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Merge remote-tracking branch 'refs/remotes/origin/dev' into dev

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oobabooga 2023-06-06 07:01:00 -03:00
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8
extensions/openai/README.md
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@ -20,6 +20,12 @@ Example:
SD_WEBUI_URL=http://127.0.0.1:7861
```
Make sure you enable it in server launch parameters. Just make sure they include:
```
--extensions openai
```
### Embeddings (alpha)
Embeddings requires ```sentence-transformers``` installed, but chat and completions will function without it loaded. The embeddings endpoint is currently using the HuggingFace model: ```sentence-transformers/all-mpnet-base-v2``` for embeddings. This produces 768 dimensional embeddings (the same as the text-davinci-002 embeddings), which is different from OpenAI's current default ```text-embedding-ada-002``` model which produces 1536 dimensional embeddings. The model is small-ish and fast-ish. This model and embedding size may change in the future.
@ -42,7 +48,7 @@ Almost everything you use it with will require you to set a dummy OpenAI API key
With the [official python openai client](https://github.com/openai/openai-python), you can set the OPENAI_API_BASE environment variable before you import the openai module, like so:
```
OPENAI_API_KEY=dummy
OPENAI_API_KEY=sk-dummy
OPENAI_API_BASE=http://127.0.0.1:5001/v1
```

192
extensions/openai/script.py
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@ -20,6 +20,7 @@ params = {
debug = True if 'OPENEDAI_DEBUG' in os.environ else False
# Slightly different defaults for OpenAI's API
# Data type is important, Ex. use 0.0 for a float 0
default_req_params = {
'max_new_tokens': 200,
'temperature': 1.0,
@ -44,14 +45,14 @@ default_req_params = {
'no_repeat_ngram_size': 0,
'num_beams': 1,
'penalty_alpha': 0.0,
'length_penalty': 1,
'length_penalty': 1.0,
'early_stopping': False,
'mirostat_mode': 0,
'mirostat_tau': 5,
'mirostat_tau': 5.0,
'mirostat_eta': 0.1,
'ban_eos_token': False,
'skip_special_tokens': True,
'custom_stopping_strings': [],
'custom_stopping_strings': ['\n###'],
}
# Optional, install the module and download the model to enable
@ -64,8 +65,6 @@ except ImportError:
st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
embedding_model = None
standard_stopping_strings = ['\nsystem:', '\nuser:', '\nhuman:', '\nassistant:', '\n###', ]
# little helper to get defaults if arg is present but None and should be the same type as default.
def default(dic, key, default):
val = dic.get(key, default)
@ -86,31 +85,6 @@ def clamp(value, minvalue, maxvalue):
return max(minvalue, min(value, maxvalue))
def deduce_template():
# Alpaca is verbose so a good default prompt
default_template = (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
)
# Use the special instruction/input/response template for anything trained like Alpaca
if shared.settings['instruction_template'] in ['Alpaca', 'Alpaca-Input']:
return default_template
try:
instruct = yaml.safe_load(open(f"characters/instruction-following/{shared.settings['instruction_template']}.yaml", 'r'))
template = instruct['turn_template']
template = template\
.replace('<|user|>', instruct.get('user', ''))\
.replace('<|bot|>', instruct.get('bot', ''))\
.replace('<|user-message|>', '{instruction}\n{input}')
return instruct.get('context', '') + template[:template.find('<|bot-message|>')].rstrip(' ')
except:
return default_template
def float_list_to_base64(float_list):
# Convert the list to a float32 array that the OpenAPI client expects
float_array = np.array(float_list, dtype="float32")
@ -139,8 +113,27 @@ class Handler(BaseHTTPRequestHandler):
"Origin, Accept, X-Requested-With, Content-Type, "
"Access-Control-Request-Method, Access-Control-Request-Headers, "
"Authorization"
)
)
def openai_error(self, message, code = 500, error_type = 'APIError', param = '', internal_message = ''):
self.send_response(code)
self.send_access_control_headers()
self.send_header('Content-Type', 'application/json')
self.end_headers()
error_resp = {
'error': {
'message': message,
'code': code,
'type': error_type,
'param': param,
}
}
if internal_message:
error_resp['internal_message'] = internal_message
response = json.dumps(error_resp)
self.wfile.write(response.encode('utf-8'))
def do_OPTIONS(self):
self.send_response(200)
self.send_access_control_headers()
@ -150,42 +143,24 @@ class Handler(BaseHTTPRequestHandler):
def do_GET(self):
if self.path.startswith('/v1/models'):
self.send_response(200)
self.send_access_control_headers()
self.send_header('Content-Type', 'application/json')
self.end_headers()
