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Add mlx-vlm (#6119)

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* Add mlx-vlm

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Add to CI workflows

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Add requirements-mps.txt

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Simplify

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

---------

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
This commit is contained in:
Ettore Di Giacinto
2025-08-23 23:05:30 +02:00
committed by GitHub
parent 1ba66d00f5
commit 6971f71a6c
12 changed files with 731 additions and 2 deletions
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backend/python/mlx-vlm/backend.py Normal file
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#!/usr/bin/env python3
import asyncio
from concurrent import futures
import argparse
import signal
import sys
import os
from typing import List
import time
import backend_pb2
import backend_pb2_grpc
import grpc
from mlx_vlm import load, generate, stream_generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config, load_image
import mlx.core as mx
import base64
import io
from PIL import Image
import tempfile
_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):
"""
A gRPC servicer that implements the Backend service defined in backend.proto.
"""
def _is_float(self, s):
"""Check if a string can be converted to float."""
try:
float(s)
return True
except ValueError:
return False
def _is_int(self, s):
"""Check if a string can be converted to int."""
try:
int(s)
return True
except ValueError:
return False
def Health(self, request, context):
"""
Returns a health check message.
Args:
request: The health check request.
context: The gRPC context.
Returns:
backend_pb2.Reply: The health check reply.
"""
return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
async def LoadModel(self, request, context):
"""
Loads a multimodal vision-language model using MLX-VLM.
Args:
request: The load model request.
context: The gRPC context.
Returns:
backend_pb2.Result: The load model result.
"""
try:
print(f"Loading MLX-VLM model: {request.Model}", file=sys.stderr)
print(f"Request: {request}", file=sys.stderr)
# Parse options like in the diffusers backend
options = request.Options
self.options = {}
# The options are a list of strings in this form optname:optvalue
# We store all the options in a dict for later use
for opt in options:
if ":" not in opt:
continue
key, value = opt.split(":", 1) # Split only on first colon to handle values with colons
# Convert numeric values to appropriate types
if self._is_float(value):
value = float(value)
elif self._is_int(value):
value = int(value)
elif value.lower() in ["true", "false"]:
value = value.lower() == "true"
self.options[key] = value
print(f"Options: {self.options}", file=sys.stderr)
# Load model and processor using MLX-VLM
# mlx-vlm load function returns (model, processor) instead of (model, tokenizer)
self.model, self.processor = load(request.Model)
# Load model config for chat template support
self.config = load_config(request.Model)
except Exception as err:
print(f"Error loading MLX-VLM model {err=}, {type(err)=}", file=sys.stderr)
return backend_pb2.Result(success=False, message=f"Error loading MLX-VLM model: {err}")
print("MLX-VLM model loaded successfully", file=sys.stderr)
return backend_pb2.Result(message="MLX-VLM model loaded successfully", success=True)
async def Predict(self, request, context):
"""
Generates text based on the given prompt and sampling parameters using MLX-VLM with multimodal support.
Args:
request: The predict request.
context: The gRPC context.
Returns:
backend_pb2.Reply: The predict result.
"""
temp_files = []
try:
# Process images and audios from request
image_paths = []
audio_paths = []
# Process images
if request.Images:
for img_data in request.Images:
img_path = self.load_image_from_base64(img_data)
if img_path:
image_paths.append(img_path)
temp_files.append(img_path)
# Process audios
if request.Audios:
for audio_data in request.Audios:
audio_path = self.load_audio_from_base64(audio_data)
if audio_path:
audio_paths.append(audio_path)
temp_files.append(audio_path)
# Prepare the prompt with multimodal information
prompt = self._prepare_prompt(request, num_images=len(image_paths), num_audios=len(audio_paths))
# Build generation parameters using request attributes and options
max_tokens, generation_params = self._build_generation_params(request)
