# Copyright © 2023-2024 Apple Inc. import argparse import copy import heapq import json import logging import pickle import platform import socket import time import uuid import warnings from collections import deque from dataclasses import dataclass, field 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, generation_stream, stream_generate from .models.cache import ( can_trim_prompt_cache, make_prompt_cache, trim_prompt_cache, ) from .sample_utils import make_logits_processors, make_sampler from .utils import load, sharded_load def get_system_fingerprint(): gpu_arch = mx.device_info()["architecture"] return f"{__version__}-{mx.__version__}-{platform.platform()}-{gpu_arch}" def parse_size(x): sizes = {"M": 1e6, "G": 1e9, "MB": 1e6, "GB": 1e9, "": 1} split = 0 for xi in x: if not (xi.isdigit() or xi == "."): break split += 1 digits = float(x[:split]) size = (x[split:]).strip().upper() return int(digits * sizes[size]) class StopCondition(NamedTuple): stop_met: bool trim_length: int trim_text_length: int def stopping_criteria( tokens: List[int], eos_token_ids: set, stop_id_sequences: List[List[int]], stop_words: List[str], ) -> StopCondition: """ Determines whether the token generation should stop based on predefined conditions. Args: tokens (List[int]): The current sequence of generated tokens. eos_token_ids (set): The token IDs that represents the end-of-sequence. If the last token in ``tokens`` is in the set, the generation should stop. stop_id_sequences (List[List[[int]]): A list of integer lists, each representing a sequence of token IDs. If the end of the `tokens` list matches any of these sequences, the generation should stop. stop_words (List[str]): The stop words that correspond to the ``stop_id_sequences``. Returns: StopCondition: A named tuple indicating whether the stop condition has been met (`stop_met`) and how many tokens should be trimmed from the end if it has (`trim_length`) as well as the text that should be trimmed. """ if tokens and tokens[-1] in eos_token_ids: return StopCondition(stop_met=True, trim_length=0, trim_text_length=0) for stop_ids, stop_word in zip(stop_id_sequences, stop_words): if len(tokens) >= len(stop_ids): if tokens[-len(stop_ids) :] == stop_ids: return StopCondition( stop_met=True, trim_length=len(stop_ids), trim_text_length=len(stop_word), ) return StopCondition(stop_met=False, trim_length=0, trim_text_length=0) def sequence_overlap(s1: Sequence, s2: Sequence) -> bool: """ Checks if a suffix of s1 has overlap with a prefix of s2 Args: s1 (Sequence): The first sequence s2 (Sequence): The second sequence Returns: bool: If the two sequences have overlap """ max_overlap = min(len(s1), len(s2)) return any(s1[-i:] == s2[:i] for i in range(1, max_overlap + 1)) 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 if role_mapping is not None else 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", None) 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", False): for tool_call in tool_calls: if func := tool_call.get("function", False): if args := func.get("arguments", False): func["arguments"] = json.loads(args) class LRUPromptCache: @dataclass class CacheEntry: prompt_cache: List[Any] nbytes: int class CacheOrder: def __init__(self): self._lru_checkpoints = deque() self._lru = deque() def __len__(self): return len(self._lru) + len(self._lru_checkpoints) def push(self, model, tokens, checkpoint: bool = False): c = self._lru_checkpoints if checkpoint else self._lru c.append((model, tokens)) def remove(self, model, tokens): try: self._lru.remove((model, tokens)) except ValueError: self._lru_checkpoints.remove((model, tokens)) def pop(self): if len(self._lru) >= len(self._lru_checkpoints): return self._lru.popleft() else: return self._lru_checkpoints.popleft() @dataclass class SearchResult: model: Any exact: List[int] shorter: List[int] longer: List[int] common_prefix: int def __init__(self, max_size: int = 10, max_bytes: int = 1 << 63): self.max_size = max_size self.max_bytes = max_bytes self._cache = {} self._lru = self.CacheOrder() self._n_bytes = 0 def __len__(self): return len(self._lru) @property def nbytes(self): return self._n_bytes def _search(self, model, tokens): """Search the cache for a prompt cache. Return exact or close match.""" if model not in self._cache: return