mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-08-31 11:54:37 +08:00
Separate model conversion and loading
This commit is contained in:
bofenghuang
parent
81183c3091
commit
21f28ccd55
@ -2,19 +2,200 @@
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import argparse
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import copy
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import hashlib
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import json
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import os
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import urllib
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import warnings
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from dataclasses import asdict
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from pathlib import Path
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from typing import List
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import mlx.core as mx
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import mlx.nn as nn
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import numpy as np
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import torch
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from mlx.utils import tree_flatten, tree_map, tree_unflatten
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from tqdm import tqdm
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from whisper.load_models import load_torch_model, torch_to_mlx
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from whisper import torch_whisper
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from whisper.whisper import ModelDimensions, Whisper
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MODEL_DTYPES = {"float16", "float32"}
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_VALID_DTYPES = {"float16", "float32"}
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_MODELS = {
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"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
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"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
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"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
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"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
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"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
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"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
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"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
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"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
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"large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
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"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
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"large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
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"large": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
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}
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# base85-encoded (n_layers, n_heads) boolean arrays indicating the cross-attention heads that are
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# highly correlated to the word-level timing, i.e. the alignment between audio and text tokens.
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_ALIGNMENT_HEADS = {
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"tiny.en": b"ABzY8J1N>@0{>%R00Bk>$p{7v037`oCl~+#00",
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"tiny": b"ABzY8bu8Lr0{>%RKn9Fp%m@SkK7Kt=7ytkO",
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"base.en": b"ABzY8;40c<0{>%RzzG;p*o+Vo09|#PsxSZm00",
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"base": b"ABzY8KQ!870{>%RzyTQH3`Q^yNP!>##QT-<FaQ7m",
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"small.en": b"ABzY8>?_)10{>%RpeA61k&I|OI3I$65C{;;pbCHh0B{qLQ;+}v00",
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"small": b"ABzY8DmU6=0{>%Rpa?J`kvJ6qF(V^F86#Xh7JUGMK}P<N0000",
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"medium.en": b"ABzY8usPae0{>%R7<zz_OvQ{)4kMa0BMw6u5rT}kRKX;$NfYBv00*Hl@qhsU00",
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"medium": b"ABzY8B0Jh+0{>%R7}kK1fFL7w6%<-Pf*t^=N)Qr&0RR9",
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"large-v1": b"ABzY8r9j$a0{>%R7#4sLmoOs{s)o3~84-RPdcFk!JR<kSfC2yj",
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"large-v2": b"ABzY8zd+h!0{>%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj",
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"large-v3": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
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"large": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
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}
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def _download(url: str, root: str) -> str:
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os.makedirs(root, exist_ok=True)
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expected_sha256 = url.split("/")[-2]
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download_target = os.path.join(root, os.path.basename(url))
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if os.path.exists(download_target) and not os.path.isfile(download_target):
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raise RuntimeError(f"{download_target} exists and is not a regular file")
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if os.path.isfile(download_target):
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with open(download_target, "rb") as f:
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model_bytes = f.read()
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if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
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return download_target
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else:
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warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
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with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
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with tqdm(
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total=int(source.info().get("Content-Length")),
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ncols=80,
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unit="iB",
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unit_scale=True,
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unit_divisor=1024,
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) as loop:
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while True:
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buffer = source.read(8192)
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if not buffer:
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break
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output.write(buffer)
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loop.update(len(buffer))
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model_bytes = open(download_target, "rb").read()
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if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
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raise RuntimeError(
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"Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."
