update python bindings
This commit is contained in:
xaedes
parent
ed6b64fb98
commit
6e88dc93bd
1 changed files with 178 additions and 42 deletions
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@ -3,7 +3,7 @@ import sys
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import glob
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import ctypes
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from ctypes import c_int, c_float, c_double, c_char_p, c_void_p, c_bool, POINTER, Structure
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from ctypes import c_int, c_float, c_double, c_char_p, c_void_p, c_bool, c_size_t, c_ubyte, POINTER, Structure
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# Load the library
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@ -19,36 +19,58 @@ llama_token_p = POINTER(llama_token)
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class llama_token_data(Structure):
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_fields_ = [
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('id', llama_token), # token id
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('p', c_float), # probability of the token
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('id', llama_token), # token id
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('p', c_float), # probability of the token
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('plog', c_float), # log probability of the token
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]
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llama_token_data_p = POINTER(llama_token_data)
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class llama_token_data_array(Structure):
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_fields_ = [
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('data', llama_token_data_p),
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('size', c_size_t),
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('sorted', c_bool),
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]
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llama_token_data_array_p = POINTER(llama_token_data_array)
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llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p)
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class llama_context_params(Structure):
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_fields_ = [
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('n_ctx', c_int), # text context
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('n_parts', c_int), # -1 for default
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('seed', c_int), # RNG seed, 0 for random
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('f16_kv', c_bool), # use fp16 for KV cache
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('logits_all', c_bool), # the llama_eval() call computes all logits, not just the last one
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('vocab_only', c_bool), # only load the vocabulary, no weights
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('use_mmap', c_bool), # use mmap if possible
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('use_mlock', c_bool), # force system to keep model in RAM
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('embedding', c_bool), # embedding mode only
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('progress_callback', llama_progress_callback), # called with a progress value between 0 and 1, pass NULL to disable
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('progress_callback_user_data', c_void_p), # context pointer passed to the progress callback
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('n_ctx', c_int), # text context
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('n_parts', c_int), # -1 for default
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('n_gpu_layers', c_int), # number of layers to store in VRAM
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('seed', c_int), # RNG seed, 0 for random
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('f16_kv', c_bool), # use fp16 for KV cache
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('logits_all', c_bool), # the llama_eval() call computes all logits, not just the last one
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('vocab_only', c_bool), # only load the vocabulary, no weights
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('use_mmap', c_bool), # use mmap if possible
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('use_mlock', c_bool), # force system to keep model in RAM
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('embedding', c_bool), # embedding mode only
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('progress_callback', llama_progress_callback), # called with a progress value between 0 and 1, pass NULL to disable
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('progress_callback_user_data', c_void_p), # context pointer passed to the progress callback
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]
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llama_context_params_p = POINTER(llama_context_params)
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llama_context_p = c_void_p
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c_size_p = POINTER(c_size_t)
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c_ubyte_p = POINTER(c_ubyte)
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c_float_p = POINTER(c_float)
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# C functions
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lib.llama_context_default_params.argtypes = []
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lib.llama_context_default_params.restype = llama_context_params
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lib.llama_mmap_supported.argtypes = []
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lib.llama_mmap_supported.restype = c_bool
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lib.llama_mlock_supported.argtypes = []
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lib.llama_mlock_supported.restype = c_bool
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lib.llama_init_from_file.argtypes = [c_char_p, llama_context_params]
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lib.llama_init_from_file.restype = llama_context_p
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@ -58,6 +80,30 @@ lib.llama_free.restype = None
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lib.llama_model_quantize.argtypes = [c_char_p, c_char_p, c_int, c_int]
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lib.llama_model_quantize.restype = c_int
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lib.llama_apply_lora_from_file.argtypes = [llama_context_p, c_char_p, c_char_p, c_int]
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lib.llama_apply_lora_from_file.restype = c_int
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lib.llama_get_kv_cache_token_count.argtypes = [llama_context_p]
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lib.llama_get_kv_cache_token_count.restype = c_int
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lib.llama_set_rng_seed.argtypes = [llama_context_p, c_int]
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lib.llama_set_rng_seed.restype = None
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lib.llama_get_state_size.argtypes = [llama_context_p]
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lib.llama_get_state_size.restype = c_size_t
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lib.llama_copy_state_data.argtypes = [llama_context_p, c_ubyte_p]
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lib.llama_copy_state_data.restype = c_size_t
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lib.llama_set_state_data.argtypes = [llama_context_p, c_ubyte_p]
