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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

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

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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

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

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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

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

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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

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

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

* [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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75
colossalai/shardformer/modeling/llama.py
View File

@@ -31,7 +31,7 @@ from colossalai.shardformer.layer._operation import (
)
from colossalai.shardformer.shard import ShardConfig
from ..layer import ColoAttention, cross_entropy_1d
from ..layer import ColoAttention, dist_cross_entropy
class LlamaPipelineForwards:
@@ -86,13 +86,20 @@ class LlamaPipelineForwards:
device = input_ids.device if input_ids is not None else inputs_embeds.device
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
hidden_states = inputs_embeds
else:
input_shape = hidden_states.shape[:-1]
batch_size, seq_length = input_shape
device = hidden_states.device
# Support SP + PP
sp_mode = shard_config.sequence_parallelism_mode
sp_group = shard_config.sequence_parallel_process_group
sp_size = shard_config.sequence_parallel_size
if sp_mode == "all_to_all" and not stage_manager.is_first_stage():
# For correct positions ids. The states will be gather along the seq dim in the attention layer later.
seq_length *= sp_size
past_seen_tokens = 0
if use_cache: # kept for BC (cache positions)
if not isinstance(past_key_values, StaticCache):
@@ -101,7 +108,7 @@ class LlamaPipelineForwards:
if cache_position is None:
if isinstance(past_key_values, StaticCache):
raise ValueError("cache_position is a required argument when using StaticCache.")
cache_position = torch.arange(past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=device)
cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_length, device=device)
seq_length_with_past = seq_length + past_seen_tokens
@@ -118,7 +125,6 @@ class LlamaPipelineForwards:
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
# embed positions, for the first stage, hidden_states is the input embeddings,
# for the other stages, hidden_states is the output of the previous stage
if shard_config.enable_flash_attention:
@@ -134,6 +140,13 @@ class LlamaPipelineForwards:
else:
attention_mask = self._update_causal_mask(attention_mask, hidden_states, cache_position)
# Support SP + PP
if stage_manager.is_first_stage():
if sp_mode in ["ring", "split_gather"]:
hidden_states = split_forward_gather_backward(hidden_states, 1, sp_group)
elif sp_mode == "all_to_all":
hidden_states = split_forward_gather_backward(hidden_states, 1, sp_group, 1 / sp_size)
if self.gradient_checkpointing and self.training and use_cache:
if use_cache:
logger.warning_once(
@@ -196,6 +209,10 @@ class LlamaPipelineForwards:
if stage_manager.is_last_stage():
hidden_states = self.norm(hidden_states)
if sp_mode == "ring" or sp_mode == "split_gather":
hidden_states = gather_forward_split_backward(hidden_states, 1, sp_group)
elif sp_mode == "all_to_all":
hidden_states = gather_forward_split_backward(hidden_states, 1, sp_group, grad_scale=sp_size)
# add hidden states from the last decoder layer
if output_hidden_states:
@@ -304,29 +321,9 @@ class LlamaPipelineForwards:
if stage_manager.is_last_stage():
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
if shard_config.enable_tensor_parallelism and shard_config.parallel_output:
new_vocab_size = logits.shape[-1]
shift_logits = shift_logits.view(-1, new_vocab_size)
loss = cross_entropy_1d(
shift_logits,
shift_labels,
process_group=shard_config.tensor_parallel_process_group,
vocab_size=self.lm_head.out_features,
dtype=self.model.dtype,
)
else:
shift_logits = shift_logits.view(-1, self.config.vocab_size)
loss = loss_fct(shift_logits, shift_labels)
loss = dist_cross_entropy(
labels, logits, shard_config, self.lm_head.out_features, self.config.vocab_size, self.model.dtype
)
if not return_dict:
output = (logits,) + outputs[1:]
@@ -529,7 +526,6 @@ def get_llama_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
)
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
@@ -649,7 +645,7 @@ def get_llama_flash_attention_model_forward(shard_config: ShardConfig, sp_mode=N
# in this case, attention_mask is a dict rather than a tensor
if shard_config.enable_flash_attention:
mask_shape = (inputs_embeds.shape[0], 1, past_seen_tokens + seq_len, past_seen_tokens + seq_len)
mask_shape = (inputs_embeds.shape[0], 1, seq_len, past_seen_tokens + seq_len)
attention_mask = ColoAttention.prepare_attn_kwargs(
mask_shape,
inputs_embeds.dtype,
@@ -814,24 +810,9 @@ def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
logits = self.lm_head(hidden_states)
logits = logits.float()
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
new_vocab_size = logits.shape[-1]
shift_logits = shift_logits.view(-1, new_vocab_size)
loss = cross_entropy_1d(
shift_logits,
shift_labels,
process_group=shard_config.tensor_parallel_process_group,
vocab_size=self.lm_head.out_features,
dtype=self.model.dtype,
)
loss = dist_cross_entropy(
labels, logits, shard_config, self.lm_head.out_features, self.config.vocab_size, self.model.dtype
)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output