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[shardformer] refactor embedding resize (#5603)

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* [branch rebase] rebase main to Feature/resize_embedding (#5554)

* fix

* [release] update version (#5411)

* [hotfix] fix typo s/keywrods/keywords etc. (#5429)

* [devops] fix compatibility (#5444)

* [devops] fix compatibility

* [hotfix] update compatibility test on pr

* [devops] fix compatibility

* [devops] record duration during comp test

* [test] decrease test duration

* fix falcon

* [shardformer] fix gathering output when using tensor parallelism (#5431)

* fix

* padding vocab_size when using pipeline parallellism

padding vocab_size when using pipeline parallellism

fix

fix

* fix

* fix

fix

fix

* fix gather output

* fix

* fix

* fix

fix resize embedding

fix resize embedding

* fix resize embedding

fix

* revert

* revert

* revert

* [doc] release Open-Sora 1.0 with model weights (#5468)

* [doc] release Open-Sora 1.0 with model weights

* [doc] release Open-Sora 1.0 with model weights

* [doc] release Open-Sora 1.0 with model weights

* [doc] update open-sora demo (#5479)

* [doc] update open-sora demo

* [doc] update open-sora demo

* [doc] update open-sora demo

* [example] add grok-1 inference (#5485)

* [misc] add submodule

* remove submodule

* [example] support grok-1 tp inference

* [example] add grok-1 inference script

* [example] refactor code

* [example] add grok-1 readme

* [exmaple] add test ci

* [exmaple] update readme

---------

Co-authored-by: Hongxin Liu <lhx0217@gmail.com>
Co-authored-by: digger yu <digger-yu@outlook.com>
Co-authored-by: binmakeswell <binmakeswell@gmail.com>

* [CI] run pre-commit (#5577)

* fix

* [release] update version (#5411)

* [hotfix] fix typo s/keywrods/keywords etc. (#5429)

* [devops] fix compatibility (#5444)

* [devops] fix compatibility

* [hotfix] update compatibility test on pr

* [devops] fix compatibility

* [devops] record duration during comp test

* [test] decrease test duration

* fix falcon

* [shardformer] fix gathering output when using tensor parallelism (#5431)

* fix

* padding vocab_size when using pipeline parallellism

padding vocab_size when using pipeline parallellism

fix

fix

* fix

* fix

fix

fix

* fix gather output

* fix

* fix

* fix

fix resize embedding

fix resize embedding

* fix resize embedding

fix

* revert

* revert

* revert

* [doc] release Open-Sora 1.0 with model weights (#5468)

* [doc] release Open-Sora 1.0 with model weights

* [doc] release Open-Sora 1.0 with model weights

* [doc] release Open-Sora 1.0 with model weights

* [doc] update open-sora demo (#5479)

* [doc] update open-sora demo

* [doc] update open-sora demo

* [doc] update open-sora demo

* [example] add grok-1 inference (#5485)

* [misc] add submodule

* remove submodule

* [example] support grok-1 tp inference

* [example] add grok-1 inference script

* [example] refactor code

* [example] add grok-1 readme

* [exmaple] add test ci

* [exmaple] update readme

* run pre-commit

---------

Co-authored-by: Hongxin Liu <lhx0217@gmail.com>
Co-authored-by: digger yu <digger-yu@outlook.com>
Co-authored-by: binmakeswell <binmakeswell@gmail.com>

* [rebase] rebase main to resize-embedding (#5581)

* [release] grok-1 314b inference (#5490)

* [release] grok-1 inference

* [release] grok-1 inference

* [release] grok-1 inference

* [example] update Grok-1 inference (#5495)

* revise grok-1 example

* remove unused arg in scripts

* prevent re-installing torch

* update readme

* revert modifying colossalai requirements

* add perf

* trivial

* add tokenizer url

* [hotfix] set return_outputs=False in examples and polish code (#5404)