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)
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return download_target
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def available_models() -> List[str]:
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"""Returns the names of available models"""
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return list(_MODELS.keys())
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def load_torch_model(
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name_or_path: str,
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download_root: str = None,
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) -> torch_whisper.Whisper:
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"""
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Load a Whisper ASR model
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Parameters
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----------
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name_or_path : str
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one of the official model names listed by `whisper.available_models()` or a local Pytorch checkpoint in OpenAI's format
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download_root: str
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path to download the model files; by default, it uses "~/.cache/whisper"
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Returns
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-------
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model : Whisper
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The Whisper ASR model instance
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"""
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if download_root is None:
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download_root = os.path.join(os.path.expanduser("~"), ".cache/whisper")
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# todo: accept alignment_heads of local Pytorch checkpoint
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alignment_heads = None
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if name_or_path in _MODELS:
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alignment_heads = _ALIGNMENT_HEADS[name_or_path]
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name_or_path = _download(_MODELS[name_or_path], download_root)
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elif not Path(name_or_path).is_file():
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raise RuntimeError(f"Model {name_or_path} is neither found in {available_models()} nor as a local path")
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with open(name_or_path, "rb") as fp:
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checkpoint = torch.load(fp)
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dims = torch_whisper.ModelDimensions(**checkpoint["dims"])
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model = torch_whisper.Whisper(dims)
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model.load_state_dict(checkpoint["model_state_dict"])
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if alignment_heads is not None:
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model.set_alignment_heads(alignment_heads)
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return model
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def convert(model, rules=None):
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params = {}
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if rules is not None and type(model) in rules:
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out = rules[type(model)](model, rules)
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return out
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if isinstance(model, torch.Tensor):
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return mx.array(model.detach().numpy())
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if isinstance(model, torch.nn.ModuleList):
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return [convert(n, rules) for n in model.children()]
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if isinstance(model, torch.nn.Conv1d):
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return {
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"weight": convert(model.weight).transpose(0, 2, 1),
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"bias": convert(model.bias),
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}
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for k, n in model.named_children():
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if k in rules:
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params.update(rules[k](n, rules))
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else:
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params[k] = convert(n, rules)
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for k, p in model.named_parameters(recurse=False):
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params[k] = convert(p)
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return params
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def torch_to_mlx(
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torch_model: torch_whisper.Whisper,
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dtype: mx.Dtype = mx.float16,
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) -> Whisper:
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def convert_rblock(model, rules):
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children = dict(model.named_children())
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mlp = list(children.pop("mlp").children())
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params = {
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"mlp1": convert(mlp[0], rules),
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"mlp2": convert(mlp[-1], rules),
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}
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for k, n in children.items():
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params[k] = convert(n, rules)
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return params
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rules = {
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torch_whisper.ResidualAttentionBlock: convert_rblock,
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}
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params = convert(torch_model, rules)
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mlx_model = Whisper(torch_model.dims, dtype)
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params = tree_map(lambda p: p.astype(dtype), params)
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mlx_model.update(params)
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return mlx_model
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def quantize(weights, config, args):
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@ -49,7 +230,7 @@ if __name__ == "__main__":
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parser.add_argument(
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"--mlx-path",
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type=str,
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default="mlx_model",
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default="mlx_models/tiny",
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help="The path to save the MLX model.",
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)
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parser.add_argument(
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@ -78,7 +259,7 @@ if __name__ == "__main__":
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)
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args = parser.parse_args()
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assert args.dtype in MODEL_DTYPES, f"dtype {args.dtype} not found in {MODEL_DTYPES}"
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assert args.dtype in _VALID_DTYPES, f"dtype {args.dtype} not found in {_VALID_DTYPES}"
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dtype = getattr(mx, args.dtype)
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print("[INFO] Loading")
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@ -1,213 +1,20 @@
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# Copyright © 2023 Apple Inc.
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import glob
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import hashlib
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import json
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import os
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import urllib
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import warnings
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from pathlib import Path
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from typing import List
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import mlx.core as mx
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import mlx.nn as nn
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import torch
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from mlx.utils import tree_map, tree_unflatten
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from tqdm import tqdm
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from mlx.utils import tree_unflatten
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from . import torch_whisper, whisper
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_MODELS = {
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"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
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"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
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"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
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"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
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"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
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"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
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"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
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"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
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"large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
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"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
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"large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
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"large": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
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}
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# base85-encoded (n_layers, n_heads) boolean arrays indicating the cross-attention heads that are
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# highly correlated to the word-level timing, i.e. the alignment between audio and text tokens.
