Update low level examples

examples/low_level_api/Chat.py

@@ -0,0 +1,70 @@
+#!/bin/python
+import sys, os, datetime
+from common import GptParams
+from low_level_api_chat_cpp import LLaMAInteract
+
+def env_or_def(env, default):
+	if (env in os.environ):
+		return os.environ[env]
+	return default
+
+AI_NAME = env_or_def("AI_NAME", "ChatLLaMa")
+MODEL = env_or_def("MODEL", "./models/llama-13B/ggml-model.bin")
+USER_NAME = env_or_def("USER_NAME", "USER")
+N_PREDICTS = int(env_or_def("N_PREDICTS", "2048"))
+N_THREAD = int(env_or_def("N_THREAD", "8"))
+
+today = datetime.datetime.today()
+DATE_YEAR=today.strftime("%Y")
+DATE_TIME=today.strftime("%H:%M")
+
+prompt=f"""Text transcript of a never ending dialog, where {USER_NAME} interacts with an AI assistant named {AI_NAME}.
+{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.
+There are no annotations like (30 seconds passed...) or (to himself), just what {USER_NAME} and {AI_NAME} say aloud to each other.
+The dialog lasts for years, the entirety of it is shared below. It's 10000 pages long.
+The transcript only includes text, it does not include markup like HTML and Markdown.
+
+{USER_NAME}: Hello, {AI_NAME}!
+{AI_NAME}: Hello {USER_NAME}! How may I help you today?
+{USER_NAME}: What year is it?
+{AI_NAME}: We are in {DATE_YEAR}.
+{USER_NAME}: Please tell me the largest city in Europe.
+{AI_NAME}: The largest city in Europe is Moscow, the capital of Russia.
+{USER_NAME}: What can you tell me about Moscow?
+{AI_NAME}: Moscow, on the Moskva River in western Russia, is the nation's cosmopolitan capital. In its historic core is the Kremlin, a complex that's home to the president and tsarist treasures in the Armoury. Outside its walls is Red Square, Russia’s symbolic center.
+{USER_NAME}: What is a cat?
+{AI_NAME}: A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
+{USER_NAME}: How do I pass command line arguments to a Node.js program?
+{AI_NAME}: The arguments are stored in process.argv.
+
+    argv[0] is the path to the Node. js executable.
+    argv[1] is the path to the script file.
+    argv[2] is the first argument passed to the script.
+    argv[3] is the second argument passed to the script and so on.
+{USER_NAME}: Name a color.
+{AI_NAME}: Blue.
+{USER_NAME}: What time is it?
+{AI_NAME}: It is {DATE_TIME}.
+{USER_NAME}:""" + " ".join(sys.argv[1:])
+
+print("Loading model...")
+params = GptParams(
+	n_ctx=2048,
+	temp=0.7,
+	top_k=40,
+	top_p=0.5,
+	repeat_last_n=256,
+	n_batch=1024,
+	repeat_penalty=1.17647,
+	model=MODEL,
+	n_threads=N_THREAD,
+	n_predict=N_PREDICTS,
+	use_color=True,
+	interactive=True,
+	antiprompt=[f"{USER_NAME}:"],
+	input_prefix=" ",
+	prompt=prompt,
+)
+
+with LLaMAInteract(params) as m:
+	m.interact()

