More interoperability to the original llama.cpp, and arguments now work

examples/low_level_api/common.py

@@ -0,0 +1,148 @@
+import os
+import argparse
+
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+# Based on https://github.com/ggerganov/llama.cpp/blob/master/examples/common.cpp
+
+
+@dataclass
+class GptParams:
+    seed: int = -1
+    n_threads: int = min(4, os.cpu_count() or 1)
+    n_predict: int = 128
+    repeat_last_n: int = 64
+    n_parts: int = -1
+    n_ctx: int = 512
+    n_batch: int = 8
+    n_keep: int = 0
+
+    top_k: int = 40
+    top_p: float = 0.95
+    temp: float = 0.80
+    repeat_penalty: float = 1.10
+
+    model: str = "./models/llama-7B/ggml-model.bin"
+    prompt: str = ""
+    input_prefix: str = " "
+
+    antiprompt: List[str] = field(default_factory=list)
+
+    memory_f16: bool = True
+    random_prompt: bool = False
+    use_color: bool = False
+    interactive: bool = False
+
+    embedding: bool = False
+    interactive_start: bool = False
+
+    instruct: bool = False
+    ignore_eos: bool = False
+    perplexity: bool = False
+    use_mlock: bool = False
+    mem_test: bool = False
+    verbose_prompt: bool = False
+
+    file: str = None
+
+    # If chat ended prematurely, append this to the conversation to fix it.
+    # Set to "\nUser:" etc.
+    # This is an alternative to input_prefix which always adds it, so it potentially duplicates "User:""
+    fix_prefix: str = " "
+    output_postfix: str = ""
+    input_echo: bool = True,
+
+    # Default instructions for Alpaca
+    # switch to "Human" and "Assistant" for Vicuna.
+    # TODO: TBD how they are gonna handle this upstream
+    instruct_inp_prefix: str="\n\n### Instruction:\n\n"
+    instruct_inp_suffix: str="\n\n### Response:\n\n"
+
+
+def gpt_params_parse(argv = None, params: Optional[GptParams] = None):
+    if params is None:
+        params = GptParams()
+
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument("-s", "--seed", type=int, default=-1, help="RNG seed (use random seed for <= 0)",dest="seed")
+    parser.add_argument("-t", "--threads", type=int, default=min(4, os.cpu_count() or 1), help="number of threads to use during computation",dest="n_threads")
+    parser.add_argument("-p", "--prompt", type=str, default="", help="initial prompt",dest="prompt")
+    parser.add_argument("-f", "--file", type=str, default=None, help="file containing initial prompt to load",dest="file")
+    parser.add_argument("-c", "--ctx_size", type=int, default=512, help="size of the prompt context",dest="n_ctx")
+    parser.add_argument("--memory_f32", action="store_false", help="use f32 instead of f16 for memory key+value",dest="memory_f16")
+    parser.add_argument("--top_p", type=float, default=0.95, help="top-p samplin",dest="top_p")
+    parser.add_argument("--top_k", type=int, default=40, help="top-k sampling",dest="top_k")
+    parser.add_argument("--temp", type=float, default=0.80, help="temperature",dest="temp")
+    parser.add_argument("--n_predict", type=int, default=128, help="number of model parts",dest="n_predict")
+    parser.add_argument("--repeat_last_n", type=int, default=64, help="last n tokens to consider for penalize ",dest="repeat_last_n")
+    parser.add_argument("--repeat_penalty", type=float, default=1.10, help="penalize repeat sequence of tokens",dest="repeat_penalty")
+    parser.add_argument("-b", "--batch_size", type=int, default=8, help="batch size for prompt processing",dest="n_batch")
+    parser.add_argument("--keep", type=int, default=0, help="number of tokens to keep from the initial prompt",dest="n_keep")
+    parser.add_argument("-m", "--model", type=str, default="./models/llama-7B/ggml-model.bin", help="model path",dest="model")
+    parser.add_argument(
+        "-i", "--interactive", action="store_true", help="run in interactive mode", dest="interactive"
+    )
+    parser.add_argument("--embedding", action="store_true", help="", dest="embedding")
+    parser.add_argument(
+        "--interactive-start",
+        action="store_true",
+        help="run in interactive mode",
+        dest="interactive"
+    )
+    parser.add_argument(
+        "--interactive-first",
+        action="store_true",
+        help="run in interactive mode and wait for input right away",
+        dest="interactive_start"
+    )
+    parser.add_argument(
+        "-ins",
+        "--instruct",
+        action="store_true",
+        help="run in instruction mode (use with Alpaca or Vicuna models)",
+        dest="instruct"
+    )
+    parser.add_argument(
+        "--color",
+        action="store_true",
+        help="colorise output to distinguish prompt and user input from generations",
+        dest="use_color"
+    )
+    parser.add_argument("--mlock", action="store_true",help="force system to keep model in RAM rather than swapping or compressing",dest="use_mlock")
+    parser.add_argument("--mtest", action="store_true",help="compute maximum memory usage",dest="mem_test")
+    parser.add_argument(
+        "-r",
+        "--reverse-prompt",
+        type=str,
+        action='append',
+        help="poll user input upon seeing PROMPT (can be\nspecified more than once for multiple prompts).",
+        dest="antiprompt"
+    )
+    parser.add_argument("--perplexity", action="store_true", help="compute perplexity over the prompt", dest="perplexity")
+    parser.add_argument("--ignore-eos", action="store_true", help="ignore end of stream token and continue generating", dest="ignore_eos")
+    parser.add_argument("--n_parts", type=int, default=-1, help="number of model parts", dest="n_parts")
+    parser.add_argument("--random-prompt", action="store_true", help="start with a randomized prompt.", dest="random_prompt")
+    parser.add_argument("--in-prefix", type=str, default="", help="string to prefix user inputs with", dest="input_prefix")
+    parser.add_argument("--fix-prefix", type=str, default="", help="append to input when generated n_predict tokens", dest="fix_prefix")
+    parser.add_argument("--out-postfix", type=str, default="", help="append to input", dest="output_postfix")
+    parser.add_argument("--input-noecho", action="store_false", help="dont output the input", dest="input_echo")
+    args = parser.parse_args(argv)
+    return args
+
+def gpt_random_prompt(rng):
+    return [
+        "So",
+        "Once upon a time",
+        "When",
+        "The",
+        "After",
+        "If",
+        "import",
+        "He",
+        "She",
+        "They",
+    ][rng % 10]
+
+if __name__ == "__main__":
+    print(GptParams(gpt_params_parse()))

examples/low_level_api/low_level_api_chat_cpp.py

@@ -6,8 +6,6 @@ Quirks:
  * The first antiprompt should be the userprompt like "\nUser:", 
    because its added when n_predict is reached (aka generation ended prematurely)
  * n_predict can be set to -1 for unlimited length responses (or just a really high value)
- * It's always in interactive mode, generation ends either by reaching an antiprompt 
-   or running out of n_predict.
  * Instruction mode adds its own antiprompt.
    You should also still be feeding the model with a "primer" prompt that 
    shows it the expected format.
@@ -59,7 +57,6 @@ specified) expect poor results""", file=sys.stderr)
 
 		# runtime args
 		self.input_consumed = 0
-		self.embd = []
 		self.n_past = 0
 		self.first_antiprompt = []
 		self.remaining_tokens = self.params.n_predict
@@ -74,7 +71,7 @@ specified) expect poor results""", file=sys.stderr)
 		self.lparams.use_mlock = self.params.use_mlock
 
 		self.ctx = llama_cpp.llama_init_from_file(self.params.model.encode("utf8"), self.lparams)
-		if (self.ctx == 0):
+		if (not self.ctx):
 			raise RuntimeError(f"error: failed to load model '{self.params.model}'")
 
 		print(file=sys.stderr)
@@ -95,7 +92,13 @@ specified) expect poor results""", file=sys.stderr)
 		# Add a space in front of the first character to match OG llama tokenizer behavior
 		self.params.prompt = " " + self.params.prompt
 
+		# Load prompt file
+		if (self.params.file):
+			with open(self.params.file) as f:
+				self.params.prompt = f.read()
+
 		# tokenize the prompt
+		self.embd = []
 		self.embd_inp = self._tokenize(self.params.prompt)
 
 		if (len(self.embd_inp) > self.params.n_ctx - 4):
@@ -384,11 +387,7 @@ The transcript only includes text, it does not include markup like HTML and Mark
 {AI_NAME}: Blue
 {USER_NAME}:"""
 	args = gpt_params_parse()
-	params = GptParams(args)
+	params = GptParams(**vars(args))
 
-	if (args.file):
-		with open(args.file) as f:
-			params.prompt = f.read()
-
-	with LLaMAInteract() as m:
+	with LLaMAInteract(params) as m:
 		m.interact()