# TODO: list all models and allow model changes via API? Lora's?
# TODO: Lora's?
# This API should list capabilities, limits and pricing...
models = [{
"id": shared.model_name, # The real chat/completions model
"object": "model",
"owned_by": "user",
"permission": []
}, {
"id": st_model, # The real sentence transformer embeddings model
"object": "model",
"owned_by": "user",
"permission": []
}, { # these are expected by so much, so include some here as a dummy
"id": "gpt-3.5-turbo", # /v1/chat/completions
"object": "model",
"owned_by": "user",
"permission": []
}, {
"id": "text-curie-001", # /v1/completions, 2k context
"object": "model",
"owned_by": "user",
"permission": []
}, {
"id": "text-davinci-002", # /v1/embeddings text-embedding-ada-002:1536, text-davinci-002:768
"object": "model",
"owned_by": "user",
"permission": []
}]
current_model_list = [ shared.model_name ] # The real chat/completions model
embeddings_model_list = [ st_model ] if embedding_model else [] # The real sentence transformer embeddings model
pseudo_model_list = [ # these are expected by so much, so include some here as a dummy
'gpt-3.5-turbo', # /v1/chat/completions
'text-curie-001', # /v1/completions, 2k context
'text-davinci-002' # /v1/embeddings text-embedding-ada-002:1536, text-davinci-002:768
]
available_model_list = get_available_models()
all_model_list = current_model_list + embeddings_model_list + pseudo_model_list + available_model_list
models.extend([{ "id": id, "object": "model", "owned_by": "user", "permission": [] } for id in get_available_models() ])
models = [{ "id": id, "object": "model", "owned_by": "user", "permission": [] } for id in all_model_list ]
response = ''
if self.path == '/v1/models':
@ -203,6 +178,7 @@ class Handler(BaseHTTPRequestHandler):
})
self.wfile.write(response.encode('utf-8'))
elif '/billing/usage' in self.path:
# Ex. /v1/dashboard/billing/usage?start_date=2023-05-01&end_date=2023-05-31
self.send_response(200)
@ -214,6 +190,7 @@ class Handler(BaseHTTPRequestHandler):
"total_usage": 0,
})
self.wfile.write(response.encode('utf-8'))
else:
self.send_error(404)
@ -227,6 +204,11 @@ class Handler(BaseHTTPRequestHandler):
print(body)
if '/completions' in self.path or '/generate' in self.path:
if not shared.model:
self.openai_error("No model loaded.")
return
is_legacy = '/generate' in self.path
is_chat = 'chat' in self.path
resp_list = 'data' if is_legacy else 'choices'
@ -238,13 +220,16 @@ class Handler(BaseHTTPRequestHandler):
cmpl_id = "chatcmpl-%d" % (created_time) if is_chat else "conv-%d" % (created_time)
# Request Parameters
# Try to use openai defaults or map them to something with the same intent
stopping_strings = default(shared.settings, 'custom_stopping_strings', [])
req_params = default_req_params.copy()
req_params['custom_stopping_strings'] = default_req_params['custom_stopping_strings'].copy()
if 'stop' in body:
if isinstance(body['stop'], str):
stopping_strings = [body['stop']]
req_params['custom_stopping_strings'].extend([body['stop']])
elif isinstance(body['stop'], list):
stopping_strings = body['stop']
req_params['custom_stopping_strings'].extend(body['stop'])
truncation_length = default(shared.settings, 'truncation_length', 2048)
truncation_length = clamp(default(body, 'truncation_length', truncation_length), 1, truncation_length)
@ -255,8 +240,6 @@ class Handler(BaseHTTPRequestHandler):
max_tokens = default(body, max_tokens_str, default(shared.settings, 'max_new_tokens', default_max_tokens))
# if the user assumes OpenAI, the max_tokens is way too large - try to ignore it unless it's small enough
req_params = default_req_params.copy()
req_params['max_new_tokens'] = max_tokens
req_params['truncation_length'] = truncation_length
req_params['temperature'] = clamp(default(body, 'temperature', default_req_params['temperature']), 0.001, 1.999) # fixup absolute 0.0
@ -319,9 +302,14 @@ class Handler(BaseHTTPRequestHandler):
'prompt': bot_prompt,
}
if instruct['user']: # WizardLM and some others have no user prompt.
req_params['custom_stopping_strings'].extend(['\n' + instruct['user'], instruct['user']])
if debug:
print(f"Loaded instruction role format: {shared.settings['instruction_template']}")
except:
req_params['custom_stopping_strings'].extend(['\nuser:'])
if debug:
print("Loaded default role format.")
