[shardformer]upgrade transformers for gpt2/gptj/whisper (#5807)

* [shardformer] fix modeling of gpt2 and gptj

* [shardformer] fix whisper modeling

* [misc] update requirements

---------

Co-authored-by: ver217 <lhx0217@gmail.com>

colossalai/shardformer/modeling/gpt2.py

@@ -738,7 +738,10 @@ def gpt2_for_sequence_classification_forward(
             sequence_lengths = -1
         else:
             if input_ids is not None:
-                sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
             else:
                 sequence_lengths = -1
                 logger.warning_once(

colossalai/shardformer/modeling/gptj.py

@@ -32,6 +32,7 @@ def _get_attention_mask(
     hidden_states: torch.Tensor,
     past_key_values: Optional[Tuple[Tuple[torch.Tensor]]],
     attention_mask: Optional[torch.FloatTensor],
+    use_flash_attention_2: bool = False,
 ) -> Optional[Union[torch.Tensor, dict]]:
     batch_size, seq_len = hidden_states.shape[:2]
     past_key_values_length = 0
@@ -47,7 +48,7 @@ def _get_attention_mask(
             attention_mask,
             is_causal=True,
         )
-    elif attention_mask is not None:
+    elif use_flash_attention_2 and attention_mask is not None:
         if batch_size <= 0:
             raise ValueError("batch_size has to be defined and > 0")
         attention_mask = attention_mask.view(batch_size, -1)
@@ -162,7 +163,9 @@ def gptj_model_forward(
 
         output_shape = input_shape + (hidden_states.size(-1),)
 
-        attention_mask = _get_attention_mask(self, shard_config, hidden_states, past_key_values, attention_mask)
+        attention_mask = _get_attention_mask(
+            self, shard_config, hidden_states, past_key_values, attention_mask, self._use_flash_attention_2
+        )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:
@@ -419,7 +422,10 @@ def gptj_for_sequence_classification_forward(
             sequence_lengths = -1
         else:
             if input_ids is not None:
-                sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
             else:
                 sequence_lengths = -1
                 logger.warning_once(
@@ -712,7 +718,9 @@ def forward(
 
         hidden_states = self.drop(hidden_states)
 
-        attention_mask = _get_attention_mask(self, shard_config, hidden_states, past_key_values, attention_mask)
+        attention_mask = _get_attention_mask(
+            self, shard_config, hidden_states, past_key_values, attention_mask, self._use_flash_attention_2
+        )
 
         output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),)
 
@@ -886,7 +894,9 @@ def forward(
         hidden_states = self.drop(hidden_states)
 
         output_shape = input_shape + (hidden_states.size(-1),)
-        attention_mask = _get_attention_mask(self, shard_config, hidden_states, past_key_values, attention_mask)
+        attention_mask = _get_attention_mask(
+            self, shard_config, hidden_states, past_key_values, attention_mask, self._use_flash_attention_2
+        )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

colossalai/shardformer/modeling/whisper.py

@@ -17,6 +17,7 @@
     SequenceClassifierOutput,
 )
 from transformers.models.whisper.modeling_whisper import (
+    _HIDDEN_STATES_START_POSITION,
     WhisperDecoder,
     WhisperEncoder,
     WhisperForAudioClassification,
@@ -166,6 +167,7 @@ def forward(
         cross_attn_head_mask=None,
         past_key_values=None,
         inputs_embeds=None,
+        position_ids=None,
         use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
@@ -199,9 +201,13 @@ def forward(
 
         # embed positions
         if input_ids is not None:
-            positions = self.embed_positions(input_ids, past_key_values_length=past_key_values_length)
+            positions = self.embed_positions(
+                input_ids, past_key_values_length=past_key_values_length, position_ids=position_ids
+            )
         else:
-            positions = self.embed_positions(inputs_embeds, past_key_values_length=past_key_values_length)
+            positions = self.embed_positions(
+                inputs_embeds, past_key_values_length=past_key_values_length, position_ids=position_ids
+            )
 
         hidden_states = inputs_embeds + positions
         hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
@@ -599,6 +605,7 @@ def whisper_decoder_forward(
         cross_attn_head_mask=None,
         past_key_values=None,
         inputs_embeds=None,
+        position_ids=None,
         use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
@@ -716,9 +723,13 @@ def whisper_decoder_forward(
 
             # embed positions
             if input_ids is not None:
-                positions = self.embed_positions(input_ids, past_key_values_length=past_key_values_length)
+                positions = self.embed_positions(
+                    input_ids, past_key_values_length=past_key_values_length, position_ids=position_ids
+                )
             else:
-                positions = self.embed_positions(inputs_embeds, past_key_values_length=past_key_values_length)
+                positions = self.embed_positions(
+                    inputs_embeds, past_key_values_length=past_key_values_length, position_ids=position_ids
+                )
 
             hidden_states = inputs_embeds + positions
             hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
@@ -841,6 +852,7 @@ def whisper_model_forward(
         encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
         past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
         decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None,
+        decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
@@ -944,6 +956,7 @@ def whisper_model_forward(
             cross_attn_head_mask=cross_attn_head_mask,
             past_key_values=past_key_values,
             inputs_embeds=decoder_inputs_embeds,
+            position_ids=decoder_position_ids,
             use_cache=use_cache,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
@@ -986,6 +999,7 @@ def whisper_for_conditional_generation_forward(
         encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
         past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
         decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None,
+        decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None,
         labels: Optional[torch.LongTensor] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
@@ -1048,6 +1062,7 @@ def whisper_for_conditional_generation_forward(
             cross_attn_head_mask=cross_attn_head_mask,
             past_key_values=past_key_values,
             decoder_inputs_embeds=decoder_inputs_embeds,
+            decoder_position_ids=decoder_position_ids,
             use_cache=use_cache,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
@@ -1118,6 +1133,12 @@ def whisper_for_audio_classification_forward(
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
+
+        if self.config.use_weighted_layer_sum:
+            output_hidden_states = True
+        elif output_hidden_states is None:
+            output_hidden_states = self.config.output_hidden_states
+
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         # audio_classification only holds encoder
@@ -1138,7 +1159,8 @@ def whisper_for_audio_classification_forward(
             return encoder_outputs
 
         if self.config.use_weighted_layer_sum:
-            hidden_states = torch.stack(encoder_outputs, dim=1)
+            hidden_states = encoder_outputs[_HIDDEN_STATES_START_POSITION]
+            hidden_states = torch.stack(hidden_states, dim=1)
             norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
             hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
         else:

colossalai/shardformer/policies/gptj.py

@@ -34,15 +34,11 @@ def preprocess(self):
         return self.model
 
     def module_policy(self):
-        from transformers.models.gptj.modeling_gptj import GPTJAttention, GPTJBlock, GPTJModel
-
-        ATTN_IMPLEMENTATION = {
-            "eager": GPTJAttention,
-        }
+        from transformers.models.gptj.modeling_gptj import GPTJ_ATTENTION_CLASSES, GPTJBlock, GPTJModel
 
         policy = {}
 
-        attn_cls = ATTN_IMPLEMENTATION[self.origin_attn_implement]
+        attn_cls = GPTJ_ATTENTION_CLASSES[self.origin_attn_implement]
 
         embedding_cls = None
         if self.shard_config.enable_tensor_parallelism:

requirements/requirements.txt

@@ -16,7 +16,7 @@ ray
 sentencepiece
 google
 protobuf
-transformers>=4.36.2,<4.40.0
+transformers==4.39.3
 peft>=0.7.1
 bitsandbytes>=0.39.0
 rpyc==6.0.0