Merge branch 'ml-explore:main' into completion_only

llms/README.md

@@ -221,6 +221,7 @@ Here are a few examples of Hugging Face models that work with this example:
 - [pfnet/plamo-13b-instruct](https://huggingface.co/pfnet/plamo-13b-instruct)
 - [stabilityai/stablelm-2-zephyr-1_6b](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b)
 - [internlm/internlm2-7b](https://huggingface.co/internlm/internlm2-7b)
+- [tiiuae/falcon-mamba-7b-instruct](https://huggingface.co/tiiuae/falcon-mamba-7b-instruct)
 
 Most
 [Mistral](https://huggingface.co/models?library=transformers,safetensors&other=mistral&sort=trending),

llms/mlx_lm/chat.py

@@ -11,6 +11,7 @@ from .utils import load, stream_generate
 DEFAULT_TEMP = 0.0
 DEFAULT_TOP_P = 1.0
 DEFAULT_SEED = 0
+DEFAULT_MAX_TOKENS = 256
 DEFAULT_MODEL = "mlx-community/Llama-3.2-3B-Instruct-4bit"
 
 
@@ -41,6 +42,13 @@ def setup_arg_parser():
         help="Set the maximum key-value cache size",
         default=None,
     )
+    parser.add_argument(
+        "--max-tokens",
+        "-m",
+        type=int,
+        default=DEFAULT_MAX_TOKENS,
+        help="Maximum number of tokens to generate",
+    )
     return parser
 
 
@@ -70,6 +78,7 @@ def main():
             model,
             tokenizer,
             prompt,
+            args.max_tokens,
             temp=args.temp,
             top_p=args.top_p,
             prompt_cache=prompt_cache,

llms/mlx_lm/models/mamba.py

@@ -23,6 +23,8 @@ class ModelArgs(BaseModelArgs):
     use_conv_bias: bool
     time_step_rank: int
     tie_word_embeddings: bool = True
+    use_bcdt_rms: bool = False
+    mixer_rms_eps: float = 1e-6
 
     def __post_init__(self):
         if not hasattr(self, "hidden_size") and hasattr(self, "d_model"):
@@ -44,6 +46,8 @@ class ModelArgs(BaseModelArgs):
 
         if self.time_step_rank == "auto":
             self.time_step_rank = math.ceil(self.hidden_size / 16)
+        if self.model_type == "falcon_mamba":
+            self.use_bcdt_rms = True
 
 
 class DepthWiseConv1d(nn.Module):
@@ -83,6 +87,11 @@ class MambaBlock(nn.Module):
         self.intermediate_size = args.intermediate_size
         self.time_step_rank = int(args.time_step_rank)
         self.use_conv_bias = args.use_conv_bias
+        self.use_bcdt_rms = args.use_bcdt_rms
+        if self.use_bcdt_rms:
+            self.mixer_norm = lambda x: mx.fast.rms_norm(
+                x, mx.ones(x.shape[-1], x.dtype), eps=args.mixer_rms_eps
+            )
 
         self.in_proj = nn.Linear(
             self.hidden_size, self.intermediate_size * 2, bias=args.use_bias
@@ -126,6 +135,8 @@ class MambaBlock(nn.Module):
             ],
             axis=-1,
         )
+        if self.use_bcdt_rms:
+            delta, B, C = map(self.mixer_norm, (delta, B, C))
         delta = nn.softplus(self.dt_proj(delta))
         new_state = mx.expand_dims(delta * x, -1) * mx.expand_dims(B, 1)
         if state is not None:

llms/mlx_lm/utils.py

@@ -29,6 +29,7 @@ from .tuner.utils import load_adapters
 MODEL_REMAPPING = {
     "mistral": "llama",  # mistral is compatible with llama
     "phi-msft": "phixtral",
+    "falcon_mamba": "mamba",
 }
 
 MAX_FILE_SIZE_GB = 5

whisper/README.md

@@ -25,7 +25,7 @@ pip install mlx-whisper
 
 At its simplest:
 
-```
+```sh
 mlx_whisper audio_file.mp3
 ```
 
@@ -35,6 +35,15 @@ Use `-f` to specify the output format and `--model` to specify the model. There
 are many other supported command line options. To see them all, run
 `mlx_whisper -h`.
 
+You can also pipe the audio content of other programs via stdin:
+
+```sh
+some-process | mlx_whisper -
+```
+
+The default output file name will be `content.*`. You can specify the name with
+the `--output-name` flag.
+
 #### API
 
 Transcribe audio with:
@@ -103,7 +112,7 @@ python convert.py --help
 ```
 
 By default, the conversion script will make the directory `mlx_models`
-and save the converted `weights.npz` and `config.json` there. 
+and save the converted `weights.npz` and `config.json` there.
 
 Each time it is run, `convert.py` will overwrite any model in the provided
 path. To save different models, make sure to set `--mlx-path` to a unique

whisper/mlx_whisper/audio.py

@@ -3,7 +3,7 @@
 import os
 from functools import lru_cache
 from subprocess import CalledProcessError, run
-from typing import Union
+from typing import Optional, Union
 
 import mlx.core as mx
 import numpy as np
@@ -21,7 +21,7 @@ FRAMES_PER_SECOND = SAMPLE_RATE // HOP_LENGTH  # 10ms per audio frame
 TOKENS_PER_SECOND = SAMPLE_RATE // N_SAMPLES_PER_TOKEN  # 20ms per audio token
 
 
-def load_audio(file: str, sr: int = SAMPLE_RATE):
+def load_audio(file: str = Optional[str], sr: int = SAMPLE_RATE, from_stdin=False):
     """
     Open an audio file and read as mono waveform, resampling as necessary
 
@@ -39,19 +39,21 @@ def load_audio(file: str, sr: int = SAMPLE_RATE):
     """
 
     # This launches a subprocess to decode audio while down-mixing
-    # and resampling as necessary.  Requires the ffmpeg CLI in PATH.
+    # and resampling as necessary. Requires the ffmpeg CLI in PATH.
+    if from_stdin:
+        cmd = ["ffmpeg", "-i", "pipe:0"]
+    else:
+        cmd = ["ffmpeg", "-nostdin", "-i", file]
+
     # fmt: off
-    cmd = [
+    cmd.extend([
-        "ffmpeg",
-        "-nostdin",
         "-threads", "0",
-        "-i", file,
         "-f", "s16le",
         "-ac", "1",
         "-acodec", "pcm_s16le",
         "-ar", str(sr),
         "-"
-    ]
+    ])
     # fmt: on
     try:
         out = run(cmd, capture_output=True, check=True).stdout

whisper/mlx_whisper/cli.py

@@ -2,9 +2,11 @@
 
 import argparse
 import os
+import pathlib
 import traceback
 import warnings
 
+from . import audio
 from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE
 from .transcribe import transcribe
 from .writers import get_writer
@@ -27,15 +29,24 @@ def build_parser():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
     )
-    parser.add_argument(
+
-        "audio", nargs="+", type=str, help="Audio file(s) to transcribe"
+    parser.add_argument("audio", nargs="+", help="Audio file(s) to transcribe")
-    )
+
     parser.add_argument(
         "--model",
         default="mlx-community/whisper-tiny",
         type=str,
         help="The model directory or hugging face repo",
     )
+    parser.add_argument(
+        "--output-name",
+        type=str,
+        default=None,
+        help=(
+            "The name of transcription/translation output files before "
+            "--output-format extensions"
+        ),
+    )
     parser.add_argument(
         "--output-dir",
         "-o",
@@ -200,6 +211,7 @@ def main():
     path_or_hf_repo: str = args.pop("model")
     output_dir: str = args.pop("output_dir")
     output_format: str = args.pop("output_format")
+    output_name: str = args.pop("output_name")
     os.makedirs(output_dir, exist_ok=True)
 
     writer = get_writer(output_format, output_dir)
@@ -219,17 +231,25 @@ def main():
         warnings.warn("--max-line-count has no effect without --max-line-width")
     if writer_args["max_words_per_line"] and writer_args["max_line_width"]:
         warnings.warn("--max-words-per-line has no effect with --max-line-width")
-    for audio_path in args.pop("audio"):
+
+    for audio_obj in args.pop("audio"):
+        if audio_obj == "-":
+            # receive the contents from stdin rather than read a file
+            audio_obj = audio.load_audio(from_stdin=True)
+
+            output_name = output_name or "content"
+        else:
+            output_name = output_name or pathlib.Path(audio_obj).stem
         try:
             result = transcribe(
-                audio_path,
+                audio_obj,
                 path_or_hf_repo=path_or_hf_repo,
                 **args,
             )
-            writer(result, audio_path, **writer_args)
+            writer(result, output_name, **writer_args)
         except Exception as e:
             traceback.print_exc()
-            print(f"Skipping {audio_path} due to {type(e).__name__}: {str(e)}")
+            print(f"Skipping {audio_obj} due to {type(e).__name__}: {str(e)}")
 
 
 if __name__ == "__main__":

whisper/mlx_whisper/writers.py

@@ -1,10 +1,8 @@
 # Copyright © 2024 Apple Inc.
 
 import json
-import os
+import pathlib
 import re
-import sys
-import zlib
 from typing import Callable, List, Optional, TextIO
 
 
@@ -43,15 +41,13 @@ class ResultWriter:
         self.output_dir = output_dir
 
     def __call__(
-        self, result: dict, audio_path: str, options: Optional[dict] = None, **kwargs
+        self, result: dict, output_name: str, options: Optional[dict] = None, **kwargs
     ):
-        audio_basename = os.path.basename(audio_path)
+        output_path = (pathlib.Path(self.output_dir) / output_name).with_suffix(
-        audio_basename = os.path.splitext(audio_basename)[0]
+            f".{self.extension}"
-        output_path = os.path.join(
-            self.output_dir, audio_basename + "." + self.extension
         )
 
-        with open(output_path, "w", encoding="utf-8") as f:
+        with output_path.open("wt", encoding="utf-8") as f:
             self.write_result(result, file=f, options=options, **kwargs)
 
     def write_result(