mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-06-27 15:57:16 +00:00
7f8b16635b
* [misc] remove config arg from initialize * [misc] remove old tensor contrusctor * [plugin] add npu support for ddp * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [devops] fix doc test ci * [test] fix test launch * [doc] update launch doc --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
226 lines
7.1 KiB
Python
226 lines
7.1 KiB
Python
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,
|
||
)
|
||
|
||
|
||
def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
|
||
# check forward
|
||
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
|
||
|
||
# unwarp the model
|
||
if org_model.__class__.__name__ == "WhisperForConditionalGeneration":
|
||
whisper = org_model.model
|
||
sharded_whisper = sharded_model.unwrap().model
|
||
else:
|
||
whisper = org_model
|
||
sharded_whisper = sharded_model.unwrap()
|
||
|
||
# check grad
|
||
if org_model.__class__.__name__ == "WhisperForAudioClassification":
|
||
col_layer_for_check = ["encoder.layers[0].self_attn.q_proj"]
|
||
row_layer_for_check = ["encoder.layers[0].self_attn.out_proj"]
|
||
else:
|
||
col_layer_for_check = [
|
||
"encoder.layers[0].self_attn.q_proj",
|
||
# 'decoder.layers[0].self_attn.q_proj'
|
||
]
|
||
row_layer_for_check = [
|
||
"encoder.layers[0].self_attn.out_proj",
|
||
#'decoder.layers[0].self_attn.out_proj'
|
||
]
|
||
|
||
# Save gradient tensors for comparison between the original model and the sharded model before optimizer step.
|
||
grads_to_check = {}
|
||
if test_config["precision"] == "fp32":
|
||
atol, rtol = 2e-4, 2e-4
|
||
else:
|
||
atol, rtol = 5e-3, 5e-3
|
||
|
||
if stage_manager is None or stage_manager.is_first_stage():
|
||
row_layer_grads = get_grad_tensors_for_check(
|
||
whisper, sharded_whisper, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1
|
||
)
|
||
col_layer_grads = get_grad_tensors_for_check(
|
||
whisper, sharded_whisper, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0
|
||
)
|
||
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 = 2e-4, 2e-4
|
||
else:
|
||
atol, rtol = 5e-3, 5e-3
|
||
|
||
if org_model.__class__.__name__ == "WhisperModel":
|
||
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 test_config["precision"] == "fp32":
|
||
atol, rtol = 1e-3, 1e-3
|
||
else:
|
||
atol, rtol = 5e-3, 5e-3
|
||
if stage_manager is None or stage_manager.is_first_stage():
|
||
check_weight(
|
||
whisper, sharded_whisper, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False
|
||
)
|
||
check_weight(
|
||
whisper, sharded_whisper, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False
|
||
)
|
||
|
||
# check grads
|
||
check_all_grad_tensors(grads_to_check)
|
||
|
||
torch.cuda.empty_cache()
|
||
|
||
|
||
# TODO fix WhisperForConditionalGeneration enable jit fused operato
|
||
# TODO（jianghai) fix fp16
|
||
@parameterize(
|
||
"test_config",
|
||
[
|
||
{
|
||
"tp_size": 2,
|
||
"pp_size": 2,
|
||
"num_microbatches": 2,
|
||
"enable_metadata_cache": False,
|
||
"enable_all_optimization": True,
|
||
"use_lazy_init": False,
|
||
"precision": "fp32",
|
||
"initial_scale": 1,
|
||
},
|
||
{
|
||
"tp_size": 1,
|
||
"pp_size": 2,
|
||
"num_microbatches": 4,
|
||
"enable_metadata_cache": False,
|
||
"use_lazy_init": False,
|
||
"precision": "fp32",
|
||
"initial_scale": 1,
|
||
},
|
||
{
|
||
"tp_size": 4,
|
||
"pp_size": 1,
|
||
"enable_all_optimization": True,
|
||
"use_lazy_init": False,
|
||
"precision": "fp32",
|
||
},
|
||
{
|
||
"tp_size": 1,
|
||
"pp_size": 4,
|
||
"num_microbatches": 4,
|
||
"enable_metadata_cache": False,
|
||
"use_lazy_init": False,
|
||
"precision": "fp32",
|
||
},
|
||
# whisper is not supported fp16 for now.
|
||
],
|
||
)
|
||
def run_whisper_test(test_config):
|
||
sub_model_zoo = model_zoo.get_sub_registry("transformers_whisper")
|
||
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
|
||
if test_config["pp_size"] > 2 and name == "transformers_whisper_for_audio_classification":
|
||
continue
|
||
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()
|
||
|
||
|
||
@parameterize(
|
||
"test_config",
|
||
[
|
||
{
|
||
"tp_size": 2,
|
||
"pp_size": 2,
|
||
"num_microbatches": 4,
|
||
"enable_metadata_cache": False,
|
||
"enable_all_optimization": False,
|
||
"use_lazy_init": False,
|
||
"precision": "fp32",
|
||
"initial_scale": 1,
|
||
},
|
||
{
|
||
"tp_size": 2,
|
||
"pp_size": 2,
|
||
"num_microbatches": 2,
|
||
"enable_metadata_cache": False,
|
||
"enable_all_optimization": False,
|
||
"use_lazy_init": False,
|
||
"precision": "fp32",
|
||
"initial_scale": 1,
|
||
},
|
||
],
|
||
)
|
||
def run_whisper_3d_test(test_config):
|
||
sub_model_zoo = model_zoo.get_sub_registry("transformers_whisper")
|
||
|
||
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()
|
||
torch.cuda.empty_cache()
|
||
|
||
|
||
def check_whisper(rank, world_size, port):
|
||
disable_existing_loggers()
|
||
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
||
run_whisper_test()
|
||
|
||
|
||
def check_whisper_3d(rank, world_size, port):
|
||
disable_existing_loggers()
|
||
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
||
run_whisper_3d_test()
|
||
|
||
|
||
@pytest.mark.dist
|
||
@rerun_if_address_is_in_use()
|
||
@clear_cache_before_run()
|
||
def test_whisper():
|
||
spawn(check_whisper, 4)
|
||
|
||
|
||
@pytest.mark.largedist
|
||
@rerun_if_address_is_in_use()
|
||
@clear_cache_before_run()
|
||
def test_whisper_3d():
|
||
spawn(check_whisper_3d, 8)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
test_whisper()
|
||
test_whisper_3d()
|