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https://github.com/hpcaitech/ColossalAI.git
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[shardformer] rewrite tests for opt/bloom/llama/vit/chatglm (#4395)
* rewrite opt tests * rewrite llama tests * rewrite bloom & vit tests * rewrite chatglm tests * fix LinearCol for classfiers * add judge for other tp layers, fix lazy init in util
This commit is contained in:
Baizhou Zhang
committed by
Hongxin Liu
Hongxin Liu
parent
21e0a42fd1
commit
7711bd524a
@@ -1,60 +1,127 @@
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import os
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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 colossalai.shardformer.layer.utils import Randomizer
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from colossalai.tensor.d_tensor.api import clear_layout_converter
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from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
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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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from tests.test_shardformer.test_model._utils import (
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build_model_from_hybrid_plugin,
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check_grad,
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check_loss,
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check_output_hidden_state,
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check_weight,
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run_forward_backward_with_hybrid_plugin,
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)
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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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def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
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assert_hf_output_close(org_output, shard_output, atol=1e-3, rtol=1e-3)
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org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \
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build_model_from_hybrid_plugin(model_fn, loss_fn, test_config)
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# do backward
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org_loss.backward()
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shard_loss.backward()
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org_loss, org_output, sharded_loss, sharded_output = \
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run_forward_backward_with_hybrid_plugin(
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org_model,
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sharded_model,
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sharded_optimizer,
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data_gen_fn,
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output_transform_fn,
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criterion,
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booster)
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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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stage_manager = booster.plugin.stage_manager
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tp_group = booster.plugin.tp_group
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# check last hidden state & loss
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if stage_manager is None or stage_manager.is_last_stage():
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if org_model.__class__.__name__ == 'ViTModel':
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check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3)
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check_loss(org_loss, sharded_loss, atol=1e-6, rtol=1e-3)
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# unwrap model
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if org_model.__class__.__name__ == 'ViTModel':
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vit_model = org_model
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shard_vit_model = sharded_model
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shard_vit_model = sharded_model.unwrap()
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else:
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vit_model = org_model.vit
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shard_vit_model = sharded_model.vit
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shard_vit_model = sharded_model.unwrap().vit
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# check grad
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col_layer_for_check = ['encoder.layer[0].attention.attention.query']
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row_layer_for_check = ['encoder.layer[0].attention.output.dense']
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check_grad(vit_model, shard_vit_model, col_layer_for_check, atol=1e-5, rtol=1e-3, dim=0, verbose=False)
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check_grad(vit_model, shard_vit_model, row_layer_for_check, atol=1e-5, rtol=1e-3, dim=1, verbose=False)
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row_layer_for_check = ['encoder.layer[0].attention.attention.query', 'embeddings.patch_embeddings.projection']
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col_layer_for_check = ['encoder.layer[0].attention.output.dense']
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if stage_manager is None or stage_manager.is_first_stage():
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check_grad(vit_model,
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shard_vit_model,
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row_layer_for_check,
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tp_group,
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atol=1e-5,
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rtol=1e-3,
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dim=0,
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verbose=False)
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check_grad(vit_model,
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shard_vit_model,
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col_layer_for_check,
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tp_group,
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atol=1e-5,
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rtol=1e-3,
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dim=1,
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verbose=False)
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# check weights after optimizer.step()
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org_optimizer.step()
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sharded_optimizer.step()
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if stage_manager is None or stage_manager.is_first_stage():
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check_weight(vit_model,
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shard_vit_model,
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col_layer_for_check,
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tp_group,
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atol=5e-3,
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rtol=1e-3,
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dim=1,
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verbose=False)
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torch.cuda.empty_cache()
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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_vit_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused):
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@parameterize('test_config', [{
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'tp_size': 2,
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'pp_size': 2,
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'num_microbatches': 4,
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'enable_fused_normalization': True,
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'use_lazy_init': False
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}, {
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'tp_size': 1,
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'pp_size': 2,
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'num_microbatches': 4,
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'enable_fused_normalization': False,
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'use_lazy_init': False
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}, {
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'tp_size': 4,
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'pp_size': 1,
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'enable_fused_normalization': True,
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'use_lazy_init': False
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}])
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def run_vit_test(test_config):
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# TODO: add test_config for TP+DP after supporting & debugging it
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# {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True}
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# TODO: add test_config for flash attention & jit operator after supporting
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# TODO: fix bug when settign lazy_init for Conv2D Layers in ViT models
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sub_model_zoo = model_zoo.get_sub_registry('transformers_vit')
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test_config['precision'] = 'float' # Do not use fp16/bf16 in testing
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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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check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
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clear_layout_converter()
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Randomizer.reset_index()
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torch.cuda.empty_cache()
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@@ -68,7 +135,7 @@ def check_vit(rank, world_size, port):
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@rerun_if_address_is_in_use()
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@clear_cache_before_run()
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def test_vit():
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spawn(check_vit, 2)
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spawn(check_vit, 4)
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if __name__ == "__main__":
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