ColossalAI/tests/test_shardformer/test_model/test_shard_t5.py

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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,
)
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
t5 = unwrap_model(org_model)
sharded_t5 = unwrap_model(sharded_model)
row_layer_for_check = ["shared", "encoder.block[0].layer[0].SelfAttention.q"]
# 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 = 1e-5, 1e-3
else:
atol, rtol = 5e-3, 5e-3
if (stage_manager is None or stage_manager.is_first_stage()) and booster.plugin.zero_stage == 0:
row_layer_grads = get_grad_tensors_for_check(
t5, sharded_t5, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0
)
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__ != "T5ForConditionalGeneration":
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 = 5e-4, 1e-3
else:
atol, rtol = 5e-3, 5e-3
if stage_manager is None or stage_manager.is_first_stage():
check_weight(t5, sharded_t5, row_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()
@parameterize(
"test_config",
[
{
"tp_size": 2,
"pp_size": 2,
"num_microbatches": 2,
"enable_all_optimization": True,
"use_lazy_init": True,
"precision": "fp16",
"initial_scale": 1,
},
{
"tp_size": 1,
"pp_size": 2,
"num_microbatches": 4,
"use_lazy_init": False,
"precision": "fp16",
"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_all_optimization": False,
"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,
},
{
"tp_size": 1,
"pp_size": 2,
"num_microbatches": 2,
"enable_all_optimization": True,
"use_lazy_init": True,
"zero_stage": 1,
"precision": "fp16",
"initial_scale": 1,
},
],
)
@clear_cache_before_run()
def run_t5_test(test_config):
sub_model_zoo = model_zoo.get_sub_registry("transformers_t5")
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
# skip 4-stage pp test for t5_encoder
if test_config["pp_size"] > 2 and name == "transformers_t5_encoder_model":
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_all_optimization": False,
"use_lazy_init": False,
"precision": "fp32",
"initial_scale": 1,
},
{
"tp_size": 2,
"pp_size": 2,
"num_microbatches": 4,
"enable_all_optimization": False,
"use_lazy_init": False,
"precision": "fp16",
"zero_stage": 1,
"initial_scale": 1,
},
],
)
def run_t5_3d_test(test_config):
sub_model_zoo = model_zoo.get_sub_registry("transformers_t5")
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_t5(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
run_t5_test()
def check_t5_3d(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
run_t5_3d_test()
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_t5():
spawn(check_t5, 4)
@pytest.mark.largedist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_t5_3d():
spawn(check_t5_3d, 8)
if __name__ == "__main__":
test_t5()
test_t5_3d()