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[FP8] rebase main (#5963)

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* add SimPO

* fix dataloader

* remove debug code

* add orpo

* fix style

* fix colossalai, transformers version

* fix colossalai, transformers version

* fix colossalai, transformers version

* fix torch colossalai version

* update transformers version

* [shardformer] DeepseekMoE support (#5871)

* [Feature] deepseek moe expert parallel implement

* [misc] fix typo, remove redundant file (#5867)

* [misc] fix typo

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* [Feature] deepseek support & unit test

* [misc] remove debug code & useless print

* [misc] fix typos (#5872)

* [Feature] remove modeling file, use auto config. (#5884)

* [misc] fix typos

* [Feature] deepseek support via auto model, remove modeling file

* [misc] delete useless file

* [misc] fix typos

* [Deepseek] remove redundant code (#5888)

* [misc] fix typos

* [Feature] deepseek support via auto model, remove modeling file

* [misc] delete useless file

* [misc] fix typos

* [misc] remove redundant code

* [Feature/deepseek] resolve comment. (#5889)

* [misc] fix typos

* [Feature] deepseek support via auto model, remove modeling file

* [misc] delete useless file

* [misc] fix typos

* [misc] remove redundant code

* [misc] mv module replacement into if branch

* [misc] add some warning message and modify some code in unit test

* [misc] fix typos

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* [Hoxfix] Fix CUDA_DEVICE_MAX_CONNECTIONS for comm overlap

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

* [Feat] Diffusion Model(PixArtAlpha/StableDiffusion3) Support (#5838)

* Diffusion Model Inference support

* Stable Diffusion 3 Support

* pixartalpha support

* [HotFix] CI,import,requirements-test for #5838 (#5892)

* [Hot Fix] CI,import,requirements-test

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* [Feature] Enable PP + SP for llama (#5868)

* fix cross-PP-stage position id length diff bug

* fix typo

* fix typo

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* use a one cross entropy func for all shardformer models

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* [ShardFormer] Add Ulysses Sequence Parallelism support for Command-R, Qwen2 and ChatGLM (#5897)

* add benchmark for sft, dpo, simpo, orpo. Add benchmarking result. Support lora with gradient checkpoint

* fix style

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

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* fix eval

* hotfix citation

* [zero] support all-gather overlap (#5898)

* [zero] support all-gather overlap

* [zero] add overlap all-gather flag

* [misc] fix typo

* [zero] update api

* fix orpo cross entropy loss

* [Auto Parallel]: Speed up intra-op plan generation by 44% (#5446)

* Remove unnecessary calls to deepcopy

* Build DimSpec's difference dict only once

This change considerably speeds up construction speed of DimSpec objects. The difference_dict is the same for each DimSpec object, so a single copy of it is enough.

* Fix documentation of DimSpec's difference method

* [ShardFormer] fix qwen2 sp (#5903)

* [compatibility] support torch 2.2 (#5875)

* Support Pytorch 2.2.2

* keep build_on_pr file and update .compatibility

* fix object_to_tensor usage when torch>=2.3.0 (#5820)

* [misc] support torch2.3 (#5893)

* [misc] support torch2.3

* [devops] update compatibility ci

* [devops] update compatibility ci

* [devops] add debug

* [devops] add debug

* [devops] add debug

* [devops] add debug

* [devops] remove debug

* [devops] remove debug

* [release] update version (#5912)

* [plugin] support all-gather overlap for hybrid parallel (#5919)

* [plugin] fixed all-gather overlap support for hybrid parallel

* add kto

* fix style, add kto data sample

* [Examples] Add lazy init to OPT and GPT examples (#5924)

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

* [ColossalChat] Hotfix for ColossalChat (#5910)

* add ignore and tiny llama

* fix path issue

* run style

* fix issue

* update bash

* add ignore and tiny llama

* fix path issue

* run style

* fix issue

* update bash

* fix ddp issue

* add Qwen 1.5 32B

* refactor tokenization

* [FIX BUG] UnboundLocalError: cannot access local variable 'default_conversation' where it is not associated with a value (#5931)

* cannot access local variable 'default_conversation' where it is not associated with a value

set default value for 'default_conversation'

