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Fix empty tensor shape issue in DynamicCache for torch.compile #42053

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Fix empty tensor shape issue in DynamicCache for torch.compile #42053

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17 changes: 13 additions & 4 deletions src/transformers/cache_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,8 +91,11 @@ class DynamicLayer(CacheLayerMixin):

def lazy_initialization(self, key_states: torch.Tensor):
self.dtype, self.device = key_states.dtype, key_states.device
self.keys = torch.tensor([], dtype=self.dtype, device=self.device)
self.values = torch.tensor([], dtype=self.dtype, device=self.device)
# Initialize with proper 4D shape: [batch_size, num_heads, 0, head_dim]
# This ensures torch.cat works correctly in torch.compile mode
batch_size, num_heads, _, head_dim = key_states.shape
self.keys = torch.zeros((batch_size, num_heads, 0, head_dim), dtype=self.dtype, device=self.device)
self.values = torch.zeros((batch_size, num_heads, 0, head_dim), dtype=self.dtype, device=self.device)
self.is_initialized = True

def update(
Expand Down Expand Up @@ -545,8 +548,14 @@ def update(
if self.keys.dim() == 4 and self.keys.shape[-2] + 1 >= self.residual_length:
self._quantized_keys = self._quantize(keys_to_return.contiguous(), axis=self.axis_key)
self._quantized_values = self._quantize(values_to_return.contiguous(), axis=self.axis_value)
self.keys = torch.tensor([], dtype=key_states.dtype, device=key_states.device)
self.values = torch.tensor([], dtype=key_states.dtype, device=key_states.device)
# Reset to proper 4D empty tensors to ensure torch.cat works correctly in torch.compile mode
batch_size, num_heads, _, head_dim = key_states.shape
self.keys = torch.zeros(
(batch_size, num_heads, 0, head_dim), dtype=key_states.dtype, device=key_states.device
)
self.values = torch.zeros(
(batch_size, num_heads, 0, head_dim), dtype=key_states.dtype, device=key_states.device
)
else:
self.keys = torch.cat([self.keys, key_states], dim=-2)
self.values = torch.cat([self.values, value_states], dim=-2)
Expand Down