print(f"Generating text with MLX-VLM - max_tokens: {max_tokens}, params: {generation_params}", file=sys.stderr)
print(f"Images: {len(image_paths)}, Audios: {len(audio_paths)}", file=sys.stderr)
# Generate text using MLX-VLM with multimodal inputs
response = generate(
model=self.model,
processor=self.processor,
prompt=prompt,
image=image_paths if image_paths else None,
audio=audio_paths if audio_paths else None,
max_tokens=max_tokens,
temperature=generation_params.get('temp', 0.6),
top_p=generation_params.get('top_p', 1.0),
verbose=False
)
return backend_pb2.Reply(message=bytes(response, encoding='utf-8'))
except Exception as e:
print(f"Error in MLX-VLM Predict: {e}", file=sys.stderr)
context.set_code(grpc.StatusCode.INTERNAL)
context.set_details(f"Generation failed: {str(e)}")
return backend_pb2.Reply(message=bytes("", encoding='utf-8'))
finally:
# Clean up temporary files
self.cleanup_temp_files(temp_files)
def Embedding(self, request, context):
"""
A gRPC method that calculates embeddings for a given sentence.
Note: MLX-VLM doesn't support embeddings directly. This method returns an error.
Args:
request: An EmbeddingRequest object that contains the request parameters.
context: A grpc.ServicerContext object that provides information about the RPC.
Returns:
An EmbeddingResult object that contains the calculated embeddings.
"""
print("Embeddings not supported in MLX-VLM backend", file=sys.stderr)
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details("Embeddings are not supported in the MLX-VLM backend.")
return backend_pb2.EmbeddingResult()
async def PredictStream(self, request, context):
"""
Generates text based on the given prompt and sampling parameters, and streams the results using MLX-VLM with multimodal support.
Args:
request: The predict stream request.
context: The gRPC context.
Yields:
backend_pb2.Reply: Streaming predict results.
"""
temp_files = []
try:
# Process images and audios from request
image_paths = []
audio_paths = []
# Process images
if request.Images:
for img_data in request.Images:
img_path = self.load_image_from_base64(img_data)
if img_path:
image_paths.append(img_path)
temp_files.append(img_path)
# Process audios
if request.Audios:
for audio_data in request.Audios:
audio_path = self.load_audio_from_base64(audio_data)
if audio_path:
audio_paths.append(audio_path)
temp_files.append(audio_path)
# Prepare the prompt with multimodal information
prompt = self._prepare_prompt(request, num_images=len(image_paths), num_audios=len(audio_paths))
# Build generation parameters using request attributes and options
max_tokens, generation_params = self._build_generation_params(request, default_max_tokens=512)
print(f"Streaming text with MLX-VLM - max_tokens: {max_tokens}, params: {generation_params}", file=sys.stderr)
print(f"Images: {len(image_paths)}, Audios: {len(audio_paths)}", file=sys.stderr)
# Stream text generation using MLX-VLM with multimodal inputs
for response in stream_generate(
model=self.model,
processor=self.processor,
prompt=prompt,
image=image_paths if image_paths else None,
audio=audio_paths if audio_paths else None,
max_tokens=max_tokens,
temperature=generation_params.get('temp', 0.6),
top_p=generation_params.get('top_p', 1.0),
):
yield backend_pb2.Reply(message=bytes(response.text, encoding='utf-8'))
except Exception as e:
print(f"Error in MLX-VLM PredictStream: {e}", file=sys.stderr)
context.set_code(grpc.StatusCode.INTERNAL)
context.set_details(f"Streaming generation failed: {str(e)}")
yield backend_pb2.Reply(message=bytes("", encoding='utf-8'))
finally:
# Clean up temporary files
self.cleanup_temp_files(temp_files)
def _prepare_prompt(self, request, num_images=0, num_audios=0):
"""
Prepare the prompt for MLX-VLM generation, handling chat templates and multimodal inputs.
Args:
request: The gRPC request containing prompt and message information.
num_images: Number of images in the request.
num_audios: Number of audio files in the request.
Returns:
str: The prepared prompt.
"""
# If tokenizer template is enabled and messages are provided instead of prompt, apply the tokenizer template
if not request.Prompt and request.UseTokenizerTemplate and request.Messages:
# Convert gRPC messages to the format expected by apply_chat_template
messages = []
for msg in request.Messages:
messages.append({"role": msg.role, "content": msg.content})
# Use mlx-vlm's apply_chat_template which handles multimodal inputs
prompt = apply_chat_template(
self.processor,
self.config,
messages,
num_images=num_images,
num_audios=num_audios
)
return prompt
