[MLX LM] Sampler refactor + a few improvements (#1094)

* starting

* refactor sampler/processor and a few improvements

* fix stream

* fix stream generate

* fix eos handling in stream generate

llms/README.md

@@ -101,7 +101,8 @@ To see a description of all the arguments you can do:
 #### Streaming
 
 For streaming generation, use the `stream_generate` function. This returns a
-generator object which streams the output text. For example,
+generator object which streams the output text, token, and log probabilities.
+For example,
 
 ```python
 from mlx_lm import load, stream_generate
@@ -116,7 +117,7 @@ prompt = tokenizer.apply_chat_template(
     messages, tokenize=False, add_generation_prompt=True
 )
 
-for t in stream_generate(model, tokenizer, prompt, max_tokens=512):
+for text, *_ in stream_generate(model, tokenizer, prompt, max_tokens=512):
     print(t, end="", flush=True)
 print()
 ```

llms/mlx_lm/cache_prompt.py

@@ -152,6 +152,7 @@ def main():
 
         model(y[:step_size][None], cache=cache)
         mx.eval([c.state for c in cache])
+        mx.metal.clear_cache()
         processed += min(y.size, step_size)
         y = y[step_size:]
         current = time.time()
@@ -165,14 +166,13 @@ def main():
         )
 
     print()
-    print(f"Peak memory: {mx.metal.get_peak_memory() / 2**30:.3f} GB")
+    print(f"Peak memory: {mx.metal.get_peak_memory() / 1e9:.3f} GB")
 
     print("Saving...")
     metadata = {}
     metadata["model"] = args.model
     metadata["chat_template"] = tokenizer.chat_template
     metadata["tokenizer_config"] = json.dumps(tokenizer_config)
-    print(f"Peak memory: {mx.metal.get_peak_memory() / 2**30:.3f} GB")
     save_prompt_cache(args.prompt_cache_file, cache, metadata)
 
 

llms/mlx_lm/chat.py

@@ -74,7 +74,7 @@ def main():
         prompt = tokenizer.apply_chat_template(
             messages, tokenize=False, add_generation_prompt=True
         )
-        for response in stream_generate(
+        for response, *_ in stream_generate(
             model,
             tokenizer,
             prompt,

llms/mlx_lm/generate.py

@@ -13,6 +13,8 @@ DEFAULT_PROMPT = "hello"
 DEFAULT_MAX_TOKENS = 100
 DEFAULT_TEMP = 0.0
 DEFAULT_TOP_P = 1.0
+DEFAULT_MIN_P = 0.0
+DEFAULT_MIN_TOKENS_TO_KEEP = 1
 DEFAULT_SEED = 0
 DEFAULT_MODEL = "mlx-community/Llama-3.2-3B-Instruct-4bit"
 DEFAULT_QUANTIZED_KV_START = 5000
@@ -52,6 +54,7 @@ def setup_arg_parser():
     )
     parser.add_argument(
         "--prompt",
+        "-p",
         default=DEFAULT_PROMPT,
         help="Message to be processed by the model ('-' reads from stdin)",
     )
@@ -68,6 +71,15 @@ def setup_arg_parser():
     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(
+        "--min-tokens-to-keep",
+        type=float,
+        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",
@@ -247,6 +259,8 @@ def main():
         formatter=formatter,
         temp=args.temp,
         top_p=args.top_p,
+        min_p=args.min_p,
+        min_tokens_to_keep=args.min_tokens_to_keep,
         max_kv_size=args.max_kv_size,
         prompt_cache=prompt_cache if using_cache else None,
         kv_bits=args.kv_bits,

llms/mlx_lm/sample_utils.py

@@ -1,10 +1,83 @@
 # Copyright © 2023-2024 Apple Inc.
 
