ColossalAI/tests/test_shardformer/test_model/test_shard_sam.py

73 lines
2.5 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=["pred_masks"])
# 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
sam = org_model
sharded_sam = sharded_model
# check grad
col_layer_for_check = ["mask_decoder.transformer.layers[0].self_attn.q_proj", "vision_encoder.layers[0].mlp.lin1"]
row_layer_for_check = ["mask_decoder.transformer.layers[0].self_attn.out_proj", "vision_encoder.layers[0].mlp.lin2"]
check_grad(sam, sharded_sam, col_layer_for_check, atol=1e-5, rtol=1e-3, dim=0, verbose=False)
check_grad(sam, sharded_sam, row_layer_for_check, atol=1e-3, rtol=1e-3, dim=1, verbose=False)
@parameterize("enable_fused_normalization", [True, False])
@parameterize("enable_tensor_parallelism", [True, False])
@parameterize("enable_flash_attention", [True, False])
def run_sam_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention):
sub_model_zoo = model_zoo.get_sub_registry("transformers_sam")
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
)
check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
torch.cuda.empty_cache()
def check_sam(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
run_sam_test()
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_sam():
spawn(check_sam, 2)
if __name__ == "__main__":
test_sam()