Better llama.cpp interoperability

Has some too many newline issues so WIP

examples/common.py

@@ -0,0 +1,135 @@
+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 = " "
+    fix_prefix: str = ""
+    output_postfix: str = ""
+    input_echo: bool = True,
+
+    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
+
+    # Default instructions for Alpaca
+    # switch to "Human" and "Assistant" for Vicuna.
+    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()
+    parser.add_argument("-h", "--help", action="store_true", help="show this help message and exit")
+    parser.add_argument("-s", "--seed", type=int, default=-1, help="",dest="seed")
+    parser.add_argument("-t", "--threads", type=int, default=1, help="",dest="n_threads")
+    parser.add_argument("-p", "--prompt", type=str, default="", help="",dest="prompt")
+    parser.add_argument("-f", "--file", type=str, default=None, help="")
+    parser.add_argument("-c", "--ctx_size", type=int, default=512, help="",dest="n_ctx")
+    parser.add_argument("--memory_f32", action="store_false", help="",dest="memory_f16")
+    parser.add_argument("--top_p", type=float, default=0.9, help="",dest="top_p")
+    parser.add_argument("--temp", type=float, default=1.0, help="",dest="temp")
+    parser.add_argument("--repeat_last_n", type=int, default=64, help="",dest="repeat_last_n")
+    parser.add_argument("--repeat_penalty", type=float, default=1.0, help="",dest="repeat_penalty")
+    parser.add_argument("-b", "--batch_size", type=int, default=8, help="",dest="n_batch")
+    parser.add_argument("--keep", type=int, default=0, help="",dest="n_keep")
+    parser.add_argument("-m", "--model", type=str, help="",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="", dest="interactive_start")
+    parser.add_argument(
+        "--interactive-first",
+        action="store_true",
+        help="run in interactive mode and wait for input right away",
+        dest="interactive"
+    )
+    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",dest="use_mlock")
+    parser.add_argument("--mtest", action="store_true",dest="mem_test")
+    parser.add_argument(
+        "-r",
+        "--reverse-prompt",
+        type=str,
+        action='append',
+        help="run in interactive mode and 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="", dest="perplexity")
+    parser.add_argument("--ignore-eos", action="store_true", help="", dest="ignore_eos")
+    parser.add_argument("--n_parts", type=int, default=-1, help="", dest="n_parts")
+    parser.add_argument("--random-prompt", action="store_true", help="", dest="random_prompt")
+    parser.add_argument("--in-prefix", type=str, default=" ", help="", dest="input_prefix")
+    parser.add_argument("--fix-prefix", type=str, default=" ", help="", dest="fix_prefix")
+    parser.add_argument("--out-postfix", type=str, default="", help="", dest="output_postfix")
+    parser.add_argument("--input-noecho", action="store_false", help="", 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_chatllama_cpp.py

@@ -12,102 +12,182 @@ Quirks:
    You should also still be feeding the model with a "primer" prompt that 
    shows it the expected format.
 """
+import sys
+from time import time
+from os import cpu_count
+
 import llama_cpp
+from common import GptParams, gpt_params_parse, gpt_random_prompt
+
+ANSI_COLOR_RESET = "\x1b[0m"
+ANSI_COLOR_YELLOW = "\x1b[33m"
+ANSI_BOLD = "\x1b[1m"
+ANSI_COLOR_GREEN = "\x1b[32m"
+
+CONSOLE_COLOR_DEFAULT = ANSI_COLOR_RESET
+CONSOLE_COLOR_PROMPT = ANSI_COLOR_YELLOW
+CONSOLE_COLOR_USER_INPUT = ANSI_BOLD + ANSI_COLOR_GREEN
 
 # A LLaMA interactive session
 class LLaMAInteract:
-	def __init__(self,
-		primer: str="",
-		model: str="./models/30B/ggml-model-q4_0.bin",
-		instruct: bool=False,
-		n_ctx: int=1024,
-		seed: int=0,
-		n_threads: int=8,
-		antiprompt: list[str]=[],
-		input_echo: bool=True,
-		n_predict: int=20,
-		n_keep: int=0,
-		n_batch: int=8,
-		repeat_last_n: int=64,
-		top_k: int=50,
-		top_p: float=1.,
-		temp: float=1.0,
-		repeat_penalty: float=1,
-		init_break: bool=True,
-		instruct_inp_prefix: str="\n\n### Instruction:\n\n",
-		instruct_inp_suffix: str="\n\n### Response:\n\n",
-	) -> None:
+	def __init__(self, params: GptParams) -> None:
 		# input args
-		self.instruct = instruct
-		self.n_threads = n_threads
-		self.input_echo = input_echo
-		self.n_predict = n_predict
-		self.n_keep = n_keep
-		self.n_batch = n_batch
-		self.repeat_last_n = repeat_last_n
-		self.top_k=top_k
-		self.top_p=top_p
-		self.temp=temp
-		self.repeat_penalty=repeat_penalty
-		self.init_break = init_break
+		self.params = params
+
+		if (self.params.perplexity):
+			raise NotImplementedError("""************
+please use the 'perplexity' tool for perplexity calculations
+************""")
+
+		if (self.params.embedding):
+			raise NotImplementedError("""************
+please use the 'embedding' tool for embedding calculations
+************""")
+
+		if (self.params.n_ctx > 2048):
+			print(f"""warning: model does not support \
+context sizes greater than 2048 tokens ({self.params.n_ctx} \
+specified) expect poor results""", file=sys.stderr)
+
+		if (self.params.seed <= 0):
+			self.params.seed = int(time())
+
+		print(f"seed = {self.params.seed}", file=sys.stderr)
+
+		if (self.params.random_prompt):
+			self.params.prompt = gpt_random_prompt(self.params.seed)
 
 		# runtime args
 		self.input_consumed = 0
 		self.embd = []
-		self.embd_inp = []
 		self.n_past = 0
 		self.first_antiprompt = []
-		self.remaining_tokens = self.n_predict
-		self.output_echo = input_echo
+		self.remaining_tokens = self.params.n_predict
+		self.output_echo = self.params.input_echo
 
 		# model load
 		self.lparams = llama_cpp.llama_context_default_params()
-		self.lparams.n_ctx = n_ctx
-		self.lparams.seed = seed
-		self.ctx = llama_cpp.llama_init_from_file(model.encode("utf8"), self.lparams)
+		self.lparams.n_ctx = self.params.n_ctx
+		self.lparams.n_parts = self.params.n_parts
+		self.lparams.seed = self.params.seed
+		self.lparams.memory_f16 = self.params.memory_f16
+		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):
+			raise RuntimeError(f"error: failed to load model '{self.params.model}'")
+
+		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)
 
 		# determine the required inference memory per token:
-		tmp = [0, 1, 2, 3]
-		llama_cpp.llama_eval(self.ctx, (llama_cpp.c_int * len(tmp))(*tmp), len(tmp), 0, self.n_threads)
-
-		# determine newline token
-		self.llama_token_newline = self._tokenize("\n", False)
-		self.inp_prefix = self._tokenize(instruct_inp_prefix)
-		self.inp_suffix = self._tokenize(instruct_inp_suffix, False)
-
-		# add instruction as antiprompt
-		if (self.instruct):
-			self.first_antiprompt.append(self._tokenize(instruct_inp_prefix.strip(), False))
-
-		# primer feed
-		if (len(primer) > 0):
-			self.embd_inp += self._tokenize(primer)
-
-		# number of tokens to keep when resetting context
-		if (self.n_keep < 0 or self.n_keep > len(self.embd_inp) or self.instruct):
-			self.n_keep = len(self.embd_inp)
+		if (self.params.mem_test):
+			tmp = [0, 1, 2, 3]
+			llama_cpp.llama_eval(self.ctx, (llama_cpp.c_int * len(tmp))(*tmp), len(tmp), 0, self.n_threads)
+			llama_cpp.llama_print_timings(self.ctx)
+			self.exit()
+			return
 
 		# create internal context
 		self.n_ctx = llama_cpp.llama_n_ctx(self.ctx)
-		self.last_n_tokens = [0]*self.n_ctx #TODO: deque doesnt support slices
+
+		# Add a space in front of the first character to match OG llama tokenizer behavior
+		self.params.prompt = " " + self.params.prompt
+
+		# tokenize the prompt
+		self.embd_inp = self._tokenize(self.params.prompt)
+
+		if (len(self.embd_inp) > self.params.n_ctx - 4):
+			raise RuntimeError(f"error: prompt is too long ({len(self.embd_inp)} tokens, max {self.params.n_ctx - 4})")
+
+		# 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)
+
+		self.inp_prefix = self._tokenize(self.params.instruct_inp_prefix)
+		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
+		if (self.params.instruct):
+			self.params.interactive_start = True
+			self.first_antiprompt.append(self._tokenize(self.params.instruct_inp_prefix.strip(), False))
+
+		# enable interactive mode if reverse prompt or interactive start is specified
+		if (len(self.params.antiprompt) != 0 or self.params.interactive_start):
+			self.params.interactive = True
+
+		# determine newline token
+		self.llama_token_newline = self._tokenize("\n", False)
+
+		if (self.params.verbose_prompt):
+			print(f"""
+prompt: '{self.params.prompt}'
+number of tokens in prompt = {len(self.embd_inp)}""", file=sys.stderr)
+
+			for i in range(len(self.embd_inp)):
+				print(f"{self.embd_inp[i]} -> '{llama_cpp.llama_token_to_str(self.ctx, self.embd_inp[i])}'", file=sys.stderr)
+
+			if (self.params.n_keep > 0):
+				print("static prompt based on n_keep: '")
+				for i in range(self.params.n_keep):
+					print(llama_cpp.llama_token_to_str(self.ctx, self.embd_inp[i]), file=sys.stderr)
+				print("'", file=sys.stderr)
+			print(file=sys.stderr)
+
+		if (self.params.interactive):
+			print("interactive mode on.", file=sys.stderr)
+
+			if (len(self.params.antiprompt) > 0):
+				for antiprompt in self.params.antiprompt:
+					print(f"Reverse prompt: '{antiprompt}'", 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},\
+top_k = {self.params.top_k},\
+top_p = {self.params.top_p},\
+repeat_last_n = {self.params.repeat_last_n},\
+repeat_penalty = {self.params.repeat_penalty}
+
+generate: n_ctx = {self.n_ctx}, \
+n_batch = {self.params.n_batch}, \
+n_predict = {self.params.n_predict}, \
+n_keep = {self.params.n_keep}
+""", file=sys.stderr)
 
 		# determine antiprompt tokens
-		for i in antiprompt:
+		for i in self.params.antiprompt:
 			self.first_antiprompt.append(self._tokenize(i, False))
 
+		self.last_n_tokens = [0]*self.n_ctx #TODO: deque doesnt support slices
+
+		if (params.interactive):
+			print("""== Running in interactive mode. ==
+ - Press Ctrl+C to interject at any time.
+ - Press Return to return control to LLaMa.
+ - If you want to submit another line, end your input in '\\'.
+
+""", file=sys.stderr)
+		self.set_color(CONSOLE_COLOR_PROMPT)
+
 	# tokenize a prompt
 	def _tokenize(self, prompt, bos=True):
 		_arr = (llama_cpp.llama_token * (len(prompt) + 1))()
 		_n = llama_cpp.llama_tokenize(self.ctx, prompt.encode("utf8"), _arr, len(_arr), bos)
 		return _arr[:_n]
 
-	# if an antiprompt is present
 	def use_antiprompt(self):
 		return len(self.first_antiprompt) > 0
 
+	def set_color(self, c):
+		if (self.params.use_color):
+			print(c)
+
 	# generate tokens
 	def generate(self):
-		while self.remaining_tokens > 0 or self.use_antiprompt():
+		while self.remaining_tokens > 0 or self.params.interactive:
 			# predict
 			if len(self.embd) > 0:
 				# infinite text generation via context swapping
@@ -115,8 +195,8 @@ class LLaMAInteract:
 				# - take the n_keep first tokens from the original prompt (via n_past)
 				# - take half of the last (n_ctx - n_keep) tokens and recompute the logits in a batch
 				if (self.n_past + len(self.embd) > self.n_ctx):
-					n_left = self.n_past - self.n_keep
-					self.n_past = self.n_keep
+					n_left = self.n_past - self.params.n_keep
+					self.n_past = self.params.n_keep
 
 					# insert n_left/2 tokens at the start of embd from last_n_tokens
 					_insert = self.last_n_tokens[
@@ -125,7 +205,7 @@ class LLaMAInteract:
 					self.embd = _insert + self.embd
 
 				if (llama_cpp.llama_eval(
-					self.ctx, (llama_cpp.llama_token * len(self.embd))(*self.embd), len(self.embd), self.n_past, self.n_threads
+					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!")
 
@@ -133,24 +213,28 @@ class LLaMAInteract:
 			self.embd = []
 			if len(self.embd_inp) <= self.input_consumed:
 				# out of user input, sample next token
-				_arr = self.last_n_tokens[-min(self.repeat_last_n, self.n_past):]
+
+				#TODO: self.params.ignore_eos
+
+				_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.top_k,
-					self.top_p,
-					self.temp,
-					self.repeat_penalty,
+					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)
 
 				# replace end of text token with newline token when in interactive mode
-				if (id == llama_cpp.llama_token_eos() and self.use_antiprompt() and not self.instruct):
+				if (id == llama_cpp.llama_token_eos() and self.params.interactive and not self.params.instruct):
 					id = self.llama_token_newline[0]
-					# tokenize and inject first reverse prompt
-					self.embd_inp += self.first_antiprompt[0]
+					if (self.use_antiprompt()):
+						# tokenize and inject first reverse prompt
+						self.embd_inp += self.first_antiprompt[0]
 
 				# add it to the context
 				self.embd.append(id)
@@ -162,7 +246,7 @@ class LLaMAInteract:
 				self.remaining_tokens -= 1
 			else:
 				# output to console if input echo is on
-				self.output_echo = self.input_echo
+				self.output_echo = self.params.input_echo
 
 				# some user input remains from prompt or interaction, forward it to processing
 				while len(self.embd_inp) > self.input_consumed:
@@ -170,7 +254,7 @@ class LLaMAInteract:
 					self.last_n_tokens.pop(0)
 					self.last_n_tokens.append(self.embd_inp[self.input_consumed])
 					self.input_consumed += 1
-					if len(self.embd) >= self.n_batch:
+					if len(self.embd) >= self.params.n_batch:
 						break
 
 			# display tokens
@@ -178,7 +262,11 @@ class LLaMAInteract:
 				for id in self.embd:
 					yield id
 
-			if (len(self.embd_inp) <= self.input_consumed):
+			# reset color to default if we there is no pending user input
+			if (self.params.input_echo and len(self.embd_inp) == self.input_consumed):
+				self.set_color(CONSOLE_COLOR_DEFAULT)
+
+			if (self.params.interactive and len(self.embd_inp) <= self.input_consumed):
 				# if antiprompt is present, stop
 				if (self.use_antiprompt()):
 					if True in [
@@ -188,26 +276,36 @@ class LLaMAInteract:
 						break
 
 				# if we are using instruction mode, and we have processed the initial prompt
-				if (self.init_break):
+				if (self.n_past > 0 and self.params.interactive_start):
 					break
 
-			# if end of generation
+			# end of text token
 			if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_token_eos():
+				if (not self.params.instruct):
+					for i in " [end of text]\n":
+						yield i
 				break
 
 			# respect n_predict even if antiprompt is present
-			if (self.use_antiprompt() and self.remaining_tokens <= 0 and self.n_predict != -1):
-				if not self.instruct:
+			if (self.params.interactive and self.remaining_tokens <= 0 and self.params.n_predict != -1):
+				# If we arent in instruction mode, fix the current generation by appending the antiprompt.
+				# Makes it so if chat ends prematurely you dont append the AI's text etc.
+				if not self.params.instruct:
 					self.embd_inp += self.first_antiprompt[0]
+				self.n_remain = self.params.n_predict
 				break
 
-		self.init_break = False
+		self.params.interactive_start = False
 
 	def __enter__(self):
 		return self
 
 	def __exit__(self, type, value, tb):
+		self.exit()
+
+	def exit(self):
 		llama_cpp.llama_free(self.ctx)
+		self.set_color(CONSOLE_COLOR_DEFAULT)
 
 	# return past text
 	def past(self):
@@ -216,18 +314,51 @@ class LLaMAInteract:
 
 	# write input
 	def input(self, prompt: str):
-		if (self.instruct and self.last_n_tokens[-len(self.inp_prefix):] != self.inp_prefix):
+		if (self.params.instruct and self.last_n_tokens[-len(self.inp_prefix):] != self.inp_prefix):
 			self.embd_inp += self.inp_prefix
 		self.embd_inp += self._tokenize(prompt)
-		if (self.instruct):
+		if (self.params.instruct):
 			self.embd_inp += self.inp_suffix
 
 	# write output
 	def output(self):
-		self.remaining_tokens = self.n_predict
+		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")
 
+	# read user input
+	def read_input(self):
+		out = ""
+		while (t := input()).endswith("\\"):
+			out += t[:-1] + "\n"
+		return out + t + "\n"
+
+	# interactive mode
+	def interact(self):
+		for i in self.output():
+			print(i,end="",flush=True)
+		self.params.input_echo = False
+
+		while self.params.interactive:
+			self.set_color(CONSOLE_COLOR_USER_INPUT)
+			if (self.params.instruct):
+				print('\n> ', end="")
+				self.input(self.read_input())
+			else:
+				print(self.params.input_prefix, end="")
+				self.input(f"{self.params.input_prefix}{self.read_input()}{self.params.output_postfix}")
+				print(self.params.output_postfix,end="")
+			self.set_color(CONSOLE_COLOR_DEFAULT)
+
+			try:
+				for i in self.output():
+					print(i,end="",flush=True)
+			except KeyboardInterrupt:
+				self.set_color(CONSOLE_COLOR_DEFAULT)
+				if not self.params.instruct:
+					print(self.params.fix_prefix,end="")
+					self.input(self.params.fix_prefix)
+
 if __name__ == "__main__":
 	from datetime import datetime
 
@@ -252,41 +383,12 @@ 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 = GptParams(args)
 
-	print("Loading model...")
-	with LLaMAInteract(prompt, 
-		model="./models/30B/ggml-model-q4_0.bin",
-		n_ctx=2048, 
-		antiprompt=[f"\n{USER_NAME}:"], 
-		repeat_last_n=256, 
-		n_predict=2048,
-		temp=0.7, top_p=0.5, top_k=40, repeat_penalty=1.17647
-	) as m:
-		print("Loaded model!")
+	if (args.file):
+		with open(args.file) as f:
+			params.prompt = f.read()
 
-		for i in m.output():
-			print(i,end="",flush=True)
-		m.input_echo = False
-
-		def inp():
-			out = ""
-			while (t := input()).endswith("\\"):
-				out += t[:-1] + "\n"
-			return out + t + "\n"
-
-		while True:
-			if (m.instruct):
-				print('\n> ', end="")
-				m.input(inp())
-			else:
-				print(f" ", end="")
-				m.input(f" {inp()}{AI_NAME}:")
-				print(f"{AI_NAME}: ",end="")
-
-			try:
-				for i in m.output():
-					print(i,end="",flush=True)
-			except KeyboardInterrupt:
-				if not m.instruct:
-					print(f"\n{USER_NAME}:",end="")
-					m.input(f"\n{USER_NAME}:")
+	with LLaMAInteract() as m:
+		m.interact()