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https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-02 09:38:05 +00:00
[pipeline] support fp32 for HybridPlugin/merge shardformer test and pipeline test into one file (#4354)
* add naive optimizer for 3DPlugin/refactor gpt2 shardformer test * merge tests of PP/DP/TP combinations into one test file * fix bug when sync grad for dp in HybridPlugin * update supported precisions for 3DPlugin/fix bug when shifting tp_degree * improve the passing of lazy_init * modify lazy_init/use sync_shared_params
This commit is contained in:
Baizhou Zhang
committed by
Hongxin Liu
Hongxin Liu
parent
f13954cd58
commit
0ceec8f9a9
@@ -160,7 +160,6 @@ def check_llama(rank, world_size, port):
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run_llama_test()
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@pytest.mark.skip('This test will fail')
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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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@@ -1,85 +1,180 @@
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import copy
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from contextlib import nullcontext
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import pytest
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import torch
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from torch import distributed as dist
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from torch.optim import Adam
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import colossalai
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from colossalai.booster import Booster
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from colossalai.booster.plugin import HybridParallelPlugin
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from colossalai.lazy.lazy_init import LazyInitContext
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from colossalai.logging import disable_existing_loggers
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from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
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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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from colossalai.tensor.d_tensor.api import (
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clear_layout_converter,
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is_customized_distributed_tensor,
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is_distributed_tensor,
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)
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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_state_dict, 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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def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
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# do backward
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use_lazy_init = False
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if 'use_lazy_init' in test_config:
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use_lazy_init = test_config.pop('use_lazy_init')
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if use_lazy_init:
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ctx = LazyInitContext()
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else:
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ctx = nullcontext()
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# prepare booster
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plugin = HybridParallelPlugin(**test_config)
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booster = Booster(plugin=plugin)
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stage_manager = plugin.stage_manager
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# prepare models and optimizers
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with ctx:
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org_model = model_fn().cuda()
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sharded_model = copy.deepcopy(org_model)
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if use_lazy_init:
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org_model = ctx.materialize(org_model)
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org_optimizer = Adam(org_model.parameters(), lr=1e-3)
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sharded_optimizer = Adam(sharded_model.parameters(), lr=1e-3)
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criterion = loss_fn
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sharded_model, sharded_optimizer, criterion, _, _ = booster.boost(sharded_model, sharded_optimizer, criterion)
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def _criterion(outputs, inputs):
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outputs = output_transform_fn(outputs)
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loss = criterion(outputs)
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return loss
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# do forward and backward
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data = data_gen_fn()
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sharded_model.train()
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if stage_manager:
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data = {
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k: v.to('cuda').repeat(4, 1) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v
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for k, v in data.items()
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}
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data_iter = iter([data])
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sharded_output = booster.execute_pipeline(data_iter,
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sharded_model,
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_criterion,
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sharded_optimizer,
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return_loss=True,
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return_outputs=True)
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sharded_loss = sharded_output['loss']
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else:
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data = {k: v.cuda() for k, v in data.items()}
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sharded_output = sharded_model(**data)
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sharded_loss = criterion(sharded_output)
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sharded_loss.backward()
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org_model.train()
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org_output = org_model(**data)
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org_loss = criterion(org_output)
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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 origin model loss\n{org_loss}\n{shard_loss}"
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if stage_manager is None or stage_manager.is_last_stage():
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# check last hidden state
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if org_model.__class__.__name__ == 'GPT2Model':
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org_hidden_state = org_output.last_hidden_state
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if stage_manager is None:
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sharded_hidden_state = sharded_output.last_hidden_state
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if stage_manager and stage_manager.is_last_stage():
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sharded_hidden_state = torch.cat([output.last_hidden_state for output in sharded_output['outputs']],
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dim=0)
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assert torch.allclose(org_hidden_state, sharded_hidden_state, atol=1e-5, rtol=1e-3), \
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f"shard model's output hidden state is not equal to origin model's last hidden state\n{org_hidden_state}\n{sharded_hidden_state}"
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# check loss
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assert torch.allclose(org_loss, sharded_loss, atol=1e-5, rtol=1e-3), \
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f"shard model loss is not equal to origin model loss\n{org_loss}\n{sharded_loss}"
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# unwrap model
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if org_model.__class__.__name__ == 'GPT2Model':
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org_model = org_model
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sharded_model = sharded_model
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sharded_model = sharded_model.unwrap()
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else:
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org_model = org_model.transformer
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sharded_model = sharded_model.transformer
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sharded_model = sharded_model.unwrap().transformer
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# check mlp grad
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org_grad = org_model.h[0].mlp.c_fc.weight.grad
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shard_grad = sharded_model.h[0].mlp.c_fc.weight.grad
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shard_weight = sharded_model.h[0].mlp.c_fc.weight
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# check weights and gradients
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if stage_manager is None or stage_manager.is_first_stage():
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if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
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shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
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shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
