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[pipeline] refactor test pipeline and remove useless utils in pipeline (#4324) 

* refactor tests

* refactor bloom model

* finish policy tests

* refactor tests

* fix test pure pipeline

* remove test pipeline and cutdown launch process

* refactor tests

* refactor bloom model

* finish policy tests

* refactor tests

* fix test pure pipeline

* remove test pipeline and cutdown launch process
pull/4445/head
Jianghai 2023-08-01 10:35:17 +08:00 committed by Hongxin Liu
parent d3c6cd66f3
commit f13954cd58
14 changed files with 138 additions and 1246 deletions
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22
colossalai/pipeline/policy/__init__.py
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from typing import Any, Dict, List, Optional, Tuple, Type
from torch import Tensor
from torch.nn import Module, Parameter
from colossalai.pipeline.stage_manager import PipelineStageManager
from .base import Policy
from .bert import BertModel, BertModelPolicy
POLICY_MAP: Dict[Type[Module], Type[Policy]] = {
BertModel: BertModelPolicy,
}
def pipeline_parallelize(
model: Module,
stage_manager: PipelineStageManager) -> Tuple[Dict[str, Parameter], Dict[str, Tensor], List[Dict[int, Tensor]]]:
if type(model) not in POLICY_MAP:
raise NotImplementedError(f"Policy for {type(model)} not implemented")
policy = POLICY_MAP[type(model)](stage_manager)
return policy.parallelize_model(model)

111
colossalai/pipeline/policy/base.py
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from typing import Any, Dict, List, Optional, Tuple
import numpy as np
from torch import Tensor
from torch.nn import Module, Parameter
from colossalai.lazy import LazyTensor
from colossalai.pipeline.stage_manager import PipelineStageManager
class Policy:
def __init__(self, stage_manager: PipelineStageManager) -> None:
self.stage_manager = stage_manager
def setup_model(self, module: Module) -> Tuple[Dict[str, Parameter], Dict[str, Tensor]]:
"""Setup model for pipeline parallel
Args:
module (Module): Module to be setup
Returns:
Tuple[Dict[str, Parameter], Dict[str, Tensor]]: Hold parameters and buffers
"""
hold_params = set()
hold_buffers = set()
def init_layer(layer: Module):
for p in layer.parameters():
if isinstance(p, LazyTensor):
p.materialize()
p.data = p.cuda()
hold_params.add(p)
for b in layer.buffers():
if isinstance(b, LazyTensor):
b.materialize()
b.data = b.cuda()
hold_buffers.add(b)
hold_layers = self.get_hold_layers(module)
for layer in hold_layers:
init_layer(layer)
hold_params_dict = {}
hold_buffers_dict = {}
# release other tensors
for n, p in module.named_parameters():
if p in hold_params:
hold_params_dict[n] = p
else:
if isinstance(p, LazyTensor):
p.materialize()
p.data = p.cuda()
p.storage().resize_(0)
for n, b in module.named_buffers():
if b in hold_buffers:
hold_buffers_dict[n] = b
else:
if isinstance(b, LazyTensor):
b.materialize()
b.data = b.cuda()
# FIXME(ver217): use meta tensor may be better
b.storage().resize_(0)
return hold_params_dict, hold_buffers_dict
def replace_forward(self, module: Module) -> None:
"""Replace module forward in place. This method should be implemented by subclass. The output of internal layers must be a dict
Args:
module (Module): _description_
"""
raise NotImplementedError
def get_hold_layers(self, module: Module) -> List[Module]:
"""Get layers that should be hold in current stage. This method should be implemented by subclass.
Args:
module (Module): Module to be setup
Returns:
List[Module]: List of layers that should be hold in current stage
"""
raise NotImplementedError
def get_shared_params(self, module: Module) -> List[Dict[int, Tensor]]:
"""Get parameters that should be shared across stages. This method should be implemented by subclass.
Args:
module (Module): Module to be setup
Returns:
List[Module]: List of parameters that should be shared across stages. E.g. [{0: module.model.embed_tokens.weight, 3: module.lm_head.weight}]
"""
raise NotImplementedError
def parallelize_model(self,
module: Module) -> Tuple[Dict[str, Parameter], Dict[str, Tensor], List[Dict[int, Tensor]]]:
"""Parallelize model for pipeline parallel
Args:
module (Module): Module to be setup
Returns:
Tuple[Dict[str, Parameter], Dict[str, Tensor], List[Dict[int, Tensor]]]: Hold parameters, buffers and shared parameters
"""
hold_params, hold_buffers = self.setup_model(module)
self.replace_forward(module)
shared_params = self.get_shared_params(module)
return hold_params, hold_buffers, shared_params

