🟩 Drop image_split_sizes in favour of image_grid_thw (#4111)

Author: qgallouedec

tests/test_utils.py

@@ -873,7 +873,7 @@ def test_with_scalar(self):
 class SplitPixelValuesByGridTester(TrlTestCase):
     def test_split_correctly_0(self):
         batch = {
-            "image_split_sizes": [4, 4],
+            "image_grid_thw": torch.tensor([[1, 2, 2], [1, 2, 2]]),
             "pixel_values": torch.arange(8 * 3).reshape(8, 3),  # Shape: [8, 3]
         }
         result = split_pixel_values_by_grid(batch)
@@ -884,7 +884,7 @@ def test_split_correctly_0(self):
 
     def test_split_correctly_1(self):
         batch = {
-            "image_split_sizes": [4, 8],
+            "image_grid_thw": torch.tensor([[1, 2, 2], [1, 2, 4]]),
             "pixel_values": torch.arange(12 * 3).reshape(12, 3),  # Shape: [12, 3]
         }
         result = split_pixel_values_by_grid(batch)
@@ -900,7 +900,7 @@ def test_missing_keys(self):
 
     def test_mismatched_length(self):
         batch = {
-            "image_split_sizes": torch.tensor([2, 2]),  # Total = 4
+            "image_grid_thw": torch.tensor([[1, 1, 2], [1, 2, 1]]),  # Total = 8
             "pixel_values": torch.randn(3, 5),  # Only 3 rows
         }
         with self.assertRaises(ValueError):

trl/experimental/gfpo/gfpo_trainer.py

@@ -93,19 +93,13 @@ def _generate_and_score_completions(self, inputs):
         # [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What color is the sky?"}]}]
         kwargs = {}
         has_images = "image" in inputs[0]
-        image_split_sizes = None
         if has_images:
             images = [example.get("image") for example in inputs]
             kwargs = {"images": [[img] for img in images]}
             for prompt in prompts:
                 if isinstance(prompt, list):  # i.e., when using conversational data
                     prepare_multimodal_messages(prompt, num_images=1)
 
-            if hasattr(self.processing_class, "_get_num_multimodal_tokens"):
-                image_sizes = [(image.height, image.width) for image in images]
-                multimodal_extra_data = self.processing_class._get_num_multimodal_tokens(image_sizes)
-                image_split_sizes = multimodal_extra_data.num_image_patches
-
         prompts_text = [maybe_apply_chat_template(example, self.processing_class)["prompt"] for example in inputs]
 
         prompt_inputs = self.processing_class(
@@ -116,13 +110,9 @@ def _generate_and_score_completions(self, inputs):
             add_special_tokens=False,
             **kwargs,
         )
-        prompt_inputs = super(_GRPOTrainer, self)._prepare_inputs(prompt_inputs)
+        prompt_inputs = super()._prepare_inputs(prompt_inputs)
         prompt_ids, prompt_mask = prompt_inputs["input_ids"], prompt_inputs["attention_mask"]
 
-        if "image_grid_thw" in prompt_inputs and image_split_sizes is None:
-            # Fallback for VLMs that require image_grid_thw but don't provide _get_num_multimodal_tokens
-            image_split_sizes = prompt_inputs["image_grid_thw"].prod(dim=1).tolist()
-
         if self.max_prompt_length is not None:
             # If max_prompt_length is set, we trim the prompt to keep only the last `max_prompt_length` tokens.
             # Then we decode those tokens back into text. We manually remove leading pad tokens from the decoded text,
@@ -407,7 +397,6 @@ def _generate_and_score_completions(self, inputs):
                     image_grid_thw=prompt_inputs.get("image_grid_thw"),
                     pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"),
                     image_sizes=prompt_inputs.get("image_sizes"),
-                    image_split_sizes=image_split_sizes,
                 )
             else:
                 old_per_token_logps = None
@@ -432,7 +421,6 @@ def _generate_and_score_completions(self, inputs):
                         image_grid_thw=prompt_inputs.get("image_grid_thw"),
                         pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"),
                         image_sizes=prompt_inputs.get("image_sizes"),
-                        image_split_sizes=image_split_sizes,
                     )
                 else:
                     with self.accelerator.unwrap_model(self.model).disable_adapter():
@@ -446,7 +434,6 @@ def _generate_and_score_completions(self, inputs):
                             image_grid_thw=prompt_inputs.get("image_grid_thw"),
                             pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"),
                             image_sizes=prompt_inputs.get("image_sizes"),
-                            image_split_sizes=image_split_sizes,
                         )
             else:
                 ref_per_token_logps = None
@@ -652,6 +639,4 @@ def _generate_and_score_completions(self, inputs):
             output["pixel_attention_mask"] = prompt_inputs["pixel_attention_mask"]
         if "image_sizes" in prompt_inputs:
             output["image_sizes"] = prompt_inputs["image_sizes"]
-        if image_split_sizes is not None:
-            output["image_split_sizes"] = image_split_sizes
         return output

