Merge branch 'zijie/add-llama-1': Add multi-model support

- Add model registry system for dynamic model loading
- Implement LlamaForCausalLM with Llama3 RoPE scaling
- Register Qwen3ForCausalLM and Qwen2ForCausalLM
- Update ModelRunner to use get_model_class() for dynamic model selection

Tested: needle 32k test PASSED

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

nanovllm/layers/rotary_embedding.py

@@ -1,4 +1,4 @@
-from functools import lru_cache
+import math
 import torch
 from torch import nn
 
@@ -48,7 +48,102 @@ class RotaryEmbedding(nn.Module):
         return query, key
 
 
-@lru_cache(1)
+class Llama3RotaryEmbedding(nn.Module):
+    """
+    Llama 3 RoPE with special frequency scaling.
+
+    Llama 3 uses a piecewise frequency adjustment:
+    - High frequencies (short wavelengths): unchanged
+    - Low frequencies (long wavelengths): scaled down by factor
+    - Medium frequencies: smoothly interpolated
+    """
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: float,
+        factor: float,
+        low_freq_factor: float,
+        high_freq_factor: float,
+        original_max_position_embeddings: int,
+    ) -> None:
+        super().__init__()
+        self.head_size = head_size
+        assert rotary_dim == head_size
+
+        # Compute base inv_freq
+        inv_freq = 1.0 / (base ** (torch.arange(0, rotary_dim, 2, dtype=torch.float) / rotary_dim))
+
+        # Apply Llama3 scaling
+        inv_freq = self._compute_llama3_inv_freq(
+            inv_freq,
+            factor,
+            low_freq_factor,
+            high_freq_factor,
+            original_max_position_embeddings,
+        )
+
+        # Build cos/sin cache
+        t = torch.arange(max_position_embeddings, dtype=torch.float)
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos()
+        sin = freqs.sin()
+        cache = torch.cat((cos, sin), dim=-1).unsqueeze_(1)
+        self.register_buffer("cos_sin_cache", cache, persistent=False)
+
+    def _compute_llama3_inv_freq(
+        self,
+        inv_freq: torch.Tensor,
+        factor: float,
+        low_freq_factor: float,
+        high_freq_factor: float,
+        original_max_position_embeddings: int,
+    ) -> torch.Tensor:
+        """
+        Apply Llama3 frequency scaling.
+
+        - wavelength > low_freq_wavelen: scale down by factor (long range, needs interpolation)
+        - wavelength < high_freq_wavelen: keep unchanged (short range, high fidelity)
+        - in between: smooth interpolation
+        """
+        old_context_len = original_max_position_embeddings
+
+        low_freq_wavelen = old_context_len / low_freq_factor
+        high_freq_wavelen = old_context_len / high_freq_factor
+
+        wavelen = 2 * math.pi / inv_freq
+
+        # Low frequency: scale down by factor
+        inv_freq_llama = torch.where(wavelen > low_freq_wavelen, inv_freq / factor, inv_freq)
+
+        # Medium frequency: smooth interpolation
+        smooth_factor = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+        smoothed_inv_freq = (1 - smooth_factor) * inv_freq_llama + smooth_factor * inv_freq
+        is_medium_freq = (wavelen >= high_freq_wavelen) & (wavelen <= low_freq_wavelen)
+        inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
+
+        return inv_freq_llama
+
+    @torch.compile
+    def forward(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        cos_sin = self.cos_sin_cache[positions]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        query = apply_rotary_emb(query, cos, sin)
+        key = apply_rotary_emb(key, cos, sin)
+        return query, key
+
+
+# Cache for RoPE instances (keyed by hashable parameters)
+_rope_cache: dict[tuple, nn.Module] = {}
+
+
 def get_rope(
     head_size: int,
     rotary_dim: int,
@@ -56,6 +151,42 @@ def get_rope(
     base: float,
     rope_scaling: dict | None = None,
 ):
-    assert rope_scaling is None
-    rotary_emb = RotaryEmbedding(head_size, rotary_dim, max_position, base)
-    return rotary_emb
+    # Create hashable cache key
+    if rope_scaling is None:
+        cache_key = (head_size, rotary_dim, max_position, base, None)
+    else:
+        rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
+        if rope_type == "llama3":
+            cache_key = (
+                head_size, rotary_dim, max_position, base, "llama3",
+                rope_scaling["factor"],
+                rope_scaling["low_freq_factor"],
+                rope_scaling["high_freq_factor"],
+                rope_scaling["original_max_position_embeddings"],
+            )
+        else:
+            cache_key = (head_size, rotary_dim, max_position, base, rope_type)
+
+    if cache_key in _rope_cache:
+        return _rope_cache[cache_key]
+
+    if rope_scaling is None:
+        rope = RotaryEmbedding(head_size, rotary_dim, max_position, base)
+    else:
+        rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
+        if rope_type == "llama3":
+            rope = Llama3RotaryEmbedding(
+                head_size,
+                rotary_dim,
+                max_position,
+                base,
+                factor=rope_scaling["factor"],
+                low_freq_factor=rope_scaling["low_freq_factor"],
+                high_freq_factor=rope_scaling["high_freq_factor"],
+                original_max_position_embeddings=rope_scaling["original_max_position_embeddings"],
+            )
+        else:
+            raise ValueError(f"Unsupported rope_type: {rope_type}")
+
+    _rope_cache[cache_key] = rope
+    return rope