523
colossalai/pipeline/policy/bert.py
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from functools import partial
from types import MethodType
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from torch.nn import CrossEntropyLoss, Module
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
)
from transformers.models.bert.modeling_bert import (
BertForPreTraining,
BertForPreTrainingOutput,
BertLMHeadModel,
BertModel,
)
from transformers.utils import ModelOutput, logging
from colossalai.pipeline.stage_manager import PipelineStageManager
from .base import Policy
logger = logging.get_logger(__name__)
def bert_model_forward(
self: BertModel,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
# labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage
):
# TODO: add explaination of the output here.
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
# debugging
# preprocess:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (output_hidden_states
if output_hidden_states is not None else self.config.output_hidden_states)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if stage_manager.is_first_stage():
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
else:
input_shape = hidden_states.size()[:-1]
batch_size, seq_length = input_shape
device = hidden_states.device
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
if output_attentions:
logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
output_attentions = False
if output_hidden_states:
logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
output_hidden_states = False
if use_cache:
logger.warning_once('use_cache=True is not supported for pipeline models at the moment.')
use_cache = False
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
attention_mask = extended_attention_mask
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
hidden_states = hidden_states if hidden_states is not None else None
if stage_manager.is_first_stage():
hidden_states = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
# inherit from bert_layer,this should be changed when we add the feature to record hidden_states
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
if self.encoder.gradient_checkpointing and self.encoder.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
use_cache = False
next_decoder_cache = () if use_cache else None
# calculate the num_layers
num_layers_per_stage = len(self.encoder.layer) // stage_manager.num_stages
start_layer = stage_manager.stage * num_layers_per_stage
end_layer = (stage_manager.stage + 1) * num_layers_per_stage
# layer_outputs
layer_outputs = hidden_states if hidden_states is not None else None
for idx, encoder_layer in enumerate(self.encoder.layer[start_layer:end_layer], start=start_layer):
if stage_manager.is_first_stage() and idx == 0:
encoder_attention_mask = encoder_extended_attention_mask
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[idx] if head_mask is not None else None
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.encoder.gradient_checkpointing and self.encoder.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, past_key_value, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + \
(layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# end of a stage loop
sequence_output = layer_outputs[0] if layer_outputs is not None else None
if stage_manager.is_last_stage():
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + layer_outputs[1:]
# return dict is not supported at this moment
else:
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# output of non-first and non-last stages: must be a dict
else:
# intermediate stage always return dict
return {
'hidden_states': hidden_states,
}
# The layer partition policy for bertmodel
class BertModelPolicy(Policy):
def __init__(
self,
stage_manager: PipelineStageManager,
num_layers: int,
):
super().__init__(stage_manager=stage_manager)
self.stage_manager = stage_manager
self.layers_per_stage = self.distribute_layers(num_layers, stage_manager.num_stages)
def get_hold_layers(self, module: BertModel) -> List[Module]:
"""
get pipeline layers for current stage
"""
hold_layers = []
if self.stage_manager.is_first_stage():
hold_layers.append(module.embeddings)
start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage)
hold_layers.extend(module.encoder.layer[start_idx:end_idx])
if self.stage_manager.is_last_stage():
hold_layers.append(module.pooler)
