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https://github.com/oobabooga/text-generation-webui.git
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[extensions/openai] use instruction templates with chat_completions (#2291)
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matatonic
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@ -142,6 +142,16 @@ class Handler(BaseHTTPRequestHandler):
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"permission": []
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})
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self.wfile.write(response.encode('utf-8'))
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elif '/billing/usage' in self.path:
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# Ex. /v1/dashboard/billing/usage?start_date=2023-05-01&end_date=2023-05-31
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self.send_response(200)
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self.send_header('Content-Type', 'application/json')
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self.end_headers()
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response = json.dumps({
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"total_usage": 0,
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})
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self.wfile.write(response.encode('utf-8'))
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else:
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self.send_error(404)
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@ -164,7 +174,8 @@ class Handler(BaseHTTPRequestHandler):
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# model = body.get('model', shared.model_name) # ignored, use existing for now
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model = shared.model_name
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created_time = int(time.time())
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cmpl_id = "conv-%d" % (created_time)
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cmpl_id = "chatcmpl-%d" % (created_time) if is_chat else "conv-%d" % (created_time)
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# Try to use openai defaults or map them to something with the same intent
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stopping_strings = default(shared.settings, 'custom_stopping_strings', [])
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@ -181,10 +192,7 @@ class Handler(BaseHTTPRequestHandler):
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max_tokens_str = 'length' if is_legacy else 'max_tokens'
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max_tokens = default(body, max_tokens_str, default(shared.settings, 'max_new_tokens', default_max_tokens))
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# hard scale this, assuming the given max is for GPT3/4, perhaps inspect the requested model and lookup the context max
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while truncation_length <= max_tokens:
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max_tokens = max_tokens // 2
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# if the user assumes OpenAI, the max_tokens is way too large - try to ignore it unless it's small enough
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req_params = {
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'max_new_tokens': max_tokens,
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@ -243,33 +251,75 @@ class Handler(BaseHTTPRequestHandler):
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object_type = ''
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if is_chat:
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# Chat Completions
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stream_object_type = 'chat.completions.chunk'
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object_type = 'chat.completions'
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messages = body['messages']
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system_msgs = []
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if 'prompt' in body: # Maybe they sent both? This is not documented in the API, but some clients seem to do this.
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system_msgs = [ body['prompt'] ]
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role_formats = {
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'user': 'user: {message}\n',
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'bot': 'assistant: {message}\n',
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'system': '{message}',
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'context': 'You are a helpful assistant. Answer as concisely as possible.',
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'prompt': 'assistant:',
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}
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# Instruct models can be much better
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try:
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instruct = yaml.safe_load(open(f"characters/instruction-following/{shared.settings['instruction_template']}.yaml", 'r'))
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template = instruct['turn_template']
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system_message_template = "{message}"
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system_message_default = instruct['context']
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bot_start = template.find('<|bot|>') # So far, 100% of instruction templates have this token
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user_message_template = template[:bot_start].replace('<|user-message|>', '{message}').replace('<|user|>', instruct['user'])
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bot_message_template = template[bot_start:].replace('<|bot-message|>', '{message}').replace('<|bot|>', instruct['bot'])
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bot_prompt = bot_message_template[:bot_message_template.find('{message}')].rstrip(' ')
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role_formats = {
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'user': user_message_template,
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'assistant': bot_message_template,
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'system': system_message_template,
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'context': system_message_default,
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'prompt': bot_prompt,
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}
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if debug:
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print(f"Loaded instruction role format: {shared.settings['instruction_template']}")
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except:
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if debug:
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print("Loaded default role format.")
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system_msgs = []
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chat_msgs = []
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# You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. Knowledge cutoff: {knowledge_cutoff} Current date: {current_date}
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context_msg = role_formats['system'].format(message=role_formats['context']) if role_formats['context'] else ''
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if context_msg:
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system_msgs.extend([context_msg])
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# Maybe they sent both? This is not documented in the API, but some clients seem to do this.
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if 'prompt' in body:
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prompt_msg = role_formats['system'].format(message=body['prompt'])
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system_msgs.extend([prompt_msg])
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for m in messages:
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role = m['role']
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content = m['content']
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# name = m.get('name', 'user')
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msg = role_formats[role].format(message=content)
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if role == 'system':
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system_msgs.extend([content.strip()])
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system_msgs.extend([msg])
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else:
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chat_msgs.extend([f"\n{role}: {content.strip()}"]) # Strip content? linefeed?
