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[shardformer] add util functions for shardformer tests/fix sync_shared_param (#4366)
* add util functions for shardformer tests & rewrite gpt2 test * fix shared_params & embedding/merging * fix precision
This commit is contained in:
Baizhou Zhang
committed by
Hongxin Liu
Hongxin Liu
parent
5c6f183192
commit
b1feeced8e
@@ -1,107 +1,48 @@
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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 (
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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.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, check_state_dict, 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(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
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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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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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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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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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# 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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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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# 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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check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3)
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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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check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3)
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# unwrap model
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if org_model.__class__.__name__ == 'GPT2Model':
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@@ -111,27 +52,19 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
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gpt2 = org_model.transformer
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sharded_gpt2 = sharded_model.unwrap().transformer
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# check grad
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col_layer_for_check = ['h[0].mlp.c_fc']
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row_layer_for_check = ['h[0].mlp.c_proj']
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check_grad(gpt2, sharded_gpt2, col_layer_for_check, atol=1e-6, rtol=1e-3, dim=1, verbose=False)
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check_grad(gpt2, sharded_gpt2, row_layer_for_check, atol=1e-6, rtol=1e-3, dim=0, verbose=False)
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row_layer_for_check = ['wte', 'h[0].mlp.c_proj']
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# check grad
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if stage_manager is None or stage_manager.is_first_stage():
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check_grad(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=1, verbose=False)
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check_grad(gpt2, sharded_gpt2, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, 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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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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check_weight(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=5e-3, rtol=1e-3, dim=1, verbose=False)
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torch.cuda.empty_cache()
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@@ -156,9 +89,11 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
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@clear_cache_before_run()
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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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# 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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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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@@ -175,7 +110,6 @@ def check_gpt2(rank, world_size, port):
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run_gpt2_test()
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@pytest.mark.skip('Have some bug caused by merge')
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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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