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* fix test data

* refactor evaluation

* remove real data path

* remove real data path

* Add n_fused as an input from native_module (#5894)

* [FIX BUG] convert env param to int in (#5934)

* [Hotfix] Fix ZeRO typo #5936

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

* [Feature] Add a switch to control whether the model checkpoint needs to be saved after each epoch ends (#5941)

* Add a switch to control whether the model checkpoint needs to be saved after each epoch ends

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* fix style

* fix style

* fix style

* [shardformer] hotfix attn mask (#5945)

* [shardformer] hotfix attn mask (#5947)

* [Feat] Distrifusion Acceleration Support for Diffusion Inference (#5895)

* Distrifusion Support source

* comp comm overlap optimization

* sd3 benchmark

* pixart distrifusion bug fix

* sd3 bug fix and benchmark

* generation bug fix

* naming fix

* add docstring, fix counter and shape error

* add reference

* readme and requirement

* [zero] hotfix update master params (#5951)

* [release] update version (#5952)

* [Chat] Fix lora (#5946)

* fix merging

* remove filepath

* fix style

* Update README.md (#5958)

* [hotfix] Remove unused plan section (#5957)

* remove readme

* fix readme

* update

* [test] add mixtral for sequence classification

* [test] add mixtral transformer test

* [moe] fix plugin

* [test] mixtra pp shard test

* [chore] handle non member group

* [zero] solve hang

* [test] pass mixtral shardformer test

* [moe] implement transit between non moe tp and ep

* [zero] solve hang

* [misc] solve booster hang by rename the variable

* solve hang when parallel mode = pp + dp

* [moe] implement submesh initialization

* [moe] add mixtral dp grad scaling when not all experts are activated

* [chore] manually revert unintended commit

* [chore] trivial fix

* [chore] arg pass & remove drop token

* [test] add mixtral modelling test

* [moe] implement tp

* [moe] test deepseek

* [moe] clean legacy code

* [Feature] MoE Ulysses Support (#5918)

* moe sp support

* moe sp bug solve

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

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

---------

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* [chore] minor fix

* [moe] init moe plugin comm setting with sp

* moe sp + ep bug fix

* [moe] finalize test (no pp)

* [moe] full test for deepseek and mixtral (pp + sp to fix)

* [chore] minor fix after rebase

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

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* [chore] solve moe ckpt test failure and some other arg pass failure

* [moe] remove ops

* [test] fix test: test_zero1_2

* [bug] fix: somehow logger hangs the program

* [moe] deepseek moe sp support

* [test] add check

* [deepseek] replace attn (a workaround for bug in transformers)

* [misc] skip redunant test

* [misc] remove debug/print code

* [moe] refactor mesh assignment

* Revert "[moe] implement submesh initialization"

This reverts commit 2f9bce6686.

* [chore] change moe_pg_mesh to private

* [misc] remove incompatible test config

* [misc] fix ci failure: change default value to false in moe plugin

* [misc] remove useless condition

* [chore] docstring

* [moe] remove force_overlap_comm flag and add warning instead

* [doc] add MoeHybridParallelPlugin docstring

* [moe] solve dp axis issue

* [chore] remove redundant test case, print string & reduce test tokens

* [feat] Dist Loader for Eval (#5950)

* support auto distributed data loader

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

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* support auto distributed data loader

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

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* fix tp error

* remove unused parameters

* remove unused

* update inference

* update docs

* update inference

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* [lora] lora support hybrid parallel plugin (#5956)

* lora support hybrid plugin

* fix

* fix

* fix

* fix

* fp8 operators for compressed communication

cast_to_fp8, cast_from_fp8, all_reduce_fp8

* fix scaling algorithm in FP8 casting

* support fp8 communication in pipeline parallelism

* add fp8_communication flag in the script

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

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* fix typo

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

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* shardformer fp8

* fix rebase

* remove all to all

* fix shardformer fp8 communication training degradation

* [fp8] support all-gather flat tensor (#5932)

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

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* fix

* Update low_level_optim.py

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This commit is contained in:
flybird11111
2024-08-06 16:29:37 +08:00
committed by GitHub
parent 53cb9606bd
commit 0c10afd372
208 changed files with 10962 additions and 2892 deletions
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54
applications/ColossalEval/examples/dataset_evaluation/inference.py
View File

