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feat: create bash library to handle install/run/test of python backends (#2286)

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* feat: create bash library to handle install/run/test of python backends

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>

* chore: minor cleanup

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>

* fix: remove incorrect LIMIT_TARGETS from parler-tts

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>

* fix: update runUnitests to handle running tests from a custom test file

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>

* chore: document runUnittests

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>

---------

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>
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backend/python/exllama/exllama.py
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@ -1,159 +0,0 @@
#!/usr/bin/env python3
import grpc
from concurrent import futures
import time
import backend_pb2
import backend_pb2_grpc
import argparse
import signal
import sys
import os, glob
from pathlib import Path
import torch
import torch.nn.functional as F
from torch import version as torch_version
from tokenizer import ExLlamaTokenizer
from generator import ExLlamaGenerator
from model import ExLlama, ExLlamaCache, ExLlamaConfig
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
# Implement the BackendServicer class with the service methods
class BackendServicer(backend_pb2_grpc.BackendServicer):
def generate(self,prompt, max_new_tokens):
self.generator.end_beam_search()
# Tokenizing the input
ids = self.generator.tokenizer.encode(prompt)
self.generator.gen_begin_reuse(ids)
initial_len = self.generator.sequence[0].shape[0]
has_leading_space = False
decoded_text = ''
for i in range(max_new_tokens):
token = self.generator.gen_single_token()
if i == 0 and self.generator.tokenizer.tokenizer.IdToPiece(int(token)).startswith('▁'):
has_leading_space = True
decoded_text = self.generator.tokenizer.decode(self.generator.sequence[0][initial_len:])
if has_leading_space:
decoded_text = ' ' + decoded_text
if token.item() == self.generator.tokenizer.eos_token_id:
break
return decoded_text
def Health(self, request, context):
return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
def LoadModel(self, request, context):
try:
# https://github.com/turboderp/exllama/blob/master/example_cfg.py
model_directory = request.ModelFile
# Locate files we need within that directory
tokenizer_path = os.path.join(model_directory, "tokenizer.model")
model_config_path = os.path.join(model_directory, "config.json")
st_pattern = os.path.join(model_directory, "*.safetensors")
model_path = glob.glob(st_pattern)[0]
# Create config, model, tokenizer and generator
config = ExLlamaConfig(model_config_path) # create config from config.json
config.model_path = model_path # supply path to model weights file
if (request.ContextSize):
config.max_seq_len = request.ContextSize # override max sequence length
config.max_attention_size = request.ContextSize**2 # Should be set to context_size^2.
# https://github.com/turboderp/exllama/issues/220#issuecomment-1720324163
# Set Rope scaling.
if (request.RopeFreqScale):
# Alpha value for Rope scaling.
# Higher value increases context but adds perplexity.
# alpha_value and compress_pos_emb are mutually exclusive.
# https://github.com/turboderp/exllama/issues/115
config.alpha_value = request.RopeFreqScale
config.calculate_rotary_embedding_base()
model = ExLlama(config) # create ExLlama instance and load the weights
tokenizer = ExLlamaTokenizer(tokenizer_path) # create tokenizer from tokenizer model file
cache = ExLlamaCache(model, batch_size = 2) # create cache for inference
generator = ExLlamaGenerator(model, tokenizer, cache) # create generator
self.generator= generator
self.model = model
self.tokenizer = tokenizer
self.cache = cache
except Exception as err:
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
return backend_pb2.Result(message="Model loaded successfully", success=True)
def Predict(self, request, context):
penalty = 1.15
if request.Penalty != 0.0:
penalty = request.Penalty
self.generator.settings.token_repetition_penalty_max = penalty
self.generator.settings.temperature = request.Temperature
self.generator.settings.top_k = request.TopK
self.generator.settings.top_p = request.TopP
tokens = 512
if request.Tokens != 0:
tokens = request.Tokens
if self.cache.batch_size == 1:
del self.cache
self.cache = ExLlamaCache(self.model, batch_size=2)
self.generator = ExLlamaGenerator(self.model, self.tokenizer, self.cache)
t = self.generate(request.Prompt, tokens)
# Remove prompt from response if present
if request.Prompt in t:
t = t.replace(request.Prompt, "")
return backend_pb2.Result(message=bytes(t, encoding='utf-8'))
def PredictStream(self, request, context):
# Implement PredictStream RPC
#for reply in some_data_generator():
# yield reply
# Not implemented yet
return self.Predict(request, context)
def serve(address):
server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
server.add_insecure_port(address)
server.start()
print("Server started. Listening on: " + address, file=sys.stderr)
# Define the signal handler function
def signal_handler(sig, frame):
print("Received termination signal. Shutting down...")
server.stop(0)
sys.exit(0)
# Set the signal handlers for SIGINT and SIGTERM
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
try:
while True:
time.sleep(_ONE_DAY_IN_SECONDS)
except KeyboardInterrupt:
server.stop(0)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run the gRPC server.")
parser.add_argument(
"--addr", default="localhost:50051", help="The address to bind the server to."
)
args = parser.parse_args()
serve(args.addr)