This commit is contained in:
commit
833126bbd3
GPG key ID:
F93C394FE9BBAFD5
14 changed files with 259 additions and 42 deletions
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@ -7,6 +7,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [Unreleased]
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## [0.2.29]
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- feat: Update llama.cpp to ggerganov/llama.cpp@4483396751c79dea540808b9cb9238245d06da2b
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- feat: Add split_mode option by @abetlen in 84615adbc6855c8384807c42f0130f9a1763f99d
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- feat: Implement GGUF metadata KV overrides by @phiharri in #1011
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- fix: Avoid "LookupError: unknown encoding: ascii" when open() called in a destructor by @yieldthought in #1012
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- fix: Fix low_level_api_chat_cpp example to match current API by @aniljava in #1086
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- fix: Fix Pydantic model parsing by @DeNeutoy in #1087
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## [0.2.28]
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- feat: Update llama.cpp to ggerganov/llama.cpp@6efb8eb30e7025b168f3fda3ff83b9b386428ad6
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@ -14,6 +14,7 @@ This package provides:
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- High-level Python API for text completion
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- OpenAI-like API
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- [LangChain compatibility](https://python.langchain.com/docs/integrations/llms/llamacpp)
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- [LlamaIndex compatibility](https://docs.llamaindex.ai/en/stable/examples/llm/llama_2_llama_cpp.html)
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- OpenAI compatible web server
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- [Local Copilot replacement](https://llama-cpp-python.readthedocs.io/en/latest/server/#code-completion)
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- [Function Calling support](https://llama-cpp-python.readthedocs.io/en/latest/server/#function-calling)
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@ -106,7 +106,7 @@ def gpt_params_parse(argv = None):
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parser.add_argument("--mirostat_lr", type=float, default=0.1, help="Mirostat learning rate, parameter eta",dest="mirostat_eta")
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parser.add_argument("-m", "--model", type=str, default="./models/llama-7B/ggml-model.bin", help="model path",dest="model")
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parser.add_argument("-p", "--prompt", type=str, default="", help="initial prompt",dest="prompt")
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parser.add_argument("-p", "--prompt", type=str, default=None, help="initial prompt",dest="prompt")
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parser.add_argument("-f", "--file", type=str, default=None, help="file containing initial prompt to load",dest="file")
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parser.add_argument("--session", type=str, default=None, help="file to cache model state in (may be large!)",dest="path_session")
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parser.add_argument("--in-prefix", type=str, default="", help="string to prefix user inputs with", dest="input_prefix")
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@ -62,7 +62,7 @@ specified) expect poor results""", file=sys.stderr)
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self.multibyte_fix = []
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# model load
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self.lparams = llama_cpp.llama_context_default_params()
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self.lparams = llama_cpp.llama_model_default_params()
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self.lparams.n_ctx = self.params.n_ctx
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self.lparams.n_parts = self.params.n_parts
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self.lparams.seed = self.params.seed
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@ -72,7 +72,11 @@ specified) expect poor results""", file=sys.stderr)
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self.model = llama_cpp.llama_load_model_from_file(
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self.params.model.encode("utf8"), self.lparams)
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self.ctx = llama_cpp.llama_new_context_with_model(self.model, self.lparams)
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# Context Params.
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self.cparams = llama_cpp.llama_context_default_params()
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self.ctx = llama_cpp.llama_new_context_with_model(self.model, self.cparams)
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if (not self.ctx):
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raise RuntimeError(f"error: failed to load model '{self.params.model}'")
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@ -244,7 +248,7 @@ n_keep = {self.params.n_keep}
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# tokenize a prompt
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def _tokenize(self, prompt, bos=True):
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_arr = (llama_cpp.llama_token * ((len(prompt) + 1) * 4))()
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_n = llama_cpp.llama_tokenize(self.ctx, prompt.encode("utf8", errors="ignore"), _arr, len(_arr), bos)
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_n = llama_cpp.llama_tokenize(self.model, prompt.encode("utf8", errors="ignore"), len(prompt), _arr, len(_arr), bos, False)
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return _arr[:_n]
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def set_color(self, c):
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@ -304,7 +308,7 @@ n_keep = {self.params.n_keep}
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self.n_past += n_eval"""
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if (llama_cpp.llama_eval(
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self.ctx, (llama_cpp.llama_token * len(self.embd))(*self.embd), len(self.embd), self.n_past, self.params.n_threads
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self.ctx, (llama_cpp.llama_token * len(self.embd))(*self.embd), len(self.embd), self.n_past
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) != 0):
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raise Exception("Failed to llama_eval!")