return hold_layers
def get_shared_params(self, module: BertModel) -> List[Dict[int, Tensor]]:
'''no shared params in bertmodel'''
return []
def replace_forward(self, module: Module) -> None:
module.forward = MethodType(partial(bert_model_forward, stage_manager=self.stage_manager), module)
def bert_for_pretraining_forward(
self: BertForPreTraining,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
next_sentence_label: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
if output_attentions:
logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
output_attentions = False
if output_hidden_states:
logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
output_hidden_states = False
if return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
outputs = bert_model_forward(self.bert,
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
stage_manager=stage_manager,
hidden_states=hidden_states if hidden_states is not None else None)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
# the last stage for pretraining model
total_loss = None
if labels is not None and next_sentence_label is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
total_loss = masked_lm_loss + next_sentence_loss
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return BertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
# intermediate stage always return dict
return {
'hidden_states': hidden_states,
}
class BertForPreTrainingPolicy(Policy):
def __init__(self, stage_manager: PipelineStageManager, num_layers: int):
super().__init__(stage_manager=stage_manager)
self.stage_manager = stage_manager
self.layers_per_stage = self.distribute_layers(num_layers, stage_manager.num_stages)
def get_hold_layers(self, module: BertForPreTraining) -> List[Module]:
"""
get pipeline layers for current stage
"""
hold_layers = []
if self.stage_manager.is_first_stage():
hold_layers.append(module.bert.embeddings)
start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage)
hold_layers.extend(module.bert.encoder.layer[start_idx:end_idx])
if self.stage_manager.is_last_stage():
hold_layers.append(module.bert.pooler)
hold_layers.append(module.cls)
return hold_layers
def get_shared_params(self, module: BertForPreTraining) -> List[Dict[int, Tensor]]:
'''no shared params in bertmodel'''
return []
def replace_forward(self, module: Module) -> None:
module.forward = MethodType(partial(bert_for_pretraining_forward, stage_manager=self.stage_manager),
module.forward)
def bert_lmhead_forward(self: BertLMHeadModel,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.Tensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_manager: Optional[PipelineStageManager] = None):
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
if output_attentions:
logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
output_attentions = False
if output_hidden_states:
logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
output_hidden_states = False
if return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
outputs = bert_model_forward(self.bert,
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
stage_manager=stage_manager,
hidden_states=hidden_states if hidden_states is not None else None)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
else:
hidden_states = outputs.get('hidden_states')
# intermediate stage always return dict
return {'hidden_states': hidden_states}
class BertLMHeadModelPolicy(Policy):
def __init__(self, stage_manager: PipelineStageManager, num_layers: int):
super().__init__(stage_manager=stage_manager)
self.stage_manager = stage_manager
self.layers_per_stage = self.distribute_layers(num_layers, stage_manager.num_stages)
def get_hold_layers(self, module: BertLMHeadModel) -> List[Module]:
"""
get pipeline layers for current stage
"""
hold_layers = []
if self.stage_manager.is_first_stage():
hold_layers.append(module.bert.embeddings)
start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage)
hold_layers.extend(module.bert.encoder.layer[start_idx:end_idx])
if self.stage_manager.is_last_stage():
hold_layers.append(module.bert.pooler)
hold_layers.append(module.cls)
return hold_layers
def get_shared_params(self, module: BertLMHeadModel) -> List[Dict[int, Tensor]]:
'''no shared params in bertmodel'''
return []
def replace_forward(self, module: Module) -> None:
module.forward = MethodType(partial(bert_lmhead_forward, stage_manager=self.stage_manager), module)