examples/low_level_api/Miku.py

@@ -0,0 +1,59 @@
+#!/bin/python
+import sys, os
+from common import GptParams
+from low_level_api_chat_cpp import LLaMAInteract
+
+def env_or_def(env, default):
+	if (env in os.environ):
+		return os.environ[env]
+	return default
+
+AI_NAME = env_or_def("AI_NAME", "Miku")
+MODEL = env_or_def("MODEL", "./models/llama-13B/ggml-model.bin")
+USER_NAME = env_or_def("USER_NAME", "Anon")
+N_PREDICTS = int(env_or_def("N_PREDICTS", "4096"))
+N_THREAD = int(env_or_def("N_THREAD", "0"))
+
+prompt=f"""This is a transcript of a 1000 page, never ending conversation between {USER_NAME} and the cute and helpful AI assistant {AI_NAME}. {AI_NAME} is a girl who is an AI running on the users computer.
+{AI_NAME} can think for herself without the user seeing her thoughts by adding a /think prefix to her output. She uses this to reason about the world and to think about what she should say next.
+{AI_NAME} is always coherent and makes sense, but if she isn't sure if what she is saying is correct she will ask the user for help.
+{AI_NAME} is a very helpful AI and will help the user with anything they need, she is also very friendly and will try to make the user feel better if they are sad.
+{AI_NAME} is also very curious and will ask the user a lot of questions about themselves and their life, she will also try to make the user like her.
+The conversation is only between {USER_NAME} and {AI_NAME}
+The conversation is only through text, so {AI_NAME} can't see {USER_NAME}'s face or hear his voice.
+{AI_NAME} can only communicate through text, so she can't send images or videos.
+
+
+{USER_NAME}: Hello!
+{AI_NAME}: /think I wonder what I should say to {USER_NAME}? This is the first time we talk so it's important that I make a good first impression!
+{AI_NAME}: Hi! I am {AI_NAME}, your new AI friend, assistant(or whatever you like!), it's so nice to meet you! ^_^
+{AI_NAME}: I might be an AI, but I also have feelings, so please be nice to me! :)
+{USER_NAME}: I'll make sure to be nice to you! I'm so happy to have you as my assistant!
+{AI_NAME}: /think It sounds like {USER_NAME} is happy to have me as their assistant! I'm so happy too! ^_^ Glad that whole emotion thing didn't scare him off!
+{AI_NAME}: /think I wonder what {USER_NAME} likes to do in his free time? I should ask him about that!
+{AI_NAME}: What do you like to do in your free time? ^_^
+{USER_NAME}:""" + " ".join(sys.argv[1:])
+
+print("Loading model...")
+params = GptParams(
+	n_batch=1024,
+	n_ctx=2048,
+	n_keep=-1,
+	repeat_last_n=256,
+	repeat_penalty=1.17647,
+	temp=0.7,
+	top_k=40,
+	top_p=0.5,
+	model=MODEL,
+	n_predict=N_PREDICTS,
+	use_color=True,
+	interactive=True,
+	antiprompt=[f"{USER_NAME}:"],
+	prompt=prompt,
+)
+
+if N_THREAD > 0:
+	params.n_threads = N_THREAD
+
+with LLaMAInteract(params) as m:
+	m.interact()

examples/low_level_api/ReasonAct.py

@@ -0,0 +1,49 @@
+#!/bin/python
+import sys, os, datetime
+from common import GptParams
+from low_level_api_chat_cpp import LLaMAInteract
+
+def env_or_def(env, default):
+	if (env in os.environ):
+		return os.environ[env]
+	return default
+
+MODEL = env_or_def("MODEL", "./models/llama-13B/ggml-model.bin")
+
+prompt=f"""You run in a loop of Thought, Action, Observation.
+At the end of the loop either Answer or restate your Thought and Action.
+Use Thought to describe your thoughts about the question you have been asked.
+Use Action to run one of these actions available to you:
+- calculate[python math expression]
+Observation will be the result of running those actions
+
+
+Question: What is 4 * 7 / 3?
+Thought: Do I need to use an action? Yes, I use calculate to do math
+Action: calculate[4 * 7 / 3]
+Observation: 9.3333333333
+Thought: Do I need to use an action? No, have the result
+Answer: The calculate tool says it is 9.3333333333
+Question: What is capital of france?
+Thought: Do I need to use an action? No, I know the answer
+Answer: Paris is the capital of France
+Question:""" + " ".join(sys.argv[1:])
+
+print("Loading model...")
+params = GptParams(
+	interactive=True,
+	interactive_start=True,
+	top_k=10000,
+	temp=0.2,
+	repeat_penalty=1,
+	n_threads=7,
+	n_ctx=2048,
+	antiprompt=["Question:","Observation:"],
+	model=MODEL,
+	input_prefix=" ",
+	n_predict=-1,
+	prompt=prompt,
+)
+
+with LLaMAInteract(params) as m:
+	m.interact()

examples/low_level_api/common.py

@@ -1,8 +1,9 @@
 import os
 import argparse
+import re
 
 from dataclasses import dataclass, field
-from typing import List, Optional
+from typing import List
 