 from functools import partial
+from typing import Callable, Dict, Optional
 
 import mlx.core as mx
 
 
+def make_sampler(
+    temp: float = 0.0,
+    top_p: float = 0.0,
+    min_p: float = 0.0,
+    min_tokens_to_keep: int = 1,
+) -> Callable[mx.array, mx.array]:
+    """
+    Make a sampler function for use with ``generate_step``.
+
+    Args:
+        temp (float): The temperature for sampling, if 0 the argmax is used.
+          Default: ``0``.
+        top_p (float, optional): Nulceus sampling, higher means model considers
+          more less likely words.
+        min_p (float, optional): The minimum value (scaled by the top token's
+          probability) that a token probability must have to be considered.
+        min_tokens_to_keep (int, optional): Minimum number of tokens that cannot
+          be filtered by min_p sampling.
+
+    Returns:
+        Callable[mx.array, mx.array]:
+            A sampler which takes log-probabilities and returns tokens.
+    """
+    if temp == 0:
+        return lambda x: mx.argmax(x, axis=-1)
+    elif top_p > 0 and top_p < 1.0:
+        return lambda x: top_p_sampling(x, top_p, temp)
+    elif min_p != 0.0:
+        return lambda x: min_p_sampling(x, min_p, min_tokens_to_keep, temp)
+    else:
+        return lambda x: categorical_sampling(x, temp)
+
+
+def make_logits_processors(
+    logit_bias: Optional[Dict[int, float]] = None,
+    repetition_penalty: Optional[float] = None,
+    repetition_context_size: Optional[int] = 20,
+):
+    """
+    Make logits processors for use with ``generate_step``.
+
+    Args:
+        repetition_penalty (float, optional): The penalty factor for repeating
+          tokens.
+        repetition_context_size (int, optional): The number of tokens to
+          consider for repetition penalty. Default: ``20``.
+        logit_bias (dictionary, optional): Additive logit bias.
+
+    Returns:
+        List[Callable[[mx.array, mx.array], mx.array]]:
+            A list of logits processors. Each processor in the list is a
+            callable which takes an array of tokens and an array of logits
+            and returns the updated logits.
+    """
+    logits_processors = []
+    if logit_bias:
+        indices = mx.array(list(logit_bias.keys()))
+        values = mx.array(list(logit_bias.values()))
+
+        def logit_bias_processor(_, logits):
+            logits[:, indices] += values
+            return logits
+
+        logits_processors.append(logit_bias_processor)
+
+    if repetition_penalty and repetition_penalty != 0.0:
+        logits_processors.append(
+            make_repetition_penalty(repetition_penalty, repetition_context_size)
+        )
+    return logits_processors
+
+
 @partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
 def min_p_sampling(
     logits: mx.array,
@@ -100,3 +173,36 @@ def top_p_sampling(logits: mx.array, top_p: float, temperature: float) -> mx.arr
 @partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
 def categorical_sampling(logits, temp):
     return mx.random.categorical(logits * (1 / temp))
+
+
+def make_repetition_penalty(penalty: float, context_size: int = 20):
+    """
+    Make repetition penalty processor.
+
+    Paper: https://arxiv.org/abs/1909.05858
+
+    Args:
+        penalty (float): The repetition penalty factor to be applied.
+        context_size (int): The number of previous tokens to use.
+            Default: ``20``.
+
+    Returns:
+        Callable[[mx.array, List[int]], mx.array]:
+            The repetition penalty processor.
+    """
+    if penalty < 0 or not isinstance(penalty, float):
+        raise ValueError(f"penalty must be a non-negative float, got {penalty}")
+
+    def repetition_penalty_processor(tokens, logits):
+        if len(tokens) > 0:
+            tokens = tokens[-context_size:]
+            selected_logits = logits[:, tokens]
+            selected_logits = mx.where(
+                selected_logits < 0,
+                selected_logits * penalty,
+                selected_logits / penalty,
+            )
+            logits[:, tokens] = selected_logits
+        return logits
+
+    return repetition_penalty_processor

llms/mlx_lm/server.py

@@ -27,7 +27,7 @@ from huggingface_hub import scan_cache_dir
 
 from ._version import __version__
 from .models.cache import make_prompt_cache
-from .utils import generate_step, load
+from .utils import load, stream_generate
 