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@ -332,7 +336,7 @@ n_keep = {self.params.n_keep}
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id = 0
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logits = llama_cpp.llama_get_logits(self.ctx)
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n_vocab = llama_cpp.llama_n_vocab(self.ctx)
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n_vocab = llama_cpp.llama_n_vocab(self.model)
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# Apply params.logit_bias map
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for key, value in self.params.logit_bias.items():
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@ -349,12 +353,20 @@ n_keep = {self.params.n_keep}
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last_n_repeat = min(len(self.last_n_tokens), repeat_last_n, self.n_ctx)
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_arr = (llama_cpp.llama_token * last_n_repeat)(*self.last_n_tokens[len(self.last_n_tokens) - last_n_repeat:])
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llama_cpp.llama_sample_repetition_penalty(self.ctx, candidates_p,
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_arr,
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last_n_repeat, llama_cpp.c_float(self.params.repeat_penalty))
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llama_cpp.llama_sample_frequency_and_presence_penalties(self.ctx, candidates_p,
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_arr,
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last_n_repeat, llama_cpp.c_float(self.params.frequency_penalty), llama_cpp.c_float(self.params.presence_penalty))
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llama_cpp.llama_sample_repetition_penalties(
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ctx=self.ctx,
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candidates=candidates_p,
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last_tokens_data = _arr,
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penalty_last_n = last_n_repeat,
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penalty_repeat = llama_cpp.c_float(self.params.repeat_penalty),
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penalty_freq = llama_cpp.c_float(self.params.frequency_penalty),
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penalty_present = llama_cpp.c_float(self.params.presence_penalty),
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)
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# NOT PRESENT IN CURRENT VERSION ?
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# llama_cpp.llama_sample_frequency_and_presence_penalti(self.ctx, candidates_p,
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# _arr,
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# last_n_repeat, llama_cpp.c_float(self.params.frequency_penalty), llama_cpp.c_float(self.params.presence_penalty))
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if not self.params.penalize_nl:
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logits[llama_cpp.llama_token_nl()] = nl_logit
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@ -473,7 +485,7 @@ n_keep = {self.params.n_keep}
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def token_to_str(self, token_id: int) -> bytes:
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size = 32
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buffer = (ctypes.c_char * size)()
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n = llama_cpp.llama_token_to_piece_with_model(
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n = llama_cpp.llama_token_to_piece(
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self.model, llama_cpp.llama_token(token_id), buffer, size)
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assert n <= size
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return bytes(buffer[:n])
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@ -532,6 +544,9 @@ n_keep = {self.params.n_keep}
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print(i,end="",flush=True)
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self.params.input_echo = False
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# Using string instead of tokens to check for antiprompt,
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# It is more reliable than tokens for interactive mode.
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generated_str = ""
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while self.params.interactive:
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self.set_color(util.CONSOLE_COLOR_USER_INPUT)
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if (self.params.instruct):
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@ -546,6 +561,10 @@ n_keep = {self.params.n_keep}
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try:
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for i in self.output():
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print(i,end="",flush=True)
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generated_str += i
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for ap in self.params.antiprompt:
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if generated_str.endswith(ap):
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raise KeyboardInterrupt
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except KeyboardInterrupt:
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self.set_color(util.CONSOLE_COLOR_DEFAULT)
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if not self.params.instruct:
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@ -561,7 +580,7 @@ if __name__ == "__main__":
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time_now = datetime.now()
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prompt = f"""Text transcript of a never ending dialog, where {USER_NAME} interacts with an AI assistant named {AI_NAME}.
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{AI_NAME} is helpful, kind, honest, friendly, good at writing and never fails to answer {USER_NAME}’s requests immediately and with details and precision.
