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#
# 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.
"""Megatron Module"""
import torch
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from nemo.utils import logging
try:
from apex.transformer import parallel_state, tensor_parallel
HAVE_APEX = True
except (ImportError, ModuleNotFoundError):
HAVE_APEX = False
_FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
_HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor)
_BF16_TYPES = (torch.BFloat16Tensor, torch.cuda.BFloat16Tensor)
def param_is_not_shared(param):
return not hasattr(param, 'shared') or not param.shared
class MegatronModule(torch.nn.Module):
"""Megatron specific extensions of torch Module with support
for pipelining."""
def __init__(self, share_token_embeddings=True):
if not HAVE_APEX:
raise ImportError(
"Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt."
)
super(MegatronModule, self).__init__()
self.share_token_embeddings = share_token_embeddings
def word_embeddings_weight(self):
if self.pre_process:
if hasattr(self, 'language_model'):
return self.language_model.embedding.word_embeddings.weight
elif hasattr(self, 'encoder_embedding'):
return self.encoder_embedding.word_embeddings.weight
elif hasattr(self, 'decoder_embedding'):
return self.decoder_embedding.word_embeddings.weight
else:
raise ValueError(
f"Pre_process is True, but no embedding is found on this rank. Looked for language_model.embedding, encoder_embedding, and decoder_embedding"
)
else:
# This is the pipeline parallel last stage.
if not self.share_token_embeddings:
raise Exception(
'word_embeddings_weight() called for last ' 'stage, but share_token_embeddings is false'
)
return self.word_embeddings.weight
def position_embeddings_weight(self):
if self.pre_process:
if hasattr(self, 'language_model'):
return self.language_model.embedding.position_embeddings.weight
elif hasattr(self, 'encoder_embedding'):
return self.encoder_embedding.position_embeddings.weight
elif hasattr(self, 'decoder_embedding'):
return self.decoder_embedding.position_embeddings.weight
else:
raise ValueError(
f"Pre_process is True, but no embedding is found on this rank. Looked for language_model.embedding, encoder_embedding, and decoder_embedding"
)
else:
# We only need position embeddings on the encoder and decoder first stages where pre_process=True
raise ValueError(f"Pre_process is False, there is no position embedding on this rank.")
def encoder_relative_position_embeddings_weight(self):
if hasattr(self, 'encoder_relative_position_embedding'):
return self.encoder_relative_position_embedding.relative_position_embedding.weight
else:
raise ValueError(
f"No encoder_relative_position_embedding found on this rank. Looking for encoder_relative_position_embedding.relative_position_embedding.weight"
)
def decoder_relative_position_embeddings_weight(self):
if hasattr(self, 'decoder_relative_position_embedding'):
return self.decoder_relative_position_embedding.relative_position_embedding.weight
else:
raise ValueError(
f"No decoder_relative_position_embedding found on this rank. Looking for decoder_relative_position_embedding.relative_position_embedding.weight"
)
def decoder_cross_attention_relative_position_embeddings_weight(self):
if hasattr(self, 'decoder_cross_attention_relative_position_embedding'):
return self.decoder_cross_attention_relative_position_embedding.relative_position_embedding.weight
else:
raise ValueError(
f"No decoder_cross_attention_relative_position_embedding found on this rank. Looking for decoder_cross_attention_relative_position_embedding.relative_position_embedding.weight"
)
def initialize_word_embeddings(self, init_method, vocab_size, hidden_size):
if not self.share_token_embeddings:
raise Exception('initialize_word_embeddings() was called but ' 'share_token_embeddings is false')
# This function just initializes the word embeddings in the final stage
# when we are using pipeline parallelism. If we aren't using pipeline
# parallelism there is nothing to do.
if parallel_state.get_pipeline_model_parallel_world_size() == 1:
return
# Parameters are shared between the word embeddings layer, and the
# heads at the end of the model. In a pipelined setup with more than
# one stage, the initial embedding layer and the head are on different
# workers, so we do the following:
# 1. Create a second copy of word_embeddings on the last stage, with
# initial parameters of 0.0.
# 2. Do an all-reduce between the first and last stage to ensure that
# the two copies of word_embeddings start off with the same
# parameter values.
# 3. In the training loop, before an all-reduce between the grads of
# the two word_embeddings layers to ensure that every applied weight
# update is the same on both stages.
if parallel_state.is_pipeline_last_stage() and not self.pre_process:
# This is relevant for T5 when the decoder is only on a single rank. It is the last stage of the pipeline and also has embeddings on this rank already.
