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https://github.com/hpcaitech/ColossalAI.git
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[pipeline] rewrite t5 tests & support multi-tensor transmitting in pipeline (#4388)
* fix remaining t5 bugs/rewrite t5 tests * fix multi-tensor communication in pipeline * rearrange test_config * fix keyerror in sync_shared_params * fix get_held_layers & Randomnizer, complete t5 tests * erase printing * fix get_held_layers through modifying _release_unheld_layers * fix _get_recursive_held_layers bug
This commit is contained in:
Baizhou Zhang
committed by
Hongxin Liu
Hongxin Liu
parent
906426cb44
commit
ed4c448488
@@ -68,16 +68,17 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
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torch.cuda.empty_cache()
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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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'enable_fused_normalization': True,
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'use_lazy_init': True
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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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}, {
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'tp_size': 4,
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@@ -1,60 +1,110 @@
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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, 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(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
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# check forward
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# the value "past_key_values" is sharded, so we ignore
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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'], atol=1e-5)
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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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org_loss.backward()
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shard_loss.backward()
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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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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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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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# check grad
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col_layer_for_check = ['encoder.block[0].layer[0].SelfAttention.q', 'shared']
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row_layer_for_check = ['encoder.block[0].layer[0].SelfAttention.relative_attention_bias']
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check_grad(org_model, sharded_model, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False)
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check_grad(org_model, sharded_model, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False)
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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 weights are tied
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if hasattr(org_model, 'lm_head'):
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assert org_model.shared.weight.data.data_ptr() == org_model.lm_head.weight.data.data_ptr()
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assert sharded_model.shared.weight.data.data_ptr() == sharded_model.lm_head.weight.data.data_ptr()
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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__ != 'T5ForConditionalGeneration':
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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-5, rtol=1e-3)
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# unwrap model
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t5 = org_model
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sharded_t5 = sharded_model.unwrap()
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row_layer_for_check = ['shared', 'encoder.block[0].layer[0].SelfAttention.q']
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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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check_grad(t5, sharded_t5, row_layer_for_check, tp_group, atol=1e-5, rtol=1e-3, dim=0)
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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(t5, sharded_t5, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, 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('use_lazy_init', [False, True])
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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_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init, enable_flash_attention,
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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': 2,
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'enable_fused_normalization': True,
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'use_lazy_init': True
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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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}, {
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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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'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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}])
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@clear_cache_before_run()
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def run_t5_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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# TODO: add test_config for flash attention & jit operator after supporting
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sub_model_zoo = model_zoo.get_sub_registry('transformers_t5')
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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, 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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# skip 4-stage pp test for t5_encoder
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if test_config['pp_size'] > 2 and name == 'transformers_t5_encoder_model':
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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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@@ -68,7 +118,7 @@ def check_t5(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_t5():
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spawn(check_t5, 2)
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spawn(check_t5, 4)
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if __name__ == "__main__":
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@@ -1,101 +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.shardformer.policies.t5 import T5BasePolicy
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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_t5.py
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def run_t5_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_t5')
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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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input_ids = inputs['input_ids']
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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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batch_size, seq_len = input_ids.shape
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hidden_size = sharded_model.config.d_model
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num_heads = sharded_model.config.num_heads
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hidden_state_shape = (batch_size, seq_len, hidden_size)
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position_bias_shape = (batch_size, num_heads, seq_len, seq_len)
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num_encoder_layers = len(sharded_model.encoder.block)
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decoder = sharded_model.__dict__.get('decoder', None)
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num_decoder_layers = len(decoder.block) if decoder else 0
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_, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(num_encoder_layers, num_decoder_layers, PP_SIZE)
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stage = stage_manager.stage
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at_first_stage = (stage == 0) or (stage == decoder_starting_stage)
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at_last_stage = (stage == decoder_starting_stage - 1) or (stage == stage_manager.num_stages - 1)
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in_decoder = stage >= decoder_starting_stage
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if not at_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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position_bias = torch.zeros(*position_bias_shape).cuda()
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encoder_decoder_position_bias = torch.zeros(*position_bias_shape).cuda()
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inputs['input_ids'] = None
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inputs['hidden_states'] = hidden_states
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inputs['position_bias'] = position_bias
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inputs['encoder_decoder_position_bias'] = encoder_decoder_position_bias
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if in_decoder:
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encoder_output_states = torch.zeros(*hidden_state_shape).cuda()
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inputs['encoder_outputs'] = (encoder_output_states,)
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sharded_model.train()
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output = sharded_model(**inputs)
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if at_last_stage:
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if name == 'transformers_t5_for_conditional_generation' and in_decoder:
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assert output.loss is not None
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else:
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if name != 'transformers_t5_encoder_model' and not in_decoder:
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output = output['encoder_outputs']
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assert output[0].shape == hidden_state_shape
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else:
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assert output['hidden_states'].shape == hidden_state_shape
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# position_bias information should be passed in T5
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assert output['position_bias'].shape == position_bias_shape
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if in_decoder:
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assert output['encoder_decoder_position_bias'].shape == position_bias_shape
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torch.cuda.empty_cache()
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def check_t5(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_t5_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_t5():
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spawn(check_t5, 4)
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if __name__ == "__main__":
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test_t5()
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