Fix llama_cpp types
This commit is contained in:
Andrei Betlen
parent
b6a9a0b6ba
commit
5e7ddfc3d6
1 changed files with 33 additions and 41 deletions
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@ -8,6 +8,7 @@ from ctypes import (
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c_void_p,
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c_bool,
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POINTER,
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_Pointer, # type: ignore
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Structure,
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Array,
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c_uint8,
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@ -252,9 +253,7 @@ _lib.llama_get_state_size.restype = c_size_t
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# Copies the state to the specified destination address.
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# Destination needs to have allocated enough memory.
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# Returns the number of bytes copied
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def llama_copy_state_data(
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ctx: llama_context_p, dest # type: Array[c_uint8]
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) -> c_size_t:
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def llama_copy_state_data(ctx: llama_context_p, dest: Array[c_uint8]) -> c_size_t:
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return _lib.llama_copy_state_data(ctx, dest)
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@ -278,9 +277,9 @@ _lib.llama_set_state_data.restype = c_size_t
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def llama_load_session_file(
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ctx: llama_context_p,
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path_session: bytes,
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tokens_out, # type: Array[llama_token]
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tokens_out: Array[llama_token],
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n_token_capacity: c_size_t,
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n_token_count_out, # type: Array[c_size_t]
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n_token_count_out: _Pointer[c_size_t],
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) -> c_size_t:
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return _lib.llama_load_session_file(
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ctx, path_session, tokens_out, n_token_capacity, n_token_count_out
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@ -300,7 +299,7 @@ _lib.llama_load_session_file.restype = c_size_t
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def llama_save_session_file(
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ctx: llama_context_p,
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path_session: bytes,
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tokens, # type: Array[llama_token]
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tokens: Array[llama_token],
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n_token_count: c_size_t,
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) -> c_size_t:
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return _lib.llama_save_session_file(ctx, path_session, tokens, n_token_count)
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@ -321,7 +320,7 @@ _lib.llama_save_session_file.restype = c_size_t
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# Returns 0 on success
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def llama_eval(
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ctx: llama_context_p,
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tokens, # type: Array[llama_token]
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tokens: Array[llama_token],
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n_tokens: c_int,
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n_past: c_int,
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n_threads: c_int,
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@ -440,8 +439,8 @@ _lib.llama_token_nl.restype = llama_token
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# @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
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def llama_sample_repetition_penalty(
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ctx: llama_context_p,
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candidates, # type: Array[llama_token_data]
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last_tokens_data, # type: Array[llama_token]
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candidates: _Pointer[llama_token_data],
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last_tokens_data: Array[llama_token],
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last_tokens_size: c_int,
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penalty: c_float,
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):
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@ -463,8 +462,8 @@ _lib.llama_sample_repetition_penalty.restype = None
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# @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
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def llama_sample_frequency_and_presence_penalties(
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ctx: llama_context_p,
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candidates, # type: Array[llama_token_data]
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last_tokens_data, # type: Array[llama_token]
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candidates: _Pointer[llama_token_data],
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last_tokens_data: Array[llama_token],
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last_tokens_size: c_int,
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alpha_frequency: c_float,
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alpha_presence: c_float,
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@ -491,10 +490,7 @@ _lib.llama_sample_frequency_and_presence_penalties.restype = None
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# @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
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def llama_sample_softmax(
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ctx: llama_context_p,
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candidates # type: Array[llama_token_data]
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):
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def llama_sample_softmax(ctx: llama_context_p, candidates: _Pointer[llama_token_data]):
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return _lib.llama_sample_softmax(ctx, candidates)
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@ -507,10 +503,10 @@ _lib.llama_sample_softmax.restype = None
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# @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
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def llama_sample_top_k(
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ctx: llama_context_p,
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candidates, # type: Array[llama_token_data]
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k: c_int,
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min_keep: c_size_t = c_size_t(1)
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ctx: llama_context_p,
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candidates: _Pointer[llama_token_data],
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k: c_int,
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min_keep: c_size_t = c_size_t(1),
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):
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return _lib.llama_sample_top_k(ctx, candidates, k, min_keep)
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@ -526,10 +522,10 @@ _lib.llama_sample_top_k.restype = None
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# @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
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def llama_sample_top_p(
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ctx: llama_context_p,
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candidates, # type: Array[llama_token_data]
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p: c_float,
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min_keep: c_size_t = c_size_t(1)
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ctx: llama_context_p,
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candidates: _Pointer[llama_token_data],
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p: c_float,
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min_keep: c_size_t = c_size_t(1),
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):
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return _lib.llama_sample_top_p(ctx, candidates, p, min_keep)
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@ -546,9 +542,9 @@ _lib.llama_sample_top_p.restype = None
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# @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
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def llama_sample_tail_free(
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ctx: llama_context_p,
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candidates, # type: Array[llama_token_data]
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candidates: _Pointer[llama_token_data],
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z: c_float,
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min_keep: c_size_t = c_size_t(1)
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min_keep: c_size_t = c_size_t(1),
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):
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return _lib.llama_sample_tail_free(ctx, candidates, z, min_keep)
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@ -565,9 +561,9 @@ _lib.llama_sample_tail_free.restype = None
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# @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
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def llama_sample_typical(
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ctx: llama_context_p,
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candidates, # type: Array[llama_token_data]
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p: c_float,
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min_keep: c_size_t = c_size_t(1)
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candidates: _Pointer[llama_token_data],
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p: c_float,
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min_keep: c_size_t = c_size_t(1),
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):
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return _lib.llama_sample_typical(ctx, candidates, p, min_keep)
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@ -582,9 +578,7 @@ _lib.llama_sample_typical.restype = None
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def llama_sample_temperature(
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ctx: llama_context_p,
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candidates, # type: Array[llama_token_data]
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temp: c_float
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ctx: llama_context_p, candidates: _Pointer[llama_token_data], temp: c_float
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):
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return _lib.llama_sample_temperature(ctx, candidates, temp)
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@ -605,11 +599,11 @@ _lib.llama_sample_temperature.restype = None
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# @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
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def llama_sample_token_mirostat(
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ctx: llama_context_p,
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candidates, # type: Array[llama_token_data]
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candidates: _Pointer[llama_token_data],
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tau: c_float,
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eta: c_float,
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eta: c_float,
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m: c_int,
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mu # type: Array[c_float]
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mu: _Pointer[c_float],
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) -> llama_token:
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return _lib.llama_sample_token_mirostat(ctx, candidates, tau, eta, m, mu)
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@ -632,10 +626,10 @@ _lib.llama_sample_token_mirostat.restype = llama_token
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# @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
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def llama_sample_token_mirostat_v2(
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ctx: llama_context_p,
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candidates, # type: Array[llama_token_data]
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tau: c_float,
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candidates: _Pointer[llama_token_data],
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tau: c_float,
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eta: c_float,
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mu # type: Array[c_float]
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mu: _Pointer[c_float],
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) -> llama_token:
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return _lib.llama_sample_token_mirostat_v2(ctx, candidates, tau, eta, mu)
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@ -652,8 +646,7 @@ _lib.llama_sample_token_mirostat_v2.restype = llama_token
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# @details Selects the token with the highest probability.
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def llama_sample_token_greedy(
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ctx: llama_context_p,
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candidates # type: Array[llama_token_data]
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ctx: llama_context_p, candidates: _Pointer[llama_token_data]
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) -> llama_token:
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return _lib.llama_sample_token_greedy(ctx, candidates)
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@ -667,8 +660,7 @@ _lib.llama_sample_token_greedy.restype = llama_token
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# @details Randomly selects a token from the candidates based on their probabilities.
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def llama_sample_token(
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ctx: llama_context_p,
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candidates # type: Array[llama_token_data]
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ctx: llama_context_p, candidates: _Pointer[llama_token_data]
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) -> llama_token:
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return _lib.llama_sample_token(ctx, candidates)
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