Gemma3 is Torch Exportable

src/transformers/integrations/executorch.py

@@ -20,15 +20,207 @@
 
 
 if is_torch_available():
-    from transformers import PreTrainedModel, StaticCache
+    from transformers import HybridCache, PreTrainedModel, StaticCache
     from transformers.pytorch_utils import is_torch_greater_or_equal, is_torch_greater_or_equal_than_2_3
 
 
+class TorchExportableModuleForDecoderOnlyLM(torch.nn.Module):
+    """
+    A recipe module designed to make a `PreTrainedModel` exportable with `torch.export`,
+    specifically for decoder-only LM with cache. This module ensures that the
+    exported model is compatible with further lowering and execution in `ExecuTorch`.
+    """
+
+    def __init__(
+        self,
+        model: PreTrainedModel,
+        max_batch_size: int = 1,
+        max_cache_len: int = 4096,
+    ):
+        """
+        Initializes the exportable module with `HybridCache`.
+
+        Args:
+            model (`PreTrainedModel`): The pretrained model to wrap.
+            max_batch_size (int): Maximum batch size for the cache.
+            max_cache_len (int): Maximum sequence length for the cache.
+
+        Raises:
+            ValueError: If the model is configured with a unsupported cache implementation.
+        """
+        super().__init__()
+
+        if model.config.cache_implementation == "static":
+            self.model = TorchExportableModuleWithStaticCache(model)
+        elif model.config.cache_implementation == "hybrid":
+            self.model = TorchExportableModuleWithHybridCache(model, max_batch_size, max_cache_len)
+        else:
+            raise ValueError(
+                f"Unsupported cache implementation in this export recipe: '{model.config.cache_implementation}'"
+            )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        cache_position: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Forward pass of the module, which is compatible with the ExecuTorch llm runner.
+
+        Args:
+            input_ids (`torch.Tensor`): Tensor representing current input token id to the module.
+            cache_position (`torch.Tensor`): Tensor representing current input position in the cache.
+
+        Returns:
+            torch.Tensor: Logits output from the model.
+        """
+        return self.model.forward(input_ids, cache_position)
+
+    def export(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        cache_position: Optional[torch.Tensor] = None,
+        dynamic_shapes: Optional[dict] = None,
+        strict: Optional[bool] = None,
+    ) -> torch.export.ExportedProgram:
+        """
+        Export the wrapped module using `torch.export`.
+
+        Args:
+            input_ids (`Optional[torch.Tensor]`):
+                Tensor representing current input token id to the module. If not provided, a default tensor will be used.
+            cache_position (`Optional[torch.Tensor]`):
+                Tensor representing current input position in the cache. If not provided, a default tensor will be used.
+            dynamic_shapes (`Optional[dict]`):
+                Dynamic shapes to use for export if specified.
+            strict(`Optional[bool]`):
+                Flag to instruct `torch.export` to use `torchdynamo`.
+        """
+        example_input_ids = input_ids if input_ids is not None else torch.tensor([[1]], dtype=torch.long)
+        example_cache_position = cache_position if cache_position is not None else torch.tensor([0], dtype=torch.long)
+
+        return torch.export.export(
+            self.model,
+            args=(example_input_ids, example_cache_position),
+            kwargs={},
+            dynamic_shapes=dynamic_shapes,
+            strict=strict if strict is not None else True,
+        )
+
+    @staticmethod
+    def generate(
+        exported_program: torch.export.ExportedProgram,
+        tokenizer,
+        prompt: str,
+        max_new_tokens: int = 20,
+        do_sample: bool = False,
+        temperature: float = 1.0,
+        top_k: int = 50,
+        top_p: float = 1.0,
+        device: str = "cpu",
+    ) -> str:
+        """
+        Generate a sequence of tokens using an exported program.
+
+        Args:
+            exported_program (`torch.export.ExportedProgram`): The exported model being used for generate.
+            tokenizer: The tokenizer to use.
+            prompt (str): The input prompt.
+            max_new_tokens (int): Maximum number of new tokens to generate.
+            do_sample (bool): Whether to use sampling or greedy decoding.
+            temperature (float): The temperature for sampling.
+            top_k (int): The number of highest probability tokens to keep for top-k sampling.
+            top_p (float): The cumulative probability for nucleus sampling.
+            device (str): The device to use.
+
+        Returns:
+            str: The generated text.
+        """
+        # Get the module from the exported program
+        exported_module = exported_program.module()
+
+        # Tokenize the prompt
+        input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+
+        # Initialize with the prompt
+        generated_ids = input_ids.clone()
+
+        # Process the prompt tokens first
+        curr_position = 0
+        for i in range(input_ids.shape[1]):
+            # Process one token at a time
+            curr_input_ids = input_ids[:, i : i + 1]
+            curr_cache_position = torch.tensor([curr_position], dtype=torch.long, device=device)
+
+            # Forward pass
+            _ = exported_module(curr_input_ids, curr_cache_position)
+            curr_position += 1
+
+        # Generate new tokens
+        for _ in range(max_new_tokens):
+            # Get the last token as input
+            curr_input_ids = generated_ids[:, -1:]
+            curr_cache_position = torch.tensor([curr_position], dtype=torch.long, device=device)
+
+            # Forward pass to get next token logits
+            outputs = exported_module(curr_input_ids, curr_cache_position)
+
+            # Get the next token ID
+            if do_sample:
+                # Apply temperature
+                if temperature > 0:
+                    logits = outputs / temperature
+                else:
+                    logits = outputs
+
+                # Apply top-k filtering
+                if top_k > 0:
+                    indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+                    logits[indices_to_remove] = float("-inf")
+
+                # Apply top-p (nucleus) filtering
+                if top_p < 1.0:
+                    sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                    cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
+
+                    # Remove tokens with cumulative probability above the threshold
+                    sorted_indices_to_remove = cumulative_probs > top_p
+                    # Shift the indices to the right to keep also the first token above the threshold
+                    sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+                    sorted_indices_to_remove[..., 0] = 0
+
+                    # Scatter sorted tensors to original indexing
+                    indices_to_remove = sorted_indices_to_remove.scatter(-1, sorted_indices, sorted_indices_to_remove)
+                    logits[indices_to_remove] = float("-inf")
+
+                # Sample from the filtered distribution
+                probs = torch.softmax(logits, dim=-1)
+                next_token_id = torch.multinomial(probs, num_samples=1)
+            else:
+                # Greedy decoding
+                next_token_id = outputs.argmax(dim=-1, keepdim=True)
+
+            # Ensure next_token_id has the right shape before concatenation
+            if next_token_id.dim() > 2:
+                next_token_id = next_token_id.squeeze(-1)
+
+            # Append to the generated sequence
+            generated_ids = torch.cat([generated_ids, next_token_id], dim=-1)
+            curr_position += 1
+
+            # Stop if we generate an EOS token
+            if next_token_id.item() == tokenizer.eos_token_id:
+                break
+
+        # Decode the generated text
+        return tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+
 class TorchExportableModuleWithStaticCache(torch.nn.Module):
     """
-    A wrapper module designed to make a `PreTrainedModel` exportable with `torch.export`,
-    specifically for use with static caching. This module ensures that the exported model
-    is compatible with further lowering and execution in `ExecuTorch`.
+    A recipe module designed to make a `PreTrainedModel` exportable with `torch.export`,
+    specifically for decoder-only LM to `StaticCache`. This module ensures that the
+    exported model is compatible with further lowering and execution in `ExecuTorch`.
 
