Update llama.cpp

Fix build examples

Exclude examples directory

Revert cmake changes

Try actions/checkout@v4

Try to update submodules

Revert

.github/workflows/test.yaml

@@ -17,7 +17,7 @@ jobs:
         python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           submodules: "true"
       - name: Set up Python ${{ matrix.python-version }}

llama_cpp/llama.py

@@ -230,8 +230,14 @@ def __init__(
         n_batch: int = 512,
         n_threads: Optional[int] = None,
         n_threads_batch: Optional[int] = None,
+        rope_scaling_type: Optional[int] = llama_cpp.LLAMA_ROPE_SCALING_UNSPECIFIED,
         rope_freq_base: float = 0.0,
         rope_freq_scale: float = 0.0,
+        yarn_ext_factor: float = float("nan"),
+        yarn_attn_factor: float = 1.0,
+        yarn_beta_fast: float = 32.0,
+        yarn_beta_slow: float = 1.0,
+        yarn_orig_ctx: int = 0,
         mul_mat_q: bool = True,
         f16_kv: bool = True,
         logits_all: bool = False,
@@ -255,30 +261,30 @@ def __init__(
 
         Args:
             model_path: Path to the model.
-            seed: Random seed. -1 for random.
-            n_ctx: Maximum context size.
-            n_batch: Maximum number of prompt tokens to batch together when calling llama_eval.
             n_gpu_layers: Number of layers to offload to GPU (-ngl). If -1, all layers are offloaded.
-            main_gpu: Main GPU to use.
-            tensor_split: Optional list of floats to split the model across multiple GPUs. If None, the model is not split.
+            main_gpu: The GPU that is used for scratch and small tensors.
+            tensor_split: How split tensors should be distributed across GPUs. If None, the model is not split.
+            vocab_only: Only load the vocabulary no weights.
+            use_mmap: Use mmap if possible.
+            use_mlock: Force the system to keep the model in RAM.
+            seed: Random seed. -1 for random.
+            n_ctx: Context size.
+            n_batch: Batch size for prompt processing (must be >= 32 to use BLAS)
+            n_threads: Number of threads to use. If None, the number of threads is automatically determined.
+            n_threads_batch: Number of threads to use for batch processing. If None, use n_threads.
+            rope_scaling_type: Type of rope scaling to use.
             rope_freq_base: Base frequency for rope sampling.
             rope_freq_scale: Scale factor for rope sampling.
-            low_vram: Use low VRAM mode.
             mul_mat_q: if true, use experimental mul_mat_q kernels
             f16_kv: Use half-precision for key/value cache.
             logits_all: Return logits for all tokens, not just the last token.
-            vocab_only: Only load the vocabulary no weights.
-            use_mmap: Use mmap if possible.
-            use_mlock: Force the system to keep the model in RAM.
             embedding: Embedding mode only.
-            n_threads: Number of threads to use. If None, the number of threads is automatically determined.
             last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque.
             lora_base: Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.
             lora_path: Path to a LoRA file to apply to the model.
             numa: Enable NUMA support. (NOTE: The initial value of this parameter is used for the remainder of the program as this value is set in llama_backend_init)
             chat_format: String specifying the chat format to use when calling create_chat_completion.
             verbose: Print verbose output to stderr.
-            kwargs: Unused keyword arguments (for additional backwards compatibility).
 
