Llama.cpp@tags/b6490

llama_cpp/__init__.py

@@ -1,4 +1,5 @@
-from .llama_cpp import *
-from .llama import *
+from .llama_cpp import *  # noqa: F401,F403
+from .llama import *  # noqa: F401,F403
+from .raw import LlamaRaw  # explicit export of thin raw API
 
 __version__ = "0.3.16"

llama_cpp/_ggml.py

@@ -2,11 +2,90 @@
 
 This module provides a minimal interface for working with ggml tensors from llama-cpp-python
 """
+import enum
 import os
 import pathlib
+import ctypes
 
 import llama_cpp._ctypes_extensions as ctypes_ext
 
 libggml_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib"
 libggml = ctypes_ext.load_shared_library("ggml", libggml_base_path)
 
+# enum ggml_log_level {
+#     GGML_LOG_LEVEL_NONE  = 0,
+#     GGML_LOG_LEVEL_DEBUG = 1,
+#     GGML_LOG_LEVEL_INFO  = 2,
+#     GGML_LOG_LEVEL_WARN  = 3,
+#     GGML_LOG_LEVEL_ERROR = 4,
+#     GGML_LOG_LEVEL_CONT  = 5, // continue previous log
+# };
+
+class GGMLLogLevel(enum.IntEnum):
+    GGML_LOG_LEVEL_NONE = 0
+    GGML_LOG_LEVEL_DEBUG = 1
+    GGML_LOG_LEVEL_INFO = 2
+    GGML_LOG_LEVEL_WARN = 3
+    GGML_LOG_LEVEL_ERROR = 4
+    GGML_LOG_LEVEL_CONT = 5 # continue previous log
+
+# // ====== ggml-opt.h ======
+
+# enum ggml_opt_build_type {
+#     GGML_OPT_BUILD_TYPE_FORWARD = 10,
+#     GGML_OPT_BUILD_TYPE_GRAD    = 20,
+#     GGML_OPT_BUILD_TYPE_OPT     = 30,
+# };
+class GGMLOptBuildType(enum.IntEnum):
+    GGML_OPT_BUILD_TYPE_FORWARD = 10
+    GGML_OPT_BUILD_TYPE_GRAD = 20
+    GGML_OPT_BUILD_TYPE_OPT = 30
+
+
+# // built-in loss types, i.e. the built-in quantities minimized by the optimizer
+# // custom loss types can be defined via mean or sum which simply reduce the outputs for all datapoints to a single value
+# enum ggml_opt_loss_type {
+#     GGML_OPT_LOSS_TYPE_MEAN,
+#     GGML_OPT_LOSS_TYPE_SUM,
+#     GGML_OPT_LOSS_TYPE_CROSS_ENTROPY,
+#     GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR,
+# };
+class GGMLOptLossType(enum.IntEnum):
+    GGML_OPT_LOSS_TYPE_MEAN = 0
+    GGML_OPT_LOSS_TYPE_SUM = 1
+    GGML_OPT_LOSS_TYPE_CROSS_ENTROPY = 2
+    GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR = 3
+
+
+# // parameters that control which optimizer is used and how said optimizer tries to find the minimal loss
+# struct ggml_opt_optimizer_params {
+#     // AdamW optimizer parameters
+#     struct {
+#         float alpha; // learning rate
+#         float beta1;
+#         float beta2;
+#         float eps;   // epsilon for numerical stability
+#         float wd;    // weight decay for AdamW, use 0.0f to disable
+#     } adamw;
+# };
+class ggml_opt_adamw_params(ctypes.Structure):
+    _fields_ = [
+        ('alpha', ctypes.c_float), # learning rate
+        ('beta1', ctypes.c_float),
+        ('beta2', ctypes.c_float),
+        ('eps',   ctypes.c_float), # epsilon for numerical stability
+        ('wd',    ctypes.c_float), # weight decay for AdamW, use 0.0f to disable
+    ]
+
+class ggml_opt_optimizer_params(ctypes.Structure):
+    _fields_ = [
+        ('adamw', ggml_opt_adamw_params), # Nested AdamW parameters
+    ]
+
+
+# // callback to calculate optimizer parameters prior to a backward pass
+# // userdata can be used to pass arbitrary data
+# typedef struct ggml_opt_optimizer_params (*ggml_opt_get_optimizer_params)(void * userdata);
+ggml_opt_get_optimizer_params = ctypes.CFUNCTYPE(
+    ctypes.POINTER(ggml_opt_optimizer_params), ctypes.c_void_p
+)