Merge a5752be9d9 into 4b2a0df237

point to the correct repository

https://github.com/ml-explore/mlx-lm

.circleci/config.yml

@@ -17,30 +17,6 @@ jobs:
             pre-commit run --all
             if ! git diff --quiet; then echo 'Style checks failed, please install pre-commit and run pre-commit run --all and push the change'; exit 1; fi
 
-  mlx_lm_build_and_test:
-    macos:
-      xcode: "15.2.0"
-    resource_class: macos.m1.large.gen1
-    steps:
-      - checkout
-      - run:
-          name: Install dependencies
-          command: |
-            brew install python@3.8
-            python3.8 -m venv env
-            source env/bin/activate
-            pip install --upgrade pip
-            pip install unittest-xml-reporting
-            cd llms/
-            pip install -e .
-      - run:
-          name: Run Python tests
-          command: |
-            source env/bin/activate
-            python -m xmlrunner discover -v llms/tests -o test-results/
-      - store_test_results:
-          path: test-results
-
 workflows:
   build_and_test:
     when:
@@ -48,7 +24,6 @@ workflows:
         pattern: "^(?!pull/)[-\\w]+$"
         value: << pipeline.git.branch >>
     jobs:
-      - mlx_lm_build_and_test
       - linux_build_and_test
 
   prb:
@@ -61,7 +36,5 @@ workflows:
           type: approval
       - apple/authenticate:
           context: pr-approval
-      - mlx_lm_build_and_test:
-          requires: [ hold ]
       - linux_build_and_test:
           requires: [ hold ]

.gitignore

@@ -6,6 +6,9 @@ __pycache__/
 # C extensions
 *.so
 
+# Vim
+*.swp
+
 # Distribution / packaging
 .Python
 build/

.pre-commit-config.yaml

@@ -1,10 +1,10 @@
 repos:
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 24.3.0
+    rev: 25.1.0
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/isort
-    rev: 5.13.2
+    rev: 6.0.0
     hooks:
     -   id: isort
         args:

ACKNOWLEDGMENTS.md

@@ -14,3 +14,4 @@ MLX Examples was developed with contributions from the following individuals:
 - Markus Enzweiler: Added the `cvae` examples.
 - Prince Canuma: Helped add support for `Starcoder2` models.
 - Shiyu Li: Added the `Segment Anything Model`.
+- Gökdeniz Gülmez: Added support for `MiniCPM`, `Helium`, `Mamba version 1`, `OLMoE` archtectures and support for `full-fine-tuning`.

README.md

@@ -4,12 +4,12 @@ This repo contains a variety of standalone examples using the [MLX
 framework](https://github.com/ml-explore/mlx).
 
 The [MNIST](mnist) example is a good starting point to learn how to use MLX.
-
-Some more useful examples are listed below.
+Some more useful examples are listed below. Check-out [MLX
+LM](https://github.com/ml-explore/mlx-lm) for a more fully featured Python
+package for LLMs with MLX.
 
 ### Text Models 
 
-- [MLX LM](llms/README.md) a package for LLM text generation, fine-tuning, and more.
 - [Transformer language model](transformer_lm) training.
 - Minimal examples of large scale text generation with [LLaMA](llms/llama),
   [Mistral](llms/mistral), and more in the [LLMs](llms) directory.
@@ -20,18 +20,23 @@ Some more useful examples are listed below.
 
 ### Image Models 
 
+- Generating images
+  - [FLUX](flux)
+  - [Stable Diffusion or SDXL](stable_diffusion)
 - Image classification using [ResNets on CIFAR-10](cifar).
-- Generating images with [Stable Diffusion or SDXL](stable_diffusion).
 - Convolutional variational autoencoder [(CVAE) on MNIST](cvae).
 
 ### Audio Models
 
 - Speech recognition with [OpenAI's Whisper](whisper).
+- Audio compression and generation with [Meta's EnCodec](encodec).
+- Music generation with [Meta's MusicGen](musicgen).
 
 ### Multimodal models
 
 - Joint text and image embeddings with [CLIP](clip).
 - Text generation from image and text inputs with [LLaVA](llava).
+- Image segmentation with [Segment Anything (SAM)](segment_anything).
 
 ### Other Models 
 
@@ -41,7 +46,7 @@ Some more useful examples are listed below.
 
 ### Hugging Face
 
-Note: You can now directly download a few converted checkpoints from the [MLX
+You can directly use or download converted checkpoints from the [MLX
 Community](https://huggingface.co/mlx-community) organization on Hugging Face.
 We encourage you to join the community and [contribute new
 models](https://github.com/ml-explore/mlx-examples/issues/155).

cifar/README.md

@@ -48,3 +48,17 @@ Note this was run on an M1 Macbook Pro with 16GB RAM.
 
 At the time of writing, `mlx` doesn't have built-in learning rate schedules.
 We intend to update this example once these features are added.
+
+## Distributed training
+
+The example also supports distributed data parallel training. You can launch a
+distributed training as follows:
+
+```shell
+$ cat >hostfile.json
+[
+    {"ssh": "host-to-ssh-to", "ips": ["ip-to-bind-to"]},
+    {"ssh": "host-to-ssh-to", "ips": ["ip-to-bind-to"]}
+]
+$ mlx.launch --verbose --hostfile hostfile.json main.py --batch 256 --epochs 5 --arch resnet20
+```

cifar/dataset.py

@@ -1,3 +1,4 @@
+import mlx.core as mx
 import numpy as np
 from mlx.data.datasets import load_cifar10
 
@@ -12,8 +13,11 @@ def get_cifar10(batch_size, root=None):
         x = x.astype("float32") / 255.0
         return (x - mean) / std
 
+    group = mx.distributed.init()
+
     tr_iter = (
         tr.shuffle()
+        .partition_if(group.size() > 1, group.size(), group.rank())
         .to_stream()
         .image_random_h_flip("image", prob=0.5)
         .pad("image", 0, 4, 4, 0.0)
@@ -25,6 +29,11 @@ def get_cifar10(batch_size, root=None):
     )
 
     test = load_cifar10(root=root, train=False)
-    test_iter = test.to_stream().key_transform("image", normalize).batch(batch_size)
+    test_iter = (
+        test.to_stream()
+        .partition_if(group.size() > 1, group.size(), group.rank())
+        .key_transform("image", normalize)
+        .batch(batch_size)
+    )
 
     return tr_iter, test_iter

cifar/main.py

@@ -23,6 +23,13 @@ parser.add_argument("--seed", type=int, default=0, help="random seed")
 parser.add_argument("--cpu", action="store_true", help="use cpu only")
 
 
+def print_zero(group, *args, **kwargs):
+    if group.rank() != 0:
+        return
+    flush = kwargs.pop("flush", True)
+    print(*args, **kwargs, flush=flush)
+
+
 def eval_fn(model, inp, tgt):
     return mx.mean(mx.argmax(model(inp), axis=1) == tgt)
 
@@ -34,9 +41,20 @@ def train_epoch(model, train_iter, optimizer, epoch):
         acc = mx.mean(mx.argmax(output, axis=1) == tgt)
         return loss, acc
 
-    losses = []
-    accs = []
-    samples_per_sec = []
+    world = mx.distributed.init()
+    losses = 0
+    accuracies = 0
+    samples_per_sec = 0
+    count = 0
+
+    def average_stats(stats, count):
+        if world.size() == 1:
+            return [s / count for s in stats]
+
+        with mx.stream(mx.cpu):
+            stats = mx.distributed.all_sum(mx.array(stats))
+            count = mx.distributed.all_sum(count)
+            return (stats / count).tolist()
 
     state = [model.state, optimizer.state]
 
@@ -44,6 +62,7 @@ def train_epoch(model, train_iter, optimizer, epoch):
     def step(inp, tgt):
         train_step_fn = nn.value_and_grad(model, train_step)
         (loss, acc), grads = train_step_fn(model, inp, tgt)
+        grads = nn.utils.average_gradients(grads)
         optimizer.update(model, grads)
         return loss, acc
 
@@ -52,69 +71,79 @@ def train_epoch(model, train_iter, optimizer, epoch):
         y = mx.array(batch["label"])
         tic = time.perf_counter()
         loss, acc = step(x, y)
-        mx.eval(state)
+        mx.eval(loss, acc, state)
         toc = time.perf_counter()
-        loss = loss.item()
-        acc = acc.item()
-        losses.append(loss)
-        accs.append(acc)
-        throughput = x.shape[0] / (toc - tic)
-        samples_per_sec.append(throughput)
+        losses += loss.item()
+        accuracies += acc.item()
+        samples_per_sec += x.shape[0] / (toc - tic)
+        count += 1
         if batch_counter % 10 == 0:
-            print(
+            l, a, s = average_stats(
+                [losses, accuracies, world.size() * samples_per_sec],
+                count,
+            )
+            print_zero(
+                world,
                 " | ".join(
                     (
                         f"Epoch {epoch:02d} [{batch_counter:03d}]",
-                        f"Train loss {loss:.3f}",
-                        f"Train acc {acc:.3f}",
-                        f"Throughput: {throughput:.2f} images/second",
-                    )
+                        f"Train loss {l:.3f}",
+                        f"Train acc {a:.3f}",
+                        f"Throughput: {s:.2f} images/second",
                     )
+                ),
             )
 
-    mean_tr_loss = mx.mean(mx.array(losses))
-    mean_tr_acc = mx.mean(mx.array(accs))
-    samples_per_sec = mx.mean(mx.array(samples_per_sec))
-    return mean_tr_loss, mean_tr_acc, samples_per_sec
+    return average_stats([losses, accuracies, world.size() * samples_per_sec], count)
 
 
 def test_epoch(model, test_iter, epoch):
-    accs = []
+    accuracies = 0
+    count = 0
     for batch_counter, batch in enumerate(test_iter):
         x = mx.array(batch["image"])
         y = mx.array(batch["label"])
         acc = eval_fn(model, x, y)
-        acc_value = acc.item()
-        accs.append(acc_value)
-    mean_acc = mx.mean(mx.array(accs))
-    return mean_acc
+        accuracies += acc.item()
+        count += 1
+
+    with mx.stream(mx.cpu):
+        accuracies = mx.distributed.all_sum(accuracies)
+        count = mx.distributed.all_sum(count)
+        return (accuracies / count).item()
 
 
 def main(args):
     mx.random.seed(args.seed)
 
+    # Initialize the distributed group and report the nodes that showed up
+    world = mx.distributed.init()
+    if world.size() > 1:
+        print(f"Starting rank {world.rank()} of {world.size()}", flush=True)
+
     model = getattr(resnet, args.arch)()
 
-    print("Number of params: {:0.04f} M".format(model.num_params() / 1e6))
+    print_zero(world, f"Number of params: {model.num_params() / 1e6:0.04f} M")
 
     optimizer = optim.Adam(learning_rate=args.lr)
 
     train_data, test_data = get_cifar10(args.batch_size)
     for epoch in range(args.epochs):
         tr_loss, tr_acc, throughput = train_epoch(model, train_data, optimizer, epoch)
-        print(
+        print_zero(
+            world,
             " | ".join(
                 (
                     f"Epoch: {epoch}",
-                    f"avg. Train loss {tr_loss.item():.3f}",
-                    f"avg. Train acc {tr_acc.item():.3f}",
-                    f"Throughput: {throughput.item():.2f} images/sec",
-                )
+                    f"avg. Train loss {tr_loss:.3f}",
+                    f"avg. Train acc {tr_acc:.3f}",
+                    f"Throughput: {throughput:.2f} images/sec",
                 )
+            ),
         )
 
         test_acc = test_epoch(model, test_data, epoch)
-        print(f"Epoch: {epoch} | Test acc {test_acc.item():.3f}")
+        print_zero(world, f"Epoch: {epoch} | Test acc {test_acc:.3f}")
 
         train_data.reset()
         test_data.reset()

clip/convert.py

@@ -63,7 +63,7 @@ def save_weights(save_path: Union[str, Path], weights: Dict[str, Any]) -> None:
         )
 
 
-def get_model_path(path_or_hf_repo: str) -> Path:
+def get_model_path(path_or_hf_repo: str, force_download: bool = False) -> Path:
     model_path = Path(path_or_hf_repo)
     if not model_path.exists():
         model_path = Path(
@@ -74,6 +74,7 @@ def get_model_path(path_or_hf_repo: str) -> Path:
                     "*.json",
                     "*.txt",
                 ],
+                force_download=force_download,
             )
         )
     return model_path
@@ -107,14 +108,20 @@ if __name__ == "__main__":
         type=str,
         default="float32",
     )
+    parser.add_argument(
+        "-f",
+        "--force-download",
+        help="Force download the model from Hugging Face.",
+        action="store_true",
+    )
     args = parser.parse_args()
 
-    torch_path = get_model_path(args.hf_repo)
+    torch_path = get_model_path(args.hf_repo, args.force_download)
     mlx_path = Path(args.mlx_path)
     mlx_path.mkdir(parents=True, exist_ok=True)
 
     print("[INFO] Loading")
-    torch_weights = torch.load(torch_path / "pytorch_model.bin")
+    torch_weights = torch.load(torch_path / "pytorch_model.bin", weights_only=True)
     print("[INFO] Converting")
     mlx_weights = {
         k: torch_to_mx(v, dtype=args.dtype) for k, v in torch_weights.items()

clip/linear_probe.py

@@ -0,0 +1,56 @@
+# Mirror of the Linear Probe Evaluation Script
+# from the official CLIP Repository.
+
+import mlx.core as mx
+import numpy as np
+from image_processor import CLIPImageProcessor
+from mlx.data.datasets import load_cifar10
+from model import CLIPModel
+from PIL import Image
+from sklearn.linear_model import LogisticRegression
+from tqdm import tqdm
+
+
+def get_cifar10(batch_size, root=None):
+    tr = load_cifar10(root=root).batch(batch_size)
+    test = load_cifar10(root=root, train=False).batch(batch_size)
+
+    return tr, test
+
+
+def get_features(model, image_proc, iter):
+    all_features = []
+    all_labels = []
+
+    for batch in tqdm(iter):
+        image, label = batch["image"], batch["label"]
+        x = image_proc([Image.fromarray(im) for im in image])
+        y = mx.array(label)
+
+        image_embeds = model.get_image_features(x)
+        mx.eval(image_embeds)
+
+        all_features.append(image_embeds)
+        all_labels.append(y)
+
+    return mx.concatenate(all_features), mx.concatenate(all_labels)
+
+
+if __name__ == "__main__":
+    model = CLIPModel.from_pretrained("mlx_model")
+    image_proc = CLIPImageProcessor.from_pretrained("mlx_model")
+
+    train_iter, test_iter = get_cifar10(batch_size=256)
+    train_features, train_labels = get_features(model, image_proc, train_iter)
+    test_features, test_labels = get_features(model, image_proc, test_iter)
+
+    # Perform logistic regression
+    # NOTE: The value of C should be determined via a hyperparameter sweep
+    # using a validation split
+    classifier = LogisticRegression(random_state=0, C=0.316, max_iter=1000, verbose=1)
+    classifier.fit(train_features, train_labels)
+
+    # Evaluate using the logistic regression classifier
+    predictions = classifier.predict(test_features)
+    accuracy = (test_labels.squeeze() == predictions).mean().item() * 100
+    print(f"Accuracy = {accuracy:.3f}")

clip/requirements.txt

@@ -1,4 +1,5 @@
 mlx
+mlx-data
 numpy
 transformers
 torch

encodec/README.md

@@ -0,0 +1,84 @@
+# EnCodec
+
+An example of Meta's EnCodec model in MLX.[^1] EnCodec is used to compress and
+generate audio.
+
+### Setup
+
+Install the requirements:
+
+```
+pip install -r requirements.txt
+```
+
+Optionally install FFmpeg and SciPy for loading and saving audio files,
+respectively.
+
+Install [FFmpeg](https://ffmpeg.org/):
+
+```
+# on macOS using Homebrew (https://brew.sh/)
+brew install ffmpeg
+```
+
+Install SciPy:
+
+```
+pip install scipy
+```
+
+### Example
+
+An example using the model:
+
+```python
+import mlx.core as mx
+from encodec import EncodecModel
+from utils import load_audio, save_audio
+
+# Load the 48 KHz model and preprocessor.
+model, processor = EncodecModel.from_pretrained("mlx-community/encodec-48khz-float32")
+
+# Load an audio file
+audio = load_audio("path/to/audio", model.sampling_rate, model.channels)
+
+# Preprocess the audio (this can also be a list of arrays for batched
+# processing).
+feats, mask = processor(audio)
+
+# Encode at the given bandwidth. A lower bandwidth results in more
+# compression but lower reconstruction quality.
+@mx.compile
+def encode(feats, mask):
+    return model.encode(feats, mask, bandwidth=3)
+
+# Decode to reconstruct the audio
+@mx.compile
+def decode(codes, scales, mask):
+    return model.decode(codes, scales, mask)
+
+
+codes, scales = encode(feats, mask)
+reconstructed = decode(codes, scales, mask)
+
+# Trim any padding:
+reconstructed = reconstructed[0, : len(audio)]
+
+# Save the audio as a wave file
+save_audio("reconstructed.wav", reconstructed, model.sampling_rate)
+```
+
+The 24 KHz, 32 KHz, and 48 KHz MLX formatted models are available in the
+[Hugging Face MLX Community](https://huggingface.co/collections/mlx-community/encodec-66e62334038300b07a43b164)
+in several data types.
+
+### Optional
+
+To convert models, use the `convert.py` script. To see the options, run:
+
+```bash
+python convert.py -h
+```
+
+[^1]: Refer to the [arXiv paper](https://arxiv.org/abs/2210.13438) and
+  [code](https://github.com/facebookresearch/encodec) for more details.

encodec/benchmarks/bench_mx.py

@@ -0,0 +1,31 @@
+# Copyright © 2024 Apple Inc.
+
+import time
+
+import mlx.core as mx
+
+from encodec import EncodecModel
+
+model, processor = EncodecModel.from_pretrained("mlx-community/encodec-48khz-float32")
+
+audio = mx.random.uniform(shape=(288000, 2))
+feats, mask = processor(audio)
+mx.eval(model, feats, mask)
+
+
+@mx.compile
+def fun():
+    codes, scales = model.encode(feats, mask, bandwidth=3)
+    reconstructed = model.decode(codes, scales, mask)
+    return reconstructed
+
+
+for _ in range(5):
+    mx.eval(fun())
+
+tic = time.time()
+for _ in range(10):
+    mx.eval(fun())
+toc = time.time()
+ms = 1000 * (toc - tic) / 10
+print(f"Time per it: {ms:.3f}")

encodec/benchmarks/bench_pt.py

@@ -0,0 +1,34 @@
+# Copyright © 2024 Apple Inc.
+
+import time
+
+import numpy as np
+import torch
+from transformers import AutoProcessor, EncodecModel
+
+processor = AutoProcessor.from_pretrained("facebook/encodec_48khz")
+audio = np.random.uniform(size=(2, 288000)).astype(np.float32)
+
+pt_model = EncodecModel.from_pretrained("facebook/encodec_48khz").to("mps")
+pt_inputs = processor(
+    raw_audio=audio, sampling_rate=processor.sampling_rate, return_tensors="pt"
+).to("mps")
+
+
+def fun():
+    pt_encoded = pt_model.encode(pt_inputs["input_values"], pt_inputs["padding_mask"])
+    pt_audio = pt_model.decode(
+        pt_encoded.audio_codes, pt_encoded.audio_scales, pt_inputs["padding_mask"]
+    )
+    torch.mps.synchronize()
+
+
+for _ in range(5):
+    fun()
+
+tic = time.time()
+for _ in range(10):
+    fun()
+toc = time.time()
+ms = 1000 * (toc - tic) / 10
+print(f"Time per it: {ms:.3f}")

encodec/convert.py

@@ -0,0 +1,212 @@
+# Copyright © 2024 Apple Inc.
+
+import argparse
+import json
+from pathlib import Path
+from textwrap import dedent
+from types import SimpleNamespace
+from typing import Any, Dict, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+from huggingface_hub import snapshot_download
+
+import encodec
+
+
+def fetch_from_hub(hf_repo: str) -> Path:
+    model_path = Path(
+        snapshot_download(
+            repo_id=hf_repo,
+            allow_patterns=["*.json", "*.safetensors"],
+        )
+    )
+    return model_path
+
+
+def upload_to_hub(path: str, upload_repo: str, hf_path: str):
+    """
+    Uploads the model to Hugging Face hub.
+
+    Args:
+        path (str): Local path to the model.
+        upload_repo (str): Name of the HF repo to upload to.
+        hf_path (str): Path to the original Hugging Face model.
+    """
+    import os
+
+    from huggingface_hub import HfApi, ModelCard, logging
+
+    content = dedent(
+        f"""
+        ---
+        language: en
+        license: other
+        library: mlx
+        tags:
+        - mlx
+        ---
+
+        The Model [{upload_repo}](https://huggingface.co/{upload_repo}) was
+        converted to MLX format from
+        [{hf_path}](https://huggingface.co/{hf_path}).
+
+        This model is intended to be used with the [EnCodec MLX
+        example](https://github.com/ml-explore/mlx-examples/tree/main/encodec).
+        """
+    )
+
+    card = ModelCard(content)
+    card.save(os.path.join(path, "README.md"))
+
+    logging.set_verbosity_info()
+
+    api = HfApi()
+    api.create_repo(repo_id=upload_repo, exist_ok=True)
+    api.upload_folder(
+        folder_path=path,
+        repo_id=upload_repo,
+        repo_type="model",
+        multi_commits=True,
+        multi_commits_verbose=True,
+    )
+    print(f"Upload successful, go to https://huggingface.co/{upload_repo} for details.")
+
+
+def save_weights(save_path: Union[str, Path], weights: Dict[str, Any]) -> None:
+    if isinstance(save_path, str):
+        save_path = Path(save_path)
+    save_path.mkdir(parents=True, exist_ok=True)
+
+    total_size = sum(v.nbytes for v in weights.values())
+    index_data = {"metadata": {"total_size": total_size}, "weight_map": {}}
+    mx.save_safetensors(
+        str(save_path / "model.safetensors"), weights, metadata={"format": "mlx"}
+    )
+
+    for weight_name in weights.keys():
+        index_data["weight_map"][weight_name] = "model.safetensors"
+
+    index_data["weight_map"] = {
+        k: index_data["weight_map"][k] for k in sorted(index_data["weight_map"])
+    }
+
+    with open(save_path / "model.safetensors.index.json", "w") as f:
+        json.dump(index_data, f, indent=4)
+
+
+def save_config(
+    config: dict,
+    config_path: Union[str, Path],
+) -> None:
+    """Save the model configuration to the ``config_path``.
+
+    The final configuration will be sorted before saving for better readability.
+
+    Args:
+        config (dict): The model configuration.
+        config_path (Union[str, Path]): Model configuration file path.
+    """
+    # Clean unused keys
+    config.pop("_name_or_path", None)
+
+    # sort the config for better readability
+    config = dict(sorted(config.items()))
+
+    # write the updated config to the config_path (if provided)
+    with open(config_path, "w") as fid:
+        json.dump(config, fid, indent=4)
+
+
+def convert(
+    upload: bool,
+    model: str,
+    dtype: str = None,
+):
+    hf_repo = f"facebook/encodec_{model}"
+    mlx_repo = f"mlx-community/encodec-{model}-{dtype}"
+    path = fetch_from_hub(hf_repo)
+    save_path = Path("mlx_models")
+
+    weights = mx.load(str(Path(path) / "model.safetensors"))
+
+    with open(path / "config.json", "r") as fid:
+        config = SimpleNamespace(**json.load(fid))
+
+    model = encodec.EncodecModel(config)
+
+    new_weights = {}
+    for k, v in weights.items():
+        basename, pname = k.rsplit(".", 1)
+        if pname == "weight_v":
+            g = weights[basename + ".weight_g"]
+            v = g * (v / mx.linalg.norm(v, axis=(1, 2), keepdims=True))
+            k = basename + ".weight"
+        elif pname in ["weight_g", "embed_avg", "cluster_size", "inited"]:
+            continue
+        elif "lstm" in basename:
+            w_or_b, ih_or_hh, ln = pname.split("_")
+            if w_or_b == "weight":
+                new_pname = "Wx" if ih_or_hh == "ih" else "Wh"
+            elif w_or_b == "bias" and ih_or_hh == "ih":
+                continue
+            else:
+                v = v + weights[k.replace("_hh_", "_ih_")]
+                new_pname = "bias"
+            k = basename + "." + ln[1:] + "." + new_pname
+        if "conv.weight" in k:
+            # Possibly a transposed conv which has a different order
+            if "decoder" in k:
+                ln = int(k.split(".")[2])
+                if "conv" in model.decoder.layers[ln] and isinstance(
+                    model.decoder.layers[ln].conv, nn.ConvTranspose1d
+                ):
+                    v = mx.moveaxis(v, 0, 2)
+                else:
+                    v = mx.moveaxis(v, 1, 2)
+            else:
+                v = mx.moveaxis(v, 1, 2)
+
+        new_weights[k] = v
+    weights = new_weights
+
+    model.load_weights(list(weights.items()))
+
+    if dtype is not None:
+        t = getattr(mx, dtype)
+        weights = {k: v.astype(t) for k, v in weights.items()}
+
+    if isinstance(save_path, str):
+        save_path = Path(save_path)
+
+    save_weights(save_path, weights)
+
+    save_config(vars(config), config_path=save_path / "config.json")
+
+    if upload:
+        upload_to_hub(save_path, mlx_repo, hf_repo)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Convert EnCodec weights to MLX.")
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="48khz",
+        help="",
+        choices=["24khz", "32khz", "48khz"],
+    )
+    parser.add_argument(
+        "--upload",
+        action="store_true",
+        help="Upload the weights to Hugging Face.",
+    )
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        help="Data type to convert the model to.",
+        default="float32",
+        choices=["float32", "bfloat16", "float16"],
+    )
+    args = parser.parse_args()
+    convert(upload=args.upload, model=args.model, dtype=args.dtype)