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lib.llama_set_state_data.restype = c_size_t
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lib.llama_load_session_file.argtypes = [llama_context_p, c_char_p, llama_token_p, c_size_t, c_size_p]
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lib.llama_load_session_file.restype = c_bool
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lib.llama_save_session_file.argtypes = [llama_context_p, c_char_p, llama_token_p, c_size_t]
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lib.llama_save_session_file.restype = c_bool
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lib.llama_eval.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_int]
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lib.llama_eval.restype = c_int
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@ -70,11 +116,14 @@ lib.llama_n_vocab.restype = c_int
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lib.llama_n_ctx.argtypes = [llama_context_p]
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lib.llama_n_ctx.restype = c_int
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lib.llama_n_embd.argtypes = [llama_context_p]
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lib.llama_n_embd.restype = c_int
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lib.llama_get_logits.argtypes = [llama_context_p]
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lib.llama_get_logits.restype = POINTER(c_float)
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lib.llama_get_logits.restype = c_float_p
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lib.llama_get_embeddings.argtypes = [llama_context_p]
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lib.llama_get_embeddings.restype = POINTER(c_float)
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lib.llama_get_embeddings.restype = c_float_p
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lib.llama_token_to_str.argtypes = [llama_context_p, llama_token]
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lib.llama_token_to_str.restype = c_char_p
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@ -85,8 +134,44 @@ lib.llama_token_bos.restype = llama_token
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lib.llama_token_eos.argtypes = []
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lib.llama_token_eos.restype = llama_token
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lib.llama_sample_top_p_top_k.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_float, c_float, c_float]
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lib.llama_sample_top_p_top_k.restype = llama_token
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lib.llama_token_nl.argtypes = []
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lib.llama_token_nl.restype = llama_token
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lib.llama_sample_repetition_penalty.argtypes = [llama_context_p, llama_token_data_array_p, llama_token_p, c_size_t, c_float]
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lib.llama_sample_repetition_penalty.restype = None
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lib.llama_sample_frequency_and_presence_penalties.argtypes = [llama_context_p, llama_token_data_array_p, llama_token_p, c_size_t, c_float, c_float]
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lib.llama_sample_frequency_and_presence_penalties.restype = None
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lib.llama_sample_softmax.argtypes = [llama_context_p, llama_token_data_array_p]
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lib.llama_sample_softmax.restype = None
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lib.llama_sample_top_k.argtypes = [llama_context_p, llama_token_data_array_p, c_int, c_size_t]
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lib.llama_sample_top_k.restype = None
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lib.llama_sample_top_p.argtypes = [llama_context_p, llama_token_data_array_p, c_float, c_size_t]
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lib.llama_sample_top_p.restype = None
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lib.llama_sample_tail_free.argtypes = [llama_context_p, llama_token_data_array_p, c_float, c_size_t]
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lib.llama_sample_tail_free.restype = None
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lib.llama_sample_typical.argtypes = [llama_context_p, llama_token_data_array_p, c_float, c_size_t]
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lib.llama_sample_typical.restype = None
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lib.llama_sample_temperature.argtypes = [llama_context_p, llama_token_data_array_p, c_float]
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lib.llama_sample_temperature.restype = None
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lib.llama_sample_token_mirostat.argtypes = [llama_context_p, llama_token_data_array_p, c_float, c_float, c_int, c_float_p]
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lib.llama_sample_token_mirostat.restype = llama_token
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lib.llama_sample_token_mirostat_v2.argtypes = [llama_context_p, llama_token_data_array_p, c_float, c_float, c_float_p]
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lib.llama_sample_token_mirostat_v2.restype = llama_token
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lib.llama_sample_token_greedy.argtypes = [llama_context_p, llama_token_data_array_p]
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lib.llama_sample_token_greedy.restype = llama_token
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lib.llama_sample_token.argtypes = [llama_context_p, llama_token_data_array_p]
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lib.llama_sample_token.restype = llama_token
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lib.llama_print_timings.argtypes = [llama_context_p]
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lib.llama_print_timings.restype = None
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@ -97,20 +182,18 @@ lib.llama_reset_timings.restype = None
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lib.llama_print_system_info.argtypes = []
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lib.llama_print_system_info.restype = c_char_p
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lib.llama_get_state_size.argtypes = [llama_context_p]
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lib.llama_get_state_size.restype = c_size_t
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lib.llama_copy_state_data.argtypes = [llama_context_p, c_ubyte_p]
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lib.llama_copy_state_data.restype = c_size_t
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lib.llama_set_state_data.argtypes = [llama_context_p, c_ubyte_p]
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lib.llama_set_state_data.restype = c_size_t
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# Python functions
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def llama_context_default_params() -> llama_context_params:
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params = lib.llama_context_default_params()
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return params
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def llama_mmap_supported() -> bool:
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return lib.llama_mmap_supported()
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def llama_mlock_supported() -> bool:
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return lib.llama_mlock_supported()
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def llama_init_from_file(path_model: str, params: llama_context_params) -> llama_context_p:
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"""Various functions for loading a ggml llama model.