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There are no annotations like (30 seconds passed...) or (to himself), just what {USER_NAME} and {AI_NAME} say aloud to each other.
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Transcript below contains only the recorded dialog between two, without any annotations like (30 seconds passed...) or (to himself), just what {USER_NAME} and {AI_NAME} say aloud to each other.
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The dialog lasts for years, the entirety of it is shared below. It's 10000 pages long.
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The transcript only includes text, it does not include markup like HTML and Markdown.
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@ -575,8 +594,11 @@ The transcript only includes text, it does not include markup like HTML and Mark
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{AI_NAME}: A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
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{USER_NAME}: Name a color.
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{AI_NAME}: Blue
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{USER_NAME}:"""
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{USER_NAME}: """
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params = gpt_params_parse()
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if params.prompt is None and params.file is None:
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params.prompt = prompt
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with LLaMAInteract(params) as m:
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m.interact()
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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__ = "0.2.28"
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__version__ = "0.2.29"
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@ -1,11 +1,15 @@
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import os
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import sys
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import sys, traceback
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# Avoid "LookupError: unknown encoding: ascii" when open() called in a destructor
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outnull_file = open(os.devnull, "w")
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errnull_file = open(os.devnull, "w")
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class suppress_stdout_stderr(object):
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# NOTE: these must be "saved" here to avoid exceptions when using
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# this context manager inside of a __del__ method
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open = open
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sys = sys
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os = os
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@ -21,9 +25,6 @@ class suppress_stdout_stderr(object):
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if not hasattr(self.sys.stdout, 'fileno') or not hasattr(self.sys.stderr, 'fileno'):
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return self # Return the instance without making changes
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self.outnull_file = self.open(self.os.devnull, "w")
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self.errnull_file = self.open(self.os.devnull, "w")
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self.old_stdout_fileno_undup = self.sys.stdout.fileno()
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self.old_stderr_fileno_undup = self.sys.stderr.fileno()
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@ -33,11 +34,11 @@ class suppress_stdout_stderr(object):
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self.old_stdout = self.sys.stdout
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self.old_stderr = self.sys.stderr
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self.os.dup2(self.outnull_file.fileno(), self.old_stdout_fileno_undup)
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self.os.dup2(self.errnull_file.fileno(), self.old_stderr_fileno_undup)
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self.os.dup2(outnull_file.fileno(), self.old_stdout_fileno_undup)
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self.os.dup2(errnull_file.fileno(), self.old_stderr_fileno_undup)
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self.sys.stdout = self.outnull_file
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self.sys.stderr = self.errnull_file
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self.sys.stdout = outnull_file
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self.sys.stderr = errnull_file
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return self
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def __exit__(self, *_):
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@ -54,6 +55,3 @@ class suppress_stdout_stderr(object):
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self.os.close(self.old_stdout_fileno)
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self.os.close(self.old_stderr_fileno)
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self.outnull_file.close()
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self.errnull_file.close()
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@ -730,11 +730,13 @@ class Llama:
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*,
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# Model Params
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n_gpu_layers: int = 0,
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split_mode: int = llama_cpp.LLAMA_SPLIT_LAYER,
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main_gpu: int = 0,
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tensor_split: Optional[List[float]] = None,
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vocab_only: bool = False,
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use_mmap: bool = True,
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use_mlock: bool = False,
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kv_overrides: Optional[Dict[str, Union[bool, int, float]]] = None,
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# Context Params
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seed: int = llama_cpp.LLAMA_DEFAULT_SEED,
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n_ctx: int = 512,
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@ -798,11 +800,13 @@ class Llama:
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Args:
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model_path: Path to the model.
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n_gpu_layers: Number of layers to offload to GPU (-ngl). If -1, all layers are offloaded.
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main_gpu: The GPU that is used for scratch and small tensors.
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split_mode: How to split the model across GPUs. See llama_cpp.LLAMA_SPLIT_* for options.
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main_gpu: 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: How split tensors should be distributed across GPUs. If None, the model is not split.
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vocab_only: Only load the vocabulary no weights.