@ -396,11 +384,6 @@ class Handler(BaseHTTPRequestHandler):
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}")
req_params['max_new_tokens'] = req_params['truncation_length'] - token_count
print(f"Warning: Set max_new_tokens = {req_params['max_new_tokens']}")
# pass with some expected stop strings.
# some strange cases of "##| Instruction: " sneaking through.
stopping_strings += standard_stopping_strings
req_params['custom_stopping_strings'] = stopping_strings
if req_params['stream']:
shared.args.chat = True
@ -423,19 +406,17 @@ class Handler(BaseHTTPRequestHandler):
chunk[resp_list][0]["message"] = {'role': 'assistant', 'content': ''}
chunk[resp_list][0]["delta"] = {'role': 'assistant', 'content': ''}
data_chunk = 'data: ' + json.dumps(chunk) + '\r\n\r\n'
chunk_size = hex(len(data_chunk))[2:] + '\r\n'
response = chunk_size + data_chunk
response = 'data: ' + json.dumps(chunk) + '\r\n\r\n'
self.wfile.write(response.encode('utf-8'))
# generate reply #######################################
if debug:
print({'prompt': prompt, 'req_params': req_params, 'stopping_strings': stopping_strings})
generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False)
print({'prompt': prompt, 'req_params': req_params})
generator = generate_reply(prompt, req_params, is_chat=False)
answer = ''
seen_content = ''
longest_stop_len = max([len(x) for x in stopping_strings])
longest_stop_len = max([len(x) for x in req_params['custom_stopping_strings']] + [0])
for a in generator:
answer = a
@ -444,7 +425,7 @@ class Handler(BaseHTTPRequestHandler):
len_seen = len(seen_content)
search_start = max(len_seen - longest_stop_len, 0)
for string in stopping_strings:
for string in req_params['custom_stopping_strings']:
idx = answer.find(string, search_start)
if idx != -1:
answer = answer[:idx] # clip it.
@ -457,7 +438,7 @@ class Handler(BaseHTTPRequestHandler):
# is completed, buffer and generate more, don't send it
buffer_and_continue = False
for string in stopping_strings:
for string in req_params['custom_stopping_strings']:
for j in range(len(string) - 1, 0, -1):
if answer[-j:] == string[:j]:
buffer_and_continue = True
@ -498,9 +479,7 @@ class Handler(BaseHTTPRequestHandler):
# So yeah... do both methods? delta and messages.
chunk[resp_list][0]['message'] = {'content': new_content}
chunk[resp_list][0]['delta'] = {'content': new_content}
data_chunk = 'data: ' + json.dumps(chunk) + '\r\n\r\n'
chunk_size = hex(len(data_chunk))[2:] + '\r\n'
response = chunk_size + data_chunk
response = 'data: ' + json.dumps(chunk) + '\r\n\r\n'
self.wfile.write(response.encode('utf-8'))
completion_token_count += len(encode(new_content)[0])
@ -527,10 +506,7 @@ class Handler(BaseHTTPRequestHandler):
chunk[resp_list][0]['message'] = {'content': ''}
chunk[resp_list][0]['delta'] = {'content': ''}
data_chunk = 'data: ' + json.dumps(chunk) + '\r\n\r\n'
chunk_size = hex(len(data_chunk))[2:] + '\r\n'
done = 'data: [DONE]\r\n\r\n'
response = chunk_size + data_chunk + done
response = 'data: ' + json.dumps(chunk) + '\r\n\r\ndata: [DONE]\r\n\r\n'
self.wfile.write(response.encode('utf-8'))
# Finished if streaming.
if debug:
@ -574,7 +550,12 @@ class Handler(BaseHTTPRequestHandler):
response = json.dumps(resp)
self.wfile.write(response.encode('utf-8'))
elif '/edits' in self.path:
if not shared.model:
self.openai_error("No model loaded.")