trl/trainer/grpo_trainer.py

@@ -791,7 +791,6 @@ def _get_per_token_logps_and_entropies(
         image_grid_thw=None,
         pixel_attention_mask=None,
         image_sizes=None,
-        image_split_sizes=None,
     ) -> dict[str, Optional[torch.Tensor]]:
         """Compute log-probs and (optionally) entropies for each token."""
         batch_size = batch_size or input_ids.size(0)  # Chunk inputs into smaller batches to reduce memory peak
@@ -804,15 +803,13 @@ def _get_per_token_logps_and_entropies(
             # Build model inputs - check if the model supports logits_to_keep (some models and VLMs don't)
             model_inputs = {"input_ids": input_ids_batch, "attention_mask": attention_mask_batch}
 
-            if image_grid_thw is not None:
+            if image_grid_thw is not None and pixel_values is not None:
                 model_inputs["image_grid_thw"] = image_grid_thw[start : start + batch_size]
-            if pixel_values is not None:
-                if image_split_sizes is not None:
-                    start_pixel_idx = sum(image_split_sizes[:start])
-                    end_pixel_idx = sum(image_split_sizes[: start + batch_size])
-                    model_inputs["pixel_values"] = pixel_values[start_pixel_idx:end_pixel_idx]
-                else:
-                    model_inputs["pixel_values"] = pixel_values[start : start + batch_size]
+                start_pixel_idx = image_grid_thw[:start].prod(-1).sum().item()
+                end_pixel_idx = image_grid_thw[: start + batch_size].prod(-1).sum().item()
+                model_inputs["pixel_values"] = pixel_values[start_pixel_idx:end_pixel_idx]
+            elif pixel_values is not None:
+                model_inputs["pixel_values"] = pixel_values[start : start + batch_size]
             if pixel_attention_mask is not None:
                 model_inputs["pixel_attention_mask"] = pixel_attention_mask[start : start + batch_size]
             if image_sizes is not None:
@@ -1078,19 +1075,13 @@ def _generate_and_score_completions(
         # [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What color is the sky?"}]}]
         kwargs = {}
         has_images = "image" in inputs[0]
-        image_split_sizes = None
         if has_images:
             images = [example.get("image") for example in inputs]
             kwargs = {"images": [[img] for img in images]}
             for prompt in prompts:
                 if isinstance(prompt, list):  # i.e., when using conversational data
                     prepare_multimodal_messages(prompt, num_images=1)
 
-            if hasattr(self.processing_class, "_get_num_multimodal_tokens"):
-                image_sizes = [(image.height, image.width) for image in images]
-                multimodal_extra_data = self.processing_class._get_num_multimodal_tokens(image_sizes)
-                image_split_sizes = multimodal_extra_data.num_image_patches
-
         prompts_text = [maybe_apply_chat_template(example, self.processing_class)["prompt"] for example in inputs]
 
         prompt_inputs = self.processing_class(
@@ -1104,10 +1095,6 @@ def _generate_and_score_completions(
         prompt_inputs = super()._prepare_inputs(prompt_inputs)
         prompt_ids, prompt_mask = prompt_inputs["input_ids"], prompt_inputs["attention_mask"]
 