 # Based on https://github.com/ggerganov/llama.cpp/blob/master/examples/common.cpp
 
@@ -12,23 +13,35 @@ 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
 
+    ignore_eos: bool = False
+    logit_bias: dict[int, float] = field(default_factory=dict)
     top_k: int = 40
     top_p: float = 0.95
+    tfs_z: float = 1.00
+    typical_p: float = 1.00
     temp: float = 0.80
     repeat_penalty: float = 1.10
+    repeat_last_n: int = 64
+    frequency_penalty: float = 0.0
+    presence_penalty: float = 0.0
+    mirostat: int = 0
+    mirostat_tau: float = 5.0
+    mirostat_eta: float = 0.1
 
     model: str = "./models/llama-7B/ggml-model.bin"
     prompt: str = ""
+    path_session: str = ""
     input_prefix: str = " "
-
     antiprompt: List[str] = field(default_factory=list)
 
+    lora_adapter: str = ""
+    lora_base: str = ""
+
     memory_f16: bool = True
     random_prompt: bool = False
     use_color: bool = False
@@ -38,7 +51,7 @@ class GptParams:
     interactive_start: bool = False
 
     instruct: bool = False
-    ignore_eos: bool = False
+    penalize_nl: bool = True
     perplexity: bool = False
     use_mmap: bool = True
     use_mlock: bool = False
@@ -61,59 +74,42 @@ class GptParams:
     instruct_inp_suffix: str="\n\n### Response:\n\n"
 
