minor fixes to cache, remove post ln option for hawk + add conv width…

… as a config option

hawk/cache.py

@@ -5,8 +5,10 @@ class RNNCache:
     state: torch.Tensor
     device: torch.device
     current_cache_size: int = 0
+    temporal_conv_width: int = 4
 
-    def __init__(self, state_dim: int, device: torch.device):
+    def __init__(self, state_dim: int, device: torch.device, temporal_conv_width: int):
+        self.conv_cache_size = self.temporal_conv_width - 1
         self.recc_state = torch.full(
             (1, 1, state_dim),
             fill_value=torch.nan,
@@ -15,7 +17,7 @@ def __init__(self, state_dim: int, device: torch.device):
         )
 
         self.conv_state = torch.full(
-            (1, 3, state_dim),  # window_size - 1
+            (1, self.conv_cache_size, state_dim),  # window_size - 1
             fill_value=torch.nan,
             dtype=torch.bfloat16,
             device=device,
@@ -27,18 +29,18 @@ def __init__(self, state_dim: int, device: torch.device):
     def update_cache(self, new_state: torch.Tensor) -> None:
         assert new_state.shape[0] == 1
         assert new_state.shape[1] == 1
-        assert new_state.ndim == 3
+        assert new_state.ndim == self.conv_cache_size
 
         self.recc_state[...] = new_state
         self.current_cache_size = 1
 
     def update_conv_cache(self, new_state: torch.Tensor) -> None:
         assert new_state.shape[0] == 1
         assert new_state.shape[1] == 1
-        assert new_state.ndim == 3
+        assert new_state.ndim == self.conv_cache_size
 
         self.conv_state = torch.roll(self.conv_state, shifts=-1, dims=1)
         self.conv_state[:, -1, :] = new_state
 
     def __repr__(self) -> str:
-        return f"RNNCache: current_cache_size={self.current_cache_size}, state_dim={self.state_dim}"
+        return f"RNNCache: {self.current_cache_size=}, {self.state_dim=}, {self.temporal_conv_width=}"

hawk/hawk.py

@@ -26,7 +26,7 @@ class HawkConfig:
     num_hidden_layers: int
     recurrent_size: int
     num_blocks: int
-    post_norm: bool = False
+    conv_width: int
 
 
 # ----
@@ -96,11 +96,6 @@ def __init__(self, config: HawkConfig, use_cache: bool = False):
             config.hidden_size, 2 * config.recurrent_size, bias=False
         )
 
-        if config.post_norm:
-            self.norm = RMSNorm(config.recurrent_size)
-        else:
-            self.norm = nn.Identity()
-
         self.rg_lru_input_gate = BlockDiagonalLinear(
             width=config.recurrent_size, num_blocks=self.config.num_blocks
         )
@@ -119,10 +114,9 @@ def __init__(self, config: HawkConfig, use_cache: bool = False):
 
         self.use_cache = use_cache
 
-        self.temporal_width = 4
         self.conv1d = Conv1D(
             width=config.recurrent_size,
-            temporal_width=self.temporal_width,
+            temporal_width=self.config.conv_width,
         )
 
         self.reset_parameters()
@@ -138,7 +132,7 @@ def forget_init(self, w: torch.Tensor) -> torch.Tensor:
         return rnn_param_init(w, min_rad=0.9, max_rad=0.999)
 
     def epilogue(self, gate, h):
-        return self.resid_proj(F.gelu(gate) * self.norm(h))
+        return self.resid_proj(F.gelu(gate) * h)
 
     def inference_prologue(self, x):
         # inference-only prologue function