mirror of https://github.com/hpcaitech/ColossalAI
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
import pytest
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import torch
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import colossalai
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from colossalai.logging import disable_existing_loggers
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from colossalai.testing import (
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assert_hf_output_close,
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clear_cache_before_run,
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parameterize,
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rerun_if_address_is_in_use,
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spawn,
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)
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from tests.kit.model_zoo import model_zoo
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from tests.test_shardformer.test_model._utils import build_model, check_grad, run_forward
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def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
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# check forward
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org_output, org_loss, shard_output, shard_loss = run_forward(
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org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn
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)
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assert_hf_output_close(org_output, shard_output, ignore_keys=["pred_masks"])
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# do backward
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org_loss.backward()
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shard_loss.backward()
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assert torch.allclose(
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org_loss, shard_loss, atol=1e-5
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), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
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# check grad
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sam = org_model
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sharded_sam = sharded_model
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# check grad
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col_layer_for_check = ["mask_decoder.transformer.layers[0].self_attn.q_proj", "vision_encoder.layers[0].mlp.lin1"]
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row_layer_for_check = ["mask_decoder.transformer.layers[0].self_attn.out_proj", "vision_encoder.layers[0].mlp.lin2"]
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check_grad(sam, sharded_sam, col_layer_for_check, atol=1e-5, rtol=1e-3, dim=0, verbose=False)
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check_grad(sam, sharded_sam, row_layer_for_check, atol=1e-3, rtol=1e-3, dim=1, verbose=False)
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@parameterize("enable_fused_normalization", [True, False])
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@parameterize("enable_tensor_parallelism", [True, False])
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@parameterize("enable_flash_attention", [True, False])
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def run_sam_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention):
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sub_model_zoo = model_zoo.get_sub_registry("transformers_sam")
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for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
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org_model, sharded_model = build_model(
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model_fn, enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention
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)
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check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
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torch.cuda.empty_cache()
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def check_sam(rank, world_size, port):
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disable_existing_loggers()
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colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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run_sam_test()
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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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@clear_cache_before_run()
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def test_sam():
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spawn(check_sam, 2)
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if __name__ == "__main__":
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test_sam()
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