return
self.send_response(200)
self.send_access_control_headers()
self.send_header('Content-Type', 'application/json')
@ -586,15 +567,42 @@ class Handler(BaseHTTPRequestHandler):
instruction = body['instruction']
input = body.get('input', '')
instruction_template = deduce_template()
# Request parameters
req_params = default_req_params.copy()
# Alpaca is verbose so a good default prompt
default_template = (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
)
instruction_template = default_template
req_params['custom_stopping_strings'] = [ '\n###' ]
# Use the special instruction/input/response template for anything trained like Alpaca
if not (shared.settings['instruction_template'] in ['Alpaca', 'Alpaca-Input']):
try:
instruct = yaml.safe_load(open(f"characters/instruction-following/{shared.settings['instruction_template']}.yaml", 'r'))
template = instruct['turn_template']
template = template\
.replace('<|user|>', instruct.get('user', ''))\
.replace('<|bot|>', instruct.get('bot', ''))\
.replace('<|user-message|>', '{instruction}\n{input}')
instruction_template = instruct.get('context', '') + template[:template.find('<|bot-message|>')].rstrip(' ')
if instruct['user']:
req_params['custom_stopping_strings'] = [ '\n' + instruct['user'], instruct['user'] ]
except:
pass
edit_task = instruction_template.format(instruction=instruction, input=input)
truncation_length = default(shared.settings, 'truncation_length', 2048)
token_count = len(encode(edit_task)[0])
max_tokens = truncation_length - token_count
req_params = default_req_params.copy()
req_params['max_new_tokens'] = max_tokens
req_params['truncation_length'] = truncation_length
req_params['temperature'] = clamp(default(body, 'temperature', default_req_params['temperature']), 0.001, 1.999) # fixup absolute 0.0
@ -605,7 +613,7 @@ class Handler(BaseHTTPRequestHandler):
if debug:
print({'edit_template': edit_task, 'req_params': req_params, 'token_count': token_count})
generator = generate_reply(edit_task, req_params, stopping_strings=standard_stopping_strings, is_chat=False)
generator = generate_reply(edit_task, req_params, is_chat=False)
answer = ''
for a in generator:
@ -636,6 +644,7 @@ class Handler(BaseHTTPRequestHandler):
response = json.dumps(resp)
self.wfile.write(response.encode('utf-8'))
elif '/images/generations' in self.path and 'SD_WEBUI_URL' in os.environ:
# Stable Diffusion callout wrapper for txt2img
# Low effort implementation for compatibility. With only "prompt" being passed and assuming DALL-E
@ -682,6 +691,7 @@ class Handler(BaseHTTPRequestHandler):
response = json.dumps(resp)
self.wfile.write(response.encode('utf-8'))
elif '/embeddings' in self.path and embedding_model is not None:
self.send_response(200)
self.send_access_control_headers()
@ -715,6 +725,7 @@ class Handler(BaseHTTPRequestHandler):
if debug:
print(f"Embeddings return size: {len(embeddings[0])}, number: {len(embeddings)}")
self.wfile.write(response.encode('utf-8'))
elif '/moderations' in self.path:
# for now do nothing, just don't error.
self.send_response(200)
@ -763,6 +774,7 @@ class Handler(BaseHTTPRequestHandler):
}]
})
self.wfile.write(response.encode('utf-8'))
else:
print(self.path, self.headers)
self.send_error(404)

2
requirements.txt
View File

@ -19,7 +19,7 @@ git+https://github.com/huggingface/transformers@e45e756d22206ca8fa9fb057c8c3d8fa
git+https://github.com/huggingface/accelerate@0226f750257b3bf2cadc4f189f9eef0c764a0467
bitsandbytes==0.39.0; platform_system != "Windows"
https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.39.0-py3-none-any.whl; platform_system == "Windows"
llama-cpp-python==0.1.56; platform_system != "Windows"
llama-cpp-python==0.1.57; platform_system != "Windows"
https://github.com/abetlen/llama-cpp-python/releases/download/v0.1.56/llama_cpp_python-0.1.56-cp310-cp310-win_amd64.whl; platform_system == "Windows"
https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.2.0/auto_gptq-0.2.0+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.2.0/auto_gptq-0.2.0+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux"