This commit is contained in:
commit
966f8cb64f
GPG key ID:
F93C394FE9BBAFD5
6 changed files with 115 additions and 28 deletions
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@ -7,6 +7,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [Unreleased]
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## [0.2.28]
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- feat: Update llama.cpp to ggerganov/llama.cpp@6efb8eb30e7025b168f3fda3ff83b9b386428ad6
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- feat: Add ability to pass in penalize_nl param by @shankinson in #1068
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- fix: print_grammar to stderr by @turian in #1052
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## [0.2.27]
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- feat: Update llama.cpp to ggerganov/llama.cpp@b3a7c20b5c035250257d2b62851c379b159c899a
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@ -1,4 +1,4 @@
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from .llama_cpp import *
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from .llama import *
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__version__ = "0.2.27"
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__version__ = "0.2.28"
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@ -1201,6 +1201,7 @@ class Llama:
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mirostat_mode: int = 0,
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mirostat_tau: float = 5.0,
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mirostat_eta: float = 0.1,
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penalize_nl: bool = True,
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logits_processor: Optional[LogitsProcessorList] = None,
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stopping_criteria: Optional[StoppingCriteriaList] = None,
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grammar: Optional[LlamaGrammar] = None,
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@ -1261,6 +1262,7 @@ class Llama:
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mirostat_eta=mirostat_eta,
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logits_processor=logits_processor,
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grammar=grammar,
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penalize_nl=penalize_nl,
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)
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if stopping_criteria is not None and stopping_criteria(
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self._input_ids, self._scores[-1, :]
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@ -104,7 +104,7 @@ LLAMA_DEFAULT_SEED = 0xFFFFFFFF
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# define LLAMA_MAX_RNG_STATE (64*1024)
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LLAMA_MAX_RNG_STATE = 64 * 1024
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#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
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# define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
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LLAMA_FILE_MAGIC_GGLA = 0x67676C61
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# define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
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@ -112,8 +112,8 @@ LLAMA_FILE_MAGIC_GGSN = 0x6767736E
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# define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
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LLAMA_SESSION_MAGIC = LLAMA_FILE_MAGIC_GGSN
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# define LLAMA_SESSION_VERSION 3
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LLAMA_SESSION_VERSION = 3
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# define LLAMA_SESSION_VERSION 4
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LLAMA_SESSION_VERSION = 4
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# struct llama_model;
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@ -179,6 +179,9 @@ LLAMA_TOKEN_TYPE_BYTE = 6
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# LLAMA_FTYPE_MOSTLY_Q5_K_S = 16, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q5_K_M = 17, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q6_K = 18, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors
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# LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
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# };
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@ -199,6 +202,9 @@ LLAMA_FTYPE_MOSTLY_Q4_K_M = 15
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LLAMA_FTYPE_MOSTLY_Q5_K_S = 16
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LLAMA_FTYPE_MOSTLY_Q5_K_M = 17
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LLAMA_FTYPE_MOSTLY_Q6_K = 18
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LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19
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LLAMA_FTYPE_MOSTLY_IQ2_XS = 20
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LLAMA_FTYPE_MOSTLY_Q2_K_S = 21
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LLAMA_FTYPE_GUESSED = 1024
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# enum llama_rope_scaling_type {
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@ -214,6 +220,14 @@ LLAMA_ROPE_SCALING_LINEAR = 1
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LLAMA_ROPE_SCALING_YARN = 2
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LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN
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# enum llama_split_mode {
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# LLAMA_SPLIT_NONE = 0, // single GPU
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# LLAMA_SPLIT_LAYER = 1, // split layers and KV across GPUs
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# LLAMA_SPLIT_ROW = 2, // split rows across GPUs
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# };
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LLAMA_SPLIT_NONE = 0
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LLAMA_SPLIT_LAYER = 1
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LLAMA_SPLIT_ROW = 2
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# typedef struct llama_token_data {
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# llama_token id; // token id
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@ -222,11 +236,12 @@ LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN
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# } llama_token_data;
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class llama_token_data(Structure):
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"""Used to store token data
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Attributes:
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id (llama_token): token id
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logit (float): log-odds of the token
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p (float): probability of the token"""
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_fields_ = [
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("id", llama_token),
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("logit", c_float),
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@ -244,11 +259,12 @@ llama_token_data_p = POINTER(llama_token_data)
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# } llama_token_data_array;
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class llama_token_data_array(Structure):
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"""Used to sample tokens given logits
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Attributes:
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data (ctypes.Array[llama_token_data]): token data
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size (int): size of the array
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sorted (bool): whether the array is sorted"""
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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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@ -303,7 +319,8 @@ class llama_batch(Structure):
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token (ctypes.Array[llama_token]): the token ids of the input (used when embd is NULL)
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embd (ctypes.Array[ctypes.c_float]): token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
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pos (ctypes.Array[ctypes.Array[llama_pos]]): the positions of the respective token in the sequence
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seq_id (ctypes.Array[ctypes.Array[llama_seq_id]]): the sequence to which the respective token belongs"""
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seq_id (ctypes.Array[ctypes.Array[llama_seq_id]]): the sequence to which the respective token belongs
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"""
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_fields_ = [
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("n_tokens", c_int32),
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@ -318,6 +335,7 @@ class llama_batch(Structure):
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("all_seq_id", llama_seq_id),
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]
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# enum llama_model_kv_override_type {
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# LLAMA_KV_OVERRIDE_INT,
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# LLAMA_KV_OVERRIDE_FLOAT,
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@ -327,6 +345,7 @@ LLAMA_KV_OVERRIDE_INT = 0
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LLAMA_KV_OVERRIDE_FLOAT = 1
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LLAMA_KV_OVERRIDE_BOOL = 2
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# struct llama_model_kv_override {
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# char key[128];
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# enum llama_model_kv_override_type tag;
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@ -343,6 +362,7 @@ class llama_model_kv_override_value(CtypesUnion):
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("bool_value", c_bool),
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]
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class llama_model_kv_override(Structure):
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_fields_ = [
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("key", ctypes.c_char * 128),
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@ -350,15 +370,25 @@ class llama_model_kv_override(Structure):
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("value", llama_model_kv_override_value),
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]
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# struct llama_model_params {
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# int32_t n_gpu_layers; // number of layers to store in VRAM
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# int32_t main_gpu; // the GPU that is used for scratch and small tensors
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# const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
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# enum llama_split_mode split_mode; // how to split the model across multiple GPUs
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# // main_gpu interpretation depends on split_mode:
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# // LLAMA_SPLIT_NONE: the GPU that is used for the entire model
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# // LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results
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# // LLAMA_SPLIT_LAYER: ignored
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# int32_t main_gpu;
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# // proportion of the model (layers or rows) to offload to each GPU, size: LLAMA_MAX_DEVICES
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# const float * tensor_split;
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# // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
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# // If the provided progress_callback returns true, model loading continues.
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# // If it returns false, model loading is immediately aborted.
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# llama_progress_callback progress_callback;
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# // context pointer passed to the progress callback
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# void * progress_callback_user_data;
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@ -372,19 +402,22 @@ class llama_model_kv_override(Structure):
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# };
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class llama_model_params(Structure):
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"""Parameters for llama_model
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Attributes:
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n_gpu_layers (int): number of layers to store in VRAM
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main_gpu (int): the GPU that is used for scratch and small tensors
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tensor_split (ctypes.Array[ctypes.c_float]): how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
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split_mode (int): how to split the model across multiple GPUs
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main_gpu (int): the GPU that is used for the entire model. main_gpu interpretation depends on split_mode: LLAMA_SPLIT_NONE: the GPU that is used for the entire model LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results LLAMA_SPLIT_LAYER: ignored
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tensor_split (ctypes.Array[ctypes.c_float]): proportion of the model (layers or rows) to offload to each GPU, size: LLAMA_MAX_DEVICES
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progress_callback (llama_progress_callback): called with a progress value between 0.0 and 1.0. Pass NULL to disable. If the provided progress_callback returns true, model loading continues. If it returns false, model loading is immediately aborted.
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progress_callback_user_data (ctypes.c_void_p): context pointer passed to the progress callback
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kv_overrides (ctypes.Array[llama_model_kv_override]): override key-value pairs of the model meta data
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vocab_only (bool): only load the vocabulary, no weights
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use_mmap (bool): use mmap if possible
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use_mlock (bool): force system to keep model in RAM"""
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_fields_ = [
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("n_gpu_layers", c_int32),
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("split_mode", c_int),
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("main_gpu", c_int32),
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("tensor_split", c_float_p),
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("progress_callback", llama_progress_callback),
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@ -416,6 +449,7 @@ class llama_model_params(Structure):
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# enum ggml_type type_k; // data type for K cache
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# enum ggml_type type_v; // data type for V cache
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# // Keep the booleans together to avoid misalignment during copy-by-value.
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# bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
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# bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
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@ -424,7 +458,7 @@ class llama_model_params(Structure):
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# };
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class llama_context_params(Structure):
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"""Parameters for llama_context
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Attributes:
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seed (int): RNG seed, -1 for random
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n_ctx (int): text context, 0 = from model
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@ -444,7 +478,9 @@ class llama_context_params(Structure):
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mul_mat_q (bool): if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
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logits_all (bool): the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
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embedding (bool): embedding mode only
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offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU"""
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offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU
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"""
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_fields_ = [
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("seed", c_uint32),
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("n_ctx", c_uint32),
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@ -493,14 +529,16 @@ It might not exist for progress report where '.' is output repeatedly."""
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# } llama_model_quantize_params;
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class llama_model_quantize_params(Structure):
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"""Parameters for llama_model_quantize
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Attributes:
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nthread (int): number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
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ftype (int): quantize to this llama_ftype
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allow_requantize (bool): allow quantizing non-f32/f16 tensors
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quantize_output_tensor (bool): quantize output.weight
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only_copy (bool): only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
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pure (bool): disable k-quant mixtures and quantize all tensors to the same type"""
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pure (bool): disable k-quant mixtures and quantize all tensors to the same type
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"""
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_fields_ = [
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("nthread", c_int32),
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("ftype", c_int),
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@ -745,13 +783,16 @@ def llama_n_ctx(ctx: llama_context_p) -> int:
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_lib.llama_n_ctx.argtypes = [llama_context_p]
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_lib.llama_n_ctx.restype = c_uint32
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# LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
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def llama_n_batch(ctx: llama_context_p) -> int:
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return _lib.llama_n_batch(ctx)
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_lib.llama_n_batch.argtypes = [llama_context_p]
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_lib.llama_n_batch.restype = c_uint32
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# LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
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def llama_vocab_type(model: llama_model_p) -> int:
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return _lib.llama_vocab_type(model)
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@ -1080,7 +1121,7 @@ _lib.llama_kv_cache_view_init.restype = llama_kv_cache_view
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# // Free a KV cache view. (use only for debugging purposes)
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# LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
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def llama_kv_cache_view_free(view: "ctypes.pointer[llama_kv_cache_view]"): # type: ignore
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def llama_kv_cache_view_free(view: "ctypes.pointer[llama_kv_cache_view]"): # type: ignore
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"""Free a KV cache view. (use only for debugging purposes)"""
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return _lib.llama_kv_cache_view_free(view)
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@ -1091,7 +1132,7 @@ _lib.llama_kv_cache_view_free.restype = None
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# // Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)
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# LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view);
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def llama_kv_cache_view_update(ctx: llama_context_p, view: "ctypes.pointer[llama_kv_cache_view]"): # type: ignore
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def llama_kv_cache_view_update(ctx: llama_context_p, view: "ctypes.pointer[llama_kv_cache_view]"): # type: ignore