         Raises:
             ValueError: If the model path does not exist.
@@ -332,12 +338,30 @@ def __init__(
         self.context_params.n_batch = self.n_batch
         self.context_params.n_threads = self.n_threads
         self.context_params.n_threads_batch = self.n_threads_batch
+        self.context_params.rope_scaling_type = (
+            rope_scaling_type if rope_scaling_type is not None else llama_cpp.LLAMA_ROPE_SCALING_UNSPECIFIED
+        )
         self.context_params.rope_freq_base = (
             rope_freq_base if rope_freq_base != 0.0 else 0
         )
         self.context_params.rope_freq_scale = (
             rope_freq_scale if rope_freq_scale != 0.0 else 0
         )
+        self.context_params.yarn_ext_factor = (
+            yarn_ext_factor if yarn_ext_factor != 0.0 else 0
+        )
+        self.context_params.yarn_attn_factor = (
+            yarn_attn_factor if yarn_attn_factor != 0.0 else 0
+        )
+        self.context_params.yarn_beta_fast = (
+            yarn_beta_fast if yarn_beta_fast != 0.0 else 0
+        )
+        self.context_params.yarn_beta_slow = (
+            yarn_beta_slow if yarn_beta_slow != 0.0 else 0
+        )
+        self.context_params.yarn_orig_ctx = (
+            yarn_orig_ctx if yarn_orig_ctx != 0 else 0
+        )
         self.context_params.mul_mat_q = mul_mat_q
         self.context_params.f16_kv = f16_kv
         self.context_params.logits_all = logits_all
@@ -1671,8 +1695,14 @@ def __getstate__(self):
             n_batch=self.n_batch,
             n_threads=self.context_params.n_threads,
             n_threads_batch=self.context_params.n_threads_batch,
+            rope_scaling_type=self.context_params.rope_scaling_type,
             rope_freq_base=self.context_params.rope_freq_base,
             rope_freq_scale=self.context_params.rope_freq_scale,
+            yarn_ext_factor=self.context_params.yarn_ext_factor,
+            yarn_attn_factor=self.context_params.yarn_attn_factor,
+            yarn_beta_fast=self.context_params.yarn_beta_fast,
+            yarn_beta_slow=self.context_params.yarn_beta_slow,
+            yarn_orig_ctx=self.context_params.yarn_orig_ctx,
             mul_mat_q=self.context_params.mul_mat_q,
             f16_kv=self.context_params.f16_kv,
             logits_all=self.context_params.logits_all,
@@ -1709,6 +1739,12 @@ def __setstate__(self, state):
             n_threads_batch=state["n_threads_batch"],
             rope_freq_base=state["rope_freq_base"],
             rope_freq_scale=state["rope_freq_scale"],
+            rope_scaling_type=state["rope_scaling_type"],
+            yarn_ext_factor=state["yarn_ext_factor"],
+            yarn_attn_factor=state["yarn_attn_factor"],
+            yarn_beta_fast=state["yarn_beta_fast"],
+            yarn_beta_slow=state["yarn_beta_slow"],
+            yarn_orig_ctx=state["yarn_orig_ctx"],
             mul_mat_q=state["mul_mat_q"],
             f16_kv=state["f16_kv"],
             logits_all=state["logits_all"],

llama_cpp/llama_cpp.py

@@ -192,6 +192,18 @@ def _load_shared_library(lib_base_name: str):
 LLAMA_FTYPE_MOSTLY_Q6_K = 18
 LLAMA_FTYPE_GUESSED = 1024
 
+# enum llama_rope_scaling_type {
+#     LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
+#     LLAMA_ROPE_SCALING_NONE        = 0,
+#     LLAMA_ROPE_SCALING_LINEAR      = 1,
+#     LLAMA_ROPE_SCALING_YARN        = 2,
+#     LLAMA_ROPE_SCALING_MAX_VALUE   = LLAMA_ROPE_SCALING_YARN,
+# };
+LLAMA_ROPE_SCALING_UNSPECIFIED = -1
+LLAMA_ROPE_SCALING_NONE = 0
+LLAMA_ROPE_SCALING_LINEAR = 1
+LLAMA_ROPE_SCALING_YARN = 2
+LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN
 
 # typedef struct llama_token_data {
 #     llama_token id; // token id
@@ -308,10 +320,16 @@ class llama_model_params(Structure):
 #     uint32_t n_batch;         // prompt processing maximum batch size
 #     uint32_t n_threads;       // number of threads to use for generation
 #     uint32_t n_threads_batch; // number of threads to use for batch processing
+#     int8_t   rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
 