220
colossalai/pipeline/policy/bloom.py
View File

@ -1,220 +0,0 @@
import warnings
from functools import partial
from types import MethodType
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from torch.nn import CrossEntropyLoss, Module
from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
from transformers.models.bloom.modeling_bloom import BloomModel
from transformers.utils import logging
from colossalai.pipeline.stage_manager import PipelineStageManager
from .base import Policy
logger = logging.get_logger(__name__)
def bloom_model_forward(
self: BloomModel,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
**deprecated_arguments,
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (output_hidden_states
if output_hidden_states is not None else self.config.output_hidden_states)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# add warnings here
if output_attentions:
logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
output_attentions = False
if output_hidden_states:
logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
output_hidden_states = False
if use_cache:
logger.warning_once('use_cache=True is not supported for pipeline models at the moment.')
use_cache = False
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape batch_size x num_heads x N x N
# head_mask has shape n_layer x batch x num_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
# case: First stage of training
if stage_manager.is_first_stage():
# check input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
hidden_states = self.word_embeddings_layernorm(inputs_embeds)
# initialize in the first stage and then pass to the next stage
else:
input_shape = hidden_states.shape[:-1]
batch_size, seq_length = input_shape
# extra recording tensor should be generated in the first stage
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
use_cache = False
if past_key_values is None:
past_key_values = tuple([None] * len(self.h))
# Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values[0] is not None:
past_key_values_length = past_key_values[0][0].shape[2] # source_len
seq_length_with_past = seq_length_with_past + past_key_values_length
if attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
else:
attention_mask = attention_mask.to(hidden_states.device)
alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
# causal_mask is constructed every stage and its input is passed through different stages
causal_mask = self._prepare_attn_mask(
attention_mask,
input_shape=(batch_size, seq_length),
past_key_values_length=past_key_values_length,
)
# calculate the num_layers
num_layers_per_stage = len(self.h) // stage_manager.num_stages
start_layer = stage_manager.stage * num_layers_per_stage
end_layer = (stage_manager.stage + 1) * num_layers_per_stage
for i, (block, layer_past) in enumerate(zip(self.h[start_layer:end_layer], past_key_values[start_layer:end_layer])):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
alibi,
causal_mask,
layer_past,
head_mask[i],
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=causal_mask,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
alibi=alibi,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + \
(outputs[2 if use_cache else 1],)
if stage_manager.is_last_stage():
# Add last hidden state
hidden_states = self.ln_f(hidden_states)
# TODO: deal with all_hidden_states, all_self_attentions, presents
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
# attention_mask is not returned ; presents = past_key_values
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class BloomModelPolicy(Policy):
def __init__(self, stage_manager: PipelineStageManager, num_layers: int, num_stages: int):
super().__init__(stage_manager=stage_manager)
self.stage_manager = stage_manager
self.layers_per_stage = self.distribute_layers(num_layers, num_stages)
def get_hold_layers(self, module: BloomModel) -> List[Module]:
"""
get pipeline layers for current stage
"""
hold_layers = []
if self.stage_manager.is_first_stage():
hold_layers.append(module.word_embeddings)
hold_layers.append(module.word_embeddings_layernorm)
start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage)
hold_layers.extend(module.h[start_idx:end_idx])
if self.stage_manager.is_last_stage():
hold_layers.append(module.ln_f)
return hold_layers
def get_shared_params(self, module: BloomModel) -> List[Dict[int, Tensor]]:
'''no shared params in bloommodel'''
pass
def replace_forward(self, module: Module) -> None:
module.forward = MethodType(partial(bloom_model_forward, stage_manager=self.stage_manager), module.model)

1
colossalai/pipeline/schedule/one_f_one_b.py
View File

@ -76,7 +76,6 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
# for the first stage, input_obj is None # for the first stage, input_obj is None
# for the non-first stage, input_obj is the output of the previous stage and it's must be a dict # for the non-first stage, input_obj is the output of the previous stage and it's must be a dict
output_obj = model_forward(model, micro_batch, input_obj) output_obj = model_forward(model, micro_batch, input_obj)
if self.stage_manager.is_last_stage(): if self.stage_manager.is_last_stage():
loss = criterion(output_obj, micro_batch) / self.num_microbatches loss = criterion(output_obj, micro_batch) / self.num_microbatches
if accum_loss is not None: if accum_loss is not None:

2
colossalai/shardformer/policies/bert.py
View File

@ -315,7 +315,7 @@ class BertForMaskedLMPolicy(BertPolicy):
def module_policy(self): def module_policy(self):
policy = super().module_policy() policy = super().module_policy()
policy = self.add_lm_head_policy(policy) policy = self.add_lm_head_policy(policy)
mpolicy = self.add_lm_prediction_policy(policy) policy = self.add_lm_prediction_policy(policy)
from transformers.models.bert.modeling_bert import BertForMaskedLM from transformers.models.bert.modeling_bert import BertForMaskedLM
if self.pipeline_stage_manager: if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BertForMaskedLM, self.set_pipeline_forward(model_cls=BertForMaskedLM,

64
tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py
View File

@ -1,64 +0,0 @@
import pytest
import torch
import torch.distributed as dist
from transformers.models.bert import BertConfig
from transformers.models.bert.modeling_bert import BertForPreTraining
import colossalai
from colossalai.cluster import ProcessGroupMesh
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.policies.base_policy import Policy
from colossalai.shardformer.policies.bert import BertForPreTrainingPolicy
from colossalai.shardformer.shard import ShardConfig
from colossalai.testing import rerun_if_address_is_in_use, spawn
def check_bert_for_pretraining_policy():
configuration = BertConfig()
model = BertForPreTraining(configuration)
DP_DIM, PP_DIM = 0, 1
DP_SIZE, PP_SIZE = 2, 2
RANK_TO_COORDINATE = {
0: (0, 0),
1: (0, 1),
2: (1, 0),
3: (1, 1),
}
PP_RANKS_IN_GROUP = {
0: [0, 1],
1: [0, 1],
2: [2, 3],
3: [2, 3],
}
pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
# print(pg_mesh)
stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
rank = dist.get_rank()
model_policy = BertForPreTrainingPolicy()
model_policy.set_model(model)
model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False)
model_policy.set_shard_config(model_config)
layers = model_policy.get_held_layers()
if stage_manager.is_first_stage():
assert len(layers) == 6 + 1
else:
assert len(layers) == 6 + 2
def run_dist_policy(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost')
check_bert_for_pretraining_policy()
@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_bert_for_pretraining_policy():
spawn(run_dist_policy, 4)
if __name__ == "__main__":
"""test the bert for pretraining model forward and bert for pretraining model policy"""
test_bert_for_pretraining_policy()