encodec/encodec.py

@@ -0,0 +1,741 @@
+# Copyright © 2024 Apple Inc.
+
+import functools
+import json
+import math
+from pathlib import Path
+from types import SimpleNamespace
+from typing import List, Optional, Tuple, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+
+_lstm_kernel = mx.fast.metal_kernel(
+    name="lstm",
+    input_names=["x", "h_in", "cell", "hidden_size", "time_step", "num_time_steps"],
+    output_names=["hidden_state", "cell_state"],
+    header="""
+    template <typename T>
+    T sigmoid(T x) {
+        auto y = 1 / (1 + metal::exp(-metal::abs(x)));
+        return (x < 0) ? 1 - y : y;
+    }
+    """,
+    source="""
+        uint b = thread_position_in_grid.x;
+        uint d = hidden_size * 4;
+
+        uint elem = b * d + thread_position_in_grid.y;
+        uint index = elem;
+        uint x_index = b * num_time_steps * d + time_step * d + index;
+
+        auto i = sigmoid(h_in[index] + x[x_index]);
+        index += hidden_size;
+        x_index += hidden_size;
+        auto f = sigmoid(h_in[index] + x[x_index]);
+        index += hidden_size;
+        x_index += hidden_size;
+        auto g = metal::precise::tanh(h_in[index] + x[x_index]);
+        index += hidden_size;
+        x_index += hidden_size;
+        auto o = sigmoid(h_in[index] + x[x_index]);
+
+        cell_state[elem] = f * cell[elem] + i * g;
+        hidden_state[elem] = o * metal::precise::tanh(cell_state[elem]);
+    """,
+)
+
+
+def lstm_custom(x, h_in, cell, time_step):
+    assert x.ndim == 3, "Input to LSTM must have 3 dimensions."
+    out_shape = cell.shape
+    return _lstm_kernel(
+        inputs=[x, h_in, cell, out_shape[-1], time_step, x.shape[-2]],
+        output_shapes=[out_shape, out_shape],
+        output_dtypes=[h_in.dtype, h_in.dtype],
+        grid=(x.shape[0], h_in.size // 4, 1),
+        threadgroup=(256, 1, 1),
+    )
+
+
+class LSTM(nn.Module):
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        bias: bool = True,
+    ):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.Wx = mx.zeros((4 * hidden_size, input_size))
+        self.Wh = mx.zeros((4 * hidden_size, hidden_size))
+        self.bias = mx.zeros((4 * hidden_size,)) if bias else None
+
+    def __call__(self, x, hidden=None, cell=None):
+        if self.bias is not None:
+            x = mx.addmm(self.bias, x, self.Wx.T)
+        else:
+            x = x @ self.Wx.T
+
+        all_hidden = []
+
+        B = x.shape[0]
+        cell = cell or mx.zeros((B, self.hidden_size), x.dtype)
+        for t in range(x.shape[-2]):
+            if hidden is None:
+                hidden = mx.zeros((B, self.hidden_size * 4), x.dtype)
+            else:
+                hidden = hidden @ self.Wh.T
+            hidden, cell = lstm_custom(x, hidden, cell, t)
+            all_hidden.append(hidden)
+
+        return mx.stack(all_hidden, axis=-2)
+
+
+class EncodecConv1d(nn.Module):
+    """Conv1d with asymmetric or causal padding and normalization."""
+
+    def __init__(
+        self,
+        config,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        dilation: int = 1,
+    ):
+        super().__init__()
+        self.causal = config.use_causal_conv
+        self.pad_mode = config.pad_mode
+        self.norm_type = config.norm_type
+
+        self.conv = nn.Conv1d(
+            in_channels, out_channels, kernel_size, stride, dilation=dilation
+        )
+        if self.norm_type == "time_group_norm":
+            self.norm = nn.GroupNorm(1, out_channels, pytorch_compatible=True)
+
+        self.stride = stride
+
+        # Effective kernel size with dilations.
+        self.kernel_size = (kernel_size - 1) * dilation + 1
+
+        self.padding_total = kernel_size - stride
+
+    def _get_extra_padding_for_conv1d(
+        self,
+        hidden_states: mx.array,
+    ) -> mx.array:
+        length = hidden_states.shape[1]
+        n_frames = (length - self.kernel_size + self.padding_total) / self.stride + 1
+        n_frames = int(math.ceil(n_frames)) - 1
+        ideal_length = n_frames * self.stride + self.kernel_size - self.padding_total
+        return ideal_length - length
+
+    def _pad1d(
+        self,
+        hidden_states: mx.array,
+        paddings: Tuple[int, int],
+        mode: str = "zero",
+        value: float = 0.0,
+    ):
+        if mode != "reflect":
+            return mx.pad(
+                hidden_states, paddings, mode="constant", constant_values=value
+            )
+
+        length = hidden_states.shape[1]
+        prefix = hidden_states[:, 1 : paddings[0] + 1][:, ::-1]
+        suffix = hidden_states[:, max(length - (paddings[1] + 1), 0) : -1][:, ::-1]
+        return mx.concatenate([prefix, hidden_states, suffix], axis=1)
+
+    def __call__(self, hidden_states):
+        extra_padding = self._get_extra_padding_for_conv1d(hidden_states)
+
+        if self.causal:
+            # Left padding for causal
+            hidden_states = self._pad1d(
+                hidden_states, (self.padding_total, extra_padding), mode=self.pad_mode
+            )
+        else:
+            # Asymmetric padding required for odd strides
+            padding_right = self.padding_total // 2
+            padding_left = self.padding_total - padding_right
+            hidden_states = self._pad1d(
+                hidden_states,
+                (padding_left, padding_right + extra_padding),
+                mode=self.pad_mode,
+            )
+
+        hidden_states = self.conv(hidden_states)
+
+        if self.norm_type == "time_group_norm":
+            hidden_states = self.norm(hidden_states)
+
+        return hidden_states
+
+
+class EncodecConvTranspose1d(nn.Module):
+    """ConvTranspose1d with asymmetric or causal padding and normalization."""
+
+    def __init__(
+        self,
+        config,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+    ):
+        super().__init__()
+        self.causal = config.use_causal_conv
+        self.trim_right_ratio = config.trim_right_ratio
+        self.norm_type = config.norm_type
+        self.conv = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride)
+        if config.norm_type == "time_group_norm":
+            self.norm = nn.GroupNorm(1, out_channels, pytorch_compatible=True)
+        self.padding_total = kernel_size - stride
+
+    def __call__(self, hidden_states):
+        hidden_states = self.conv(hidden_states)
+
+        if self.norm_type == "time_group_norm":
+            hidden_states = self.norm(hidden_states)
+
+        if self.causal:
+            padding_right = math.ceil(self.padding_total * self.trim_right_ratio)
+        else:
+            padding_right = self.padding_total // 2
+
+        padding_left = self.padding_total - padding_right
+
+        end = hidden_states.shape[1] - padding_right
+        hidden_states = hidden_states[:, padding_left:end, :]
+        return hidden_states
+
+
+class EncodecLSTM(nn.Module):
+    def __init__(self, config, dimension):
+        super().__init__()
+        self.lstm = [LSTM(dimension, dimension) for _ in range(config.num_lstm_layers)]
+
+    def __call__(self, hidden_states):
+        h = hidden_states
+        for lstm in self.lstm:
+            h = lstm(h)
+        return h + hidden_states
+
+
+class EncodecResnetBlock(nn.Module):
+    """
+    Residual block from SEANet model as used by EnCodec.
+    """
+
+    def __init__(self, config, dim: int, dilations: List[int]):
+        super().__init__()
+        kernel_sizes = (config.residual_kernel_size, 1)
+        if len(kernel_sizes) != len(dilations):
+            raise ValueError("Number of kernel sizes should match number of dilations")
+
+        hidden = dim // config.compress
+        block = []
+        for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)):
+            in_chs = dim if i == 0 else hidden
+            out_chs = dim if i == len(kernel_sizes) - 1 else hidden
+            block += [nn.ELU()]
+            block += [
+                EncodecConv1d(config, in_chs, out_chs, kernel_size, dilation=dilation)
+            ]
+        self.block = block
+
+        if getattr(config, "use_conv_shortcut", True):
+            self.shortcut = EncodecConv1d(config, dim, dim, kernel_size=1)
+        else:
+            self.shortcut = nn.Identity()
+
+    def __call__(self, hidden_states):
+        residual = hidden_states
+        for layer in self.block:
+            hidden_states = layer(hidden_states)
+
+        return self.shortcut(residual) + hidden_states
+
+
+class EncodecEncoder(nn.Module):
+    """SEANet encoder as used by EnCodec."""
+
+    def __init__(self, config):
+        super().__init__()
+        model = [
+            EncodecConv1d(
+                config, config.audio_channels, config.num_filters, config.kernel_size
+            )
+        ]
+        scaling = 1
+
+        for ratio in reversed(config.upsampling_ratios):
+            current_scale = scaling * config.num_filters
+            for j in range(config.num_residual_layers):
+                model += [
+                    EncodecResnetBlock(
+                        config, current_scale, [config.dilation_growth_rate**j, 1]
+                    )
+                ]
+            model += [nn.ELU()]
+            model += [
+                EncodecConv1d(
+                    config,
+                    current_scale,
+                    current_scale * 2,
+                    kernel_size=ratio * 2,
+                    stride=ratio,
+                )
+            ]
+            scaling *= 2
+
+        model += [EncodecLSTM(config, scaling * config.num_filters)]
+        model += [nn.ELU()]
+        model += [
+            EncodecConv1d(
+                config,
+                scaling * config.num_filters,
+                config.hidden_size,
+                config.last_kernel_size,
+            )
+        ]
+
+        self.layers = model
+
+    def __call__(self, hidden_states):
+        for layer in self.layers:
+            hidden_states = layer(hidden_states)
+        return hidden_states
+
+
+class EncodecDecoder(nn.Module):
+    """SEANet decoder as used by EnCodec."""
+
+    def __init__(self, config):
+        super().__init__()
+        scaling = int(2 ** len(config.upsampling_ratios))
+        model = [
+            EncodecConv1d(
+                config,
+                config.hidden_size,
+                scaling * config.num_filters,
+                config.kernel_size,
+            )
+        ]
+
+        model += [EncodecLSTM(config, scaling * config.num_filters)]
+
+        for ratio in config.upsampling_ratios:
+            current_scale = scaling * config.num_filters
+            model += [nn.ELU()]
+            model += [
+                EncodecConvTranspose1d(
+                    config,
+                    current_scale,
+                    current_scale // 2,
+                    kernel_size=ratio * 2,
+                    stride=ratio,
+                )
+            ]
+            for j in range(config.num_residual_layers):
+                model += [
+                    EncodecResnetBlock(
+                        config, current_scale // 2, (config.dilation_growth_rate**j, 1)
+                    )
+                ]
+            scaling //= 2
+
+        model += [nn.ELU()]
+        model += [
+            EncodecConv1d(
+                config,
+                config.num_filters,
+                config.audio_channels,
+                config.last_kernel_size,
+            )
+        ]
+        self.layers = model
+
+    def __call__(self, hidden_states):
+        for layer in self.layers:
+            hidden_states = layer(hidden_states)
+        return hidden_states
+
+
+class EncodecEuclideanCodebook(nn.Module):
+    """Codebook with Euclidean distance."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.embed = mx.zeros((config.codebook_size, config.codebook_dim))
+
+    def quantize(self, hidden_states):
+        embed = self.embed.T
+        scaled_states = hidden_states.square().sum(axis=1, keepdims=True)
+        dist = -(
+            scaled_states
+            - 2 * hidden_states @ embed
+            + embed.square().sum(axis=0, keepdims=True)
+        )
+        embed_ind = dist.argmax(axis=-1)
+        return embed_ind
+
+    def encode(self, hidden_states):
+        shape = hidden_states.shape
+        hidden_states = hidden_states.reshape((-1, shape[-1]))
+        embed_ind = self.quantize(hidden_states)
+        embed_ind = embed_ind.reshape(*shape[:-1])
+        return embed_ind
+
+    def decode(self, embed_ind):
+        return self.embed[embed_ind]
+
+
+class EncodecVectorQuantization(nn.Module):
+    """
+    Vector quantization implementation. Currently supports only euclidean distance.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.codebook = EncodecEuclideanCodebook(config)
+
+    def encode(self, hidden_states):
+        return self.codebook.encode(hidden_states)
+
+    def decode(self, embed_ind):
+        return self.codebook.decode(embed_ind)
+
+
+class EncodecResidualVectorQuantizer(nn.Module):
+    """Residual Vector Quantizer."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.codebook_size = config.codebook_size
+
+        hop_length = np.prod(config.upsampling_ratios)
+        self.frame_rate = math.ceil(config.sampling_rate / hop_length)
+        self.num_quantizers = int(
+            1000 * config.target_bandwidths[-1] // (self.frame_rate * 10)
+        )
+        self.layers = [
+            EncodecVectorQuantization(config) for _ in range(self.num_quantizers)
+        ]
+
+    def get_num_quantizers_for_bandwidth(
+        self, bandwidth: Optional[float] = None
+    ) -> int:
+        """Return num_quantizers based on specified target bandwidth."""
+        bw_per_q = math.log2(self.codebook_size) * self.frame_rate
+        num_quantizers = self.num_quantizers
+        if bandwidth is not None and bandwidth > 0.0:
+            num_quantizers = int(max(1, math.floor(bandwidth * 1000 / bw_per_q)))
+        return num_quantizers
+
+    def encode(
+        self, embeddings: mx.array, bandwidth: Optional[float] = None
+    ) -> mx.array:
+        """
+        Encode a given input array with the specified frame rate at the given
+        bandwidth. The RVQ encode method sets the appropriate number of
+        quantizers to use and returns indices for each quantizer.
+        """
+        num_quantizers = self.get_num_quantizers_for_bandwidth(bandwidth)
+        residual = embeddings
+        all_indices = []
+        for layer in self.layers[:num_quantizers]:
+            indices = layer.encode(residual)
+            quantized = layer.decode(indices)
+            residual = residual - quantized
+            all_indices.append(indices)
+        out_indices = mx.stack(all_indices, axis=1)
+        return out_indices
+
+    def decode(self, codes: mx.array) -> mx.array:
+        """Decode the given codes to the quantized representation."""
+        quantized_out = None
+        for i, indices in enumerate(codes.split(codes.shape[1], axis=1)):
+            layer = self.layers[i]
+            quantized = layer.decode(indices.squeeze(1))
+            if quantized_out is None:
+                quantized_out = quantized
+            else:
+                quantized_out = quantized + quantized_out
+        return quantized_out
+
+
+class EncodecModel(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.encoder = EncodecEncoder(config)
+        self.decoder = EncodecDecoder(config)
+        self.quantizer = EncodecResidualVectorQuantizer(config)
+
+    def _encode_frame(
+        self, input_values: mx.array, bandwidth: float, padding_mask: mx.array
+    ) -> Tuple[mx.array, Optional[mx.array]]:
+        """
+        Encodes the given input using the underlying VQVAE.
+        """
+        length = input_values.shape[1]
+        duration = length / self.config.sampling_rate
+
+        if (
+            self.config.chunk_length_s is not None
+            and duration > 1e-5 + self.config.chunk_length_s
+        ):
+            raise RuntimeError(
+                f"Duration of frame ({duration}) is longer than chunk {self.config.chunk_length_s}"
+            )
+
+        scale = None
+        if self.config.normalize:
+            # if the padding is non zero
+            input_values = input_values * padding_mask[..., None]
+            mono = mx.sum(input_values, axis=2, keepdims=True) / input_values.shape[2]
+            scale = mono.square().mean(axis=1, keepdims=True).sqrt() + 1e-8
+            input_values = input_values / scale
+
+        embeddings = self.encoder(input_values)
+        codes = self.quantizer.encode(embeddings, bandwidth)
+        return codes, scale
+
+    def encode(
+        self,
+        input_values: mx.array,
+        padding_mask: mx.array = None,
+        bandwidth: Optional[float] = None,
+    ) -> Tuple[mx.array, Optional[mx.array]]:
+        """
+        Encodes the input audio waveform into discrete codes.
+
+        Args:
+            input_values (mx.array): The input audio waveform with shape
+                ``(batch_size, channels, sequence_length)``.
+            padding_mask (mx.array): Padding mask used to pad the ``input_values``.
+            bandwidth (float, optional): The target bandwidth. Must be one of
+                ``config.target_bandwidths``. If ``None``, uses the smallest
+                possible bandwidth. bandwidth is represented as a thousandth of
+                what it is, e.g. 6kbps bandwidth is represented as bandwidth == 6.0
+
+        Returns:
+            A list of frames containing the discrete encoded codes for the
+            input audio waveform, along with rescaling factors for each chunk
+            when ``config.normalize==True``. Each frame is a tuple ``(codebook,
+            scale)``, with ``codebook`` of shape ``(batch_size, num_codebooks,
+            frames)``.
+        """
+
+        if bandwidth is None:
+            bandwidth = self.config.target_bandwidths[0]
+        if bandwidth not in self.config.target_bandwidths:
+            raise ValueError(
+                f"This model doesn't support the bandwidth {bandwidth}. "
+                f"Select one of {self.config.target_bandwidths}."
+            )
+
+        _, input_length, channels = input_values.shape
+
+        if channels < 1 or channels > 2:
+            raise ValueError(
+                f"Number of audio channels must be 1 or 2, but got {channels}"
+            )
+
+        chunk_length = self.chunk_length
+        if chunk_length is None:
+            chunk_length = input_length
+            stride = input_length
+        else:
+            stride = self.chunk_stride
+
+        if padding_mask is None:
+            padding_mask = mx.ones(input_values.shape[:2], dtype=mx.bool_)
+        encoded_frames = []
+        scales = []
+
+        step = chunk_length - stride
+        if (input_length % stride) != step:
+            raise ValueError(
+                "The input length is not properly padded for batched chunked "
+                "encoding. Make sure to pad the input correctly."
+            )
+
+        for offset in range(0, input_length - step, stride):
+            mask = padding_mask[:, offset : offset + chunk_length].astype(mx.bool_)
+            frame = input_values[:, offset : offset + chunk_length]
+            encoded_frame, scale = self._encode_frame(frame, bandwidth, mask)
+            encoded_frames.append(encoded_frame)
+            scales.append(scale)
+
+        encoded_frames = mx.stack(encoded_frames)
+
+        return (encoded_frames, scales)
+
+    @staticmethod
+    def _linear_overlap_add(frames: List[mx.array], stride: int):
+        if len(frames) == 0:
+            raise ValueError("`frames` cannot be an empty list.")
+
+        dtype = frames[0].dtype
+        N, frame_length, C = frames[0].shape
+        total_size = stride * (len(frames) - 1) + frames[-1].shape[1]
+
+        time_vec = mx.linspace(0, 1, frame_length + 2, dtype=dtype)[1:-1]
+        weight = 0.5 - (time_vec - 0.5).abs()
+
+        weight = weight[:, None]
+        sum_weight = mx.zeros((total_size, 1), dtype=dtype)
+        out = mx.zeros((N, total_size, C), dtype=dtype)
+        offset = 0
+
+        for frame in frames:
+            frame_length = frame.shape[1]
+            out[:, offset : offset + frame_length] += weight[:frame_length] * frame
+            sum_weight[offset : offset + frame_length] += weight[:frame_length]
+            offset += stride
+
+        return out / sum_weight
+
+    def _decode_frame(
+        self, codes: mx.array, scale: Optional[mx.array] = None
+    ) -> mx.array:
+        embeddings = self.quantizer.decode(codes)
+        outputs = self.decoder(embeddings)
+        if scale is not None:
+            outputs = outputs * scale
+        return outputs
+
+    @property
+    def channels(self):
+        return self.config.audio_channels
+
+    @property
+    def sampling_rate(self):
+        return self.config.sampling_rate
+
+    @property
+    def chunk_length(self):
+        if self.config.chunk_length_s is None:
+            return None
+        else:
+            return int(self.config.chunk_length_s * self.config.sampling_rate)
+
+    @property
+    def chunk_stride(self):
+        if self.config.chunk_length_s is None or self.config.overlap is None:
+            return None
+        else:
+            return max(1, int((1.0 - self.config.overlap) * self.chunk_length))
+
+    def decode(
+        self,
+        audio_codes: mx.array,
+        audio_scales: Union[mx.array, List[mx.array]],
+        padding_mask: Optional[mx.array] = None,
+    ) -> Tuple[mx.array, mx.array]:
+        """
+        Decodes the given frames into an output audio waveform.
+
+        Note that the output might be a bit bigger than the input. In that
+        case, any extra steps at the end should be trimmed.
+
+        Args:
+            audio_codes (mx.array): Discret code embeddings of shape
+                ``(batch_size, nb_chunks, chunk_length)``.
+            audio_scales (mx.array): Scaling factor for each input.
+            padding_mask (mx.array): Padding mask.
+        """
+        chunk_length = self.chunk_length
+        if chunk_length is None:
+            if audio_codes.shape[1] != 1:
+                raise ValueError(f"Expected one frame, got {len(audio_codes)}")
+            audio_values = self._decode_frame(audio_codes[:, 0], audio_scales[0])
+        else:
+            decoded_frames = []
+
+            for frame, scale in zip(audio_codes, audio_scales):
+                frames = self._decode_frame(frame, scale)
+                decoded_frames.append(frames)
+
+            audio_values = self._linear_overlap_add(
+                decoded_frames, self.chunk_stride or 1
+            )
+
+        # truncate based on padding mask
+        if padding_mask is not None and padding_mask.shape[1] < audio_values.shape[1]:
+            audio_values = audio_values[:, : padding_mask.shape[1]]
+        return audio_values
+
+    @classmethod
+    def from_pretrained(cls, path_or_repo: str):
+        from huggingface_hub import snapshot_download
+
+        path = Path(path_or_repo)
+        if not path.exists():
+            path = Path(
+                snapshot_download(
+                    repo_id=path_or_repo,
+                    allow_patterns=["*.json", "*.safetensors", "*.model"],
+                )
+            )
+
+        with open(path / "config.json", "r") as f:
+            config = SimpleNamespace(**json.load(f))
+
+        model = EncodecModel(config)
+        model.load_weights(str(path / "model.safetensors"))
+        processor = functools.partial(
+            preprocess_audio,
+            sampling_rate=config.sampling_rate,
+            chunk_length=model.chunk_length,
+            chunk_stride=model.chunk_stride,
+        )
+        mx.eval(model)
+        return model, processor
+
+
+def preprocess_audio(
+    raw_audio: Union[mx.array, List[mx.array]],
+    sampling_rate: int = 24000,
+    chunk_length: Optional[int] = None,
+    chunk_stride: Optional[int] = None,
+):
+    r"""
+    Prepare inputs for the EnCodec model.
+
+    Args:
+        raw_audio (mx.array or List[mx.array]): The sequence or batch of
+            sequences to be processed.
+        sampling_rate (int): The sampling rate at which the audio waveform
+            should be digitalized.
+        chunk_length (int, optional): The model's chunk length.
+        chunk_stride (int, optional): The model's chunk stride.
+    """
+    if not isinstance(raw_audio, list):
+        raw_audio = [raw_audio]
+
+    raw_audio = [x[..., None] if x.ndim == 1 else x for x in raw_audio]
+
+    max_length = max(array.shape[0] for array in raw_audio)
+    if chunk_length is not None:
+        max_length += chunk_length - (max_length % chunk_stride)
+
+    inputs = []
+    masks = []
+    for x in raw_audio:
+        length = x.shape[0]
+        mask = mx.ones((length,), dtype=mx.bool_)
+        difference = max_length - length
+        if difference > 0:
+            mask = mx.pad(mask, (0, difference))
+            x = mx.pad(x, ((0, difference), (0, 0)))
+        inputs.append(x)
+        masks.append(mask)
+    return mx.stack(inputs), mx.stack(masks)

encodec/example.py

@@ -0,0 +1,39 @@
+# Copyright © 2024 Apple Inc.
+
+import mlx.core as mx
+from utils import load_audio, save_audio
+
+from encodec import EncodecModel
+
+# Load the 48 KHz model and preprocessor.
+model, processor = EncodecModel.from_pretrained("mlx-community/encodec-48khz-float32")
+
+# Load an audio file
+audio = load_audio("/path/to/audio", model.sampling_rate, model.channels)
+
+# Preprocess the audio (this can also be a list of arrays for batched
+# processing).
+feats, mask = processor(audio)
+
+
+# Encode at the given bandwidth. A lower bandwidth results in more
+# compression but lower reconstruction quality.
+@mx.compile
+def encode(feats, mask):
+    return model.encode(feats, mask, bandwidth=3)
+
+
+# Decode to reconstruct the audio
+@mx.compile
+def decode(codes, scales, mask):
+    return model.decode(codes, scales, mask)
+
+
+codes, scales = encode(feats, mask)
+reconstructed = decode(codes, scales, mask)
+
+# Trim any padding:
+reconstructed = reconstructed[0, : len(audio)]
+
+# Save the audio as a wave file
+save_audio("reconstructed.wav", reconstructed, model.sampling_rate)

encodec/requirements.txt

@@ -0,0 +1,3 @@
+mlx>=0.18
+numpy
+huggingface_hub

encodec/test.py

@@ -0,0 +1,67 @@
+# Copyright © 2024 Apple Inc.
+
+import mlx.core as mx
+import numpy as np
+import torch
+from transformers import AutoProcessor
+from transformers import EncodecModel as PTEncodecModel
+
+from encodec import EncodecModel, preprocess_audio
+
+
+def compare_processors():
+    np.random.seed(0)
+    audio_length = 95500
+    audio = np.random.uniform(size=(2, audio_length)).astype(np.float32)
+
+    processor = AutoProcessor.from_pretrained("facebook/encodec_48khz")
+
+    pt_inputs = processor(
+        raw_audio=audio, sampling_rate=processor.sampling_rate, return_tensors="pt"
+    )
+    mx_inputs = preprocess_audio(
+        mx.array(audio).T,
+        processor.sampling_rate,
+        processor.chunk_length,
+        processor.chunk_stride,
+    )
+
+    assert np.array_equal(pt_inputs["input_values"], mx_inputs[0].moveaxis(2, 1))
+    assert np.array_equal(pt_inputs["padding_mask"], mx_inputs[1])
+
+
+def compare_models():
+    pt_model = PTEncodecModel.from_pretrained("facebook/encodec_48khz")
+    mx_model, _ = EncodecModel.from_pretrained("mlx-community/encodec-48khz-float32")
+
+    np.random.seed(0)
+    audio_length = 190560
+    audio = np.random.uniform(size=(1, audio_length, 2)).astype(np.float32)
+    mask = np.ones((1, audio_length), dtype=np.int32)
+    pt_encoded = pt_model.encode(
+        torch.tensor(audio).moveaxis(2, 1), torch.tensor(mask)[None]
+    )
+    mx_encoded = mx_model.encode(mx.array(audio), mx.array(mask))
+    pt_codes = pt_encoded.audio_codes.numpy()
+    mx_codes = mx_encoded[0]
+    assert np.array_equal(pt_codes, mx_codes), "Encoding codes mismatch"
+
+    for mx_scale, pt_scale in zip(mx_encoded[1], pt_encoded.audio_scales):
+        if mx_scale is not None:
+            pt_scale = pt_scale.numpy()
+            assert np.allclose(pt_scale, mx_scale, atol=1e-3, rtol=1e-4)
+
+    pt_audio = pt_model.decode(
+        pt_encoded.audio_codes, pt_encoded.audio_scales, torch.tensor(mask)[None]
+    )
+    pt_audio = pt_audio[0].squeeze().T.detach().numpy()
+    mx_audio = mx_model.decode(*mx_encoded, mx.array(mask))
+    mx_audio = mx_audio.squeeze()
+    assert np.allclose(
+        pt_audio, mx_audio, atol=1e-4, rtol=1e-4
+    ), "Decoding audio mismatch"
+
+
+if __name__ == "__main__":
+    compare_processors()
+    compare_models()

