baalajimaestro/llama.cpp
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Fix candidates type

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This commit is contained in:
Andrei Betlen 2023-05-05 14:00:30 -04:00
parent 5e7ddfc3d6
commit 6702d2abfd
1 changed files with 11 additions and 11 deletions
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22
llama_cpp/llama_cpp.py
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@ -439,7 +439,7 @@ _lib.llama_token_nl.restype = llama_token
# @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
def llama_sample_repetition_penalty(
ctx: llama_context_p,
candidates: _Pointer[llama_token_data],
candidates: _Pointer[llama_token_data_array],
last_tokens_data: Array[llama_token],
last_tokens_size: c_int,
penalty: c_float,
@ -462,7 +462,7 @@ _lib.llama_sample_repetition_penalty.restype = None
# @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
def llama_sample_frequency_and_presence_penalties(
ctx: llama_context_p,
candidates: _Pointer[llama_token_data],
candidates: _Pointer[llama_token_data_array],
last_tokens_data: Array[llama_token],
last_tokens_size: c_int,
alpha_frequency: c_float,
@ -504,7 +504,7 @@ _lib.llama_sample_softmax.restype = None
# @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
def llama_sample_top_k(
ctx: llama_context_p,
candidates: _Pointer[llama_token_data],
candidates: _Pointer[llama_token_data_array],
k: c_int,
min_keep: c_size_t = c_size_t(1),
):
@ -523,7 +523,7 @@ _lib.llama_sample_top_k.restype = None
# @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
def llama_sample_top_p(
ctx: llama_context_p,
candidates: _Pointer[llama_token_data],
candidates: _Pointer[llama_token_data_array],
p: c_float,
min_keep: c_size_t = c_size_t(1),
):
@ -542,7 +542,7 @@ _lib.llama_sample_top_p.restype = None
# @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
def llama_sample_tail_free(
ctx: llama_context_p,
candidates: _Pointer[llama_token_data],
candidates: _Pointer[llama_token_data_array],
z: c_float,
min_keep: c_size_t = c_size_t(1),
):
@ -561,7 +561,7 @@ _lib.llama_sample_tail_free.restype = None
# @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
def llama_sample_typical(
ctx: llama_context_p,
candidates: _Pointer[llama_token_data],
candidates: _Pointer[llama_token_data_array],
p: c_float,
min_keep: c_size_t = c_size_t(1),
):
@ -578,7 +578,7 @@ _lib.llama_sample_typical.restype = None
def llama_sample_temperature(
ctx: llama_context_p, candidates: _Pointer[llama_token_data], temp: c_float
ctx: llama_context_p, candidates: _Pointer[llama_token_data_array], temp: c_float
):
return _lib.llama_sample_temperature(ctx, candidates, temp)
@ -599,7 +599,7 @@ _lib.llama_sample_temperature.restype = None
# @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.
def llama_sample_token_mirostat(
ctx: llama_context_p,
candidates: _Pointer[llama_token_data],
candidates: _Pointer[llama_token_data_array],
tau: c_float,
eta: c_float,
m: c_int,
@ -626,7 +626,7 @@ _lib.llama_sample_token_mirostat.restype = llama_token
# @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.
def llama_sample_token_mirostat_v2(
ctx: llama_context_p,
candidates: _Pointer[llama_token_data],
candidates: _Pointer[llama_token_data_array],
tau: c_float,
eta: c_float,
mu: _Pointer[c_float],
@ -646,7 +646,7 @@ _lib.llama_sample_token_mirostat_v2.restype = llama_token
# @details Selects the token with the highest probability.
def llama_sample_token_greedy(
ctx: llama_context_p, candidates: _Pointer[llama_token_data]
ctx: llama_context_p, candidates: _Pointer[llama_token_data_array]
) -> llama_token:
return _lib.llama_sample_token_greedy(ctx, candidates)
@ -660,7 +660,7 @@ _lib.llama_sample_token_greedy.restype = llama_token
# @details Randomly selects a token from the candidates based on their probabilities.
def llama_sample_token(
ctx: llama_context_p, candidates: _Pointer[llama_token_data]
ctx: llama_context_p, candidates: _Pointer[llama_token_data_array]
) -> llama_token:
return _lib.llama_sample_token(ctx, candidates)