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Allocate (almost) all memory needed for the model.
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@ -125,6 +208,30 @@ def llama_model_quantize(fname_inp: str, fname_out: str, itype: c_int, qk: c_int
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"""Returns 0 on success"""
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return lib.llama_model_quantize(fname_inp.encode('utf-8'), fname_out.encode('utf-8'), itype, qk)
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def llama_apply_lora_from_file(ctx: llama_context_p, path_lora: str, path_base_model: str, n_threads: c_int) -> c_int:
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return lib.llama_apply_lora_from_file(ctx, path_lora.encode('utf-8'), path_base_model.encode('utf-8'), n_threads)
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def llama_get_kv_cache_token_count(ctx: llama_context_p) -> c_int:
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return lib.llama_get_kv_cache_token_count(ctx)
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def llama_set_rng_seed(ctx: llama_context_p, seed: c_int):
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return lib.llama_set_rng_seed(ctx, seed)
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def llama_get_state_size(ctx: llama_context_p) -> c_size_t:
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return lib.llama_get_state_size(ctx)
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def llama_copy_state_data(ctx: llama_context_p, dst: c_ubyte_p) -> c_size_t:
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return lib.llama_copy_state_data(ctx, dst)
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def llama_set_state_data(ctx: llama_context_p, src: c_ubyte_p) -> c_size_t:
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return lib.llama_set_state_data(ctx, src)
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def llama_load_session_file(ctx: llama_context_p, path_session: str, tokens_out: llama_token_p, n_token_capacity: c_size_t, n_token_count_out: c_size_p) -> c_bool:
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return lib.llama_load_session_file(ctx, path_session.encode('utf-8'), tokens_out, n_token_capacity, n_token_count_out)
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def llama_save_session_file(ctx: llama_context_p, path_session: str, tokens: llama_token_p, n_token_count: c_size_t) -> c_bool:
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return lib.llama_save_session_file(ctx, path_session.encode('utf-8'), tokens, n_token_count)
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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:
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"""Run the llama inference to obtain the logits and probabilities for the next token.