64
tests/test_pipeline/test_policy/test_bert_lm_head_model.py
View File

@ -1,64 +0,0 @@
import pytest
import torch
import torch.distributed as dist
from transformers.models.bert import BertConfig
from transformers.models.bert.modeling_bert import BertLMHeadModel
import colossalai
from colossalai.cluster import ProcessGroupMesh
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.policies.base_policy import Policy
from colossalai.shardformer.policies.bert import BertLMHeadModelPolicy
from colossalai.shardformer.shard import ShardConfig
from colossalai.testing import rerun_if_address_is_in_use, spawn
def check_bert_lmhead_policy():
configuration = BertConfig()
model = BertLMHeadModel(configuration)
DP_DIM, PP_DIM = 0, 1
DP_SIZE, PP_SIZE = 2, 2
RANK_TO_COORDINATE = {
0: (0, 0),
1: (0, 1),
2: (1, 0),
3: (1, 1),
}
PP_RANKS_IN_GROUP = {
0: [0, 1],
1: [0, 1],
2: [2, 3],
3: [2, 3],
}
pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
# print(pg_mesh)
stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
rank = dist.get_rank()
model_policy = BertLMHeadModelPolicy()
model_policy.set_model(model)
model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False)
model_policy.set_shard_config(model_config)
layers = model_policy.get_held_layers()
if stage_manager.is_first_stage():
assert len(layers) == 6 + 1
else:
assert len(layers) == 6 + 2
def run_dist_policy(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost')
check_bert_lmhead_policy()
@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_bert_lmhead_policy():
spawn(run_dist_policy, 4)
if __name__ == "__main__":
"""test the bert for lm head model policy"""
test_bert_lmhead_policy()

66
tests/test_pipeline/test_policy/test_bert_model.py
View File

@ -1,66 +0,0 @@
'''
In the test policy we only test policy: held layers and others, as the tests for forward logic are done in test_shardformer/test_model
'''
import pytest
import torch.distributed as dist
from transformers.models.bert.modeling_bert import BertModel
import colossalai
from colossalai.cluster import ProcessGroupMesh
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.policies.base_policy import Policy
from colossalai.shardformer.policies.bert import BertModelPolicy
from colossalai.shardformer.shard import ShardConfig
from colossalai.testing import rerun_if_address_is_in_use, spawn
def check_bert_model_policy():
model = BertModel.from_pretrained('bert-base-uncased')
DP_DIM, PP_DIM = 0, 1
DP_SIZE, PP_SIZE = 2, 2
RANK_TO_COORDINATE = {
0: (0, 0),
1: (0, 1),
2: (1, 0),
3: (1, 1),
}
PP_RANKS_IN_GROUP = {
0: [0, 1],
1: [0, 1],
2: [2, 3],
3: [2, 3],
}
pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
# print(pg_mesh)
stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
rank = dist.get_rank()
model_policy = BertModelPolicy()
model_policy.set_model(model)
model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False)
model_policy.set_shard_config(model_config)
layers = model_policy.get_held_layers()
if stage_manager.is_first_stage():
assert len(layers) == 6 + 1
else:
assert len(layers) == 6 + 1
def run_dist_policy(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost')
check_bert_model_policy()
@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_bert_model_policy():
spawn(run_dist_policy, 4)
if __name__ == "__main__":
"""test the bert model policy"""
test_bert_model_policy()