encodec/utils.py

@@ -0,0 +1,52 @@
+# Copyright © 2024 Apple Inc.
+
+import mlx.core as mx
+import numpy as np
+
+
+def save_audio(file: str, audio: mx.array, sampling_rate: int):
+    """
+    Save audio to a wave (.wav) file.
+    """
+    from scipy.io.wavfile import write
+
+    audio = (audio * 32767).astype(mx.int16)
+    write(file, sampling_rate, np.array(audio))
+
+
+def load_audio(file: str, sampling_rate: int, channels: int):
+    """
+    Read audio into an mx.array, resampling if necessary.
+
+    Args:
+        file (str): The audio file to open.
+        sampling_rate (int): The sample rate to resample the audio at if needed.
+        channels (int): The number of audio channels.
+
+    Returns:
+        An mx.array containing the audio waveform in float32.
+    """
+    from subprocess import CalledProcessError, run
+
+    # This launches a subprocess to decode audio while down-mixing
+    # and resampling as necessary.  Requires the ffmpeg CLI in PATH.
+    # fmt: off
+    cmd = [
+        "ffmpeg",
+        "-nostdin",
+        "-threads", "0",
+        "-i", file,
+        "-f", "s16le",
+        "-ac", str(channels),
+        "-acodec", "pcm_s16le",
+        "-ar", str(sampling_rate),
+        "-"
+    ]
+    # fmt: on
+    try:
+        out = run(cmd, capture_output=True, check=True).stdout
+    except CalledProcessError as e:
+        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+
+    out = mx.array(np.frombuffer(out, np.int16))
+    return out.reshape(-1, channels).astype(mx.float32) / 32767.0

flux/README.md

@@ -0,0 +1,281 @@
+FLUX
+====
+
+FLUX implementation in MLX. The implementation is ported directly from
+[https://github.com/black-forest-labs/flux](https://github.com/black-forest-labs/flux)
+and the model weights are downloaded directly from the Hugging Face Hub.
+
+The goal of this example is to be clean, educational and to allow for
+experimentation with finetuning FLUX models as well as adding extra
+functionality such as in-/outpainting, guidance with custom losses etc.
+
+![MLX image](static/generated-mlx.png)    
+*Image generated using FLUX-dev in MLX and the prompt 'An image in the style of
+tron emanating futuristic technology with the word "MLX" in the center with
+capital red letters.'*
+
+Installation
+------------
+
+The dependencies are minimal, namely:
+
+- `huggingface-hub` to download the checkpoints.
+- `regex` for the tokenization
+- `tqdm`, `PIL`, and `numpy` for the scripts
+- `sentencepiece` for the T5 tokenizer
+- `datasets` for using an HF dataset directly
+
+You can install all of the above with the `requirements.txt` as follows:
+
+    pip install -r requirements.txt
+
+
+Usage
+---------
+
+You can use the following command to generate an image, using `--output` to specify the storage location of the image, defaulting to `out.png`.
+
+```shell
+python txt2image.py --model schnell \
+    --n-images 1 \
+    --image-size 256x512 \
+    --verbose \
+    'A photo of an astronaut riding a horse on Mars.'
+```
+
+For more parameters, please use the `--help` command to view.
+
+```shell
+python txt2image.py --help
+```
+
+Inference
+---------
+
+Inference in this example is similar to the stable diffusion example. The
+classes to get you started are `FluxPipeline` from the `flux` module.
+
+```python
+import mlx.core as mx
+from flux import FluxPipeline
+
+# This will download all the weights from HF hub
+flux = FluxPipeline("flux-schnell")
+
+# Make a generator that returns the latent variables from the reverse diffusion
+# process
+latent_generator = flux.generate_latents(
+    "A photo of an astronaut riding a horse on Mars",
+    num_steps=4,
+    latent_size=(32, 64),  # 256x512 image
+)
+
+# The first return value of the generator contains the conditioning and the
+# random noise at the beginning of the diffusion process.
+conditioning = next(latent_generator)
+(
+    x_T,                # The initial noise
+    x_positions,        # The integer positions used for image positional encoding
+    t5_conditioning,    # The T5 features from the text prompt
+    t5_positions,       # Integer positions for text (normally all 0s)
+    clip_conditioning,  # The clip text features from the text prompt
+) = conditioning
+
+# Returning the conditioning as the first output from the generator allows us
+# to unload T5 and clip before running the diffusion transformer.
+mx.eval(conditioning)
+
+# Evaluate each diffusion step
+for x_t in latent_generator:
+    mx.eval(x_t)
+
+# Note that we need to pass the latent size because it is collapsed and
+# patchified in x_t and we need to unwrap it.
+img = flux.decode(x_t, latent_size=(32, 64))
+```
+
+The above are essentially the implementation of the `txt2image.py` script
+except for some additional logic to quantize and/or load trained adapters. One
+can use the script as follows:
+
+```shell
+python txt2image.py \
+    --n-images 4 \
+    --n-rows 2 \
+    --image-size 256x512 \
+    'A photo of an astronaut riding a horse on Mars.'
+```
+
+### Experimental Options
+
+FLUX pads the prompt to a specific size of 512 tokens for the dev model and
+256 for the schnell model. Not applying padding results in faster generation
+but it is not clear how it may affect the generated images. To enable that
+option in this example pass `--no-t5-padding` to the `txt2image.py` script or
+instantiate the pipeline with `FluxPipeline("flux-schnell", t5_padding=False)`.
+
+Finetuning
+----------
+
+The `dreambooth.py` script supports LoRA finetuning of FLUX-dev (and schnell
+but ymmv) on a provided image dataset. The dataset folder must have an
+`train.jsonl` file with the following format:
+
+```jsonl
+{"image": "path-to-image-relative-to-dataset", "prompt": "Prompt to use with this image"}
+{"image": "path-to-image-relative-to-dataset", "prompt": "Prompt to use with this image"}
+...
+```
+
+The training script by default trains for 600 iterations with a batch size of
+1, gradient accumulation of 4 and LoRA rank of 8. Run `python dreambooth.py
+--help` for the list of hyperparameters you can tune.
+
+> [!Note]
+> FLUX finetuning requires approximately 50GB of RAM. QLoRA is coming soon and
+> should reduce this number significantly.
+
+### Training Example
+
+This is a step-by-step finetuning example. We will be using the data from
+[https://github.com/google/dreambooth](https://github.com/google/dreambooth).
+In particular, we will use `dog6` which is a popular example for showcasing
+dreambooth [^1].
+
+The training images are the following 5 images [^2]:
+
+![dog6](static/dog6.png)
+
+We start by making the following `train.jsonl` file and placing it in the same
+folder as the images.
+
+```jsonl
+{"image": "00.jpg", "prompt": "A photo of sks dog"}
+{"image": "01.jpg", "prompt": "A photo of sks dog"}
+{"image": "02.jpg", "prompt": "A photo of sks dog"}
+{"image": "03.jpg", "prompt": "A photo of sks dog"}
+{"image": "04.jpg", "prompt": "A photo of sks dog"}
+```
+
+Subsequently we finetune FLUX using the following command:
+
+```shell
+python dreambooth.py \
+    --progress-prompt 'A photo of an sks dog lying on the sand at a beach in Greece' \
+    --progress-every 600 --iterations 1200 --learning-rate 0.0001 \
+    --lora-rank 4 --grad-accumulate 8 \
+    path/to/dreambooth/dataset/dog6
+```
+
+Or you can directly use the pre-processed Hugging Face dataset
+[mlx-community/dreambooth-dog6](https://huggingface.co/datasets/mlx-community/dreambooth-dog6)
+for fine-tuning.
+
+```shell
+python dreambooth.py \
+    --progress-prompt 'A photo of an sks dog lying on the sand at a beach in Greece' \
+    --progress-every 600 --iterations 1200 --learning-rate 0.0001 \
+    --lora-rank 4 --grad-accumulate 8 \
+    mlx-community/dreambooth-dog6
+```
+
+The training requires approximately 50GB of RAM and on an M2 Ultra it takes a
+bit more than 1 hour.
+
+### Using the Adapter
+
+The adapters are saved in `mlx_output` and can be used directly by the
+`txt2image.py` script. For instance,
+
+```shell
+python txt2image.py --model dev --save-raw --image-size 512x512 --n-images 1 \
+    --adapter mlx_output/final_adapters.safetensors \
+    --fuse-adapter \
+    --no-t5-padding \
+    'A photo of an sks dog lying on the sand at a beach in Greece'
+```
+
+generates an image that looks like the following,
+
+![dog image](static/dog-r4-g8-1200.png)
+
+and of course we can pass `--image-size 512x1024` to get larger images with
+different aspect ratios,
+
+![wide dog image](static/dog-r4-g8-1200-512x1024.png)
+
+The arguments that are relevant to the adapters are of course `--adapter` and
+`--fuse-adapter`. The first defines the path to an adapter to apply to the
+model and the second fuses the adapter back into the model to get a bit more
+speed during generation.
+
+[^1]: Refer to the [arXiv paper](https://arxiv.org/abs/2208.12242) for more details.
+[^2]: The images are from unsplash by https://unsplash.com/@alvannee .
+
+
+Distributed Computation
+------------------------
+
+The FLUX example supports distributed computation during both generation and
+training. See the [distributed communication
+documentation](https://ml-explore.github.io/mlx/build/html/usage/distributed.html)
+for information on how to set-up MLX for distributed communication. The rest of
+this section assumes you can launch distributed MLX programs using `mlx.launch
+--hostfile hostfile.json`.
+
+### Distributed Finetuning
+
+Distributed finetuning scales very well with FLUX and all one has to do is
+adjust the gradient accumulation and training iterations so that the batch
+size remains the same. For instance, to replicate the following training
+
+```shell
+python dreambooth.py \
+    --progress-prompt 'A photo of an sks dog lying on the sand at a beach in Greece' \
+    --progress-every 600 --iterations 1200 --learning-rate 0.0001 \
+    --lora-rank 4 --grad-accumulate 8 \
+    mlx-community/dreambooth-dog6
+```
+
+On 4 machines we simply run
+
+```shell
+mlx.launch --verbose --hostfile hostfile.json -- python dreambooth.py \
+    --progress-prompt 'A photo of an sks dog lying on the sand at a beach in Greece' \
+    --progress-every 150 --iterations 300 --learning-rate 0.0001 \
+    --lora-rank 4 --grad-accumulate 2 \
+    mlx-community/dreambooth-dog6
+```
+
+Note the iterations that changed to 300 from 1200 and the gradient accumulations to 2 from 8.
+
+### Distributed Inference
+
+Distributed inference can be divided in two different approaches. The first
+approach is the data-parallel approach, where each node generates its own
+images and shares them at the end. The second approach is the model-parallel
+approach where the model is shared across the nodes and they collaboratively
+generate the images.
+
+The `txt2image.py` script will attempt to choose the best approach depending on
+how many images are being generated across the nodes. The model-parallel
+approach can be forced by passing the argument `--force-shard`.
+
+For better performance in the model-parallel approach we suggest that you use a
+[thunderbolt
+ring](https://ml-explore.github.io/mlx/build/html/usage/distributed.html#getting-started-with-ring).
+
+All you have to do once again is use `mlx.launch` as follows
+
+```shell
+mlx.launch --verbose --hostfile hostfile.json -- \
+    python txt2image.py --model schnell \
+    --n-images 8 \
+    --image-size 512x512 \
+    --verbose \
+    'A photo of an astronaut riding a horse on Mars'
+```
+
+for model-parallel generation you may want to also pass `--env
+MLX_METAL_FAST_SYNCH=1` to `mlx.launch` which is an experimental setting that
+reduces the CPU/GPU synchronization overhead.

flux/dreambooth.py

@@ -0,0 +1,292 @@
+# Copyright © 2024 Apple Inc.
+
+import argparse
+import time
+from functools import partial
+from pathlib import Path
+
+import mlx.core as mx
+import mlx.nn as nn
+import mlx.optimizers as optim
+import numpy as np
+from mlx.nn.utils import average_gradients
+from mlx.utils import tree_flatten, tree_map, tree_reduce
+from PIL import Image
+
+from flux import FluxPipeline, Trainer, load_dataset, save_config
+
+
+def generate_progress_images(iteration, flux, args):
+    """Generate images to monitor the progress of the finetuning."""
+    out_dir = Path(args.output_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    out_file = out_dir / f"{iteration:07d}_progress.png"
+    print(f"Generating {str(out_file)}", flush=True)
+
+    # Generate some images and arrange them in a grid
+    n_rows = 2
+    n_images = 4
+    x = flux.generate_images(
+        args.progress_prompt,
+        n_images,
+        args.progress_steps,
+    )
+    x = mx.pad(x, [(0, 0), (4, 4), (4, 4), (0, 0)])
+    B, H, W, C = x.shape
+    x = x.reshape(n_rows, B // n_rows, H, W, C).transpose(0, 2, 1, 3, 4)
+    x = x.reshape(n_rows * H, B // n_rows * W, C)
+    x = mx.pad(x, [(4, 4), (4, 4), (0, 0)])
+    x = (x * 255).astype(mx.uint8)
+
+    # Save them to disc
+    im = Image.fromarray(np.array(x))
+    im.save(out_file)
+
+
+def save_adapters(adapter_name, flux, args):
+    out_dir = Path(args.output_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    out_file = out_dir / adapter_name
+    print(f"Saving {str(out_file)}")
+
+    mx.save_safetensors(
+        str(out_file),
+        dict(tree_flatten(flux.flow.trainable_parameters())),
+        metadata={
+            "lora_rank": str(args.lora_rank),
+            "lora_blocks": str(args.lora_blocks),
+        },
+    )
+
+
+def setup_arg_parser():
+    """Set up and return the argument parser."""
+    parser = argparse.ArgumentParser(
+        description="Finetune Flux to generate images with a specific subject"
+    )
+
+    parser.add_argument(
+        "--model",
+        default="dev",
+        choices=[
+            "dev",
+            "schnell",
+        ],
+        help="Which flux model to train",
+    )
+    parser.add_argument(
+        "--guidance", type=float, default=4.0, help="The guidance factor to use."
+    )
+    parser.add_argument(
+        "--iterations",
+        type=int,
+        default=600,
+        help="How many iterations to train for",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=1,
+        help="The batch size to use when training the stable diffusion model",
+    )
+    parser.add_argument(
+        "--resolution",
+        type=lambda x: tuple(map(int, x.split("x"))),
+        default=(512, 512),
+        help="The resolution of the training images",
+    )
+    parser.add_argument(
+        "--num-augmentations",
+        type=int,
+        default=5,
+        help="Augment the images by random cropping and panning",
+    )
+    parser.add_argument(
+        "--progress-prompt",
+        required=True,
+        help="Use this prompt when generating images for evaluation",
+    )
+    parser.add_argument(
+        "--progress-steps",
+        type=int,
+        default=50,
+        help="Use this many steps when generating images for evaluation",
+    )
+    parser.add_argument(
+        "--progress-every",
+        type=int,
+        default=50,
+        help="Generate images every PROGRESS_EVERY steps",
+    )
+    parser.add_argument(
+        "--checkpoint-every",
+        type=int,
+        default=50,
+        help="Save the model every CHECKPOINT_EVERY steps",
+    )
+    parser.add_argument(
+        "--lora-blocks",
+        type=int,
+        default=-1,
+        help="Train the last LORA_BLOCKS transformer blocks",
+    )
+    parser.add_argument(
+        "--lora-rank", type=int, default=8, help="LoRA rank for finetuning"
+    )
+    parser.add_argument(
+        "--warmup-steps", type=int, default=100, help="Learning rate warmup"
+    )
+    parser.add_argument(
+        "--learning-rate", type=float, default="1e-4", help="Learning rate for training"
+    )
+    parser.add_argument(
+        "--grad-accumulate",
+        type=int,
+        default=4,
+        help="Accumulate gradients for that many iterations before applying them",
+    )
+    parser.add_argument(
+        "--output-dir", default="mlx_output", help="Folder to save the checkpoints in"
+    )
+
+    parser.add_argument("dataset")
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_arg_parser()
+    args = parser.parse_args()
+
+    output_path = Path(args.output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+    save_config(vars(args), output_path / "adapter_config.json")
+
+    # Load the model and set it up for LoRA training. We use the same random
+    # state when creating the LoRA layers so all workers will have the same
+    # initial weights.
+    mx.random.seed(0x0F0F0F0F)
+    flux = FluxPipeline("flux-" + args.model)
+    flux.flow.freeze()
+    flux.linear_to_lora_layers(args.lora_rank, args.lora_blocks)
+
+    # Reset the seed to a different seed per worker if we are in distributed
+    # mode so that each worker is working on different data, diffusion step and
+    # random noise.
+    mx.random.seed(0xF0F0F0F0 + mx.distributed.init().rank())
+
+    # Report how many parameters we are training
+    trainable_params = tree_reduce(
+        lambda acc, x: acc + x.size, flux.flow.trainable_parameters(), 0
+    )
+    print(f"Training {trainable_params / 1024 ** 2:.3f}M parameters", flush=True)
+
+    # Set up the optimizer and training steps. The steps are a bit verbose to
+    # support gradient accumulation together with compilation.
+    warmup = optim.linear_schedule(0, args.learning_rate, args.warmup_steps)
+    cosine = optim.cosine_decay(
+        args.learning_rate, args.iterations // args.grad_accumulate
+    )
+    lr_schedule = optim.join_schedules([warmup, cosine], [args.warmup_steps])
+    optimizer = optim.Adam(learning_rate=lr_schedule)
+    state = [flux.flow.state, optimizer.state, mx.random.state]
+
+    @partial(mx.compile, inputs=state, outputs=state)
+    def single_step(x, t5_feat, clip_feat, guidance):
+        loss, grads = nn.value_and_grad(flux.flow, flux.training_loss)(
+            x, t5_feat, clip_feat, guidance
+        )
+        grads = average_gradients(grads)
+        optimizer.update(flux.flow, grads)
+
+        return loss
+
+    @partial(mx.compile, inputs=state, outputs=state)
+    def compute_loss_and_grads(x, t5_feat, clip_feat, guidance):
+        return nn.value_and_grad(flux.flow, flux.training_loss)(
+            x, t5_feat, clip_feat, guidance
+        )
+
+    @partial(mx.compile, inputs=state, outputs=state)
+    def compute_loss_and_accumulate_grads(x, t5_feat, clip_feat, guidance, prev_grads):
+        loss, grads = nn.value_and_grad(flux.flow, flux.training_loss)(
+            x, t5_feat, clip_feat, guidance
+        )
+        grads = tree_map(lambda a, b: a + b, prev_grads, grads)
+        return loss, grads
+
+    @partial(mx.compile, inputs=state, outputs=state)
+    def grad_accumulate_and_step(x, t5_feat, clip_feat, guidance, prev_grads):
+        loss, grads = nn.value_and_grad(flux.flow, flux.training_loss)(
+            x, t5_feat, clip_feat, guidance
+        )
+        grads = tree_map(
+            lambda a, b: (a + b) / args.grad_accumulate,
+            prev_grads,
+            grads,
+        )
+        grads = average_gradients(grads)
+        optimizer.update(flux.flow, grads)
+
+        return loss
+
+    # We simply route to the appropriate step based on whether we have
+    # gradients from a previous step and whether we should be performing an
+    # update or simply computing and accumulating gradients in this step.
+    def step(x, t5_feat, clip_feat, guidance, prev_grads, perform_step):
+        if prev_grads is None:
+            if perform_step:
+                return single_step(x, t5_feat, clip_feat, guidance), None
+            else:
+                return compute_loss_and_grads(x, t5_feat, clip_feat, guidance)
+        else:
+            if perform_step:
+                return (
+                    grad_accumulate_and_step(
+                        x, t5_feat, clip_feat, guidance, prev_grads
+                    ),
+                    None,
+                )
+            else:
+                return compute_loss_and_accumulate_grads(
+                    x, t5_feat, clip_feat, guidance, prev_grads
+                )
+
+    dataset = load_dataset(args.dataset)
+    trainer = Trainer(flux, dataset, args)
+    trainer.encode_dataset()
+
+    guidance = mx.full((args.batch_size,), args.guidance, dtype=flux.dtype)
+
+    # An initial generation to compare
+    generate_progress_images(0, flux, args)
+
+    grads = None
+    losses = []
+    tic = time.time()
+    for i, batch in zip(range(args.iterations), trainer.iterate(args.batch_size)):
+        loss, grads = step(*batch, guidance, grads, (i + 1) % args.grad_accumulate == 0)
+        mx.eval(loss, grads, state)
+        losses.append(loss.item())
+
+        if (i + 1) % 10 == 0:
+            toc = time.time()
+            peak_mem = mx.metal.get_peak_memory() / 1024**3
+            print(
+                f"Iter: {i + 1} Loss: {sum(losses) / 10:.3f} "
+                f"It/s: {10 / (toc - tic):.3f} "
+                f"Peak mem: {peak_mem:.3f} GB",
+                flush=True,
+            )
+
+        if (i + 1) % args.progress_every == 0:
+            generate_progress_images(i + 1, flux, args)
+
+        if (i + 1) % args.checkpoint_every == 0:
+            save_adapters(f"{i + 1:07d}_adapters.safetensors", flux, args)
+
+        if (i + 1) % 10 == 0:
+            losses = []
+            tic = time.time()
+
+    save_adapters("final_adapters.safetensors", flux, args)
+    print("Training successful.")

flux/flux/__init__.py

@@ -0,0 +1,16 @@
+# Copyright © 2024 Apple Inc.
+
+from .datasets import Dataset, load_dataset
+from .flux import FluxPipeline
+from .lora import LoRALinear
+from .sampler import FluxSampler
+from .trainer import Trainer
+from .utils import (
+    load_ae,
+    load_clip,
+    load_clip_tokenizer,
+    load_flow_model,
+    load_t5,
+    load_t5_tokenizer,
+    save_config,
+)

flux/flux/autoencoder.py

@@ -0,0 +1,357 @@
+# Copyright © 2024 Apple Inc.
+
+from dataclasses import dataclass
+from typing import List
+
+import mlx.core as mx
+import mlx.nn as nn
+from mlx.nn.layers.upsample import upsample_nearest
+
+
+@dataclass
+class AutoEncoderParams:
+    resolution: int
+    in_channels: int
+    ch: int
+    out_ch: int
+    ch_mult: List[int]
+    num_res_blocks: int
+    z_channels: int
+    scale_factor: float
+    shift_factor: float
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = nn.GroupNorm(
+            num_groups=32,
+            dims=in_channels,
+            eps=1e-6,
+            affine=True,
+            pytorch_compatible=True,
+        )
+        self.q = nn.Linear(in_channels, in_channels)
+        self.k = nn.Linear(in_channels, in_channels)
+        self.v = nn.Linear(in_channels, in_channels)
+        self.proj_out = nn.Linear(in_channels, in_channels)
+
+    def __call__(self, x: mx.array) -> mx.array:
+        B, H, W, C = x.shape
+
+        y = x.reshape(B, 1, -1, C)
+        y = self.norm(y)
+        q = self.q(y)
+        k = self.k(y)
+        v = self.v(y)
+        y = mx.fast.scaled_dot_product_attention(q, k, v, scale=C ** (-0.5))
+        y = self.proj_out(y)
+
+        return x + y.reshape(B, H, W, C)
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        self.norm1 = nn.GroupNorm(
+            num_groups=32,
+            dims=in_channels,
+            eps=1e-6,
+            affine=True,
+            pytorch_compatible=True,
+        )
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        self.norm2 = nn.GroupNorm(
+            num_groups=32,
+            dims=out_channels,
+            eps=1e-6,
+            affine=True,
+            pytorch_compatible=True,
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut = nn.Linear(in_channels, out_channels)
+
+    def __call__(self, x):
+        h = x
+        h = self.norm1(h)
+        h = nn.silu(h)
+        h = self.conv1(h)
+
+        h = self.norm2(h)
+        h = nn.silu(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.conv = nn.Conv2d(
+            in_channels, in_channels, kernel_size=3, stride=2, padding=0
+        )
+
+    def __call__(self, x: mx.array):
+        x = mx.pad(x, [(0, 0), (0, 1), (0, 1), (0, 0)])
+        x = self.conv(x)
+        return x
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.conv = nn.Conv2d(
+            in_channels, in_channels, kernel_size=3, stride=1, padding=1
+        )
+
+    def __call__(self, x: mx.array):
+        x = upsample_nearest(x, (2, 2))
+        x = self.conv(x)
+        return x
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        resolution: int,
+        in_channels: int,
+        ch: int,
+        ch_mult: list[int],
+        num_res_blocks: int,
+        z_channels: int,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        # downsampling
+        self.conv_in = nn.Conv2d(
+            in_channels, self.ch, kernel_size=3, stride=1, padding=1
+        )
+
+        curr_res = resolution
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = []
+        block_in = self.ch
+        for i_level in range(self.num_resolutions):
+            block = []
+            attn = []  # TODO: Remove the attn, nobody appends anything to it
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out
+            down = {}
+            down["block"] = block
+            down["attn"] = attn
+            if i_level != self.num_resolutions - 1:
+                down["downsample"] = Downsample(block_in)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = {}
+        self.mid["block_1"] = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.mid["attn_1"] = AttnBlock(block_in)
+        self.mid["block_2"] = ResnetBlock(in_channels=block_in, out_channels=block_in)
+
+        # end
+        self.norm_out = nn.GroupNorm(
+            num_groups=32, dims=block_in, eps=1e-6, affine=True, pytorch_compatible=True
+        )
+        self.conv_out = nn.Conv2d(
+            block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1
+        )
+
+    def __call__(self, x: mx.array):
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level]["block"][i_block](hs[-1])
+
+                # TODO: Remove the attn
+                if len(self.down[i_level]["attn"]) > 0:
+                    h = self.down[i_level]["attn"][i_block](h)
+
+                hs.append(h)
+
+            if i_level != self.num_resolutions - 1:
+                hs.append(self.down[i_level]["downsample"](hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid["block_1"](h)
+        h = self.mid["attn_1"](h)
+        h = self.mid["block_2"](h)
+
+        # end
+        h = self.norm_out(h)
+        h = nn.silu(h)
+        h = self.conv_out(h)
+
+        return h
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        ch: int,
+        out_ch: int,
+        ch_mult: list[int],
+        num_res_blocks: int,
+        in_channels: int,
+        resolution: int,
+        z_channels: int,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.ffactor = 2 ** (self.num_resolutions - 1)
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        block_in = ch * ch_mult[self.num_resolutions - 1]
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.z_shape = (1, z_channels, curr_res, curr_res)
+
+        # z to block_in
+        self.conv_in = nn.Conv2d(
+            z_channels, block_in, kernel_size=3, stride=1, padding=1
+        )
+
+        # middle
+        self.mid = {}
+        self.mid["block_1"] = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.mid["attn_1"] = AttnBlock(block_in)
+        self.mid["block_2"] = ResnetBlock(in_channels=block_in, out_channels=block_in)
+
+        # upsampling
+        self.up = []
+        for i_level in reversed(range(self.num_resolutions)):
+            block = []
+            attn = []  # TODO: Remove the attn, nobody appends anything to it
+
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks + 1):
+                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out
+            up = {}
+            up["block"] = block
+            up["attn"] = attn
+            if i_level != 0:
+                up["upsample"] = Upsample(block_in)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = nn.GroupNorm(
+            num_groups=32, dims=block_in, eps=1e-6, affine=True, pytorch_compatible=True
+        )
+        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
+
+    def __call__(self, z: mx.array):
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        h = self.mid["block_1"](h)
+        h = self.mid["attn_1"](h)
+        h = self.mid["block_2"](h)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level]["block"][i_block](h)
+
+                # TODO: Remove the attn
+                if len(self.up[i_level]["attn"]) > 0:
+                    h = self.up[i_level]["attn"][i_block](h)
+
+            if i_level != 0:
+                h = self.up[i_level]["upsample"](h)
+
+        # end
+        h = self.norm_out(h)
+        h = nn.silu(h)
+        h = self.conv_out(h)
+
+        return h
+
+
+class DiagonalGaussian(nn.Module):
+    def __call__(self, z: mx.array):
+        mean, logvar = mx.split(z, 2, axis=-1)
+        if self.training:
+            std = mx.exp(0.5 * logvar)
+            eps = mx.random.normal(shape=z.shape, dtype=z.dtype)
+            return mean + std * eps
+        else:
+            return mean
+
+
+class AutoEncoder(nn.Module):
+    def __init__(self, params: AutoEncoderParams):
+        super().__init__()
+        self.encoder = Encoder(
+            resolution=params.resolution,
+            in_channels=params.in_channels,
+            ch=params.ch,
+            ch_mult=params.ch_mult,
+            num_res_blocks=params.num_res_blocks,
+            z_channels=params.z_channels,
+        )
+        self.decoder = Decoder(
+            resolution=params.resolution,
+            in_channels=params.in_channels,
+            ch=params.ch,
+            out_ch=params.out_ch,
+            ch_mult=params.ch_mult,
+            num_res_blocks=params.num_res_blocks,
+            z_channels=params.z_channels,
+        )
+        self.reg = DiagonalGaussian()
+
+        self.scale_factor = params.scale_factor
+        self.shift_factor = params.shift_factor
+
+    def sanitize(self, weights):
+        new_weights = {}
+        for k, w in weights.items():
+            if w.ndim == 4:
+                w = w.transpose(0, 2, 3, 1)
+                w = w.reshape(-1).reshape(w.shape)
+                if w.shape[1:3] == (1, 1):
+                    w = w.squeeze((1, 2))
+            new_weights[k] = w
+        return new_weights
+
+    def encode(self, x: mx.array):
+        z = self.reg(self.encoder(x))
+        z = self.scale_factor * (z - self.shift_factor)
+        return z
+
+    def decode(self, z: mx.array):
+        z = z / self.scale_factor + self.shift_factor
+        return self.decoder(z)
+
+    def __call__(self, x: mx.array):
+        return self.decode(self.encode(x))