* fix: simplify merge_batch

* fix: use return_outputs=False to eliminate extra memory consumption

* feat: add return_outputs warning

* style: remove `return_outputs=False` as it is the default value

* [release] grok-1 inference benchmark (#5500)

* [release] grok-1 inference benchmark

* [release] grok-1 inference benchmark

* [release] grok-1 inference benchmark

* [release] grok-1 inference benchmark

* [release] grok-1 inference benchmark

* [shardformer]Fix lm parallel. (#5480)

* fix

* padding vocab_size when using pipeline parallellism

padding vocab_size when using pipeline parallellism

fix

fix

* fix

* fix

fix

fix

* fix gather output

* fix

* fix

* fix

fix resize embedding

fix resize embedding

* fix resize embedding

fix

* revert

* revert

* revert

* fix lm forward distribution

* fix

* test ci

* fix

* [fix] fix grok-1 example typo (#5506)

* [devops] fix example test ci (#5504)

* Fix ColoTensorSpec for py11 (#5440)

* fixed layout converter caching and updated tester

* Empty-Commit

* [shardformer] update colo attention to support custom mask (#5510)

* [feature] refactor colo attention (#5462)

* [extension] update api

* [feature] add colo attention

* [feature] update sdpa

* [feature] update npu attention

* [feature] update flash-attn

* [test] add flash attn test

* [test] update flash attn test

* [shardformer] update modeling to fit colo attention (#5465)

* [misc] refactor folder structure

* [shardformer] update llama flash-attn

* [shardformer] fix llama policy

* [devops] update tensornvme install

* [test] update llama test

* [shardformer] update colo attn kernel dispatch

* [shardformer] update blip2

* [shardformer] update chatglm

* [shardformer] update gpt2

* [shardformer] update gptj

* [shardformer] update opt

* [shardformer] update vit

* [shardformer] update colo attention mask prep

* [shardformer] update whisper

* [test] fix shardformer tests (#5514)

* [test] fix shardformer tests

* [test] fix shardformer tests

* [format] applied code formatting on changed files in pull request 5510 (#5517)

Co-authored-by: github-actions <github-actions@github.com>

* [shardformer] fix pipeline forward error if custom layer distribution is used (#5189)

* Use self.[distribute_layers|get_stage_index] to exploit custom layer distribution

* Change static methods for t5 layer distribution to member functions

* Change static methods for whisper layer distribution to member functions

* Replace whisper policy usage with self one

* Fix test case to use non-static layer distribution methods

* fix: fix typo

---------

Co-authored-by: Wenhao Chen <cwher@outlook.com>

* [Fix] Grok-1 use tokenizer from the same pretrained path (#5532)

* [fix] use tokenizer from the same pretrained path

* trust remote code

* [ColossalChat] Update RLHF V2 (#5286)

* Add dpo. Fix sft, ppo, lora. Refactor all

* fix and tested ppo

* 2 nd round refactor

* add ci tests

* fix ci

* fix ci

* fix readme, style

* fix readme style

* fix style, fix benchmark

* reproduce benchmark result, remove useless files

* rename to ColossalChat

* use new image

* fix ci workflow

* fix ci

* use local model/tokenizer for ci tests

* fix ci

* fix ci

* fix ci

* fix ci timeout

* fix rm progress bar. fix ci timeout

* fix ci

* fix ci typo

* remove 3d plugin from ci temporary

* test environment

* cannot save optimizer

* support chat template

* fix readme

* fix path

* test ci locally

* restore build_or_pr

* fix ci data path

* fix benchmark

* fix ci, move ci tests to 3080, disable fast tokenizer

* move ci to 85

* support flash attention 2

* add all-in-one data preparation script. Fix colossal-llama2-chat chat template

* add hardware requirements

* move ci test data

* fix save_model, add unwrap

* fix missing bos

* fix missing bos; support grad accumulation with gemini

* fix ci

* fix ci

* fix ci

* fix llama2 chat template config

* debug sft

* debug sft

* fix colossalai version requirement

* fix ci

* add sanity check to prevent NaN loss

* fix requirements

* add dummy data generation script

* add dummy data generation script

* add dummy data generation script

* add dummy data generation script

* update readme

* update readme

* update readme and ignore

* fix logger bug

* support parallel_output

* modify data preparation logic

* fix tokenization

* update lr

* fix inference

* run pre-commit

---------

Co-authored-by: Tong Li <tong.li352711588@gmail.com>

* [shardformer, pipeline] add `gradient_checkpointing_ratio` and heterogenous shard policy for llama (#5508)