63
tests/test_pipeline/test_policy/test_bloom_model.py
View File

@ -1,63 +0,0 @@
import pytest
import torch
import torch.distributed as dist
from transformers.models.bloom import BloomConfig, BloomModel
import colossalai
from colossalai.cluster import ProcessGroupMesh
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.policies.base_policy import Policy
from colossalai.shardformer.policies.bloom import BloomModelPolicy
from colossalai.shardformer.shard import ShardConfig
from colossalai.testing import rerun_if_address_is_in_use, spawn
def check_bloom_model_policy():
# create a BloomModel
configuration = BloomConfig()
model = BloomModel(configuration)
DP_DIM, PP_DIM = 0, 1
DP_SIZE, PP_SIZE = 2, 2
RANK_TO_COORDINATE = {
0: (0, 0),
1: (0, 1),
2: (1, 0),
3: (1, 1),
}
PP_RANKS_IN_GROUP = {
0: [0, 1],
1: [0, 1],
2: [2, 3],
3: [2, 3],
}
pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
# print(pg_mesh)
stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
rank = dist.get_rank()
model_policy = BloomModelPolicy()
model_policy.set_model(model)
model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False)
model_policy.set_shard_config(model_config)
layers = model_policy.get_held_layers()
if stage_manager.is_first_stage():
assert len(layers) == 1 + 2
else:
assert len(layers) == 1 + 1
def run_dist_policy(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost')
check_bloom_model_policy()
@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_bloom_model_policy():
spawn(run_dist_policy, 4)
if __name__ == "__main__":
"""test the bloom model policy"""
test_bloom_model_policy()

3
tests/test_shardformer/test_model/test_shard_bert.py
View File

@ -2,7 +2,10 @@ import pytest
import torch import torch
import colossalai import colossalai
from colossalai.cluster import ProcessGroupMesh
from colossalai.logging import disable_existing_loggers from colossalai.logging import disable_existing_loggers
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.policies.auto_policy import get_autopolicy
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import ( from colossalai.testing import (
assert_hf_output_close, assert_hf_output_close,

72
tests/test_shardformer/test_model/test_shard_bert_pipeline.py
View File

@ -5,6 +5,8 @@ import colossalai
from colossalai.cluster import ProcessGroupMesh from colossalai.cluster import ProcessGroupMesh
from colossalai.logging import disable_existing_loggers from colossalai.logging import disable_existing_loggers
from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.policies.auto_policy import get_autopolicy
from colossalai.shardformer.shard import ShardConfig
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import ( from colossalai.testing import (
assert_hf_output_close, assert_hf_output_close,
@ -17,38 +19,28 @@ from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): def check_bert_model_policy(name, model: torch.nn.Module, stage_manager: PipelineStageManager):
# check forward stage_manager = stage_manager
pass policy = get_autopolicy(model)
policy.set_model(model)
model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False)
policy.set_shard_config(model_config)
layers = policy.get_held_layers()
if stage_manager.is_first_stage():
assert len(layers) == 1 + 1
else:
if name == "transformers_bert":
assert len(layers) == 1 + 1
elif name in [
"transformers_bert_for_sequence_classification", "transformers_bert_for_token_classification",
"transformers_bert_for_mcq"
]:
assert len(layers) == 1 + 3
else:
assert len(layers) == 1 + 2
@parameterize('enable_fused_normalization', [False]) def check_bert_model_pipeline_forward(name, sharded_model, stage_manager: PipelineStageManager):
@parameterize('enable_tensor_parallelism', [False])
@parameterize('use_lazy_init', [False])
#TODO: merge this into test_shard_bert
def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
DP_DIM, PP_DIM = 0, 1
DP_SIZE, PP_SIZE = 2, 2
RANK_TO_COORDINATE = {
0: (0, 0),
1: (0, 1),
2: (1, 0),
3: (1, 1),
}
PP_RANKS_IN_GROUP = {
0: [0, 1],
1: [0, 1],
2: [2, 3],
3: [2, 3],
}
pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
sub_model_zoo = model_zoo.get_sub_registry('transformers_bert')
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization,
enable_tensor_parallelism, use_lazy_init)
if name == 'transformers_bert_for_mcq': if name == 'transformers_bert_for_mcq':
x = torch.randint(0, 1000, (2, 3, 3)).cuda() x = torch.randint(0, 1000, (2, 3, 3)).cuda()
attention_mask = torch.ones_like(x).cuda() attention_mask = torch.ones_like(x).cuda()
@ -77,6 +69,24 @@ def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, use_laz
stage_manager=stage_manager) stage_manager=stage_manager)
assert output[0].shape[0] == 2 assert output[0].shape[0] == 2
@parameterize('enable_fused_normalization', [False])
@parameterize('enable_tensor_parallelism', [False])
@parameterize('use_lazy_init', [False])
#TODO: merge this into test_shard_bert
def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
PP_DIM = 0
PP_SIZE = 2
pg_mesh = ProcessGroupMesh(PP_SIZE)
stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
sub_model_zoo = model_zoo.get_sub_registry('transformers_bert')
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization,
enable_tensor_parallelism, use_lazy_init)
check_bert_model_policy(name, org_model, stage_manager)
check_bert_model_pipeline_forward(name, sharded_model, stage_manager)
torch.cuda.empty_cache() torch.cuda.empty_cache()
@ -90,7 +100,7 @@ def check_bert(rank, world_size, port):
@rerun_if_address_is_in_use() @rerun_if_address_is_in_use()
@clear_cache_before_run() @clear_cache_before_run()
def test_bert(): def test_bert():
spawn(check_bert, 4) spawn(check_bert, 2)
if __name__ == "__main__": if __name__ == "__main__":

