mirror of https://github.com/hpcaitech/ColossalAI
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
import pytest
|
|
import torch
|
|
|
|
import colossalai
|
|
from colossalai.logging import disable_existing_loggers
|
|
from colossalai.testing import (
|
|
assert_hf_output_close,
|
|
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, check_grad, run_forward
|
|
|
|
|
|
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
|
|
# check forward
|
|
org_output, org_loss, shard_output, shard_loss = run_forward(
|
|
org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn
|
|
)
|
|
assert_hf_output_close(org_output, shard_output, ignore_keys=["past_key_values"])
|
|
|
|
# do backward
|
|
org_loss.backward()
|
|
shard_loss.backward()
|
|
|
|
assert torch.allclose(
|
|
org_loss, shard_loss, atol=1e-5
|
|
), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
|
|
|
|
# check grad
|
|
|
|
blip2 = org_model
|
|
sharded_blip2 = sharded_model
|
|
|
|
# check grad
|
|
col_layer_for_check = [
|
|
"vision_model.encoder.layers[0].self_attn.qkv",
|
|
"qformer.encoder.layer[0].attention.attention.query",
|
|
"language_model.model.decoder.layers[0].self_attn.k_proj",
|
|
]
|
|
row_layer_for_check = [
|
|
"vision_model.encoder.layers[0].self_attn.projection",
|
|
"qformer.encoder.layer[0].attention.output.dense",
|
|
"language_model.model.decoder.layers[0].self_attn.out_proj",
|
|
]
|
|
check_grad(blip2, sharded_blip2, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False)
|
|
check_grad(blip2, sharded_blip2, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False)
|
|
|
|
|
|
@parameterize("enable_fused_normalization", [True, False])
|
|
@parameterize("enable_tensor_parallelism", [True, False])
|
|
@parameterize("enable_flash_attention", [True, False])
|
|
@parameterize("enable_jit_fused", [True, False])
|
|
def run_blip2_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused):
|
|
sub_model_zoo = model_zoo.get_sub_registry("transformers_blip2")
|
|
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
|
|
org_model, sharded_model = build_model(
|
|
model_fn, enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused
|
|
)
|
|
check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
|
|
|
|
torch.cuda.empty_cache()
|
|
|
|
|
|
def check_blip2(rank, world_size, port):
|
|
disable_existing_loggers()
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
run_blip2_test()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@rerun_if_address_is_in_use()
|
|
@clear_cache_before_run()
|
|
def test_blip2():
|
|
spawn(check_blip2, 2)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_blip2()
|