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"""Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)"""
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return _lib.llama_kv_cache_view_update(ctx, view)
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@ -1251,6 +1292,40 @@ _lib.llama_kv_cache_seq_shift.argtypes = [
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]
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_lib.llama_kv_cache_seq_shift.restype = None
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# // Integer division of the positions by factor of `d > 1`
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# // If the KV cache is RoPEd, the KV data is updated accordingly
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# // p0 < 0 : [0, p1]
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# // p1 < 0 : [p0, inf)
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# LLAMA_API void llama_kv_cache_seq_div(
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# struct llama_context * ctx,
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# llama_seq_id seq_id,
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# llama_pos p0,
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# llama_pos p1,
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# int d);
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def llama_kv_cache_seq_div(
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ctx: llama_context_p,
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seq_id: Union[llama_seq_id, int],
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p0: Union[llama_pos, int],
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p1: Union[llama_pos, int],
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d: Union[c_int, int],
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):
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"""Integer division of the positions by factor of `d > 1`
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If the KV cache is RoPEd, the KV data is updated accordingly
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p0 < 0 : [0, p1]
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p1 < 0 : [p0, inf)"""
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return _lib.llama_kv_cache_seq_div(ctx, seq_id, p0, p1, d)
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_lib.llama_kv_cache_seq_div.argtypes = [
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llama_context_p,
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llama_seq_id,
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llama_pos,
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llama_pos,
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c_int,
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]
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_lib.llama_kv_cache_seq_div.restype = None
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# //
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# // State / sessions
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# //
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@ -2063,10 +2138,11 @@ def llama_sample_temp(
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temp: Union[c_float, float],
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):
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"""Temperature sampling described in academic paper "Generating Long Sequences with Sparse Transformers" https://arxiv.org/abs/1904.10509
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Parameters:
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candidates: A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
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temp: The temperature value to use for the sampling. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text."""
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temp: The temperature value to use for the sampling. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
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"""
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return _lib.llama_sample_temp(ctx, candidates, temp)
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@ -2111,10 +2187,11 @@ def llama_sample_grammar(
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grammar, # type: llama_grammar_p
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):
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"""Apply constraints from grammar
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Parameters:
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candidates: A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
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grammar: A grammar object containing the rules and constraints to apply to the generated text."""
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grammar: A grammar object containing the rules and constraints to apply to the generated text.
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"""
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return _lib.llama_sample_grammar(ctx, candidates, grammar)
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@ -2148,13 +2225,14 @@ def llama_sample_token_mirostat(
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mu, # type: _Pointer[c_float]
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) -> int:
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"""Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
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Parameters:
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candidates: A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
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tau: The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
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eta: The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||||
m: The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
|
||||
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."""
|
||||
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.
|
||||
"""
|
||||
return _lib.llama_sample_token_mirostat(ctx, candidates, tau, eta, m, mu)
|
||||
|
||||
|
||||
|
|
@ -2188,12 +2266,13 @@ def llama_sample_token_mirostat_v2(
|
|||
mu, # type: _Pointer[c_float]
|
||||
) -> int:
|
||||
"""Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||||
|
||||
|
||||
Parameters:
|
||||
candidates: A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||||
tau: The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
|
||||
eta: The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||||
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."""
|
||||
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.
|
||||
"""
|
||||
return _lib.llama_sample_token_mirostat_v2(ctx, candidates, tau, eta, mu)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -72,7 +72,7 @@ class LlamaGrammar:
|
|||
)
|
||||
if verbose:
|
||||
print(f"{cls.from_string.__name__} grammar:", file=sys.stderr)
|
||||
print_grammar(sys.stdout, parsed_grammar)
|
||||
print_grammar(sys.stderr, parsed_grammar)
|
||||
print(file=sys.stderr)
|
||||
return cls(parsed_grammar)
|
||||
|
||||
|
|
|
|||
2
vendor/llama.cpp
vendored
2
vendor/llama.cpp
vendored
|
|
@ -1 +1 @@
|
|||
Subproject commit b3a7c20b5c035250257d2b62851c379b159c899a
|
||||
Subproject commit 76484fbfd355df388f71d6edaa98e1692a74de7e
|
||||
Loading…
Reference in a new issue