# Copyright © 2023-2024 Apple Inc. import argparse import contextlib import copy import functools import json import sys import time from collections import deque from dataclasses import dataclass from functools import partial from typing import ( Any, Callable, Generator, List, Optional, Sequence, Tuple, Union, ) import mlx.core as mx import mlx.nn as nn from mlx.utils import tree_reduce from transformers import PreTrainedTokenizer from .models import cache from .models.cache import ( ArraysCache, BatchKVCache, BatchRotatingKVCache, CacheList, KVCache, QuantizedKVCache, RotatingKVCache, TokenBuffer, load_prompt_cache, ) from .sample_utils import make_sampler from .tokenizer_utils import TokenizerWrapper from .utils import does_model_support_input_embeddings, load DEFAULT_PROMPT = "hello" DEFAULT_MAX_TOKENS = 100 DEFAULT_TEMP = 0.0 DEFAULT_TOP_P = 1.0 DEFAULT_MIN_P = 0.0 DEFAULT_TOP_K = 0 DEFAULT_XTC_PROBABILITY = 0.0 DEFAULT_XTC_THRESHOLD = 0.0 DEFAULT_MIN_TOKENS_TO_KEEP = 1 DEFAULT_SEED = None DEFAULT_MODEL = "mlx-community/Llama-3.2-3B-Instruct-4bit" DEFAULT_QUANTIZED_KV_START = 5000 def str2bool(string): return string.lower() not in ["false", "f"] def setup_arg_parser(): """Set up and return the argument parser.""" parser = argparse.ArgumentParser(description="LLM inference script") parser.add_argument( "--model", type=str, help=( "The path to the local model directory or Hugging Face repo. " f"If no model is specified, then {DEFAULT_MODEL} is used." ), default=None, ) parser.add_argument( "--trust-remote-code", action="store_true", help="Enable trusting remote code for tokenizer", ) parser.add_argument( "--adapter-path", type=str, help="Optional path for the trained adapter weights and config.", ) parser.add_argument( "--extra-eos-token", type=str, default=(), nargs="+", help="Add tokens in the list of eos tokens that stop generation.", ) parser.add_argument( "--system-prompt", default=None, help="System prompt to be used for the chat template", ) parser.add_argument( "--prompt", "-p", default=DEFAULT_PROMPT, help="Message to be processed by the model ('-' reads from stdin)", ) parser.add_argument( "--prefill-response", default=None, help="Prefill response to be used for the chat template", ) parser.add_argument( "--max-tokens", "-m", type=int, default=DEFAULT_MAX_TOKENS, help="Maximum number of tokens to generate", ) parser.add_argument( "--temp", type=float, default=DEFAULT_TEMP, help="Sampling temperature" ) parser.add_argument( "--top-p", type=float, default=DEFAULT_TOP_P, help="Sampling top-p" ) parser.add_argument( "--min-p", type=float, default=DEFAULT_MIN_P, help="Sampling min-p" ) parser.add_argument( "--top-k", type=int, default=DEFAULT_TOP_K, help="Sampling top-k" ) parser.add_argument( "--xtc-probability", type=float, default=DEFAULT_XTC_PROBABILITY, help="Probability of XTC sampling to happen each next token", ) parser.add_argument( "--xtc-threshold", type=float, default=0.0, help="Thresold the probs of each next token candidate to be sampled by XTC", ) parser.add_argument( "--min-tokens-to-keep", type=int, default=DEFAULT_MIN_TOKENS_TO_KEEP, help="Minimum tokens to keep for min-p sampling.", ) parser.add_argument( "--seed", type=int, default=DEFAULT_SEED, help="PRNG seed", ) parser.add_argument( "--ignore-chat-template", action="store_true", help="Use the raw prompt without the tokenizer's chat template.", ) parser.add_argument( "--use-default-chat-template", action="store_true", help="Use the default chat template", ) parser.add_argument( "--chat-template-config", help="Additional config for `apply_chat_template`. Should be a dictionary of" " string keys to values represented as a JSON decodable string.", default=None, ) parser.add_argument( "--verbose", type=str2bool, default=True, help="Log verbose output when 'True' or 'T' or only print the response when 'False' or 'F'", ) parser.add_argument( "--max-kv-size", type=int, help="Set the maximum key-value cache size", default=None, ) parser.add_argument( "--prompt-cache-file", type=str, default=None, help="A file containing saved KV caches to avoid recomputing them", ) parser.add_argument( "--quantize-activations", "-qa", action="store_true", help="Quantize activations using the same quantization config as the corresponding layer.", ) parser.add_argument( "--kv-bits", type=int, help="Number of bits for KV cache quantization. Defaults to no quantization.", default=None, ) parser.add_argument( "--kv-group-size", type=int, help="Group size for KV cache quantization.", default=64, ) parser.add_argument( "--quantized-kv-start", help="When --kv-bits is set, start quantizing the KV cache " "from this step onwards.", type=int, default=DEFAULT_QUANTIZED_KV_START, ) 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, ) return parser # A stream on the default device just for generation generation_stream = mx.new_thread_local_stream(mx.default_device()) @contextlib.contextmanager def wired_limit(model: nn.Module, streams: Optional[List[mx.Stream]] = None): """ A context manager to temporarily change the wired limit. Note, the wired limit should not be changed during an async eval. If an async eval could be running pass in the streams to synchronize with prior to exiting the context manager. """ if not mx.metal.is_available(): try: yield finally: pass else: model_bytes = tree_reduce( lambda acc, x: acc + x.nbytes if isinstance(x, mx.array) else acc, model, 0 ) max_rec_size = mx.device_info()["max_recommended_working_set_size"] if model_bytes > 0.9 * max_rec_size: model_mb = model_bytes // 2**20 max_rec_mb = max_rec_size // 2**20 print( f"[WARNING] Generating with a model that requires {model_mb} MB " f"which is close to the maximum recommended size of {max_rec_mb} " "MB. This can be slow. See the documentation for possible work-arounds: " "https://github.com/ml-explore/mlx-lm/tree/main#large-models" ) old_limit = mx.set_wired_limit(max_rec_size) try: yield finally: if streams is not None: for s in streams: mx.synchronize(s) else: mx.synchronize() mx.set_wired_limit(old_limit) @dataclass class GenerationResponse: """ The output of :func:`stream_generate`. Args: text (str): The next segment of decoded text. This can be an empty string. token (int): The next token. from_draft (bool): Whether the token was generated by the draft model. logprobs (mx.array): A vector of log probabilities. prompt_tokens (int): The number of tokens in the prompt. prompt_tps (float): The prompt processing tokens-per-second. generation_tokens (int): The number of generated tokens. generation_tps (float): The tokens-per-second for generation. peak_memory (float): The peak memory used so far in GB. finish_reason (str): The reason the response is being sent: "length", "stop" or `None` """ text: str token: int logprobs: mx.array from_draft: bool prompt_tokens: int prompt_tps: float generation_tokens: int generation_tps: float peak_memory: float finish_reason: Optional[str] = None def maybe_quantize_kv_cache(prompt_cache, quantized_kv_start, kv_group_size, kv_bits): if kv_bits is None: return for e, c in enumerate(prompt_cache): if hasattr(c, "to_quantized") and c.offset >= quantized_kv_start: prompt_cache[e] = c.to_quantized(group_size=kv_group_size, bits=kv_bits) def generate_step( prompt: mx.array, model: nn.Module, *, max_tokens: int = 256, sampler: Optional[Callable[[mx.array], mx.array]] = None, logits_processors: Optional[List[Callable[[mx.array, mx.array], mx.array]]] = None, max_kv_size: Optional[int] = None, prompt_cache: Optional[Any] = None, prefill_step_size: int = 2048, kv_bits: Optional[int] = None, kv_group_size: int = 64, quantized_kv_start: int = 0, prompt_progress_callback: Optional[Callable[[int, int], None]] = None, input_embeddings: Optional[mx.array] = None, ) -> Generator[Tuple[mx.array, mx.array], None, None]: """ A generator producing token ids based on the given prompt from the model. Args: prompt (mx.array): The input prompt. model (nn.Module): The model to use for generation. max_tokens (int): The maximum number of tokens. Use``-1`` for an infinite generator. Default: ``256``. sampler (Callable[mx.array, mx.array], optional): A sampler for sampling a token from a vector of log probabilities. Default: ``None``. logits_processors (List[Callable[[mx.array, mx.array], mx.array]], optional): A list of functions that take tokens and logits and return the processed logits. Default: ``None``. max_kv_size (int, optional): Maximum size of the key-value cache. Old entries (except the first 4 tokens) will be overwritten. prompt_cache (List[Any], optional): A pre-computed prompt cache. Note, if provided, the cache will be updated in place. prefill_step_size (int): Step size for processing the prompt. kv_bits (int, optional): Number of bits to use for KV cache quantization. None implies no cache quantization. Default: ``None``. kv_group_size (int): Group size for KV cache quantization. Default: ``64``. quantized_kv_start (int): Step to begin using a quantized KV cache. when ``kv_bits`` is non-None. Default: ``0``. prompt_progress_callback (Callable[[int, int], None]): A call-back which takes the prompt tokens processed so far and the total number of prompt tokens. input_embeddings (mx.array, optional): Input embeddings to use instead of or in conjunction with prompt tokens. Default: ``None``. Yields: Tuple[mx.array, mx.array]: One token and a vector of log probabilities. """ if input_embeddings is not None: if not does_model_support_input_embeddings(model): raise ValueError("Model does not support input embeddings.") elif len(prompt) > 0 and len(prompt) != len(input_embeddings): raise ValueError( f"When providing input_embeddings, their sequence length ({len(input_embeddings)}) " f"must match the sequence length of the prompt ({len(prompt)}), or the " "prompt must be empty." ) elif len(prompt) == 0: raise ValueError( "Either input_embeddings or prompt (or both) must be provided." ) tokens = None # Create the KV cache for generation if prompt_cache is None: prompt_cache = cache.make_prompt_cache( model, max_kv_size=max_kv_size, ) prompt_progress_callback = prompt_progress_callback or (lambda *_: None) quantize_cache_fn = functools.partial( maybe_quantize_kv_cache, quantized_kv_start=quantized_kv_start, kv_group_size=kv_group_size, kv_bits=kv_bits, ) sampler = sampler or (lambda x: mx.argmax(x, axis=-1)) def _model_call(input_tokens: mx.array, input_embeddings: Optional[mx.array]): if input_embeddings is not None: return model( input_tokens, cache=prompt_cache, input_embeddings=input_embeddings ) else: return model(input_tokens, cache=prompt_cache) def _step(input_tokens: mx.array, input_embeddings: Optional[mx.array] = None): nonlocal tokens with mx.stream(generation_stream): logits = _model_call( input_tokens=input_tokens[None], input_embeddings=( input_embeddings[None] if input_embeddings is not None else None ), ) logits = logits[:, -1, :] if logits_processors and len(input_tokens) > 0: tokens = ( mx.concat([tokens, input_tokens]) if tokens is not None else input_tokens ) for processor in logits_processors: logits = processor(tokens, logits) quantize_cache_fn(prompt_cache) logprobs = logits - mx.logsumexp(logits, keepdims=True) sampled = sampler(logprobs) return sampled, logprobs.squeeze(0) with mx.stream(generation_stream): total_prompt_tokens = ( len(input_embeddings) if input_embeddings is not None else len(prompt) ) prompt_processed_tokens = 0 prompt_progress_callback(prompt_processed_tokens, total_prompt_tokens) while total_prompt_tokens - prompt_processed_tokens > 1: remaining = (total_prompt_tokens - prompt_processed_tokens) - 1 n_to_process = min(prefill_step_size, remaining) _model_call( input_tokens=prompt[:n_to_process][None], input_embeddings=( input_embeddings[:n_to_process][None] if input_embeddings is not None else None ), ) quantize_cache_fn(prompt_cache) mx.eval([c.state for c in prompt_cache]) prompt_processed_tokens += n_to_process prompt_progress_callback(prompt_processed_tokens, total_prompt_tokens) prompt = prompt[n_to_process:] input_embeddings = ( input_embeddings[n_to_process:] if input_embeddings is not None else input_embeddings ) mx.clear_cache() y, logprobs = _step(input_tokens=prompt, input_embeddings=input_embeddings) mx.async_eval(y, logprobs) n = 0 while True: if n != max_tokens: next_y, next_logprobs = _step(y) mx.async_eval(next_y, next_logprobs) if n == 0: mx.eval(y) prompt_progress_callback(total_prompt_tokens, total_prompt_tokens) if n == max_tokens: break yield y.item(), logprobs if n % 256 == 0: mx.clear_cache() y, logprobs = next_y, next_logprobs n += 1 def speculative_generate_step( prompt: mx.array, model: nn.Module, draft_model: nn.Module, *, num_draft_tokens: int = 2, max_tokens: int = 256, sampler: Optional[Callable[[mx.array], mx.array]] = None, logits_processors: Optional[List[Callable[[mx.array, mx.array], mx.array]]] = None, prompt_cache: Optional[Any] = None, prefill_step_size: int = 512, kv_bits: Optional[int] = None, kv_group_size: int = 64, quantized_kv_start: int = 0, ) -> Generator[Tuple[mx.array, mx.array, bool], None, None]: """ A generator producing token ids based on the given prompt from the model. Args: prompt (mx.array): The input prompt. model (nn.Module): The model to use for generation. draft_model (nn.Module): The draft model for speculative decoding. num_draft_tokens (int, optional): The number of draft tokens for speculative decoding. Default: ``2``. max_tokens (int): The maximum number of tokens. Use``-1`` for an infinite generator. Default: ``256``. sampler (Callable[[mx.array], mx.array], optional): A sampler for sampling a token from a vector of log probabilities. Default: ``None``. logits_processors (List[Callable[[mx.array, mx.array], mx.array]], optional): A list of functions that take tokens and logits and return the processed logits. Default: ``None``. prompt_cache (List[Any], optional): A pre-computed prompt cache. Note, if provided, the cache will be updated in place. The cache must be trimmable. prefill_step_size (int): Step size for processing the prompt. kv_bits (int, optional): Number of bits to use for KV cache quantization. None implies no cache quantization. Default: ``None``. kv_group_size (int): Group size for KV cache quantization. Default: ``64``. quantized_kv_start (int): Step to begin using a quantized KV cache. when ``kv_bits`` is non-None. Default: ``0``. Yields: Tuple[mx.array, mx.array, bool]: One token, a vector of log probabilities, and a bool indicating if the token was generated by the draft model """ y = prompt.astype(mx.uint32) prev_tokens = None # Create the KV cache for generation if prompt_cache is None: model_cache = cache.make_prompt_cache(model) draft_cache = cache.make_prompt_cache(draft_model) else: model_cache = prompt_cache[: len(model.layers)] draft_cache = prompt_cache[len(model.layers) :] if not cache.can_trim_prompt_cache(model_cache): types = {type(c).