 
 def get_system_fingerprint():
@@ -64,7 +64,7 @@ def stopping_criteria(
           end if it has (`trim_length`).
     """
     if tokens and tokens[-1] == eos_token_id:
-        return StopCondition(stop_met=True, trim_length=1)
+        return StopCondition(stop_met=True, trim_length=0)
 
     for stop_ids in stop_id_sequences:
         if len(tokens) >= len(stop_ids):
@@ -253,7 +253,7 @@ class APIHandler(BaseHTTPRequestHandler):
         self.max_tokens = self.body.get("max_completion_tokens", None)
         if self.max_tokens is None:
             self.max_tokens = self.body.get("max_tokens", 512)
-        self.temperature = self.body.get("temperature", 1.0)
+        self.temperature = self.body.get("temperature", 0.0)
         self.top_p = self.body.get("top_p", 1.0)
         self.repetition_penalty = self.body.get("repetition_penalty", 1.0)
         self.repetition_context_size = self.body.get("repetition_context_size", 20)
@@ -290,10 +290,7 @@ class APIHandler(BaseHTTPRequestHandler):
 
         # Call endpoint specific method
         prompt = endpoints[self.path]()
-
+        self.handle_completion(prompt, stop_id_sequences)
-        # Call method based on response type
-        method = self.handle_stream if self.stream else self.handle_completion
-        method(prompt, stop_id_sequences)
 
     def validate_model_parameters(self):
         """
@@ -452,32 +449,40 @@ class APIHandler(BaseHTTPRequestHandler):
             stop_id_sequences (List[List[int]]): A list of stop words passed
               to the stopping_criteria function
         """
-        detokenizer = self.tokenizer.detokenizer
-        detokenizer.reset()
         tokens = []
         finish_reason = "length"
         stop_sequence_suffix = None
-        logging.debug(f"Starting completion:")
+        if self.stream:
+            self.end_headers()
+            logging.debug(f"Starting stream:")
+        else:
+            logging.debug(f"Starting completion:")
         token_logprobs = []
         top_tokens = []
 
         prompt = self.get_prompt_cache(prompt)
 
-        for _, (token, logprobs) in zip(
+        text = ""
-            range(self.max_tokens),
+        tic = time.perf_counter()
-            generate_step(
+        for n, (segment, token, logprobs) in enumerate(
-                prompt=mx.array(prompt),
+            stream_generate(
                 model=self.model,
+                tokenizer=self.tokenizer,
+                prompt=prompt,
+                max_tokens=self.max_tokens,
                 temp=self.temperature,
-                top_p=self.top_p,
                 repetition_penalty=self.repetition_penalty,
                 repetition_context_size=self.repetition_context_size,
                 logit_bias=self.logit_bias,
                 prompt_cache=self.prompt_cache.cache,
             ),
         ):
-            detokenizer.add_token(token)
+            if n == 0:
-            logging.debug(detokenizer.text)
+                prompt_time = time.perf_counter() - tic
+                tic = time.perf_counter()
+
+            text += segment
+            logging.debug(text)
             tokens.append(token)
 
             if self.logprobs > 0:
@@ -498,121 +503,63 @@ class APIHandler(BaseHTTPRequestHandler):
                     stop_sequence_suffix = self.tokenizer.decode(
                         tokens[-stop_condition.trim_length :]
                     )
+                    text = text[: -len(stop_sequence_suffix)]
                 break
 
