder_attn_mask,
                                              sentence_labels)

    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
                                       keep_vars=False):
        """For easy load when model is combined with other heads,
        add an extra key."""

        state_dict_ = {}
        state_dict_[self._language_model_key] \
            = self.language_model.state_dict_for_save_checkpoint(
            destination, prefix, keep_vars)
        if self.post_process:
            state_dict_[self._lm_head_key] \
                = self.lm_head.state_dict_for_save_checkpoint(
                destination, prefix, keep_vars)
        if self.post_process and self.add_binary_head:
            state_dict_[self._binary_head_key] \
                = self.binary_head.state_dict(destination, prefix, keep_vars)
        # Save word_embeddings.
        if self.post_process and not self.pre_process:
            state_dict_[self._word_embeddings_for_head_key] \
                = self.word_embeddings.state_dict(destination, prefix, keep_vars)
        return state_dict_

    def load_state_dict(self, state_dict, strict=True):
        """Customized load."""

        self.language_model.load_state_dict(
            state_dict[self._language_model_key], strict=strict)
        if self.post_process:
            self.lm_head.load_state_dict(
                state_dict[self._lm_head_key], strict=strict)
        if self.post_process and self.add_binary_head:
            self.binary_head.load_state_dict(
                state_dict[self._binary_head_key], strict=strict)
        # Load word_embeddings.
        if self.post_process and not self.pre_process:
            self.word_embeddings.load_state_dict(
                state_dict[self._word_embeddings_for_head_key], strict=strict)

    def post_language_model_processing(self, encoder_inputs, 
                                       encoder_outputs, 
                                       encoder_labels,
                                       pooled_outputs,
                                       lm_head, 
                                       binary_head,
                                       logit_weights,
                                       fp16_lm_cross_entropy,
                                       decoder_inputs,
                                       decoder_outputs,
                                       decoder_labels,
                                       attention_mask,
                                       sentence_labels
                                       ):
        # Output.
        describe_tensor = {            
            "attention_mask" : attention_mask[0]
        }
        
        sent_loss = None
        if self.cal_sent_loss:
            binary_logits = binary_head(pooled_outputs)
            sent_loss = F.cross_entropy(binary_logits.view(-1, 2).float(),
                                sentence_labels.view(-1),
                                ignore_index=-1)
            
        encoder_loss = None
        if self.cal_encoder_loss:
            encoder_outputs_logits_ = lm_head(encoder_outputs, logit_weights)
            if fp16_lm_cross_entropy:
                assert encoder_outputs_logits_.dtype == torch.half
                encoder_outputs_logits = encoder_outputs_logits_
            else:
                encoder_outputs_logits = encoder_outputs_logits_.float()
            encoder_loss = mpu.vocab_parallel_cross_entropy(encoder_outputs_logits, encoder_labels.transpose(0, 1).contiguous())
            describe_tensor["encoder_inputs"] = encoder_inputs[0]
            describe_tensor["encoder_outputs"] = encoder_outputs_logits[0].transpose(0, 1)
            describe_tensor["encoder_labels"] = encoder_labels[0]

        decoder_loss = None
        if self.cal_decoder_loss:
            decoder_outputs_logits_ = decoder_outputs
            if fp16_lm_cross_entropy:
                assert decoder_outputs_logits_.dtype == torch.half
                decoder_outputs_logits = decoder_outputs_logits_
            else:
                decoder_outputs_logits = decoder_outputs_logits_.float()
            decoder_loss = mpu.vocab_parallel_cross_entropy(decoder_outputs_logits, decoder_labels.transpose(0, 1).contiguous())
            describe_tensor["encoder_inputs"] = encoder_inputs[0]
            describe_tensor["decoder_inputs"] = decoder_inputs[0]
            describe_tensor["decoder_outputs"] = decoder_outputs_logits[0].transpose(0, 1)
            describe_tensor["decoder_labels"] = decoder_labels[0]

        return encoder_loss, decoder_loss, sent_loss, describe_tensor