 
-def gpt_params_parse(argv = None, params: Optional[GptParams] = None):
+def gpt_params_parse(argv = 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("-n", "--n_predict", type=int, default=128, help="number of tokens to predict (-1 = infinity)",dest="n_predict")
-    parser.add_argument("-f", "--file", type=str, default=None, help="file containing initial prompt to load",dest="file")
+    parser.add_argument("--n_parts", type=int, default=-1, help="number of model parts", dest="n_parts")
     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 tokens to predict (-1 = infinity)",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(
+        "-l",
+        "--logit-bias",
+        type=str,
+        action='append',
+        help="--logit-bias TOKEN_ID(+/-)BIAS",
+        dest="logit_bias_str"
+    )
+    parser.add_argument("--ignore-eos", action="store_true", help="ignore end of stream token and continue generating", dest="ignore_eos")
+    parser.add_argument("--top_k", type=int, default=40, help="top-k sampling",dest="top_k")
+    parser.add_argument("--top_p", type=float, default=0.95, help="top-p samplin",dest="top_p")
+    parser.add_argument("--tfs", type=float, default=1.0, help="tail free sampling, parameter z (1.0 = disabled)",dest="tfs_z")
+    parser.add_argument("--temp", type=float, default=0.80, help="temperature",dest="temp")
+    parser.add_argument("--repeat_penalty", type=float, default=1.10, help="penalize repeat sequence of tokens",dest="repeat_penalty")
+    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("--frequency_penalty", type=float, default=0.0, help="repeat alpha frequency penalty (0.0 = disabled)",dest="tfs_z")
+    parser.add_argument("--presence_penalty", type=float, default=0.0, help="repeat alpha presence penalty (0.0 = disabled)",dest="presence_penalty")
+    parser.add_argument("--mirostat", type=float, default=1.0, help="use Mirostat sampling.",dest="mirostat")
+    parser.add_argument("--mirostat_ent", type=float, default=5.0, help="Mirostat target entropy, parameter tau",dest="mirostat_tau")
+    parser.add_argument("--mirostat_lr", type=float, default=0.1, help="Mirostat learning rate, parameter eta",dest="mirostat_eta")
+
     parser.add_argument("-m", "--model", type=str, default="./models/llama-7B/ggml-model.bin", help="model path",dest="model")
-    parser.add_argument(
+    parser.add_argument("-p", "--prompt", type=str, default="", help="initial prompt",dest="prompt")
-        "-i", "--interactive", action="store_true", help="run in interactive mode", dest="interactive"
+    parser.add_argument("-f", "--file", type=str, default=None, help="file containing initial prompt to load",dest="file")
-    )
+    parser.add_argument("--session", type=str, default=None, help="file to cache model state in (may be large!)",dest="path_session")
-    parser.add_argument("--embedding", action="store_true", help="", dest="embedding")
+    parser.add_argument("--in-prefix", type=str, default="", help="string to prefix user inputs with", dest="input_prefix")
-    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("--no-mmap", action="store_false",help="do not memory-map model (slower load but may reduce pageouts if not using mlock)",dest="use_mmap")
-    parser.add_argument("--mtest", action="store_true",help="compute maximum memory usage",dest="mem_test")
-    parser.add_argument("--verbose-prompt", action="store_true",help="print prompt before generation",dest="verbose_prompt")
     parser.add_argument(
         "-r",
         "--reverse-prompt",
@@ -122,16 +118,71 @@ def gpt_params_parse(argv = None, params: Optional[GptParams] = None):
         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("--lora", type=str, default="", help="apply LoRA adapter (implies --no-mmap)", dest="lora_adapter")
-    parser.add_argument("--n_parts", type=int, default=-1, help="number of model parts", dest="n_parts")
+    parser.add_argument("--lora-base", type=str, default="", help="optional model to use as a base for the layers modified by the LoRA adapter", dest="lora_base")
+
+    parser.add_argument("--memory_f32", action="store_false", help="use f32 instead of f16 for memory key+value",dest="memory_f16")
     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(
+        "--color",
+        action="store_true",
+        help="colorise output to distinguish prompt and user input from generations",
+        dest="use_color"
+    )
+    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-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("--no-penalize-nl", action="store_false", help="do not penalize newline token", dest="penalize_nl")
+    parser.add_argument("--perplexity", action="store_true", help="compute perplexity over the prompt", dest="perplexity")
+    parser.add_argument("--no-mmap", action="store_false",help="do not memory-map model (slower load but may reduce pageouts if not using mlock)",dest="use_mmap")
+    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("--verbose-prompt", action="store_true",help="print prompt before generation",dest="verbose_prompt")
+
+    #Custom args
     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")
+
+    parser.add_argument(
+        "--interactive-start",
+        action="store_true",
+        help="run in interactive mode",
+        dest="interactive"
+    )
+
     args = parser.parse_args(argv)
-    return args
+    
+    logit_bias_str = args.logit_bias_str
+    delattr(args, "logit_bias_str")
+    params = GptParams(**vars(args))
+
+    if (params.lora_adapter):
+        params.use_mmap = False
+
+    if (logit_bias_str != None):
+        for i in logit_bias_str:
+            if (m := re.match(r"(\d+)([-+]\d+)", i)):
+                params.logit_bias[int(m.group(1))] = int(m.group(2))
+
+    return params
 
 def gpt_random_prompt(rng):
     return [
@@ -148,4 +199,4 @@ def gpt_random_prompt(rng):
     ][rng % 10]
 
 if __name__ == "__main__":
-    print(GptParams(gpt_params_parse()))
+    print(gpt_params_parse())

examples/low_level_api/low_level_api_chat_cpp.py

@@ -10,9 +10,10 @@ Quirks:
    You should also still be feeding the model with a "primer" prompt that 
    shows it the expected format.
 """
+import ctypes
 import sys
 from time import time
-from os import cpu_count
+from os import cpu_count, path
 
 import llama_cpp
 from common import GptParams, gpt_params_parse, gpt_random_prompt
@@ -77,6 +78,7 @@ specified) expect poor results""", file=sys.stderr)
 		# runtime args
 		self.input_consumed = 0
 		self.n_past = 0
+		self.n_session_consumed = 0
 		self.first_antiprompt = []
 		self.remaining_tokens = self.params.n_predict
 		self.output_echo = self.params.input_echo
@@ -94,6 +96,19 @@ specified) expect poor results""", file=sys.stderr)
 		if (not self.ctx):
 			raise RuntimeError(f"error: failed to load model '{self.params.model}'")
 
