Fix export for gemma4 and add Integration tests (#45285)

* start updating tests

* start making them pass

* more

* fix

* fix export

* fix

* oupsi

* review comments

* fix expectations for a10

* style

* rename tests

Author: Cyrilvallez

src/transformers/cache_utils.py

@@ -982,18 +982,38 @@ def update_recurrent_state(self, recurrent_states: torch.Tensor, layer_idx: int,
         return recurrent_states
 
     def early_initialization(
-        self, batch_size: int, num_heads: int, head_dim: int, dtype: torch.dtype, device: torch.device
+        self,
+        batch_size: int,
+        num_heads: int | list[int],
+        head_dim: int | list[int],
+        dtype: torch.dtype,
+        device: torch.device,
     ):
         """
         Initialize all the layers in advance (it's otherwise lazily initialized on the first `update` call).
         This is useful for our `export` recipes, as `export` needs everything in advance.
         """
-        # Note that the initialization needs all dimensions (except -2), as well as device and dtype, so we use
-        # this fake tensor approach. It has size 0 on the -2 dimension, so it does not allocate any data (it only
-        # creates an empty tensor with correct shape, dtype and device), which is very efficient and practical
-        fake_kv_tensor = torch.zeros((batch_size, num_heads, 0, head_dim), dtype=dtype, device=device)
-        # Init all layers
-        for layer in self.layers:
+        # To allow different num_heads and head_dim depending on layers, we accept lists
+        if isinstance(num_heads, int):
+            num_heads = [num_heads] * len(self)
+        if isinstance(head_dim, int):
+            head_dim = [head_dim] * len(self)
+
+        if len(num_heads) != len(self.layers):
+            raise ValueError(
+                f"`num_head` was provided as a list of length {len(num_heads)}, but the Cache currently has {len(self.layers)} layers"
+            )
+        if len(head_dim) != len(self.layers):
+            raise ValueError(
+                f"`head_dim` was provided as a list of length {len(num_heads)}, but the Cache currently has {len(self.layers)} layers"
+            )
+
+        for layer, layer_num_heads, layer_head_dim in zip(self.layers, num_heads, head_dim):
+            # Note that the initialization needs all dimensions (except -2), as well as device and dtype, so we use
+            # this fake tensor approach. It has size 0 on the -2 dimension, so it does not allocate any data (it only
+            # creates an empty tensor with correct shape, dtype and device), which is very efficient and practical
+            fake_kv_tensor = torch.zeros((batch_size, layer_num_heads, 0, layer_head_dim), dtype=dtype, device=device)
+            # Init the layer
             layer.lazy_initialization(fake_kv_tensor, fake_kv_tensor)
 
     def get_seq_length(self, layer_idx: int = 0) -> int:

src/transformers/integrations/executorch.py

@@ -443,6 +443,28 @@ def generate(
         return tokenizer.decode(generated_ids[0], skip_special_tokens=True)
 
 
+def get_head_shapes(config) -> tuple[int | list[int], int | list[int]]:
+    """Returns a tuple `(num_heads, head_dim)` containing either 2 ints, or a list of int with the value for each
+    layer."""
+    # Gemma4 has different head_dim and num_heads depending on layer type
+    if hasattr(config, "global_head_dim"):
+        head_dim = [
+            config.global_head_dim if layer == "full_attention" else config.head_dim
+            for layer in config.layer_types[: -config.num_kv_shared_layers]
+        ]
+        num_heads = [
+            config.num_global_key_value_heads
+            if layer == "full_attention" and config.attention_k_eq_v
+            else config.num_key_value_heads
+            for layer in config.layer_types[: -config.num_kv_shared_layers]
+        ]
+    else:
+        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
+
+    return num_heads, head_dim
+
+
 class TorchExportableModuleWithStaticCache(torch.nn.Module):
     """
     A recipe module designed to make a `PreTrainedModel` exportable with `torch.export`,
@@ -523,9 +545,8 @@ def __init__(
         # simple StaticLayer... It means that any generation beyond the window is unfortunately unsupported
         for i, layer in enumerate(self.static_cache.layers):
             if isinstance(layer, StaticSlidingWindowLayer):
-                self.static_cache.layers[i] = StaticLayer(layer.max_cache_len)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
+                self.static_cache.layers[i] = StaticLayer(max_cache_len)
+        num_heads, head_dim = get_head_shapes(config)
         dtype = self.model.dtype
         # We need this call to initialize all the layers (otherwise it's done lazily, which is not exportable)
         self.static_cache.early_initialization(batch_size, num_heads, head_dim, dtype, device)
@@ -702,9 +723,8 @@ def __init__(
         # simple StaticLayer... It means that any generation beyond the window is unfortunately unsupported
         for i, layer in enumerate(self.cache.layers):
             if isinstance(layer, StaticSlidingWindowLayer):
-                self.cache.layers[i] = StaticLayer(layer.max_cache_len)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
+                self.cache.layers[i] = StaticLayer(max_cache_len)
+        num_heads, head_dim = get_head_shapes(config)
         dtype = self.model.dtype
         # We need this call to initialize all the layers (otherwise it's done lazily, which is not exportable)
         self.cache.early_initialization(batch_size, num_heads, head_dim, dtype, device)
@@ -856,9 +876,8 @@ def __init__(self, model, max_static_cache_length, batch_size):
         # simple StaticLayer... It means that any generation beyond the window is unfortunately unsupported
         for i, layer in enumerate(self.static_cache.layers):
             if isinstance(layer, StaticSlidingWindowLayer):
-                self.static_cache.layers[i] = StaticLayer(layer.max_cache_len)
-        head_dim = getattr(self.config, "head_dim", self.config.hidden_size // self.config.num_attention_heads)
-        num_heads = getattr(self.config, "num_key_value_heads", self.config.num_attention_heads)
+                self.static_cache.layers[i] = StaticLayer(max_static_cache_length)
+        num_heads, head_dim = get_head_shapes(self.config)
         self.static_cache.early_initialization(batch_size, num_heads, head_dim, torch.float32, model_device)
         self.cache = EncoderDecoderCache(self.static_cache, DynamicCache(config=self.config))