Initial merged FP16 model commit

llama_rope_scaled_monkey_patch.py

@@ -0,0 +1,65 @@
+import torch
+import transformers
+import transformers.models.llama.modeling_llama
+from einops import rearrange
+import random
+
+# This monkey patch file is not needed if using ExLlama, or if using `trust_remote_code=True``
+
+class ScaledRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+        self.register_buffer("inv_freq", inv_freq)
+        
+        max_position_embeddings = 8192
+
+        # Build here to make `torch.jit.trace` work.
+        self.max_seq_len_cached = max_position_embeddings
+        t = torch.arange(
+            self.max_seq_len_cached,
+            device=self.inv_freq.device,
+            dtype=self.inv_freq.dtype,
+        )
+
+        self.scale = 1 / 4
+        t *= self.scale
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer(
+            "cos_cached", emb.cos()[None, None, :, :], persistent=False
+        )
+        self.register_buffer(
+            "sin_cached", emb.sin()[None, None, :, :], persistent=False
+        )
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
+        if seq_len > self.max_seq_len_cached:
+            self.max_seq_len_cached = seq_len
+            t = torch.arange(
+                self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype
+            )
+            t *= self.scale
+            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+            # Different from paper, but it uses a different permutation in order to obtain the same calculation
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+            self.register_buffer(
+                "cos_cached", emb.cos()[None, None, :, :], persistent=False
+            )
+            self.register_buffer(
+                "sin_cached", emb.sin()[None, None, :, :], persistent=False
+            )
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+def replace_llama_rope_with_scaled_rope():
+    transformers.models.llama.modeling_llama.LlamaRotaryEmbedding = (
+        ScaledRotaryEmbedding
+    )