Add high_freq_factor to ModelArgs

examples/models/llama/model_args.py

@@ -46,6 +46,7 @@ class ModelArgs:
     rope_freq_base: float = 10000.0  # The base frequency for RoPE. Keep it for BC.
     use_scaled_rope: bool = False  # Use scaled RoPE, introduced in llama3.1.
     rope_scale_factor: int = 8
+    high_freq_factor: int = 4
     # Additional Model Metadata needed at runtime
     bos_idx: int = 1
     eos_idx: int = 3

examples/models/llama/rope.py

@@ -17,10 +17,9 @@
 # ======================== Stock Implementation ========================
 
 
-def apply_scaling(freqs: torch.Tensor, scale_factor: int):
+def apply_scaling(freqs: torch.Tensor, scale_factor: int, high_freq_factor: int):
     # Values obtained from grid search
     low_freq_factor = 1
-    high_freq_factor = 4
     old_context_len = 8192  # original llama3 length
 
     low_freq_wavelen = old_context_len / low_freq_factor
@@ -47,14 +46,15 @@ def precompute_freqs_cis(
     theta: float = 10000.0,
     use_scaled: bool = False,
     scale_factor: Optional[int] = None,
+    high_freq_factor: int = 4,
 ):
     freqs = 1.0 / (
         theta ** (torch.arange(0, dim, 2, device="cpu")[: (dim // 2)].float() / dim)
     )
     t = torch.arange(end, device=freqs.device)  # pyre-ignore
     if use_scaled:
         assert scale_factor is not None
-        freqs = apply_scaling(freqs, scale_factor)  # pyre-ignore
+        freqs = apply_scaling(freqs, scale_factor, high_freq_factor)  # pyre-ignore
     freqs = torch.outer(t, freqs).float()
     freqs_cos = torch.cos(freqs)
     freqs_sin = torch.sin(freqs)
@@ -242,6 +242,7 @@ def __init__(self, params: ModelArgs):
                 precompute_freqs_cis,
                 use_scaled=self.params.use_scaled_rope,
                 scale_factor=self.params.rope_scale_factor,
+                high_freq_factor=self.params.high_freq_factor,
             )
             self.apply_rotary_emb = RotaryEmbedding()