# Copyright © 2023-2024 Apple Inc. import argparse import json import logging import pickle import platform import socket import time import uuid import warnings from collections import deque from dataclasses import dataclass, replace from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer from pathlib import Path from queue import Empty as QueueEmpty from queue import Queue from threading import Thread from typing import ( Any, Callable, Dict, List, Literal, NamedTuple, Optional, Sequence, Tuple, Union, ) import mlx.core as mx from huggingface_hub import scan_cache_dir from ._version import __version__ from .generate import ( BatchGenerator, SequenceStateMachine, stream_generate, ) from .models.cache import ( LRUPromptCache, make_prompt_cache, ) from .sample_utils import make_logits_processors, make_sampler from .utils import _parse_size, load, sharded_load def get_system_fingerprint(): gpu_arch = mx.device_info()["architecture"] return f"{__version__}-{mx.__version__}-{platform.platform()}-{gpu_arch}" class ToolCallFormatter: def __init__(self, tool_parser, tools, streaming=False): self._idx = 0 self._tool_parser = tool_parser self._tools = tools self._streaming = streaming def _format(self, tc): tc_id = tc.pop("id", None) or str(uuid.uuid4()) tc["arguments"] = json.dumps(tc["arguments"], ensure_ascii=False) out = { "function": tc, "type": "function", "id": tc_id, } if self._streaming: out["index"] = self._idx self._idx += 1 return out def __call__(self, tool_calls): if not tool_calls: return [] result = [] for tool_text in tool_calls: try: parsed = self._tool_parser(tool_text, self._tools) except (ValueError, json.JSONDecodeError) as e: logging.warning( f"Failed to parse tool call ({type(e).__name__}: {e}) — " f"tool text was likely truncated mid-generation." ) continue if not isinstance(parsed, list): parsed = [parsed] result.extend(self._format(tc) for tc in parsed) return result def convert_chat(messages: List[dict], role_mapping: Optional[dict] = None): default_role_mapping = { "system_prompt": ( "A chat between a curious user and an artificial intelligence " "assistant. The assistant follows the given rules no matter what." ), "system": "ASSISTANT's RULE: ", "user": "USER: ", "assistant": "ASSISTANT: ", "stop": "\n", } role_mapping = role_mapping or default_role_mapping prompt = "" for line in messages: role_prefix = role_mapping.get(line["role"], "") stop = role_mapping.get("stop", "") content = line.get("content", "") prompt += f"{role_prefix}{content}{stop}" prompt += role_mapping.get("assistant", "") return prompt.rstrip() def process_message_content(messages): """ Convert message content to a format suitable for `apply_chat_template`. The function operates on messages in place. It converts the 'content' field to a string instead of a list of text fragments. Args: message_list (list): A list of dictionaries, where each dictionary may have a 'content' key containing a list of dictionaries with 'type' and 'text' keys. Raises: ValueError: If the 'content' type is not supported or if 'text' is missing. """ for message in messages: content = message.get("content") if isinstance(content, list): text_fragments = [ fragment["text"] for fragment in content if fragment["type"] == "text" ] if len(text_fragments) != len(content): raise ValueError("Only 'text' content type is supported.") message["content"] = "".join(text_fragments) elif content is None: message["content"] = "" if tool_calls := message.get("tool_calls"): for tool_call in tool_calls: if func := tool_call.get("function"): if args := func.get("arguments"): func["arguments"] = json.loads(args) @dataclass class ModelDescription: model: str draft: str adapter: str @dataclass class SamplingArguments: temperature: float top_p: float top_k: int min_p: float xtc_probability: float xtc_threshold: float @dataclass class LogitsProcessorArguments: logit_bias: Optional[Dict[int, float]] repetition_penalty: float repetition_context_size: int presence_penalty: float presence_context_size: int frequency_penalty: float frequency_context_size: int @dataclass class GenerationArguments: model: ModelDescription sampling: SamplingArguments logits: LogitsProcessorArguments stop_words: List[str] max_tokens: int num_draft_tokens: int logprobs: bool top_logprobs: int seed: Optional[int] chat_template_kwargs: Optional[Dict[str, Any]] @dataclass class CompletionRequest: request_type: Literal["chat", "text"] prompt: str messages: List[Any] tools: Optional[List[Any]] role_mapping: Optional[Dict[str, Any]] @dataclass class GenerationContext: has_tool_calling: bool has_thinking: bool tool_parser: Callable[[str, Any], Dict] sequences: Dict[Tuple[int], str] prompt: List[int] prompt_cache_count: int = -1 _should_stop: bool = False def stop(self): self._should_stop = True @dataclass class Response: text: str token: int state: str match: Tuple[int] logprob: float finish_reason: Optional[str] top_tokens: Tuple[Dict[str, Any]] def _process_control_tokens(ctx, token_stream): buffer_size = max(len(s) for s in ctx.sequences) buffered_stream = deque() for tok in token_stream: buffered_stream.append(tok) if tok.match is not None: popped = [buffered_stream.pop() for _ in tok.match] for t in reversed(popped): buffered_stream.append(replace(t, text="")) if len(buffered_stream) >= buffer_size: yield buffered_stream.popleft() while len(buffered_stream) > 0: yield buffered_stream.popleft() class TimeBudget: def __init__(self, budget=0.5, iterations=25, sync_frequency=10): self._is_distributed = mx.distributed.init().size() > 1 self._budget = budget self._iterations = iterations self._sync_frequency = sync_frequency self._start = None self._current_iterations = None self._loops = 0 self._time_spent = 0 def __iter__(self): self._start = time.time() self._current_iterations = 0 return self def __next__(self): if not self._is_distributed: if time.time() - self._start > self._budget: raise StopIteration() return None self._current_iterations += 1 if self._current_iterations <= self._iterations: return None self._loops += 1 self._time_spent += time.time() - self._start if self._loops % self._sync_frequency == 0: loop_time = mx.distributed.all_sum(self._time_spent).item() avg_loop_time = loop_time / ( mx.distributed.init().size() * self._sync_frequency ) factor = self._budget / avg_loop_time self._iterations = max(round(self._iterations * factor), 1) self._loops = 0 self._time_spent = 0 raise StopIteration() class ModelProvider: def __init__(self, cli_args: argparse.Namespace): """Load models on demand and persist them across the whole process.""" self.cli_args = cli_args self.model_key = None self.model = None self.tokenizer = None self.draft_model = None self.is_batchable = False group = mx.distributed.init() self.pipeline_group = group if group.size() > 1 and cli_args.pipeline else None self.tensor_group = ( group if group.size() > 1 and not cli_args.pipeline else None ) self.is_distributed = group.size() > 1 # Maps model and adapter paths the actual paths to be used. Used to # map 'default_model' to the provided model by cli argument but could # be used for more in the future. self._model_map = {} self._adapter_map = {} self._draft_model_map = {} self._model_map["default_model"] = self.cli_args.model self._adapter_map["default_model"] = self.cli_args.adapter_path self._draft_model_map["default_model"] = self.cli_args.draft_model # Build the tokenizer config for later use in load self._tokenizer_config = { "trust_remote_code": True if cli_args.trust_remote_code else None } if cli_args.chat_template: self._tokenizer_config["chat_template"] = cli_args.chat_template def _load(self, model_path, adapter_path=None, draft_model_path=None): if self.is_distributed and ( adapter_path is not None or draft_model_path is not None ): raise ValueError( "Loading with adapters or draft models not supported in distributed mode" ) # Remove the old model if it exists. self.model_key = None self.model = None self.tokenizer = None self.draft_model = None # Load the model and tokenizer if self.is_distributed: model, tokenizer = sharded_load( model_path, pipeline_group=self.pipeline_group, tensor_group=self.tensor_group, tokenizer_config=self._tokenizer_config, ) else: model, tokenizer = load( model_path, adapter_path=adapter_path, tokenizer_config=self._tokenizer_config, ) # Use the default chat template if needed if self.cli_args.use_default_chat_template: if tokenizer.chat_template is None: tokenizer.chat_template = tokenizer.default_chat_template # Load the draft model for speculative decoding draft_model = None if draft_model_path is not None: draft_model, draft_tokenizer = load(draft_model_path) if draft_tokenizer.vocab_size != tokenizer.vocab_size: logging.warning( "Draft model tokenizer does not match model tokenizer. " "Speculative decoding may not work as expected." ) # Compute batchability is_batchable = draft_model is None is_batchable = is_batchable and all( hasattr(c, "merge") for c in make_prompt_cache(model) ) # Update the member variables self.model_key = (model_path, adapter_path, draft_model_path) self.model = model self.tokenizer = tokenizer self.draft_model = draft_model self.is_batchable = is_batchable def load_default(self): if self._model_map["default_model"] is not None: self.load("default_model", None, "default_model") def load(self, model_path, adapter_path=None, draft_model_path=None): model_path = self._model_map.get(model_path, model_path) adapter_path = self._adapter_map.get(model_path, adapter_path) draft_model_path = self._draft_model_map.get(draft_model_path, draft_model_path) model_key = (model_path, adapter_path, draft_model_path) if self.model_key != model_key: self._load(*model_key) return self.model, self.tokenizer def _make_sampler(args, tokenizer): return make_sampler( args.sampling.temperature, top_p=args.sampling.top_p, top_k=args.sampling.top_k, min_p=args.sampling.min_p, xtc_probability=args.sampling.xtc_probability, xtc_threshold=args.sampling.xtc_threshold, xtc_special_tokens=[ tokenizer.eos_token_id, tokenizer.encode("\n"), ], ) def _make_logits_processors(args): return make_logits_processors( args.logits.logit_bias, args.logits.repetition_penalty, args.logits.repetition_context_size, args.logits.presence_penalty, args.logits.presence_context_size, args.logits.frequency_penalty, args.logits.frequency_context_size, ) def _format_top_logprobs(logprobs, top_n, tokenizer) -> Tuple[Dict[str, Any]]: """Returns info dicts for the top `top_n` tokens from `logprobs`""" if top_n <= 0: return () sorted_indices = mx.argpartition(-logprobs, kth=top_n - 1) top_indices = sorted_indices[:top_n].tolist() top_probs = logprobs[top_indices].tolist() txts = tokenizer.convert_ids_to_tokens(top_indices) return tuple( {"id": i, "token": s, "logprob": g} for i, s, g in zip(top_indices, txts, top_probs) ) class ResponseGenerator: def __init__(self, model_provider: ModelProvider, prompt_cache: LRUPromptCache): self.model_provider = model_provider self.prompt_cache = prompt_cache self.requests = Queue() self._state_machine_cache = {} self._time_budget = TimeBudget() self._is_distributed = mx.distributed.init().size() > 1 self._rank = mx.distributed.init().rank() self._stop = False self._generation_thread = Thread(target=self._generate) self._generation_thread.start() def stop_and_join(self): self._stop = True self._generation_thread.join() def join(self): self._generation_thread.join() def _log_cache_stats(self): n_sequences = len(self.prompt_cache) n_bytes = self.prompt_cache.nbytes logging.info(f"Prompt Cache: {n_sequences} sequences, {n_bytes / 1e9:.2f} GB") for cache_type, stats in self.prompt_cache.stats_by_type().items(): n_sequences = stats["n_sequences"] n_bytes = stats["n_bytes"] logging.info( f"- {cache_type}: {n_sequences} sequences, {n_bytes / 1e9:.2f} GB" ) def _next_request(self, timeout=None): request = None if not self._is_distributed or self._rank == 0: try: if timeout is not None: request = self.requests.get(timeout=timeout) else: request = self.requests.get_nowait() except QueueEmpty: pass return self._share_request(request) def _share_object(self, obj): if not self._is_distributed: return obj if self._rank == 0: if obj is None: mx.eval(mx.distributed.all_sum(0)) return None data = mx.array(pickle.dumps(obj)) mx.eval(mx.distributed.all_sum(data.size)) mx.eval(mx.distributed.all_sum(data)) return obj else: size = mx.distributed.all_sum(0).item() if size == 0: return None data = mx.zeros(size, dtype=mx.uint8) data = mx.distributed.all_sum(data) return pickle.loads(data) def _share_request(self, request): if not self._is_distributed: return request shareable = request[1:] if request is not None else None shareable = self._share_object(shareable) if shareable is None: return None rq = request[0] if request is not None else Queue() return rq, *shareable def _tokenize(self, tokenizer, request, args): """Tokenize a request and split the prompt into segments. Returns a tuple * prompt - Full list of tokens * segments - A list of lists of tokens. Up to 3 segments that correspond to system prompt, context, thinking tail. * segment_types - A string per segment indicating if the segment is a system prompt or a user prompt or nothing special. * initial state - A string that contains the initial state of the state machine (normal or thinking depending on whether we have tail or not) """ if request.request_type == "chat": messages = request.messages tools = request.tools role_mapping = request.role_mapping if tokenizer.has_chat_template: process_message_content(messages) if tools and not tokenizer.has_tool_calling: logging.warning( "Received tools but model does not support tool calling. " "If you think this is an error, file an issue here: " "https://github.com/ml-explore/mlx-lm/issues" ) chat_template_args = self.model_provider.cli_args.chat_template_args if args.chat_template_kwargs: chat_template_args = chat_template_args.copy() chat_template_args.update(args.chat_template_kwargs) template_kwargs = dict( tools=tools, tokenize=True, **chat_template_args, ) prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, **template_kwargs, ) else: prompt = tokenizer.encode(convert_chat(messages, role_mapping)) return prompt, [prompt], ["assistant"], "normal" else: prompt = tokenizer.encode(request.prompt) return prompt, [prompt], ["assistant"], "normal" # If we are here it means we have a chat request so we need to search # for segments for better cache management. # Choose the initial state among only reasoning or normal initial_state = "normal" if tokenizer.has_thinking: think_start = tokenizer.rfind_think_start(prompt) think_end = tokenizer.rfind_think_end(prompt) if think_start > think_end: initial_state = "reasoning" # It is not a user message so no segmentation needed. if messages[-1]["role"] != "user": return prompt, [prompt], ["assistant"], initial_state segments = [] segment_types = [] # Find where the system prompt ends and add it as a segment. num_system = 0 sys_end = 0 for m in messages: if m["role"] == "system": num_system += 1 else: break if num_system > 0: sys_tokens = tokenizer.apply_chat_template( messages[:num_system] + [{"role": "user", "content": ""}], add_generation_prompt=False, **template_kwargs, ) for i, (a, b) in enumerate(zip(sys_tokens, prompt)): if a != b: sys_end = i break if sys_end > 0 and sys_end < len(prompt): segments.append(prompt[:sys_end]) segment_types.append("system") # Find a tail segment that contains thinking tokens (small up to 11 # tokens) tail_start = len(prompt) if tokenizer.has_thinking: think_start = tokenizer.rfind_think_start(prompt, start=tail_start - 11) if think_start >= 0: tail_start = think_start # Finalize the segments and return if sys_end < tail_start: segments.append(prompt[sys_end:tail_start]) segment_types.append("user") if tail_start < len(prompt): segments.append(prompt[tail_start:]) segment_types.append("assistant") if not segments: segments = [prompt] segment_types = ["assistant"] return prompt, segments, segment_types, initial_state def _make_state_machine( self, model_key, tokenizer, stop_words, initial_state="normal" ): """Make a new SequenceStateMachine or fetch it if we 've made it before. Return also a dictionary that maps the token sequences in the state machine to their strings. """ cache_key = (model_key, tuple(stop_words), initial_state) rs = self._state_machine_cache.get(cache_key) if rs is not None: return rs # Will hold the state machine transitions and the sequences map to # strings. transitions = {} sequences = {} # Add all the stop sequences common_stops = [] for t in tokenizer.eos_token_ids: sequences[(t,)] = tokenizer.convert_ids_to_tokens(t) common_stops.append(((t,), None)) for w in stop_words: t = tuple(tokenizer.encode(w, add_special_tokens=False)) sequences[t] = w common_stops.append((t, None)) # From normal to stop transitions["normal"] = list(common_stops) # Reasoning related transitions if tokenizer.has_thinking: ts = tokenizer.think_start_tokens te = tokenizer.think_end_tokens transitions["normal"].append((ts, "reasoning")) transitions["reasoning"] = [(te, "normal")] transitions["reasoning"].extend(common_stops) sequences[ts] = tokenizer.think_start sequences[te] = tokenizer.think_end # Tool calling relating transitions if tokenizer.has_tool_calling: ts = tokenizer.tool_call_start_tokens te = tokenizer.tool_call_end_tokens transitions["normal"].append((ts, "tool")) transitions["tool"] = [(te, "normal")] if te else [] transitions["tool"].extend(common_stops) sequences[ts] = tokenizer.tool_call_start if te: sequences[te] = tokenizer.tool_call_end sm = SequenceStateMachine(transitions, initial=initial_state) if len(self._state_machine_cache) > 100: self._state_machine_cache.clear() self._state_machine_cache[cache_key] = (sm, sequences) return sm, sequences def _is_batchable(self, args): return self.model_provider.is_batchable and args.seed is None def _generate(self): # Local thread stream that we 'll pass to the BatchGenerator to make # sure that all generation runs in the same stream as the # synchronization messages. generation_stream = mx.default_stream(mx.default_device()) # Load the default model if it is given self.model_provider.load_default() current_model = None current_sampling = None current_tokenizer = None current_model_key = None batch_generator = None drain_batch = False batch_results = {} unprocessed_requests = [] def get_next_request(timeout=None): if unprocessed_requests: return unprocessed_requests.pop() else: return self._next_request(timeout) if self._is_distributed: seed = mx.distributed.all_sum(mx.random.state[0]).view(mx.uint64).item() mx.random.seed(seed) while not self._stop: request = None if not drain_batch: timeout = ( None if (batch_generator is not None and len(batch_results) > 0) else 0.1 ) request = get_next_request(timeout=timeout) # We got a request if request is not None: rqueue, request, args = request # Can it be added to the current batch? if ( batch_generator is not None and current_model == args.model and self._is_batchable(args) ): try: prompt, segments, segment_types, initial_state = self._tokenize( current_tokenizer, request, args ) except Exception as e: rqueue.put(e) continue sm, sequences = self._make_state_machine( self.model_provider.model_key, tokenizer, args.stop_words, initial_state, ) self._log_cache_stats() cache, rest = self.prompt_cache.fetch_nearest_cache( current_model_key, prompt ) prompt_cache_count = len(prompt) - len(rest) N = prompt_cache_count while N > 0: if N >= len(segments[0]): N -= len(segments.pop(0)) segment_types.pop(0) else: segments[0] = segments[0][N:] break ctx = GenerationContext( has_tool_calling=tokenizer.has_tool_calling, has_thinking=tokenizer.has_thinking, tool_parser=tokenizer.tool_parser, sequences=sequences, prompt=prompt, prompt_cache_count=prompt_cache_count, ) rqueue.put(ctx) (uid,) = batch_generator.insert_segments( segments=[segments], max_tokens=[args.max_tokens], caches=[cache], all_tokens=[prompt[:prompt_cache_count]], samplers=[_make_sampler(args, tokenizer)], logits_processors=[_make_logits_processors(args)], state_machines=[sm], ) batch_results[uid] = { "ctx": ctx, "rqueue": rqueue, "detokenizer": tokenizer.detokenizer, "segment_types": segment_types[::-1], "top_logprobs": args.top_logprobs, } # just making sure we don't leave a reference around del cache if self.model_provider.cli_args.prompt_cache_bytes is not None: total = self.model_provider.cli_args.prompt_cache_bytes active = batch_generator.prompt_cache_nbytes self.prompt_cache.trim_to(n_bytes=total - active) continue # No batch generator. Load the model and if it's not # batchable serve sequential, o/w make a batch generaotr and # serve batched elif batch_generator is None: try: model, tokenizer = self.model_provider.load( args.model.model, args.model.adapter, args.model.draft ) except Exception as e: rqueue.put(e) continue if not self._is_batchable(args): self._serve_single((rqueue, request, args)) continue current_model = args.model current_tokenizer = tokenizer current_model_key = self.model_provider.model_key batch_results = {} batch_generator = BatchGenerator( model, completion_batch_size=self.cli_args.decode_concurrency, prefill_batch_size=self.cli_args.prompt_concurrency, prefill_step_size=self.cli_args.prefill_step_size, stream=generation_stream, ) unprocessed_requests.append((rqueue, request, args)) continue # We have a batch but this request cannot be added to the # batch so drain it to process the request. else: drain_batch = True unprocessed_requests.append((rqueue, request, args)) continue # No request so serve from the current batch elif batch_generator is not None: if len(batch_results) == 0: if drain_batch: current_model = None current_sampling = None current_tokenizer = None current_model_key = None batch_generator.close() batch_generator = None drain_batch = False continue uids_to_remove = [] for _ in self._time_budget: prompt_responses, gen_responses = batch_generator.next() if not prompt_responses and not gen_responses: break # Progress report for prompt processing for r in prompt_responses: result = batch_results[r.uid] result["rqueue"].put(r.progress) if result["ctx"]._should_stop: uids_to_remove.append(r.uid) # Save the caches at end of segments eos_ids = [ r.uid for r in prompt_responses if r.end_of_segment and not r.end_of_prompt and batch_results[r.uid]["segment_types"] ] caches = batch_generator.extract_cache(eos_ids) for uid, (cache, cache_key) in caches.items(): self.prompt_cache.insert_cache( self.model_provider.model_key, cache_key[:], cache, cache_type=batch_results[uid]["segment_types"].pop(), ) del caches for r in gen_responses: result = batch_results[r.uid] result["detokenizer"].add_token(r.token) result["rqueue"].put( Response( result["detokenizer"].last_segment, r.token, r.current_state, r.match_sequence, r.logprobs[r.token].item(), r.finish_reason, _format_top_logprobs( r.logprobs, result["top_logprobs"], current_tokenizer, ), ) ) if r.finish_reason is not None: result["rqueue"].put(None) self.prompt_cache.insert_cache( current_model_key, r.all_tokens[:], r.prompt_cache, cache_type="assistant", ) del batch_results[r.uid] if result["ctx"]._should_stop: uids_to_remove.append(r.uid) uids_to_remove = self._share_object(uids_to_remove) if uids_to_remove: batch_generator.remove(uids_to_remove) for uid in uids_to_remove: # It may have already been removed during # generation batch_results.pop(uid, None) def _serve_single(self, request): rqueue, request, args = request # Define the progress callback def progress(tokens_processed, tokens_total): rqueue.put((tokens_processed, tokens_total)) try: # Load the model and tokenizer model = self.model_provider.model tokenizer = self.model_provider.tokenizer draft_model = self.model_provider.draft_model # Prepare the prompt and state machine prompt, _, _, initial_state = self._tokenize(tokenizer, request, args) sm, sequences = self._make_state_machine( self.model_provider.model_key, tokenizer, args.stop_words, initial_state=initial_state, ) sm_state = sm.make_state() # Start the generation context ctx = GenerationContext( has_thinking=tokenizer.has_thinking, has_tool_calling=tokenizer.has_tool_calling, tool_parser=tokenizer.tool_parser, sequences=sequences, prompt=prompt, ) rqueue.put(ctx) # Seed if requested if args.seed is not None: mx.random.seed(args.seed) # Make the sampler and logit processor sampler = _make_sampler(args, tokenizer) logits_processors = _make_logits_processors(args) # Load the KV cache self._log_cache_stats() cache, rest = self.prompt_cache.fetch_nearest_cache( self.model_provider.model_key, prompt ) ctx.prompt_cache_count = len(prompt) - len(rest) cache_key = prompt[:] if cache is None: cache = make_prompt_cache(self.model_provider.model) if self.model_provider.draft_model