rls2.5: fix return_dict_in_generate issue (#3333)

Author: blzheng

intel_extension_for_pytorch/transformers/generation/beam_sample.py

@@ -2,41 +2,18 @@
 from torch import nn
 import torch.distributed as dist
 import warnings
-from typing import Optional, Tuple, Union, List
+from typing import Optional, Union, List
 from transformers.generation.stopping_criteria import (
     StoppingCriteriaList,
     validate_stopping_criteria,
 )
 from transformers.generation.logits_process import LogitsProcessorList
 from transformers.generation.beam_search import BeamScorer
-from transformers.utils import ModelOutput
 import time
-
-
-class GenerateBeamDecoderOnlyOutput(ModelOutput):
-    sequences: torch.LongTensor = None
-    sequences_scores: Optional[torch.FloatTensor] = None
-    scores: Optional[Tuple[torch.FloatTensor]] = None
-    logits: Optional[Tuple[torch.FloatTensor]] = None
-    beam_indices: Optional[torch.LongTensor] = None
-    attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
-
-
-class GenerateBeamEncoderDecoderOutput(ModelOutput):
-    sequences: torch.LongTensor = None
-    sequences_scores: Optional[torch.FloatTensor] = None
-    scores: Optional[Tuple[torch.FloatTensor]] = None
-    logits: Optional[Tuple[torch.FloatTensor]] = None
-    beam_indices: Optional[torch.LongTensor] = None
-    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
-
+from transformers.generation.utils import (
+    GenerateBeamDecoderOnlyOutput,
+    GenerateBeamEncoderDecoderOutput,
+)
 
 GenerateBeamOutput = Union[
     GenerateBeamDecoderOnlyOutput, GenerateBeamEncoderDecoderOutput

intel_extension_for_pytorch/transformers/generation/beam_search.py

@@ -2,36 +2,18 @@
 from torch import nn
 import torch.distributed as dist
 from ...utils._logger import logger, WarningType
-from typing import Optional, Tuple, Union, List
+from typing import Optional, Union, List
 from transformers.generation.stopping_criteria import (
     StoppingCriteriaList,
     validate_stopping_criteria,
 )
 from transformers.generation.logits_process import LogitsProcessorList
 from transformers.generation.beam_search import BeamScorer
-from transformers.utils import ModelOutput
 import time
-
-
-class BeamSearchEncoderDecoderOutput(ModelOutput):
-    sequences: torch.LongTensor = None
-    sequences_scores: Optional[torch.FloatTensor] = None
-    scores: Optional[Tuple[torch.FloatTensor]] = None
-    beam_indices: Optional[torch.LongTensor] = None
-    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-
-
-class BeamSearchDecoderOnlyOutput(ModelOutput):
-    sequences: torch.LongTensor = None
-    sequences_scores: Optional[torch.FloatTensor] = None
-    scores: Optional[Tuple[torch.FloatTensor]] = None
-    beam_indices: Optional[torch.LongTensor] = None
-    attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+from transformers.generation.utils import (
+    BeamSearchEncoderDecoderOutput,
+    BeamSearchDecoderOnlyOutput,
+)
 
 
 BeamSearchOutput = Union[BeamSearchEncoderDecoderOutput, BeamSearchDecoderOnlyOutput]
@@ -55,6 +37,7 @@ def _beam_search(
 ) -> Union[BeamSearchOutput, torch.LongTensor]:
     new_generation_config = model_kwargs.pop("generation_config", None)
     if new_generation_config is not None:
+        return_dict_in_generate = new_generation_config.return_dict_in_generate
         if new_generation_config.do_sample:
             return self._beam_sample(
                 input_ids,

intel_extension_for_pytorch/transformers/generation/greedy_search.py

@@ -1,33 +1,19 @@
 import torch
 import torch.distributed as dist
 from ...utils._logger import logger, WarningType
-from typing import Optional, Tuple, Union, List
+from typing import Optional, Union, List
 from transformers.generation.stopping_criteria import (
     StoppingCriteriaList,
     validate_stopping_criteria,
 )
 from transformers.generation.logits_process import LogitsProcessorList
 from transformers.generation.streamers import BaseStreamer
-from transformers.utils import ModelOutput
 import time
 
-
-class GreedySearchDecoderOnlyOutput(ModelOutput):
-    sequences: torch.LongTensor = None
-    scores: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-
-
-class GreedySearchEncoderDecoderOutput(ModelOutput):
-    sequences: torch.LongTensor = None
-    scores: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-
+from transformers.generation.utils import (
+    GreedySearchDecoderOnlyOutput,
+    GreedySearchEncoderDecoderOutput,
+)
 
 GreedySearchOutput = Union[
     GreedySearchEncoderDecoderOutput, GreedySearchDecoderOnlyOutput

intel_extension_for_pytorch/transformers/generation/sample.py

@@ -2,33 +2,18 @@
 from torch import nn
 import torch.distributed as dist
 import warnings
-from typing import Optional, Tuple, Union, List
+from typing import Optional, Union, List
 from transformers.generation.stopping_criteria import (
     StoppingCriteriaList,
     validate_stopping_criteria,
 )
 from transformers.generation.logits_process import LogitsProcessorList
 from transformers.generation.streamers import BaseStreamer
-from transformers.utils import ModelOutput
 import time
-
-
-class SampleEncoderDecoderOutput(ModelOutput):
-    sequences: torch.LongTensor = None
-    scores: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-
-
-class SampleDecoderOnlyOutput(ModelOutput):
-    sequences: torch.LongTensor = None
-    scores: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-
+from transformers.generation.utils import (
+    SampleEncoderDecoderOutput,
+    SampleDecoderOnlyOutput,
+)
 
 SampleOutput = Union[SampleEncoderDecoderOutput, SampleDecoderOnlyOutput]
 
@@ -52,6 +37,7 @@ def _sample(
 ) -> Union[SampleOutput, torch.LongTensor]:
     new_generation_config = model_kwargs.pop("generation_config", None)
     if new_generation_config is not None:
+        return_dict_in_generate = new_generation_config.return_dict_in_generate
         if not new_generation_config.do_sample:
             pad_token_id = new_generation_config._pad_token_tensor
             eos_token_id = new_generation_config._eos_token_tensor

tests/cpu/test_ipex_optimize_transformers.py

@@ -523,6 +523,13 @@ def test_generate_functions(self):
                     ipex_res = ipex_m.generate(input_ids, **generate_kwargs)
                     ref_res = ref_m.generate(input_ids, **generate_kwargs)
                     self.assertEqual(ipex_res, ref_res)
+                    ipex_res_dict = ipex_m.generate(
+                        input_ids, return_dict_in_generate=True, **generate_kwargs
+                    )
+                    ref_res_dict = ref_m.generate(
+                        input_ids, return_dict_in_generate=True, **generate_kwargs
+                    )
+                    self.assertEqual(ipex_res_dict.sequences, ref_res_dict.sequences)
 
     def test_cache_weight_for_large_batch(self):
         config = AutoConfig.from_pretrained(