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use_mmap: Use mmap if possible.
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use_mlock: Force the system to keep the model in RAM.
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kv_overrides: Key-value overrides for the model.
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seed: RNG seed, -1 for random
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n_ctx: Text context, 0 = from model
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n_batch: Prompt processing maximum batch size
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@ -848,6 +852,7 @@ class Llama:
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self.model_params.n_gpu_layers = (
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0x7FFFFFFF if n_gpu_layers == -1 else n_gpu_layers
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) # 0x7FFFFFFF is INT32 max, will be auto set to all layers
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self.model_params.split_mode = split_mode
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self.model_params.main_gpu = main_gpu
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self.tensor_split = tensor_split
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self._c_tensor_split = None
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@ -866,6 +871,34 @@ class Llama:
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self.model_params.use_mmap = use_mmap if lora_path is None else False
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self.model_params.use_mlock = use_mlock
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self.kv_overrides = kv_overrides
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if kv_overrides is not None:
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n_overrides = len(kv_overrides)
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self._kv_overrides_array = llama_cpp.llama_model_kv_override * (n_overrides + 1)
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self._kv_overrides_array_keys = []
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for k, v in kv_overrides.items():
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key_buf = ctypes.create_string_buffer(k.encode("utf-8"))
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self._kv_overrides_array_keys.append(key_buf)
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self._kv_overrides_array[i].key = key_buf
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if isinstance(v, int):
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self._kv_overrides_array[i].tag = llama_cpp.LLAMA_KV_OVERRIDE_INT
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self._kv_overrides_array[i].value.int_value = v
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elif isinstance(v, float):
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self._kv_overrides_array[i].tag = llama_cpp.LLAMA_KV_OVERRIDE_FLOAT
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self._kv_overrides_array[i].value.float_value = v
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elif isinstance(v, bool):
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self._kv_overrides_array[i].tag = llama_cpp.LLAMA_KV_OVERRIDE_BOOL
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self._kv_overrides_array[i].value.bool_value = v
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else:
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raise ValueError(f"Unknown value type for {k}: {v}")
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self._kv_overrides_array_sentinel_key = b'\0'
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# null array sentinel
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self._kv_overrides_array[n_overrides].key = self._kv_overrides_array_sentinel_key
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self.model_params.kv_overrides = self._kv_overrides_array
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self.n_batch = min(n_ctx, n_batch) # ???
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self.n_threads = n_threads or max(multiprocessing.cpu_count() // 2, 1)
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self.n_threads_batch = n_threads_batch or max(
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@ -2143,11 +2176,13 @@ class Llama:
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model_path=self.model_path,
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# Model Params
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n_gpu_layers=self.model_params.n_gpu_layers,
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split_mode=self.model_params.split_mode,
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main_gpu=self.model_params.main_gpu,
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tensor_split=self.tensor_split,
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vocab_only=self.model_params.vocab_only,
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use_mmap=self.model_params.use_mmap,
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use_mlock=self.model_params.use_mlock,
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kv_overrides=self.kv_overrides,
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# Context Params
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seed=self.context_params.seed,
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n_ctx=self.context_params.n_ctx,
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@ -2185,11 +2220,13 @@ class Llama:
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model_path=state["model_path"],
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# Model Params
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n_gpu_layers=state["n_gpu_layers"],
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split_mode=state["split_mode"],
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main_gpu=state["main_gpu"],
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tensor_split=state["tensor_split"],
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vocab_only=state["vocab_only"],
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use_mmap=state["use_mmap"],
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use_mlock=state["use_mlock"],
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kv_overrides=state["kv_overrides"],
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# Context Params
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seed=state["seed"],
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n_ctx=state["n_ctx"],
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@ -229,6 +229,7 @@ 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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# float logit; // log-odds of the token
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@ -395,6 +396,7 @@ class llama_model_kv_override(Structure):
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# // override key-value pairs of the model meta data
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# const struct llama_model_kv_override * kv_overrides;
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# // Keep the booleans together to avoid misalignment during copy-by-value.
