mirror of https://github.com/hpcaitech/ColossalAI
247 lines
7.8 KiB
Python
247 lines
7.8 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.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 (
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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 (
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build_model_from_hybrid_plugin,
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check_all_grad_tensors,
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check_loss,
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check_output_hidden_state,
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check_weight,
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get_grad_tensors_for_check,
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run_forward_backward_with_hybrid_plugin,
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)
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def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
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# check forward
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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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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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stage_manager = booster.plugin.stage_manager
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tp_group = booster.plugin.tp_group
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# unwarp the model
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if org_model.__class__.__name__ == 'WhisperForConditionalGeneration':
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whisper = org_model.model
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sharded_whisper = sharded_model.unwrap().model
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else:
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whisper = org_model
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sharded_whisper = sharded_model.unwrap()
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# check grad
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if org_model.__class__.__name__ == 'WhisperForAudioClassification':
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col_layer_for_check = ['encoder.layers[0].self_attn.q_proj']
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row_layer_for_check = ['encoder.layers[0].self_attn.out_proj']
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else:
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col_layer_for_check = [
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'encoder.layers[0].self_attn.q_proj',
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# 'decoder.layers[0].self_attn.q_proj'
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]
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row_layer_for_check = [
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'encoder.layers[0].self_attn.out_proj',
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#'decoder.layers[0].self_attn.out_proj'
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]
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# Save gradient tensors for comparison between the original model and the sharded model before optimizer step.
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grads_to_check = {}
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if test_config['precision'] == 'fp32':
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atol, rtol = 2e-4, 2e-4
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else:
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atol, rtol = 5e-3, 5e-3
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if stage_manager is None or stage_manager.is_first_stage():
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row_layer_grads = get_grad_tensors_for_check(whisper,
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sharded_whisper,
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row_layer_for_check,
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tp_group,
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atol=atol,
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rtol=rtol,
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dim=1)
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col_layer_grads = get_grad_tensors_for_check(whisper,
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sharded_whisper,
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col_layer_for_check,
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tp_group,
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atol=atol,
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rtol=rtol,
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dim=0)
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grads_to_check.update(col_layer_grads)
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grads_to_check.update(row_layer_grads)
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# optimizer executes step
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org_optimizer.step()
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sharded_optimizer.step()
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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 test_config['precision'] == 'fp32':
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atol, rtol = 2e-4, 2e-4
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else:
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atol, rtol = 5e-3, 5e-3
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if org_model.__class__.__name__ == 'WhisperModel':
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check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
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check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
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# check weights
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if test_config['precision'] == 'fp32':
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atol, rtol = 1e-3, 1e-3
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else:
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atol, rtol = 5e-3, 5e-3
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if stage_manager is None or stage_manager.is_first_stage():
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check_weight(whisper,
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sharded_whisper,
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row_layer_for_check,
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tp_group,
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atol=atol,
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rtol=rtol,
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dim=1,
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verbose=False)
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check_weight(whisper,
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sharded_whisper,
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col_layer_for_check,
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tp_group,
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atol=atol,
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rtol=rtol,
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dim=0,
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verbose=False)
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# check grads
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check_all_grad_tensors(grads_to_check)
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torch.cuda.empty_cache()
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#TODO fix WhisperForConditionalGeneration enable jit fused operato
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# TODO（jianghai) fix fp16
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@parameterize(
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'test_config',
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[
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{
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'tp_size': 2,
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'pp_size': 2,
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'num_microbatches': 2,
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'enable_all_optimization': True,
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'use_lazy_init': True,
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'precision': 'fp32',
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'initial_scale': 1,
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},
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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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'use_lazy_init': False,
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'precision': 'fp32',
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'initial_scale': 1,
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},
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{
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'tp_size': 4,
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'pp_size': 1,
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'enable_all_optimization': True,
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'use_lazy_init': False,
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'precision': 'fp32',
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},
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{
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'tp_size': 1,
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'pp_size': 4,
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'num_microbatches': 4,
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'use_lazy_init': False,
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'precision': 'fp32',
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},
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# whisper is not supported fp16 for now.
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])
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def run_whisper_test(test_config):
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sub_model_zoo = model_zoo.get_sub_registry('transformers_whisper')
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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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if test_config['pp_size'] > 2 and name == 'transformers_whisper_for_audio_classification':
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continue
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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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@parameterize('test_config', [
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{
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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_all_optimization': False,
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'use_lazy_init': False,
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'precision': 'fp32',
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'initial_scale': 1,
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},
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{
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'tp_size': 2,
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'pp_size': 2,
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'num_microbatches': 2,
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'enable_all_optimization': False,
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'use_lazy_init': False,
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'precision': 'fp32',
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'initial_scale': 1,
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},
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])
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def run_whisper_3d_test(test_config):
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sub_model_zoo = model_zoo.get_sub_registry('transformers_whisper')
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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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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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torch.cuda.empty_cache()
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def check_whisper(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_whisper_test()
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def check_whisper_3d(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_whisper_3d_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_whisper():
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spawn(check_whisper, 4)
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@pytest.mark.largedist
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@rerun_if_address_is_in_use()
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@clear_cache_before_run()
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def test_whisper_3d():
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spawn(check_whisper_3d, 8)
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if __name__ == "__main__":
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test_whisper()
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test_whisper_3d()
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