-        if "image_grid_thw" in prompt_inputs and image_split_sizes is None:
-            # Fallback for VLMs that require image_grid_thw but don't provide _get_num_multimodal_tokens
-            image_split_sizes = prompt_inputs["image_grid_thw"].prod(dim=1).tolist()
-
         if self.max_prompt_length is not None:
             # If max_prompt_length is set, we trim the prompt to keep only the last `max_prompt_length` tokens.
             # Then we decode those tokens back into text. We manually remove leading pad tokens from the decoded text,
@@ -1392,7 +1379,6 @@ def _generate_and_score_completions(
                     image_grid_thw=prompt_inputs.get("image_grid_thw"),
                     pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"),
                     image_sizes=prompt_inputs.get("image_sizes"),
-                    image_split_sizes=image_split_sizes,
                 )
             else:
                 old_per_token_logps = None
@@ -1417,7 +1403,6 @@ def _generate_and_score_completions(
                         image_grid_thw=prompt_inputs.get("image_grid_thw"),
                         pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"),
                         image_sizes=prompt_inputs.get("image_sizes"),
-                        image_split_sizes=image_split_sizes,
                     )
                 else:
                     with self.accelerator.unwrap_model(self.model).disable_adapter():
@@ -1431,7 +1416,6 @@ def _generate_and_score_completions(
                             image_grid_thw=prompt_inputs.get("image_grid_thw"),
                             pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"),
                             image_sizes=prompt_inputs.get("image_sizes"),
-                            image_split_sizes=image_split_sizes,
                         )
             else:
                 ref_per_token_logps = None
@@ -1580,8 +1564,6 @@ def _generate_and_score_completions(
             output["pixel_attention_mask"] = prompt_inputs["pixel_attention_mask"]
         if "image_sizes" in prompt_inputs:
             output["image_sizes"] = prompt_inputs["image_sizes"]
-        if image_split_sizes is not None:
-            output["image_split_sizes"] = image_split_sizes
         return output
 
     def compute_liger_loss(self, unwrapped_model, inputs):
@@ -1656,7 +1638,6 @@ def _compute_loss(self, model, inputs):
             image_grid_thw=inputs.get("image_grid_thw"),
             pixel_attention_mask=inputs.get("pixel_attention_mask"),
             image_sizes=inputs.get("image_sizes"),
-            image_split_sizes=inputs.get("image_split_sizes"),
         )
 
         if self.top_entropy_quantile < 1.0:

trl/trainer/rloo_trainer.py

@@ -777,7 +777,6 @@ def _get_per_token_logps_and_entropies(
         image_grid_thw=None,
         pixel_attention_mask=None,
         image_sizes=None,
-        image_split_sizes=None,
     ) -> dict[str, Optional[torch.Tensor]]:
         """Compute log-probs and (optionally) entropies for each token."""
         batch_size = batch_size or input_ids.size(0)  # Chunk inputs into smaller batches to reduce memory peak
@@ -790,15 +789,13 @@ def _get_per_token_logps_and_entropies(
             # Build model inputs - check if the model supports logits_to_keep (some models and VLMs don't)
             model_inputs = {"input_ids": input_ids_batch, "attention_mask": attention_mask_batch}
 
-            if image_grid_thw is not None:
+            if image_grid_thw is not None and pixel_values is not None:
                 model_inputs["image_grid_thw"] = image_grid_thw[start : start + batch_size]
-            if pixel_values is not None:
-                if image_split_sizes is not None:
-                    start_pixel_idx = sum(image_split_sizes[:start])
-                    end_pixel_idx = sum(image_split_sizes[: start + batch_size])
-                    model_inputs["pixel_values"] = pixel_values[start_pixel_idx:end_pixel_idx]
-                else:
-                    model_inputs["pixel_values"] = pixel_values[start : start + batch_size]
+                start_pixel_idx = image_grid_thw[:start].prod(-1).sum().item()
+                end_pixel_idx = image_grid_thw[: start + batch_size].prod(-1).sum().item()
+                model_inputs["pixel_values"] = pixel_values[start_pixel_idx:end_pixel_idx]
+            elif pixel_values is not None:
+                model_inputs["pixel_values"] = pixel_values[start : start + batch_size]
             if pixel_attention_mask is not None:
                 model_inputs["pixel_attention_mask"] = pixel_attention_mask[start : start + batch_size]
             if image_sizes is not None:
@@ -1064,19 +1061,13 @@ def _generate_and_score_completions(
         # [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What color is the sky?"}]}]
         kwargs = {}
         has_images = "image" in inputs[0]
-        image_split_sizes = None
         if has_images:
             images = [example.get("image") for example in inputs]
             kwargs = {"images": [[img] for img in images]}
             for prompt in prompts:
                 if isinstance(prompt, list):  # i.e., when using conversational data
                     prepare_multimodal_messages(prompt, num_images=1)
 