     Note:
         This class is specifically designed to support export process using `torch.export`
@@ -178,6 +370,94 @@ def generate(
         return torch.tensor([response_tokens], dtype=torch.long)
 
 
+class TorchExportableModuleWithHybridCache(torch.nn.Module):
+    """
+    A recipe module designed to make a `PreTrainedModel` exportable with `torch.export`,
+    specifically for decoder-only LM to `HybridCache`. This module ensures that the
+    exported model is compatible with further lowering and execution in `ExecuTorch`.
+    """
+
+    def __init__(
+        self,
+        model: PreTrainedModel,
+        max_batch_size: int = 1,
+        max_cache_len: int = 4096,
+    ):
+        """
+        Initializes the exportable module with `HybridCache`.
+
+        Args:
+            model (`PreTrainedModel`): The pretrained model to wrap.
+            max_batch_size (int): Maximum batch size for the cache.
+            max_cache_len (int): Maximum sequence length for the cache.
+
+        Raises:
+            AssertionError: If the model doesn't have the expected configuration for HybridCache.
+        """
+        super().__init__()
+        self.model = model
+
+        # Verify the model is configured for HybridCache
+        if not self.model.config.use_cache:
+            raise AssertionError("Model must have caching enabled")
+
+        if (
+            not hasattr(self.model.config, "cache_implementation")
+            or self.model.config.cache_implementation != "hybrid"
+        ):
+            raise AssertionError("Model must use 'hybrid' cache implementation")
+
+        # Initialize the HybridCache
+        self.cache = HybridCache(
+            config=self.model.config,
+            max_batch_size=max_batch_size,
+            max_cache_len=max_cache_len,
+            device=self.model.device,
+            dtype=self.model.dtype,
+        )
+
+        # Register all key and value cache tensors as buffers
+        for i in range(len(self.cache.key_cache)):
+            self.register_buffer(f"key_cache_{i}", self.cache.key_cache[i], persistent=False)
+            self.register_buffer(f"value_cache_{i}", self.cache.value_cache[i], persistent=False)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        cache_position: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Forward pass of the module, which is compatible with the ExecuTorch llm runner.
+
+        Args:
+            input_ids (`torch.Tensor`): Tensor representing current input token id to the module.
+            cache_position (`torch.Tensor`): Tensor representing current input position in the cache.
+
+        Returns:
+            torch.Tensor: Logits output from the model.
+        """
+        batch_size, seq_len = input_ids.shape
+
+        # Generate position_ids from cache_position
+        position_ids = cache_position.unsqueeze(0).expand(batch_size, -1)
+
+        # Create attention mask (always ones for token-by-token generation)
+        attention_mask = torch.ones((batch_size, seq_len), dtype=torch.long, device=input_ids.device)
+
+        # Forward pass with the model
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=self.cache,
+            use_cache=True,
+            cache_position=cache_position,
+        )
+
+        # Return only the logits to simplify the export
+        return outputs.logits
+
+
 def convert_and_export_with_cache(
     model: PreTrainedModel,
     example_input_ids: Optional[torch.Tensor] = None,