self.SearchResult(model, None, None, None, 0) current = self._cache[model] last_cache_index = -1 index = 0 while index < len(tokens) and tokens[index] in current: current = current[tokens[index]] if "cache" in current: last_cache_index = index index += 1 # Exact match no need to search for longer or shorter caches if last_cache_index == len(tokens) - 1: return self.SearchResult(model, tokens, None, None, 0) # Find the shorter cache shorter = None if last_cache_index > 0: shorter = tokens[: last_cache_index + 1] # Check for caches that are longer longer = None common_prefix = index if index > 0: best = None stack = [(current, [])] while stack: current, extra = stack.pop() if "cache" in current: if best is None or len(extra) < len(best): best = extra else: for tok in current: stack.append((current[tok], extra + [tok])) longer = tokens[:index] + best return self.SearchResult(model, None, shorter, longer, common_prefix) def _get(self, model, tokens): current = self._cache[model] for tok in tokens: current = current[tok] return current["cache"] def _delete(self, model, tokens): path = [self._cache[model]] for tok in tokens: path.append(path[-1][tok]) cache_bytes = path[-1]["cache"].nbytes self._n_bytes -= cache_bytes del path[-1]["cache"] for i in reversed(range(len(tokens))): d_prev, d, t = path[i], path[i + 1], tokens[i] if len(d) > 0: break del d_prev[t] def fetch_nearest_cache(self, model, tokens): result = self._search(model, tokens) if result.exact is not None: cache_entry = self._get(result.model, result.exact) return copy.deepcopy(cache_entry.prompt_cache), [] short_length = len(result.shorter) if result.shorter is not None else 0 if result.longer is not None and result.common_prefix > short_length: cache_entry = self._get(result.model, result.longer) if can_trim_prompt_cache(cache_entry.prompt_cache): cache = copy.deepcopy(cache_entry.prompt_cache) prefix = min(len(tokens) - 1, result.common_prefix) num_to_trim = len(result.longer) - prefix trim_prompt_cache(cache, num_to_trim) return cache, tokens[prefix:] if short_length > 0: cache_entry = self._get(result.model, result.shorter) return copy.deepcopy(cache_entry.prompt_cache), tokens[short_length:] return None, tokens def insert_cache(self, model, tokens, prompt_cache, checkpoint: bool = False): is_trimmable = can_trim_prompt_cache(prompt_cache) if model not in self._cache: self._cache[model] = {} current = self._cache[model] for i, tok in enumerate(tokens): if tok not in current: current[tok] = {} if is_trimmable and "cache" in current: self._n_bytes -= current["cache"].nbytes del current["cache"] self._lru.remove(model, tokens[:i]) current = current[tok] if "cache" in current: self._lru.remove(model, tokens) else: cache_bytes = sum(c.nbytes for c in prompt_cache) current["cache"] = self.CacheEntry(prompt_cache, cache_bytes) self._n_bytes += cache_bytes self._lru.push(model, tokens, checkpoint=checkpoint) if len(self._lru) > self.max_size: model, tokens = self._lru.pop() self._delete(model, tokens) while self._n_bytes > self.max_bytes and len(self._lru) > 1: model, tokens = self._lru.pop() self._delete(model, tokens) def trim_to( self, *, n_sequences: Optional[int] = None, n_bytes: Optional[int] = None ): n_sequences = max(0, n_sequences) if n_sequences is not None else 1 << 63 n_bytes = max(0, n_bytes) if n_bytes is not None else 1 << 63 while len(self._lru) > n_sequences: model, tokens = self._lru.pop() self._delete(model, tokens) while self._n_bytes > n_bytes: model, tokens = self._lru.pop() self._delete(model, tokens) def log_cache_stats(self): ncaches, nbytes = len(self), self.nbytes ntok = ( len(self._lru._lru_checkpoints[-1][1]) if len(self._lru._lru_checkpoints) > 0 else 0 ) logging.info( f"KV Caches: {ncaches} seq, {nbytes/1e9:.2f} GB, latest user cache {ntok} tokens" ) @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 tool_call_start: str tool_call_end: str tool_parser: Callable[[str, Any], Dict] has_thinking: bool think_start_id: int think_end_id: int think_end: str eos_token_ids: set stop_token_sequences: List[List[int]] 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 logprob: float finish_reason: Optional[str] top_tokens: Tuple[Dict[str, Any]] 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: self._loops += 1 self._time_spent += time.time() - self._start if self._loops % self._sync_frequency == 0: with mx.stream(generation_stream): 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 # Preload the default model if it is provided self.default_model_map = {} if self.cli_args.model is not None: self.default_model_map[self.cli_args.model] = "default_model" self.load(self.cli_args.model, draft_model_path="default_model") # Added in adapter_path to load dynamically def load(self, model_path, adapter_path=None, draft_model_path=None): model_path = self.default_model_map.get(model_path, model_path) if self.model_key == (model_path, adapter_path, draft_model_path): return self.model, self.tokenizer # Remove the old model if it exists. self.model = None self.tokenizer = None self.model_key = None self.draft_model = None # Building tokenizer_config tokenizer_config = { "trust_remote_code": True if self.cli_args.trust_remote_code else None } if self.cli_args.chat_template: tokenizer_config["chat_template"] = self.cli_args.chat_template if model_path == "default_model": if self.cli_args.model is None: raise ValueError( "A model path has to be given as a CLI " "argument or in the HTTP request" ) adapter_path = adapter_path or self.cli_args.adapter_path # TODO: Generalize distributed load if self.is_distributed: model, tokenizer = sharded_load( self.cli_args.model, self.pipeline_group, self.tensor_group ) else: model, tokenizer = load( self.cli_args.model, adapter_path=adapter_path, tokenizer_config=tokenizer_config, ) else: # TODO: Generalize distributed load if self.is_distributed: model, tokenizer = sharded_load( model_path, self.pipeline_group, self.tensor_group ) else: model, tokenizer = load( model_path, adapter_path=adapter_path, tokenizer_config=tokenizer_config, ) if self.cli_args.use_default_chat_template: if tokenizer.chat_template is None: tokenizer.chat_template = tokenizer.default_chat_template self.model_key = (model_path, adapter_path, draft_model_path) self.model = model self.tokenizer = tokenizer def validate_draft_tokenizer(draft_tokenizer): # Check if tokenizers are compatible 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." ) # Load draft model if specified if ( draft_model_path == "default_model" and self.cli_args.draft_model is not None ): self.draft_model, draft_tokenizer = load(self.cli_args.draft_model) validate_draft_tokenizer(draft_tokenizer) elif draft_model_path is not None and draft_model_path != "default_model": self.draft_model, draft_tokenizer = load(draft_model_path) validate_draft_tokenizer(draft_tokenizer) if self.draft_model is None: self.is_batchable = all( hasattr(c, "merge") for c in make_prompt_cache(self.model) ) 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_logprobs, tokenizer) -> Tuple[Dict[str, Any]]: """Returns info dicts for the top `top_logprobs` tokens from `logprobs`""" if top_logprobs <= 0: return () sorted_indices = mx.argpartition(-logprobs, kth=top_logprobs - 1) top_indices = sorted_indices[:top_logprobs].tolist() top_logprobs = 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_logprobs) ) 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._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 _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 with mx.stream(generation_stream): if self._rank == 0: if obj is None: mx.eval(mx.distributed.all_sum(0)) return None else: 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 else: 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): 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) return tokenizer.apply_chat_template( messages, tools=tools, add_generation_prompt=True, tokenize=True, **chat_template_args, ) else: return tokenizer.encode(convert_chat(messages, role_mapping)) else: return tokenizer.encode(request.prompt) def _compute_prompt_checkpoint(self, tokenizer, request, prompt): if request.request_type != "chat": return False, -1 if request.messages[-1]["role"] != "user": return False, -1 # Save the KV cache at the end of the prompt just before # the think start token which will likely be removed in the # next turn. prompt_checkpoint = -1 if tokenizer.has_thinking: for i in range(1, min(11, len(prompt)) - 1, 1): if prompt[-i] == tokenizer.think_start_id: prompt_checkpoint = -i - 1 break return True, prompt_checkpoint def _is_batchable(self, args): if not self.model_provider.is_batchable: return False if args.seed is not None: return False return True def _generate(self): 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) def progress_callback(info): for uid, processed, total in info: if uid in batch_results: batch_results[uid]["rqueue"].put((min(processed, total), total)) def checkpoint_callback(prompts): for uid, prompt_end, cache in prompts: rs = batch_results[uid] if not rs["checkpoint"]: continue self.prompt_cache.insert_cache( current_model_key, rs["cache_key"][:-prompt_end], list(cache), checkpoint=True, ) 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 = self._tokenize(current_tokenizer, request, args) except Exception as e: rqueue.put(e) continue ctx = GenerationContext( has_tool_calling=tokenizer.has_tool_calling, tool_call_start=tokenizer.tool_call_start, tool_call_end=tokenizer.tool_call_end, tool_parser=tokenizer.tool_parser, has_thinking=tokenizer.has_thinking, think_start_id=tokenizer.think_start_id, think_end=tokenizer.think_end, think_end_id=tokenizer.think_end_id, eos_token_ids=tokenizer.eos_token_ids, stop_token_sequences=[ tokenizer.encode(stop_word, add_special_tokens=False) for stop_word in args.stop_words ], prompt=prompt, ) rqueue.put(ctx) self.prompt_cache.log_cache_stats() cache, rest = self.prompt_cache.fetch_nearest_cache( current_model_key, prompt ) ctx.prompt_cache_count = len(prompt) - len(rest) if cache is None: cache = make_prompt_cache(self.model_provider.model) do_checkpoint, checkpoint_position = ( self._compute_prompt_checkpoint(tokenizer, request, prompt) ) (uid,) = batch_generator.insert( [rest], args.max_tokens, caches=[cache], samplers=[_make_sampler(args, tokenizer)], logits_processors=[_make_logits_processors(args)], prompt_checkpoints=[checkpoint_position], ) batch_results[uid] = { "ctx": ctx, "cache_key": prompt[:], "rqueue": rqueue, "detokenizer": tokenizer.detokenizer, "checkpoint": do_checkpoint, } # 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, stop_tokens=tokenizer.eos_token_ids, 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, prompt_progress_callback=progress_callback, prompt_checkpoint_callback=checkpoint_callback, ) 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: responses = batch_generator.next() if not responses: break for r in responses: result = batch_results[r.uid] result["cache_key"].append(r.token) if r.finish_reason != "stop": result["detokenizer"].add_token(r.token) result["rqueue"].put( Response( result["detokenizer"].last_segment, r.token, r.logprobs[r.token].item(), r.finish_reason, _format_top_logprobs( r.logprobs, args.top_logprobs, current_tokenizer ), ) ) if r.finish_reason is not None: result["rqueue"].put(None) self.prompt_cache.insert_cache( current_model_key, result["cache_key"], r.prompt_cache ) 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: with mx.stream(generation_stream): caches = batch_generator.remove( uids_to_remove, return_prompt_caches=True ) for uid, prompt_cache in caches.items(): if uid not in batch_results: continue result = batch_results[uid] self.prompt_cache.insert_cache( current_model_key, result["cache_key"], prompt_cache ) del batch_results[uid] 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 prompt = self._tokenize(tokenizer, request, args) # Start the generation context ctx = GenerationContext( has_tool_calling=tokenizer.has_tool_calling, tool_call_start=tokenizer.tool_call_start, tool_call_end=tokenizer.tool_call_end, tool_parser=tokenizer.tool_parser, has_thinking=tokenizer.has_thinking, think_start_id=tokenizer.think_start_id, think_end=tokenizer.think_end, think_end_id=tokenizer.think_end_id, eos_token_ids=tokenizer.eos_token_ids, stop_token_sequences=[ tokenizer.encode(stop_word, add_special_tokens=False) for stop_word in args.stop_words ], 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.prompt_cache.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, ): rqueue.put( Response( gen.text, gen.token, gen.logprobs[gen.token].item(), gen.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 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, _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): self.send_header("Access-Control-Allow-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 = int(self.headers["Content-Length"]) 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 indent = "\t" # Backslashes can't be inside of f-strings logging.debug(f"Incoming Request Body: {json.dumps(self.body, indent=indent)}") assert isinstance( self.body, dict ), f"Request should be dict, but got {type(self.body)}" # 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_model_parameters(self): """ Validate the model parameters passed in the request for the correct types and values. """ if not isinstance(self.stream, bool): raise ValueError("stream must be a boolean") if not isinstance(self.max_tokens, int) or self.max_tokens < 0: raise ValueError("max_tokens must be a non-negative integer") if not isinstance(self.temperature, (float, int)) or self.temperature < 0: raise ValueError("temperature must be a non-negative float") if not isinstance(self.top_p, (float, int)) or self.top_p < 0 or self.top_p > 1: raise ValueError("top_p must be a float between 0 and 1") if not isinstance(self.top_k, int) or self.top_k < 0: raise ValueError("top_k must be a non-negative integer") if not isinstance(self.min_p, (float, int)) or self.min_p < 0 or self.min_p > 1: raise ValueError("min_p must be a float between 0 and 1") if not isinstance(self.num_draft_tokens, int) or self.num_draft_tokens < 0: raise ValueError("num_draft_tokens must be a non-negative integer") if ( not isinstance(self.repetition_penalty, (float, int)) or self.repetition_penalty < 0 ): raise ValueError("repetition_penalty must be a non-negative float") if ( not isinstance(self.repetition_context_size, int) or self.repetition_context_size < 0 ): raise ValueError("repetition_context_size must be a non-negative integer") if not isinstance(self.presence_penalty, (float, int)): raise ValueError("Presence penalty must be must be a float") if ( not isinstance(self.presence_context_size, int) or self.presence_context_size < 0 ): raise ValueError("presence_context_size must be a non-negative integer") if not isinstance(self.frequency_penalty, (float, int)): raise ValueError("Presence penalty must be must be a float") if ( not isinstance(self.frequency_context_size, int) or self.frequency_context_size < 0 ): raise ValueError("frequency_context_size must be a non-negative integer") if not isinstance(self.logprobs, bool): raise ValueError("logprobs must be a boolean") if self.top_logprobs != -1 and not (0 < self.top_logprobs <= 10): raise ValueError( f"top_logprobs must be between 1 and 10 but got {self.top_logprobs:,}" ) if self.logit_bias is not None: if not isinstance(self.logit_bias, dict): raise ValueError("logit_bias must be a dict of int to float") try: self.logit_bias = {int(k): v for k, v in self.logit_bias.items()} except ValueError: raise ValueError("logit_bias must be a dict of int to float") if not ( isinstance(self.xtc_probability, float) and 0.00 <= self.xtc_probability <= 1.00 ): raise ValueError(f"xtc_probability must be a float between 0.00 and 1.00") if not ( isinstance(self.xtc_threshold, float) and 0.00 <= self.xtc_threshold <= 0.50 ): raise ValueError(f"xtc_threshold must be a float between 0.00 and 0.5") if not isinstance(self.requested_model, str): raise ValueError("model must be a string") if self.adapter is not None and not isinstance(self.adapter, str): raise ValueError("adapter must be a string") if self.seed is not None and not isinstance(self.seed, int): raise ValueError("seed must be an integer") 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", "content": text, "reasoning": reasoning_text, "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 passed to the stopping_criteria function """ 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, ) # Create keepalive callback to send SSE comments during long prompt processing def keepalive_callback(processed_tokens, total_tokens): logging.info( f"Prompt processing progress: {processed_tokens}/{total_tokens}" ) if self.stream: try: # Send SSE comment for keepalive - invisible to clients but keeps connection alive self.wfile.write( f": keepalive {processed_tokens}/{total_tokens}\n\n".encode() ) self.wfile.flush() except (BrokenPipeError, ConnectionResetError, OSError): # Client disconnected, ignore pass # 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": f"{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:") # Variables to save the tool calls in as they are being generated by # the model. in_tool_call = False made_tool_call = False tool_calls = [] tool_text = "" tool_idx = 0 def format_tool_call(tool_call): nonlocal tool_idx tool_call_id = tool_call.pop("id", None) or str(uuid.uuid4()) tool_call["arguments"] = json.dumps( tool_call["arguments"], ensure_ascii=False ) out = { "function": tool_call, "type": "function", "id": tool_call_id, } if self.stream: out["index"] = tool_idx tool_idx += 1 return out def parse_tools(tool_calls): if not tool_calls: return [] result = [] for tool_text in tool_calls: parsed = ctx.tool_parser(tool_text, request.tools) if isinstance(parsed, list): result.extend(format_tool_call(tc) for tc in parsed) else: result.append(format_tool_call(parsed)) return result # Start out in reasoning if the model is a reasoning model and the # prompt has an open think token but no closing think token in_reasoning = False if ctx.has_thinking: for i in range(len(ctx.prompt) - 1, -1, -1): if ctx.prompt[i] == ctx.think_end_id: break elif ctx.prompt[i] == ctx.think_start_id: in_reasoning = True break reasoning_text = "" # Variables to save the generated tokens and the corresponding probs tokens = [] token_logprobs = [] top_tokens = [] # Variables to save the generated text text = "" segment = "" # Well finally save the reason for stopping finish_reason = "length" # Process the generated tokens for gen in response: logging.debug(gen.text) # Gather the text in tool calling or text variables if in_reasoning: if gen.text == ctx.think_end: in_reasoning = False else: reasoning_text += gen.text elif ctx.has_tool_calling and gen.text == ctx.tool_call_start: made_tool_call = True in_tool_call = True elif in_tool_call: if gen.text == ctx.tool_call_end: tool_calls.append(tool_text) tool_text = "" in_tool_call = False else: tool_text += gen.text else: text += gen.text segment += gen.text # Save the token and its logprob tokens.append(gen.token) if args.logprobs: token_logprobs.append(gen.logprob) # If requested save the k top logprobs if args.top_logprobs > 0: top_tokens.append(gen.top_tokens) # Check if we should stop early stop_condition = stopping_criteria( tokens, ctx.eos_token_ids, ctx.stop_token_sequences, stop_words, ) if stop_condition.stop_met: finish_reason = "tool_calls" if made_tool_call else "stop" ctx.stop() tokens = tokens[: len(tokens) - stop_condition.trim_length] text = text[: len(text) - stop_condition.trim_text_length] segment = "" break if self.stream and not in_tool_call: # If the end of tokens overlaps with a stop sequence, generate new # tokens until we know if the stop sequence is hit or not if any( ( sequence_overlap(tokens, sequence) for sequence in ctx.stop_token_sequences ) ): continue elif segment or tool_calls or reasoning_text: response = self.generate_response( segment, None, tool_calls=parse_tools(tool_calls), reasoning_text=reasoning_text, ) self.wfile.write(f"data: {json.dumps(response)}\n\n".encode()) self.wfile.flush() reasoning_text = "" segment = "" tool_calls = [] if gen.finish_reason is not None: finish_reason = gen.finish_reason # Flush any remaining tool text (e.g. when tool_call_end is empty) if in_tool_call and tool_text: tool_calls.append(tool_text) if self.stream: response = self.generate_response( segment, finish_reason, tool_calls=parse_tools(tool_calls), reasoning_text=reasoning_text, ) self.wfile.write(f"data: {json.dumps(response)}\n\n".encode()) self.wfile.flush() if self.stream_options is not None and self.stream_options["include_usage"]: response = self.completion_usage_response( len(ctx.prompt), len(tokens), ctx.prompt_cache_count, ) self.wfile.write(f"data: {json.dumps(response)}\n\n".encode()) self.wfile.flush() self.wfile.write("data: [DONE]\n\n".encode()) self.wfile.flush() else: response = 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=parse_tools(tool_calls), ) response_json = json.dumps(response).encode() indent = "\t" # Backslashes can't be inside of f-strings logging.debug(f"Outgoing Response: {json.dumps(response, indent=indent)}") # Send an additional Content-Length header when it is known self.send_header("Content-Length", str(len(response_json))) self.end_headers() self.wfile.write(response_json) self.wfile.flush() 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( "--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()