@@ -5,6 +5,8 @@ from typing import Dict, List
import torch.distributed as dist
from colossal_eval import dataset, models, utils
from colossal_eval.dataset.base import DistributedDataset
from torch.utils.data import DataLoader, DistributedSampler
import colossalai
from colossalai.accelerator import get_accelerator
@@ -13,6 +15,7 @@ from colossalai.logging import get_dist_logger
from colossalai.shardformer import ShardConfig
logger = get_dist_logger()
os.environ["TOKENIZERS_PARALLELISM"] = "false"
def rm_and_merge(
@@ -54,7 +57,8 @@ def rm_and_merge(
)
else:
rank_answers = utils.jload(directory)
answers["data"].extend(rank_answers["data"])
deduplidate_answers = [x for x in rank_answers["data"] if x not in answers["data"]]
answers["data"].extend(deduplidate_answers)
answers["inference_kwargs"] = rank_answers["inference_kwargs"]
for r in range(dp_size):
@@ -65,7 +69,7 @@ def rm_and_merge(
os.remove(directory)
except Exception as e:
print(e)
print(len(answers["data"]))
all_answers[category] = answers
all_answers_with_dataset_class["inference_results"] = all_answers
@@ -108,7 +112,12 @@ def main(args):
tp_rank = coordinates[TP_AXIS]
shard_config = (
ShardConfig(tensor_parallel_process_group=tp_group, enable_tensor_parallelism=args.tp_size > 1)
ShardConfig(
tensor_parallel_process_group=tp_group,
enable_tensor_parallelism=args.tp_size > 1,
parallel_output=False,
enable_all_optimization=True,
)
if args.tp_size > 1
else None
)
@@ -183,6 +192,7 @@ def main(args):
model_name = model_parameter["name"]
model_class = eval(f"models.{model_parameter['model_class']}")
paramerters = model_parameter["parameters"]
batch_size = paramerters["batch_size"]
paramerters.update({"logger": logger})
paramerters.update({"prompt_template": utils.prompt_templates[paramerters["prompt_template"]]})
paramerters.update({"shard_config": shard_config})
@@ -192,7 +202,6 @@ def main(args):
raise ValueError(f"Model class {model_parameter['model_class']} is not a subclass of BaseModel.")
for dataset_name, split_data in inference_data.items():
start = 0
prev_questions = None
for category, category_data in split_data.items():
num_turn = category_data["inference_kwargs"].get("turns", 1)
@@ -201,26 +210,33 @@ def main(args):
raise Exception(f"Dataset {dataset_name} doesn't have few-shot data for category {category}!")
answers_to_dump = copy.deepcopy(category_data)
partition_size = len(category_data["data"]) // dp_size
redundant = len(category_data["data"]) % dp_size
# Ensure that the amount of data for inference is as consistent as possible across different processes.
lengths = [partition_size for _ in range(dp_size)]
for j in range(redundant):
lengths[(j + start) % dp_size] += 1
start = (start + redundant) % dp_size
for turn in range(num_turn):
if turn == 0:
questions = category_data["data"][
sum(lengths[0:dp_rank]) : sum(lengths[0:dp_rank]) + lengths[dp_rank]
]
dist_dataset = DistributedDataset(category_data["data"])
else:
questions = prev_questions
dist_dataset = DistributedDataset(prev_questions)
sampler = DistributedSampler(
dist_dataset,
num_replicas=pg_mesh.size(DP_AXIS),
rank=pg_mesh.coordinate(DP_AXIS),
shuffle=False,
)
questions_loader = DataLoader(
dist_dataset,
batch_size=batch_size,
sampler=sampler,
num_workers=8,
pin_memory=True,
collate_fn=lambda x: x,
)
category_data["inference_kwargs"]["dataset"] = dataset_name
category_data["inference_kwargs"]["category"] = category
answers_per_rank = model_.inference(
questions, inference_kwargs=category_data["inference_kwargs"], debug=debug_args[dataset_name]
data_loader=questions_loader,
inference_kwargs=category_data["inference_kwargs"],
debug=debug_args[dataset_name],
)
prev_questions = answers_per_rank