src/transformers/models/cohere2/modeling_cohere2.py

@@ -351,7 +351,7 @@ def forward(
                 # In case we are beyond the sliding window, we need to correctly offset the mask slicing
                 offset = cache_position[-1] - effective_seq_len + 1
                 # Should only be used when beyond the sliding window (i.e. offset > 0)
-                offset = max(0, offset)
+                offset = torch.clamp(offset, min=0)
                 # equivalent to: `attention_mask = attention_mask[:, :, :, offset : offset + effective_seq_len]`,
                 # but without data-dependent slicing (i.e. torch.compile friendly)
                 mask_indexes = torch.arange(

src/transformers/models/cohere2/modular_cohere2.py

@@ -400,7 +400,7 @@ def forward(
                 # In case we are beyond the sliding window, we need to correctly offset the mask slicing
                 offset = cache_position[-1] - effective_seq_len + 1
                 # Should only be used when beyond the sliding window (i.e. offset > 0)
-                offset = max(0, offset)
+                offset = torch.clamp(offset, min=0)
                 # equivalent to: `attention_mask = attention_mask[:, :, :, offset : offset + effective_seq_len]`,
                 # but without data-dependent slicing (i.e. torch.compile friendly)
                 mask_indexes = torch.arange(

src/transformers/models/gemma2/modeling_gemma2.py

@@ -317,7 +317,7 @@ def forward(
                 # In case we are beyond the sliding window, we need to correctly offset the mask slicing
                 offset = cache_position[-1] - effective_seq_len + 1
                 # Should only be used when beyond the sliding window (i.e. offset > 0)
-                offset = max(0, offset)
+                offset = torch.clamp(offset, min=0)
                 # equivalent to: `attention_mask = attention_mask[:, :, :, offset : offset + effective_seq_len]`,
                 # but without data-dependent slicing (i.e. torch.compile friendly)
                 mask_indexes = torch.arange(

src/transformers/models/gemma2/modular_gemma2.py

@@ -364,7 +364,7 @@ def forward(
                 # In case we are beyond the sliding window, we need to correctly offset the mask slicing
                 offset = cache_position[-1] - effective_seq_len + 1
                 # Should only be used when beyond the sliding window (i.e. offset > 0)
-                offset = max(0, offset)
+                offset = torch.clamp(offset, min=0)
                 # equivalent to: `attention_mask = attention_mask[:, :, :, offset : offset + effective_seq_len]`,
                 # but without data-dependent slicing (i.e. torch.compile friendly)
                 mask_indexes = torch.arange(

src/transformers/models/gemma3/modeling_gemma3.py

@@ -410,7 +410,7 @@ def forward(
                 # In case we are beyond the sliding window, we need to correctly offset the mask slicing
                 offset = cache_position[-1] - effective_seq_len + 1
                 # Should only be used when beyond the sliding window (i.e. offset > 0)
-                offset = max(0, offset)
+                offset = torch.clamp(offset, min=0)
                 # equivalent to: `attention_mask = attention_mask[:, :, :, offset : offset + effective_seq_len]`,
                 # but without data-dependent slicing (i.e. torch.compile friendly)
                 mask_indexes = torch.arange(

src/transformers/models/gemma3/modular_gemma3.py

@@ -494,7 +494,7 @@ def forward(
                 # In case we are beyond the sliding window, we need to correctly offset the mask slicing
                 offset = cache_position[-1] - effective_seq_len + 1
                 # Should only be used when beyond the sliding window (i.e. offset > 0)
-                offset = max(0, offset)
+                offset = torch.clamp(offset, min=0)
                 # equivalent to: `attention_mask = attention_mask[:, :, :, offset : offset + effective_seq_len]`,
                 # but without data-dependent slicing (i.e. torch.compile friendly)
                 mask_indexes = torch.arange(