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# bool vocab_only; // only load the vocabulary, no weights
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# bool use_mmap; // use mmap if possible
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@ -407,7 +409,7 @@ class llama_model_params(Structure):
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n_gpu_layers (int): number of layers to store in VRAM
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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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tensor_split (ctypes.Array[ctypes.c_float]): proportion of the model (layers or rows) to offload to each GPU, size: LLAMA_MAX_DEVICES
|
||||
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.
|
||||
progress_callback_user_data (ctypes.c_void_p): context pointer passed to the progress callback
|
||||
kv_overrides (ctypes.Array[llama_model_kv_override]): override key-value pairs of the model meta data
|
||||
|
|
@ -526,6 +528,7 @@ It might not exist for progress report where '.' is output repeatedly."""
|
|||
# bool quantize_output_tensor; // quantize output.weight
|
||||
# bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
|
||||
# bool pure; // disable k-quant mixtures and quantize all tensors to the same type
|
||||
# void * imatrix; // pointer to importance matrix data
|
||||
# } llama_model_quantize_params;
|
||||
class llama_model_quantize_params(Structure):
|
||||
"""Parameters for llama_model_quantize
|
||||
|
|
@ -537,6 +540,7 @@ class llama_model_quantize_params(Structure):
|
|||
quantize_output_tensor (bool): quantize output.weight
|
||||
only_copy (bool): only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
|
||||
pure (bool): disable k-quant mixtures and quantize all tensors to the same type
|
||||
imatrix (ctypes.c_void_p): pointer to importance matrix data
|
||||
"""
|
||||
|
||||
_fields_ = [
|
||||
|
|
@ -545,6 +549,8 @@ class llama_model_quantize_params(Structure):
|
|||
("allow_requantize", c_bool),
|
||||
("quantize_output_tensor", c_bool),
|
||||
("only_copy", c_bool),
|
||||
("pure", c_bool),
|
||||
("imatrix", c_void_p),
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -1956,14 +1962,39 @@ _lib.llama_sample_repetition_penalties.restype = None
|
|||
|
||||
|
||||
# /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
|
||||
# /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
|
||||
# /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
|
||||
# /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
|
||||
# LLAMA_API void llama_sample_classifier_free_guidance(
|
||||
# struct llama_context * ctx,
|
||||
# /// @param logits Logits extracted from the original generation context.
|
||||
# /// @param logits_guidance Logits extracted from a separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
|
||||
# /// @param scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
|
||||
# LLAMA_API void llama_sample_apply_guidance(
|
||||
# struct llama_context * ctx,
|
||||
# float * logits,
|
||||
# float * logits_guidance,
|
||||
# float scale);
|
||||
def llama_sample_apply_guidance(
|
||||
ctx: llama_context_p,
|
||||
logits, # type: _Pointer[c_float]
|
||||
logits_guidance, # type: _Pointer[c_float]
|
||||
scale: Union[c_float, float],
|
||||
):
|
||||
"""Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806"""
|
||||
return _lib.llama_sample_apply_guidance(ctx, logits, logits_guidance, scale)
|
||||
|
||||
|
||||
_lib.llama_sample_apply_guidance.argtypes = [
|
||||
llama_context_p,
|
||||
c_float_p,
|
||||
c_float_p,
|
||||
c_float,
|
||||
]
|
||||
_lib.llama_sample_apply_guidance.restype = None