flux/flux/clip.py

@@ -0,0 +1,154 @@
+# Copyright © 2024 Apple Inc.
+
+from dataclasses import dataclass
+from typing import List, Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+
+_ACTIVATIONS = {"quick_gelu": nn.gelu_fast_approx, "gelu": nn.gelu}
+
+
+@dataclass
+class CLIPTextModelConfig:
+    num_layers: int = 23
+    model_dims: int = 1024
+    num_heads: int = 16
+    max_length: int = 77
+    vocab_size: int = 49408
+    hidden_act: str = "quick_gelu"
+
+    @classmethod
+    def from_dict(cls, config):
+        return cls(
+            num_layers=config["num_hidden_layers"],
+            model_dims=config["hidden_size"],
+            num_heads=config["num_attention_heads"],
+            max_length=config["max_position_embeddings"],
+            vocab_size=config["vocab_size"],
+            hidden_act=config["hidden_act"],
+        )
+
+
+@dataclass
+class CLIPOutput:
+    # The last_hidden_state indexed at the EOS token and possibly projected if
+    # the model has a projection layer
+    pooled_output: Optional[mx.array] = None
+
+    # The full sequence output of the transformer after the final layernorm
+    last_hidden_state: Optional[mx.array] = None
+
+    # A list of hidden states corresponding to the outputs of the transformer layers
+    hidden_states: Optional[List[mx.array]] = None
+
+
+class CLIPEncoderLayer(nn.Module):
+    """The transformer encoder layer from CLIP."""
+
+    def __init__(self, model_dims: int, num_heads: int, activation: str):
+        super().__init__()
+
+        self.layer_norm1 = nn.LayerNorm(model_dims)
+        self.layer_norm2 = nn.LayerNorm(model_dims)
+
+        self.attention = nn.MultiHeadAttention(model_dims, num_heads, bias=True)
+
+        self.linear1 = nn.Linear(model_dims, 4 * model_dims)
+        self.linear2 = nn.Linear(4 * model_dims, model_dims)
+
+        self.act = _ACTIVATIONS[activation]
+
+    def __call__(self, x, attn_mask=None):
+        y = self.layer_norm1(x)
+        y = self.attention(y, y, y, attn_mask)
+        x = y + x
+
+        y = self.layer_norm2(x)
+        y = self.linear1(y)
+        y = self.act(y)
+        y = self.linear2(y)
+        x = y + x
+
+        return x
+
+
+class CLIPTextModel(nn.Module):
+    """Implements the text encoder transformer from CLIP."""
+
+    def __init__(self, config: CLIPTextModelConfig):
+        super().__init__()
+
+        self.token_embedding = nn.Embedding(config.vocab_size, config.model_dims)
+        self.position_embedding = nn.Embedding(config.max_length, config.model_dims)
+        self.layers = [
+            CLIPEncoderLayer(config.model_dims, config.num_heads, config.hidden_act)
+            for i in range(config.num_layers)
+        ]
+        self.final_layer_norm = nn.LayerNorm(config.model_dims)
+
+    def _get_mask(self, N, dtype):
+        indices = mx.arange(N)
+        mask = indices[:, None] < indices[None]
+        mask = mask.astype(dtype) * (-6e4 if dtype == mx.float16 else -1e9)
+        return mask
+
+    def sanitize(self, weights):
+        new_weights = {}
+        for key, w in weights.items():
+            # Remove prefixes
+            if key.startswith("text_model."):
+                key = key[11:]
+            if key.startswith("embeddings."):
+                key = key[11:]
+            if key.startswith("encoder."):
+                key = key[8:]
+
+            # Map attention layers
+            if "self_attn." in key:
+                key = key.replace("self_attn.", "attention.")
+            if "q_proj." in key:
+                key = key.replace("q_proj.", "query_proj.")
+            if "k_proj." in key:
+                key = key.replace("k_proj.", "key_proj.")
+            if "v_proj." in key:
+                key = key.replace("v_proj.", "value_proj.")
+
+            # Map ffn layers
+            if "mlp.fc1" in key:
+                key = key.replace("mlp.fc1", "linear1")
+            if "mlp.fc2" in key:
+                key = key.replace("mlp.fc2", "linear2")
+
+            new_weights[key] = w
+
+        return new_weights
+
+    def __call__(self, x):
+        # Extract some shapes
+        B, N = x.shape
+        eos_tokens = x.argmax(-1)
+
+        # Compute the embeddings
+        x = self.token_embedding(x)
+        x = x + self.position_embedding.weight[:N]
+
+        # Compute the features from the transformer
+        mask = self._get_mask(N, x.dtype)
+        hidden_states = []
+        for l in self.layers:
+            x = l(x, mask)
+            hidden_states.append(x)
+
+        # Apply the final layernorm and return
+        x = self.final_layer_norm(x)
+        last_hidden_state = x
+
+        # Select the EOS token
+        pooled_output = x[mx.arange(len(x)), eos_tokens]
+
+        return CLIPOutput(
+            pooled_output=pooled_output,
+            last_hidden_state=last_hidden_state,
+            hidden_states=hidden_states,
+        )

flux/flux/datasets.py

@@ -0,0 +1,75 @@
+import json
+from pathlib import Path
+
+from PIL import Image
+
+
+class Dataset:
+    def __getitem__(self, index: int):
+        raise NotImplementedError()
+
+    def __len__(self):
+        raise NotImplementedError()
+
+
+class LocalDataset(Dataset):
+    prompt_key = "prompt"
+
+    def __init__(self, dataset: str, data_file):
+        self.dataset_base = Path(dataset)
+        with open(data_file, "r") as fid:
+            self._data = [json.loads(l) for l in fid]
+
+    def __len__(self):
+        return len(self._data)
+
+    def __getitem__(self, index: int):
+        item = self._data[index]
+        image = Image.open(self.dataset_base / item["image"])
+        return image, item[self.prompt_key]
+
+
+class LegacyDataset(LocalDataset):
+    prompt_key = "text"
+
+    def __init__(self, dataset: str):
+        self.dataset_base = Path(dataset)
+        with open(self.dataset_base / "index.json") as f:
+            self._data = json.load(f)["data"]
+
+
+class HuggingFaceDataset(Dataset):
+
+    def __init__(self, dataset: str):
+        from datasets import load_dataset as hf_load_dataset
+
+        self._df = hf_load_dataset(dataset)["train"]
+
+    def __len__(self):
+        return len(self._df)
+
+    def __getitem__(self, index: int):
+        item = self._df[index]
+        return item["image"], item["prompt"]
+
+
+def load_dataset(dataset: str):
+    dataset_base = Path(dataset)
+    data_file = dataset_base / "train.jsonl"
+    legacy_file = dataset_base / "index.json"
+
+    if data_file.exists():
+        print(f"Load the local dataset {data_file} .", flush=True)
+        dataset = LocalDataset(dataset, data_file)
+    elif legacy_file.exists():
+        print(f"Load the local dataset {legacy_file} .")
+        print()
+        print("     WARNING: 'index.json' is deprecated in favor of 'train.jsonl'.")
+        print("              See the README for details.")
+        print(flush=True)
+        dataset = LegacyDataset(dataset)
+    else:
+        print(f"Load the Hugging Face dataset {dataset} .", flush=True)
+        dataset = HuggingFaceDataset(dataset)
+
+    return dataset

flux/flux/flux.py

@@ -0,0 +1,246 @@
+# Copyright © 2024 Apple Inc.
+
+from typing import Tuple
+
+import mlx.core as mx
+import mlx.nn as nn
+from mlx.utils import tree_unflatten
+from tqdm import tqdm
+
+from .lora import LoRALinear
+from .sampler import FluxSampler
+from .utils import (
+    load_ae,
+    load_clip,
+    load_clip_tokenizer,
+    load_flow_model,
+    load_t5,
+    load_t5_tokenizer,
+)
+
+
+class FluxPipeline:
+    def __init__(self, name: str, t5_padding: bool = True):
+        self.dtype = mx.bfloat16
+        self.name = name
+        self.t5_padding = t5_padding
+
+        self.ae = load_ae(name)
+        self.flow = load_flow_model(name)
+        self.clip = load_clip(name)
+        self.clip_tokenizer = load_clip_tokenizer(name)
+        self.t5 = load_t5(name)
+        self.t5_tokenizer = load_t5_tokenizer(name)
+        self.sampler = FluxSampler(name)
+
+    def ensure_models_are_loaded(self):
+        mx.eval(
+            self.ae.parameters(),
+            self.flow.parameters(),
+            self.clip.parameters(),
+            self.t5.parameters(),
+        )
+
+    def reload_text_encoders(self):
+        self.t5 = load_t5(self.name)
+        self.clip = load_clip(self.name)
+
+    def tokenize(self, text):
+        t5_tokens = self.t5_tokenizer.encode(text, pad=self.t5_padding)
+        clip_tokens = self.clip_tokenizer.encode(text)
+        return t5_tokens, clip_tokens
+
+    def _prepare_latent_images(self, x):
+        b, h, w, c = x.shape
+
+        # Pack the latent image to 2x2 patches
+        x = x.reshape(b, h // 2, 2, w // 2, 2, c)
+        x = x.transpose(0, 1, 3, 5, 2, 4).reshape(b, h * w // 4, c * 4)
+
+        # Create positions ids used to positionally encode each patch. Due to
+        # the way RoPE works, this results in an interesting positional
+        # encoding where parts of the feature are holding different positional
+        # information. Namely, the first part holds information independent of
+        # the spatial position (hence 0s), the 2nd part holds vertical spatial
+        # information and the last one horizontal.
+        i = mx.zeros((h // 2, w // 2), dtype=mx.int32)
+        j, k = mx.meshgrid(mx.arange(h // 2), mx.arange(w // 2), indexing="ij")
+        x_ids = mx.stack([i, j, k], axis=-1)
+        x_ids = mx.repeat(x_ids.reshape(1, h * w // 4, 3), b, 0)
+
+        return x, x_ids
+
+    def _prepare_conditioning(self, n_images, t5_tokens, clip_tokens):
+        # Prepare the text features
+        txt = self.t5(t5_tokens)
+        if len(txt) == 1 and n_images > 1:
+            txt = mx.broadcast_to(txt, (n_images, *txt.shape[1:]))
+        txt_ids = mx.zeros((n_images, txt.shape[1], 3), dtype=mx.int32)
+
+        # Prepare the clip text features
+        vec = self.clip(clip_tokens).pooled_output
+        if len(vec) == 1 and n_images > 1:
+            vec = mx.broadcast_to(vec, (n_images, *vec.shape[1:]))
+
+        return txt, txt_ids, vec
+
+    def _denoising_loop(
+        self,
+        x_t,
+        x_ids,
+        txt,
+        txt_ids,
+        vec,
+        num_steps: int = 35,
+        guidance: float = 4.0,
+        start: float = 1,
+        stop: float = 0,
+    ):
+        B = len(x_t)
+
+        def scalar(x):
+            return mx.full((B,), x, dtype=self.dtype)
+
+        guidance = scalar(guidance)
+        timesteps = self.sampler.timesteps(
+            num_steps,
+            x_t.shape[1],
+            start=start,
+            stop=stop,
+        )
+        for i in range(num_steps):
+            t = timesteps[i]
+            t_prev = timesteps[i + 1]
+
+            pred = self.flow(
+                img=x_t,
+                img_ids=x_ids,
+                txt=txt,
+                txt_ids=txt_ids,
+                y=vec,
+                timesteps=scalar(t),
+                guidance=guidance,
+            )
+            x_t = self.sampler.step(pred, x_t, t, t_prev)
+
+            yield x_t
+
+    def generate_latents(
+        self,
+        text: str,
+        n_images: int = 1,
+        num_steps: int = 35,
+        guidance: float = 4.0,
+        latent_size: Tuple[int, int] = (64, 64),
+        seed=None,
+    ):
+        # Set the PRNG state
+        if seed is not None:
+            mx.random.seed(seed)
+
+        # Create the latent variables
+        x_T = self.sampler.sample_prior((n_images, *latent_size, 16), dtype=self.dtype)
+        x_T, x_ids = self._prepare_latent_images(x_T)
+
+        # Get the conditioning
+        t5_tokens, clip_tokens = self.tokenize(text)
+        txt, txt_ids, vec = self._prepare_conditioning(n_images, t5_tokens, clip_tokens)
+
+        # Yield the conditioning for controlled evaluation by the caller
+        yield (x_T, x_ids, txt, txt_ids, vec)
+
+        # Yield the latent sequences from the denoising loop
+        yield from self._denoising_loop(
+            x_T, x_ids, txt, txt_ids, vec, num_steps=num_steps, guidance=guidance
+        )
+
+    def decode(self, x, latent_size: Tuple[int, int] = (64, 64)):
+        h, w = latent_size
+        x = x.reshape(len(x), h // 2, w // 2, -1, 2, 2)
+        x = x.transpose(0, 1, 4, 2, 5, 3).reshape(len(x), h, w, -1)
+        x = self.ae.decode(x)
+        return mx.clip(x + 1, 0, 2) * 0.5
+
+    def generate_images(
+        self,
+        text: str,
+        n_images: int = 1,
+        num_steps: int = 35,
+        guidance: float = 4.0,
+        latent_size: Tuple[int, int] = (64, 64),
+        seed=None,
+        reload_text_encoders: bool = True,
+        progress: bool = True,
+    ):
+        latents = self.generate_latents(
+            text, n_images, num_steps, guidance, latent_size, seed
+        )
+        mx.eval(next(latents))
+
+        if reload_text_encoders:
+            self.reload_text_encoders()
+
+        for x_t in tqdm(latents, total=num_steps, disable=not progress, leave=True):
+            mx.eval(x_t)
+
+        images = []
+        for i in tqdm(range(len(x_t)), disable=not progress, desc="generate images"):
+            images.append(self.decode(x_t[i : i + 1]))
+            mx.eval(images[-1])
+        images = mx.concatenate(images, axis=0)
+        mx.eval(images)
+
+        return images
+
+    def training_loss(
+        self,
+        x_0: mx.array,
+        t5_features: mx.array,
+        clip_features: mx.array,
+        guidance: mx.array,
+    ):
+        # Get the text conditioning
+        txt = t5_features
+        txt_ids = mx.zeros(txt.shape[:-1] + (3,), dtype=mx.int32)
+        vec = clip_features
+
+        # Prepare the latent input
+        x_0, x_ids = self._prepare_latent_images(x_0)
+
+        # Forward process
+        t = self.sampler.random_timesteps(*x_0.shape[:2], dtype=self.dtype)
+        eps = mx.random.normal(x_0.shape, dtype=self.dtype)
+        x_t = self.sampler.add_noise(x_0, t, noise=eps)
+        x_t = mx.stop_gradient(x_t)
+
+        # Do the denoising
+        pred = self.flow(
+            img=x_t,
+            img_ids=x_ids,
+            txt=txt,
+            txt_ids=txt_ids,
+            y=vec,
+            timesteps=t,
+            guidance=guidance,
+        )
+
+        return (pred + x_0 - eps).square().mean()
+
+    def linear_to_lora_layers(self, rank: int = 8, num_blocks: int = -1):
+        """Swap the linear layers in the transformer blocks with LoRA layers."""
+        all_blocks = self.flow.double_blocks + self.flow.single_blocks
+        all_blocks.reverse()
+        num_blocks = num_blocks if num_blocks > 0 else len(all_blocks)
+        for i, block in zip(range(num_blocks), all_blocks):
+            loras = []
+            for name, module in block.named_modules():
+                if isinstance(module, nn.Linear):
+                    loras.append((name, LoRALinear.from_base(module, r=rank)))
+            block.update_modules(tree_unflatten(loras))
+
+    def fuse_lora_layers(self):
+        fused_layers = []
+        for name, module in self.flow.named_modules():
+            if isinstance(module, LoRALinear):
+                fused_layers.append((name, module.fuse()))
+        self.flow.update_modules(tree_unflatten(fused_layers))

flux/flux/layers.py

@@ -0,0 +1,321 @@
+# Copyright © 2024 Apple Inc.
+
+import math
+from dataclasses import dataclass
+from functools import partial
+from typing import List, Optional, Tuple
+
+import mlx.core as mx
+import mlx.nn as nn
+
+
+def _rope(pos: mx.array, dim: int, theta: float):
+    scale = mx.arange(0, dim, 2, dtype=mx.float32) / dim
+    omega = 1.0 / (theta**scale)
+    x = pos[..., None] * omega
+    cosx = mx.cos(x)
+    sinx = mx.sin(x)
+    pe = mx.stack([cosx, -sinx, sinx, cosx], axis=-1)
+    pe = pe.reshape(*pe.shape[:-1], 2, 2)
+
+    return pe
+
+
+@partial(mx.compile, shapeless=True)
+def _ab_plus_cd(a, b, c, d):
+    return a * b + c * d
+
+
+def _apply_rope(x, pe):
+    s = x.shape
+    x = x.reshape(*s[:-1], -1, 1, 2)
+    x = _ab_plus_cd(x[..., 0], pe[..., 0], x[..., 1], pe[..., 1])
+    return x.reshape(s)
+
+
+def _attention(q: mx.array, k: mx.array, v: mx.array, pe: mx.array):
+    B, H, L, D = q.shape
+
+    q = _apply_rope(q, pe)
+    k = _apply_rope(k, pe)
+    x = mx.fast.scaled_dot_product_attention(q, k, v, scale=D ** (-0.5))
+
+    return x.transpose(0, 2, 1, 3).reshape(B, L, -1)
+
+
+def timestep_embedding(
+    t: mx.array, dim: int, max_period: int = 10000, time_factor: float = 1000.0
+):
+    half = dim // 2
+    freqs = mx.arange(0, half, dtype=mx.float32) / half
+    freqs = freqs * (-math.log(max_period))
+    freqs = mx.exp(freqs)
+
+    x = (time_factor * t)[:, None] * freqs[None]
+    x = mx.concatenate([mx.cos(x), mx.sin(x)], axis=-1)
+
+    return x.astype(t.dtype)
+
+
+class EmbedND(nn.Module):
+    def __init__(self, dim: int, theta: int, axes_dim: List[int]):
+        super().__init__()
+
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def __call__(self, ids: mx.array):
+        n_axes = ids.shape[-1]
+        pe = mx.concatenate(
+            [_rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
+            axis=-3,
+        )
+
+        return pe[:, None]
+
+
+class MLPEmbedder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int):
+        super().__init__()
+        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
+        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+    def __call__(self, x: mx.array) -> mx.array:
+        return self.out_layer(nn.silu(self.in_layer(x)))
+
+
+class QKNorm(nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.query_norm = nn.RMSNorm(dim)
+        self.key_norm = nn.RMSNorm(dim)
+
+    def __call__(self, q: mx.array, k: mx.array) -> tuple[mx.array, mx.array]:
+        return self.query_norm(q), self.key_norm(k)
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.norm = QKNorm(head_dim)
+        self.proj = nn.Linear(dim, dim)
+
+    def __call__(self, x: mx.array, pe: mx.array) -> mx.array:
+        H = self.num_heads
+        B, L, _ = x.shape
+        qkv = self.qkv(x)
+        q, k, v = mx.split(qkv, 3, axis=-1)
+        q = q.reshape(B, L, H, -1).transpose(0, 2, 1, 3)
+        k = k.reshape(B, L, H, -1).transpose(0, 2, 1, 3)
+        v = v.reshape(B, L, H, -1).transpose(0, 2, 1, 3)
+        q, k = self.norm(q, k)
+        x = _attention(q, k, v, pe)
+        x = self.proj(x)
+        return x
+
+
+@dataclass
+class ModulationOut:
+    shift: mx.array
+    scale: mx.array
+    gate: mx.array
+
+
+class Modulation(nn.Module):
+    def __init__(self, dim: int, double: bool):
+        super().__init__()
+        self.is_double = double
+        self.multiplier = 6 if double else 3
+        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
+
+    def __call__(self, x: mx.array) -> Tuple[ModulationOut, Optional[ModulationOut]]:
+        x = self.lin(nn.silu(x))
+        xs = mx.split(x[:, None, :], self.multiplier, axis=-1)
+
+        mod1 = ModulationOut(*xs[:3])
+        mod2 = ModulationOut(*xs[3:]) if self.is_double else None
+
+        return mod1, mod2
+
+
+class DoubleStreamBlock(nn.Module):
+    def __init__(
+        self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False
+    ):
+        super().__init__()
+
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.img_mod = Modulation(hidden_size, double=True)
+        self.img_norm1 = nn.LayerNorm(hidden_size, affine=False, eps=1e-6)
+        self.img_attn = SelfAttention(
+            dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias
+        )
+
+        self.img_norm2 = nn.LayerNorm(hidden_size, affine=False, eps=1e-6)
+        self.img_mlp = nn.Sequential(
+            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            nn.GELU(approx="tanh"),
+            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+        )
+
+        self.txt_mod = Modulation(hidden_size, double=True)
+        self.txt_norm1 = nn.LayerNorm(hidden_size, affine=False, eps=1e-6)
+        self.txt_attn = SelfAttention(
+            dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias
+        )
+
+        self.txt_norm2 = nn.LayerNorm(hidden_size, affine=False, eps=1e-6)
+        self.txt_mlp = nn.Sequential(
+            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            nn.GELU(approx="tanh"),
+            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+        )
+
+        self.sharding_group = None
+
+    def __call__(
+        self, img: mx.array, txt: mx.array, vec: mx.array, pe: mx.array
+    ) -> Tuple[mx.array, mx.array]:
+        B, L, _ = img.shape
+        _, S, _ = txt.shape
+        H = self.num_heads
+
+        img_mod1, img_mod2 = self.img_mod(vec)
+        txt_mod1, txt_mod2 = self.txt_mod(vec)
+
+        # prepare image for attention
+        img_modulated = self.img_norm1(img)
+        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+        img_qkv = self.img_attn.qkv(img_modulated)
+        img_q, img_k, img_v = mx.split(img_qkv, 3, axis=-1)
+        img_q = img_q.reshape(B, L, H, -1).transpose(0, 2, 1, 3)
+        img_k = img_k.reshape(B, L, H, -1).transpose(0, 2, 1, 3)
+        img_v = img_v.reshape(B, L, H, -1).transpose(0, 2, 1, 3)
+        img_q, img_k = self.img_attn.norm(img_q, img_k)
+
+        # prepare txt for attention
+        txt_modulated = self.txt_norm1(txt)
+        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+        txt_qkv = self.txt_attn.qkv(txt_modulated)
+        txt_q, txt_k, txt_v = mx.split(txt_qkv, 3, axis=-1)
+        txt_q = txt_q.reshape(B, S, H, -1).transpose(0, 2, 1, 3)
+        txt_k = txt_k.reshape(B, S, H, -1).transpose(0, 2, 1, 3)
+        txt_v = txt_v.reshape(B, S, H, -1).transpose(0, 2, 1, 3)
+        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k)
+
+        # run actual attention
+        q = mx.concatenate([txt_q, img_q], axis=2)
+        k = mx.concatenate([txt_k, img_k], axis=2)
+        v = mx.concatenate([txt_v, img_v], axis=2)
+
+        attn = _attention(q, k, v, pe)
+        txt_attn, img_attn = mx.split(attn, [S], axis=1)
+
+        # Project - cat - average - split
+        txt_attn = self.txt_attn.proj(txt_attn)
+        img_attn = self.img_attn.proj(img_attn)
+        if self.sharding_group is not None:
+            attn = mx.concatenate([txt_attn, img_attn], axis=1)
+            attn = mx.distributed.all_sum(attn, group=self.sharding_group)
+            txt_attn, img_attn = mx.split(attn, [S], axis=1)
+
+        # calculate the img bloks
+        img = img + img_mod1.gate * img_attn
+        img_mlp = self.img_mlp(
+            (1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift
+        )
+
+        # calculate the txt bloks
+        txt = txt + txt_mod1.gate * txt_attn
+        txt_mlp = self.txt_mlp(
+            (1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift
+        )
+
+        if self.sharding_group is not None:
+            txt_img = mx.concatenate([txt_mlp, img_mlp], axis=1)
+            txt_img = mx.distributed.all_sum(txt_img, group=self.sharding_group)
+            txt_mlp, img_mlp = mx.split(txt_img, [S], axis=1)
+
+        # finalize the img/txt blocks
+        img = img + img_mod2.gate * img_mlp
+        txt = txt + txt_mod2.gate * txt_mlp
+
+        return img, txt
+
+
+class SingleStreamBlock(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qk_scale: Optional[float] = None,
+    ):
+        super().__init__()
+        self.hidden_dim = hidden_size
+        self.num_heads = num_heads
+        head_dim = hidden_size // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        # qkv and mlp_in
+        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
+        # proj and mlp_out
+        self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
+
+        self.norm = QKNorm(head_dim)
+
+        self.hidden_size = hidden_size
+        self.pre_norm = nn.LayerNorm(hidden_size, affine=False, eps=1e-6)
+
+        self.mlp_act = nn.GELU(approx="tanh")
+        self.modulation = Modulation(hidden_size, double=False)
+
+    def __call__(self, x: mx.array, vec: mx.array, pe: mx.array):
+        B, L, _ = x.shape
+        H = self.num_heads
+
+        mod, _ = self.modulation(vec)
+        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+
+        q, k, v, mlp = mx.split(
+            self.linear1(x_mod),
+            [self.hidden_size, 2 * self.hidden_size, 3 * self.hidden_size],
+            axis=-1,
+        )
+        q = q.reshape(B, L, H, -1).transpose(0, 2, 1, 3)
+        k = k.reshape(B, L, H, -1).transpose(0, 2, 1, 3)
+        v = v.reshape(B, L, H, -1).transpose(0, 2, 1, 3)
+        q, k = self.norm(q, k)
+
+        # compute attention
+        y = _attention(q, k, v, pe)
+
+        # compute activation in mlp stream, cat again and run second linear layer
+        y = self.linear2(mx.concatenate([y, self.mlp_act(mlp)], axis=2))
+        return x + mod.gate * y
+
+
+class LastLayer(nn.Module):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, affine=False, eps=1e-6)
+        self.linear = nn.Linear(
+            hidden_size, patch_size * patch_size * out_channels, bias=True
+        )
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)
+        )
+
+    def __call__(self, x: mx.array, vec: mx.array):
+        shift, scale = mx.split(self.adaLN_modulation(vec), 2, axis=1)
+        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = self.linear(x)
+        return x

flux/flux/lora.py

@@ -6,47 +6,40 @@ import mlx.core as mx
 import mlx.nn as nn
 
 
-class DoRALinear(nn.Module):
+class LoRALinear(nn.Module):
     @staticmethod
-    def from_linear(
+    def from_base(
         linear: nn.Linear,
         r: int = 8,
         dropout: float = 0.0,
-        scale: float = 20.0,
+        scale: float = 1.0,
     ):
-        # TODO support quantized weights in DoRALinear
         output_dims, input_dims = linear.weight.shape
-        if isinstance(linear, nn.QuantizedLinear):
-            raise ValueError("DoRALinear does not yet support quantization.")
-        dora_lin = DoRALinear(
+        lora_lin = LoRALinear(
             input_dims=input_dims,
             output_dims=output_dims,
             r=r,
             dropout=dropout,
             scale=scale,
         )
-        dora_lin.linear = linear
-        return dora_lin
+        lora_lin.linear = linear
+        return lora_lin
 