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chat_msgs.extend([msg])
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# You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. Knowledge cutoff: {knowledge_cutoff} Current date: {current_date}
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system_msg = 'You are assistant, a large language model. Answer as concisely as possible.'
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if system_msgs:
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# can't really truncate the system messages
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system_msg = '\n'.join(system_msgs)
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if system_msg[-1] != '\n':
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system_msg = system_msg + '\n'
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system_token_count = len(encode(system_msg)[0])
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remaining_tokens = req_params['truncation_length'] - req_params['max_new_tokens'] - system_token_count
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remaining_tokens = req_params['truncation_length'] - system_token_count
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chat_msg = ''
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while chat_msgs:
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@ -279,25 +329,15 @@ class Handler(BaseHTTPRequestHandler):
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chat_msg = new_msg + chat_msg
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remaining_tokens -= new_size
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else:
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# TODO: clip a message to fit?
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# ie. user: ...<clipped message>
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print(f"Warning: too many messages for context size, dropping {len(chat_msgs) + 1} oldest message(s).")
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break
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if len(chat_msgs) > 0:
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print(f"truncating chat messages, dropping {len(chat_msgs)} messages.")
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if system_msg:
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prompt = 'system: ' + system_msg + '\n' + chat_msg + '\nassistant:'
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else:
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prompt = chat_msg + '\nassistant:'
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prompt = system_msg + chat_msg + role_formats['prompt']
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token_count = len(encode(prompt)[0])
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# pass with some expected stop strings.
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# some strange cases of "##| Instruction: " sneaking through.
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stopping_strings += standard_stopping_strings
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req_params['custom_stopping_strings'] = stopping_strings
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else:
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# Text Completions
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stream_object_type = 'text_completion.chunk'
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object_type = 'text_completion'
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@ -312,9 +352,16 @@ class Handler(BaseHTTPRequestHandler):
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token_count = len(encode(prompt)[0])
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if token_count >= req_params['truncation_length']:
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new_len = int(len(prompt) * (float(shared.settings['truncation_length']) - req_params['max_new_tokens']) / token_count)
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new_len = int(len(prompt) * shared.settings['truncation_length'] / token_count)
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prompt = prompt[-new_len:]
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print(f"truncating prompt to {new_len} characters, was {token_count} tokens. Now: {len(encode(prompt)[0])} tokens.")
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new_token_count = len(encode(prompt)[0])
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print(f"Warning: truncating prompt to {new_len} characters, was {token_count} tokens. Now: {new_token_count} tokens.")
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token_count = new_token_count
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if req_params['truncation_length'] - token_count < req_params['max_new_tokens']:
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print(f"Warning: Ignoring max_new_tokens ({req_params['max_new_tokens']}), too large for the remaining context. Remaining tokens: {req_params['truncation_length'] - token_count}")
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req_params['max_new_tokens'] = req_params['truncation_length'] - token_count
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print(f"Warning: Set max_new_tokens = {req_params['max_new_tokens']}")
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# pass with some expected stop strings.
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# some strange cases of "##| Instruction: " sneaking through.
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@ -338,11 +385,9 @@ class Handler(BaseHTTPRequestHandler):
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if stream_object_type == 'text_completion.chunk':
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chunk[resp_list][0]["text"] = ""
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else:
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# This is coming back as "system" to the openapi cli, not sure why.
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# So yeah... do both methods? delta and messages.
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chunk[resp_list][0]["message"] = {'role': 'assistant', 'content': ''}
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chunk[resp_list][0]["delta"] = {'role': 'assistant', 'content': ''}
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# { "role": "assistant" }
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response = 'data: ' + json.dumps(chunk) + '\n'
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self.wfile.write(response.encode('utf-8'))
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@ -449,7 +494,7 @@ class Handler(BaseHTTPRequestHandler):
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if debug:
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if answer and answer[0] == ' ':
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answer = answer[1:]
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print({'response': answer})
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print({'answer': answer}, chunk)
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return
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# strip extra leading space off new generated content
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