 #     // ref: https://github.com/ggerganov/llama.cpp/pull/2054
-#     float rope_freq_base;  // RoPE base frequency, 0 = from model
-#     float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
+#     float    rope_freq_base;   // RoPE base frequency, 0 = from model
+#     float    rope_freq_scale;  // RoPE frequency scaling factor, 0 = from model
+#     float    yarn_ext_factor;  // YaRN extrapolation mix factor, NaN = from model
+#     float    yarn_attn_factor; // YaRN magnitude scaling factor
+#     float    yarn_beta_fast;   // YaRN low correction dim
+#     float    yarn_beta_slow;   // YaRN high correction dim
+#     uint32_t yarn_orig_ctx;    // YaRN original context size
 
 
 #     // Keep the booleans together to avoid misalignment during copy-by-value.
@@ -327,8 +345,14 @@ class llama_context_params(Structure):
         ("n_batch", c_uint32),
         ("n_threads", c_uint32),
         ("n_threads_batch", c_uint32),
+        ("rope_scaling_type", c_int8),
         ("rope_freq_base", c_float),
         ("rope_freq_scale", c_float),
+        ("yarn_ext_factor", c_float),
+        ("yarn_attn_factor", c_float),
+        ("yarn_beta_fast", c_float),
+        ("yarn_beta_slow", c_float),
+        ("yarn_orig_ctx", c_uint32),
         ("mul_mat_q", c_bool),
         ("f16_kv", c_bool),
         ("logits_all", c_bool),