assert not parallel_state.is_pipeline_first_stage()
self._word_embeddings_for_head_key = 'word_embeddings_for_head'
# set word_embeddings weights to 0 here, then copy first
# stage's weights using all_reduce below.
self.word_embeddings = tensor_parallel.VocabParallelEmbedding(
vocab_size, hidden_size, init_method=init_method
)
self.word_embeddings.weight.data.fill_(0)
self.word_embeddings.weight.shared = True
# Zero out initial weights for decoder embedding.
# NOTE: We don't currently support T5 with the interleaved schedule.
# This is the case where PP > 1 and we're on the decoder first stage.
if not parallel_state.is_pipeline_first_stage(ignore_virtual=True) and self.pre_process:
if hasattr(self, 'language_model'):
# Zero params for GPT
self.language_model.embedding.zero_parameters()
else:
# Zero decoder embeddings for T5
assert hasattr(self, 'decoder_embedding')
self.decoder_embedding.zero_parameters()
def sync_initial_word_embeddings(self):
if torch.distributed.is_initialized():
if parallel_state.is_rank_in_embedding_group() and self.share_token_embeddings:
torch.distributed.all_reduce(
self.word_embeddings_weight().data, group=parallel_state.get_embedding_group()
)
else:
logging.warning(
"WARNING! Distributed processes aren't initialized, so "
"word embeddings in the last layer are not synchronized. "
"If you are just manipulating a model this is fine, but "
"this needs to be handled manually. If you are training "
"something is definitely wrong."
)
def sync_initial_position_embeddings(self):
# Ensure that the encoder first stage and decoder first have the same
# initial position embedding parameter values.
# NOTE: We don't currently support T5 with the interleaved schedule.
if (
parallel_state.is_rank_in_position_embedding_group()
and parallel_state.get_pipeline_model_parallel_split_rank() is not None
):
# TODO: Support tokentype embedding.
# self.language_model.embedding.cuda()
position_embeddings = self.position_embeddings_weight()
torch.distributed.all_reduce(position_embeddings.data, group=parallel_state.get_position_embedding_group())
def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False):
"""Use this function to override the state dict for
saving checkpoints."""
return self.state_dict(destination, prefix, keep_vars)
def sync_initial_encoder_relative_position_embeddings(self):
# Ensure that all encoder RPE stages have the same weights.
if parallel_state.is_rank_in_encoder_relative_position_embedding_group():
position_embeddings = self.encoder_relative_position_embeddings_weight()
torch.distributed.all_reduce(
position_embeddings.data, group=parallel_state.get_encoder_relative_position_embedding_group()
)
def sync_initial_decoder_relative_position_embeddings(self):
if parallel_state.is_rank_in_decoder_relative_position_embedding_group():
position_embeddings = self.decoder_relative_position_embeddings_weight()
torch.distributed.all_reduce(
position_embeddings.data, group=parallel_state.get_decoder_relative_position_embedding_group()
)
def sync_initial_decoder_cross_attention_relative_position_embeddings(self):
if parallel_state.is_rank_in_decoder_relative_position_embedding_group():
position_embeddings = self.decoder_cross_attention_relative_position_embeddings_weight()
torch.distributed.all_reduce(
position_embeddings.data, group=parallel_state.get_decoder_relative_position_embedding_group()
)
def conversion_helper(val, conversion):
"""Apply conversion to val. Recursively apply conversion if `val`
#is a nested tuple/list structure."""
if not isinstance(val, (tuple, list)):
return conversion(val)
rtn = [conversion_helper(v, conversion) for v in val]
if isinstance(val, tuple):
rtn = tuple(rtn)
return rtn
def fp32_to_float16(val, float16_converter):
"""Convert fp32 `val` to fp16/bf16"""
def half_conversion(val):
val_typecheck = val
if isinstance(val_typecheck, (Parameter, Variable)):
val_typecheck = val.data
if isinstance(val_typecheck, _FLOAT_TYPES):
val = float16_converter(val)
return val
return conversion_helper(val, half_conversion)
def float16_to_fp32(val):
"""Convert fp16/bf16 `val` to fp32"""
def float_conversion(val):
val_typecheck = val
if isinstance(val_typecheck, (Parameter, Variable)):
val_typecheck = val.data
if isinstance(val_typecheck, (_BF16_TYPES, _HALF_TYPES)):
val = val.float()
return val
return conversion_helper(val, float_conversion)
class Float16Module(MegatronModule):
def __init__(self, module, precision):
if not HAVE_APEX:
raise ImportError(
"Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt."