71
tests/test_shardformer/test_model/test_shard_bloom_pipeline.py
View File

@ -5,7 +5,9 @@ import colossalai
from colossalai.cluster import ProcessGroupMesh from colossalai.cluster import ProcessGroupMesh
from colossalai.logging import disable_existing_loggers from colossalai.logging import disable_existing_loggers
from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.policies.auto_policy import get_autopolicy
from colossalai.shardformer.policies.base_policy import Policy from colossalai.shardformer.policies.base_policy import Policy
from colossalai.shardformer.shard import ShardConfig
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import ( from colossalai.testing import (
assert_hf_output_close, assert_hf_output_close,
@ -18,9 +20,37 @@ from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): def check_bloom_model_policy(name, model: torch.nn.Module, stage_manager: PipelineStageManager):
# check forward policy = get_autopolicy(model)
pass policy.set_model(model)
model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False)
policy.set_shard_config(model_config)
layers = policy.get_held_layers()
if stage_manager.is_first_stage():
assert len(layers) == 0 + 2
else:
if name == 'transformers_bloom':
assert len(layers) == 1 + 1
elif name == 'transformers_bloom_for_token_classification':
assert len(layers) == 1 + 3
else:
assert len(layers) == 1 + 2
def check_bloom_model_pipeline_forward(name, sharded_model, stage_manager: PipelineStageManager):
if stage_manager.stage == 0:
x = torch.randint(0, 1000, (1, 3)).cuda()
attention_mask = torch.ones_like(x).cuda()
output = sharded_model(input_ids=x, attention_mask=attention_mask)
assert output['hidden_states'].shape == (1, 3, 64)
else:
attention_mask = torch.ones((1, 3)).cuda()
hidden_states = torch.randint(0, 1000, (1, 3, 64)).to(torch.float32).cuda()
output = sharded_model(
hidden_states=hidden_states,
attention_mask=attention_mask,
)
assert output[0].shape[0] == 1
@parameterize('enable_fused_normalization', [False]) @parameterize('enable_fused_normalization', [False])
@ -28,40 +58,17 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
@parameterize('use_lazy_init', [False]) @parameterize('use_lazy_init', [False])
#TODO: merge this into test_shard_bloom #TODO: merge this into test_shard_bloom
def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
DP_DIM, PP_DIM = 0, 1 PP_DIM = 0
DP_SIZE, PP_SIZE = 2, 2 PP_SIZE = 2
RANK_TO_COORDINATE = { pg_mesh = ProcessGroupMesh(PP_SIZE)
0: (0, 0),
1: (0, 1),
2: (1, 0),
3: (1, 1),
}
PP_RANKS_IN_GROUP = {
0: [0, 1],
1: [0, 1],
2: [2, 3],
3: [2, 3],
}
pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
stage_manager = PipelineStageManager(pg_mesh, PP_DIM) stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom')
x = torch.randint(0, 1000, (1, 3)).cuda()
hidden_states = torch.randint(0, 1000, (1, 3, 64)).to(torch.float32).cuda()
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization,
enable_tensor_parallelism, use_lazy_init) enable_tensor_parallelism, use_lazy_init)
if stage_manager.stage == 0: check_bloom_model_policy(name, org_model, stage_manager)
attention_mask = torch.ones_like(x).cuda() check_bloom_model_pipeline_forward(name, sharded_model, stage_manager)
output = sharded_model(input_ids=x, attention_mask=attention_mask)
assert output['hidden_states'].shape == (1, 3, 64)
else:
attention_mask = torch.ones((1, 3)).cuda()
output = sharded_model(
hidden_states=hidden_states,
attention_mask=attention_mask,
)
assert output[0].shape[0] == 1
torch.cuda.empty_cache() torch.cuda.empty_cache()
@ -76,7 +83,7 @@ def check_bloom(rank, world_size, port):
@rerun_if_address_is_in_use() @rerun_if_address_is_in_use()
@clear_cache_before_run() @clear_cache_before_run()
def test_bloom(): def test_bloom():
spawn(check_bloom, 4) spawn(check_bloom, 2)
if __name__ == "__main__": if __name__ == "__main__":