-            if hasattr(self.processing_class, "_get_num_multimodal_tokens"):
-                image_sizes = [(image.height, image.width) for image in images]
-                multimodal_extra_data = self.processing_class._get_num_multimodal_tokens(image_sizes)
-                image_split_sizes = multimodal_extra_data.num_image_patches
-
         prompts_text = [maybe_apply_chat_template(example, self.processing_class)["prompt"] for example in inputs]
 
         prompt_inputs = self.processing_class(
@@ -1090,10 +1081,6 @@ def _generate_and_score_completions(
         prompt_inputs = super()._prepare_inputs(prompt_inputs)
         prompt_ids, prompt_mask = prompt_inputs["input_ids"], prompt_inputs["attention_mask"]
 
-        if "image_grid_thw" in prompt_inputs and image_split_sizes is None:
-            # Fallback for VLMs that require image_grid_thw but don't provide _get_num_multimodal_tokens
-            image_split_sizes = prompt_inputs["image_grid_thw"].prod(dim=1).tolist()
-
         if self.max_prompt_length is not None:
             # If max_prompt_length is set, we trim the prompt to keep only the last `max_prompt_length` tokens.
             # Then we decode those tokens back into text. We manually remove leading pad tokens from the decoded text,
@@ -1346,7 +1333,6 @@ def _generate_and_score_completions(
                 image_grid_thw=prompt_inputs.get("image_grid_thw"),
                 pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"),
                 image_sizes=prompt_inputs.get("image_sizes"),
-                image_split_sizes=image_split_sizes,
             )
             old_logps = (old_per_token_logps * completion_mask).sum(1)  # mask out padding and tokens after EOS
 
@@ -1363,7 +1349,6 @@ def _generate_and_score_completions(
                         image_grid_thw=prompt_inputs.get("image_grid_thw"),
                         pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"),
                         image_sizes=prompt_inputs.get("image_sizes"),
-                        image_split_sizes=image_split_sizes,
                     )
                 else:
                     with self.accelerator.unwrap_model(self.model).disable_adapter():
@@ -1377,7 +1362,6 @@ def _generate_and_score_completions(
                             image_grid_thw=prompt_inputs.get("image_grid_thw"),
                             pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"),
                             image_sizes=prompt_inputs.get("image_sizes"),
-                            image_split_sizes=image_split_sizes,
                         )
             else:
                 ref_per_token_logps = None
@@ -1498,8 +1482,6 @@ def _generate_and_score_completions(
             output["pixel_attention_mask"] = prompt_inputs["pixel_attention_mask"]
         if "image_sizes" in prompt_inputs:
             output["image_sizes"] = prompt_inputs["image_sizes"]
-        if image_split_sizes is not None:
-            output["image_split_sizes"] = image_split_sizes
         return output
 
     @profiling_decorator
@@ -1527,7 +1509,6 @@ def _compute_loss(self, model, inputs):
             image_grid_thw=inputs.get("image_grid_thw"),
             pixel_attention_mask=inputs.get("pixel_attention_mask"),
             image_sizes=inputs.get("image_sizes"),
-            image_split_sizes=inputs.get("image_split_sizes"),
         )
 
         logps = (per_token_logps * completion_mask).sum(1)  # mask out padding and tokens after EOS

trl/trainer/utils.py

@@ -1783,10 +1783,10 @@ def split_pixel_values_by_grid(batch: dict[str, torch.Tensor]) -> dict[str, Unio
     Splits `batch["pixel_values"]` into a list of tensors based on the product of each row in
     `batch["image_grid_thw"]`, while keeping other entries unchanged.
     """
-    if "image_split_sizes" not in batch or "pixel_values" not in batch:
+    if "image_grid_thw" not in batch or "pixel_values" not in batch:
         return batch
 
-    lengths = batch["image_split_sizes"]  # [batch_size]
+    lengths = batch["image_grid_thw"].prod(-1).tolist()  # [batch_size]
     pixel_values = batch["pixel_values"]  # [total, feature_dim]
 
     if sum(lengths) != pixel_values.size(0):