[shardformer]update t5 tests for using all optimizations.

colossalai/shardformer/README.md

@@ -31,7 +31,7 @@
 
 ### Quick Start
 
-The sample API usage is given below(If you enable the use of flash attention, please install xformers.):
+The sample API usage is given below(If you enable the use of flash attention, please install `flash_attn`. In addition, xformers's `cutlass_op` provide a supplementary optimization, It requires that the sequence length be a multiple of 8.):
 
 ``` python
 from colossalai.shardformer import ShardConfig, Shard

tests/kit/model_zoo/transformers/t5.py

@@ -16,8 +16,8 @@ def data_gen_for_encoder_only():
     # config = T5Config(decoder_start_token_id=0)
     # tokenizer = T5Tokenizer.from_pretrained("t5-small")
     # input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids
-    input_ids = torch.Tensor([[13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 1, 12]]).long()
-    attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]).long()
+    input_ids = torch.Tensor([[13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 1, 12, 1627, 5, 1, 12]]).long()
+    attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]).long()
     return dict(input_ids=input_ids, attention_mask=attention_mask)
 
 
@@ -26,7 +26,7 @@ def data_gen_for_conditional_generation():
     #
     # labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids
     data = data_gen_for_encoder_only()
-    labels = torch.Tensor([[644, 4598, 229, 19250, 5, 1, 644, 4598, 229, 19250, 5, 1]]).long()
+    labels = torch.Tensor([[644, 4598, 229, 19250, 5, 1, 644, 4598, 229, 19250, 5, 1, 229, 19250, 5, 1]]).long()
     data['labels'] = labels
     return data
 
@@ -35,7 +35,7 @@ def data_gen_for_t5_model():
     # decoder_inputs_ids is obtained with the following code
     # decoder_input_ids = model._shift_right(input_ids)
     data = data_gen_for_encoder_only()
-    decoder_input_ids = torch.Tensor([[0, 13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 5]]).long()
+    decoder_input_ids = torch.Tensor([[0, 13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 5, 19, 1627, 5, 5]]).long()
     data['decoder_input_ids'] = decoder_input_ids
     return data
 

tests/test_shardformer/test_model/test_shard_t5.py

@@ -37,11 +37,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
 
     # 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__ != 'T5ForConditionalGeneration':
-            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
     t5 = org_model
@@ -50,14 +54,22 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     row_layer_for_check = ['shared', 'encoder.block[0].layer[0].SelfAttention.q']
 
     # check weights and gradients
+    if test_config['precision'] == 'fp32':
+        atol, rtol = 1e-5, 1e-3
+    else:
+        atol, rtol = 5e-3, 5e-3
     if stage_manager is None or stage_manager.is_first_stage():
-        check_grad(t5, sharded_t5, row_layer_for_check, tp_group, atol=1e-5, rtol=1e-3, dim=0)
+        check_grad(t5, sharded_t5, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0)
 
     # check weights after optimizer.step()
     org_optimizer.step()
     sharded_optimizer.step()
+    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(t5, sharded_t5, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, verbose=False)
+        check_weight(t5, sharded_t5, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False)
 
     torch.cuda.empty_cache()
 
@@ -66,23 +78,29 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     'tp_size': 2,
     'pp_size': 2,
     'num_microbatches': 2,
-    'enable_fused_normalization': True,
-    'use_lazy_init': True
+    'enable_all_optimization': True,
+    'use_lazy_init': True,
+    'precision': 'fp16',
+    'initial_scale': 1,
 }, {
     'tp_size': 1,
     'pp_size': 2,
     'num_microbatches': 4,
-    'use_lazy_init': False
+    'use_lazy_init': False,
+    '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',
 }, {
     'tp_size': 1,
     'pp_size': 4,
     'num_microbatches': 4,
-    'use_lazy_init': False
+    'use_lazy_init': False,
+    'precision': 'fp32',
 }])
 @clear_cache_before_run()
 def run_t5_test(test_config):
@@ -93,7 +111,6 @@ def run_t5_test(test_config):
     # TODO: add test_config for flash attention & jit operator after supporting
 
     sub_model_zoo = model_zoo.get_sub_registry('transformers_t5')
-    test_config['precision'] = 'float'    # Do not use fp16/bf16 in testing
 
     for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():