Merge branch 'synapse_1_20' into transformers_future

examples/image-to-text/README.md

@@ -168,6 +168,8 @@ python3 ../gaudi_spawn.py \
     --lora_target_modules '".*(language_model).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$"'
 ```
 
+The single card training command for llava-hf/llava-1.5-7b-hf is similar.
+
 >  For different models, please adjust training parameters and `lora_target_modules`. Such as replace `lora_target_modules`
 >  with below for HuggingFaceM4/idefics2-8b.
 >  '".*(text_model|modality_projection|perceiver_resampler).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$"'

examples/image-to-text/run_image2text_lora_finetune.py

@@ -298,7 +298,58 @@ def __call__(self, examples):
         return batch
 
 
-def eval(processor, model, dataset, batch_size, use_lazy_mode, use_hpu_graphs, max_seq_length):
+class LLavaDataCollator:
+    def __init__(self, processor, max_seq_length):
+        self.processor = processor
+
+        num_image_tokens = (self.processor.image_processor.crop_size["height"] // self.processor.patch_size) * (
+            self.processor.image_processor.crop_size["width"] // self.processor.patch_size
+        ) + 1
+        if self.processor.vision_feature_select_strategy == "default":
+            num_image_tokens -= 1
+
+        # text length + image length
+        self.max_seq_length = max_seq_length + num_image_tokens
+
+    def __call__(self, examples):
+        texts = []
+        images = []
+
+        keys = list(examples[0].keys())
+        if not all(key in ["image", "query", "answers"] for key in keys):
+            raise ValueError("Unsupported dataset format")
+        for example in examples:
+            image = example["image"]
+            question = example["query"]["en"]
+            answer = random.choice(example["answers"])
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": "Answer briefly."},
+                        {"type": "image"},
+                        {"type": "text", "text": question},
+                    ],
+                },
+                {"role": "assistant", "content": [{"type": "text", "text": answer}]},
+            ]
+            text = self.processor.apply_chat_template(messages, add_generation_prompt=False)
+            texts.append(text.strip())
+            images.append(image)
+
+        batch = self.processor(
+            images, texts, return_tensors="pt", padding="max_length", truncation=True, max_length=self.max_seq_length
+        )
+
+        labels = batch["input_ids"].clone()
+        if self.processor.tokenizer.pad_token_id is not None:
+            labels[labels == self.processor.tokenizer.pad_token_id] = -100
+        batch["labels"] = labels
+
+        return batch
+
+
+def eval(processor, model, dataset, batch_size, use_lazy_mode, use_hpu_graphs, max_seq_length, model_type):
     from tqdm import tqdm
 
     answers_unique = []
@@ -307,7 +358,6 @@ def eval(processor, model, dataset, batch_size, use_lazy_mode, use_hpu_graphs, m
     for i in tqdm(range(0, len(dataset), batch_size)):
         examples = dataset[i : i + batch_size]
         answers_unique.extend(examples["answers"])
-        images = [[im] for im in examples["image"]]
         texts = []
         for q in examples["query"]:
             messages = [
@@ -322,14 +372,31 @@ def eval(processor, model, dataset, batch_size, use_lazy_mode, use_hpu_graphs, m
             ]
             text = processor.apply_chat_template(messages, add_generation_prompt=True)
             texts.append(text.strip())
-        inputs = processor(
-            text=texts,
-            images=images,
-            return_tensors="pt",
-            padding="max_length",
-            truncation=True,
-            max_length=max_seq_length,
-        )
+
+        if model_type is not None and model_type == "llava":
+            images = []
+            for im in examples["image"]:
+                images.append(im)
+
+            inputs = processor(
+                images,
+                texts,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=max_seq_length,
+                padding_side="left",
+            )
+        else:
+            images = [[im] for im in examples["image"]]
+            inputs = processor(
+                text=texts,
+                images=images,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=max_seq_length,
+            )
         inputs = {k: v.to("hpu") for k, v in inputs.items()}
         generated_ids = model.generate(
             **inputs, max_new_tokens=64, ignore_eos=False, lazy_mode=use_lazy_mode, hpu_graphs=use_hpu_graphs
@@ -346,6 +413,22 @@ def eval(processor, model, dataset, batch_size, use_lazy_mode, use_hpu_graphs, m
     return anls
 
 
+def find_all_linear_names(model):
+    cls = torch.nn.Linear
+    lora_module_names = set()
+    multimodal_keywords = ["mm_projector", "vision_tower", "vision_resampler"]
+    for name, module in model.named_modules():
+        if any(mm_keyword in name for mm_keyword in multimodal_keywords):
+            continue
+        if isinstance(module, cls):
+            names = name.split(".")