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tokens + n_tokens is the provided batch of new tokens to process
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@ -145,7 +252,10 @@ def llama_n_vocab(ctx: llama_context_p) -> c_int:
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def llama_n_ctx(ctx: llama_context_p) -> c_int:
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return lib.llama_n_ctx(ctx)
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def llama_get_logits(ctx: llama_context_p):
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def llama_n_embd(ctx: llama_context_p) -> c_int:
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return lib.llama_n_embd(ctx)
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def llama_get_logits(ctx: llama_context_p) -> c_float_p:
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"""Token logits obtained from the last call to llama_eval()
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The logits for the last token are stored in the last row
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Can be mutated in order to change the probabilities of the next token
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@ -153,7 +263,7 @@ def llama_get_logits(ctx: llama_context_p):
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Cols: n_vocab"""
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return lib.llama_get_logits(ctx)
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def llama_get_embeddings(ctx: llama_context_p):
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def llama_get_embeddings(ctx: llama_context_p) -> c_float_p:
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"""Get the embeddings for the input
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shape: [n_embd] (1-dimensional)"""
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return lib.llama_get_embeddings(ctx)
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@ -168,8 +278,44 @@ def llama_token_bos() -> llama_token:
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def llama_token_eos() -> llama_token:
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return lib.llama_token_eos()
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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:
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return lib.llama_sample_top_p_top_k(ctx, last_n_tokens_data, last_n_tokens_size, top_k, top_p, temp, repeat_penalty)
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def llama_token_nl() -> llama_token:
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return lib.llama_token_nl()
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def llama_sample_repetition_penalty(ctx: llama_context_p, candidates: llama_token_data_array_p, last_tokens: llama_token_p, last_tokens_size: c_size_t, penalty: float):
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lib.llama_sample_repetition_penalty(ctx, candidates, last_tokens, last_tokens_size, penalty)
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def llama_sample_frequency_and_presence_penalties(ctx: llama_context_p, candidates: llama_token_data_array_p, last_tokens: llama_token_p, last_tokens_size: c_size_t, alpha_frequency: float, alpha_presence: float):
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lib.llama_sample_frequency_and_presence_penalties(ctx, candidates, last_tokens, last_tokens_size, alpha_frequency, alpha_presence)
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def llama_sample_softmax(ctx: llama_context_p, candidates: llama_token_data_array_p):
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lib.llama_sample_softmax(ctx, candidates)
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def llama_sample_top_k(ctx: llama_context_p, candidates: llama_token_data_array_p, k: c_int, min_keep: c_size_t):
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lib.llama_sample_top_k(ctx, candidates, k, min_keep)
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def llama_sample_top_p(ctx: llama_context_p, candidates: llama_token_data_array_p, p: float, min_keep: c_size_t):
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lib.llama_sample_top_p(ctx, candidates, c_float(p), c_size_t(min_keep))
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def llama_sample_tail_free(ctx: llama_context_p, candidates: llama_token_data_array_p, z: float, min_keep: c_size_t):
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lib.llama_sample_tail_free(ctx, candidates, z, min_keep)
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def llama_sample_typical(ctx: llama_context_p, candidates: llama_token_data_array_p, p: float, min_keep: c_size_t):
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lib.llama_sample_typical(ctx, candidates, p, min_keep)
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def llama_sample_temperature(ctx: llama_context_p, candidates: llama_token_data_array_p, temp: float):
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lib.llama_sample_temperature(ctx, candidates, temp)
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def llama_sample_token_mirostat(ctx: llama_context_p, candidates: llama_token_data_array_p, tau: float, eta: float, m: c_int, mu: c_float_p) -> llama_token:
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return lib.llama_sample_token_mirostat(ctx, candidates, tau, eta, m, mu)
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def llama_sample_token_mirostat_v2(ctx: llama_context_p, candidates: llama_token_data_array_p, tau: float, eta: float, mu: c_float_p) -> llama_token:
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return lib.llama_sample_token_mirostat_v2(ctx, candidates, tau, eta, mu)
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def llama_sample_token_greedy(ctx: llama_context_p, candidates: llama_token_data_array_p) -> llama_token:
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return lib.llama_sample_token_greedy(ctx, candidates)
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def llama_sample_token(ctx: llama_context_p, candidates: llama_token_data_array_p) -> llama_token:
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return lib.llama_sample_token(ctx, candidates)
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def llama_print_timings(ctx: llama_context_p):
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lib.llama_print_timings(ctx)
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@ -177,15 +323,5 @@ def llama_print_timings(ctx: llama_context_p):
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def llama_reset_timings(ctx: llama_context_p):
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lib.llama_reset_timings(ctx)
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def llama_print_system_info() -> str:
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"""Print system informaiton"""
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return lib.llama_print_system_info().decode('utf-8')
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def llama_get_state_size(ctx: llama_context_p) -> c_size_t:
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return lib.llama_get_state_size(ctx)
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def llama_copy_state_data(ctx: llama_context_p, dst: c_ubyte_p) -> c_size_t:
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return lib.llama_copy_state_data(ctx, dst)
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def llama_set_state_data(ctx: llama_context_p, src: c_ubyte_p) -> c_size_t:
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return lib.llama_set_state_data(ctx, src)
|
||||
def llama_print_system_info() -> c_char_p:
|
||||
return lib.llama_print_system_info()
|
||||
Loading…
Add table
Add a link
Reference in a new issue