add python wrapper

https://gist.github.com/abetlen/2b90e5f153f6efd00931d098de5c73ce

py/llama_cpp/llama.py

@@ -0,0 +1,173 @@
+import os
+import sys
+import glob
+import ctypes
+
+from ctypes import c_int, c_float, c_double, c_char_p, c_void_p, c_bool, POINTER, Structure
+
+
+# Load the library
+if sys.platform == 'win32':
+    lib = ctypes.cdll.LoadLibrary(next(iter(glob.glob(os.path.join(os.path.dirname(__file__), '..', '..', '**', 'llama.dll'), recursive=True))))
+else:
+    lib = ctypes.cdll.LoadLibrary(next(iter(glob.glob(os.path.join(os.path.dirname(__file__), '..', '..', '**', 'libllama.so'), recursive=True))))
+
+
+# C types
+llama_token = c_int
+llama_token_p = POINTER(llama_token)
+
+class llama_token_data(Structure):
+    _fields_ = [
+        ('id', llama_token), # token id
+        ('p', c_float), # probability of the token
+        ('plog', c_float), # log probability of the token
+    ]
+
+llama_token_data_p = POINTER(llama_token_data)
+llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p)
+class llama_context_params(Structure):
+    _fields_ = [
+        ('n_ctx', c_int), # text context
+        ('n_parts', c_int), # -1 for default
+        ('seed', c_int), # RNG seed, 0 for random
+        ('f16_kv', c_bool), # use fp16 for KV cache
+        ('logits_all', c_bool), # the llama_eval() call computes all logits, not just the last one
+        ('vocab_only', c_bool), # only load the vocabulary, no weights
+        ('use_mmap', c_bool), # use mmap if possible
+        ('use_mlock', c_bool), # force system to keep model in RAM
+        ('embedding', c_bool), # embedding mode only
+        ('progress_callback', llama_progress_callback), # called with a progress value between 0 and 1, pass NULL to disable
+        ('progress_callback_user_data', c_void_p), # context pointer passed to the progress callback
+    ]
+
+llama_context_params_p = POINTER(llama_context_params)
+
+llama_context_p = c_void_p
+
+# C functions
+lib.llama_context_default_params.argtypes = []
+lib.llama_context_default_params.restype = llama_context_params
+
+lib.llama_init_from_file.argtypes = [c_char_p, llama_context_params]
+lib.llama_init_from_file.restype = llama_context_p
+
+lib.llama_free.argtypes = [llama_context_p]
+lib.llama_free.restype = None
+
+lib.llama_model_quantize.argtypes = [c_char_p, c_char_p, c_int, c_int]
+lib.llama_model_quantize.restype = c_int
+
+lib.llama_eval.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_int]
+lib.llama_eval.restype = c_int
+
+lib.llama_tokenize.argtypes = [llama_context_p, c_char_p, llama_token_p, c_int, c_bool]
+lib.llama_tokenize.restype = c_int
+
+lib.llama_n_vocab.argtypes = [llama_context_p]
+lib.llama_n_vocab.restype = c_int
+
+lib.llama_n_ctx.argtypes = [llama_context_p]
+lib.llama_n_ctx.restype = c_int
+
+lib.llama_get_logits.argtypes = [llama_context_p]
+lib.llama_get_logits.restype = POINTER(c_float)
+
+lib.llama_get_embeddings.argtypes = [llama_context_p]
+lib.llama_get_embeddings.restype = POINTER(c_float)
+
+lib.llama_token_to_str.argtypes = [llama_context_p, llama_token]
+lib.llama_token_to_str.restype = c_char_p
+
+lib.llama_token_bos.argtypes = []
+lib.llama_token_bos.restype = llama_token
+
+lib.llama_token_eos.argtypes = []
+lib.llama_token_eos.restype = llama_token
+
+lib.llama_sample_top_p_top_k.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_float, c_float, c_float]
+lib.llama_sample_top_p_top_k.restype = llama_token
+
+lib.llama_print_timings.argtypes = [llama_context_p]
+lib.llama_print_timings.restype = None
+
+lib.llama_reset_timings.argtypes = [llama_context_p]
+lib.llama_reset_timings.restype = None
+
+lib.llama_print_system_info.argtypes = []
+lib.llama_print_system_info.restype = c_char_p
+
+# Python functions