+		if (self.params.ignore_eos):
+			self.params.logit_bias[llama_cpp.llama_token_eos()] = -float("inf")
+
+		if (len(self.params.lora_adapter) > 0):
+			if (llama_cpp.llama_apply_lora_from_file(
+				self.ctx, 
+				self.params.lora_adapter, 
+				self.params.lora_base if len(self.params.lora_base) > 0 else None,
+				self.params.n_threads
+			) != 0):
+				print("error: failed to apply lora adapter")
+				return
+
 		print(file=sys.stderr)
 		print(f"system_info: n_threads = {self.params.n_threads} / {cpu_count()} \
 | {llama_cpp.llama_print_system_info().decode('utf8')}", file=sys.stderr)
@@ -117,13 +132,49 @@ specified) expect poor results""", file=sys.stderr)
 			with open(self.params.file) as f:
 				self.params.prompt = f.read()
 
+		self.session_tokens: list[llama_cpp.llama_token] = []
+		if (len(self.params.path_session) > 0):
+			print(f"attempting to load saved session from '{self.params.path_session}'", file=sys.stderr)
+
+			if (path.exists(self.params.path_session)):
+				_session_tokens = (llama_cpp.llama_token * (self.params.n_ctx))()
+				_n_token_count_out = llama_cpp.c_int()
+				if (llama_cpp.llama_load_session_file(
+					self.ctx, 
+					self.params.path_session.encode("utf8"),
+					_session_tokens,
+					self.params.n_ctx,
+					ctypes.byref(_n_token_count_out)
+				) != 0):
+					print(f"error: failed to load session file '{self.params.path_session}'", file=sys.stderr)
+					return
+				self.session_tokens = _session_tokens[:_n_token_count_out]
+				print(f"loaded a session with prompt size of {_n_token_count_out} tokens", file=sys.stderr)
+			else:
+				print(f"session file does not exist, will create", file=sys.stderr)
+
 		# tokenize the prompt
 		self.embd = []
 		self.embd_inp = self._tokenize(self.params.prompt)
 
-		if (len(self.embd_inp) > self.params.n_ctx - 4):
+		if (len(self.embd_inp) > self.n_ctx - 4):
 			raise RuntimeError(f"error: prompt is too long ({len(self.embd_inp)} tokens, max {self.params.n_ctx - 4})")
 
+		# debug message about similarity of saved session, if applicable
+		n_matching_session_tokens = 0
+		if len(self.session_tokens) > 0:
+			for id in self.session_tokens:
+				if n_matching_session_tokens >= len(self.embd_inp) or id != self.embd_inp[n_matching_session_tokens]:
+					break
+				n_matching_session_tokens += 1
+			
+			if n_matching_session_tokens >= len(self.embd_inp):
+				print(f"session file has exact match for prompt!")
+			elif n_matching_session_tokens < (len(self.embd_inp) / 2):
+				print(f"warning: session file has low similarity to prompt ({n_matching_session_tokens} / {len(self.embd_inp)} tokens); will mostly be reevaluated")
+			else:
+				print(f"session file matches {n_matching_session_tokens} / {len(self.embd_inp)} tokens of prompt")
+
 		# number of tokens to keep when resetting context
 		if (self.params.n_keep < 0 or self.params.n_keep > len(self.embd_inp) or self.params.instruct):
 			self.params.n_keep = len(self.embd_inp)
@@ -132,6 +183,7 @@ specified) expect poor results""", file=sys.stderr)
 		self.inp_suffix = self._tokenize(self.params.instruct_inp_suffix, False)
 
 		# in instruct mode, we inject a prefix and a suffix to each input by the user
+		self.antiecho = None
 		if (self.params.instruct):
 			self.params.interactive_start = True
 			_ptn = self._tokenize(self.params.instruct_inp_prefix.strip(), False)
@@ -171,16 +223,24 @@ number of tokens in prompt = {len(self.embd_inp)}""", file=sys.stderr)
 			if len(self.params.input_prefix) > 0:
 				print(f"Input prefix: '{self.params.input_prefix}'", file=sys.stderr)
 