elif request.Prompt:
# If we have a direct prompt but also have images/audio, we need to format it properly
if num_images > 0 or num_audios > 0:
# Create a simple message structure for multimodal prompt
messages = [{"role": "user", "content": request.Prompt}]
prompt = apply_chat_template(
self.processor,
self.config,
messages,
num_images=num_images,
num_audios=num_audios
)
return prompt
else:
return request.Prompt
else:
# Fallback to empty prompt with multimodal template if we have media
if num_images > 0 or num_audios > 0:
messages = [{"role": "user", "content": ""}]
prompt = apply_chat_template(
self.processor,
self.config,
messages,
num_images=num_images,
num_audios=num_audios
)
return prompt
else:
return ""
def _build_generation_params(self, request, default_max_tokens=200):
"""
Build generation parameters from request attributes and options for MLX-VLM.
Args:
request: The gRPC request.
default_max_tokens: Default max_tokens if not specified.
Returns:
tuple: (max_tokens, generation_params dict)
"""
# Extract max_tokens
max_tokens = getattr(request, 'Tokens', default_max_tokens)
if max_tokens == 0:
max_tokens = default_max_tokens
# Extract generation parameters from request attributes
temp = getattr(request, 'Temperature', 0.0)
if temp == 0.0:
temp = 0.6 # Default temperature
top_p = getattr(request, 'TopP', 0.0)
if top_p == 0.0:
top_p = 1.0 # Default top_p
# Initialize generation parameters for MLX-VLM
generation_params = {
'temp': temp,
'top_p': top_p,
}
# Add seed if specified
seed = getattr(request, 'Seed', 0)
if seed != 0:
mx.random.seed(seed)
# Override with options if available
if hasattr(self, 'options'):
# Max tokens from options
if 'max_tokens' in self.options:
max_tokens = self.options['max_tokens']
# Generation parameters from options
param_option_mapping = {
'temp': 'temp',
'temperature': 'temp', # alias
'top_p': 'top_p',
}
for option_key, param_key in param_option_mapping.items():
if option_key in self.options:
generation_params[param_key] = self.options[option_key]
# Handle seed from options
if 'seed' in self.options:
mx.random.seed(self.options['seed'])
return max_tokens, generation_params
def load_image_from_base64(self, image_data: str):
"""
Load an image from base64 encoded data.
Args:
image_data (str): Base64 encoded image data.
Returns:
PIL.Image or str: The loaded image or path to the image.
"""
try:
decoded_data = base64.b64decode(image_data)
image = Image.open(io.BytesIO(decoded_data))
# Save to temporary file for mlx-vlm
with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
image.save(tmp_file.name, format='JPEG')
return tmp_file.name
except Exception as e:
print(f"Error loading image from base64: {e}", file=sys.stderr)
return None
def load_audio_from_base64(self, audio_data: str):
"""
Load audio from base64 encoded data.
Args:
audio_data (str): Base64 encoded audio data.
Returns:
str: Path to the loaded audio file.
"""
try:
decoded_data = base64.b64decode(audio_data)
# Save to temporary file for mlx-vlm
with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
tmp_file.write(decoded_data)
return tmp_file.name
except Exception as e:
print(f"Error loading audio from base64: {e}", file=sys.stderr)
return None
def cleanup_temp_files(self, file_paths: List[str]):
"""
Clean up temporary files.
Args:
file_paths (List[str]): List of file paths to clean up.
"""
for file_path in file_paths:
try:
if file_path and os.path.exists(file_path):
os.remove(file_path)
except Exception as e:
print(f"Error removing temporary file {file_path}: {e}", file=sys.stderr)
async def serve(address):
# Start asyncio gRPC server
server = grpc.aio.server(migration_thread_pool=futures.ThreadPoolExecutor(max_workers=MAX_WORKERS),
options=[
('grpc.max_message_length', 50 * 1024 * 1024), # 50MB
('grpc.max_send_message_length', 50 * 1024 * 1024), # 50MB
('grpc.max_receive_message_length', 50 * 1024 * 1024), # 50MB
])
# Add the servicer to the server
backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
# Bind the server to the address
server.add_insecure_port(address)
# Gracefully shutdown the server on SIGTERM or SIGINT
loop = asyncio.get_event_loop()
for sig in (signal.SIGINT, signal.SIGTERM):
loop.add_signal_handler(
sig, lambda: asyncio.ensure_future(server.stop(5))
)
# Start the server
await server.start()
print("Server started. Listening on: " + address, file=sys.stderr)
# Wait for the server to be terminated
await server.wait_for_termination()
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()
asyncio.run(serve(args.addr))