Skip device placement for past key values in decoder models

src/transformers/modeling_utils.py

@@ -1052,6 +1052,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
     main_input_name = "input_ids"
     _auto_class = None
     _no_split_modules = None
+    _skip_keys_device_placement = None
     _keep_in_fp32_modules = None
 
     # a list of `re` patterns of `state_dict` keys that should be removed from the list of missing
@@ -2887,7 +2888,10 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
         # Dispatch model with hooks on all devices if necessary
         if device_map is not None:
-            dispatch_model(model, device_map=device_map, offload_dir=offload_folder, offload_index=offload_index)
+            kwargs = {"device_map": device_map, "offload_dir": offload_folder, "offload_index": offload_index}
+            if "skip_keys" in inspect.signature(dispatch_model).parameters:
+                kwargs["skip_keys"] = model._skip_keys_device_placement
+            dispatch_model(model, **kwargs)
 
         if output_loading_info:
             if loading_info is None:

src/transformers/models/bart/modeling_bart.py

@@ -509,6 +509,7 @@ class BartPretrainedModel(PreTrainedModel):
     supports_gradient_checkpointing = True
     _keys_to_ignore_on_load_unexpected = [r"encoder.version", r"decoder.version"]
     _no_split_modules = [r"BartEncoderLayer", r"BartDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
 
     def _init_weights(self, module):
         std = self.config.init_std

src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py

@@ -1597,6 +1597,7 @@ class BigBirdPegasusPreTrainedModel(PreTrainedModel):
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
     _no_split_modules = ["BigBirdPegasusEncoderLayer", "BigBirdPegasusDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
 
     def _init_weights(self, module):
         std = self.config.init_std

src/transformers/models/blip_2/modeling_blip_2.py

@@ -286,6 +286,7 @@ class Blip2PreTrainedModel(PreTrainedModel):
         r"language_model.lm_head.weight",
     ]
     _no_split_modules = ["Blip2Attention", "T5Block", "OPTDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
     _keep_in_fp32_modules = ["wo"]
 
     def _init_weights(self, module):

src/transformers/models/bloom/modeling_bloom.py

@@ -481,6 +481,7 @@ class BloomPreTrainedModel(PreTrainedModel):
     base_model_prefix = "transformer"
     supports_gradient_checkpointing = True
     _no_split_modules = ["BloomBlock"]
+    _skip_keys_device_placement = "past_key_values"
 
     def __init__(self, *inputs, **kwargs):
         super().__init__(*inputs, **kwargs)

src/transformers/models/bridgetower/modeling_bridgetower.py

@@ -982,6 +982,7 @@ class BridgeTowerPreTrainedModel(PreTrainedModel):
     base_model_prefix = "bridgetower"
     supports_gradient_checkpointing = False
     _no_split_modules = ["BridgeTowerSelfAttention", "BridgeTowerResidualAttention"]
+    _skip_keys_device_placement = "past_key_values"
 
     def _init_weights(self, module):
         if isinstance(module, BridgeTowerVisionModel):

src/transformers/models/codegen/modeling_codegen.py

@@ -315,6 +315,7 @@ class CodeGenPreTrainedModel(PreTrainedModel):
     base_model_prefix = "transformer"
     supports_gradient_checkpointing = True
     _no_split_modules = ["CodeGenBlock"]
+    _skip_keys_device_placement = "past_key_values"
 
     def __init__(self, *inputs, **kwargs):
         super().__init__(*inputs, **kwargs)

src/transformers/models/gpt2/modeling_gpt2.py

@@ -449,6 +449,7 @@ class GPT2PreTrainedModel(PreTrainedModel):
     is_parallelizable = True
     supports_gradient_checkpointing = True
     _no_split_modules = ["GPT2Block"]
+    _skip_keys_device_placement = "past_key_values"
 
     def __init__(self, *inputs, **kwargs):
         super().__init__(*inputs, **kwargs)

src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py

@@ -372,6 +372,7 @@ class GPTBigCodePreTrainedModel(PreTrainedModel):
     base_model_prefix = "transformer"
     supports_gradient_checkpointing = True
     _no_split_modules = ["GPTBigCodeBlock"]
+    _skip_keys_device_placement = "past_key_values"
 
     def __init__(self, *inputs, **kwargs):
         super().__init__(*inputs, **kwargs)

src/transformers/models/gpt_neo/modeling_gpt_neo.py

@@ -363,6 +363,7 @@ class GPTNeoPreTrainedModel(PreTrainedModel):
     base_model_prefix = "transformer"
     supports_gradient_checkpointing = True
     _no_split_modules = ["GPTNeoBlock"]
+    _skip_keys_device_placement = "past_key_values"
 
     def __init__(self, *inputs, **kwargs):
         super().__init__(*inputs, **kwargs)

src/transformers/models/gpt_neox/modeling_gpt_neox.py

@@ -62,6 +62,7 @@ class GPTNeoXPreTrainedModel(PreTrainedModel):
     base_model_prefix = "gpt_neox"
     supports_gradient_checkpointing = True
     _no_split_modules = ["GPTNeoXLayer"]
+    _skip_keys_device_placement = "past_key_values"
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py

@@ -50,6 +50,7 @@ class GPTNeoXJapanesePreTrainedModel(PreTrainedModel):
     base_model_prefix = "gpt_neox_japanese"
     supports_gradient_checkpointing = True
     _no_split_modules = ["GPTNeoXJapaneseLayer"]
+    _skip_keys_device_placement = "past_key_values"
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/gptj/modeling_gptj.py

@@ -340,6 +340,7 @@ class GPTJPreTrainedModel(PreTrainedModel):
     is_parallelizable = True
     supports_gradient_checkpointing = True
     _no_split_modules = ["GPTJBlock"]
+    _skip_keys_device_placement = "past_key_values"
 
     def __init__(self, *inputs, **kwargs):
         super().__init__(*inputs, **kwargs)

src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py

@@ -692,6 +692,7 @@ class GPTSanJapanesePreTrainedModel(PreTrainedModel):
     base_model_prefix = "gptsan_japanese"
     supports_gradient_checkpointing = False
     _no_split_modules = ["GPTSanJapaneseBlock"]
+    _skip_keys_device_placement = "past_key_values"
 
     @property
     def dummy_inputs(self):

src/transformers/models/llama/modeling_llama.py

@@ -342,6 +342,7 @@ class LlamaPreTrainedModel(PreTrainedModel):
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
     _no_split_modules = ["LlamaDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
     _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
 
     def _init_weights(self, module):