llama_cpp/server/app.py

@@ -41,11 +41,7 @@ class Settings(BaseSettings):
         default=None,
         description="The alias of the model to use for generating completions.",
     )
-    seed: int = Field(default=llama_cpp.LLAMA_DEFAULT_SEED, description="Random seed. -1 for random.")
-    n_ctx: int = Field(default=2048, ge=1, description="The context size.")
-    n_batch: int = Field(
-        default=512, ge=1, description="The batch size to use per eval."
-    )
+    # Model Params
     n_gpu_layers: int = Field(
         default=0,
         ge=-1,
@@ -60,17 +56,6 @@ class Settings(BaseSettings):
         default=None,
         description="Split layers across multiple GPUs in proportion.",
     )
-    rope_freq_base: float = Field(
-        default=0.0, description="RoPE base frequency"
-    )
-    rope_freq_scale: float = Field(
-        default=0.0, description="RoPE frequency scaling factor"
-    )
-    mul_mat_q: bool = Field(
-        default=True, description="if true, use experimental mul_mat_q kernels"
-    )
-    f16_kv: bool = Field(default=True, description="Whether to use f16 key/value.")
-    logits_all: bool = Field(default=True, description="Whether to return logits.")
     vocab_only: bool = Field(
         default=False, description="Whether to only return the vocabulary."
     )
@@ -82,17 +67,59 @@ class Settings(BaseSettings):
         default=llama_cpp.llama_mlock_supported(),
         description="Use mlock.",
     )
-    embedding: bool = Field(default=True, description="Whether to use embeddings.")
+    # Context Params
+    seed: int = Field(default=llama_cpp.LLAMA_DEFAULT_SEED, description="Random seed. -1 for random.")
+    n_ctx: int = Field(default=2048, ge=1, description="The context size.")
+    n_batch: int = Field(
+        default=512, ge=1, description="The batch size to use per eval."
+    )
     n_threads: int = Field(
         default=max(multiprocessing.cpu_count() // 2, 1),
         ge=1,
         description="The number of threads to use.",
     )
+    n_threads_batch: int = Field(
+        default=max(multiprocessing.cpu_count() // 2, 1),
+        ge=0,
+        description="The number of threads to use when batch processing.",
+    )
+    rope_scaling_type: int = Field(
+        default=llama_cpp.LLAMA_ROPE_SCALING_UNSPECIFIED
+    )
+    rope_freq_base: float = Field(
+        default=0.0, description="RoPE base frequency"
+    )
+    rope_freq_scale: float = Field(
+        default=0.0, description="RoPE frequency scaling factor"
+    )
+    yarn_ext_factor: float = Field(
+        default=float("nan")
+    )
+    yarn_attn_factor: float = Field(
+        default=1.0
+    )
+    yarn_beta_fast: float = Field(
+        default=32.0
+    )
+    yarn_beta_slow: float = Field(
+        default=1.0
+    )
+    yarn_orig_ctx: int = Field(
+        default=0
+    )
+    mul_mat_q: bool = Field(
+        default=True, description="if true, use experimental mul_mat_q kernels"
+    )
+    f16_kv: bool = Field(default=True, description="Whether to use f16 key/value.")
+    logits_all: bool = Field(default=True, description="Whether to return logits.")
+    embedding: bool = Field(default=True, description="Whether to use embeddings.")
+    # Sampling Params
     last_n_tokens_size: int = Field(
         default=64,
         ge=0,
         description="Last n tokens to keep for repeat penalty calculation.",
     )
+    # LoRA Params
     lora_base: Optional[str] = Field(
         default=None,
         description="Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model."
@@ -101,14 +128,17 @@ class Settings(BaseSettings):
         default=None,
         description="Path to a LoRA file to apply to the model.",
     )
+    # Backend Params
     numa: bool = Field(
         default=False,
         description="Enable NUMA support.",
     )
+    # Chat Format Params
     chat_format: str = Field(
         default="llama-2",
         description="Chat format to use.",
     )
+    # Cache Params
     cache: bool = Field(
         default=False,
         description="Use a cache to reduce processing times for evaluated prompts.",
@@ -121,9 +151,11 @@ class Settings(BaseSettings):
         default=2 << 30,
         description="The size of the cache in bytes. Only used if cache is True.",
     )
+    # Misc
     verbose: bool = Field(
         default=True, description="Whether to print debug information."
     )
+    # Server Params
     host: str = Field(default="localhost", description="Listen address")
     port: int = Field(default=8000, description="Listen port")
     interrupt_requests: bool = Field(
@@ -345,27 +377,41 @@ def create_app(settings: Optional[Settings] = None):
     global llama
     llama = llama_cpp.Llama(
         model_path=settings.model,
-        seed=settings.seed,
-        n_ctx=settings.n_ctx,
-        n_batch=settings.n_batch,
+        # Model Params
         n_gpu_layers=settings.n_gpu_layers,
         main_gpu=settings.main_gpu,
         tensor_split=settings.tensor_split,
+        vocab_only=settings.vocab_only,
+        use_mmap=settings.use_mmap,
+        use_mlock=settings.use_mlock,
+        # Context Params
+        seed=settings.seed,
+        n_ctx=settings.n_ctx,
+        n_batch=settings.n_batch,
+        n_threads=settings.n_threads,
+        n_threads_batch=settings.n_threads_batch,
+        rope_scaling_type=settings.rope_scaling_type,
         rope_freq_base=settings.rope_freq_base,
         rope_freq_scale=settings.rope_freq_scale,
+        yarn_ext_factor=settings.yarn_ext_factor,
+        yarn_attn_factor=settings.yarn_attn_factor,
+        yarn_beta_fast=settings.yarn_beta_fast,
+        yarn_beta_slow=settings.yarn_beta_slow,
+        yarn_orig_ctx=settings.yarn_orig_ctx,
         mul_mat_q=settings.mul_mat_q,
         f16_kv=settings.f16_kv,
         logits_all=settings.logits_all,
-        vocab_only=settings.vocab_only,
-        use_mmap=settings.use_mmap,
-        use_mlock=settings.use_mlock,
         embedding=settings.embedding,
-        n_threads=settings.n_threads,
+        # Sampling Params
         last_n_tokens_size=settings.last_n_tokens_size,
+        # LoRA Params
         lora_base=settings.lora_base,
         lora_path=settings.lora_path,
+        # Backend Params
         numa=settings.numa,
+        # Chat Format Params
         chat_format=settings.chat_format,
+        # Misc
         verbose=settings.verbose,
     )
     if settings.cache: