Fix low_level_api_chat_cpp example to match current API (#1086)

* Fix low_level_api_chat_cpp to match current API

* Fix low_level_api_chat_cpp to match current API

* Using None instead of empty string to so that default prompt template can be used if no prompt provided

---------

Co-authored-by: Anil Pathak <[email protected]>

Author: aniljava

examples/low_level_api/common.py

@@ -106,7 +106,7 @@ def gpt_params_parse(argv = None):
     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("-p", "--prompt", type=str, default="", help="initial prompt",dest="prompt")
+    parser.add_argument("-p", "--prompt", type=str, default=None, 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("--session", type=str, default=None, help="file to cache model state in (may be large!)",dest="path_session")
     parser.add_argument("--in-prefix", type=str, default="", help="string to prefix user inputs with", dest="input_prefix")

examples/low_level_api/low_level_api_chat_cpp.py

@@ -62,7 +62,7 @@ def __init__(self, params: GptParams) -> None:
 		self.multibyte_fix = []
 
 		# model load
-		self.lparams = llama_cpp.llama_context_default_params()
+		self.lparams = llama_cpp.llama_model_default_params()
 		self.lparams.n_ctx = self.params.n_ctx
 		self.lparams.n_parts = self.params.n_parts
 		self.lparams.seed = self.params.seed
@@ -72,7 +72,11 @@ def __init__(self, params: GptParams) -> None:
 
 		self.model = llama_cpp.llama_load_model_from_file(
 			self.params.model.encode("utf8"), self.lparams)
-		self.ctx = llama_cpp.llama_new_context_with_model(self.model, self.lparams)
+
+		# Context Params.
+		self.cparams = llama_cpp.llama_context_default_params()
+
+		self.ctx = llama_cpp.llama_new_context_with_model(self.model, self.cparams)
 		if (not self.ctx):
 			raise RuntimeError(f"error: failed to load model '{self.params.model}'")
 
@@ -244,7 +248,7 @@ def __init__(self, params: GptParams) -> None:
 	# tokenize a prompt
 	def _tokenize(self, prompt, bos=True):
 		_arr = (llama_cpp.llama_token * ((len(prompt) + 1) * 4))()
-		_n = llama_cpp.llama_tokenize(self.ctx, prompt.encode("utf8", errors="ignore"), _arr, len(_arr), bos)
+		_n = llama_cpp.llama_tokenize(self.model, prompt.encode("utf8", errors="ignore"), len(prompt), _arr, len(_arr), bos, False)
 		return _arr[:_n]
 
 	def set_color(self, c):
@@ -304,7 +308,7 @@ def generate(self):
 					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
+					self.ctx, (llama_cpp.llama_token * len(self.embd))(*self.embd), len(self.embd), self.n_past
 				) != 0):
 					raise Exception("Failed to llama_eval!")
 
@@ -332,7 +336,7 @@ def generate(self):
 				id = 0
 
 				logits = llama_cpp.llama_get_logits(self.ctx)
-				n_vocab = llama_cpp.llama_n_vocab(self.ctx)
+				n_vocab = llama_cpp.llama_n_vocab(self.model)
 
 				# Apply params.logit_bias map
 				for key, value in self.params.logit_bias.items():
@@ -349,12 +353,20 @@ def generate(self):
 				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, llama_cpp.c_float(self.params.repeat_penalty))
-				llama_cpp.llama_sample_frequency_and_presence_penalties(self.ctx, candidates_p,
-					_arr,
-					last_n_repeat, llama_cpp.c_float(self.params.frequency_penalty), llama_cpp.c_float(self.params.presence_penalty))
+				llama_cpp.llama_sample_repetition_penalties(
+					ctx=self.ctx,
+					candidates=candidates_p,
+					last_tokens_data = _arr,
+					penalty_last_n = last_n_repeat,
+					penalty_repeat = llama_cpp.c_float(self.params.repeat_penalty),
+					penalty_freq = llama_cpp.c_float(self.params.frequency_penalty),
+					penalty_present = llama_cpp.c_float(self.params.presence_penalty),
+				)
+				
+				# NOT PRESENT IN CURRENT VERSION ?
+				# llama_cpp.llama_sample_frequency_and_presence_penalti(self.ctx, candidates_p,
+				# 	_arr,
+				# 	last_n_repeat, llama_cpp.c_float(self.params.frequency_penalty), llama_cpp.c_float(self.params.presence_penalty))
 
 				if not self.params.penalize_nl:
 					logits[llama_cpp.llama_token_nl()] = nl_logit
@@ -473,7 +485,7 @@ def exit(self):
 	def token_to_str(self, token_id: int) -> bytes:
 		size = 32
 		buffer = (ctypes.c_char * size)()
-		n = llama_cpp.llama_token_to_piece_with_model(
+		n = llama_cpp.llama_token_to_piece(
 			self.model, llama_cpp.llama_token(token_id), buffer, size)
 		assert n <= size
 		return bytes(buffer[:n])
@@ -532,6 +544,9 @@ def interact(self):
 			print(i,end="",flush=True)
 		self.params.input_echo = False
 
+        # Using string instead of tokens to check for antiprompt,
+		# It is more reliable than tokens for interactive mode.
+		generated_str = ""
 		while self.params.interactive:
 			self.set_color(util.CONSOLE_COLOR_USER_INPUT)
 			if (self.params.instruct):
@@ -546,6 +561,10 @@ def interact(self):
 			try:
 				for i in self.output():
 					print(i,end="",flush=True)
+					generated_str += i
+					for ap in self.params.antiprompt:
+						if generated_str.endswith(ap):							
+							raise KeyboardInterrupt
 			except KeyboardInterrupt:
 				self.set_color(util.CONSOLE_COLOR_DEFAULT)
 				if not self.params.instruct:
@@ -561,7 +580,7 @@ def interact(self):
 	time_now = datetime.now()
 	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.
+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.
 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.
 
@@ -575,8 +594,11 @@ def interact(self):
 {AI_NAME}: A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
 {USER_NAME}: Name a color.
 {AI_NAME}: Blue
-{USER_NAME}:"""
+{USER_NAME}:   """
+	
 	params = gpt_params_parse()
+	if params.prompt is None and params.file is None:
+		params.prompt = prompt
 
 	with LLaMAInteract(params) as m:
 		m.interact()