-    def to_linear(self, de_quantize: bool = False):
+    def fuse(self):
         linear = self.linear
         bias = "bias" in linear
         weight = linear.weight
-
-        # Use the same type as the linear weight if not quantized
         dtype = weight.dtype
 
         output_dims, input_dims = weight.shape
         fused_linear = nn.Linear(input_dims, output_dims, bias=bias)
 
-        lora_b = (self.scale * self.lora_b.T).astype(dtype)
-        lora_a = self.lora_a.T.astype(dtype)
-        weight = weight + lora_b @ lora_a
-        norm_scale = self.m / mx.linalg.norm(weight, axis=1)
-        fused_linear.weight = norm_scale[:, None] * weight
-
+        lora_b = self.scale * self.lora_b.T
+        lora_a = self.lora_a.T
+        fused_linear.weight = weight + (lora_b @ lora_a).astype(dtype)
         if bias:
             fused_linear.bias = linear.bias
+
         return fused_linear
 
     def __init__(
@@ -55,13 +48,14 @@ class DoRALinear(nn.Module):
         output_dims: int,
         r: int = 8,
         dropout: float = 0.0,
-        scale: float = 20.0,
+        scale: float = 1.0,
         bias: bool = False,
     ):
         super().__init__()
 
         # Regular linear layer weights
         self.linear = nn.Linear(input_dims, output_dims, bias=bias)
+
         self.dropout = nn.Dropout(p=dropout)
 
         # Scale for low-rank update
@@ -75,21 +69,8 @@ class DoRALinear(nn.Module):
             shape=(input_dims, r),
         )
         self.lora_b = mx.zeros(shape=(r, output_dims))
-        self.m = mx.linalg.norm(self.linear.weight, axis=1)
 
     def __call__(self, x):
-        # Regular LoRA (without a bias)
-        y = x @ self.linear.weight.T
+        y = self.linear(x)
         z = (self.dropout(x) @ self.lora_a) @ self.lora_b
-        out = y + (self.scale * z).astype(x.dtype)
-
-        # Compute the norm of the adapted weights
-        adapted = self.linear.weight + (self.scale * self.lora_b.T) @ self.lora_a.T
-        denom = mx.stop_gradient(mx.linalg.norm(adapted, axis=1))
-
-        # Remove the norm and scale by the learned magnitude
-        out = (self.m / denom) * out
-
-        if "bias" in self.linear:
-            out = out + self.linear.bias
-        return out
+        return y + (self.scale * z).astype(x.dtype)

flux/flux/model.py

@@ -0,0 +1,178 @@
+# Copyright © 2024 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+from mlx.nn.layers.distributed import shard_inplace, shard_linear
+
+from .layers import (
+    DoubleStreamBlock,
+    EmbedND,
+    LastLayer,
+    MLPEmbedder,
+    SingleStreamBlock,
+    timestep_embedding,
+)
+
+
+@dataclass
+class FluxParams:
+    in_channels: int
+    vec_in_dim: int
+    context_in_dim: int
+    hidden_size: int
+    mlp_ratio: float
+    num_heads: int
+    depth: int
+    depth_single_blocks: int
+    axes_dim: list[int]
+    theta: int
+    qkv_bias: bool
+    guidance_embed: bool
+
+
+class Flux(nn.Module):
+    def __init__(self, params: FluxParams):
+        super().__init__()
+
+        self.params = params
+        self.in_channels = params.in_channels
+        self.out_channels = self.in_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(
+                f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
+            )
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(
+                f"Got {params.axes_dim} but expected positional dim {pe_dim}"
+            )
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.pe_embedder = EmbedND(
+            dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim
+        )
+        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
+        self.guidance_in = (
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
+            if params.guidance_embed
+            else nn.Identity()
+        )
+        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
+
+        self.double_blocks = [
+            DoubleStreamBlock(
+                self.hidden_size,
+                self.num_heads,
+                mlp_ratio=params.mlp_ratio,
+                qkv_bias=params.qkv_bias,
+            )
+            for _ in range(params.depth)
+        ]
+
+        self.single_blocks = [
+            SingleStreamBlock(
+                self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio
+            )
+            for _ in range(params.depth_single_blocks)
+        ]
+
+        self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
+
+    def sanitize(self, weights):
+        new_weights = {}
+        for k, w in weights.items():
+            if k.startswith("model.diffusion_model."):
+                k = k[22:]
+            if k.endswith(".scale"):
+                k = k[:-6] + ".weight"
+            for seq in ["img_mlp", "txt_mlp", "adaLN_modulation"]:
+                if f".{seq}." in k:
+                    k = k.replace(f".{seq}.", f".{seq}.layers.")
+                    break
+            new_weights[k] = w
+        return new_weights
+
+    def shard(self, group: Optional[mx.distributed.Group] = None):
+        group = group or mx.distributed.init()
+        N = group.size()
+        if N == 1:
+            return
+
+        for block in self.double_blocks:
+            block.num_heads //= N
+            block.img_attn.num_heads //= N
+            block.txt_attn.num_heads //= N
+            block.sharding_group = group
+            block.img_attn.qkv = shard_linear(
+                block.img_attn.qkv, "all-to-sharded", segments=3, group=group
+            )
+            block.txt_attn.qkv = shard_linear(
+                block.txt_attn.qkv, "all-to-sharded", segments=3, group=group
+            )
+            shard_inplace(block.img_attn.proj, "sharded-to-all", group=group)
+            shard_inplace(block.txt_attn.proj, "sharded-to-all", group=group)
+            block.img_mlp.layers[0] = shard_linear(
+                block.img_mlp.layers[0], "all-to-sharded", group=group
+            )
+            block.txt_mlp.layers[0] = shard_linear(
+                block.txt_mlp.layers[0], "all-to-sharded", group=group
+            )
+            shard_inplace(block.img_mlp.layers[2], "sharded-to-all", group=group)
+            shard_inplace(block.txt_mlp.layers[2], "sharded-to-all", group=group)
+
+        for block in self.single_blocks:
+            block.num_heads //= N
+            block.hidden_size //= N
+            block.linear1 = shard_linear(
+                block.linear1,
+                "all-to-sharded",
+                segments=[1 / 7, 2 / 7, 3 / 7],
+                group=group,
+            )
+            block.linear2 = shard_linear(
+                block.linear2, "sharded-to-all", segments=[1 / 5], group=group
+            )
+
+    def __call__(
+        self,
+        img: mx.array,
+        img_ids: mx.array,
+        txt: mx.array,
+        txt_ids: mx.array,
+        timesteps: mx.array,
+        y: mx.array,
+        guidance: Optional[mx.array] = None,
+    ) -> mx.array:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        img = self.img_in(img)
+        vec = self.time_in(timestep_embedding(timesteps, 256))
+        if self.params.guidance_embed:
+            if guidance is None:
+                raise ValueError(
+                    "Didn't get guidance strength for guidance distilled model."
+                )
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        ids = mx.concatenate([txt_ids, img_ids], axis=1)
+        pe = self.pe_embedder(ids).astype(img.dtype)
+
+        for block in self.double_blocks:
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+
+        img = mx.concatenate([txt, img], axis=1)
+        for block in self.single_blocks:
+            img = block(img, vec=vec, pe=pe)
+        img = img[:, txt.shape[1] :, ...]
+
+        img = self.final_layer(img, vec)
+
+        return img

flux/flux/sampler.py

@@ -0,0 +1,57 @@
+# Copyright © 2024 Apple Inc.
+
+import math
+from functools import lru_cache
+
+import mlx.core as mx
+
+
+class FluxSampler:
+    def __init__(self, name: str, base_shift: float = 0.5, max_shift: float = 1.15):
+        self._base_shift = base_shift
+        self._max_shift = max_shift
+        self._schnell = "schnell" in name
+
+    def _time_shift(self, x, t):
+        x1, x2 = 256, 4096
+        t1, t2 = self._base_shift, self._max_shift
+        exp_mu = math.exp((x - x1) * (t2 - t1) / (x2 - x1) + t1)
+        t = exp_mu / (exp_mu + (1 / t - 1))
+        return t
+
+    @lru_cache
+    def timesteps(
+        self, num_steps, image_sequence_length, start: float = 1, stop: float = 0
+    ):
+        t = mx.linspace(start, stop, num_steps + 1)
+
+        if not self._schnell:
+            t = self._time_shift(image_sequence_length, t)
+
+        return t.tolist()
+
+    def random_timesteps(self, B, L, dtype=mx.float32, key=None):
+        if self._schnell:
+            # TODO: Should we upweigh 1 and 0.75?
+            t = mx.random.randint(1, 5, shape=(B,), key=key)
+            t = t.astype(dtype) / 4
+        else:
+            t = mx.random.uniform(shape=(B,), dtype=dtype, key=key)
+            t = self._time_shift(L, t)
+
+        return t
+
+    def sample_prior(self, shape, dtype=mx.float32, key=None):
+        return mx.random.normal(shape, dtype=dtype, key=key)
+
+    def add_noise(self, x, t, noise=None, key=None):
+        noise = (
+            noise
+            if noise is not None
+            else mx.random.normal(x.shape, dtype=x.dtype, key=key)
+        )
+        t = t.reshape([-1] + [1] * (x.ndim - 1))
+        return x * (1 - t) + t * noise
+
+    def step(self, pred, x_t, t, t_prev):
+        return x_t + (t_prev - t) * pred

flux/flux/t5.py

@@ -0,0 +1,244 @@
+# Copyright © 2024 Apple Inc.
+
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Tuple
+
+import mlx.core as mx
+import mlx.nn as nn
+
+_SHARED_REPLACEMENT_PATTERNS = [
+    (".block.", ".layers."),
+    (".k.", ".key_proj."),
+    (".o.", ".out_proj."),
+    (".q.", ".query_proj."),
+    (".v.", ".value_proj."),
+    ("shared.", "wte."),
+    ("lm_head.", "lm_head.linear."),
+    (".layer.0.layer_norm.", ".ln1."),
+    (".layer.1.layer_norm.", ".ln2."),
+    (".layer.2.layer_norm.", ".ln3."),
+    (".final_layer_norm.", ".ln."),
+    (
+        "layers.0.layer.0.SelfAttention.relative_attention_bias.",
+        "relative_attention_bias.embeddings.",
+    ),
+]
+
+_ENCODER_REPLACEMENT_PATTERNS = [
+    (".layer.0.SelfAttention.", ".attention."),
+    (".layer.1.DenseReluDense.", ".dense."),
+]
+
+
+@dataclass
+class T5Config:
+    vocab_size: int
+    num_layers: int
+    num_heads: int
+    relative_attention_num_buckets: int
+    d_kv: int
+    d_model: int
+    feed_forward_proj: str
+    tie_word_embeddings: bool
+
+    d_ff: Optional[int] = None
+    num_decoder_layers: Optional[int] = None
+    relative_attention_max_distance: int = 128
+    layer_norm_epsilon: float = 1e-6
+
+    @classmethod
+    def from_dict(cls, config):
+        return cls(
+            vocab_size=config["vocab_size"],
+            num_layers=config["num_layers"],
+            num_heads=config["num_heads"],
+            relative_attention_num_buckets=config["relative_attention_num_buckets"],
+            d_kv=config["d_kv"],
+            d_model=config["d_model"],
+            feed_forward_proj=config["feed_forward_proj"],
+            tie_word_embeddings=config["tie_word_embeddings"],
+            d_ff=config.get("d_ff", 4 * config["d_model"]),
+            num_decoder_layers=config.get("num_decoder_layers", config["num_layers"]),
+            relative_attention_max_distance=config.get(
+                "relative_attention_max_distance", 128
+            ),
+            layer_norm_epsilon=config.get("layer_norm_epsilon", 1e-6),
+        )
+
+
+class RelativePositionBias(nn.Module):
+    def __init__(self, config: T5Config, bidirectional: bool):
+        self.bidirectional = bidirectional
+        self.num_buckets = config.relative_attention_num_buckets
+        self.max_distance = config.relative_attention_max_distance
+        self.n_heads = config.num_heads
+        self.embeddings = nn.Embedding(self.num_buckets, self.n_heads)
+
+    @staticmethod
+    def _relative_position_bucket(rpos, bidirectional, num_buckets, max_distance):
+        num_buckets = num_buckets // 2 if bidirectional else num_buckets
+        max_exact = num_buckets // 2
+
+        abspos = rpos.abs()
+        is_small = abspos < max_exact
+
+        scale = (num_buckets - max_exact) / math.log(max_distance / max_exact)
+        buckets_large = (mx.log(abspos / max_exact) * scale).astype(mx.int16)
+        buckets_large = mx.minimum(max_exact + buckets_large, num_buckets - 1)
+
+        buckets = mx.where(is_small, abspos, buckets_large)
+        if bidirectional:
+            buckets = buckets + (rpos > 0) * num_buckets
+        else:
+            buckets = buckets * (rpos < 0)
+
+        return buckets
+
+    def __call__(self, query_length: int, key_length: int, offset: int = 0):
+        """Compute binned relative position bias"""
+        context_position = mx.arange(offset, query_length)[:, None]
+        memory_position = mx.arange(key_length)[None, :]
+
+        # shape (query_length, key_length)
+        relative_position = memory_position - context_position
+        relative_position_bucket = self._relative_position_bucket(
+            relative_position,
+            bidirectional=self.bidirectional,
+            num_buckets=self.num_buckets,
+            max_distance=self.max_distance,
+        )
+
+        # shape (query_length, key_length, num_heads)
+        values = self.embeddings(relative_position_bucket)
+
+        # shape (num_heads, query_length, key_length)
+        return values.transpose(2, 0, 1)
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        inner_dim = config.d_kv * config.num_heads
+        self.num_heads = config.num_heads
+        self.query_proj = nn.Linear(config.d_model, inner_dim, bias=False)
+        self.key_proj = nn.Linear(config.d_model, inner_dim, bias=False)
+        self.value_proj = nn.Linear(config.d_model, inner_dim, bias=False)
+        self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False)
+
+    def __call__(
+        self,
+        queries: mx.array,
+        keys: mx.array,
+        values: mx.array,
+        mask: Optional[mx.array],
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> [mx.array, Tuple[mx.array, mx.array]]:
+        queries = self.query_proj(queries)
+        keys = self.key_proj(keys)
+        values = self.value_proj(values)
+
+        num_heads = self.num_heads
+        B, L, _ = queries.shape
+        _, S, _ = keys.shape
+        queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)
+
+        if cache is not None:
+            key_cache, value_cache = cache
+            keys = mx.concatenate([key_cache, keys], axis=3)
+            values = mx.concatenate([value_cache, values], axis=2)
+
+        values_hat = mx.fast.scaled_dot_product_attention(
+            queries, keys, values, scale=1.0, mask=mask.astype(queries.dtype)
+        )
+        values_hat = values_hat.transpose(0, 2, 1, 3).reshape(B, L, -1)
+
+        return self.out_proj(values_hat), (keys, values)
+
+
+class DenseActivation(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        mlp_dims = config.d_ff or config.d_model * 4
+        self.gated = config.feed_forward_proj.startswith("gated")
+        if self.gated:
+            self.wi_0 = nn.Linear(config.d_model, mlp_dims, bias=False)
+            self.wi_1 = nn.Linear(config.d_model, mlp_dims, bias=False)
+        else:
+            self.wi = nn.Linear(config.d_model, mlp_dims, bias=False)
+        self.wo = nn.Linear(mlp_dims, config.d_model, bias=False)
+        activation = config.feed_forward_proj.removeprefix("gated-")
+        if activation == "relu":
+            self.act = nn.relu
+        elif activation == "gelu":
+            self.act = nn.gelu
+        elif activation == "silu":
+            self.act = nn.silu
+        else:
+            raise ValueError(f"Unknown activation: {activation}")
+
+    def __call__(self, x):
+        if self.gated:
+            hidden_act = self.act(self.wi_0(x))
+            hidden_linear = self.wi_1(x)
+            x = hidden_act * hidden_linear
+        else:
+            x = self.act(self.wi(x))
+        return self.wo(x)
+
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        self.attention = MultiHeadAttention(config)
+        self.ln1 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.ln2 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.dense = DenseActivation(config)
+
+    def __call__(self, x, mask):
+        y = self.ln1(x)
+        y, _ = self.attention(y, y, y, mask=mask)
+        x = x + y
+
+        y = self.ln2(x)
+        y = self.dense(y)
+        return x + y
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        self.layers = [
+            TransformerEncoderLayer(config) for i in range(config.num_layers)
+        ]
+        self.ln = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.relative_attention_bias = RelativePositionBias(config, bidirectional=True)
+
+    def __call__(self, x: mx.array):
+        pos_bias = self.relative_attention_bias(x.shape[1], x.shape[1])
+        pos_bias = pos_bias.astype(x.dtype)
+        for layer in self.layers:
+            x = layer(x, mask=pos_bias)
+        return self.ln(x)
+
+
+class T5Encoder(nn.Module):
+    def __init__(self, config: T5Config):
+        self.wte = nn.Embedding(config.vocab_size, config.d_model)
+        self.encoder = TransformerEncoder(config)
+
+    def sanitize(self, weights):
+        new_weights = {}
+        for k, w in weights.items():
+            for old, new in _SHARED_REPLACEMENT_PATTERNS:
+                k = k.replace(old, new)
+            if k.startswith("encoder."):
+                for old, new in _ENCODER_REPLACEMENT_PATTERNS:
+                    k = k.replace(old, new)
+            new_weights[k] = w
+        return new_weights
+
+    def __call__(self, inputs: mx.array):
+        return self.encoder(self.wte(inputs))

flux/flux/tokenizers.py

@@ -0,0 +1,185 @@
+# Copyright © 2024 Apple Inc.
+
+import mlx.core as mx
+import regex
+from sentencepiece import SentencePieceProcessor
+
+
+class CLIPTokenizer:
+    """A simple port of CLIPTokenizer from https://github.com/huggingface/transformers/ ."""
+
+    def __init__(self, bpe_ranks, vocab, max_length=77):
+        self.max_length = max_length
+        self.bpe_ranks = bpe_ranks
+        self.vocab = vocab
+        self.pat = regex.compile(
+            r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
+            regex.IGNORECASE,
+        )
+
+        self._cache = {self.bos: self.bos, self.eos: self.eos}
+
+    @property
+    def bos(self):
+        return "<|startoftext|>"
+
+    @property
+    def bos_token(self):
+        return self.vocab[self.bos]
+
+    @property
+    def eos(self):
+        return "<|endoftext|>"
+
+    @property
+    def eos_token(self):
+        return self.vocab[self.eos]
+
+    def bpe(self, text):
+        if text in self._cache:
+            return self._cache[text]
+
+        unigrams = list(text[:-1]) + [text[-1] + "</w>"]
+        unique_bigrams = set(zip(unigrams, unigrams[1:]))
+
+        if not unique_bigrams:
+            return unigrams
+
+        # In every iteration try to merge the two most likely bigrams. If none
+        # was merged we are done.
+        #
+        # Ported from https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip.py
+        while unique_bigrams:
+            bigram = min(
+                unique_bigrams, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))
+            )
+            if bigram not in self.bpe_ranks:
+                break
+
+            new_unigrams = []
+            skip = False
+            for a, b in zip(unigrams, unigrams[1:]):
+                if skip:
+                    skip = False
+                    continue
+
+                if (a, b) == bigram:
+                    new_unigrams.append(a + b)
+                    skip = True
+
+                else:
+                    new_unigrams.append(a)
+
+            if not skip:
+                new_unigrams.append(b)
+
+            unigrams = new_unigrams
+            unique_bigrams = set(zip(unigrams, unigrams[1:]))
+
+        self._cache[text] = unigrams
+
+        return unigrams
+
+    def tokenize(self, text, prepend_bos=True, append_eos=True):
+        if isinstance(text, list):
+            return [self.tokenize(t, prepend_bos, append_eos) for t in text]
+
+        # Lower case cleanup and split according to self.pat. Hugging Face does
+        # a much more thorough job here but this should suffice for 95% of
+        # cases.
+        clean_text = regex.sub(r"\s+", " ", text.lower())
+        tokens = regex.findall(self.pat, clean_text)
+
+        # Split the tokens according to the byte-pair merge file
+        bpe_tokens = [ti for t in tokens for ti in self.bpe(t)]
+
+        # Map to token ids and return
+        tokens = [self.vocab[t] for t in bpe_tokens]
+        if prepend_bos:
+            tokens = [self.bos_token] + tokens
+        if append_eos:
+            tokens.append(self.eos_token)
+
+        if len(tokens) > self.max_length:
+            tokens = tokens[: self.max_length]
+            if append_eos:
+                tokens[-1] = self.eos_token
+
+        return tokens
+
+    def encode(self, text):
+        if not isinstance(text, list):
+            return self.encode([text])
+
+        tokens = self.tokenize(text)
+        length = max(len(t) for t in tokens)
+        for t in tokens:
+            t.extend([self.eos_token] * (length - len(t)))
+
+        return mx.array(tokens)
+
+
+class T5Tokenizer:
+    def __init__(self, model_file, max_length=512):
+        self._tokenizer = SentencePieceProcessor(model_file)
+        self.max_length = max_length
+
+    @property
+    def pad(self):
+        try:
+            return self._tokenizer.id_to_piece(self.pad_token)
+        except IndexError:
+            return None
+
+    @property
+    def pad_token(self):
+        return self._tokenizer.pad_id()
+
+    @property
+    def bos(self):
+        try:
+            return self._tokenizer.id_to_piece(self.bos_token)
+        except IndexError:
+            return None
+
+    @property
+    def bos_token(self):
+        return self._tokenizer.bos_id()
+
+    @property
+    def eos(self):
+        try:
+            return self._tokenizer.id_to_piece(self.eos_token)
+        except IndexError:
+            return None
+
+    @property
+    def eos_token(self):
+        return self._tokenizer.eos_id()
+
+    def tokenize(self, text, prepend_bos=True, append_eos=True, pad=True):
+        if isinstance(text, list):
+            return [self.tokenize(t, prepend_bos, append_eos, pad) for t in text]
+
+        tokens = self._tokenizer.encode(text)
+
+        if prepend_bos and self.bos_token >= 0:
+            tokens = [self.bos_token] + tokens
+        if append_eos and self.eos_token >= 0:
+            tokens.append(self.eos_token)
+        if pad and len(tokens) < self.max_length and self.pad_token >= 0:
+            tokens += [self.pad_token] * (self.max_length - len(tokens))
+
+        return tokens
+
+    def encode(self, text, pad=True):
+        if not isinstance(text, list):
+            return self.encode([text], pad=pad)
+
+        pad_token = self.pad_token if self.pad_token >= 0 else 0
+        tokens = self.tokenize(text, pad=pad)
+        length = max(len(t) for t in tokens)
+        for t in tokens:
+            t.extend([pad_token] * (length - len(t)))
+
+        return mx.array(tokens)

flux/flux/trainer.py

@@ -0,0 +1,98 @@
+import mlx.core as mx
+import numpy as np
+from PIL import Image, ImageFile
+from tqdm import tqdm
+
+from .datasets import Dataset
+from .flux import FluxPipeline
+
+
+class Trainer:
+
+    def __init__(self, flux: FluxPipeline, dataset: Dataset, args):
+        self.flux = flux
+        self.dataset = dataset
+        self.args = args
+        self.latents = []
+        self.t5_features = []
+        self.clip_features = []
+
+    def _random_crop_resize(self, img):
+        resolution = self.args.resolution
+        width, height = img.size
+
+        a, b, c, d = mx.random.uniform(shape=(4,), stream=mx.cpu).tolist()
+
+        # Random crop the input image between 0.8 to 1.0 of its original dimensions
+        crop_size = (
+            max((0.8 + 0.2 * a) * width, resolution[0]),
+            max((0.8 + 0.2 * b) * height, resolution[1]),
+        )
+        pan = (width - crop_size[0], height - crop_size[1])
+        img = img.crop(
+            (
+                pan[0] * c,
+                pan[1] * d,
+                crop_size[0] + pan[0] * c,
+                crop_size[1] + pan[1] * d,
+            )
+        )
+
+        # Fit the largest rectangle with the ratio of resolution in the image
+        # rectangle.
+        width, height = crop_size
+        ratio = resolution[0] / resolution[1]
+        r1 = (height * ratio, height)
+        r2 = (width, width / ratio)
+        r = r1 if r1[0] <= width else r2
+        img = img.crop(
+            (
+                (width - r[0]) / 2,
+                (height - r[1]) / 2,
+                (width + r[0]) / 2,
+                (height + r[1]) / 2,
+            )
+        )
+
+        # Finally resize the image to resolution
+        img = img.resize(resolution, Image.LANCZOS)
+
+        return mx.array(np.array(img))
+
+    def _encode_image(self, input_img: ImageFile.ImageFile, num_augmentations: int):
+        for i in range(num_augmentations):
+            img = self._random_crop_resize(input_img)
+            img = (img[:, :, :3].astype(self.flux.dtype) / 255) * 2 - 1
+            x_0 = self.flux.ae.encode(img[None])
+            x_0 = x_0.astype(self.flux.dtype)
+            mx.eval(x_0)
+            self.latents.append(x_0)
+
+    def _encode_prompt(self, prompt):
+        t5_tok, clip_tok = self.flux.tokenize([prompt])
+        t5_feat = self.flux.t5(t5_tok)
+        clip_feat = self.flux.clip(clip_tok).pooled_output
+        mx.eval(t5_feat, clip_feat)
+        self.t5_features.append(t5_feat)
+        self.clip_features.append(clip_feat)
+
+    def encode_dataset(self):
+        """Encode the images & prompt in the latent space to prepare for training."""
+        self.flux.ae.eval()
+        for image, prompt in tqdm(self.dataset, desc="encode dataset"):
+            self._encode_image(image, self.args.num_augmentations)
+            self._encode_prompt(prompt)
+
+    def iterate(self, batch_size):
+        xs = mx.concatenate(self.latents)
+        t5 = mx.concatenate(self.t5_features)
+        clip = mx.concatenate(self.clip_features)
+        mx.eval(xs, t5, clip)
+        n_aug = self.args.num_augmentations
+        while True:
+            x_indices = mx.random.permutation(len(self.latents))
+            c_indices = x_indices // n_aug
+            for i in range(0, len(self.latents), batch_size):
+                x_i = x_indices[i : i + batch_size]
+                c_i = c_indices[i : i + batch_size]
+                yield xs[x_i], t5[c_i], clip[c_i]