__name__ for c in model_cache if not c.is_trimmable()} raise ValueError( f"Speculative decoding requires a trimmable prompt cache " f"(got {types})." ) sampler = sampler or (lambda x: mx.argmax(x, axis=-1)) quantize_cache_fn = functools.partial( maybe_quantize_kv_cache, quantized_kv_start=quantized_kv_start, kv_group_size=kv_group_size, kv_bits=kv_bits, ) def _process_and_sample(tokens, logits): if logits_processors: for processor in logits_processors: logits = processor(tokens, logits) logprobs = logits - mx.logsumexp(logits, axis=-1, keepdims=True) y = sampler(logprobs) return y, logprobs def _step(model, cache, y, n_predict=1): with mx.stream(generation_stream): logits = model(y[None], cache=cache) logits = logits[:, -n_predict:, :] quantize_cache_fn(cache) if logits_processors: nonlocal prev_tokens out_y, out_logprobs = [], [] if n_predict > 1: y = y[: -(n_predict - 1)] for i in range(n_predict): prev_tokens = ( mx.concatenate([prev_tokens, y]) if prev_tokens is not None else y ) y, logprobs = _process_and_sample(prev_tokens, logits[:, i, :]) out_y.append(y) out_logprobs.append(logprobs) return mx.concatenate(out_y, axis=0), mx.concatenate( out_logprobs, axis=0 ) else: return _process_and_sample(None, logits.squeeze(0)) def _prefill(model, cache, y): while y.size > 1: n_to_process = min(prefill_step_size, y.size - 1) model(y[:n_to_process][None], cache=cache) quantize_cache_fn(cache) mx.eval([c.state for c in cache]) y = y[n_to_process:] mx.clear_cache() return y def _rewind_cache(num_draft, num_accept): cache.trim_prompt_cache(model_cache, num_draft - num_accept) cache.trim_prompt_cache(draft_cache, max(num_draft - num_accept - 1, 0)) def _draft_generate(y, num_draft): if num_draft == 0: return mx.array([], mx.uint32) ys = [] for _ in range(num_draft): y, _ = _step(draft_model, draft_cache, y) mx.async_eval(y) ys.append(y) return mx.concatenate(ys) with mx.stream(generation_stream): draft_y = _prefill(draft_model, draft_cache, y) y = _prefill(model, model_cache, y) ntoks = 0 # Set these so the finally block doesn't raise num_draft = 0 n = 0 try: while True: num_draft = min(max_tokens - ntoks, num_draft_tokens) draft_tokens = _draft_generate(draft_y, num_draft) if prev_tokens is not None: prev_tokens = prev_tokens[: prev_tokens.size - y.size - num_draft + 1] y = mx.concatenate([y, draft_tokens]) tokens, logprobs = _step(model, model_cache, y, num_draft + 1) mx.eval(tokens, draft_tokens) draft_tokens = draft_tokens.tolist() tokens = tokens.tolist() n = 0 while n < num_draft: tn, dtn, lpn = tokens[n], draft_tokens[n], logprobs[n] if tn != dtn: break n += 1 ntoks += 1 yield tn, lpn, True if ntoks == max_tokens: break if ntoks < max_tokens: ntoks += 1 yield tokens[n], logprobs[n], False if ntoks == max_tokens: break y = mx.array([tokens[n]], mx.uint32) draft_y = y # If we accepted all the draft tokens, include the last # draft token in the next draft step since it hasn't been # processed yet by the draft model if n == num_draft: draft_y = mx.concatenate( [mx.array(draft_tokens[-1:], mx.uint32), draft_y] ) if prev_tokens is not None: prev_tokens = prev_tokens[: -max(num_draft - n, 1)] _rewind_cache(num_draft, n) finally: _rewind_cache(num_draft, n) def stream_generate( model: nn.Module, tokenizer: Union[PreTrainedTokenizer, TokenizerWrapper], prompt: Union[str, mx.array, List[int]], max_tokens: int = 256, draft_model: Optional[nn.Module] = None, **kwargs, ) -> Generator[GenerationResponse, None, None]: """ A generator producing text based on the given prompt from the model. Args: model (nn.Module): The model to use for generation. tokenizer (PreTrainedTokenizer): The tokenizer. prompt (Union[str, mx.array, List[int]]): The input prompt string or integer tokens. max_tokens (int): The maximum number of tokens to generate. Default: ``256``. draft_model (Optional[nn.Module]): An optional draft model. If provided then speculative decoding is used. The draft model must use the same tokenizer as the main model. Default: ``None``. kwargs: The remaining options get passed to :func:`generate_step`. See :func:`generate_step` for more details. Yields: GenerationResponse: An instance containing the generated text segment and associated metadata. See :class:`GenerationResponse` for details. """ if not isinstance(tokenizer, TokenizerWrapper): tokenizer = TokenizerWrapper(tokenizer) if not isinstance(prompt, mx.array): if isinstance(prompt, str): # Try to infer if special tokens are needed add_special_tokens = tokenizer.bos_token is None or not prompt.startswith( tokenizer.bos_token ) prompt = tokenizer.encode(prompt, add_special_tokens=add_special_tokens) prompt = mx.array(prompt) detokenizer = tokenizer.detokenizer kwargs["max_tokens"] = max_tokens if draft_model is None: kwargs.pop("num_draft_tokens", None) token_generator = generate_step(prompt, model, **kwargs) # from_draft always false for non-speculative generation token_generator = ( (token, logprobs, False) for token, logprobs in token_generator ) else: kwargs.pop("max_kv_size", None) kwargs.pop("prompt_progress_callback", None) token_generator = speculative_generate_step( prompt, model, draft_model, **kwargs ) with wired_limit(model, [generation_stream]): tic = time.perf_counter() for n, (token, logprobs, from_draft) in enumerate(token_generator): if n == 0: prompt_time = time.perf_counter() - tic prompt_tps = prompt.size / prompt_time tic = time.perf_counter() if token in tokenizer.eos_token_ids: break detokenizer.add_token(token) if (n + 1) == max_tokens: break yield GenerationResponse( text=detokenizer.last_segment, token=token, logprobs=logprobs, from_draft=from_draft, prompt_tokens=prompt.size, prompt_tps=prompt_tps, generation_tokens=n + 1, generation_tps=(n + 1) / (time.perf_counter() - tic), peak_memory=mx.get_peak_memory() / 1e9, finish_reason=None, ) detokenizer.finalize() yield GenerationResponse( text=detokenizer.last_segment, token=token, logprobs=logprobs, from_draft=from_draft, prompt_tokens=prompt.size, prompt_tps=prompt_tps, generation_tokens=n + 1, generation_tps=(n + 1) / (time.perf_counter() - tic), peak_memory=mx.get_peak_memory() / 1e9, finish_reason="stop" if token in tokenizer.eos_token_ids else "length", ) def generate( model: nn.Module, tokenizer: Union[PreTrainedTokenizer, TokenizerWrapper], prompt: Union[str, List[int]], verbose: bool = False, **kwargs, ) -> str: """ Generate a complete response from the model. Args: model (nn.Module): The language model. tokenizer (PreTrainedTokenizer): The tokenizer. prompt (Union[str, List[int]]): The input prompt string or integer tokens. verbose (bool): If ``True``, print tokens and timing information. Default: ``False``. kwargs: The remaining options get passed to :func:`stream_generate`. See :func:`stream_generate` for more details. """ if verbose: print("=" * 10) text = "" for response in stream_generate(model, tokenizer, prompt, **kwargs): if verbose: print(response.text, end="", flush=True) text += response.text if verbose: print() print("=" * 10) if len(text) == 0: print("No text generated for this prompt") return print( f"Prompt: {response.prompt_tokens} tokens, " f"{response.prompt_tps:.3f} tokens-per-sec" ) print( f"Generation: {response.generation_tokens} tokens, " f"{response.generation_tps:.3f} tokens-per-sec" ) print(f"Peak memory: {response.peak_memory:.3f} GB") return text def _left_pad_prompts(prompts, max_length=None): if max_length is None: max_length = max(len(p) for p in prompts) return mx.array([[0] * (max_length - len(p)) + p for p in prompts]) def _right_pad_prompts(prompts, max_length=None): if max_length is None: max_length = max(len(p) for p in prompts) return mx.array([p + [0] * (max_length - len(p)) for p in prompts]) @dataclass class BatchStats: """ An data object to hold generation stats. Args: prompt_tokens (int): The number of prompt tokens processed. prompt_tps (float): The prompt processing tokens-per-second. prompt_time (float): The time in seconds spent in prompt processing. generation_tokens (int): The number of generated tokens. generation_tps (float): The tokens-per-second for generation. generation_time (float): The time in seconds spent in generation . peak_memory (float): The peak memory used so far in GB. """ prompt_tokens: int = 0 prompt_tps: float = 0 prompt_time: float = 0 generation_tokens: int = 0 generation_tps: float = 0 generation_time: float = 0 peak_memory: float = 0 def _make_cache(model, left_padding, max_kv_size): """ Convert a list of regular caches into their corresponding batch-aware caches. """ def to_batch_cache(c): if type(c) is KVCache: return BatchKVCache(left_padding) elif isinstance(c, ArraysCache): c.left_padding = mx.array(left_padding) return c elif isinstance(c, RotatingKVCache): if c.keep > 0: raise ValueError("RotatingKVCache with keep tokens is not supported.") return BatchRotatingKVCache(c.max_size, left_padding) elif isinstance(c, CacheList): return CacheList(*(to_batch_cache(sub_c) for sub_c in c.caches)) else: raise ValueError(f"{type(c)} does not yet support batching") if hasattr(model, "make_cache"): cache = model.make_cache() return [to_batch_cache(c) for c in cache] else: if max_kv_size is not None: return [ BatchRotatingKVCache(max_kv_size, left_padding) for _ in model.layers ] return [BatchKVCache(left_padding) for _ in model.layers] def _merge_caches(caches): batch_cache = [] if not caches: return batch_cache for i in range(len(caches[0])): if hasattr(caches[0][i], "merge"): batch_cache.append(caches[0][i].merge([c[i] for c in caches])) else: raise ValueError( f"{type(caches[0][i])} does not yet support batching with history" ) return batch_cache def _extend_cache(cache_a, cache_b): if not cache_a: return cache_b if not cache_b: return cache_a for ca, cb in zip(cache_a, cache_b): ca.extend(cb) return cache_a def _build_trie(sequences): """Build an Aho-Corasick trie from the provided sequences See https://en.wikipedia.org/wiki/Aho–Corasick_algorithm . """ trie = {} for idx, seq in enumerate(sequences): node = trie try: for tok in seq: node = node.setdefault(tok, {}) node["__match__"] = (tuple(seq), idx) except TypeError: node = node.setdefault(seq, {}) node["__match__"] = ((seq,), idx) # BFS to set failure links and propagate matches. queue = deque() for key, child in trie.items(): if key == "__match__": continue child["__fail__"] = trie queue.append(child) while queue: parent = queue.popleft() for key, child in parent.items(): if key in ("__fail__", "__match__"): continue queue.append(child) fail = parent["__fail__"] while key not in fail and fail is not trie: fail = fail["__fail__"] child["__fail__"] = fail[key] if key in fail else trie if "__match__" not in child and "__match__" in child["__fail__"]: child["__match__"] = child["__fail__"]["__match__"] return trie def _step_trie(node, trie, x): """One step in the Aho-Corasick trie.""" while x not in node and node is not trie: node = node["__fail__"] if x in node: node = node[x] return node class SequenceStateMachine: """A state machine that uses one Aho-Corasick trie per state to efficiently track state across a generated sequence. The transitions are provided as state -> [(sequence, new_state)]. Example: sm = SequenceStateMachine( transitions={ "normal": [ (think_start_tokens, "reasoning"), (tool_start_tokens, "tool"), (eos, None), ], "reasoning": [ (think_end_tokens, "normal"), (eos, None), ], "tool": [ (tool_end_tokens, None), (eos, None) ], }, initial="normal" ) """ def __init__(self, transitions={}, initial="normal"): self._initial = initial self._states = {} for src, edges in transitions.items(): sequences, dst = zip(*edges) self._states[src] = (_build_trie(sequences), dst) if not self._states: self._states[initial] = (_build_trie([]), []) def __deepcopy__(self, memo): new = object.__new__(SequenceStateMachine) new._initial = self._initial new._states = self._states return new def make_state(self): return (self._initial, self._states[self._initial][0], self._states) @staticmethod def match(state, x): s, n, states = state n = _step_trie(n, states[s][0], x) seq = None match = n.get("__match__") if match is not None: seq = match[0] s = states[s][1][match[1]] n = states[s][0] if s is not None else None return (s, n, states), seq, s class PromptProcessingBatch: """ A batch processor for prompt tokens with support for incremental processing. This class handles batched prompt processing, managing KV caches and preparing tokens for generation. It supports extending, filtering, and splitting batches. """ @dataclass class Response: uid: int progress: tuple end_of_segment: bool end_of_prompt: bool def __init__( self, model: nn.Module, uids: List[int], caches: List[List[Any]], tokens: Optional[List[List[int]]] = None, prefill_step_size: int = 2048, samplers: Optional[List[Callable[[mx.array], mx.array]]] = None, fallback_sampler: Optional[Callable[[mx.array], mx.array]] = None, logits_processors: Optional[ List[List[Callable[[mx.array, mx.array], mx.array]]] ] = None, state_machines: Optional[List[SequenceStateMachine]] = None, max_tokens: Optional[List[int]] = None, ): self.model = model self.uids = uids self.prompt_cache = _merge_caches(caches) self.tokens = tokens if tokens is not None else [[] for _ in uids] self.prefill_step_size = prefill_step_size self.samplers = samplers if samplers is not None else [] self.fallback_sampler = fallback_sampler or (lambda x: mx.argmax(x, axis=-1)) self.logits_processors = ( logits_processors if logits_processors is not None else [] ) self.state_machines = ( state_machines if state_machines is not None else [SequenceStateMachine()] * len(uids) ) self.max_tokens = ( max_tokens if max_tokens is not None else [DEFAULT_MAX_TOKENS] * len(self.uids) ) def __len__(self): return len(self.uids) def extract_cache(self, idx: int) -> List[Any]: return [c.extract(idx) for c in self.prompt_cache] def extend(self, batch): if not any(self.samplers): self.samplers = [None] * len(self.uids) if not any(self.logits_processors): self.logits_processors = [None] * len(self.uids) samplers = batch.samplers if any(batch.samplers) else [None] * len(batch.uids) logits_processors = ( batch.logits_processors if any(batch.logits_processors) else [None] * len(batch.uids) ) self.uids.extend(batch.uids) self.prompt_cache = _extend_cache(self.prompt_cache, batch.prompt_cache) self.tokens.extend(batch.tokens) self.samplers.extend(samplers) self.logits_processors.extend(logits_processors) self.max_tokens.extend(batch.max_tokens) self.state_machines.extend(batch.state_machines) def _copy(self): new_batch = self.__class__.__new__(self.__class__) new_batch.model = self.model new_batch.uids = list(self.uids) new_batch.prompt_cache = copy.deepcopy(self.prompt_cache) new_batch.tokens = list(self.tokens) new_batch.prefill_step_size = self.prefill_step_size new_batch.samplers = list(self.samplers) new_batch.fallback_sampler = self.fallback_sampler new_batch.logits_processors = list(self.logits_processors) new_batch.state_machines = list(self.state_machines) new_batch.max_tokens = list(self.max_tokens) return new_batch def split(self, indices: List[int]): indices = sorted(indices) indices_left = sorted(set(range(len(self.uids))) - set(indices)) new_batch = self._copy() self.filter(indices_left) new_batch.filter(indices) return new_batch def filter(self, keep: List[int]): self.uids = [self.uids[idx] for idx in keep] if not keep: self.prompt_cache.clear() else: for c in self.prompt_cache: c.filter(keep) self.tokens = [self.tokens[idx] for idx in keep] if any(self.samplers): self.samplers = [self.samplers[idx] for idx in keep] else: self.samplers = [None] * len(keep) if any(self.logits_processors): self.logits_processors = [self.logits_processors[idx] for idx in keep] else: self.logits_processors = [[]] * len(keep) self.max_tokens = [self.max_tokens[idx] for idx in keep] self.state_machines = [self.state_machines[idx] for idx in keep] def prompt(self, tokens: List[List[int]]): """ Process prompt tokens through the model. Args: tokens: List of token sequences to process. """ if len(self.uids) != len(tokens): raise ValueError("The batch length doesn't match the number of inputs") if not tokens: return # Add the tokens to the self.tokens so they represent the tokens # contained in the KV Cache. for sti, ti in zip(self.tokens, tokens): sti += ti # Calculate if we need to pad lengths = [len(p) for p in tokens] max_length = max(lengths) padding = [max_length - l for l in lengths] max_padding = max(padding) # Prepare the caches and inputs. Right pad if needed otherwise just # cast to array. if max_padding > 0: tokens = _right_pad_prompts(tokens, max_length=max_length) for c in self.prompt_cache: c.prepare(lengths=lengths, right_padding=padding) else: tokens = mx.array(tokens) # Actual prompt processing loop while tokens.shape[1] > 0: n_to_process = min(self.prefill_step_size, tokens.shape[1]) self.model(tokens[:, :n_to_process], cache=self.prompt_cache) mx.eval([c.state for c in self.prompt_cache]) mx.clear_cache() tokens = tokens[:, n_to_process:] # Finalize the cache if there was any padding if max_padding > 0: for c in self.prompt_cache: c.finalize() mx.eval([c.state for c in self.prompt_cache]) mx.clear_cache() def generate(self, tokens: List[List[int]]): """ Transition from prompt processing to generation. Args: tokens: Final tokens for each sequence to start generation. Returns: A GenerationBatch ready for token generation. """ if any(len(t) > 1 for t in tokens): self.prompt([t[:-1] for t in tokens]) last_token = mx.array([t[-1] for t in tokens]) generation = GenerationBatch( self.model, self.uids, last_token, self.prompt_cache, self.tokens, self.samplers, self.fallback_sampler, self.logits_processors, self.state_machines, self.max_tokens, ) self.uids = [] self.prompt_cache = [] self.tokens = [] self.samplers = [] self.logits_processors = [] self.max_tokens = [] return generation @classmethod def empty( cls, model: nn.Module, fallback_sampler: Callable[[mx.array], mx.array], prefill_step_size: int = 2048, ): return cls( model=model, fallback_sampler=fallback_sampler, prefill_step_size=prefill_step_size, uids=[], caches=[], tokens=[], samplers=[], logits_processors=[], max_tokens=[], state_machines=[], ) class GenerationBatch: """ A batched token generator that manages multiple sequences in parallel. This class handles the generation phase after prompt processing, managing KV caches, sampling, and stop sequence detection for multiple sequences. """ @dataclass class Response: uid: int token: int logprobs: mx.array finish_reason: Optional[str] current_state: Optional[str] match_sequence: Optional[List[int]] prompt_cache: Optional[List[Any]] all_tokens: Optional[List[int]] def __init__( self, model: nn.Module, uids: List[int], inputs: mx.array, prompt_cache: List[Any], tokens: List[List[int]], samplers: Optional[List[Callable[[mx.array], mx.array]]], fallback_sampler: Callable[[mx.array], mx.array], logits_processors: Optional[ List[List[Callable[[mx.array, mx.array], mx.array]]] ], state_machines: List[SequenceStateMachine], max_tokens: List[int], ): self.model = model self.uids = uids self.prompt_cache = prompt_cache self.tokens = tokens self.samplers = samplers self.fallback_sampler = fallback_sampler self.logits_processors = logits_processors self.state_machines = state_machines self.max_tokens = max_tokens if self.samplers and len(self.samplers) != len(self.uids): raise ValueError("Insufficient number of samplers provided") if self.logits_processors and len(self.logits_processors) != len(self.uids): raise ValueError("Insufficient number of logits_processors provided") self._current_tokens = None self._current_logprobs = [] self._next_tokens = inputs self._next_logprobs = [] self._token_context = [TokenBuffer(t) for t in tokens] self._num_tokens = [0] * len(self.uids) self._matcher_states = [m.make_state() for m in state_machines] if self.uids: self._step() def __len__(self): return len(self.uids) def extend(self, batch): """Extend this batch with another generation batch.""" self.uids.extend(batch.uids) self.prompt_cache = _extend_cache(self.prompt_cache, batch.prompt_cache) self.tokens.extend(batch.tokens) self.samplers.extend(batch.samplers) self.logits_processors.extend(batch.logits_processors) self.max_tokens.extend(batch.max_tokens) self.state_machines.extend(batch.state_machines) if self._current_tokens is None: self._current_tokens = batch._current_tokens self._current_logprobs = batch._current_logprobs elif batch._current_tokens is not None: self._current_tokens = mx.concatenate( [self._current_tokens, batch._current_tokens] ) self._current_logprobs.extend(batch._current_logprobs) if