|
||||
|
||||
|
||||
# LLAMA_API DEPRECATED(void llama_sample_classifier_free_guidance(
|
||||
# struct llama_context * ctx,
|
||||
# llama_token_data_array * candidates,
|
||||
# struct llama_context * guidance_ctx,
|
||||
# float scale);
|
||||
# struct llama_context * guidance_ctx,
|
||||
# float scale),
|
||||
# "use llama_sample_apply_guidance() instead");
|
||||
def llama_sample_classifier_free_guidance(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
|
|||
|
|
@ -1433,7 +1433,6 @@ class SchemaConverter:
|
|||
|
||||
def visit(self, schema: Dict[str, Any], name: str) -> str:
|
||||
schema_type: Optional[str] = schema.get("type") # type: ignore
|
||||
assert isinstance(schema_type, str), f"Unrecognized schema: {schema}"
|
||||
rule_name = name or "root"
|
||||
|
||||
if "$defs" in schema:
|
||||
|
|
|
|||
|
|
@ -197,7 +197,36 @@ async def authenticate(
|
|||
|
||||
|
||||
@router.post(
|
||||
"/v1/completions", summary="Completion", dependencies=[Depends(authenticate)]
|
||||
"/v1/completions",
|
||||
summary="Completion",
|
||||
dependencies=[Depends(authenticate)],
|
||||
response_model= Union[
|
||||
llama_cpp.CreateCompletionResponse,
|
||||
str,
|
||||
],
|
||||
responses={
|
||||
"200": {
|
||||
"description": "Successful Response",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"anyOf": [
|
||||
{"$ref": "#/components/schemas/CreateCompletionResponse"}
|
||||
],
|
||||
"title": "Completion response, when stream=False",
|
||||
}
|
||||
},
|
||||
"text/event-stream":{
|
||||
"schema": {
|
||||
"type": "string",
|
||||
"title": "Server Side Streaming response, when stream=True. " +
|
||||
"See SSE format: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format", # noqa: E501
|
||||
"example": """data: {... see CreateCompletionResponse ...} \\n\\n data: ... \\n\\n ... data: [DONE]"""
|
||||
}
|
||||
}
|
||||
},
|
||||
}
|
||||
},
|
||||
)
|
||||
@router.post(
|
||||
"/v1/engines/copilot-codex/completions",
|
||||
|
|
@ -280,7 +309,33 @@ async def create_embedding(
|
|||
|
||||
|
||||
@router.post(
|
||||
"/v1/chat/completions", summary="Chat", dependencies=[Depends(authenticate)]
|
||||
"/v1/chat/completions", summary="Chat", dependencies=[Depends(authenticate)],
|
||||
response_model= Union[
|
||||
llama_cpp.ChatCompletion, str
|
||||
],
|
||||
responses={
|
||||
"200": {
|
||||
"description": "Successful Response",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"anyOf": [
|
||||
{"$ref": "#/components/schemas/CreateChatCompletionResponse"}
|
||||
],
|
||||
"title": "Completion response, when stream=False",
|
||||
}
|
||||
},
|
||||
"text/event-stream":{
|
||||
"schema": {
|
||||
"type": "string",
|
||||
"title": "Server Side Streaming response, when stream=True" +
|
||||
"See SSE format: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format", # noqa: E501
|
||||
"example": """data: {... see CreateChatCompletionResponse ...} \\n\\n data: ... \\n\\n ... data: [DONE]"""
|
||||
}
|
||||
}
|
||||
},
|
||||
}
|
||||
},
|
||||
)
|
||||
async def create_chat_completion(
|
||||
request: Request,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from typing import Optional, Union, List
|
||||
from typing import Dict, Optional, Union, List
|
||||
|
||||
import llama_cpp
|
||||
|
||||
|
|
@ -71,6 +71,23 @@ class LlamaProxy:
|
|||
chat_handler = llama_cpp.llama_chat_format.Llava15ChatHandler(
|
||||
clip_model_path=settings.clip_model_path, verbose=settings.verbose
|
||||
)
|
||||
|
||||
kv_overrides: Optional[Dict[str, Union[bool, int, float]]] = None
|
||||
if settings.kv_overrides is not None:
|
||||
assert isinstance(settings.kv_overrides, list)
|
||||
kv_overrides = {}
|
||||
for kv in settings.kv_overrides:
|
||||
key, value = kv.split("=")
|
||||
if ":" in value:
|
||||
value_type, value = value.split(":")
|
||||
if value_type == "bool":
|
||||
kv_overrides[key] = value.lower() in ["true", "1"]
|
||||
elif value_type == "int":
|
||||
kv_overrides[key] = int(value)
|
||||
elif value_type == "float":
|
||||
kv_overrides[key] = float(value)
|
||||
else:
|
||||
raise ValueError(f"Unknown value type {value_type}")
|
||||
|
||||
_model = llama_cpp.Llama(
|
||||
model_path=settings.model,
|
||||
|
|
@ -81,6 +98,7 @@ class LlamaProxy:
|
|||
vocab_only=settings.vocab_only,
|
||||
use_mmap=settings.use_mmap,
|
||||
use_mlock=settings.use_mlock,
|
||||
kv_overrides=kv_overrides,
|
||||
# Context Params
|
||||
seed=settings.seed,
|
||||
n_ctx=settings.n_ctx,
|
||||
|
|
|
|||
|
|
@ -28,6 +28,10 @@ class ModelSettings(BaseSettings):
|
|||
ge=-1,
|
||||
description="The number of layers to put on the GPU. The rest will be on the CPU. Set -1 to move all to GPU.",
|
||||
)
|
||||
split_mode: int = Field(
|
||||
default=llama_cpp.LLAMA_SPLIT_LAYER,
|
||||
description="The split mode to use.",
|
||||
)
|
||||
main_gpu: int = Field(
|
||||
default=0,
|
||||
ge=0,
|
||||
|
|
@ -48,11 +52,15 @@ class ModelSettings(BaseSettings):
|
|||
default=llama_cpp.llama_mlock_supported(),
|
||||
description="Use mlock.",
|
||||
)
|
||||
kv_overrides: Optional[List[str]] = Field(
|
||||
default=None,
|
||||
description="List of model kv overrides in the format key=type:value where type is one of (bool, int, float). Valid true values are (true, TRUE, 1), otherwise false.",
|
||||
)
|
||||
# Context Params
|
||||
seed: int = Field(
|
||||
default=llama_cpp.LLAMA_DEFAULT_SEED, description="Random seed. -1 for random."
|
||||
)
|
||||
n_ctx: int = Field(default=2048, ge=1, description="The context size.")
|
||||
n_ctx: int = Field(default=2048, ge=0, description="The context size.")
|
||||
n_batch: int = Field(
|
||||
default=512, ge=1, description="The batch size to use per eval."
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import llama_cpp
|
||||
import json
|
||||
|
||||
tree = """
|
||||
leaf ::= "."
|
||||
|
|
@ -6,8 +7,46 @@ node ::= leaf | "(" node node ")"
|
|||
root ::= node
|
||||
"""
|
||||
|
||||
|
||||
def test_grammar_from_string():
|
||||
grammar = llama_cpp.LlamaGrammar.from_string(tree)
|
||||
assert grammar._n_rules == 3
|
||||
assert grammar._start_rule_index == 2
|
||||
assert grammar.grammar is not None
|
||||
|
||||
|
||||
def test_composed_pydantic_grammar():
|
||||
"""
|
||||
from pydantic import BaseModel
|
||||
|
||||
class A(BaseModel):
|
||||
a: int
|
||||
|
||||
class B(BaseModel):
|
||||
a: A
|
||||
b: int
|
||||
"""
|
||||
|
||||
# This schema corresponds to the grammar in the comment above.
|
||||
# We don't use the pydantic models directly to avoid the dependency.
|
||||
schema = {
|
||||
"$defs": {
|
||||
"A": {
|
||||
"properties": {"a": {"title": "A", "type": "integer"}},
|
||||
"required": ["a"],
|
||||
"title": "A",
|
||||
"type": "object",
|
||||
}
|
||||
},
|
||||
"properties": {
|
||||
"a": {"$ref": "#/$defs/A"},
|
||||
"b": {"title": "B", "type": "integer"},
|
||||
},
|
||||
"required": ["a", "b"],
|
||||
"title": "B",
|
||||
"type": "object",
|
||||
}
|
||||
|
||||
grammar = llama_cpp.LlamaGrammar.from_json_schema(json.dumps(schema))
|
||||
|
||||
assert grammar.grammar is not None
|
||||
|
|
|
|||
2
vendor/llama.cpp
vendored
2
vendor/llama.cpp
vendored
|
|
@ -1 +1 @@
|
|||
Subproject commit 76484fbfd355df388f71d6edaa98e1692a74de7e
|
||||
Subproject commit 5c999609013a30c06e6fd28be8db5c2074bcc196
|
||||
Loading…
Reference in a new issue