-        self.prompt_cache.tokens.extend(tokens)
+            if self.stream:
-        detokenizer.finalize()
+                # If the end of tokens overlaps with a stop sequence, generate new
-        text = (
+                # tokens until we know if the stop sequence is hit or not
-            detokenizer.text
+                if any(
-            if stop_sequence_suffix is None
+                    (
-            else detokenizer.text[: -len(stop_sequence_suffix)]
+                        sequence_overlap(tokens, sequence)
-        )
+                        for sequence in stop_id_sequences
-        response = self.generate_response(
-            text,
-            finish_reason,
-            len(prompt),
-            len(tokens),
-            token_logprobs=token_logprobs,
-            top_tokens=top_tokens,
-            tokens=tokens,
-        )
-
-        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 handle_stream(
-        self,
-        prompt: List[int],
-        stop_id_sequences: List[List[int]],
-    ):
-        """
-        Generate response to prompt and foward it to the client using a Server
-        Sent Events (SSE) stream.
-
-        Args:
-            prompt (mx.array): The tokenized prompt
-            stop_id_sequences (List[List[int]]): A list of stop words passed to
-              the stopping_criteria function
-        """
-        # No additional headers are needed, call end_headers
-        self.end_headers()
-
-        detokenizer = self.tokenizer.detokenizer
-        detokenizer.reset()
-        tokens = []
-
-        stop_sequence_suffix = None
-        logging.debug(f"Starting stream:")
-
-        prompt = self.get_prompt_cache(prompt)
-
-        for _, (token, _) in zip(
-            range(self.max_tokens),
-            generate_step(
-                prompt=mx.array(prompt),
-                model=self.model,
-                temp=self.temperature,
-                top_p=self.top_p,
-                repetition_penalty=self.repetition_penalty,
-                repetition_context_size=self.repetition_context_size,
-                prompt_cache=self.prompt_cache.cache,
-            ),
-        ):
-            detokenizer.add_token(token)
-            logging.debug(detokenizer.text)
-            tokens.append(token)
-
-            stop_condition = stopping_criteria(
-                tokens,
-                stop_id_sequences,
-                self.tokenizer.eos_token_id,
-            )
-            if stop_condition.stop_met:
-                if stop_condition.trim_length:
-                    stop_sequence_suffix = self.tokenizer.decode(
-                        tokens[-stop_condition.trim_length :]
                     )
-                break
+                ):
-
+                    continue
-            # If the end of tokens overlaps with a stop sequence, generate new
+                elif segment:
-            # tokens until we know if the stop sequence is hit or not
+                    response = self.generate_response(segment, None)
-            if any(
+                    self.wfile.write(f"data: {json.dumps(response)}\n\n".encode())
-                (sequence_overlap(tokens, sequence) for sequence in stop_id_sequences)
+                    self.wfile.flush()
-            ):
-                continue
-
-            new_text = detokenizer.last_segment
-            if new_text:
-                response = self.generate_response(new_text, None)
-                self.wfile.write(f"data: {json.dumps(response)}\n\n".encode())
-                self.wfile.flush()
 
         self.prompt_cache.tokens.extend(tokens)
 
-        # check is there any remaining text to send
+        gen_time = time.perf_counter() - tic
-        detokenizer.finalize()
+        prompt_tps = len(prompt) / prompt_time
-        last_segment = detokenizer.last_segment
+        gen_tps = len(tokens) / gen_time
-        if last_segment:
+        peak_mem = mx.metal.get_peak_memory() / 1e9
-            if stop_sequence_suffix is not None:
+        logging.debug(f"Prompt: {prompt_tps:.3f} tokens-per-sec")
-                last_segment = last_segment[: -len(stop_sequence_suffix)]
+        logging.debug(f"Generation: {gen_tps:.3f} tokens-per-sec")
-            response = self.generate_response(last_segment, "length")
+        logging.debug(f"Peak memory: {peak_mem:.3f} GB")
+
+        if self.stream:
+            response = self.generate_response(segment, finish_reason)
             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(prompt), len(tokens))
+                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(prompt),
+                len(tokens),
+                token_logprobs=token_logprobs,
+                top_tokens=top_tokens,
+                tokens=tokens,
+            )
+            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)}")
 
-        if self.stream_options is not None and self.stream_options["include_usage"]:
+            # Send an additional Content-Length header when it is known
-            response = self.completion_usage_response(len(prompt), len(tokens))
+            self.send_header("Content-Length", str(len(response_json)))
-            self.wfile.write(f"data: {json.dumps(response)}\n\n".encode())
+            self.end_headers()
-
+            self.wfile.write(response_json)
-        self.wfile.write("data: [DONE]\n\n".encode())
+            self.wfile.flush()
-        self.wfile.flush()
 
     def completion_usage_response(
         self,

llms/mlx_lm/tuner/trainer.py

@@ -285,7 +285,7 @@ def train(
             it_sec = args.steps_per_report / (stop - start)
             tokens_sec = float(n_tokens) / (stop - start)
             trained_tokens += n_tokens
-            peak_mem = mx.metal.get_peak_memory() / 2**30
+            peak_mem = mx.metal.get_peak_memory() / 1e9
             if rank == 0:
                 print(
                     f"Iter {it}: Train loss {train_loss:.3f}, "

llms/mlx_lm/utils.py

@@ -20,7 +20,7 @@ from transformers import PreTrainedTokenizer
 
 # Local imports
 from .models import cache
-from .sample_utils import categorical_sampling, min_p_sampling, top_p_sampling
+from .sample_utils import make_logits_processors, make_sampler
 from .tokenizer_utils import TokenizerWrapper, load_tokenizer
 from .tuner.utils import dequantize as dequantize_model
 from .tuner.utils import load_adapters
@@ -34,6 +34,9 @@ MODEL_REMAPPING = {
 
 MAX_FILE_SIZE_GB = 5
 
+# A stream on the default device just for generation
+generation_stream = mx.new_stream(mx.default_device())
+
 
 class ModelNotFoundError(Exception):
     def __init__(self, message):
@@ -137,29 +140,6 @@ def get_model_path(path_or_hf_repo: str, revision: Optional[str] = None) -> Path
     return model_path
 
 
-def apply_repetition_penalty(logits: mx.array, tokens: mx.array, penalty: float):
-    """
-    Apply repetition penalty to specific logits based on the given context.
-
-    Paper: https://arxiv.org/abs/1909.05858
-
-    Args:
-        logits (mx.array): The logits produced by the language model.
-        tokens (mx.array): A list of N previous tokens.
-        penalty (float): The repetition penalty factor to be applied.
-
-    Returns:
-        logits (mx.array): Logits with repetition penalty applied to generated tokens.
-    """
-    if len(tokens) > 0:
-        selected_logits = logits[:, tokens]
-        selected_logits = mx.where(
-            selected_logits < 0, selected_logits * penalty, selected_logits / penalty
-        )
-        logits[:, tokens] = selected_logits
-    return logits
-
-
 def maybe_quantize_kv_cache(prompt_cache, quantized_kv_start, kv_group_size, kv_bits):
     if (
         kv_bits is not None
@@ -185,7 +165,7 @@ def generate_step(
     max_kv_size: Optional[int] = None,
     prompt_cache: Optional[Any] = None,
     logit_bias: Optional[Dict[int, float]] = None,
-    logits_processor: Optional[List[Callable[[mx.array, mx.array], mx.array]]] = None,
+    logits_processors: Optional[List[Callable[[mx.array, mx.array], mx.array]]] = None,
     kv_bits: Optional[int] = None,
     kv_group_size: int = 64,
     quantized_kv_start: int = 0,
@@ -214,7 +194,7 @@ def generate_step(
         prompt_cache (List[Any], optional): A pre-computed prompt cache. Note, if
           provided, the cache will be updated in place.
         logit_bias (dictionary, optional): Additive logit bias.
-        logits_processor (List[Callable[[mx.array, mx.array], mx.array]], optional):
+        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``.
         kv_bits (int, optional): Number of bits to use for KV cache quantization.
@@ -224,53 +204,9 @@ def generate_step(
             when ``kv_bits`` is non-None. Default: ``0``.
 
     Yields:
-        Generator[Tuple[mx.array, mx.array], None, None]: A generator producing
+        Tuple[mx.array, mx.array]: One token and a vector of log probabilities.
-          one token and a vector of log probabilities.
     """
 