* feat: add `GradientCheckpointConfig` and `PipelineGradientCheckpointConfig`

* feat: apply `GradientCheckpointConfig` to policy and llama_forward

* feat: move `distribute_layer` and `get_stage_index` to PipelineStageManager

* fix: add optional args for `distribute_layer` and `get_stage_index`

* fix: fix changed API calls

* test: update llama tests

* style: polish `GradientCheckpointConfig`

* fix: fix pipeline utils tests

* fix incorrect sharding without zero (#5545)

Co-authored-by: Edenzzzz <wtan45@wisc.edu>

* [shardformer] Sequence Parallelism Optimization (#5533)

* sequence parallel optimization

* validate sequence parallel in llama (code to be polished)

* shardformer api writing

* integrate sequence parallel in ShardFormer

* fix pp bugs and sp bugs for LlaMa model

* integrating ring-based sequence parallelism into ShardFormer

* [sequence parallelism]: Add fused megatron function

* integrating ring-based sequence parallelism into ShardFormer

---------

Co-authored-by: linsj20 <linsj20@mails.tsinghua.edu.cn>

* fix bugs when useing sp and flashattention together

* fix operation function name

* support flash attention for ulysses-style sp

* clarify sp process group

* fix compatibility bugs in moe plugin

* fix fused linear bugs

* fix linear layer test

* support gpt model all-to-all sp

* modify shard data dimension (meant to be dim=-1)

* support megtron-style sp and distributed attn for llama model

* [shardformer] add megatron sp to llama

* support llama7B 128k with distributed attention

* [shardformer] robustness enhancement

* add block attn

* sp mode 1: keep input as a complete sequence

* fix sp compatability

* finish sp mode 3 support for gpt

* using all_to_all_single when batch size is 1

* support mode 2 sp in gpt2 (#5)

* [shardformer] add megatron sp to llama

* support llama7B 128k with distributed attention

* [shardformer] robustness enhancement

* add block attn

* sp mode 1: keep input as a complete sequence

* fix sp compatability

* refactor ring implementation

* support mode 2 sp in gpt2

* polish code

* enable distributed attn mask when using sp mode 2 and 3 in llama

* automatically enable flash attn when using sp mode 2 and 3 in llama

* inplace attn mask

* add zero2 support for sequence parallel

* polish code

* fix bugs

* fix gemini checkpoint io

* loose tensor checking atol and rtol

* add comment

* fix llama layernorm grad

* fix zero grad

* fix zero grad

* fix conflict

* update split and gather auto grad func

* sequence parallel: inside text split (#6)

* polish code (part 1)

* polish code (part 2)

* polish code (part 2.5)

* polish code (part 3)

* sequence parallel: inside text split

* miscellaneous minor fixes

* polish code

* fix ulysses style ZeRO

* sequence parallel: inside text split

* miscellaneous minor fixes

* disaggregate sp group and dp group for  sp

* fix llama and gpt sp

* polish code

* move ulysses grad sync to ddp (#9)

* remove zero_stage and unbind the grad sync for alltoall sp

* add 2d group creation test

* move ulysses grad sync to ddp

* add 2d group creation test

* remove useless code

* change shard config not to enable sp when enable_all_optimizations

* add sp warnings for several model

* remove useless code

---------

Co-authored-by: linsj20 <linsj20@mails.tsinghua.edu.cn>

* [hotfix] quick fixes to make legacy tutorials runnable (#5559)