flux/flux/utils.py

@@ -0,0 +1,230 @@
+# Copyright © 2024 Apple Inc.
+
+import json
+import os
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional, Union
+
+import mlx.core as mx
+from huggingface_hub import hf_hub_download
+
+from .autoencoder import AutoEncoder, AutoEncoderParams
+from .clip import CLIPTextModel, CLIPTextModelConfig
+from .model import Flux, FluxParams
+from .t5 import T5Config, T5Encoder
+from .tokenizers import CLIPTokenizer, T5Tokenizer
+
+
+@dataclass
+class ModelSpec:
+    params: FluxParams
+    ae_params: AutoEncoderParams
+    ckpt_path: Optional[str]
+    ae_path: Optional[str]
+    repo_id: Optional[str]
+    repo_flow: Optional[str]
+    repo_ae: Optional[str]
+
+
+configs = {
+    "flux-dev": ModelSpec(
+        repo_id="black-forest-labs/FLUX.1-dev",
+        repo_flow="flux1-dev.safetensors",
+        repo_ae="ae.safetensors",
+        ckpt_path=os.getenv("FLUX_DEV"),
+        params=FluxParams(
+            in_channels=64,
+            vec_in_dim=768,
+            context_in_dim=4096,
+            hidden_size=3072,
+            mlp_ratio=4.0,
+            num_heads=24,
+            depth=19,
+            depth_single_blocks=38,
+            axes_dim=[16, 56, 56],
+            theta=10_000,
+            qkv_bias=True,
+            guidance_embed=True,
+        ),
+        ae_path=os.getenv("AE"),
+        ae_params=AutoEncoderParams(
+            resolution=256,
+            in_channels=3,
+            ch=128,
+            out_ch=3,
+            ch_mult=[1, 2, 4, 4],
+            num_res_blocks=2,
+            z_channels=16,
+            scale_factor=0.3611,
+            shift_factor=0.1159,
+        ),
+    ),
+    "flux-schnell": ModelSpec(
+        repo_id="black-forest-labs/FLUX.1-schnell",
+        repo_flow="flux1-schnell.safetensors",
+        repo_ae="ae.safetensors",
+        ckpt_path=os.getenv("FLUX_SCHNELL"),
+        params=FluxParams(
+            in_channels=64,
+            vec_in_dim=768,
+            context_in_dim=4096,
+            hidden_size=3072,
+            mlp_ratio=4.0,
+            num_heads=24,
+            depth=19,
+            depth_single_blocks=38,
+            axes_dim=[16, 56, 56],
+            theta=10_000,
+            qkv_bias=True,
+            guidance_embed=False,
+        ),
+        ae_path=os.getenv("AE"),
+        ae_params=AutoEncoderParams(
+            resolution=256,
+            in_channels=3,
+            ch=128,
+            out_ch=3,
+            ch_mult=[1, 2, 4, 4],
+            num_res_blocks=2,
+            z_channels=16,
+            scale_factor=0.3611,
+            shift_factor=0.1159,
+        ),
+    ),
+}
+
+
+def load_flow_model(name: str, hf_download: bool = True):
+    # Get the safetensors file to load
+    ckpt_path = configs[name].ckpt_path
+
+    # Download if needed
+    if (
+        ckpt_path is None
+        and configs[name].repo_id is not None
+        and configs[name].repo_flow is not None
+        and hf_download
+    ):
+        ckpt_path = hf_hub_download(configs[name].repo_id, configs[name].repo_flow)
+
+    # Make the model
+    model = Flux(configs[name].params)
+
+    # Load the checkpoint if needed
+    if ckpt_path is not None:
+        weights = mx.load(ckpt_path)
+        weights = model.sanitize(weights)
+        model.load_weights(list(weights.items()))
+
+    return model
+
+
+def load_ae(name: str, hf_download: bool = True):
+    # Get the safetensors file to load
+    ckpt_path = configs[name].ae_path
+
+    # Download if needed
+    if (
+        ckpt_path is None
+        and configs[name].repo_id is not None
+        and configs[name].repo_ae is not None
+        and hf_download
+    ):
+        ckpt_path = hf_hub_download(configs[name].repo_id, configs[name].repo_ae)
+
+    # Make the autoencoder
+    ae = AutoEncoder(configs[name].ae_params)
+
+    # Load the checkpoint if needed
+    if ckpt_path is not None:
+        weights = mx.load(ckpt_path)
+        weights = ae.sanitize(weights)
+        ae.load_weights(list(weights.items()))
+
+    return ae
+
+
+def load_clip(name: str):
+    # Load the config
+    config_path = hf_hub_download(configs[name].repo_id, "text_encoder/config.json")
+    with open(config_path) as f:
+        config = CLIPTextModelConfig.from_dict(json.load(f))
+
+    # Make the clip text encoder
+    clip = CLIPTextModel(config)
+
+    # Load the weights
+    ckpt_path = hf_hub_download(configs[name].repo_id, "text_encoder/model.safetensors")
+    weights = mx.load(ckpt_path)
+    weights = clip.sanitize(weights)
+    clip.load_weights(list(weights.items()))
+
+    return clip
+
+
+def load_t5(name: str):
+    # Load the config
+    config_path = hf_hub_download(configs[name].repo_id, "text_encoder_2/config.json")
+    with open(config_path) as f:
+        config = T5Config.from_dict(json.load(f))
+
+    # Make the T5 model
+    t5 = T5Encoder(config)
+
+    # Load the weights
+    model_index = hf_hub_download(
+        configs[name].repo_id, "text_encoder_2/model.safetensors.index.json"
+    )
+    weight_files = set()
+    with open(model_index) as f:
+        for _, w in json.load(f)["weight_map"].items():
+            weight_files.add(w)
+    weights = {}
+    for w in weight_files:
+        w = f"text_encoder_2/{w}"
+        w = hf_hub_download(configs[name].repo_id, w)
+        weights.update(mx.load(w))
+    weights = t5.sanitize(weights)
+    t5.load_weights(list(weights.items()))
+
+    return t5
+
+
+def load_clip_tokenizer(name: str):
+    vocab_file = hf_hub_download(configs[name].repo_id, "tokenizer/vocab.json")
+    with open(vocab_file, encoding="utf-8") as f:
+        vocab = json.load(f)
+
+    merges_file = hf_hub_download(configs[name].repo_id, "tokenizer/merges.txt")
+    with open(merges_file, encoding="utf-8") as f:
+        bpe_merges = f.read().strip().split("\n")[1 : 49152 - 256 - 2 + 1]
+    bpe_merges = [tuple(m.split()) for m in bpe_merges]
+    bpe_ranks = dict(map(reversed, enumerate(bpe_merges)))
+
+    return CLIPTokenizer(bpe_ranks, vocab, max_length=77)
+
+
+def load_t5_tokenizer(name: str, pad: bool = True):
+    model_file = hf_hub_download(configs[name].repo_id, "tokenizer_2/spiece.model")
+    return T5Tokenizer(model_file, 256 if "schnell" in name else 512)
+
+
+def save_config(
+    config: dict,
+    config_path: Union[str, Path],
+) -> None:
+    """Save the model configuration to the ``config_path``.
+
+    The final configuration will be sorted before saving for better readability.
+
+    Args:
+        config (dict): The model configuration.
+        config_path (Union[str, Path]): Model configuration file path.
+    """
+    # Sort the config for better readability
+    config = dict(sorted(config.items()))
+
+    # Write the config to the provided file
+    with open(config_path, "w") as fid:
+        json.dump(config, fid, indent=4)

flux/generate_interactive.py

@@ -0,0 +1,109 @@
+import argparse
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+from flux import FluxPipeline
+
+
+def print_zero(group, *args, **kwargs):
+    if group.rank() == 0:
+        flush = kwargs.pop("flush", True)
+        print(*args, **kwargs, flush=flush)
+
+
+def quantization_predicate(name, m):
+    return hasattr(m, "to_quantized") and m.weight.shape[1] % 512 == 0
+
+
+def to_latent_size(image_size):
+    h, w = image_size
+    h = ((h + 15) // 16) * 16
+    w = ((w + 15) // 16) * 16
+
+    if (h, w) != image_size:
+        print(
+            "Warning: The image dimensions need to be divisible by 16px. "
+            f"Changing size to {h}x{w}."
+        )
+
+    return (h // 8, w // 8)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Generate images from a textual prompt using FLUX"
+    )
+    parser.add_argument("--quantize", "-q", action="store_true")
+    parser.add_argument("--model", choices=["schnell", "dev"], default="schnell")
+    parser.add_argument("--output", default="out.png")
+    args = parser.parse_args()
+
+    flux = FluxPipeline("flux-" + args.model, t5_padding=True)
+
+    if args.quantize:
+        nn.quantize(flux.flow, class_predicate=quantization_predicate)
+        nn.quantize(flux.t5, class_predicate=quantization_predicate)
+        nn.quantize(flux.clip, class_predicate=quantization_predicate)
+
+    group = mx.distributed.init()
+    if group.size() > 1:
+        flux.flow.shard(group)
+
+    print_zero(group, "Loading models")
+    flux.ensure_models_are_loaded()
+
+    def print_help():
+        print_zero(group, "The command list:")
+        print_zero(group, "- 'q' to exit")
+        print_zero(group, "- 's HxW' to change the size of the image")
+        print_zero(group, "- 'n S' to change the number of steps")
+        print_zero(group, "- 'h' to print this help")
+
+    print_zero(group, "FLUX interactive session")
+    print_help()
+    seed = 0
+    size = (512, 512)
+    latent_size = to_latent_size(size)
+    steps = 50 if args.model == "dev" else 4
+    while True:
+        prompt = input(">> " if group.rank() == 0 else "")
+        if prompt == "q":
+            break
+        if prompt == "h":
+            print_help()
+            continue
+        if prompt.startswith("s "):
+            size = tuple([int(xi) for xi in prompt[2:].split("x")])
+            print_zero(group, "Setting the size to", size)
+            latent_size = to_latent_size(size)
+            continue
+        if prompt.startswith("n "):
+            steps = int(prompt[2:])
+            print_zero(group, "Setting the steps to", steps)
+            continue
+
+        seed += 1
+        latents = flux.generate_latents(
+            prompt,
+            n_images=1,
+            num_steps=steps,
+            latent_size=latent_size,
+            guidance=4.0,
+            seed=seed,
+        )
+        print_zero(group, "Processing prompt")
+        mx.eval(next(latents))
+        print_zero(group, "Generating latents")
+        for xt in tqdm(latents, total=steps, disable=group.rank() > 0):
+            mx.eval(xt)
+        print_zero(group, "Generating image")
+        xt = flux.decode(xt, latent_size)
+        xt = (xt * 255).astype(mx.uint8)
+        mx.eval(xt)
+        im = Image.fromarray(np.array(xt[0]))
+        im.save(args.output)
+        print_zero(group, "Saved at", args.output, end="\n\n")

flux/requirements.txt

@@ -0,0 +1,7 @@
+mlx>=0.18.1
+huggingface-hub
+regex
+numpy
+tqdm
+Pillow
+sentencepiece

flux/txt2image.py

@@ -0,0 +1,175 @@
+# Copyright © 2024 Apple Inc.
+
+import argparse
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+from flux import FluxPipeline
+
+
+def to_latent_size(image_size):
+    h, w = image_size
+    h = ((h + 15) // 16) * 16
+    w = ((w + 15) // 16) * 16
+
+    if (h, w) != image_size:
+        print(
+            "Warning: The image dimensions need to be divisible by 16px. "
+            f"Changing size to {h}x{w}."
+        )
+
+    return (h // 8, w // 8)
+
+
+def quantization_predicate(name, m):
+    return hasattr(m, "to_quantized") and m.weight.shape[1] % 512 == 0
+
+
+def load_adapter(flux, adapter_file, fuse=False):
+    weights, lora_config = mx.load(adapter_file, return_metadata=True)
+    rank = int(lora_config["lora_rank"])
+    num_blocks = int(lora_config["lora_blocks"])
+    flux.linear_to_lora_layers(rank, num_blocks)
+    flux.flow.load_weights(list(weights.items()), strict=False)
+    if fuse:
+        flux.fuse_lora_layers()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Generate images from a textual prompt using FLUX"
+    )
+    parser.add_argument("prompt")
+    parser.add_argument("--model", choices=["schnell", "dev"], default="schnell")
+    parser.add_argument("--n-images", type=int, default=4)
+    parser.add_argument(
+        "--image-size", type=lambda x: tuple(map(int, x.split("x"))), default=(512, 512)
+    )
+    parser.add_argument("--steps", type=int)
+    parser.add_argument("--guidance", type=float, default=4.0)
+    parser.add_argument("--n-rows", type=int, default=1)
+    parser.add_argument("--decoding-batch-size", type=int, default=1)
+    parser.add_argument("--quantize", "-q", action="store_true")
+    parser.add_argument("--preload-models", action="store_true")
+    parser.add_argument("--output", default="out.png")
+    parser.add_argument("--save-raw", action="store_true")
+    parser.add_argument("--seed", type=int)
+    parser.add_argument("--verbose", "-v", action="store_true")
+    parser.add_argument("--adapter")
+    parser.add_argument("--fuse-adapter", action="store_true")
+    parser.add_argument("--no-t5-padding", dest="t5_padding", action="store_false")
+    parser.add_argument("--force-shard", action="store_true")
+    args = parser.parse_args()
+
+    # Load the models
+    flux = FluxPipeline("flux-" + args.model, t5_padding=args.t5_padding)
+    args.steps = args.steps or (50 if args.model == "dev" else 2)
+
+    if args.adapter:
+        load_adapter(flux, args.adapter, fuse=args.fuse_adapter)
+
+    if args.quantize:
+        nn.quantize(flux.flow, class_predicate=quantization_predicate)
+        nn.quantize(flux.t5, class_predicate=quantization_predicate)
+        nn.quantize(flux.clip, class_predicate=quantization_predicate)
+
+    # Figure out what kind of distributed generation we should do
+    group = mx.distributed.init()
+    n_images = args.n_images
+    should_gather = False
+    if group.size() > 1:
+        if args.force_shard or n_images < group.size() or n_images % group.size() != 0:
+            flux.flow.shard(group)
+        else:
+            n_images //= group.size()
+            should_gather = True
+
+        # If we are sharding we should have the same seed and if we are doing
+        # data parallel generation we should have different seeds
+        if args.seed is None:
+            args.seed = mx.distributed.all_sum(mx.random.randint(0, 2**20)).item()
+        if should_gather:
+            args.seed = args.seed + group.rank()
+
+    if args.preload_models:
+        flux.ensure_models_are_loaded()
+
+    # Make the generator
+    latent_size = to_latent_size(args.image_size)
+    latents = flux.generate_latents(
+        args.prompt,
+        n_images=n_images,
+        num_steps=args.steps,
+        latent_size=latent_size,
+        guidance=args.guidance,
+        seed=args.seed,
+    )
+
+    # First we get and eval the conditioning
+    conditioning = next(latents)
+    mx.eval(conditioning)
+    peak_mem_conditioning = mx.get_peak_memory() / 1024**3
+    mx.reset_peak_memory()
+
+    # The following is not necessary but it may help in memory constrained
+    # systems by reusing the memory kept by the text encoders.
+    del flux.t5
+    del flux.clip
+
+    # Actual denoising loop
+    for x_t in tqdm(latents, total=args.steps, disable=group.rank() > 0):
+        mx.eval(x_t)
+
+    # The following is not necessary but it may help in memory constrained
+    # systems by reusing the memory kept by the flow transformer.
+    del flux.flow
+    peak_mem_generation = mx.get_peak_memory() / 1024**3
+    mx.reset_peak_memory()
+
+    # Decode them into images
+    decoded = []
+    for i in tqdm(range(0, n_images, args.decoding_batch_size)):
+        decoded.append(flux.decode(x_t[i : i + args.decoding_batch_size], latent_size))
+        mx.eval(decoded[-1])
+    peak_mem_decoding = mx.get_peak_memory() / 1024**3
+    peak_mem_overall = max(
+        peak_mem_conditioning, peak_mem_generation, peak_mem_decoding
+    )
+
+    # Gather them if each node has different images
+    decoded = mx.concatenate(decoded, axis=0)
+    if should_gather:
+        decoded = mx.distributed.all_gather(decoded)
+        mx.eval(decoded)
+
+    if args.save_raw:
+        *name, suffix = args.output.split(".")
+        name = ".".join(name)
+        x = decoded
+        x = (x * 255).astype(mx.uint8)
+        for i in range(len(x)):
+            im = Image.fromarray(np.array(x[i]))
+            im.save(".".join([name, str(i), suffix]))
+    else:
+        # Arrange them on a grid
+        x = decoded
+        x = mx.pad(x, [(0, 0), (4, 4), (4, 4), (0, 0)])
+        B, H, W, C = x.shape
+        x = x.reshape(args.n_rows, B // args.n_rows, H, W, C).transpose(0, 2, 1, 3, 4)
+        x = x.reshape(args.n_rows * H, B // args.n_rows * W, C)
+        x = (x * 255).astype(mx.uint8)
+
+        # Save them to disc
+        im = Image.fromarray(np.array(x))
+        im.save(args.output)
+
+    # Report the peak memory used during generation
+    if args.verbose and group.rank() == 0:
+        print(f"Peak memory used for the text:       {peak_mem_conditioning:.3f}GB")
+        print(f"Peak memory used for the generation: {peak_mem_generation:.3f}GB")
+        print(f"Peak memory used for the decoding:   {peak_mem_decoding:.3f}GB")
+        print(f"Peak memory used overall:            {peak_mem_overall:.3f}GB")

gan/main.py

@@ -0,0 +1,195 @@
+import mnist
+
+import argparse
+
+import mlx.core as mx
+import mlx.nn as nn
+import mlx.optimizers as optim
+
+from tqdm import tqdm
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Generator Block
+def GenBlock(in_dim:int,out_dim:int):
+    return nn.Sequential(
+        nn.Linear(in_dim,out_dim),
+        nn.BatchNorm(out_dim, 0.8),
+        nn.LeakyReLU(0.2)
+    )
+
+# Generator Model
+class Generator(nn.Module):
+
+    def __init__(self, z_dim:int = 32, im_dim:int = 784, hidden_dim: int = 256):
+        super(Generator, self).__init__()
+
+        self.gen = nn.Sequential(
+            GenBlock(z_dim, hidden_dim),
+            GenBlock(hidden_dim, hidden_dim * 2),
+            GenBlock(hidden_dim * 2, hidden_dim * 4),
+
+            nn.Linear(hidden_dim * 4,im_dim),
+        )
+        
+    def __call__(self, noise):
+        x = self.gen(noise)
+        return mx.tanh(x)
+
+# make 2D noise with shape n_samples x z_dim
+def get_noise(n_samples:list[int], z_dim:int)->list[int]:
+    return mx.random.normal(shape=(n_samples, z_dim))
+
+#---------------------------------------------#
+
+# Discriminator Block
+def DisBlock(in_dim:int,out_dim:int):
+    return nn.Sequential(
+        nn.Linear(in_dim,out_dim),
+        nn.LeakyReLU(negative_slope=0.2),
+        nn.Dropout(0.3),
+    )
+
+# Discriminator Model
+class Discriminator(nn.Module):
+
+    def __init__(self,im_dim:int = 784, hidden_dim:int = 256):
+        super(Discriminator, self).__init__()
+
+        self.disc = nn.Sequential(
+            DisBlock(im_dim, hidden_dim * 4),
+            DisBlock(hidden_dim * 4, hidden_dim * 2),
+            DisBlock(hidden_dim * 2, hidden_dim),
+            
+            nn.Linear(hidden_dim,1),
+            nn.Sigmoid()
+        )
+        
+    def __call__(self, noise):
+        return self.disc(noise)
+    
+# Discriminator Loss
+def disc_loss(gen, disc, real, num_images, z_dim):
+    
+    noise =  mx.array(get_noise(num_images, z_dim))
+    fake_images = gen(noise)
+        
+    fake_disc = disc(fake_images)
+    
+    fake_labels = mx.zeros((fake_images.shape[0],1))
+            
+    fake_loss = mx.mean(nn.losses.binary_cross_entropy(fake_disc,fake_labels,with_logits=True))
+        
+    real_disc = mx.array(disc(real))
+    real_labels = mx.ones((real.shape[0],1))
+    
+    real_loss = mx.mean(nn.losses.binary_cross_entropy(real_disc,real_labels,with_logits=True))
+    
+    disc_loss = (fake_loss + real_loss) / 2.0
+
+    return disc_loss
+
+# Genearator Loss
+def gen_loss(gen, disc, num_images, z_dim):
+
+    noise = mx.array(get_noise(num_images, z_dim))
+    
+    fake_images = gen(noise)
+    fake_disc = mx.array(disc(fake_images))
+
+    fake_labels = mx.ones((fake_images.shape[0],1))
+            
+    gen_loss = nn.losses.binary_cross_entropy(fake_disc,fake_labels,with_logits=True)
+    
+    return mx.mean(gen_loss)
+
+# make batch of images
+def batch_iterate(batch_size: int, ipt: list[int])-> list[int]:
+    perm = np.random.permutation(len(ipt))
+    for s in range(0, len(ipt), batch_size):
+        ids = perm[s : s + batch_size]
+        yield ipt[ids]
+
+# plot batch of images at epoch steps
+def show_images(epoch_num:int,imgs:list[int],num_imgs:int = 25):
+    if (imgs.shape[0] > 0): 
+        fig,axes = plt.subplots(5, 5, figsize=(5, 5))
+        
+        for i, ax in enumerate(axes.flat):
+            img = mx.array(imgs[i]).reshape(28,28)
+            ax.imshow(img,cmap='gray')
+            ax.axis('off')
+        plt.tight_layout()
+        plt.savefig('gen_images/img_{}.png'.format(epoch_num))
+        plt.show()
+
+def main(args:dict):
+    seed = 42
+    n_epochs = 500
+    z_dim = 128
+    batch_size = 128
+    lr = 2e-5
+    
+    mx.random.seed(seed)
+
+    # Load the data
+    train_images,*_ = map(np.array, getattr(mnist,'mnist')())
+    
+    # Normalization images => [-1,1]
+    train_images = train_images * 2.0 - 1.0
+    
+    gen = Generator(z_dim)
+    mx.eval(gen.parameters())
+    gen_opt = optim.Adam(learning_rate=lr, betas=[0.5, 0.999])
+
+    disc = Discriminator()
+    mx.eval(disc.parameters())
+    disc_opt = optim.Adam(learning_rate=lr, betas=[0.5, 0.999])
+
+    # TODO training...
+
+    D_loss_grad = nn.value_and_grad(disc, disc_loss)
+    G_loss_grad = nn.value_and_grad(gen, gen_loss)
+
+    for epoch in tqdm(range(n_epochs)):
+
+        for idx,real in enumerate(batch_iterate(batch_size, train_images)):
+                    
+            # TODO Train Discriminator
+            D_loss,D_grads = D_loss_grad(gen, disc,mx.array(real), batch_size, z_dim)
+
+            # Update optimizer
+            disc_opt.update(disc, D_grads)
+            
+            # Update gradients
+            mx.eval(disc.parameters(), disc_opt.state)
+
+            # TODO Train Generator
+            G_loss,G_grads = G_loss_grad(gen, disc, batch_size, z_dim)
+            
+            # Update optimizer
+            gen_opt.update(gen, G_grads)
+            
+            # Update gradients
+            mx.eval(gen.parameters(), gen_opt.state)        
+            
+        if epoch%100==0:
+                print("Epoch: {}, iteration: {}, Discriminator Loss:{}, Generator Loss: {}".format(epoch,idx,D_loss,G_loss))
+                fake_noise = mx.array(get_noise(batch_size, z_dim))
+                fake = gen(fake_noise)
+                show_images(epoch,fake)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Train a simple GAN on MNIST with MLX.")
+    parser.add_argument("--gpu", action="store_true", help="Use the Metal back-end.")
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="mnist",
+        choices=["mnist", "fashion_mnist"],
+        help="The dataset to use.",
+    )
+    args = parser.parse_args()
+    if not args.gpu:
+        mx.set_default_device(mx.cpu)
+    main(args)

gan/mnist.py

@@ -0,0 +1,83 @@
+# Copyright © 2023 Apple Inc.
+
+import gzip
+import os
+import pickle
+from urllib import request
+
+import numpy as np
+
+
+def mnist(
+    save_dir="/tmp",
+    base_url="https://raw.githubusercontent.com/fgnt/mnist/master/",
+    filename="mnist.pkl",
+):
+    """
+    Load the MNIST dataset in 4 tensors: train images, train labels,
+    test images, and test labels.
+
+    Checks `save_dir` for already downloaded data otherwise downloads.
+
+    Download code modified from:
+      https://github.com/hsjeong5/MNIST-for-Numpy
+    """
+
+    def download_and_save(save_file):
+        filename = [
+            ["training_images", "train-images-idx3-ubyte.gz"],
+            ["test_images", "t10k-images-idx3-ubyte.gz"],
+            ["training_labels", "train-labels-idx1-ubyte.gz"],
+            ["test_labels", "t10k-labels-idx1-ubyte.gz"],
+        ]
+
+        mnist = {}
+        for name in filename:
+            out_file = os.path.join("/tmp", name[1])
+            request.urlretrieve(base_url + name[1], out_file)
+        for name in filename[:2]:
+            out_file = os.path.join("/tmp", name[1])
+            with gzip.open(out_file, "rb") as f:
+                mnist[name[0]] = np.frombuffer(f.read(), np.uint8, offset=16).reshape(
+                    -1, 28 * 28
+                )
+        for name in filename[-2:]:
+            out_file = os.path.join("/tmp", name[1])
+            with gzip.open(out_file, "rb") as f:
+                mnist[name[0]] = np.frombuffer(f.read(), np.uint8, offset=8)
+        with open(save_file, "wb") as f:
+            pickle.dump(mnist, f)
+
+    save_file = os.path.join(save_dir, filename)
+    if not os.path.exists(save_file):
+        download_and_save(save_file)
+    with open(save_file, "rb") as f:
+        mnist = pickle.load(f)
+
+    def preproc(x):
+        return x.astype(np.float32) / 255.0
+
+    mnist["training_images"] = preproc(mnist["training_images"])
+    mnist["test_images"] = preproc(mnist["test_images"])
+    return (
+        mnist["training_images"],
+        mnist["training_labels"].astype(np.uint32),
+        mnist["test_images"],
+        mnist["test_labels"].astype(np.uint32),
+    )
+
+
+def fashion_mnist(save_dir="/tmp"):
+    return mnist(
+        save_dir,
+        base_url="http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/",
+        filename="fashion_mnist.pkl",
+    )
+
+
+if __name__ == "__main__":
+    train_x, train_y, test_x, test_y = mnist()
+    assert train_x.shape == (60000, 28 * 28), "Wrong training set size"
+    assert train_y.shape == (60000,), "Wrong training set size"
+    assert test_x.shape == (10000, 28 * 28), "Wrong test set size"
+    assert test_y.shape == (10000,), "Wrong test set size"

llava/generate.py

@@ -79,10 +79,10 @@ def load_image(image_source):
 def prepare_inputs(processor, image, prompt):
     if isinstance(image, str):
         image = load_image(image)
-    inputs = processor(prompt, image, return_tensors="np")
+    inputs = processor(image, prompt, return_tensors="np")
     pixel_values = mx.array(inputs["pixel_values"])
     input_ids = mx.array(inputs["input_ids"])
-    return input_ids, pixel_values
+    return pixel_values, input_ids
 
 
 def load_model(model_path, tokenizer_config={}):
@@ -126,8 +126,7 @@ def main():
     processor, model = load_model(args.model, tokenizer_config)
 
     prompt = codecs.decode(args.prompt, "unicode_escape")
-
-    input_ids, pixel_values = prepare_inputs(processor, args.image, prompt)
+    pixel_values, input_ids = prepare_inputs(processor, args.image, prompt)
 
     print(prompt)
     generated_text = generate_text(

llava/llava.py

@@ -68,11 +68,10 @@ class LlavaModel(nn.Module):
         input_ids: Optional[mx.array] = None,
         pixel_values: Optional[mx.array] = None,
     ):
-        if pixel_values is None:
-            return self.language_model(input_ids)
-
         # Get the input embeddings from the language model
         inputs_embeds = self.language_model.model.embed_tokens(input_ids)
+        if pixel_values is None:
+            return inputs_embeds
 