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all_shard_grad = torch.cat(shard_grad_list, dim=1)
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else:
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all_shard_grad = shard_grad
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assert torch.allclose(
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org_grad, all_shard_grad,
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atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}"
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shard_weight = sharded_model.h[0].mlp.c_fc.weight
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org_grad = org_model.h[0].mlp.c_fc.weight.grad
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shard_grad = sharded_model.h[0].mlp.c_fc.weight.grad
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# check embedding weights
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org_grad = org_model.wte.weight.grad
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shard_grad = sharded_model.wte.weight.grad
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shard_weight = sharded_model.wte.weight
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if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
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shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(plugin.tp_size)]
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dist.all_gather(shard_grad_list, shard_grad, plugin.tp_group)
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shard_grad = torch.cat(shard_grad_list, dim=1)
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assert torch.allclose(org_grad, shard_grad, atol=1e-5, rtol=1e-3), \
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f"shard model grad is not equal to origin model grad\n{org_grad}\n{shard_grad}"
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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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org_weight = org_model.h[0].mlp.c_fc.weight
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shard_weight = sharded_model.h[0].mlp.c_fc.weight
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if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
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shard_weight_list = [torch.zeros([*shard_weight.shape]).to('cuda') for _ in range(plugin.tp_size)]
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dist.all_gather(shard_weight_list, shard_weight, plugin.tp_group)
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shard_weight = torch.cat(shard_weight_list, dim=1)
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assert torch.allclose(org_weight, shard_weight, atol=5e-3, rtol=1e-3), \
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f"shard model weight is not equal to origin model weight\n{org_weight}\n{shard_weight}"
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if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
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shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
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shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
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all_shard_grad = torch.cat(shard_grad_list, dim=0)
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else:
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all_shard_grad = shard_grad
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assert torch.allclose(
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org_grad, all_shard_grad,
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atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}"
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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('use_lazy_init', [False, True])
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@parameterize('test_config', [{
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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': True
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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_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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@clear_cache_before_run()
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def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
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sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt')
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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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use_lazy_init)
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check_state_dict(org_model, sharded_model, name=name)
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check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
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def run_gpt2_test(test_config):
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# TODO: add plugin_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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sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt')
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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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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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@@ -93,7 +188,7 @@ def check_gpt2(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_gpt2():
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spawn(check_gpt2, 2)
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spawn(check_gpt2, 4)
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if __name__ == "__main__":
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@@ -1,72 +0,0 @@
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import pytest
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import torch
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import colossalai
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from colossalai.cluster import ProcessGroupMesh
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from colossalai.logging import disable_existing_loggers
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from colossalai.pipeline.stage_manager import PipelineStageManager
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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_pipeline_model
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def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
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# TODO: add tests for forward/backward later
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pass
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@parameterize('enable_tensor_parallelism', [False])
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@parameterize('enable_fused_normalization', [False])
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@parameterize('use_lazy_init', [False])
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#TODO: merge this into test_shard_gpt2
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def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
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DP_DIM, PP_DIM = 0, 1
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DP_SIZE, PP_SIZE = 2, 2
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pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
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stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
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sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt')
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for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items():
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inputs = data_gen_fn()
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inputs = {k: v.cuda() for k, v in inputs.items()}
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_, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization,
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enable_tensor_parallelism, use_lazy_init)
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input_ids = inputs['input_ids']
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batch_size, seq_len = input_ids.shape
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hidden_size = sharded_model.config.n_embd
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hidden_state_shape = (batch_size, seq_len, hidden_size)
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if not stage_manager.is_first_stage():
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# change inputs if not the first stage
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hidden_states = torch.zeros(*hidden_state_shape).cuda()
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inputs['input_ids'] = None
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inputs['hidden_states'] = hidden_states
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sharded_model.train()
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output = sharded_model(**inputs)
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if stage_manager.is_last_stage():
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if name == 'transformers_gpt':
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assert output[0].shape == hidden_state_shape
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else:
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assert output.loss is not None
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else:
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assert output['hidden_states'].shape == hidden_state_shape
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torch.cuda.empty_cache()
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def check_gpt2(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_gpt2_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_gpt2():
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spawn(check_gpt2, 4)
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if __name__ == "__main__":
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test_gpt2()
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