[shardformer] update tests for all optimization (#4413)

[shardformer] update tests for all optimization

colossalai/shardformer/modeling/bert.py

@@ -1048,9 +1048,12 @@ def get_bert_flash_attention_forward():
                 final_attention_mask = final_attention_mask * scale + attention_mask
             else:
                 final_attention_mask = attention_mask
+
+        if final_attention_mask is not None:
             batch_size, src_len = query_layer.size()[0], query_layer.size()[2]
             tgt_len = key_layer.size()[2]
-            final_attention_mask = final_attention_mask.expand(batch_size, self.num_attention_heads, src_len, tgt_len)
+            final_attention_mask = final_attention_mask.expand(batch_size, self.num_attention_heads, src_len,
+                                                               tgt_len).contiguous()
 
         query_layer = query_layer.permute(0, 2, 1, 3).contiguous()
         key_layer = key_layer.permute(0, 2, 1, 3).contiguous()

tests/kit/model_zoo/transformers/bert.py

@@ -69,21 +69,30 @@ def data_gen_for_mcq():
     # data['labels'] = torch.tensor([0], dtype=torch.int64)
     input_ids = torch.tensor([[[
         101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591,
-        4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102, 102
+        4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102, 102, 5442,
+        1012, 102, 102
     ],
                                [
                                    101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037,
                                    4825, 1010, 2003, 3591, 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2096,
-                                   2218, 1999, 1996, 2192, 1012, 102, 0, 0
+                                   2218, 1999, 1996, 2192, 1012, 102, 0, 0, 1012, 102, 0, 0
+                               ]]])
+    token_type_ids = torch.tensor([[[
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+        1, 1, 1
+    ],
+                                    [
+                                        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
+                                        1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0
+                                    ]]])
+    attention_mask = torch.tensor([[[
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+        1, 1, 1
+    ],
+                                    [
+                                        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+                                        1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0
                                     ]]])
-    token_type_ids = torch.tensor(
-        [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
-           0]]])
-    attention_mask = torch.tensor(
-        [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-          [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
-           0]]])
     labels = torch.tensor([0], dtype=torch.int64)
 
     return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels)

tests/test_shardformer/test_model/test_shard_bert.py

@@ -36,10 +36,14 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     tp_group = booster.plugin.tp_group
     # check last hidden state & loss
     if stage_manager is None or stage_manager.is_last_stage():
+        if test_config['precision'] == 'fp32':
+            atol, rtol = 1e-5, 1e-3
+        else:
+            atol, rtol = 5e-3, 5e-3
         if org_model.__class__.__name__ == 'BertModel':
-            check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3)
+            check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
 
-        check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3)
+        check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
     # unwrap model
     if org_model.__class__.__name__ == 'BertModel':
         bert = org_model
@@ -51,17 +55,25 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     col_layer_for_check = ['encoder.layer[0].output.dense']
     row_layer_for_check = ['embeddings.word_embeddings', 'encoder.layer[0].intermediate.dense']
 
+    if test_config['precision'] == 'fp32':
+        atol, rtol = 1e-4, 1e-3
+    else:
+        atol, rtol = 5e-3, 5e-3
     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_grad(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
+        check_grad(bert, sharded_bert, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False)
 
     # check weights after optimizer.step()
     org_optimizer.step()
     sharded_optimizer.step()
+    if test_config['precision'] == 'fp32':
+        atol, rtol = 5e-3, 1e-3
+    else:
+        atol, rtol = 5e-3, 5e-3
     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)
+        check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
 
     torch.cuda.empty_cache()
 
@@ -70,23 +82,26 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     'tp_size': 1,
     'pp_size': 2,
     'num_microbatches': 4,
-    'use_lazy_init': True
+    'use_lazy_init': True,
+    'precision': 'fp32',
 }, {
     'tp_size': 2,
     'pp_size': 2,
-    'num_microbatches': 4,
-    'enable_fused_normalization': False,
-    'use_lazy_init': False
+    'num_microbatches': 2,
+    'enable_all_optimization': True,
+    'use_lazy_init': True,
+    'precision': 'fp16',
+    'initial_scale': 1,
 }, {
     'tp_size': 4,
     'pp_size': 1,
-    'enable_fused_normalization': True,
-    'use_lazy_init': False
+    'enable_all_optimization': True,
+    'use_lazy_init': False,
+    'precision': 'fp32',
 }])
 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():
         check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)