+def llama_context_default_params() -> llama_context_params:
+    params = lib.llama_context_default_params()
+    return params
+
+def llama_init_from_file(path_model: str, params: llama_context_params) -> llama_context_p:
+    """Various functions for loading a ggml llama model.
+    Allocate (almost) all memory needed for the model.
+    Return NULL on failure """
+    return lib.llama_init_from_file(path_model.encode('utf-8'), params)
+
+def llama_free(ctx: llama_context_p):
+    """Free all allocated memory"""
+    lib.llama_free(ctx)
+
+def llama_model_quantize(fname_inp: str, fname_out: str, itype: c_int, qk: c_int) -> c_int:
+    """Returns 0 on success"""
+    return lib.llama_model_quantize(fname_inp.encode('utf-8'), fname_out.encode('utf-8'), itype, qk)
+
+def llama_eval(ctx: llama_context_p, tokens: llama_token_p, n_tokens: c_int, n_past: c_int, n_threads: c_int) -> c_int:
+    """Run the llama inference to obtain the logits and probabilities for the next token.
+    tokens + n_tokens is the provided batch of new tokens to process
+    n_past is the number of tokens to use from previous eval calls
+    Returns 0 on success"""
+    return lib.llama_eval(ctx, tokens, n_tokens, n_past, n_threads)
+
+def llama_tokenize(ctx: llama_context_p, text: str, tokens: llama_token_p, n_max_tokens: c_int, add_bos: c_bool) -> c_int:
+    """Convert the provided text into tokens.
+    The tokens pointer must be large enough to hold the resulting tokens.
+    Returns the number of tokens on success, no more than n_max_tokens
+    Returns a negative number on failure - the number of tokens that would have been returned"""
+    return lib.llama_tokenize(ctx, text.encode('utf-8'), tokens, n_max_tokens, add_bos)
+
+def llama_n_vocab(ctx: llama_context_p) -> c_int:
+    return lib.llama_n_vocab(ctx)
+
+def llama_n_ctx(ctx: llama_context_p) -> c_int:
+    return lib.llama_n_ctx(ctx)
+
+def llama_get_logits(ctx: llama_context_p):
+    """Token logits obtained from the last call to llama_eval()
+    The logits for the last token are stored in the last row
+    Can be mutated in order to change the probabilities of the next token
+    Rows: n_tokens
+    Cols: n_vocab"""
+    return lib.llama_get_logits(ctx)
+
+def llama_get_embeddings(ctx: llama_context_p):
+    """Get the embeddings for the input
+    shape: [n_embd] (1-dimensional)"""
+    return lib.llama_get_embeddings(ctx)
+
+def llama_token_to_str(ctx: llama_context_p, token: int) -> str:
+    """Token Id -> String. Uses the vocabulary in the provided context"""
+    return lib.llama_token_to_str(ctx, token).decode('utf-8')
+
+def llama_token_bos() -> llama_token:
+    return lib.llama_token_bos()
+
+def llama_token_eos() -> llama_token:
+    return lib.llama_token_eos()
+
+def llama_sample_top_p_top_k(ctx: llama_context_p, last_n_tokens_data: llama_token_p, last_n_tokens_size: c_int, top_k: c_int, top_p: c_float, temp: c_float, repeat_penalty: c_float) -> llama_token:
+    return lib.llama_sample_top_p_top_k(ctx, last_n_tokens_data, last_n_tokens_size, top_k, top_p, temp, repeat_penalty)
+
+def llama_print_timings(ctx: llama_context_p):
+    lib.llama_print_timings(ctx)
+
+def llama_reset_timings(ctx: llama_context_p):
+    lib.llama_reset_timings(ctx)
+
+def llama_print_system_info() -> str:
+    """Print system informaiton"""
+    return lib.llama_print_system_info().decode('utf-8')

setup.py

@@ -0,0 +1,15 @@
+
+from setuptools import setup, find_packages
+import glob, os
+
+setup(
+    name='llama_cpp',
+    version='0.0.1',
+    author='Anonymous',
+    author_email='',
+    license='All rights reserved',
+    packages=find_packages(where='py'),
+    package_dir={'': 'py'},
+    install_requires=[],
+    entry_points={'console_scripts': []},
+)