[InstructBlip] Add accelerate support for instructblip (#24488)

* add accelerate support for instructblip

* add `_keep_in_fp32_modules`

* dynamically adapt `_no_split_modules`

* better fix

* same logic for `_keep_in_fp32_modules`

src/transformers/models/instructblip/modeling_instructblip.py

@@ -281,6 +281,8 @@ class InstructBlipPreTrainedModel(PreTrainedModel):
         r"language_model.decoder.embed_tokens.weight",
         r"language_model.lm_head.weight",
     ]
+    _no_split_modules = ["InstructBlipAttention", "InstructBlipQFormerMultiHeadAttention"]
+    _keep_in_fp32_modules = []
 
     # Copied from transformers.models.blip_2.modeling_blip_2.Blip2PreTrainedModel._init_weights with Blip2->InstructBlip
     def _init_weights(self, module):
@@ -1264,11 +1266,18 @@ def __init__(self, config: InstructBlipConfig):
         self.qformer = InstructBlipQFormerModel(config.qformer_config)
 
         self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size)
+
         if config.use_decoder_only_language_model:
             language_model = AutoModelForCausalLM.from_config(config.text_config)
         else:
             language_model = AutoModelForSeq2SeqLM.from_config(config.text_config)
 
+        if language_model._no_split_modules is not None:
+            self._no_split_modules.extend(language_model._no_split_modules)
+
+        if language_model._keep_in_fp32_modules is not None:
+            self._keep_in_fp32_modules.extend(language_model._keep_in_fp32_modules)
+
         self.language_model = language_model
 
         # Initialize weights and apply final processing
@@ -1422,7 +1431,7 @@ def forward(
 
         if attention_mask is None:
             attention_mask = torch.ones_like(input_ids)
-        attention_mask = torch.cat([language_model_attention_mask, attention_mask], dim=1)
+        attention_mask = torch.cat([language_model_attention_mask.to(attention_mask.device), attention_mask], dim=1)
 
         if self.config.use_decoder_only_language_model:
             outputs = self.language_model(