deprecate input_batch in model inputs

Author: tianyu-l

torchtitan/experiments/forge/example_train.py

@@ -158,10 +158,10 @@ def forward_backward_step(
         parallel_dims = self.parallel_dims
 
         inputs = input_dict["input"]
-        extra_args = {}
+        extra_kwargs = {}
 
         if getattr(self.model_args, "use_flex_attn", False):
-            extra_args["attention_masks"] = model_parts[0].get_attention_masks(
+            extra_kwargs["attention_masks"] = model_parts[0].get_attention_masks(
                 input_batch=inputs,
                 tokenizer=self.tokenizer,
             )
@@ -187,17 +187,15 @@ def forward_backward_step(
                 if self.pp_has_first_stage:
                     self.pp_schedule.step(
                         inputs,
-                        **extra_args,
+                        **extra_kwargs,
                         target=targets,
                         losses=losses,
-                        input_batch=inputs,
                     )
                 else:
                     self.pp_schedule.step(
-                        **extra_args,
+                        **extra_kwargs,
                         target=targets,
                         losses=losses,
-                        input_batch=inputs,
                     )
 
             # accumulate losses across pipeline microbatches
@@ -215,7 +213,7 @@ def forward_backward_step(
             with self.train_context(optional_context_parallel_ctx):
                 assert len(model_parts) == 1
                 with self.maybe_enable_amp:
-                    pred = model_parts[0](inputs, **extra_args)
+                    pred = model_parts[0](inputs, **extra_kwargs)
                     loss = self.loss_fn(pred, labels)
                 # need to free to before bwd to avoid peaking memory
                 del pred

torchtitan/experiments/vlm/model/model.py

@@ -96,7 +96,6 @@ def forward(
         grid_thw: torch.Tensor,
         special_tokens: SpecialTokens,
         attention_masks: AttentionMasksType | None = None,
-        input_batch: torch.Tensor | None = None,
     ):
         # passthrough for nonexistent layers, allows easy configuration of pipeline parallel stages
         h_BSD = self.tok_embeddings(tokens) if self.tok_embeddings else tokens

torchtitan/models/deepseek_v3/model/model.py

@@ -411,7 +411,6 @@ def forward(
         self,
         tokens: torch.Tensor,
         attention_masks: AttentionMasksType | None = None,
-        input_batch: torch.Tensor | None = None,
     ):
         """
         Forward pass for the Transformer model.
@@ -421,10 +420,6 @@ def forward(
                 If pipeline parallelism is enabled, this will be the input token indices
                 for the ranks on the first pipeline stage. This will be the activation of the
                 previous pipeline stage if the current rank is not on the first stage.
-            input_batch (torch.Tensor): The input batch read from the dataloader.
-                This will always be the input batch regardless of the pipeline stage.
-                This field is required for non-first PP stages to perform document
-                masking attention (to analyze the boundary of the document).
 
         Returns:
             torch.Tensor: Logits tensor of shape (batch_size, vocab_size).

torchtitan/models/llama3/model/model.py

@@ -478,7 +478,6 @@ def forward(
         self,
         tokens: torch.Tensor,
         attention_masks: AttentionMasksType | None = None,
-        input_batch: torch.Tensor | None = None,
     ):
         """
         Perform a forward pass through the Transformer model.
@@ -488,10 +487,6 @@ def forward(
                 If pipeline parallelism is enabled, this will be the input token indices
                 for the ranks on the first pipeline stage. This will be the activation of the
                 previous pipeline stage if the current rank is not on the first stage.
-            input_batch (torch.Tensor): The input batch read from the dataloader.
-                This will always be the input batch regardless of the pipeline stage.
-                This field is required for non-first PP stages to perform document
-                masking attention (to analyze the boundary of the document).
 
         Returns:
             torch.Tensor: Output logits after applying the Transformer model.

torchtitan/models/llama4/model/model.py

@@ -539,7 +539,6 @@ def forward(
         self,
         tokens: torch.Tensor,
         attention_masks: AttentionMasksType | None = None,
-        input_batch: torch.Tensor | None = None,
     ):
         """
         Perform a forward pass through the Transformer model.
@@ -549,10 +548,6 @@ def forward(
                 If pipeline parallelism is enabled, this will be the input token indices
                 for the ranks on the first pipeline stage. This will be the activation of the
                 previous pipeline stage if the current rank is not on the first stage.
-            input_batch (torch.Tensor): The input batch read from the dataloader.
-                This will always be the input batch regardless of the pipeline stage.
-                This field is required for non-first PP stages to perform document
-                masking attention (to analyze the boundary of the document).
 
         Returns:
             torch.Tensor: Output logits after applying the Transformer model.

torchtitan/models/qwen3/model/model.py

@@ -470,7 +470,6 @@ def forward(
         self,
         tokens: torch.Tensor,
         attention_masks: AttentionMasksType | None = None,
-        input_batch: torch.Tensor | None = None,
     ):
         """
         Perform a forward pass through the Transformer model.
@@ -480,10 +479,6 @@ def forward(
                 If pipeline parallelism is enabled, this will be the input token indices
                 for the ranks on the first pipeline stage. This will be the activation of the
                 previous pipeline stage if the current rank is not on the first stage.
-            input_batch (torch.Tensor): The input batch read from the dataloader.
-                This will always be the input batch regardless of the pipeline stage.
-                This field is required for non-first PP stages to perform document
-                masking attention (to analyze the boundary of the document).
 
         Returns:
             torch.Tensor: Output logits after applying the Transformer model.

torchtitan/train.py

@@ -422,11 +422,11 @@ def forward_backward_step(
         extra_inputs = {k: v for k, v in input_dict.items() if k != "input"}
         # For arguments, like attention_masks, we have to put them in a separate
         # dict as extra_inputs are not forwarded to other stages in PP, but
-        # extra_args are.
-        extra_args = {}
+        # extra_kwargs are.
+        extra_kwargs = {}
 
         if getattr(self.model_args, "use_flex_attn", False):
-            extra_args["attention_masks"] = model_parts[0].get_attention_masks(
+            extra_kwargs["attention_masks"] = model_parts[0].get_attention_masks(
                 input_batch=inputs,
                 tokenizer=self.tokenizer,
                 extra_inputs=extra_inputs,
@@ -457,17 +457,15 @@ def forward_backward_step(
                     self.pp_schedule.step(
                         inputs,
                         **extra_inputs,
-                        **extra_args,
+                        **extra_kwargs,
                         target=targets,
                         losses=losses,
-                        input_batch=inputs,
                     )
                 else:
                     self.pp_schedule.step(
-                        **extra_args,
+                        **extra_kwargs,
                         target=targets,
                         losses=losses,
-                        input_batch=inputs,
                     )
 
             # accumulate losses across pipeline microbatches
@@ -485,7 +483,7 @@ def forward_backward_step(
             with self.train_context(optional_context_parallel_ctx):
                 assert len(model_parts) == 1
                 with self.maybe_enable_amp:
-                    pred = model_parts[0](inputs, **extra_inputs, **extra_args)
+                    pred = model_parts[0](inputs, **extra_inputs, **extra_kwargs)
                     loss = self.loss_fn(pred, labels)
                 # need to free pred before bwd to avoid peaking memory
                 del pred