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_ALIGNMENT_HEADS = {
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"tiny.en": b"ABzY8J1N>@0{>%R00Bk>$p{7v037`oCl~+#00",
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"tiny": b"ABzY8bu8Lr0{>%RKn9Fp%m@SkK7Kt=7ytkO",
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"base.en": b"ABzY8;40c<0{>%RzzG;p*o+Vo09|#PsxSZm00",
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"base": b"ABzY8KQ!870{>%RzyTQH3`Q^yNP!>##QT-<FaQ7m",
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"small.en": b"ABzY8>?_)10{>%RpeA61k&I|OI3I$65C{;;pbCHh0B{qLQ;+}v00",
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"small": b"ABzY8DmU6=0{>%Rpa?J`kvJ6qF(V^F86#Xh7JUGMK}P<N0000",
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"medium.en": b"ABzY8usPae0{>%R7<zz_OvQ{)4kMa0BMw6u5rT}kRKX;$NfYBv00*Hl@qhsU00",
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"medium": b"ABzY8B0Jh+0{>%R7}kK1fFL7w6%<-Pf*t^=N)Qr&0RR9",
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"large-v1": b"ABzY8r9j$a0{>%R7#4sLmoOs{s)o3~84-RPdcFk!JR<kSfC2yj",
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"large-v2": b"ABzY8zd+h!0{>%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj",
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"large-v3": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
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"large": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
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}
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def _download(url: str, root: str) -> str:
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os.makedirs(root, exist_ok=True)
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expected_sha256 = url.split("/")[-2]
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download_target = os.path.join(root, os.path.basename(url))
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if os.path.exists(download_target) and not os.path.isfile(download_target):
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raise RuntimeError(f"{download_target} exists and is not a regular file")
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if os.path.isfile(download_target):
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with open(download_target, "rb") as f:
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model_bytes = f.read()
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if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
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return download_target
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else:
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warnings.warn(
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f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"
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)
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with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
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with tqdm(
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total=int(source.info().get("Content-Length")),
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ncols=80,
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unit="iB",
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unit_scale=True,
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unit_divisor=1024,
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) as loop:
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while True:
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buffer = source.read(8192)
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if not buffer:
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break
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output.write(buffer)
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loop.update(len(buffer))
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model_bytes = open(download_target, "rb").read()
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if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
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raise RuntimeError(
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"Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."
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)
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return download_target
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def available_models() -> List[str]:
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"""Returns the names of available models"""
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return list(_MODELS.keys())
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def load_torch_model(
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name_or_path: str,
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download_root: str = None,
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) -> torch_whisper.Whisper:
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"""
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Load a Whisper ASR model
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Parameters
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----------
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name_or_path : str
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one of the official model names listed by `whisper.available_models()` or a local Pytorch checkpoint
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download_root: str
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path to download the model files; by default, it uses "~/.cache/whisper"
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||||
|
||||
Returns
|
||||
-------
|
||||
model : Whisper
|
||||
The Whisper ASR model instance
|
||||
"""
|
||||
|
||||
if download_root is None:
|
||||
download_root = os.path.join(os.path.expanduser("~"), ".cache/whisper")
|
||||
|
||||
if name_or_path in _MODELS:
|
||||
name_or_path = _download(_MODELS[name_or_path], download_root)
|
||||
elif not Path(name_or_path).is_file():
|
||||
raise RuntimeError(f"Model {name_or_path} is neither found in {available_models()} nor as a local path")
|
||||
|
||||
alignment_heads = _ALIGNMENT_HEADS.get(name_or_path)
|
||||
|
||||
with open(name_or_path, "rb") as fp:
|
||||
checkpoint = torch.load(fp)
|
||||
|
||||
dims = torch_whisper.ModelDimensions(**checkpoint["dims"])
|
||||
model = torch_whisper.Whisper(dims)
|
||||
model.load_state_dict(checkpoint["model_state_dict"])
|
||||
|
||||
if alignment_heads is not None:
|
||||
model.set_alignment_heads(alignment_heads)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
def convert(model, rules=None):
|
||||
params = {}
|
||||
if rules is not None and type(model) in rules:
|
||||
out = rules[type(model)](model, rules)
|
||||
return out
|
||||
if isinstance(model, torch.Tensor):
|
||||
return mx.array(model.detach().numpy())
|
||||
if isinstance(model, torch.nn.ModuleList):
|
||||
return [convert(n, rules) for n in model.children()]
|
||||
if isinstance(model, torch.nn.Conv1d):
|
||||
return {
|
||||
"weight": convert(model.weight).transpose(0, 2, 1),
|
||||
"bias": convert(model.bias),
|
||||
}
|
||||
for k, n in model.named_children():
|
||||
if k in rules:
|
||||
params.update(rules[k](n, rules))
|
||||
else:
|
||||
params[k] = convert(n, rules)
|
||||
for k, p in model.named_parameters(recurse=False):
|
||||
params[k] = convert(p)
|
||||
return params
|
||||
|
||||
|
||||
def torch_to_mlx(
|
||||
torch_model: torch_whisper.Whisper,
|
||||
dtype: mx.Dtype = mx.float16,
|
||||
) -> whisper.Whisper:
|
||||
def convert_rblock(model, rules):
|
||||
children = dict(model.named_children())
|
||||
mlp = list(children.pop("mlp").children())
|
||||
params = {
|
||||
"mlp1": convert(mlp[0], rules),
|
||||
"mlp2": convert(mlp[-1], rules),
|
||||
}
|
||||
for k, n in children.items():
|
||||
params[k] = convert(n, rules)
|
||||
return params
|
||||
|
||||
rules = {
|
||||
torch_whisper.ResidualAttentionBlock: convert_rblock,
|
||||
}
|
||||
|
||||
params = convert(torch_model, rules)
|
||||
|
||||
mlx_model = whisper.Whisper(torch_model.dims, dtype)
|
||||
params = tree_map(lambda p: p.astype(dtype), params)
|
||||
mlx_model.update(params)
|
||||
return mlx_model
|
||||
from . import whisper
|
||||
|
||||
|
||||
def load_model(
|
||||
name_or_path: str,
|
||||
download_root: str = None,
|
||||
folder: str,
|
||||
dtype: mx.Dtype = mx.float32,
|
||||
) -> whisper.Whisper:
|
||||
if name_or_path in _MODELS:
|
||||
print(f"[INFO] Loading and converting {name_or_path} model")
|
||||
return torch_to_mlx(load_torch_model(name_or_path, download_root), dtype)
|
||||
elif not (glob.glob(f"{name_or_path}/weights.npz") and glob.glob(f"{name_or_path}/config.json")):
|
||||
raise ValueError(
|
||||
f"{name_or_path} not found in {available_models()}. Ensure that weights.npz and config.json files are"
|
||||
" present in the specified path"
|
||||
)
|
||||
|
||||
model_path = Path(name_or_path)
|
||||
model_path = Path(folder)
|
||||
|
||||
with open(str(model_path / "config.json"), "r") as f:
|
||||
config = json.loads(f.read())
|
||||
@ -225,7 +32,5 @@ def load_model(
|
||||
nn.QuantizedLinear.quantize_module(model, **quantization)
|
||||
|
||||
model.update(weights)
|
||||
|
||||
mx.eval(model.parameters())
|
||||
|
||||
return model
|
||||
|
||||
@ -40,20 +40,20 @@ def _format_timestamp(seconds: float):
|
||||
|
||||
class ModelHolder:
|
||||
model = None
|
||||
model_name_or_path = None
|
||||
model_path = None
|
||||
|
||||
@classmethod
|
||||
def get_model(cls, model_name_or_path: str, dtype: mx.Dtype):
|
||||
if cls.model is None or model_name_or_path != cls.model_name_or_path:
|
||||
cls.model = load_model(model_name_or_path, dtype=dtype)
|
||||
cls.model_name_or_path = model_name_or_path
|
||||
def get_model(cls, model_path: str, dtype: mx.Dtype):
|
||||
if cls.model is None or model_path != cls.model_path:
|
||||
cls.model = load_model(model_path, dtype=dtype)
|
||||
cls.model_path = model_path
|
||||
return cls.model
|
||||
|
||||
|
||||
def transcribe(
|
||||
audio: Union[str, np.ndarray, mx.array],
|
||||
*,
|
||||
model_name_or_path: str = "tiny",
|
||||
model_path: str = "mlx_models/tiny",
|
||||
verbose: Optional[bool] = None,
|
||||
temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
|
||||
compression_ratio_threshold: Optional[float] = 2.4,
|
||||
@ -73,10 +73,8 @@ def transcribe(
|
||||
audio: Union[str, np.ndarray, mx.array]
|
||||
The path to the audio file to open, or the audio waveform
|
||||
|
||||
model_name_or_path: str
|
||||
The Whisper model. Can be any of ["tiny", "base", "small", "medium", "large"],
|
||||
or a local folder in MLX format.
|
||||
Default is "tiny".
|
||||
model_path: str
|
||||
The path to the Whisper model that has been converted to MLX format.
|
||||
|
||||
verbose: bool
|
||||
Whether to display the text being decoded to the console. If True, displays all the details,
|
||||
@ -116,7 +114,7 @@ def transcribe(
|
||||
"""
|
||||
|
||||
dtype = mx.float16 if decode_options.get("fp16", True) else mx.float32
|
||||
model = ModelHolder.get_model(model_name_or_path, dtype)
|
||||
model = ModelHolder.get_model(model_path, dtype)
|
||||
|
||||
# Pad 30-seconds of silence to the input audio, for slicing
|
||||
mel = log_mel_spectrogram(audio, n_mels=model.dims.n_mels, padding=N_SAMPLES)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user