Co-authored-by: Edenzzzz <wtan45@wisc.edu>

* [fix] fix typo s/muiti-node /multi-node etc. (#5448)

* [hotfix] fix typo s/get_defualt_parser /get_default_parser (#5548)

* [devops] remove post commit ci (#5566)

* [devops] remove post commit ci

* [misc] run pre-commit on all files

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

---------

Co-authored-by: binmakeswell <binmakeswell@gmail.com>
Co-authored-by: Yuanheng Zhao <54058983+yuanheng-zhao@users.noreply.github.com>
Co-authored-by: Wenhao Chen <cwher@outlook.com>
Co-authored-by: Hongxin Liu <lhx0217@gmail.com>
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* [shardformer]enable padding vocabulary size. (#5489)

* padding vocab_size when using pipeline parallellism

padding vocab_size when using pipeline parallellism

fix

fix

* fix

* fix

fix

fix

* fix gather output

* fix

* fix

* fix

fix resize embedding

fix resize embedding

* fix resize embedding

fix

* revert

* revert

* revert

* padding vocab

* padding vocabe

* fix

* fix

* fxi

* test ci

* fix

fix

fix

fix

* fix

fix

* fix

* fix

* Update hybrid_parallel_plugin.py

fix

fix

fix

* fix

fix

* fix

fix

* fix

* resolve super init

resolve super init

resolve super init

resolve super init

* resolve comments

* fix

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* vocab checkpointio

* padding vocab_size when using pipeline parallellism

padding vocab_size when using pipeline parallellism

fix

fix

* fix

fix

fix

* fix

* fix

fix resize embedding

fix resize embedding

* fix resize embedding

fix

* revert

* revert

* padding vocab

* fix

* fix

fix

* fix

fix

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix ci

* fix

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix

* cherry-pick

* revert moe modify

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix

fix

fix

fix

fix

fix

fix

fix

* resolve comments

resolve comments

resolve comments

resolve comments

resolve comments

* ptensor

ptensor

resolve comments

fix

fix

fix

fix

fix

resolve comments

resolve comments

resolve comments

resolve comments

resolve comments

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Hongxin Liu <lhx0217@gmail.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* fix rebase

* fix rebase

---------

Co-authored-by: Hongxin Liu <lhx0217@gmail.com>
Co-authored-by: digger yu <digger-yu@outlook.com>
Co-authored-by: binmakeswell <binmakeswell@gmail.com>
Co-authored-by: Yuanheng Zhao <54058983+yuanheng-zhao@users.noreply.github.com>
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This commit is contained in:
flybird11111
2024-04-18 16:10:18 +08:00
committed by GitHub
parent 3788fefc7a
commit a0ad587c24
36 changed files with 1352 additions and 289 deletions
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111
colossalai/shardformer/layer/embedding.py
View File