is not None: cache += make_prompt_cache(self.model_provider.draft_model) # Process the prompt and generate tokens for gen in stream_generate( model=model, tokenizer=tokenizer, prompt=rest, max_tokens=args.max_tokens, sampler=sampler, logits_processors=logits_processors, prompt_cache=cache, draft_model=draft_model, num_draft_tokens=args.num_draft_tokens, prompt_progress_callback=progress, prefill_step_size=self.cli_args.prefill_step_size, ): finish_reason = gen.finish_reason sm_state, match_sequence, current_state = sm.match(sm_state, gen.token) if match_sequence is not None and current_state is None: finish_reason = "stop" rqueue.put( Response( gen.text, gen.token, current_state, match_sequence, gen.logprobs[gen.token].item(), finish_reason, _format_top_logprobs( gen.logprobs, args.top_logprobs, tokenizer ), ) ) cache_key.append(gen.token) if ctx._should_stop: if self._is_distributed: raise NotImplementedError() break if finish_reason is not None: break rqueue.put(None) # Save the KV cache again self.prompt_cache.insert_cache( self.model_provider.model_key, cache_key, cache ) except Exception as e: rqueue.put(e) def generate( self, request: CompletionRequest, generation_args: GenerationArguments, progress_callback: Optional[Callable[[int, int], None]] = None, ): response_queue = Queue() self.requests.put((response_queue, request, generation_args)) def _inner(): while True: response = response_queue.get() if response is None: break if isinstance(response, Exception): raise response if isinstance(response, tuple): if progress_callback is not None: progress_callback(*response) continue yield response ctx = response_queue.get() if isinstance(ctx, Exception): raise ctx return ctx, _process_control_tokens(ctx, _inner()) @property def cli_args(self): return self.model_provider.cli_args class APIHandler(BaseHTTPRequestHandler): def __init__( self, response_generator: ResponseGenerator, *args, system_fingerprint: Optional[str] = None, **kwargs, ): """ Create static request specific metadata """ self.created = int(time.time()) self.response_generator = response_generator self.system_fingerprint = system_fingerprint or get_system_fingerprint() super().__init__(*args, **kwargs) def _set_cors_headers(self): allowed_origins = self.response_generator.cli_args.allowed_origins origin = self.headers.get("Origin") if "*" in allowed_origins: self.send_header("Access-Control-Allow-Origin", "*") elif origin in allowed_origins: self.send_header("Access-Control-Allow-Origin", origin) self.send_header("Vary", "Origin") self.send_header("Access-Control-Allow-Methods", "*") self.send_header("Access-Control-Allow-Headers", "*") def _set_completion_headers(self, status_code: int = 200): self.send_response(status_code) self.send_header("Content-type", "application/json") self._set_cors_headers() def _set_stream_headers(self, status_code: int = 200): self.send_response(status_code) self.send_header("Content-type", "text/event-stream") self.send_header("Cache-Control", "no-cache") self._set_cors_headers() def do_OPTIONS(self): self._set_completion_headers(204) self.end_headers() def do_POST(self): """ Respond to a POST request from a client. """ request_factories = { "/v1/completions": self.handle_text_completions, "/v1/chat/completions": self.handle_chat_completions, "/chat/completions": self.handle_chat_completions, } if self.path not in request_factories: self._set_completion_headers(404) self.end_headers() self.wfile.write(b"Not Found") return # Fetch and parse request body content_length = self.headers.get("Content-Length") if content_length is None: self._set_completion_headers(411) self.end_headers() self.wfile.write( json.dumps({"error": "Content-Length header is required"}).encode() ) return try: content_length = int(content_length) except ValueError: self._set_completion_headers(400) self.end_headers() self.wfile.write( json.dumps({"error": "Invalid Content-Length header"}).encode() ) return raw_body = self.rfile.read(content_length) try: self.body = json.loads(raw_body.decode()) except json.JSONDecodeError as e: logging.error(f"JSONDecodeError: {e} - Raw body: {raw_body.decode()}") self._set_completion_headers(400) self.end_headers() self.wfile.write( json.dumps({"error": f"Invalid JSON in request body: {e}"}).encode() ) return if logging.getLogger().isEnabledFor(logging.DEBUG): debug_body = json.dumps(self.body, indent="\t") logging.debug(f"Incoming Request Body: {debug_body}") if not isinstance(self.body, dict): debug_body = json.dumps(self.body, indent="\t") logging.error(f"Invalid Request Body: {debug_body}") self._set_completion_headers(400) self.end_headers() self.wfile.write( json.dumps({"error": "Request should be a JSON dictionary"}).encode() ) return # Extract request parameters from the body self.stream = self.body.get("stream", False) self.stream_options = self.body.get("stream_options", None) self.requested_model = self.body.get("model", "default_model") self.requested_draft_model = self.body.get("draft_model", "default_model") self.num_draft_tokens = self.body.get( "num_draft_tokens", self.response_generator.cli_args.num_draft_tokens ) self.adapter = self.body.get("adapters", None) self.max_tokens = self.body.get("max_completion_tokens", None) if self.max_tokens is None: self.max_tokens = self.body.get( "max_tokens", self.response_generator.cli_args.max_tokens ) self.temperature = self.body.get( "temperature", self.response_generator.cli_args.temp ) self.top_p = self.body.get("top_p", self.response_generator.cli_args.top_p) self.top_k = self.body.get("top_k", self.response_generator.cli_args.top_k) self.min_p = self.body.get("min_p", self.response_generator.cli_args.min_p) self.repetition_penalty = self.body.get("repetition_penalty", 0.0) self.repetition_context_size = self.body.get("repetition_context_size", 20) self.presence_penalty = self.body.get("presence_penalty", 0.0) self.presence_context_size = self.body.get("presence_context_size", 20) self.frequency_penalty = self.body.get("frequency_penalty", 0.0) self.frequency_context_size = self.body.get("frequency_context_size", 20) self.xtc_probability = self.body.get("xtc_probability", 0.0) self.xtc_threshold = self.body.get("xtc_threshold", 0.0) self.logit_bias = self.body.get("logit_bias", None) self.logprobs = self.body.get("logprobs", False) self.top_logprobs = self.body.get("top_logprobs", -1) self.seed = self.body.get("seed", None) self.chat_template_kwargs = self.body.get("chat_template_kwargs") self.validate_model_parameters() # Get stop sequences stop_words = self.body.get("stop") stop_words = stop_words or [] stop_words = [stop_words] if isinstance(stop_words, str) else stop_words # Create the completion request request = request_factories[self.path]() self.handle_completion(request, stop_words) def _validate( self, name, expected_type, min_val=None, max_val=None, optional=False, whitelist=None, ): value = getattr(self, name) if optional and value is None: return if not isinstance(value, expected_type): try: allowed = tuple(et.__name__ for et in expected_type) except TypeError: allowed = expected_type.__name__ raise ValueError(f"{name} must be of type {allowed}") if whitelist is not None and value in whitelist: return if min_val is not None and value < min_val: raise ValueError(f"{name} must be at least {min_val}") if max_val is not None and value > max_val: raise ValueError(f"{name} must be at most {max_val}") def validate_model_parameters(self): """Validate that the passed model parameters have correct types and values.""" self._validate("stream", bool) self._validate("max_tokens", int, min_val=0) self._validate("temperature", (float, int), min_val=0) self._validate("top_p", (float, int), min_val=0, max_val=1) self._validate("top_k", int, min_val=0) self._validate("min_p", (float, int), min_val=0, max_val=1) self._validate("num_draft_tokens", int, min_val=0) self._validate("repetition_penalty", (float, int), min_val=0) self._validate("repetition_context_size", int, min_val=0) self._validate("presence_penalty", (float, int)) self._validate("presence_context_size", int, min_val=0) self._validate("frequency_penalty", (float, int)) self._validate("frequency_context_size", int, min_val=0) self._validate("logprobs", bool) self._validate("top_logprobs", int, min_val=0, max_val=11, whitelist=[-1]) self._validate("xtc_probability", float, min_val=0, max_val=1) self._validate("xtc_threshold", float, min_val=0, max_val=1) self._validate("requested_model", str) self._validate("adapter", str, optional=True) self._validate("seed", int, optional=True) self._validate("logit_bias", dict, optional=True) if self.logit_bias is not None: try: self.logit_bias = {int(k): float(v) for k, v in self.logit_bias.items()} except ValueError: raise ValueError("logit_bias must be a dict of int to float") def generate_response( self, text: str, finish_reason: Union[Literal["length", "stop"], None], prompt_token_count: Optional[int] = None, completion_token_count: Optional[int] = None, prompt_cache_count: Optional[int] = None, token_logprobs: Optional[List[float]] = None, top_tokens: Optional[List[Tuple[Dict[str, Any]]]] = None, tokens: Optional[List[int]] = None, tool_calls: Optional[List[str]] = None, reasoning_text: Optional[str] = None, ) -> dict: """ Generate a single response packet based on response type (stream or not), completion type and parameters. Args: text (str): Text generated by model finish_reason (Union[Literal["length", "stop"], None]): The reason the response is being sent: "length", "stop" or `None`. prompt_token_count (Optional[int]): The number of tokens in the prompt, used to populate the "usage" field (not used when stream). completion_token_count (Optional[int]): The number of tokens in the response, used to populate the "usage" field (not used when stream). prompt_cache_count (Optional[int]): The portion of prompt_token_count that was found in the cache when servicing the request. token_logprobs (Optional[List[float]]): The log probabilities per token, in token order. top_tokens (Optional[List[Tuple[Dict[str, Any]]]]): List of outputs from _format_top_logprobs, giving info on the top N tokens at each token position. tokens (Optional[List[int]]): List of tokens to return with logprobs structure tool_calls (Optional[List[str]]): List of tool calls. reasoning_text (Optional[str]): The reasoning text generated by the model. Returns: dict: A dictionary containing the response, in the same format as OpenAI's API. """ token_logprobs = token_logprobs or [] top_logprobs = top_tokens or [] tool_calls = tool_calls or [] # Static response response = { "id": self.request_id, "system_fingerprint": self.system_fingerprint, "object": self.object_type, "model": self.requested_model, "created": self.created, "choices": [ { "index": 0, "finish_reason": finish_reason, }, ], } if top_logprobs: response["choices"][0]["logprobs"] = { "content": [ dict(i[0], top_logprobs=i) if i else {} for i in top_logprobs ] } elif token_logprobs: response["choices"][0]["logprobs"] = { "content": [ dict(id=i, logprob=g) for i, g in zip(tokens, token_logprobs) ] } if not self.stream: if not ( isinstance(prompt_token_count, int) and isinstance(completion_token_count, int) ): raise ValueError( "Response type is complete, but token counts not provided" ) response["usage"] = { "prompt_tokens": prompt_token_count, "completion_tokens": completion_token_count, "total_tokens": prompt_token_count + completion_token_count, } if prompt_cache_count is not None and prompt_cache_count >= 0: response["usage"]["prompt_tokens_details"] = { "cached_tokens": prompt_cache_count, } choice = response["choices"][0] # Add dynamic response if self.object_type.startswith("chat.completion"): key_name = "delta" if self.stream else "message" choice[key_name] = {"role": "assistant"} if text: choice[key_name]["content"] = text if reasoning_text: choice[key_name]["reasoning"] = reasoning_text if tool_calls: choice[key_name]["tool_calls"] = tool_calls elif self.object_type == "text_completion": choice.update(text=text) else: raise ValueError(f"Unsupported response type: {self.object_type}") return response def handle_completion(self, request: CompletionRequest, stop_words: List[str]): """ Generate a response to a prompt and send it to the client in a single batch. Args: prompt (List[int]): The tokenized prompt. stop_words (List[str]): A list of stop words """ args = GenerationArguments( model=ModelDescription( model=self.requested_model, draft=self.requested_draft_model, adapter=self.adapter, ), sampling=SamplingArguments( temperature=self.temperature, top_p=self.top_p, top_k=self.top_k, min_p=self.min_p, xtc_probability=self.xtc_probability, xtc_threshold=self.xtc_threshold, ), logits=LogitsProcessorArguments( logit_bias=self.logit_bias, repetition_penalty=self.repetition_penalty, repetition_context_size=self.repetition_context_size, presence_penalty=self.presence_penalty, presence_context_size=self.presence_context_size, frequency_penalty=self.frequency_penalty, frequency_context_size=self.frequency_context_size, ), stop_words=stop_words, max_tokens=self.max_tokens, num_draft_tokens=self.num_draft_tokens, logprobs=self.logprobs, top_logprobs=self.top_logprobs, seed=self.seed, chat_template_kwargs=self.chat_template_kwargs, ) # Keep connection allive during long prompt processing (and also log # the progress) def keepalive_callback(processed, total): logging.info(f"Prompt processing progress: {processed}/{total}") if self.stream: msg = f": keepalive {processed}/{total}\n\n".encode() self.wfile.write(msg) self.wfile.flush() # Create the token generator try: ctx, response = self.response_generator.generate( request, args, progress_callback=keepalive_callback, ) except Exception as e: self._set_completion_headers(404) self.end_headers() self.wfile.write(json.dumps({"error": str(e)}).encode()) return # Prepare the headers if self.stream: self._set_stream_headers(200) self.end_headers() logging.debug("Starting stream:") else: self._set_completion_headers(200) logging.debug("Starting completion:") # Tool call formatter tool_formatter = ToolCallFormatter(ctx.tool_parser, request.tools, self.stream) # Variables to save the generated text, tokens, logprobs, tools etc prev_state = None finish_reason = "stop" reasoning_text = "" made_tool_call = False tool_text = "" tool_calls = [] text = "" tokens = [] token_logprobs = [] top_tokens = [] try: for gen in response: logging.debug(gen.text) # Collect the text according to our current state and state # transitions. Reasoning or tool or normal text. if gen.state == "reasoning": reasoning_text += gen.text elif gen.state == "tool": tool_text += gen.text elif gen.state == "normal": if prev_state == "tool": tool_calls.append(tool_text) tool_text = "" made_tool_call = True text += gen.text # Add the tokens and logprobs to the vars. tokens.append(gen.token) if args.logprobs: token_logprobs.append(gen.logprob) if args.top_logprobs > 0: top_tokens.append(gen.top_tokens) if ( self.stream and gen.state != "tool" and (text or tool_calls or reasoning_text) ): resp = self.generate_response( text, None, tool_calls=tool_formatter(tool_calls), reasoning_text=reasoning_text, ) self.wfile.write(f"data: {json.dumps(resp)}\n\n".encode()) self.wfile.flush() reasoning_text = "" text = "" tool_calls = [] if gen.finish_reason is not None: finish_reason = gen.finish_reason prev_state = gen.state if prev_state == "tool" and tool_text: tool_calls.append(tool_text) made_tool_call = True if finish_reason == "stop" and made_tool_call: finish_reason = "tool_calls" if self.stream: resp = self.generate_response( text, finish_reason, tool_calls=tool_formatter(tool_calls), reasoning_text=reasoning_text, ) self.wfile.write(f"data: {json.dumps(resp)}\n\n".encode()) self.wfile.flush() if ( self.stream_options is not None and self.stream_options["include_usage"] ): resp = self.completion_usage_response( len(ctx.prompt), len(tokens), ctx.prompt_cache_count, ) self.wfile.write(f"data: {json.dumps(resp)}\n\n".encode()) self.wfile.flush() self.wfile.write("data: [DONE]\n\n".encode()) self.wfile.flush() else: resp = self.generate_response( text, finish_reason, len(ctx.prompt), len(tokens), ctx.prompt_cache_count, token_logprobs=token_logprobs, top_tokens=top_tokens, tokens=tokens, reasoning_text=reasoning_text, tool_calls=tool_formatter(tool_calls), ) if logging.getLogger().isEnabledFor(logging.DEBUG): response_debug = json.dumps(resp, indent="\t") logging.debug(f"Outgoing Response: {response_debug}") response_json = json.dumps(resp).encode() self.send_header("Content-Length", str(len(response_json))) self.end_headers() self.wfile.write(response_json) self.wfile.flush() finally: ctx.stop() def completion_usage_response( self, prompt_token_count: Optional[int] = None, completion_token_count: Optional[int] = None, prompt_cache_count: Optional[int] = None, ): response = { "id": self.request_id, "system_fingerprint": self.system_fingerprint, "object": "chat.completion", "model": self.requested_model, "created": self.created, "choices": [], "usage": { "prompt_tokens": prompt_token_count, "completion_tokens": completion_token_count, "total_tokens": prompt_token_count + completion_token_count, }, } if prompt_cache_count is not None and prompt_cache_count >= 0: response["usage"]["prompt_tokens_details"] = { "cached_tokens": prompt_cache_count, } return response def handle_chat_completions(self) -> CompletionRequest: """ Handle a chat completion request. Returns: mx.array: A mx.array of the tokenized prompt from the request body """ body = self.body assert "messages" in body, "Request did not contain messages" # Determine response type self.request_id = f"chatcmpl-{uuid.uuid4()}" self.object_type = "chat.completion.chunk" if self.stream else "chat.completion" return CompletionRequest( "chat", "", body["messages"], body.get("tools") or None, body.get("role_mapping"), ) def handle_text_completions(self) -> CompletionRequest: """ Handle a text completion request. Returns: mx.array: A mx.array of the tokenized prompt from the request body """ # Determine response type self.request_id = f"cmpl-{uuid.uuid4()}" self.object_type = "text_completion" assert "prompt" in self.body, "Request did not contain a prompt" return CompletionRequest( "text", self.body["prompt"], [], None, None, ) def do_GET(self): """ Respond to a GET request from a client. """ if self.path.startswith("/v1/models"): self.handle_models_request() elif self.path == "/health": self.handle_health_check() else: self._set_completion_headers(404) self.end_headers() self.wfile.write(b"Not Found") def handle_health_check(self): """ Handle a GET request for the /health endpoint. """ self._set_completion_headers(200) self.end_headers() self.wfile.write('{"status": "ok"}'.encode()) self.wfile.flush() def handle_models_request(self): """ Handle a GET request for the /v1/models endpoint. """ self._set_completion_headers(200) self.end_headers() files = ["config.json", "model.safetensors.index.json", "tokenizer_config.json"] parts = self.path.split("/") filter_repo_id = None if len(parts) > 3: filter_repo_id = "/".join(parts[3:]) def probably_mlx_lm(repo): if repo.repo_type != "model": return False if "main" not in repo.refs: return False if filter_repo_id is not None and repo.repo_id != filter_repo_id: return False file_names = {f.file_path.name for f in repo.refs["main"].files} return all(f in file_names for f in files) # Scan the cache directory for downloaded mlx models hf_cache_info = scan_cache_dir() downloaded_models = [ repo for repo in hf_cache_info.repos if probably_mlx_lm(repo) ] # Create a list of available models models = [ { "id": repo.repo_id, "object": "model", "created": self.created, } for repo in downloaded_models ] if self.response_generator.cli_args.model: model_path = Path(self.response_generator.cli_args.model) if model_path.exists(): model_id = str(model_path.resolve()) models.append( { "id": model_id, "object": "model", "created": self.created, } ) response = {"object": "list", "data": models} response_json = json.dumps(response).encode() self.wfile.write(response_json) self.wfile.flush() def _run_http_server( host: str, port: int, response_generator, server_class=ThreadingHTTPServer, handler_class=APIHandler, ): server_address = (host, port) infos = socket.getaddrinfo( *server_address, type=socket.SOCK_STREAM, flags=socket.AI_PASSIVE ) server_class.address_family, _, _, _, server_address = next(iter(infos)) httpd = server_class( server_address, lambda *args, **kwargs: handler_class( response_generator, system_fingerprint=get_system_fingerprint(), *args, **kwargs, ), ) warnings.warn( "mlx_lm.server is not recommended for production as " "it only implements basic security checks." ) logging.info(f"Starting httpd at {host} on port {port}...") try: httpd.serve_forever() except KeyboardInterrupt: httpd.shutdown() response_generator.stop_and_join() def run( host: str, port: int, model_provider: ModelProvider, server_class=ThreadingHTTPServer, handler_class=APIHandler, ): group = mx.distributed.init() prompt_cache = LRUPromptCache(model_provider.cli_args.prompt_cache_size) response_generator = ResponseGenerator(model_provider, prompt_cache) if group.rank() == 0: _run_http_server(host, port, response_generator) else: response_generator.join() def main(): parser = argparse.ArgumentParser(description="MLX Http Server.") parser.add_argument( "--model", type=str, help="The path to the MLX model weights, tokenizer, and config", ) parser.add_argument( "--adapter-path", type=str, help="Optional path for the trained adapter weights and config.", ) parser.add_argument( "--host", type=str, default="127.0.0.1", help="Host for the HTTP server (default: 127.0.0.1)", ) parser.add_argument( "--port", type=int, default=8080, help="Port for the HTTP server (default: 8080)", ) parser.add_argument( "--allowed-origins", type=lambda x: x.split(","), default="*", help="Allowed origins (default: *)", ) parser.add_argument( "--draft-model", type=str, help="A model to be used for speculative decoding.", default=None, ) parser.add_argument( "--num-draft-tokens", type=int, help="Number of tokens to draft when using speculative decoding.", default=3, ) parser.add_argument( "--trust-remote-code", action="store_true", help="Enable trusting remote code for tokenizer", ) parser.add_argument( "--log-level", type=str, default="INFO", choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"], help="Set the logging level (default: INFO)", ) parser.add_argument( "--chat-template", type=str, default="", help="Specify a chat template for the tokenizer", required=False, ) parser.add_argument( "--use-default-chat-template", action="store_true", help="Use the default chat template", ) parser.add_argument( "--temp", type=float, default=0.0, help="Default sampling temperature (default: 0.0)", ) parser.add_argument( "--top-p", type=float, default=1.0, help="Default nucleus sampling top-p (default: 1.0)", ) parser.add_argument( "--top-k", type=int, default=0, help="Default top-k sampling (default: 0, disables top-k)", ) parser.add_argument( "--min-p", type=float, default=0.0, help="Default min-p sampling (default: 0.0, disables min-p)", ) parser.add_argument( "--max-tokens", type=int, default=512, help="Default maximum number of tokens to generate (default: 512)", ) parser.add_argument( "--chat-template-args", type=json.loads, help="""A JSON formatted string of arguments for the tokenizer's apply_chat_template, e.g. '{"enable_thinking":false}'""", default="{}", ) parser.add_argument( "--decode-concurrency", type=int, default=32, help="When a request is batchable then decode that many requests in parallel", ) parser.add_argument( "--prompt-concurrency", type=int, default=8, help="When a request is batchable then process that many prompts in parallel", ) parser.add_argument( "--prefill-step-size", type=int, default=2048, help="Step size for prefill processing (default: 2048)", ) parser.add_argument( "--prompt-cache-size", type=int, default=10, help="Maximum number of distinct KV caches to hold in the prompt cache", ) parser.add_argument( "--prompt-cache-bytes", type=_parse_size, help="Maximum size in bytes of the KV caches", ) parser.add_argument( "--pipeline", action="store_true", help="Use pipelining instead of tensor parallelism", ) args = parser.parse_args() if mx.metal.is_available(): wired_limit = mx.device_info()["max_recommended_working_set_size"] mx.set_wired_limit(wired_limit) logging.basicConfig( level=getattr(logging, args.log_level.upper(), None), format="%(asctime)s - %(levelname)s - %(message)s", ) run(args.host, args.port, ModelProvider(args)) if __name__ == "__main__": print( "Calling `python -m mlx_lm.server...` directly is deprecated." " Use `mlx_lm.server...` or `python -m mlx_lm server ...` instead." ) main()