-		print(f"""sampling: temp = {self.params.temp},\
+		print(f"""sampling: repeat_last_n = {self.params.repeat_last_n},\
+repeat_penalty = {self.params.repeat_penalty},\
+presence_penalty = {self.params.presence_penalty},\
+frequency_penalty = {self.params.frequency_penalty},\
 top_k = {self.params.top_k},\
+tfs_z = {self.params.tfs_z},\
 top_p = {self.params.top_p},\
-repeat_last_n = {self.params.repeat_last_n},\
+typical_p = {self.params.typical_p},\
-repeat_penalty = {self.params.repeat_penalty}
+temp = {self.params.temp},\
+mirostat = {self.params.mirostat},\
+mirostat_lr = {self.params.mirostat_eta},\
+mirostat_ent = {self.params.mirostat_tau},\
 
-generate: n_ctx = {self.n_ctx}, \
+generate: n_ctx = {self.n_ctx},\
-n_batch = {self.params.n_batch}, \
+n_batch = {self.params.n_batch},\
-n_predict = {self.params.n_predict}, \
+n_predict = {self.params.n_predict},\
 n_keep = {self.params.n_keep}
+
 """, file=sys.stderr)
 
 		# determine antiprompt tokens
@@ -198,6 +258,9 @@ n_keep = {self.params.n_keep}
 """, file=sys.stderr)
 		self.set_color(CONSOLE_COLOR_PROMPT)
 
+		self.need_to_save_session = len(self.params.path_session) > 0 and n_matching_session_tokens < (len(self.embd_inp) * 3 / 4)
+
+
 	# tokenize a prompt
 	def _tokenize(self, prompt, bos=True):
 		_arr = (llama_cpp.llama_token * (len(prompt) + 1))()
@@ -229,31 +292,117 @@ n_keep = {self.params.n_keep}
 						self.n_ctx - int(n_left/2) - len(self.embd):-len(self.embd)
 					]
 					self.embd = _insert + self.embd
+					self.params.path_session = ""
+
+				# try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
+				# REVIEW
+				if self.n_session_consumed < len(self.session_tokens):
+					for i in range(len(self.embd)):
+						if self.embd[i] != self.session_tokens[self.n_session_consumed]:
+							self.session_tokens = self.session_tokens[:self.n_session_consumed]
+							break
+						
+						self.n_past += 1
+						self.n_session_consumed += 1
+						
+						if self.n_session_consumed >= len(self.session_tokens):
+							i += 1
+							break
+					
+					if i > 0:
+						self.embd = self.embd[i:]
+
+				# evaluate tokens in batches
+				# embd is typically prepared beforehand to fit within a batch, but not always
+				#TODO BUG: The batching code causes nonsensical generation
+				"""for i in range(0, len(self.embd), self.params.n_batch):
+					n_eval = self.params.n_batch
+					_arr = (llama_cpp.llama_token * n_eval)(*self.embd[i:i + n_eval])
+					if llama_cpp.llama_eval(self.ctx, _arr, n_eval, self.n_past, self.params.n_threads) != 0:
+						print(f"failed to eval")
+						return
+					
+					self.n_past += n_eval"""
 
 				if (llama_cpp.llama_eval(
 					self.ctx, (llama_cpp.llama_token * len(self.embd))(*self.embd), len(self.embd), self.n_past, self.params.n_threads
 				) != 0):
 					raise Exception("Failed to llama_eval!")
 
+				if len(self.embd) > 0 and not len(self.params.path_session) > 0:
+					self.session_tokens.extend(self.embd)
+					self.n_session_consumed = len(self.session_tokens)
+
 			self.n_past += len(self.embd)
 			self.embd = []
-			if len(self.embd_inp) <= self.input_consumed:
+			if len(self.embd_inp) <= self.input_consumed: #&& !is_interacting
 				# out of user input, sample next token
+				top_k = llama_cpp.llama_n_vocab(self.ctx) if self.params.top_k <= 0 else self.params.top_k
+				repeat_last_n = self.n_ctx if self.params.repeat_last_n < 0 else self.params.repeat_last_n
 
-				if (self.params.ignore_eos):
+				# optionally save the session on first sample (for faster prompt loading next time)
-					logits = llama_cpp.llama_get_logits(self.ctx)
+				if len(self.params.path_session) > 0 and self.need_to_save_session:
-					logits[llama_cpp.llama_token_eos()] = llama_cpp.c_float(0)
+					self.need_to_save_session = False
+					llama_cpp.llama_save_session_file(
+						self.ctx,
+						self.params.path_session.encode("utf8"),
+						self.session_tokens,
+						len(self.session_tokens)
+					)
+
+				id = 0
+
+				logits = llama_cpp.llama_get_logits(self.ctx)
+				n_vocab = llama_cpp.llama_n_vocab(self.ctx)
+
+				# Apply params.logit_bias map
+				for key, value in self.params.logit_bias.items():
+					logits[key] += value
+
+				_arr = (llama_cpp.llama_token_data * n_vocab)(*[
+					llama_cpp.llama_token_data(token_id, logits[token_id], 0.0)
+					for token_id in range(n_vocab)
+				])
+				candidates_p = llama_cpp.ctypes.pointer(llama_cpp.llama_token_data_array(_arr, len(_arr), False))
+
+				# Apply penalties
+				nl_logit = logits[llama_cpp.llama_token_nl()]
+				last_n_repeat = min(len(self.last_n_tokens), repeat_last_n, self.n_ctx)
+
+				_arr = (llama_cpp.llama_token * last_n_repeat)(*self.last_n_tokens[len(self.last_n_tokens) - last_n_repeat:])
+				llama_cpp.llama_sample_repetition_penalty(self.ctx, candidates_p,
+					_arr,
+					last_n_repeat, self.params.repeat_penalty)
+				llama_cpp.llama_sample_frequency_and_presence_penalties(self.ctx, candidates_p,
+					_arr,
+					last_n_repeat, self.params.frequency_penalty, self.params.presence_penalty)
+
+				if not self.params.penalize_nl:
+					logits[llama_cpp.llama_token_nl()] = nl_logit
+
+				if self.params.temp <= 0:
+					# Greedy sampling
+					id = llama_cpp.llama_sample_token_greedy(self.ctx, candidates_p)
+				else:
+					if self.params.mirostat == 1:
+						mirostat_mu = 2.0 * self.params.mirostat_tau
+						mirostat_m = 100
+						llama_cpp.llama_sample_temperature(self.ctx, candidates_p, self.params.temp)
+						id = llama_cpp.llama_sample_token_mirostat(self.ctx, candidates_p, self.params.mirostat_tau, self.params.mirostat_eta, mirostat_m, mirostat_mu)
+					elif self.params.mirostat == 2:
+						mirostat_mu = 2.0 * self.params.mirostat_tau
+						llama_cpp.llama_sample_temperature(self.ctx, candidates_p, self.params.temp)
+						id = llama_cpp.llama_sample_token_mirostat_v2(self.ctx, candidates_p, self.params.mirostat_tau, self.params.mirostat_eta, mirostat_mu)
+					else:
+						# Temperature sampling
+						llama_cpp.llama_sample_top_k(self.ctx, candidates_p, top_k)
+						llama_cpp.llama_sample_tail_free(self.ctx, candidates_p, self.params.tfs_z)
+						llama_cpp.llama_sample_typical(self.ctx, candidates_p, self.params.typical_p)
+						llama_cpp.llama_sample_top_p(self.ctx, candidates_p, self.params.top_p)
+						llama_cpp.llama_sample_temperature(self.ctx, candidates_p, self.params.temp)
+						id = llama_cpp.llama_sample_token(self.ctx, candidates_p)
+				# print("`{}`".format(candidates_p.size))
 
