mirror of https://github.com/hpcaitech/ColossalAI
77 lines
2.9 KiB
Python
77 lines
2.9 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(org_model, sharded_model, data_gen_fn,
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output_transform_fn, loss_fn)
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assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'])
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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(org_loss, shard_loss,
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atol=1e-5), 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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blip2 = org_model
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sharded_blip2 = sharded_model
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# check grad
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col_layer_for_check = [
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'vision_model.encoder.layers[0].self_attn.qkv', 'qformer.encoder.layer[0].attention.attention.query',
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'language_model.model.decoder.layers[0].self_attn.k_proj'
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]
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row_layer_for_check = [
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'vision_model.encoder.layers[0].self_attn.projection', 'qformer.encoder.layer[0].attention.output.dense',
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'language_model.model.decoder.layers[0].self_attn.out_proj'
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]
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check_grad(blip2, sharded_blip2, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False)
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check_grad(blip2, sharded_blip2, row_layer_for_check, atol=1e-6, rtol=1e-5, 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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@parameterize('enable_jit_fused', [True, False])
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def run_blip2_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused):
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sub_model_zoo = model_zoo.get_sub_registry('transformers_blip2')
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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(model_fn, enable_fused_normalization, enable_tensor_parallelism,
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enable_flash_attention, enable_jit_fused)
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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_blip2(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_blip2_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_blip2():
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spawn(check_blip2, 2)
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if __name__ == "__main__":
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test_blip2()
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