70
tests/test_shardformer/test_model/test_shard_llama_pipeline.py
View File

@ -5,7 +5,9 @@ import colossalai
from colossalai.cluster import ProcessGroupMesh from colossalai.cluster import ProcessGroupMesh
from colossalai.logging import disable_existing_loggers from colossalai.logging import disable_existing_loggers
from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.policies.auto_policy import get_autopolicy
from colossalai.shardformer.policies.base_policy import Policy from colossalai.shardformer.policies.base_policy import Policy
from colossalai.shardformer.shard import ShardConfig
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import ( from colossalai.testing import (
assert_hf_output_close, assert_hf_output_close,
@ -18,9 +20,35 @@ from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): def check_llama_model_policy(name, model: torch.nn.Module, stage_manager: PipelineStageManager):
# check forward policy = get_autopolicy(model)
pass policy.set_model(model)
model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False)
policy.set_shard_config(model_config)
layers = policy.get_held_layers()
if stage_manager.is_first_stage():
assert len(layers) == 2 + 1
else:
if name == "transformers_llama":
assert len(layers) == 2 + 1
else:
assert len(layers) == 2 + 2
def check_llama_model_pipeline_forward(name, sharded_model, stage_manager: PipelineStageManager):
x = torch.randint(0, 1000, (2, 3)).cuda()
if stage_manager.stage == 0:
attention_mask = torch.ones_like(x).cuda()
output = sharded_model(input_ids=x, attention_mask=attention_mask)
assert output['hidden_states'].shape == (2, 3, 128)
else:
hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda()
attention_mask = torch.ones((2, 3)).cuda()
output = sharded_model(
hidden_states=hidden_states,
attention_mask=attention_mask,
)
assert output[0] is not None
@parameterize('enable_fused_normalization', [False]) @parameterize('enable_fused_normalization', [False])
@ -28,40 +56,18 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
@parameterize('use_lazy_init', [False]) @parameterize('use_lazy_init', [False])
#TODO: merge this into test_shard_llama #TODO: merge this into test_shard_llama
def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
DP_DIM, PP_DIM = 0, 1 PP_DIM = 0
DP_SIZE, PP_SIZE = 2, 2 PP_SIZE = 2
RANK_TO_COORDINATE = { pg_mesh = ProcessGroupMesh(PP_SIZE)
0: (0, 0),
1: (0, 1),
2: (1, 0),
3: (1, 1),
}
PP_RANKS_IN_GROUP = {
0: [0, 1],
1: [0, 1],
2: [2, 3],
3: [2, 3],
}
pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
stage_manager = PipelineStageManager(pg_mesh, PP_DIM) stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') sub_model_zoo = model_zoo.get_sub_registry('transformers_llama')
x = torch.randint(0, 1000, (2, 3)).cuda()
hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda()
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization,
enable_tensor_parallelism, use_lazy_init) enable_tensor_parallelism, use_lazy_init)
if stage_manager.stage == 0: check_llama_model_policy(name, org_model, stage_manager)
attention_mask = torch.ones_like(x).cuda() check_llama_model_pipeline_forward(name, sharded_model, stage_manager)
output = sharded_model(input_ids=x, attention_mask=attention_mask)
assert output['hidden_states'].shape == (2, 3, 128)
else:
attention_mask = torch.ones((2, 3)).cuda()
output = sharded_model(
hidden_states=hidden_states,
attention_mask=attention_mask,
)
assert output[0] is not None
torch.cuda.empty_cache() torch.cuda.empty_cache()
@ -76,7 +82,7 @@ def check_llama(rank, world_size, port):
@rerun_if_address_is_in_use() @rerun_if_address_is_in_use()
@clear_cache_before_run() @clear_cache_before_run()
def test_llama(): def test_llama():
spawn(check_llama, 4) spawn(check_llama, 2)
if __name__ == "__main__": if __name__ == "__main__":