         # Get the ouptut hidden states from the vision model
         *_, hidden_states = self.vision_tower(
@@ -105,31 +104,21 @@ class LlavaModel(nn.Module):
         self, image_features, inputs_embeds, input_ids
     ):
         image_token_index = self.config.image_token_index
-        num_images, num_image_patches, embed_dim = image_features.shape
+        batch_size, num_image_patches, embed_dim = image_features.shape
 
         # Positions of <image> tokens in input_ids, assuming batch size is 1
-        image_positions = np.where(input_ids[0] == image_token_index)[0].tolist()
-
-        if len(image_positions) != num_images:
-            raise ValueError(
-                f"The number of image tokens ({len(image_positions)}) does not "
-                f" match the number of image inputs ({num_images})."
+        image_positions = mx.array(
+            np.where(input_ids[0] == image_token_index)[0], mx.uint32
         )
 
-        text_segments = []
-        start_idx = 0
+        if len(image_positions) != num_image_patches:
+            raise ValueError(
+                f"The number of image tokens ({len(image_positions)}) does not "
+                f" match the number of image patches ({num_image_patches})."
+            )
 
-        for position in image_positions:
-            text_segments.append(inputs_embeds[:, start_idx:position])
-            start_idx = position + 1
-
-        image_embeddings = mx.split(image_features, image_features.shape[0])
-        final_embeddings = [v for p in zip(text_segments, image_embeddings) for v in p]
-        final_embeddings += [inputs_embeds[:, start_idx:]]
-
-        # Create a final embedding of shape
-        # (1, num_image_patches*num_images + sequence_len, embed_dim)
-        return mx.concatenate(final_embeddings, axis=1)
+        inputs_embeds[0, image_positions] = image_features
+        return inputs_embeds
 
     def __call__(self, input_ids: mx.array, pixel_values: mx.array, cache=None):
         input_embddings = self.get_input_embeddings(input_ids, pixel_values)

llms/CONTRIBUTING.md

@@ -1,47 +0,0 @@
-# Contributing to MLX LM 
-
-Below are some tips to port LLMs available on Hugging Face to MLX.
-
-Before starting checkout the [general contribution
-guidelines](https://github.com/ml-explore/mlx-examples/blob/main/CONTRIBUTING.md).
-
-Next, from this directory, do an editable install:
-
-```shell
-pip install -e .
-```
-
-Then check if the model has weights in the
-[safetensors](https://huggingface.co/docs/safetensors/index) format. If not
-[follow instructions](https://huggingface.co/spaces/safetensors/convert) to
-convert it.
-
-After that, add the model file to the
-[`mlx_lm/models`](https://github.com/ml-explore/mlx-examples/tree/main/llms/mlx_lm/models)
-directory. You can see other examples there. We recommend starting from a model
-that is similar to the model you are porting.
-
-Make sure the name of the new model file is the same as the `model_type` in the
-`config.json`, for example
-[starcoder2](https://huggingface.co/bigcode/starcoder2-7b/blob/main/config.json#L17).
-
-To determine the model layer names, we suggest either:
-
-- Refer to the Transformers implementation if you are familiar with the
-  codebase.
-- Load the model weights and check the weight names which will tell you about
-  the model structure.
-- Look at the names of the weights by inspecting `model.safetensors.index.json`
-  in the Hugging Face repo.
-
-To add LoRA support edit
-[`mlx_lm/tuner/utils.py`](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/tuner/utils.py#L27-L60)
-
-Finally, add a test for the new modle type to the [model
-tests](https://github.com/ml-explore/mlx-examples/blob/main/llms/tests/test_models.py).
-
-From the `llms/` directory, you can run the tests with:
-
-```shell
-python -m unittest discover tests/
-```

llms/MANIFEST.in

@@ -1,2 +0,0 @@
-include mlx_lm/requirements.txt
-recursive-include mlx_lm/ *.py

llms/README.md

@@ -1,169 +1,6 @@
-## Generate Text with LLMs and MLX
+# MOVE NOTICE 
 
-The easiest way to get started is to install the `mlx-lm` package:
+The mlx-lm package has moved to a [new repo](https://github.com/ml-explore/mlx-lm).
 
-**With `pip`**:
-
-```sh
-pip install mlx-lm
-```
-
-**With `conda`**:
-
-```sh
-conda install -c conda-forge mlx-lm
-```
-
-The `mlx-lm` package also has:
-
-- [LoRA and QLoRA fine-tuning](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/LORA.md)
-- [Merging models](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/MERGE.md)
-- [HTTP model serving](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/SERVER.md)
-
-### Python API
-
-You can use `mlx-lm` as a module:
-
-```python
-from mlx_lm import load, generate
-
-model, tokenizer = load("mlx-community/Mistral-7B-Instruct-v0.3-4bit")
-
-response = generate(model, tokenizer, prompt="hello", verbose=True)
-```
-
-To see a description of all the arguments you can do:
-
-```
->>> help(generate)
-```
-
-The `mlx-lm` package also comes with functionality to quantize and optionally
-upload models to the Hugging Face Hub.
-
-You can convert models in the Python API with:
-
-```python
-from mlx_lm import convert
-
-repo = "mistralai/Mistral-7B-Instruct-v0.3"
-upload_repo = "mlx-community/My-Mistral-7B-Instruct-v0.3-4bit"
-
-convert(repo, quantize=True, upload_repo=upload_repo)
-```
-
-This will generate a 4-bit quantized Mistral 7B and upload it to the repo
-`mlx-community/My-Mistral-7B-Instruct-v0.3-4bit`. It will also save the
-converted model in the path `mlx_model` by default.
-
-To see a description of all the arguments you can do:
-
-```
->>> help(convert)
-```
-
-#### Streaming
-
-For streaming generation, use the `stream_generate` function. This returns a
-generator object which streams the output text. For example,
-
-```python
-from mlx_lm import load, stream_generate
-
-repo = "mlx-community/Mistral-7B-Instruct-v0.3-4bit"
-model, tokenizer = load(repo)
-
-prompt = "Write a story about Einstein"
-
-for t in stream_generate(model, tokenizer, prompt, max_tokens=512):
-    print(t, end="", flush=True)
-print()
-```
-
-### Command Line
-
-You can also use `mlx-lm` from the command line with:
-
-```
-mlx_lm.generate --model mistralai/Mistral-7B-Instruct-v0.3 --prompt "hello"
-```
-
-This will download a Mistral 7B model from the Hugging Face Hub and generate
-text using the given prompt.
-
-For a full list of options run:
-
-```
-mlx_lm.generate --help
-```
-
-To quantize a model from the command line run:
-
-```
-mlx_lm.convert --hf-path mistralai/Mistral-7B-Instruct-v0.3 -q
-```
-
-For more options run:
-
-```
-mlx_lm.convert --help
-```
-
-You can upload new models to Hugging Face by specifying `--upload-repo` to
-`convert`. For example, to upload a quantized Mistral-7B model to the
-[MLX Hugging Face community](https://huggingface.co/mlx-community) you can do:
-
-```
-mlx_lm.convert \
-    --hf-path mistralai/Mistral-7B-Instruct-v0.3 \
-    -q \
-    --upload-repo mlx-community/my-4bit-mistral
-```
-
-### Supported Models
-
-The example supports Hugging Face format Mistral, Llama, and Phi-2 style
-models.  If the model you want to run is not supported, file an
-[issue](https://github.com/ml-explore/mlx-examples/issues/new) or better yet,
-submit a pull request.
-
-Here are a few examples of Hugging Face models that work with this example:
-
-- [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
-- [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
-- [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct)
-- [01-ai/Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat)
-- [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
-- [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
-- [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B)
-- [pfnet/plamo-13b](https://huggingface.co/pfnet/plamo-13b)
-- [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)
-
-Most
-[Mistral](https://huggingface.co/models?library=transformers,safetensors&other=mistral&sort=trending),
-[Llama](https://huggingface.co/models?library=transformers,safetensors&other=llama&sort=trending),
-[Phi-2](https://huggingface.co/models?library=transformers,safetensors&other=phi&sort=trending),
-and
-[Mixtral](https://huggingface.co/models?library=transformers,safetensors&other=mixtral&sort=trending)
-style models should work out of the box.
-
-For some models (such as `Qwen` and `plamo`) the tokenizer requires you to
-enable the `trust_remote_code` option. You can do this by passing
-`--trust-remote-code` in the command line. If you don't specify the flag
-explicitly, you will be prompted to trust remote code in the terminal when
-running the model. 
-
-For `Qwen` models you must also specify the `eos_token`. You can do this by
-passing `--eos-token "<|endoftext|>"` in the command
-line. 
-
-These options can also be set in the Python API. For example:
-
-```python
-model, tokenizer = load(
-    "qwen/Qwen-7B",
-    tokenizer_config={"eos_token": "<|endoftext|>", "trust_remote_code": True},
-)
-```
+The package has been removed from the MLX Examples repo. Send new contributions
+and issues to the MLX LM repo.

llms/gguf_llm/generate.py

@@ -40,7 +40,7 @@ def generate(
     if len(tokens) == 0:
         print("No tokens generated for this prompt")
         return
-    prompt_tps = prompt.size / prompt_time
+    prompt_tps = len(prompt) / prompt_time
     gen_tps = (len(tokens) - 1) / gen_time
     print(f"Prompt: {prompt_tps:.3f} tokens-per-sec")
     print(f"Generation: {gen_tps:.3f} tokens-per-sec")

llms/gguf_llm/models.py

@@ -19,10 +19,10 @@ class ModelArgs:
     rms_norm_eps: float
     vocab_size: int
     context_length: int
-    num_key_value_heads: int = None
+    num_key_value_heads: Optional[int] = None
     rope_theta: float = 10000
     rope_traditional: bool = False
-    model_type: str = None
+    model_type: Optional[str] = None
     rope_scaling: Optional[Dict[str, Union[float, str]]] = None
 
     def __post_init__(self):
@@ -54,7 +54,7 @@ class Attention(nn.Module):
 
         dim = args.hidden_size
         self.n_heads = n_heads = args.num_attention_heads
-        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
+        self.n_kv_heads = n_kv_heads = args.num_key_value_heads or n_heads
 
         self.repeats = n_heads // n_kv_heads
 
@@ -66,7 +66,7 @@ class Attention(nn.Module):
         self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
         self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
         rope_scale = (
-            1 / args.rope_scaling["factor"]
+            1 / float(args.rope_scaling["factor"])
             if args.rope_scaling is not None and args.rope_scaling["type"] == "linear"
             else 1
         )
@@ -254,7 +254,7 @@ def translate_weight_names(name):
     return name
 
 
-def load(gguf_file: str, repo: str = None):
+def load(gguf_file: str, repo: Optional[str] = None):
     # If the gguf_file exists, try to load model from it.
     # Otherwise try to download and cache from the HF repo
     if not Path(gguf_file).exists():

llms/llama/convert.py

@@ -7,6 +7,7 @@ import glob
 import json
 import shutil
 from pathlib import Path
+from typing import Dict
 
 import mlx.core as mx
 import mlx.nn as nn
@@ -149,7 +150,8 @@ def quantize(weights, config, args):
 def make_shards(weights: dict, max_file_size_gibibyte: int = 15):
     max_file_size_bytes = max_file_size_gibibyte << 30
     shards = []
-    shard, shard_size = {}, 0
+    shard: Dict[str, mx.array] = {}
+    shard_size = 0
     for k, v in weights.items():
         if shard_size + v.nbytes > max_file_size_bytes:
             shards.append(shard)

llms/mixtral/mixtral.py

@@ -23,7 +23,7 @@ class ModelArgs:
     n_kv_heads: int
     norm_eps: float
     vocab_size: int
-    moe: dict = None
+    moe: dict
 
 
 class Attention(nn.Module):
@@ -91,7 +91,6 @@ class FeedForward(nn.Module):
 class MOEFeedForward(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
-
         self.num_experts = args.moe["num_experts"]
         self.num_experts_per_tok = args.moe["num_experts_per_tok"]
         self.experts = [FeedForward(args) for _ in range(self.num_experts)]
@@ -115,7 +114,6 @@ class MOEFeedForward(nn.Module):
             yt = (yt * st).sum(axis=-1)
             y.append(yt[None, :])
         y = mx.concatenate(y)
-
         return y.reshape(orig_shape)
 
 

llms/mlx_lm/LORA.md

@@ -1,266 +0,0 @@
-# Fine-Tuning with LoRA or QLoRA
-
-You can use use the `mlx-lm` package to fine-tune an LLM with low rank
-adaptation (LoRA) for a target task.[^lora] The example also supports quantized
-LoRA (QLoRA).[^qlora] LoRA fine-tuning works with the following model families:
-
-- Mistral
-- Llama
-- Phi2
-- Mixtral
-- Qwen2
-- Gemma
-- OLMo
-- MiniCPM
-- InternLM2
-
-## Contents
-
-- [Run](#Run)
-  - [Fine-tune](#Fine-tune)
-  - [Evaluate](#Evaluate)
-  - [Generate](#Generate)
-- [Fuse](#Fuse)
-- [Data](#Data)
-- [Memory Issues](#Memory-Issues)
-
-## Run
-
-The main command is `mlx_lm.lora`. To see a full list of command-line options run:
-
-```shell
-mlx_lm.lora --help
-```
-
-Note, in the following the `--model` argument can be any compatible Hugging
-Face repo or a local path to a converted model.
-
-You can also specify a YAML config with `-c`/`--config`. For more on the format see the
-[example YAML](examples/lora_config.yaml). For example:
-
-```shell
-mlx_lm.lora --config /path/to/config.yaml
-```
-
-If command-line flags are also used, they will override the corresponding
-values in the config.
-
-### Fine-tune
-
-To fine-tune a model use:
-
-```shell
-mlx_lm.lora \
-    --model <path_to_model> \
-    --train \
-    --data <path_to_data> \
-    --iters 600
-```
-
-The `--data` argument must specify a path to a `train.jsonl`, `valid.jsonl`
-when using `--train` and a path to a `test.jsonl` when using `--test`. For more
-details on the data format see the section on [Data](#Data).
-
-For example, to fine-tune a Mistral 7B you can use `--model
-mistralai/Mistral-7B-v0.1`.
-
-If `--model` points to a quantized model, then the training will use QLoRA,
-otherwise it will use regular LoRA.
-
-By default, the adapter config and weights are saved in `adapters/`. You can
-specify the output location with `--adapter-path`.
-
-You can resume fine-tuning with an existing adapter with
-`--resume-adapter-file <path_to_adapters.safetensors>`.
-
-### Evaluate
-
-To compute test set perplexity use:
-
-```shell
-mlx_lm.lora \
-    --model <path_to_model> \
-    --adapter-path <path_to_adapters> \
-    --data <path_to_data> \
-    --test
-```
-
-### Generate
-
-For generation use `mlx_lm.generate`:
-
-```shell
-mlx_lm.generate \
-    --model <path_to_model> \
-    --adapter-path <path_to_adapters> \
-    --prompt "<your_model_prompt>"
-```
-
-## Fuse
-
-You can generate a model fused with the low-rank adapters using the
-`mlx_lm.fuse` command. This command also allows you to optionally:
-
-- Upload the fused model to the Hugging Face Hub.
-- Export the fused model to GGUF. Note GGUF support is limited to Mistral,
-  Mixtral, and Llama style models in fp16 precision.
-
-To see supported options run:
-
-```shell
-mlx_lm.fuse --help
-```
-
-To generate the fused model run:
-
-```shell
-mlx_lm.fuse --model <path_to_model>
-```
-
-This will by default load the adapters from `adapters/`, and save the fused
-model in the path `lora_fused_model/`. All of these are configurable.
-
-To upload a fused model, supply the `--upload-repo` and `--hf-path` arguments
-to `mlx_lm.fuse`. The latter is the repo name of the original model, which is
-useful for the sake of attribution and model versioning.
-
-For example, to fuse and upload a model derived from Mistral-7B-v0.1, run:
-
-```shell
-mlx_lm.fuse \
-    --model mistralai/Mistral-7B-v0.1 \
-    --upload-repo mlx-community/my-lora-mistral-7b \
-    --hf-path mistralai/Mistral-7B-v0.1
-```
-
-To export a fused model to GGUF, run:
-
-```shell
-mlx_lm.fuse \
-    --model mistralai/Mistral-7B-v0.1 \
-    --export-gguf
-```
-
-This will save the GGUF model in `lora_fused_model/ggml-model-f16.gguf`. You
-can specify the file name with `--gguf-path`.
-
-## Data
-
-The LoRA command expects you to provide a dataset with `--data`. The MLX
-Examples GitHub repo has an [example of the WikiSQL
-data](https://github.com/ml-explore/mlx-examples/tree/main/lora/data) in the
-correct format.
-
-For fine-tuning (`--train`), the data loader expects a `train.jsonl` and a
-`valid.jsonl` to be in the data directory. For evaluation (`--test`), the data
-loader expects a `test.jsonl` in the data directory.
-
-Currently, `*.jsonl` files support three data formats: `chat`,
-`completions`, and `text`. Here are three examples of these formats:
-
-`chat`:
-
-```jsonl
-{
-  "messages": [
-    {
-      "role": "system",
-      "content": "You are a helpful assistant."
-    },
-    {
-      "role": "user",
-      "content": "Hello."
-    },
-    {
-      "role": "assistant",
-      "content": "How can I assistant you today."
-    }
-  ]
-}
-```
-
-`completions`:
-
-```jsonl
-{
-  "prompt": "What is the capital of France?",
-  "completion": "Paris."
-}
-```
-
-`text`:
-
-```jsonl
-{
-  "text": "This is an example for the model."
-}
-```
-
-Note, the format is automatically determined by the dataset. Note also, keys in
-each line not expected by the loader will be ignored.
-
-For the `chat` and `completions` formats, Hugging Face [chat
-templates](https://huggingface.co/blog/chat-templates) are used. This applies
-the model's chat template by default. If the model does not have a chat
-template, then Hugging Face will use a default. For example, the final text in
-the `chat` example above with Hugging Face's default template becomes:
-
-```text
-<|im_start|>system
-You are a helpful assistant.<|im_end|>
-<|im_start|>user
-Hello.<|im_end|>
-<|im_start|>assistant
-How can I assistant you today.<|im_end|>
-```
-
-If you are unsure of the format to use, the `chat` or `completions` are good to
-start with. For custom requirements on the format of the dataset, use the
-`text` format to assemble the content yourself.
-
-## Memory Issues
-
-Fine-tuning a large model with LoRA requires a machine with a decent amount
-of memory. Here are some tips to reduce memory use should you need to do so:
-
-1. Try quantization (QLoRA). You can use QLoRA by generating a quantized model
-   with `convert.py` and the `-q` flag. See the [Setup](#setup) section for
-   more details.
-
-2. Try using a smaller batch size with `--batch-size`. The default is `4` so
-   setting this to `2` or `1` will reduce memory consumption. This may slow
-   things down a little, but will also reduce the memory use.
-
-3. Reduce the number of layers to fine-tune with `--lora-layers`. The default
-   is `16`, so you can try `8` or `4`. This reduces the amount of memory
-   needed for back propagation. It may also reduce the quality of the
-   fine-tuned model if you are fine-tuning with a lot of data.
-
-4. Longer examples require more memory. If it makes sense for your data, one thing
-   you can do is break your examples into smaller
-   sequences when making the `{train, valid, test}.jsonl` files.
-
-5. Gradient checkpointing lets you trade-off memory use (less) for computation
-   (more) by recomputing instead of storing intermediate values needed by the
-   backward pass. You can use gradient checkpointing by passing the
-   `--grad-checkpoint` flag. Gradient checkpointing will be more helpful for
-   larger batch sizes or sequence lengths with smaller or quantized models.
-
-For example, for a machine with 32 GB the following should run reasonably fast:
-
-```
-mlx_lm.lora \
-    --model mistralai/Mistral-7B-v0.1 \
-    --train \
-    --batch-size 1 \
-    --lora-layers 4 \
-    --data wikisql
-```
-
-The above command on an M1 Max with 32 GB runs at about 250
-tokens-per-second, using the MLX Example
-[`wikisql`](https://github.com/ml-explore/mlx-examples/tree/main/lora/data)
-data set.
-
-[^lora]: Refer to the [arXiv paper](https://arxiv.org/abs/2106.09685) for more details on LoRA.
-[^qlora]: Refer to the paper [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314)

llms/mlx_lm/MANAGE.md

@@ -1,22 +0,0 @@
-# Managing Models
-
-You can use `mlx-lm` to manage models downloaded locally in your machine. They
-are stored in the Hugging Face cache.
-
-Scan models: 
-
-```shell
-mlx_lm.manage --scan
-```
-
-Specify a `--pattern` to get info on a single or specific set of models:
-
-```shell
-mlx_lm.manage --scan --pattern mlx-community/Mistral-7B-Instruct-v0.2-4bit
-```
-
-To delete a model (or multiple models):
-
-```shell
-mlx_lm.manage --delete --pattern mlx-community/Mistral-7B-Instruct-v0.2-4bit
-```

llms/mlx_lm/MERGE.md

@@ -1,50 +0,0 @@
-# Model Merging
-
-You can use `mlx-lm` to merge models and upload them to the Hugging
-Face hub or save them locally for LoRA fine tuning.
-
-The main command is `mlx_lm.merge`:
-
-```shell
-mlx_lm.merge --config config.yaml 
-```
-
-The merged model will be saved by default in `mlx_merged_model`. To see a
-full list of options run:
-
-```shell
-mlx_lm.merge --help
-```
-
-Here is an example `config.yaml`:
-
-```yaml
-models:
-  - OpenPipe/mistral-ft-optimized-1218
-  - mlabonne/NeuralHermes-2.5-Mistral-7B
-method: slerp
-parameters:
-  t:
-    - filter: self_attn
-      value: [0, 0.5, 0.3, 0.7, 1]
-    - filter: mlp
-      value: [1, 0.5, 0.7, 0.3, 0]
-    - value: 0.5
-```
-
-The `models` field is a list of Hugging Face repo ids. The first model in the
-list is treated as the base model into which the remaining models are merged.
-
-The `method` field is the merging method. Right now `slerp` is the only
-supported method.
-
-The `parameters` are the corresponding parameters for the given `method`.
-Each parameter is a list with `filter` determining which layer the parameter
-applies to and `value` determining the actual value used. The last item in
-the list without a `filter` field is the default.
-
-If `value` is a list, it specifies the start and end values for the
-corresponding segment of blocks. In the example above, the models have 32
-blocks. For blocks 1-8, the layers with `self_attn` in the name will use the
-values `np.linspace(0, 0.5, 8)`, the same layers in the next 8 blocks (9-16)
-will use `np.linspace(0.5, 0.3, 8)`, and so on.

llms/mlx_lm/README.md

@@ -1,10 +0,0 @@
-## Generate Text with MLX and :hugs: Hugging Face
-
-This an example of large language model text generation that can pull models from
-the Hugging Face Hub.
-
-For more information on this example, see the [README](../README.md) in the
-parent directory.
-
-This package also supports fine tuning with LoRA or QLoRA. For more information
-see the [LoRA documentation](LORA.md).

llms/mlx_lm/SERVER.md

@@ -1,76 +0,0 @@
-# HTTP Model Server
-
-You use `mlx-lm` to make an HTTP API for generating text with any supported
-model. The HTTP API is intended to be similar to the [OpenAI chat
-API](https://platform.openai.com/docs/api-reference).
-
-> [!NOTE]  
-> The MLX LM server is not recommended for production as it only implements
-> basic security checks.
-
-Start the server with: 
-
-```shell
-mlx_lm.server --model <path_to_model_or_hf_repo>
-```
-
-For example:
-
-```shell
-mlx_lm.server --model mistralai/Mistral-7B-Instruct-v0.1
-```
-
-This will start a text generation server on port `8080` of the `localhost`
-using Mistral 7B instruct. The model will be downloaded from the provided
-Hugging Face repo if it is not already in the local cache.
-
-To see a full list of options run:
-
-```shell
-mlx_lm.server --help
-```
-
-You can make a request to the model by running:
-
-```shell
-curl localhost:8080/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -d '{
-     "messages": [{"role": "user", "content": "Say this is a test!"}],
-     "temperature": 0.7
-   }'
-```
-
-### Request Fields
-
-- `messages`: An array of message objects representing the conversation
-  history. Each message object should have a role (e.g. user, assistant) and
-  content (the message text).
-
-- `role_mapping`: (Optional) A dictionary to customize the role prefixes in
-  the generated prompt. If not provided, the default mappings are used.
-
-- `stop`: (Optional) An array of strings or a single string. Thesse are
-  sequences of tokens on which the generation should stop.
-
-- `max_tokens`: (Optional) An integer specifying the maximum number of tokens
-  to generate. Defaults to `100`.
-
-- `stream`: (Optional) A boolean indicating if the response should be
-  streamed. If true, responses are sent as they are generated. Defaults to
-  false.
-
-- `temperature`: (Optional) A float specifying the sampling temperature.
-  Defaults to `1.0`.
-
-- `top_p`: (Optional) A float specifying the nucleus sampling parameter.
-  Defaults to `1.0`.
-
-- `repetition_penalty`: (Optional) Applies a penalty to repeated tokens.
-  Defaults to `1.0`.
-
-- `repetition_context_size`: (Optional) The size of the context window for
-  applying repetition penalty. Defaults to `20`.
-
-- `logit_bias`: (Optional) A dictionary mapping token IDs to their bias
-  values. Defaults to `None`.