@@ -21,10 +21,10 @@ from colossalai.tensor.d_tensor.api import (
)
from ._operation import gather_forward_split_backward, reduce_forward
from .parallel_module import ParallelModule
from .parallel_module import PaddingParallelModule, ParallelModule
from .utils import create_randomizer_with_offset
__all__ = ["Embedding1D", "VocabParallelEmbedding1D"]
__all__ = ["Embedding1D", "VocabParallelEmbedding1D", "PaddingEmbedding"]
class Embedding1D(ParallelModule):
@@ -161,7 +161,80 @@ class Embedding1D(ParallelModule):
return output_parallel
class VocabParallelEmbedding1D(ParallelModule):
class PaddingEmbedding(PaddingParallelModule):
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: torch.dtype = None,
device: torch.device = None,
weight: Optional[nn.Parameter] = None,
make_vocab_size_divisible_by: int = 64,
*args,
**kwargs,
):
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
self.embed_args = args
self.embed_kwargs = kwargs
self.padding_idx = padding_idx
if num_embeddings % make_vocab_size_divisible_by != 0:
self.num_embeddings = (
num_embeddings + make_vocab_size_divisible_by - (num_embeddings % make_vocab_size_divisible_by)
)
# create weight and bias
if weight is None:
factory_kwargs = {"device": device, "dtype": dtype}
weight = nn.Parameter(torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs))
else:
weight.data = weight.data.to(device=device, dtype=dtype)
super().__init__(self.num_embeddings, num_embeddings, weight)
if weight is None:
self.reset_parameters()
def reset_parameters(self) -> None:
init.normal_(self.weight)
self._fill_padding_idx_with_zero()
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None:
with torch.no_grad():
self.weight[self.padding_idx].fill_(0)
def forward(self, input: Tensor) -> Tensor:
return F.embedding(input, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
@staticmethod
def from_native_module(
module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs
) -> PaddingParallelModule:
r"""
Convert a native pytorch embedding module to a parallel module.
"""
LazyInitContext.materialize(module)
# get the origin attributes
num_embeddings = module.num_embeddings
embedding_dim = module.embedding_dim
padding_idx = module.padding_idx
device = module.weight.device
# create the parallel module
padding_embedding = PaddingEmbedding(
num_embeddings=num_embeddings,
embedding_dim=embedding_dim,
padding_idx=padding_idx,
device=device,
weight=module.weight,
*args,
**kwargs,
)
return padding_embedding
class VocabParallelEmbedding1D(PaddingParallelModule):
r"""Embedding parallelized in the vocabulary dimension.
Args:
@@ -201,10 +274,10 @@ class VocabParallelEmbedding1D(ParallelModule):
process_group: ProcessGroup = None,
weight: Optional[nn.Parameter] = None,
weight_initializer: Callable = init.normal_(),
make_vocab_size_divisible_by: int = 64,
*args,
**kwargs,
):
super().__init__()
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
self.embed_args = args
@@ -214,8 +287,23 @@ class VocabParallelEmbedding1D(ParallelModule):
tensor_parallel_size = dist.get_world_size(group=process_group)
tensor_parallel_rank = dist.get_rank(group=process_group)
self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size)
self.num_embeddings = self.num_embeddings_per_partition
# generate weight and bias
if weight is None:
factory_kwargs = {"device": device, "dtype": dtype}
weight = nn.Parameter(torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs))
else:
weight.data = weight.data.to(device=device, dtype=dtype)
# calculate new padding size
multiple = make_vocab_size_divisible_by * tensor_parallel_size
if num_embeddings % multiple != 0:
self.num_embeddings = num_embeddings + multiple - (num_embeddings % multiple)
# resize vocabulary size
super().__init__(self.num_embeddings, num_embeddings, weight)
# deal with tensor parallelism
self.num_embeddings_per_partition = divide(self.num_embeddings, tensor_parallel_size)
self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition
self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition
@@ -226,13 +314,6 @@ class VocabParallelEmbedding1D(ParallelModule):
seed = torch.random.initial_seed()
self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group)
# parameter
if weight is None:
factory_kwargs = {"device": device, "dtype": dtype}
self.weight = nn.Parameter(torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs))
else:
weight.data = weight.data.to(device=device, dtype=dtype)
self.weight = weight
if not is_distributed_tensor(self.weight):
sharded_weight = shard_rowwise(self.weight.data, process_group)
sharded_tensor_to_existing_param(sharded_weight, self.weight)
@@ -243,7 +324,7 @@ class VocabParallelEmbedding1D(ParallelModule):
@staticmethod
def from_native_module(
module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs
) -> ParallelModule:
) -> PaddingParallelModule:
r"""
Convert a native pytorch embedding module to a parallel module.
"""
@@ -303,11 +384,9 @@ class VocabParallelEmbedding1D(ParallelModule):
# Mask the input.
masked_input = input_.clone() - self.vocab_start_index
masked_input[input_mask] = 0
output_parallel = F.embedding(
masked_input, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs
)
# Mask the output embedding.
embedding_output = output_parallel.clone()
embedding_output[input_mask, :] = 0.0