)
super().__init__()
self.precision = precision
if precision == 16:
self.add_module('module', module.half())
def float16_converter(val):
return val.half()
elif precision == 'bf16':
self.add_module('module', module.bfloat16())
def float16_converter(val):
return val.bfloat16()
else:
raise Exception(
f'precision {precision} is not supported. Float16Module (megatron_amp_O2) supports '
'only fp16 and bf16.'
)
self.float16_converter = float16_converter
def set_input_tensor(self, input_tensor):
return self.module.set_input_tensor(input_tensor)
def forward(self, *inputs, **kwargs):
# Note: Legacy checkpoints didn't have pre-process.
if getattr(self.module, 'pre_process', True):
inputs = fp32_to_float16(inputs, self.float16_converter)
outputs = self.module(*inputs, **kwargs)
if parallel_state.is_pipeline_last_stage():
outputs = float16_to_fp32(outputs)
return outputs
def state_dict(self, destination=None, prefix='', keep_vars=False):
return self.module.state_dict(destination, prefix, keep_vars)
def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False):
return self.module.state_dict_for_save_checkpoint(destination, prefix, keep_vars)
def word_embeddings_weight(self):
if self.module.pre_process:
if hasattr(self.module, 'language_model'):
return self.module.language_model.embedding.word_embeddings.weight
elif hasattr(self.module, 'encoder_embedding'):
return self.module.encoder_embedding.word_embeddings.weight
elif hasattr(self.module, 'decoder_embedding'):
return self.module.decoder_embedding.word_embeddings.weight
else:
raise ValueError(
f"Pre_process is True, but no embedding is found on this rank. Looked for language_model.embedding, encoder_embedding, and decoder_embedding"
)
else:
# This is the pipeline parallel last stage.
if not self.share_token_embeddings:
raise Exception(
'word_embeddings_weight() called for last ' 'stage, but share_token_embeddings is false'
)
return self.module.word_embeddings.weight
def position_embeddings_weight(self):
if self.module.pre_process:
if hasattr(self.module, 'language_model'):
return self.module.language_model.embedding.position_embeddings.weight
elif hasattr(self.module, 'encoder_embedding'):
return self.module.encoder_embedding.position_embeddings.weight
elif hasattr(self.module, 'decoder_embedding'):
return self.module.decoder_embedding.position_embeddings.weight
else:
raise ValueError(
f"Pre_process is True, but no embedding is found on this rank. Looked for language_model.position_embeddings, encoder_embedding.position_embedding_weight, and decoder_embedding.position_embedding_weight"
)
else:
# We only need position embeddings on the encoder and decoder first stages where pre_process=True
raise ValueError(f"Pre_process is False, there is no position embedding on this rank.")
def encoder_relative_position_embeddings_weight(self):
if hasattr(self.module, 'encoder_relative_position_embedding'):
return self.module.encoder_relative_position_embedding.relative_position_embedding.weight
else:
raise ValueError(
f"No encoder_relative_position_embedding found on this rank. Looking for encoder_relative_position_embedding.relative_position_embedding.weight"
)
def decoder_relative_position_embeddings_weight(self):
if hasattr(self.module, 'decoder_relative_position_embedding'):
return self.module.decoder_relative_position_embedding.relative_position_embedding.weight
else:
raise ValueError(
f"No decoder_relative_position_embedding found on this rank. Looking for decoder_relative_position_embedding.relative_position_embedding.weight"
)
def decoder_cross_attention_relative_position_embeddings_weight(self):
if hasattr(self.module, 'decoder_cross_attention_relative_position_embedding'):
return self.module.decoder_cross_attention_relative_position_embedding.relative_position_embedding.weight
else:
raise ValueError(
f"No decoder_cross_attention_relative_position_embedding found on this rank. Looking for decoder_cross_attention_relative_position_embedding.relative_position_embedding.weight"
)
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