[pipeline] rewrite bert tests and fix some bugs (hpcaitech#4409)

* add pipeline policy and bert forward to be done

* add bertmodel pipeline forward and make tests

* add Bert_Policy and test for policy

* update formatting

* update formatting

* update the code

* fix bugs

* fix name confilt

* add bloom model and policy ,revise the base class of policy

* revise

* revision

* add bert_for_pretraining

* add bert_for_pretraining forward and policy

* fix typos

* cancel warning

* change the imediate output to default dict

* change the default output of get_shared_params

* rewrite bert test

* rewrite bert test

* fix some bugs

* del pipeline tests

* del pipeline tests

* del useless print

* del useless print

* rewrite data repeats

tests/kit/model_zoo/transformers/bert.py

@@ -104,7 +104,8 @@ def data_gen_for_qa():
 output_transform_fn = lambda x: x
 
 # define loss funciton
-loss_fn_for_bert_model = lambda x: x.pooler_output.sum()
+loss_fn_for_bert_model = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state, torch.ones_like(x.last_hidden_state
+                                                                                                    ))
 loss_fn = lambda x: x.loss
 
 config = transformers.BertConfig(hidden_size=128,

tests/test_shardformer/test_model/_utils.py

@@ -131,6 +131,8 @@ def build_model_from_hybrid_plugin(model_fn: Callable, loss_fn: Callable, test_c
 def run_forward_backward_with_hybrid_plugin(org_model: Module, sharded_model: Module, sharded_optimizer: Optimizer,
                                             data_gen_fn: Callable, output_transform_fn: Callable, criterion: Callable,
                                             booster: Booster):
+    org_model.cuda()
+    sharded_model.cuda()
 
     def _criterion(outputs, inputs):
         outputs = output_transform_fn(outputs)
@@ -141,7 +143,8 @@ def _criterion(outputs, inputs):
     sharded_model.train()
     if booster.plugin.stage_manager is not None:
         data = {
-            k: v.to('cuda').repeat(4, 1) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v
+            k: v.to('cuda').repeat(*([4] + [1] *
+                                     (v.dim() - 1))) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v
             for k, v in data.items()
         }
         data_iter = iter([data])
@@ -162,6 +165,7 @@ def _criterion(outputs, inputs):
     org_model.train()
     data = {k: v.cuda() for k, v in data.items()}
     org_output = org_model(**data)
+
     org_loss = criterion(org_output)
     org_loss.backward()
 
@@ -226,7 +230,6 @@ def check_grad(org_model: Module,
                atol: float = 1e-5,
                rtol: float = 1e-3,
                verbose: bool = False):
-
     for suffix in layer_suffix:
         org_grad = getattr_(org_model, suffix).weight.grad
         shard_grad = getattr_(sharded_model, suffix).weight.grad
@@ -242,7 +245,6 @@ def check_grad(org_model: Module,
         # embedding may be resized when using tensor parallel
         if shard_grad.shape[0] > org_grad.shape[0]:
             shard_grad = shard_grad[:org_grad.shape[0], :]
-
         if verbose and dist.get_rank() == 0:
             print(f"'{suffix}' grad: {org_grad}, {shard_grad}")
 

tests/test_shardformer/test_model/test_shard_bert.py

@@ -1,65 +1,98 @@
 import pytest
 import torch
+from torch import distributed as dist
 
 import colossalai
-from colossalai.cluster import ProcessGroupMesh
 from colossalai.logging import disable_existing_loggers
-from colossalai.pipeline.stage_manager import PipelineStageManager
-from colossalai.shardformer.policies.auto_policy import get_autopolicy
-from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
-from colossalai.testing import (
-    assert_hf_output_close,
-    clear_cache_before_run,
-    parameterize,
-    rerun_if_address_is_in_use,
-    spawn,
-)
+from colossalai.shardformer.layer.utils import Randomizer
+from colossalai.tensor.d_tensor.api import clear_layout_converter
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 from tests.kit.model_zoo import model_zoo
-from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward
+from tests.test_shardformer.test_model._utils import (
+    build_model_from_hybrid_plugin,
+    check_grad,
+    check_loss,
+    check_output_hidden_state,
+    check_weight,
+    run_forward_backward_with_hybrid_plugin,
+)
 
 
-def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
-    # unwarp model
+def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
+
+    org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \
+        build_model_from_hybrid_plugin(model_fn, loss_fn, test_config)
+
+    org_loss, org_output, sharded_loss, sharded_output = \
+            run_forward_backward_with_hybrid_plugin(
+            org_model,
+            sharded_model,
+            sharded_optimizer,
+            data_gen_fn,
+            output_transform_fn,
+            criterion,
+            booster)
+    stage_manager = booster.plugin.stage_manager
+    tp_group = booster.plugin.tp_group
+    # check last hidden state & loss
+    if stage_manager is None or stage_manager.is_last_stage():
+        if org_model.__class__.__name__ == 'BertModel':
+            check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3)
+
+        check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3)
+    # unwrap model
     if org_model.__class__.__name__ == 'BertModel':
         bert = org_model
-        sharded_bert = sharded_model
+        sharded_bert = sharded_model.unwrap()
     else:
         bert = org_model.bert
-        sharded_bert = sharded_model.bert
-
-    # check forward
-    org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
-                                                                 output_transform_fn, loss_fn)
-    assert_hf_output_close(org_output, shard_output)
-
-    # do backward
-    org_loss.backward()
-    shard_loss.backward()
-
-    assert torch.allclose(org_loss, shard_loss,
-                          atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
-
-    # check grad
-    col_layer_for_check = ['encoder.layer[0].attention.self.query', 'embeddings.word_embeddings']
-    row_layer_for_check = ['encoder.layer[0].attention.output.dense']
-    check_grad(bert, sharded_bert, col_layer_for_check, atol=1e-7, rtol=1e-3, dim=0, verbose=False)
-    check_grad(bert, sharded_bert, row_layer_for_check, atol=1e-7, rtol=1e-3, dim=1, verbose=False)
-
-
-@parameterize('enable_fused_normalization', [True, False])
-@parameterize('enable_tensor_parallelism', [True, False])
-@parameterize('enable_flash_attention', [True, False])
-@parameterize('enable_jit_fused', [True, False])
-@parameterize('use_lazy_init', [False, True])
-def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused,
-                  use_lazy_init):
+        sharded_bert = sharded_model.unwrap().bert
+
+    col_layer_for_check = ['encoder.layer[0].output.dense']
+    row_layer_for_check = ['embeddings.word_embeddings', 'encoder.layer[0].intermediate.dense']
+
+    if stage_manager is None or stage_manager.is_first_stage():
+        #check_weight(bert.embeddings.word_embeddings, sharded_bert.embeddings.word_embeddings, tp_group, atol=1e-5, rtol=1e-3)
+        #check_weight(bert.encoder.layer[0].attention.self.query, sharded_bert.encoder.layer[0].attention.self.query, tp_group, atol=5e-3, rtol=1e-3)
+        check_grad(bert, sharded_bert, col_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=1, verbose=False)
+        check_grad(bert, sharded_bert, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, verbose=False)
+
+    # check weights after optimizer.step()
+    org_optimizer.step()
+    sharded_optimizer.step()
+    if stage_manager is None or stage_manager.is_first_stage():
+        check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=5e-3, rtol=1e-3, dim=1, verbose=False)
+
+    torch.cuda.empty_cache()
+
+
+@parameterize('test_config', [{
+    'tp_size': 1,
+    'pp_size': 2,
+    'num_microbatches': 4,
+    'use_lazy_init': True
+}, {
+    'tp_size': 2,
+    'pp_size': 2,
+    'num_microbatches': 4,
+    'enable_fused_normalization': False,
+    'use_lazy_init': False
+}, {
+    'tp_size': 4,
+    'pp_size': 1,
+    'enable_fused_normalization': True,
+    'use_lazy_init': False
+}])
+def run_bert_test(test_config):
+
     sub_model_zoo = model_zoo.get_sub_registry('transformers_bert')
+    test_config['precision'] = 'float'
+
     for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
-        org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism,
-                                               enable_flash_attention, enable_jit_fused, use_lazy_init)
-        check_state_dict(org_model, sharded_model, name=name)
-        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+        check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
 
+    clear_layout_converter()
+    Randomizer.reset_index()
     torch.cuda.empty_cache()
 
 
@@ -73,7 +106,7 @@ def check_bert(rank, world_size, port):
 @rerun_if_address_is_in_use()
 @clear_cache_before_run()
 def test_bert():
-    spawn(check_bert, 2)
+    spawn(check_bert, 4)
 
 
 if __name__ == "__main__":