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[shardformer]: support gpt-j, falcon, Mistral and add interleaved pipeline for bert (#5088)

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* [shardformer] implement policy for all GPT-J models and test

* [shardformer] support interleaved pipeline parallel for bert finetune

* [shardformer] shardformer support falcon (#4883)

* [shardformer]: fix interleaved pipeline for bert model (#5048)

* [hotfix]: disable seq parallel for gptj and falcon, and polish code (#5093)

* Add Mistral support for Shardformer (#5103)

* [shardformer] add tests to mistral (#5105)

---------

Co-authored-by: Pengtai Xu <henryxu880@gmail.com>
Co-authored-by: ppt0011 <143150326+ppt0011@users.noreply.github.com>
Co-authored-by: flybird11111 <1829166702@qq.com>
Co-authored-by: eric8607242 <e0928021388@gmail.com>
This commit is contained in:
Wenhao Chen
2023-11-28 16:54:42 +08:00
committed by GitHub
parent 126cf180bc
commit 7172459e74
39 changed files with 4007 additions and 234 deletions
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168
tests/test_shardformer/test_model/test_shard_mistral.py Normal file
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import os
import pytest
import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.shardformer.layer.utils import Randomizer
from colossalai.tensor.d_tensor.api import clear_layout_converter
from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import (
build_model_from_hybrid_plugin,
check_all_grad_tensors,
check_loss,
check_output_hidden_state,
check_weight,
get_grad_tensors_for_check,
run_forward_backward_with_hybrid_plugin,
unwrap_model,
)
os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
model_fn, loss_fn, test_config
)
org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin(
org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster
)
stage_manager = booster.plugin.stage_manager
tp_group = booster.plugin.tp_group
# unwrap model
mistral_model = unwrap_model(org_model, "MistralModel", "model")
shard_mistral_model = unwrap_model(sharded_model, "MistralModel", "model")
row_layer_for_check = ["layers[0].self_attn.q_proj", "embed_tokens"]
col_layer_for_check = ["layers[0].self_attn.o_proj"]
# Save gradient tensors for comparison between the original model and the sharded model before optimizer step.
grads_to_check = {}
if (stage_manager is None or stage_manager.is_first_stage()) and booster.plugin.zero_stage == 0:
if test_config["precision"] == "fp32":
atol, rtol = 5e-5, 1e-4
else:
atol, rtol = 5e-3, 5e-3
row_layer_grads = get_grad_tensors_for_check(
mistral_model,
shard_mistral_model,
row_layer_for_check,
tp_group,
atol=atol,
rtol=rtol,
dim=0,
verbose=False,
)
col_layer_grads = get_grad_tensors_for_check(
mistral_model,
shard_mistral_model,
col_layer_for_check,
tp_group,
atol=atol,
rtol=rtol,
dim=1,
verbose=False,
)
grads_to_check.update(col_layer_grads)
grads_to_check.update(row_layer_grads)
# optimizer executes step
org_optimizer.step()
sharded_optimizer.step()
# check last hidden state & loss
if stage_manager is None or stage_manager.is_last_stage():
if test_config["precision"] == "fp32":
atol, rtol = 1e-5, 1e-3
else:
atol, rtol = 5e-3, 5e-3
if org_model.__class__.__name__ == "MistralModel":
check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
# check weights
if stage_manager is None or stage_manager.is_first_stage():
if test_config["precision"] == "fp32":
atol, rtol = 1e-4, 1e-3
else:
atol, rtol = 5e-3, 5e-3
check_weight(
mistral_model,
shard_mistral_model,
col_layer_for_check,
tp_group,
atol=atol,
rtol=rtol,
dim=1,
verbose=False,
)
# check grads
check_all_grad_tensors(grads_to_check)
torch.cuda.empty_cache()
@parameterize(
"test_config",
[
{
"tp_size": 4,
"pp_size": 1,
"enable_all_optimization": True,
"use_lazy_init": False,
"precision": "fp32",
},
{
"tp_size": 2,
"pp_size": 1,
"enable_all_optimization": True,
"use_lazy_init": False,
"precision": "fp32",
},
{
"tp_size": 2,
"pp_size": 1,
"enable_all_optimization": True,
"use_lazy_init": True,
"zero_stage": 2,
"precision": "fp16",
"initial_scale": 1,
},
],
)
def run_mistral_test(test_config):
sub_model_zoo = model_zoo.get_sub_registry("transformers_mistral")
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
clear_layout_converter()
Randomizer.reset_index()
torch.cuda.empty_cache()
def check_mistral(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
run_mistral_test()
@pytest.mark.skip("This test should be run on a version of transformers not less than 4.35.2.")
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_mistral():
spawn(check_mistral, 4)
if __name__ == "__main__":
test_mistral()