self._next_tokens is None: self._next_tokens = batch._next_tokens self._next_logprobs = batch._next_logprobs elif batch._next_tokens is not None: self._next_tokens = mx.concatenate([self._next_tokens, batch._next_tokens]) self._next_logprobs.extend(batch._next_logprobs) self._token_context.extend(batch._token_context) self._num_tokens.extend(batch._num_tokens) self._matcher_states.extend(batch._matcher_states) def _step(self) -> Tuple[List[int], List[mx.array]]: """ Perform a single generation step. Returns: Tuple of token list and logprobs list. """ self._current_tokens = self._next_tokens self._current_logprobs = self._next_logprobs inputs = self._current_tokens # Forward pass logits = self.model(inputs[:, None], cache=self.prompt_cache) logits = logits[:, -1, :] # Logits processors token_context = [] if any(self.logits_processors): # Update the token context that will be used by the logits processors token_context = [ tc.update_and_fetch(inputs[i : i + 1]) for i, tc in enumerate(self._token_context) ] processed_logits = [] for e in range(len(self.uids)): sample_logits = logits[e : e + 1] for processor in self.logits_processors[e]: sample_logits = processor(token_context[e], sample_logits) processed_logits.append(sample_logits) logits = mx.concatenate(processed_logits, axis=0) # Normalize the logits logprobs = logits - mx.logsumexp(logits, axis=-1, keepdims=True) # Sample if any(self.samplers): all_samples = [] for e in range(len(self.uids)): sample_sampler = self.samplers[e] or self.fallback_sampler sampled = sample_sampler(logprobs[e : e + 1]) all_samples.append(sampled) sampled = mx.concatenate(all_samples, axis=0) else: sampled = self.fallback_sampler(logprobs) # Assign the next step to member variables and start computing it # asynchronously self._next_tokens = sampled self._next_logprobs = list(logprobs) mx.async_eval(self._next_tokens, self._next_logprobs, token_context) # Eval the current tokens and current logprobs. After that also add # them to self.tokens so that it always represents the tokens contained # in the KV Cache. mx.eval(inputs, self._current_logprobs) inputs = inputs.tolist() for sti, ti in zip(self.tokens, inputs): sti.append(ti) return inputs, self._current_logprobs def extract_cache(self, idx: int) -> List[Any]: return [c.extract(idx) for c in self.prompt_cache] def filter(self, keep: List[int]): """Filter the batch to keep only the specified indices.""" self.uids = [self.uids[idx] for idx in keep] if not keep: self.prompt_cache.clear() else: for c in self.prompt_cache: c.filter(keep) self.tokens = [self.tokens[idx] for idx in keep] if any(self.samplers): self.samplers = [self.samplers[idx] for idx in keep] if any(self.logits_processors): self.logits_processors = [self.logits_processors[idx] for idx in keep] self.max_tokens = [self.max_tokens[idx] for idx in keep] self.state_machines = [self.state_machines[idx] for idx in keep] self._next_tokens = self._next_tokens[keep] if keep else None self._next_logprobs = [self._next_logprobs[idx] for idx in keep] self._token_context = [self._token_context[idx] for idx in keep] self._num_tokens = [self._num_tokens[idx] for idx in keep] self._matcher_states = [self._matcher_states[idx] for idx in keep] def next(self) -> List[Response]: """ Generate the next batch of tokens. Returns: List of Response objects for each sequence in the batch. """ if not self.uids: return [] tokens, logprobs = self._step() keep = [] responses = [] for i in range(len(self.uids)): finish_reason = None match_sequence = None self._num_tokens[i] += 1 if self._num_tokens[i] >= self.max_tokens[i]: finish_reason = "length" self._matcher_states[i], match_sequence, current_state = ( self.state_machines[i].match(self._matcher_states[i], tokens[i]) ) if match_sequence is not None and current_state is None: finish_reason = "stop" if finish_reason is not None: responses.append( self.Response( uid=self.uids[i], token=tokens[i], logprobs=logprobs[i], finish_reason=finish_reason, current_state=current_state, match_sequence=match_sequence, prompt_cache=self.extract_cache(i), all_tokens=self.tokens[i], ) ) else: keep.append(i) responses.append( self.Response( uid=self.uids[i], token=tokens[i], logprobs=logprobs[i], finish_reason=None, match_sequence=match_sequence, current_state=current_state, prompt_cache=None, all_tokens=None, ) ) if len(keep) < len(self.uids): self.filter(keep) return responses @classmethod def empty( cls, model: nn.Module, fallback_sampler: Callable[[mx.array], mx.array], ): return cls( model=model, fallback_sampler=fallback_sampler, uids=[], inputs=mx.array([], dtype=mx.uint32), prompt_cache=[], tokens=[], samplers=[], logits_processors=[], max_tokens=[], state_machines=[], ) class BatchGenerator: """ A batch generator implements continuous batching. This class provides automatic management of prompt processing and generation batches, handling the transition between the two. It also allows for segmented prompt processing which guarantees that the generator will stop at these boundaries when processing an input. """ def __init__( self, model: nn.Module, *, max_tokens: int = 128, stop_tokens: Optional[Sequence[Sequence[int]]] = None, sampler: Optional[Callable[[mx.array], mx.array]] = None, logits_processors: Optional[ List[Callable[[mx.array, mx.array], mx.array]] ] = None, completion_batch_size: int = 32, prefill_batch_size: int = 8, prefill_step_size: int = 2048, max_kv_size: Optional[int] = None, stream=None, ): self.model = model self.max_tokens = max_tokens self.sampler = sampler or (lambda x: mx.argmax(x, axis=-1)) self.logits_processors = logits_processors or [] self.uid_count = 0 self.prefill_step_size = prefill_step_size self.prefill_batch_size = prefill_batch_size self.completion_batch_size = max(completion_batch_size, prefill_batch_size) self.max_kv_size = max_kv_size self._stream = stream or generation_stream self._default_state_machine = SequenceStateMachine( {"normal": [(seq, None) for seq in stop_tokens]} if stop_tokens else {}, initial="normal", ) self._uid_count = 0 self._prompt_batch = PromptProcessingBatch.empty( self.model, self.sampler, prefill_step_size=prefill_step_size, ) self._generation_batch = GenerationBatch.empty(self.model, self.sampler) self._unprocessed_sequences = deque() self._currently_processing = [] self._prompt_tokens_counter = 0 self._prompt_time_counter = 0 self._gen_tokens_counter = 0 self._steps_counter = 0 if mx.metal.is_available(): self._old_wired_limit = mx.set_wired_limit( mx.device_info()["max_recommended_working_set_size"] ) else: self._old_wired_limit = None @property def stream(self): return self._stream def close(self): if self._old_wired_limit is not None: mx.synchronize(self._stream) mx.set_wired_limit(self._old_wired_limit) self._old_wired_limit = None def __del__(self): self.close() @contextlib.contextmanager def stats(self, stats=None): stats = stats or BatchStats() self._prompt_tokens_counter = 0 self._prompt_time_counter = 0 self._gen_tokens_counter = 0 tic = time.perf_counter() try: yield stats finally: toc = time.perf_counter() total_time = toc - tic gen_time = total_time - self._prompt_time_counter stats.prompt_tokens += self._prompt_tokens_counter stats.prompt_time += self._prompt_time_counter stats.prompt_tps = stats.prompt_tokens / stats.prompt_time stats.generation_tokens += self._gen_tokens_counter stats.generation_time += gen_time stats.generation_tps = stats.generation_tokens / stats.generation_time stats.peak_memory = max(stats.peak_memory, mx.get_peak_memory() / 1e9) def insert( self, prompts: List[List[int]], max_tokens: Optional[List[int]] = None, caches: Optional[List[List[Any]]] = None, all_tokens: Optional[List[List[int]]] = None, samplers: Optional[List[Callable[[mx.array], mx.array]]] = None, logits_processors: Optional[ List[List[Callable[[mx.array, mx.array], mx.array]]] ] = None, state_machines: Optional[List[SequenceStateMachine]] = None, ): return self.insert_segments( [[p] for p in prompts], max_tokens, caches, all_tokens, samplers, logits_processors, state_machines, ) def insert_segments( self, segments: List[List[List[int]]], max_tokens: Optional[List[int]] = None, caches: Optional[List[List[Any]]] = None, all_tokens: Optional[List[List[int]]] = None, samplers: Optional[List[Callable[[mx.array], mx.array]]] = None, logits_processors: Optional[ List[List[Callable[[mx.array, mx.array], mx.array]]] ] = None, state_machines: Optional[List[SequenceStateMachine]] = None, ): uids = [] max_tokens = max_tokens or [self.max_tokens] * len(segments) all_tokens = all_tokens or [[] for _ in segments] samplers = samplers or [None] * len(segments) logits_processors = logits_processors or ( [self.logits_processors] * len(segments) ) state_machines = state_machines or ( [self._default_state_machine] * len(segments) ) caches = caches or [None] * len(segments) for i in range(len(segments)): if caches[i] is None: caches[i] = self._make_new_cache() for seq, m, c, at, s, lp, sm in zip( segments, max_tokens, caches, all_tokens, samplers, logits_processors, state_machines, ): seq = list(seq) if len(seq[-1]) != 1: seq.append(seq[-1][-1:]) seq[-2] = seq[-2][:-1] self._unprocessed_sequences.append( (self._uid_count, seq, m, c, at, s, lp, sm) ) uids.append(self._uid_count) self._uid_count += 1 return uids def _make_new_cache(self): if self.max_kv_size is None: return cache.make_prompt_cache(self.model) return [ ( RotatingKVCache(max_size=self.max_kv_size) if isinstance(ci, KVCache) else ci ) for ci in cache.make_prompt_cache(self.model) ] def _find_uids(self, uids): uids = set(uids) results = {} for i, uid_i in enumerate(self._generation_batch.uids): if uid_i in uids: results[uid_i] = (2, i) for i, uid_i in enumerate(self._prompt_batch.uids): if uid_i in uids: results[uid_i] = (1, i) for i, seq in enumerate(self._unprocessed_sequences): if seq[0] in uids: results[seq[0]] = (0, i) return results def extract_cache(self, uids): results = {} for uid, (stage, idx) in self._find_uids(uids).items(): if stage == 0: results[uid] = self._unprocessed_sequences[idx][3:5] elif stage == 1: results[uid] = ( self._prompt_batch.extract_cache(idx), self._prompt_batch.tokens[idx], ) else: results[uid] = ( self._generation_batch.extract_cache(idx), self._generation_batch.tokens[idx], ) return results def remove(self, uids, return_prompt_caches=False): caches = {} if return_prompt_caches: caches = self.extract_cache(uids) keep = ( set(range(len(self._unprocessed_sequences))), set(range(len(self._prompt_batch))), set(range(len(self._generation_batch))), ) for stage, idx in self._find_uids(uids).values(): keep[stage].remove(idx) if len(keep[0]) < len(self._unprocessed_sequences): self._unprocessed_sequences = deque( x for i, x in enumerate(self._unprocessed_sequences) if i in keep[0] ) if len(keep[1]) < len(self._prompt_batch): self._prompt_batch.filter(sorted(keep[1])) self._currently_processing = [ x for i, x in enumerate(self._currently_processing) if i in keep[1] ] if len(keep[2]) < len(self._generation_batch): self._generation_batch.filter(sorted(keep[2])) return caches @property def prompt_cache_nbytes(self): total = sum(c.nbytes for p in self._unprocessed_sequences for c in p[3]) total += sum(c.nbytes for c in self._prompt_batch.prompt_cache) total += sum(c.nbytes for c in self._generation_batch.prompt_cache) return total def _make_batch(self, n: int): uids = [] caches = [] tokens = [] samplers = [] logits_processors = [] max_tokens = [] state_machines = [] for _ in range(n): sequence = self._unprocessed_sequences.popleft() uids.append(sequence[0]) caches.append(sequence[3]) tokens.append(sequence[4]) samplers.append(sequence[5]) logits_processors.append(sequence[6]) max_tokens.append(sequence[2]) state_machines.append(sequence[7]) self._currently_processing.append( [sequence[1], 0, sum(len(s) for s in sequence[1])] ) return PromptProcessingBatch( model=self.model, uids=uids, caches=caches, tokens=tokens, prefill_step_size=self.prefill_step_size, samplers=samplers, fallback_sampler=self.sampler, logits_processors=logits_processors, state_machines=state_machines, max_tokens=max_tokens, ) def _next(self): generation_responses = [] prompt_responses = [] # Generate tokens first if len(self._generation_batch) > 0: generation_responses = self._generation_batch.next() self._gen_tokens_counter += len(generation_responses) self._steps_counter += 1 if self._steps_counter % 512 == 0: mx.clear_cache() # Exit early because we already have our hands full with decoding if len(self._generation_batch) >= self.completion_batch_size: return prompt_responses, generation_responses # Check if we have sequences and add them to the prompt batch n = min( self.prefill_batch_size - len(self._prompt_batch), self.completion_batch_size - len(self._generation_batch), len(self._unprocessed_sequences), ) if n > 0: self._prompt_batch.extend(self._make_batch(n)) # Split the prompt sequences to the ones moving to generation and the rest keep = [] split = [] for i, seq in enumerate(self._currently_processing): segments = seq[0] if len(segments) == 1 and len(segments[0]) == 1: split.append(i) else: keep.append(i) # Actually split off part of the prompt batch and start generation if split: last_inputs = [self._currently_processing[i][0][0] for i in split] progress = [(self._currently_processing[i][2],) * 2 for i in split] self._currently_processing = [self._currently_processing[i] for i in keep] gen_batch = self._prompt_batch.split(split).generate(last_inputs) for i, p in enumerate(progress): prompt_responses.append( PromptProcessingBatch.Response( gen_batch.uids[i], p, True, True, ) ) self._generation_batch.extend(gen_batch) # Extract the next prompts input prompts = [] for i, seq in enumerate(self._currently_processing): response = PromptProcessingBatch.Response( self._prompt_batch.uids[i], 0, False, False ) segments = seq[0] n = min(len(segments[0]), self.prefill_step_size) prompts.append(segments[0][:n]) segments[0] = segments[0][n:] if len(segments[0]) == 0: segments.pop(0) response.end_of_segment = True seq[1] += len(prompts[-1]) response.progress = (seq[1], seq[2]) prompt_responses.append(response) # Process the prompts self._prompt_tokens_counter += sum(len(p) for p in prompts) tic = time.perf_counter() self._prompt_batch.prompt(prompts) toc = time.perf_counter() self._prompt_time_counter += toc - tic return prompt_responses, generation_responses def next(self): """ Get the next batch of responses. Returns: Tuple of prompt processing responses and generation responses. """ with mx.stream(self._stream): return self._next() def next_generated(self): """ Return only generated tokens ignoring batch generation responses. Returns: List of GenerationBatch.Response objects """ with mx.stream(self._stream): while True: prompt_responses, generation_responses = self._next() if not generation_responses and prompt_responses: continue return generation_responses @dataclass class BatchResponse: """ A data object to hold a batch generation response. Args: texts: (List[str]): The generated text for each prompt. stats (BatchStats): Statistics about the generation. """ texts: List[str] stats: BatchStats caches: Optional[List[List[Any]]] def batch_generate( model, tokenizer, prompts: List[List[int]], prompt_caches: Optional[List[List[Any]]] = None, max_tokens: Union[int, List[int]] = 128, verbose: bool = False, return_prompt_caches: bool = False, **kwargs, ) -> BatchResponse: """ Generate responses for the given batch of prompts. Args: model (nn.Module): The language model. tokenizer (PreTrainedTokenizer): The tokenizer. prompts (List[List[int]]): The input prompts. prompt_caches (List[List[Any]], optional): Pre-computed prompt-caches for each input prompt. Note, unlike ``generate_step``, the caches won't be updated in-place. verbose (bool): If ``True``, print tokens and timing information. Default: ``False``. max_tokens (Union[int, List[int]): Maximum number of output tokens. This can be per prompt if a list is provided. return_prompt_caches (bool): Return the prompt caches in the batch responses. Default: ``False``. kwargs: The remaining options get passed to :obj:`BatchGenerator`. See :obj:`BatchGenerator` for more details. """ gen = BatchGenerator( model, stop_tokens=[[t] for t in tokenizer.eos_token_ids], **kwargs, ) num_samples = len(prompts) fin = 0 if verbose: print(f"[batch_generate] Finished processing 0/{num_samples} ...", end="\r") if isinstance(max_tokens, int): max_tokens = [max_tokens] * len(prompts) uids = gen.insert(prompts, max_tokens, caches=prompt_caches) results = {uid: [] for uid in uids} prompt_caches = {} with gen.stats() as stats: while responses := gen.next_generated(): for r in responses: if r.finish_reason is not None: if return_prompt_caches: prompt_caches[r.uid] = r.prompt_cache if verbose: fin += 1 print( f"[batch_generate] Finished processing {fin}/{num_samples} ...", end="\r", ) if r.finish_reason != "stop": results[r.uid].append(r.token) gen.close() if verbose: print(f"[batch_generate] Finished processing {fin}/{num_samples}") # Return results in correct order texts = [tokenizer.decode(results[uid]) for uid in uids] caches = [prompt_caches[uid] for uid in uids] if return_prompt_caches else None if verbose: print( f"[batch_generate] Prompt: {stats.prompt_tokens} tokens, {stats.prompt_tps:.3f} tokens-per-sec" ) print( f"[batch_generate] Generation: {stats.generation_tokens} tokens, " f"{stats.generation_tps:.3f} tokens-per-sec" ) print(f"[batch_generate] Peak memory: {stats.peak_memory:.3f} GB") return BatchResponse(texts, stats, caches) def main(): parser = setup_arg_parser() args = parser.parse_args() if args.seed is not None: mx.random.seed(args.seed) # Load the prompt cache and metadata if a cache file is provided using_cache = args.prompt_cache_file is not None if using_cache: prompt_cache, metadata = load_prompt_cache( args.prompt_cache_file, return_metadata=True, ) if isinstance(prompt_cache[0], QuantizedKVCache): if args.kv_bits is not None and args.kv_bits != prompt_cache[0].bits: raise ValueError( "--kv-bits does not match the kv cache loaded from --prompt-cache-file." ) if args.kv_group_size != prompt_cache[0].group_size: raise ValueError( "--kv-group-size does not match the kv cache loaded from --prompt-cache-file." ) # Building tokenizer_config tokenizer_config = ( {} if not using_cache else json.loads(metadata["tokenizer_config"]) ) tokenizer_config["trust_remote_code"] = True if args.trust_remote_code else None model_path = args.model if using_cache: if model_path is None: model_path = metadata["model"] elif model_path != metadata["model"]: raise ValueError( f"Providing a different model ({model_path}) than that " f"used to create the prompt cache ({metadata['model']}) " "is an error." ) model_path = model_path or DEFAULT_MODEL model, tokenizer = load( model_path, adapter_path=args.adapter_path, tokenizer_config=tokenizer_config, model_config={"quantize_activations": args.quantize_activations}, ) for eos_token in args.extra_eos_token: tokenizer.add_eos_token(eos_token) template_kwargs = {} if args.chat_template_config is not None: template_kwargs = json.loads(args.chat_template_config) prompt = args.prompt.replace("\\n", "\n").replace("\\t", "\t") prompt = sys.stdin.read() if prompt == "-" else prompt if not args.ignore_chat_template and tokenizer.has_chat_template: if args.system_prompt is not None: messages = [{"role": "system", "content": args.system_prompt}] else: messages = [] messages.append({"role": "user", "content": prompt}) has_prefill = args.prefill_response is not None if has_prefill: messages.append({"role": "assistant", "content": args.prefill_response}) prompt = tokenizer.apply_chat_template( messages, tokenize=False, continue_final_message=has_prefill, add_generation_prompt=not has_prefill, **template_kwargs, ) # Treat the prompt as a suffix assuming that the prefix is in the # stored kv cache. if using_cache: messages[-1]["content"] = "" test_prompt = tokenizer.apply_chat_template( messages, tokenize=False, continue_final_message=has_prefill, add_generation_prompt=not has_prefill, ) prompt = prompt[test_prompt.index("") :] prompt = tokenizer.encode(prompt, add_special_tokens=False) else: prompt = tokenizer.encode(prompt) if args.draft_model is not None: draft_model, draft_tokenizer = load(args.draft_model) if draft_tokenizer.vocab_size != tokenizer.vocab_size: raise ValueError("Draft model tokenizer does not match model tokenizer.") else: draft_model = None sampler = make_sampler( args.temp, args.top_p, args.min_p, args.min_tokens_to_keep, top_k=args.top_k, xtc_probability=args.xtc_probability, xtc_threshold=args.xtc_threshold, xtc_special_tokens=tokenizer.encode("\n") + list(tokenizer.eos_token_ids), ) response = generate( model, tokenizer, prompt, max_tokens=args.max_tokens, verbose=args.verbose, sampler=sampler, max_kv_size=args.max_kv_size, prompt_cache=prompt_cache if using_cache else None, kv_bits=args.kv_bits, kv_group_size=args.kv_group_size, quantized_kv_start=args.quantized_kv_start, draft_model=draft_model, num_draft_tokens=args.num_draft_tokens, ) if not args.verbose: print(response) if __name__ == "__main__": print( "Calling `python -m mlx_lm.generate...` directly is deprecated." " Use `mlx_lm.generate...` or `python -m mlx_lm generate ...` instead." ) main()