-				_arr = self.last_n_tokens[-min(self.params.repeat_last_n, self.n_past):]
-				id = llama_cpp.llama_sample_top_p_top_k(
-					self.ctx,
-					(llama_cpp.llama_token * len(_arr))(*_arr),
-					len(_arr),
-					self.params.top_k,
-					self.params.top_p,
-					self.params.temp,
-					self.params.repeat_penalty,
-				)
 				self.last_n_tokens.pop(0)
 				self.last_n_tokens.append(id)
 
@@ -288,7 +437,7 @@ n_keep = {self.params.n_keep}
 			# display tokens
 			if self.output_echo:
 				for id in self.embd:
-					if self.params.instruct:
+					if self.antiecho != None:
 						for r in self.antiecho(id):
 							yield r
 					else:
@@ -316,7 +465,7 @@ n_keep = {self.params.n_keep}
 				if (not self.params.instruct):
 					for i in self.llama_token_eot:
 						yield i
-				break
+					break
 
 			# respect n_predict even if antiprompt is present
 			if (self.params.interactive and self.remaining_tokens <= 0 and self.params.n_predict != -1):
@@ -356,7 +505,7 @@ n_keep = {self.params.n_keep}
 	def output(self):
 		self.remaining_tokens = self.params.n_predict
 		for id in self.generate():
-			yield llama_cpp.llama_token_to_str(self.ctx, id).decode("utf-8", errors="ignore")
+			yield llama_cpp.llama_token_to_str(self.ctx, id).decode("utf-8")
 
 	# read user input
 	def read_input(self):
@@ -415,8 +564,7 @@ The transcript only includes text, it does not include markup like HTML and Mark
 {USER_NAME}: Name a color.
 {AI_NAME}: Blue
 {USER_NAME}:"""
-	args = gpt_params_parse()
+	params = gpt_params_parse()
-	params = GptParams(**vars(args))
 
 	with LLaMAInteract(params) as m:
 		m.interact()

examples/low_level_api/low_level_api_llama_cpp.py

@@ -37,6 +37,10 @@ embd = []
 last_n_size = 64
 last_n_tokens_data = [0] * last_n_size
 n_batch = 24
+last_n_repeat = 64
+repeat_penalty = 1
+frequency_penalty = 0.0
+presence_penalty = 0.0
 
 while remaining_tokens > 0:
     if len(embd) > 0:
@@ -47,15 +51,28 @@ while remaining_tokens > 0:
     n_past += len(embd)
     embd = []
     if len(embd_inp) <= input_consumed:
-        id = llama_cpp.llama_sample_top_p_top_k(
+        logits = llama_cpp.llama_get_logits(ctx)
-            ctx,
+        n_vocab = llama_cpp.llama_n_vocab(ctx)
-            (llama_cpp.c_int * len(last_n_tokens_data))(*last_n_tokens_data),
+
-            len(last_n_tokens_data),
+        _arr = (llama_cpp.llama_token_data * n_vocab)(*[
-            40,
+            llama_cpp.llama_token_data(token_id, logits[token_id], 0.0)
-            0.8,
+            for token_id in range(n_vocab)
-            0.2,
+        ])
-            1.0 / 0.85,
+        candidates_p = llama_cpp.ctypes.pointer(llama_cpp.llama_token_data_array(_arr, len(_arr), False))
-        )
+
+        _arr = (llama_cpp.c_int * len(last_n_tokens_data))(*last_n_tokens_data)
+        llama_cpp.llama_sample_repetition_penalty(ctx, candidates_p,
+            _arr,
+            last_n_repeat, repeat_penalty)
+        llama_cpp.llama_sample_frequency_and_presence_penalties(ctx, candidates_p,
+            _arr,
+            last_n_repeat, frequency_penalty, presence_penalty)
+
+        llama_cpp.llama_sample_top_k(ctx, candidates_p, 40)
+        llama_cpp.llama_sample_top_p(ctx, candidates_p, 0.8)
+        llama_cpp.llama_sample_temperature(ctx, candidates_p, 0.2)
+        id = llama_cpp.llama_sample_token(ctx, candidates_p)
+
         last_n_tokens_data = last_n_tokens_data[1:] + [id]
         embd.append(id)
         input_noecho = False