+            lora_module_names.add(names[0] if len(names) == 1 else names[-1])
+
+    if "lm_head" in lora_module_names:  # needed for 16-bit
+        lora_module_names.remove("lm_head")
+    return list(lora_module_names)
+
+
 def main():
     parser = HfArgumentParser((ModelArguments, DataArguments, GaudiTrainingArguments, FinetuneArguments))
     if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
@@ -380,7 +463,7 @@ def main():
         do_image_splitting=model_args.do_image_splitting,
         padding_side="right",
     )
-    setattr(processor.image_processor, "pad_to_longest_edge", True)
+
     config_kwargs = {
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
@@ -395,7 +478,13 @@ def main():
     else:
         raise ValueError("Please provide value for model_name_or_path or config_name.")
 
-        # Load model
+    if config.model_type == "llava":
+        setattr(processor, "patch_size", config.vision_config.patch_size)
+        setattr(processor, "vision_feature_select_strategy", config.vision_feature_select_strategy)
+    else:
+        setattr(processor.image_processor, "pad_to_longest_edge", True)
+
+    # Load model
     if model_args.model_name_or_path:
         model_dtype = torch.bfloat16 if training_args.bf16 else None
         model = AutoModelForVision2Seq.from_pretrained(
@@ -413,11 +502,16 @@ def main():
     else:
         raise ValueError("Must provide model_name_or_path to load a pretrained CausalLM model.")
 
+    if finetune_args.lora_target_modules is None:
+        target_modules = find_all_linear_names(model)
+    else:
+        target_modules = finetune_args.lora_target_modules
+
     lora_config = LoraConfig(
         r=finetune_args.lora_rank,
         lora_alpha=finetune_args.lora_alpha,
         lora_dropout=finetune_args.lora_dropout,
-        target_modules=finetune_args.lora_target_modules,
+        target_modules=target_modules,
         init_lora_weights="gaussian",
     )
     model = get_peft_model(model, lora_config)
@@ -456,15 +550,21 @@ def main():
             if col not in (data_args.input_column_names + data_args.output_column_names)
         ]
     )
-    if hasattr(config, "image_token_id"):
-        # idefics
-        image_token_id = config.image_token_id
-    elif hasattr(config, "image_token_index"):
-        # mllama
-        image_token_id = config.image_token_index
+    if config.model_type == "llava":
+        data_collator = LLavaDataCollator(processor, max_seq_length=data_args.max_seq_length)
     else:
-        raise ValueError("Please provide value for image_token_id")
-    data_collator = MyDataCollator(processor, max_seq_length=data_args.max_seq_length, image_token_id=image_token_id)
+        if hasattr(config, "image_token_id"):
+            # idefics
+            image_token_id = config.image_token_id
+        elif hasattr(config, "image_token_index"):
+            # mllama
+            image_token_id = config.image_token_index
+        else:
+            raise ValueError("Please provide value for image_token_id")
+
+        data_collator = MyDataCollator(
+            processor, max_seq_length=data_args.max_seq_length, image_token_id=image_token_id
+        )
 
     gaudi_config = GaudiConfig()
     gaudi_config.use_fused_adam = True
@@ -509,14 +609,29 @@ def main():
             }
         ]
         text = processor.apply_chat_template(messages, add_generation_prompt=True)
-        inputs = processor(
-            text=[text.strip()],
-            images=[image],
-            return_tensors="pt",
-            padding="max_length",
-            truncation=True,
-            max_length=data_args.max_seq_length,
-        )
+
+        if config.model_type == "llava":
+            # don't expand image_token_id
+            setattr(processor, "patch_size", None)
+            setattr(processor, "vision_feature_select_strategy", None)
+            inputs = processor(
+                [image],
+                [text.strip()],
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=data_args.max_seq_length,
+                padding_side="left",
+            )
+        else:
+            inputs = processor(
+                text=[text.strip()],
+                images=[image],
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=data_args.max_seq_length,
+            )
         inputs = {k: v.to("hpu") for k, v in inputs.items()}
         generated_ids = model.generate(
             **inputs,
@@ -543,6 +658,7 @@ def main():
                 use_lazy_mode=training_args.use_lazy_mode,
                 use_hpu_graphs=training_args.use_hpu_graphs_for_inference,
                 max_seq_length=data_args.max_seq_length,
+                model_type=config.model_type,
             )
             eval_metrics = {"eval_accuracy": anls}
             trainer.log_metrics("eval", eval_metrics)