gguf : add rope_freq_base parameter for CodeLlama (#2769)

convert.py

@@ -104,6 +104,8 @@ class Params:
     n_head_kv:  int
     f_norm_eps: float
 
+    f_rope_freq_base: Optional[float] = None
+
     ftype: Optional[GGMLFileType] = None
 
     # path to the directory containing the model files
@@ -194,15 +196,16 @@ class Params:
     def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
         config = json.load(open(config_path))
 
-        n_vocab    = config["vocab_size"] if "vocab_size" in config else -1
-        n_embd     = config["dim"]
-        n_layer    = config["n_layers"]
-        n_mult     = config["multiple_of"]
-        n_ctx      = 2048 if config["norm_eps"] == 1e-06 else 4096 # hack to determine LLaMA v1 vs v2
-        n_ff       = -1
-        n_head     = config["n_heads"]
-        n_head_kv  = config["n_kv_heads"] if "n_kv_heads" in config else n_head
-        f_norm_eps = config["norm_eps"]
+        n_vocab          = config["vocab_size"] if "vocab_size" in config else -1
+        n_embd           = config["dim"]
+        n_layer          = config["n_layers"]
+        n_mult           = config["multiple_of"]
+        n_ctx            = 2048 if config["norm_eps"] == 1e-06 else 4096 # hack to determine LLaMA v1 vs v2
+        n_ff             = -1
+        n_head           = config["n_heads"]
+        n_head_kv        = config["n_kv_heads"] if "n_kv_heads" in config else n_head
+        f_norm_eps       = config["norm_eps"]
+        f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
 
         if n_vocab == -1:
             n_vocab = model["tok_embeddings.weight"].shape[0]
@@ -211,15 +214,16 @@ class Params:
             n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]
 
         return Params(
-            n_vocab    = n_vocab,
-            n_embd     = n_embd,
-            n_mult     = n_mult,
-            n_layer    = n_layer,
-            n_ctx      = n_ctx,
-            n_ff       = n_ff,
-            n_head     = n_head,
-            n_head_kv  = n_head_kv,
-            f_norm_eps = f_norm_eps,
+            n_vocab          = n_vocab,
+            n_embd           = n_embd,
+            n_mult           = n_mult,
+            n_layer          = n_layer,
+            n_ctx            = n_ctx,
+            n_ff             = n_ff,
+            n_head           = n_head,
+            n_head_kv        = n_head_kv,
+            f_norm_eps       = f_norm_eps,
+            f_rope_freq_base = f_rope_freq_base,
         )
 
     @staticmethod
@@ -754,6 +758,9 @@ class OutputFile:
         self.gguf.add_head_count_kv       (params.n_head_kv)
         self.gguf.add_layer_norm_rms_eps  (params.f_norm_eps)
 
+        if params.f_rope_freq_base:
+            self.gguf.add_rope_freq_base(params.f_rope_freq_base)
+
         if params.ftype:
             self.gguf.add_file_type(params.ftype)
 

gguf.py

@@ -47,6 +47,7 @@ KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
 
 # RoPE
 KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
+KEY_ROPE_FREQ_BASE       = "{arch}.rope.freq_base"
 KEY_ROPE_SCALE_LINEAR    = "{arch}.rope.scale_linear"
 
 # tokenization
@@ -663,7 +664,10 @@ class GGUFWriter:
         self.add_uint32(
             KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count)
 
-    def add_rope_scale_linear(self, value:  float):
+    def add_rope_freq_base(self, value: float):
+        self.add_float32(KEY_ROPE_FREQ_BASE.format(arch=self.arch), value)
+
+    def add_rope_scale_linear(self, value: float):
         self.add_float32(KEY_ROPE_SCALE_LINEAR.format(arch=self.arch), value)
 
     def add_tokenizer_model(self, model: str):

llama.cpp

@@ -195,6 +195,7 @@ enum llm_kv {
     LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
 
     LLM_KV_ROPE_DIMENSION_COUNT,
+    LLM_KV_ROPE_FREQ_BASE,
     LLM_KV_ROPE_SCALE_LINEAR,
 
     LLM_KV_TOKENIZER_MODEL,
@@ -238,6 +239,7 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,   "%s.attention.layer_norm_rms_epsilon" },
 
     { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count" },
+    { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"       },
     { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"    },
 
     { LLM_KV_TOKENIZER_MODEL,               "tokenizer.ggml.model"              },
@@ -1561,12 +1563,26 @@ static void llm_load_hparams(
     hparams.n_head_kv = hparams.n_head;
     GGUF_GET_KEY(ctx, hparams.n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV));
 
-    // TODO: manually setting rope scale should override this
+    // TODO: manually setting rope freq base and scale should override this
+    // FIXME: partial fix when the param specified is not the default value, but
+    //        will not work for overriding the model value to the params default
+
+    llama_context_params defaults = llama_context_default_params();
+
+    // rope_freq_base
+    {
+        float ropebase = 10000.0f;
+        GGUF_GET_KEY(ctx, ropebase, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
+        if (ropebase != 10000.0f && rope_freq_base == defaults.rope_freq_base) {
+            rope_freq_base = ropebase;
+        }
+    }
+
     // rope_freq_scale (inverse of the kv) is optional
     {
         float ropescale = 1.0f;
         GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
-        if (ropescale != 1.0f) {
+        if (ropescale != 1.0f && rope_freq_scale == defaults.rope_freq_scale) {
             rope_freq_scale = 1.0f/ropescale;
         }
     }