-    def sample(logits: mx.array) -> Tuple[mx.array, float]:
-        logprobs = logits - mx.logsumexp(logits)
-
-        if temp == 0:
-            token = mx.argmax(logits, axis=-1)
-        else:
-            if top_p > 0 and top_p < 1.0:
-                token = top_p_sampling(logits, top_p, temp)
-            elif min_p != 0.0:
-                token = min_p_sampling(logits, min_p, min_tokens_to_keep, temp)
-            else:
-                token = categorical_sampling(logits, temp)
-
-        return token, logprobs
-
-    if repetition_penalty and (
-        repetition_penalty < 0 or not isinstance(repetition_penalty, float)
-    ):
-        raise ValueError(
-            f"repetition_penalty must be a non-negative float, got {repetition_penalty}"
-        )
-
-    logits_processor = logits_processor or []
-
-    if repetition_penalty:
-
-        def repetition_penalty_processor(tokens, logits):
-            return apply_repetition_penalty(
-                logits, tokens[-repetition_context_size:], repetition_penalty
-            )
-
-        logits_processor.append(repetition_penalty_processor)
-
-    if logit_bias:
-        indices = mx.array(list(logit_bias.keys()))
-        values = mx.array(list(logit_bias.values()))
-
-        def logit_bias_processor(_, logits):
-            logits[:, indices] += values
-            return logits
-
-        logits_processor.append(logit_bias_processor)
-
     y = prompt
     tokens = None
 
@@ -283,24 +219,31 @@ def generate_step(
     elif len(prompt_cache) != len(model.layers):
         raise ValueError("Wrong number of layers in the prompt cache.")
 
+    sampler = make_sampler(temp, top_p, min_p, min_tokens_to_keep)
+    logits_processors = logits_processors or []
+    logits_processors.extend(
+        make_logits_processors(logit_bias, repetition_penalty, repetition_context_size)
+    )
+
     def _step(y):
+        with mx.stream(generation_stream):
+            logits = model(y[None], cache=prompt_cache)
+            logits = logits[:, -1, :]
 
-        logits = model(y[None], cache=prompt_cache)
+            if logits_processors:
-        logits = logits[:, -1, :]
+                nonlocal tokens
+                tokens = mx.concat([tokens, y]) if tokens is not None else y
 
-        if logits_processor:
+                for processor in logits_processors:
-            nonlocal tokens
+                    logits = processor(tokens, logits)
-            tokens = mx.concat([tokens, y]) if tokens is not None else y
 
-            for processor in logits_processor:
+            maybe_quantize_kv_cache(
-                logits = processor(tokens, logits)
+                prompt_cache, quantized_kv_start, kv_group_size, kv_bits
+            )
 
-        maybe_quantize_kv_cache(
+            logprobs = logits - mx.logsumexp(logits, keepdims=True)
-            prompt_cache, quantized_kv_start, kv_group_size, kv_bits
+            y = sampler(logprobs)
-        )
+            return y, logprobs.squeeze(0)
-
-        y, logprobs = sample(logits)
-        return y, logprobs.squeeze(0)
 
     while y.size > prefill_step_size:
         model(y[:prefill_step_size][None], cache=prompt_cache)
@@ -325,43 +268,51 @@ def generate_step(
 def stream_generate(
     model: nn.Module,
     tokenizer: Union[PreTrainedTokenizer, TokenizerWrapper],
-    prompt: str,
+    prompt: Union[str, List[int]],
     max_tokens: int = 100,
     **kwargs,
-) -> Union[str, Generator[str, None, None]]:
+) -> Generator[Tuple[str, int, mx.array], None, None]:
     """
     A generator producing text 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 ma
+        tokenizer (PreTrainedTokenizer): The tokenizer.
+        prompt (Union[str, List[int]]): The input prompt string or integer tokens.
+        max_tokens (int): The maximum number of tokens. Default: ``100``.
         kwargs: The remaining options get passed to :func:`generate_step`.
           See :func:`generate_step` for more details.
 
     Yields:
-        Generator[Tuple[mx.array, mx.array]]: A generator producing text.
+        Tuple[str, int, mx.array]:
+            The next text segment, token, and vector of log probabilities.
     """
     if not isinstance(tokenizer, TokenizerWrapper):
         tokenizer = TokenizerWrapper(tokenizer)
 
-    prompt_tokens = mx.array(tokenizer.encode(prompt))
+    prompt_tokens = mx.array(
+        prompt if isinstance(prompt, list) else tokenizer.encode(prompt)
+    )
     detokenizer = tokenizer.detokenizer
 
-    detokenizer.reset()
+    with wired_limit(model, [generation_stream]):
-    for n, (token, _) in zip(
+        detokenizer.reset()
-        range(max_tokens),
+        for n, (token, logits) in zip(
-        generate_step(prompt_tokens, model, **kwargs),
+            range(max_tokens),
-    ):
+            generate_step(prompt_tokens, model, **kwargs),
-        if token == tokenizer.eos_token_id:
+        ):
-            break
+            if token == tokenizer.eos_token_id:
-        detokenizer.add_token(token)
+                break
 
-        # Yield the last segment if streaming
+            detokenizer.add_token(token)
-        yield detokenizer.last_segment
 
-    detokenizer.finalize()
+            if n == (max_tokens - 1):
-    yield detokenizer.last_segment
+                break
+
+            yield detokenizer.last_segment, token, logits
+
+        detokenizer.finalize()
+        yield detokenizer.last_segment, token, logits
 
 
 def generate(
@@ -372,7 +323,7 @@ def generate(
     verbose: bool = False,
     formatter: Optional[Callable] = None,
     **kwargs,
-) -> Union[str, Generator[str, None, None]]:
+) -> str:
     """
     Generate a complete response from the model.
 
@@ -398,7 +349,7 @@ def generate(
     prompt_tokens = mx.array(tokenizer.encode(prompt))
     detokenizer = tokenizer.detokenizer
 
-    with wired_limit(model):
+    with wired_limit(model, [generation_stream]):
         tic = time.perf_counter()
         detokenizer.reset()
         for n, (token, logprobs) in zip(
@@ -416,8 +367,7 @@ def generate(
                 if formatter:
                     # We have to finalize so that the prob corresponds to the last segment
                     detokenizer.finalize()
-                    with mx.stream(mx.cpu):
+                    prob = mx.exp(logprobs[token]).item()
-                        prob = mx.exp(logprobs[token]).item()
                     formatter(detokenizer.last_segment, prob)
                 else:
                     print(detokenizer.last_segment, end="", flush=True)
@@ -438,7 +388,7 @@ def generate(
                 f"Prompt: {prompt_tokens.size} tokens, {prompt_tps:.3f} tokens-per-sec"
             )
             print(f"Generation: {token_count} tokens, {gen_tps:.3f} tokens-per-sec")
-            peak_mem = mx.metal.get_peak_memory() / 2**30
+            peak_mem = mx.metal.get_peak_memory() / 1e9
             print(f"Peak memory: {peak_mem:.3f} GB")
 
         return detokenizer.text
@@ -623,7 +573,9 @@ def upload_to_hub(path: str, upload_repo: str, hf_path: str):
         f"""
         # {upload_repo}
 
-        The Model [{upload_repo}](https://huggingface.co/{upload_repo}) was converted to MLX format from [{hf_path}](https://huggingface.co/{hf_path}) using mlx-lm version **{__version__}**.
+        The Model [{upload_repo}](https://huggingface.co/{upload_repo}) was
+        converted to MLX format from [{hf_path}](https://huggingface.co/{hf_path})
+        using mlx-lm version **{__version__}**.
 
         ## Use with mlx
 

llms/tests/test_generate.py

@@ -46,7 +46,7 @@ class TestGenerate(unittest.TestCase):
             "hello",
             max_tokens=5,
             verbose=False,
-            logits_processor=[logits_processor],
+            logits_processors=[logits_processor],
         )
         self.assertEqual(len(all_toks), len(init_toks) + 5)
 

llms/tests/test_prompt_cache.py

@@ -299,7 +299,7 @@ class TestPromptCache(unittest.TestCase):
         ):
             i += 1
             self.assertEqual(tok, toks[i])
-            self.assertTrue(mx.allclose(logits, all_logits[i], rtol=1e-2))
+            self.assertTrue(mx.allclose(logits, all_logits[i], rtol=2e-2))
 
 
 if __name__ == "__main__":