llms/mlx_lm/UPLOAD.md

@@ -1,37 +0,0 @@
-### Packaging for PyPI
-
-Install `build` and `twine`:
-
-```
-pip install --user --upgrade build
-pip install --user --upgrade twine
-```
-
-Generate the source distribution and wheel:
-
-```
-python -m build
-```
-
-> [!warning]
-> Use a test server first
-
-#### Test Upload
-
-Upload to test server:
-
-```
-python -m twine upload --repository testpypi dist/*
-```
-
-Install from test server and check that it works:
-
-```
-python -m pip install --index-url https://test.pypi.org/simple/ --no-deps mlx-lm
-```
-
-#### Upload
-
-```
-python -m twine upload dist/*
-```

llms/mlx_lm/__init__.py

@@ -1,4 +0,0 @@
-# Copyright © 2023-2024 Apple Inc.
-
-from .utils import convert, generate, load, stream_generate
-from .version import __version__

llms/mlx_lm/convert.py

@@ -1,62 +0,0 @@
-# Copyright © 2023-2024 Apple Inc.
-
-import argparse
-
-from .utils import convert
-
-
-def configure_parser() -> argparse.ArgumentParser:
-    """
-    Configures and returns the argument parser for the script.
-
-    Returns:
-        argparse.ArgumentParser: Configured argument parser.
-    """
-    parser = argparse.ArgumentParser(
-        description="Convert Hugging Face model to MLX format"
-    )
-
-    parser.add_argument("--hf-path", type=str, help="Path to the Hugging Face model.")
-    parser.add_argument(
-        "--mlx-path", type=str, default="mlx_model", help="Path to save the MLX model."
-    )
-    parser.add_argument(
-        "-q", "--quantize", help="Generate a quantized model.", action="store_true"
-    )
-    parser.add_argument(
-        "--q-group-size", help="Group size for quantization.", type=int, default=64
-    )
-    parser.add_argument(
-        "--q-bits", help="Bits per weight for quantization.", type=int, default=4
-    )
-    parser.add_argument(
-        "--dtype",
-        help="Type to save the parameters, ignored if -q is given.",
-        type=str,
-        choices=["float16", "bfloat16", "float32"],
-        default="float16",
-    )
-    parser.add_argument(
-        "--upload-repo",
-        help="The Hugging Face repo to upload the model to.",
-        type=str,
-        default=None,
-    )
-    parser.add_argument(
-        "-d",
-        "--dequantize",
-        help="Dequantize a quantized model.",
-        action="store_true",
-        default=False,
-    )
-    return parser
-
-
-def main():
-    parser = configure_parser()
-    args = parser.parse_args()
-    convert(**vars(args))
-
-
-if __name__ == "__main__":
-    main()

llms/mlx_lm/examples/lora_config.yaml

@@ -1,71 +0,0 @@
-# The path to the local model directory or Hugging Face repo.
-model: "mlx_model"
-# Whether or not to train (boolean)
-train: true
-
-# Directory with {train, valid, test}.jsonl files
-data: "/path/to/training/data"
-
-# The PRNG seed
-seed: 0
-
-# Number of layers to fine-tune
-lora_layers: 16
-
-# Minibatch size.
-batch_size: 4
-
-# Iterations to train for.
-iters: 1000
-
-# Number of validation batches, -1 uses the entire validation set.
-val_batches: 25
-
-# Adam learning rate.
-learning_rate: 1e-5
-
-# Number of training steps between loss reporting.
-steps_per_report: 10
-
-# Number of training steps between validations.
-steps_per_eval: 200
-
-# Load path to resume training with the given adapter weights.
-resume_adapter_file: null
-
-# Save/load path for the trained adapter weights.
-adapter_path: "adapters"
-
-# Save the model every N iterations.
-save_every: 100
-
-# Evaluate on the test set after training
-test: false
-
-# Number of test set batches, -1 uses the entire test set.
-test_batches: 100
-
-# Maximum sequence length.
-max_seq_length: 2048
-
-# Use gradient checkpointing to reduce memory use.
-grad_checkpoint: false
-
-# Use DoRA instead of LoRA.
-use_dora: false
-
-# LoRA parameters can only be specified in a config file
-lora_parameters:
-  # The layer keys to apply LoRA to.
-  # These will be applied for the last lora_layers
-  keys: ["self_attn.q_proj", "self_attn.v_proj"]
-  rank: 8
-  scale: 20.0
-  dropout: 0.0
-
-# Schedule can only be specified in a config file, uncomment to use.
-#lr_schedule:
-#  name: cosine_decay
-#  warmup: 100 # 0 for no warmup
-#  warmup_init: 1e-7 # 0 if not specified
-#  arguments: [1e-5, 1000, 1e-7] # passed to scheduler

llms/mlx_lm/examples/merge_config.yaml

@@ -1,11 +0,0 @@
-models: 
-  - OpenPipe/mistral-ft-optimized-1218
-  - mlabonne/NeuralHermes-2.5-Mistral-7B
-method: slerp
-parameters:
-  t:
-    - filter: self_attn
-      value: [0, 0.5, 0.3, 0.7, 1]
-    - filter: mlp
-      value: [1, 0.5, 0.7, 0.3, 0]
-    - value: 0.5

llms/mlx_lm/fuse.py

@@ -1,131 +0,0 @@
-import argparse
-import glob
-import shutil
-from pathlib import Path
-
-from mlx.utils import tree_flatten, tree_unflatten
-
-from .gguf import convert_to_gguf
-from .tuner.dora import DoRALinear
-from .tuner.lora import LoRALinear, LoRASwitchLinear
-from .tuner.utils import apply_lora_layers, dequantize
-from .utils import (
-    fetch_from_hub,
-    get_model_path,
-    save_config,
-    save_weights,
-    upload_to_hub,
-)
-
-
-def parse_arguments() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Fuse fine-tuned adapters into the base model."
-    )
-    parser.add_argument(
-        "--model",
-        default="mlx_model",
-        help="The path to the local model directory or Hugging Face repo.",
-    )
-    parser.add_argument(
-        "--save-path",
-        default="lora_fused_model",
-        help="The path to save the fused model.",
-    )
-    parser.add_argument(
-        "--adapter-path",
-        type=str,
-        default="adapters",
-        help="Path to the trained adapter weights and config.",
-    )
-    parser.add_argument(
-        "--hf-path",
-        type=str,
-        default=None,
-        help="Path to the original Hugging Face model. Required for upload if --model is a local directory.",
-    )
-    parser.add_argument(
-        "--upload-repo",
-        help="The Hugging Face repo to upload the model to.",
-        type=str,
-        default=None,
-    )
-    parser.add_argument(
-        "--de-quantize",
-        help="Generate a de-quantized model.",
-        action="store_true",
-    )
-    parser.add_argument(
-        "--export-gguf",
-        help="Export model weights in GGUF format.",
-        action="store_true",
-    )
-    parser.add_argument(
-        "--gguf-path",
-        help="Path to save the exported GGUF format model weights. Default is ggml-model-f16.gguf.",
-        default="ggml-model-f16.gguf",
-        type=str,
-    )
-    return parser.parse_args()
-
-
-def main() -> None:
-    print("Loading pretrained model")
-    args = parse_arguments()
-
-    model_path = get_model_path(args.model)
-    model, config, tokenizer = fetch_from_hub(model_path)
-
-    model.freeze()
-    model = apply_lora_layers(model, args.adapter_path)
-
-    fused_linears = [
-        (n, m.to_linear())
-        for n, m in model.named_modules()
-        if isinstance(m, (LoRASwitchLinear, LoRALinear, DoRALinear))
-    ]
-
-    model.update_modules(tree_unflatten(fused_linears))
-
-    if args.de_quantize:
-        print("De-quantizing model")
-        model = dequantize(model)
-
-    weights = dict(tree_flatten(model.parameters()))
-
-    save_path = Path(args.save_path)
-
-    save_weights(save_path, weights)
-
-    py_files = glob.glob(str(model_path / "*.py"))
-    for file in py_files:
-        shutil.copy(file, save_path)
-
-    tokenizer.save_pretrained(save_path)
-
-    if args.de_quantize:
-        config.pop("quantization", None)
-
-    save_config(config, config_path=save_path / "config.json")
-
-    if args.export_gguf:
-        model_type = config["model_type"]
-        if model_type not in ["llama", "mixtral", "mistral"]:
-            raise ValueError(
-                f"Model type {model_type} not supported for GGUF conversion."
-            )
-        convert_to_gguf(model_path, weights, config, str(save_path / args.gguf_path))
-
-    if args.upload_repo is not None:
-        hf_path = args.hf_path or (
-            args.model if not Path(args.model).exists() else None
-        )
-        if hf_path is None:
-            raise ValueError(
-                "Must provide original Hugging Face repo to upload local model."
-            )
-        upload_to_hub(args.save_path, args.upload_repo, hf_path)
-
-
-if __name__ == "__main__":
-    main()

llms/mlx_lm/generate.py

@@ -1,161 +0,0 @@
-# Copyright © 2023-2024 Apple Inc.
-
-import argparse
-
-import mlx.core as mx
-
-from .utils import generate, load
-
-DEFAULT_MODEL_PATH = "mlx_model"
-DEFAULT_PROMPT = "hello"
-DEFAULT_MAX_TOKENS = 100
-DEFAULT_TEMP = 0.6
-DEFAULT_TOP_P = 1.0
-DEFAULT_SEED = 0
-
-
-def setup_arg_parser():
-    """Set up and return the argument parser."""
-    parser = argparse.ArgumentParser(description="LLM inference script")
-    parser.add_argument(
-        "--model",
-        type=str,
-        default="mlx_model",
-        help="The path to the local model directory or Hugging Face repo.",
-    )
-    parser.add_argument(
-        "--adapter-path",
-        type=str,
-        help="Optional path for the trained adapter weights and config.",
-    )
-    parser.add_argument(
-        "--trust-remote-code",
-        action="store_true",
-        help="Enable trusting remote code for tokenizer",
-    )
-    parser.add_argument(
-        "--eos-token",
-        type=str,
-        default=None,
-        help="End of sequence token for tokenizer",
-    )
-    parser.add_argument(
-        "--prompt", default=DEFAULT_PROMPT, help="Message to be processed by the model"
-    )
-    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("--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(
-        "--colorize",
-        action="store_true",
-        help="Colorize output based on T[0] probability",
-    )
-    parser.add_argument(
-        "--cache-limit-gb",
-        type=int,
-        default=None,
-        help="Set the MLX cache limit in GB",
-        required=False,
-    )
-    return parser
-
-
-def colorprint(color, s):
-    color_codes = {
-        "black": 30,
-        "red": 31,
-        "green": 32,
-        "yellow": 33,
-        "blue": 34,
-        "magenta": 35,
-        "cyan": 36,
-        "white": 39,
-    }
-    ccode = color_codes.get(color, 30)
-    print(f"\033[1m\033[{ccode}m{s}\033[0m", end="", flush=True)
-
-
-def colorprint_by_t0(s, t0):
-    if t0 > 0.95:
-        color = "white"
-    elif t0 > 0.70:
-        color = "green"
-    elif t0 > 0.30:
-        color = "yellow"
-    else:
-        color = "red"
-    colorprint(color, s)
-
-
-def main():
-    parser = setup_arg_parser()
-    args = parser.parse_args()
-
-    mx.random.seed(args.seed)
-
-    if args.cache_limit_gb is not None:
-        mx.metal.set_cache_limit(args.cache_limit_gb * 1024 * 1024 * 1024)
-
-    # Building tokenizer_config
-    tokenizer_config = {"trust_remote_code": True if args.trust_remote_code else None}
-    if args.eos_token is not None:
-        tokenizer_config["eos_token"] = args.eos_token
-
-    model, tokenizer = load(
-        args.model,
-        adapter_path=args.adapter_path,
-        tokenizer_config=tokenizer_config,
-    )
-
-    if args.use_default_chat_template:
-        if tokenizer.chat_template is None:
-            tokenizer.chat_template = tokenizer.default_chat_template
-
-    if not args.ignore_chat_template and (
-        hasattr(tokenizer, "apply_chat_template")
-        and tokenizer.chat_template is not None
-    ):
-        messages = [{"role": "user", "content": args.prompt}]
-        prompt = tokenizer.apply_chat_template(
-            messages, tokenize=False, add_generation_prompt=True
-        )
-    else:
-        prompt = args.prompt
-
-    formatter = colorprint_by_t0 if args.colorize else None
-
-    generate(
-        model,
-        tokenizer,
-        prompt,
-        args.max_tokens,
-        verbose=True,
-        formatter=formatter,
-        temp=args.temp,
-        top_p=args.top_p,
-    )
-
-
-if __name__ == "__main__":
-    main()

llms/mlx_lm/gguf.py

@@ -1,313 +0,0 @@
-import re
-from enum import IntEnum
-from pathlib import Path
-from typing import Iterable, Optional, Set, Tuple, Union
-
-import mlx.core as mx
-from transformers import AutoTokenizer
-
-
-class TokenType(IntEnum):
-    NORMAL = 1
-    UNKNOWN = 2
-    CONTROL = 3
-    USER_DEFINED = 4
-    UNUSED = 5
-    BYTE = 6
-
-
-class GGMLFileType(IntEnum):
-    GGML_TYPE_F16 = 1
-
-
-# copied from https://github.com/ggerganov/llama.cpp/blob/master/convert.py#L455
-class HfVocab:
-    def __init__(
-        self,
-        fname_tokenizer: Path,
-        fname_added_tokens: Optional[Union[Path, None]] = None,
-    ) -> None:
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            fname_tokenizer,
-            cache_dir=fname_tokenizer,
-            local_files_only=True,
-        )
-        self.added_tokens_list = []
-        self.added_tokens_dict = dict()
-        self.added_tokens_ids = set()
-        for tok, tokidx in sorted(
-            self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]
-        ):
-            if tokidx >= self.tokenizer.vocab_size:
-                self.added_tokens_list.append(tok)
-                self.added_tokens_dict[tok] = tokidx
-                self.added_tokens_ids.add(tokidx)
-        self.specials = {
-            tok: self.tokenizer.get_vocab()[tok]
-            for tok in self.tokenizer.all_special_tokens
-        }
-        self.special_ids = set(self.tokenizer.all_special_ids)
-        self.vocab_size_base = self.tokenizer.vocab_size
-        self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
-        self.fname_tokenizer = fname_tokenizer
-        self.fname_added_tokens = fname_added_tokens
-
-    def hf_tokens(self) -> Iterable[Tuple[bytes, float, TokenType]]:
-        reverse_vocab = {
-            id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()
-        }
-        for token_id in range(self.vocab_size_base):
-            if token_id in self.added_tokens_ids:
-                continue
-            token_text = reverse_vocab[token_id].encode("utf-8")
-            yield token_text, self.get_token_score(token_id), self.get_token_type(
-                token_id, token_text, self.special_ids
-            )
-
-    def get_token_type(
-        self, token_id: int, token_text: bytes, special_ids: Set[int]
-    ) -> TokenType:
-        if re.fullmatch(rb"<0x[0-9A-Fa-f]{2}>", token_text):
-            return TokenType.BYTE
-        return TokenType.CONTROL if token_id in special_ids else TokenType.NORMAL
-
-    def get_token_score(self, token_id: int) -> float:
-        return -1000.0
-
-    def added_tokens(self) -> Iterable[Tuple[bytes, float, TokenType]]:
-        for text in self.added_tokens_list:
-            if text in self.specials:
-                toktype = self.get_token_type(
-                    self.specials[text], b"", self.special_ids
-                )
-                score = self.get_token_score(self.specials[text])
-            else:
-                toktype = TokenType.USER_DEFINED
-                score = -1000.0
-            yield text.encode("utf-8"), score, toktype
-
-    def has_newline_token(self):
-        return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab
-
-    def all_tokens(self) -> Iterable[Tuple[bytes, float, TokenType]]:
-        yield from self.hf_tokens()
-        yield from self.added_tokens()
-
-    def __repr__(self) -> str:
-        return f"<HfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
-
-    @staticmethod
-    def load(path: Path) -> "HfVocab":
-        added_tokens_path = path.parent / "added_tokens.json"
-        return HfVocab(path, added_tokens_path if added_tokens_path.exists() else None)
-
-
-def translate_weight_names(name):
-    name = name.replace("model.layers.", "blk.")
-    # for mixtral gate
-    name = name.replace("block_sparse_moe.gate", "ffn_gate_inp")
-    # for mixtral experts ffns
-    pattern = r"block_sparse_moe\.experts\.(\d+)\.w1\.weight"
-    replacement = r"ffn_gate.\1.weight"
-    name = re.sub(pattern, replacement, name)
-    pattern = r"block_sparse_moe\.experts\.(\d+)\.w2\.weight"
-    replacement = r"ffn_down.\1.weight"
-    name = re.sub(pattern, replacement, name)
-    pattern = r"block_sparse_moe\.experts\.(\d+)\.w3\.weight"
-    replacement = r"ffn_up.\1.weight"
-    name = re.sub(pattern, replacement, name)
-
-    name = name.replace("mlp.gate_proj", "ffn_gate")
-    name = name.replace("mlp.down_proj", "ffn_down")
-    name = name.replace("mlp.up_proj", "ffn_up")
-    name = name.replace("self_attn.q_proj", "attn_q")
-    name = name.replace("self_attn.k_proj", "attn_k")
-    name = name.replace("self_attn.v_proj", "attn_v")
-    name = name.replace("self_attn.o_proj", "attn_output")
-    name = name.replace("input_layernorm", "attn_norm")
-    name = name.replace("post_attention_layernorm", "ffn_norm")
-    name = name.replace("model.embed_tokens", "token_embd")
-    name = name.replace("model.norm", "output_norm")
-    name = name.replace("lm_head", "output")
-    return name
-
-
-def permute_weights(weights, n_head, n_head_kv=None):
-    if n_head_kv is not None and n_head != n_head_kv:
-        n_head = n_head_kv
-    reshaped = weights.reshape(
-        n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]
-    )
-    swapped = reshaped.swapaxes(1, 2)
-    final_shape = weights.shape
-    return swapped.reshape(final_shape)
-
-
-def prepare_metadata(config, vocab):
-    metadata = {
-        "general.name": "llama",
-        "llama.context_length": (
-            mx.array(config["max_position_embeddings"], dtype=mx.uint32)
-            if config.get("max_position_embeddings") is not None
-            else None
-        ),
-        "llama.embedding_length": (
-            mx.array(config["hidden_size"], dtype=mx.uint32)
-            if config.get("hidden_size") is not None
-            else None
-        ),
-        "llama.block_count": (
-            mx.array(config["num_hidden_layers"], dtype=mx.uint32)
-            if config.get("num_hidden_layers") is not None
-            else None
-        ),
-        "llama.feed_forward_length": (
-            mx.array(config["intermediate_size"], dtype=mx.uint32)
-            if config.get("intermediate_size") is not None
-            else None
-        ),
-        "llama.rope.dimension_count": (
-            mx.array(
-                config["hidden_size"] // config["num_attention_heads"], dtype=mx.uint32
-            )
-            if config.get("hidden_size") is not None
-            and config.get("num_attention_heads") is not None
-            else None
-        ),
-        "llama.attention.head_count": (
-            mx.array(config["num_attention_heads"], dtype=mx.uint32)
-            if config.get("num_attention_heads") is not None
-            else None
-        ),
-        "llama.attention.head_count_kv": (
-            mx.array(
-                config.get("num_key_value_heads", config["num_attention_heads"]),
-                dtype=mx.uint32,
-            )
-            if config.get("num_attention_heads") is not None
-            else None
-        ),
-        "llama.expert_count": (
-            mx.array(config.get("num_local_experts", None), dtype=mx.uint32)
-            if config.get("num_local_experts") is not None
-            else None
-        ),
-        "llama.expert_used_count": (
-            mx.array(config.get("num_experts_per_tok", None), dtype=mx.uint32)
-            if config.get("num_experts_per_tok") is not None
-            else None
-        ),
-        "llama.attention.layer_norm_rms_epsilon": (
-            mx.array(config.get("rms_norm_eps", 1e-05))
-            if config.get("rms_norm_eps") is not None
-            else None
-        ),
-        "llama.rope.freq_base": (
-            mx.array(config.get("rope_theta", 10000), dtype=mx.float32)
-            if config.get("rope_theta") is not None
-            else None
-        ),
-    }
-
-    rope_scaling = config.get("rope_scaling")
-    if rope_scaling is not None and (typ := rope_scaling.get("type")):
-        rope_factor = rope_scaling.get("factor")
-        f_rope_scale = rope_factor
-        if typ == "linear":
-            rope_scaling_type = "linear"
-            metadata["llama.rope.scaling.type"] = rope_scaling_type
-            metadata["llama.rope.scaling.factor"] = mx.array(f_rope_scale)
-
-    metadata["general.file_type"] = mx.array(
-        GGMLFileType.GGML_TYPE_F16.value,
-        dtype=mx.uint32,
-    )
-    metadata["general.quantization_version"] = mx.array(
-        GGMLFileType.GGML_TYPE_F16.value,
-        dtype=mx.uint32,
-    )
-    metadata["general.name"] = config.get("_name_or_path", "llama").split("/")[-1]
-    metadata["general.architecture"] = "llama"
-    metadata["general.alignment"] = mx.array(32, dtype=mx.uint32)
-
-    # add metadata for vocab
-    metadata["tokenizer.ggml.model"] = "llama"
-    tokens = []
-    scores = []
-    toktypes = []
-    for text, score, toktype in vocab.all_tokens():
-        tokens.append(text)
-        scores.append(score)
-        toktypes.append(toktype.value)
-    assert len(tokens) == vocab.vocab_size
-    metadata["tokenizer.ggml.tokens"] = tokens
-    metadata["tokenizer.ggml.scores"] = mx.array(scores, dtype=mx.float32)
-    metadata["tokenizer.ggml.token_type"] = mx.array(toktypes, dtype=mx.uint32)
-    metadata["tokenizer.ggml.bos_token_id"] = mx.array(
-        vocab.tokenizer.bos_token_id, dtype=mx.uint32
-    )
-    metadata["tokenizer.ggml.eos_token_id"] = mx.array(
-        vocab.tokenizer.eos_token_id, dtype=mx.uint32
-    )
-    metadata["tokenizer.ggml.unknown_token_id"] = mx.array(
-        vocab.tokenizer.unk_token_id, dtype=mx.uint32
-    )
-
-    metadata = {k: v for k, v in metadata.items() if v is not None}
-    return metadata
-
-
-def convert_to_gguf(
-    model_path: Union[str, Path],
-    weights: dict,
-    config: dict,
-    output_file_path: str,
-):
-    if isinstance(model_path, str):
-        model_path = Path(model_path)
-
-    quantization = config.get("quantization", None)
-    if quantization:
-        raise NotImplementedError(
-            "Conversion of quantized models is not yet supported."
-        )
-    print("Converting to GGUF format")
-    # https://github.com/ggerganov/llama.cpp/blob/master/convert.py#L1182 seems relate to llama.cpp's multihead attention
-    weights = {
-        k: (
-            permute_weights(
-                v, config["num_attention_heads"], config["num_attention_heads"]
-            )
-            if "self_attn.q_proj.weight" in k
-            else (
-                permute_weights(
-                    v, config["num_attention_heads"], config["num_key_value_heads"]
-                )
-                if "self_attn.k_proj.weight" in k
-                else v
-            )
-        )
-        for k, v in weights.items()
-    }
-
-    # rename weights for gguf format
-    weights = {translate_weight_names(k): v for k, v in weights.items()}
-
-    if not (model_path / "tokenizer.json").exists():
-        raise ValueError("Tokenizer json not found")
-
-    vocab = HfVocab.load(model_path)
-    metadata = prepare_metadata(config, vocab)
-
-    weights = {
-        k: (
-            v.astype(mx.float32).astype(mx.float16)
-            if v.dtype == mx.bfloat16
-            else v.astype(mx.float32) if "norm" in k else v
-        )
-        for k, v in weights.items()
-    }
-
-    output_file_path = output_file_path
-    mx.save_gguf(output_file_path, weights, metadata)
-    print(f"Converted GGUF model saved as: {output_file_path}")

llms/mlx_lm/lora.py

@@ -1,278 +0,0 @@
-# Copyright © 2024 Apple Inc.
-
-import argparse
-import math
-import re
-import types
-from pathlib import Path
-
-import mlx.nn as nn
-import mlx.optimizers as optim
-import numpy as np
-import yaml
-
-from .tokenizer_utils import TokenizerWrapper
-from .tuner.datasets import load_dataset
-from .tuner.trainer import TrainingArgs, TrainingCallback, evaluate, train
-from .tuner.utils import (
-    apply_lora_layers,
-    build_schedule,
-    linear_to_lora_layers,
-    print_trainable_parameters,
-)
-from .utils import load, save_config
-
-yaml_loader = yaml.SafeLoader
-yaml_loader.add_implicit_resolver(
-    "tag:yaml.org,2002:float",
-    re.compile(
-        """^(?:
-     [-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)?
-    |[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+)
-    |\\.[0-9_]+(?:[eE][-+][0-9]+)?
-    |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]*
-    |[-+]?\\.(?:inf|Inf|INF)
-    |\\.(?:nan|NaN|NAN))$""",
-        re.X,
-    ),
-    list("-+0123456789."),
-)
-
-CONFIG_DEFAULTS = {
-    "model": "mlx_model",
-    "train": False,
-    "data": "data/",
-    "seed": 0,
-    "lora_layers": 16,
-    "batch_size": 4,
-    "iters": 1000,
-    "val_batches": 25,
-    "learning_rate": 1e-5,
-    "steps_per_report": 10,
-    "steps_per_eval": 200,
-    "resume_adapter_file": None,
-    "adapter_path": "adapters",
-    "save_every": 100,
-    "test": False,
-    "test_batches": 500,
-    "max_seq_length": 2048,
-    "lr_schedule": None,
-    "lora_parameters": {"rank": 8, "alpha": 16, "dropout": 0.0, "scale": 10.0},
-    "use_dora": False,
-}
-
-
-def build_parser():
-    parser = argparse.ArgumentParser(description="LoRA or QLoRA finetuning.")
-    parser.add_argument(
-        "--model",
-        help="The path to the local model directory or Hugging Face repo.",
-    )
-
-    # Training args
-    parser.add_argument(
-        "--train",
-        action="store_true",
-        help="Do training",
-        default=None,
-    )
-    parser.add_argument(
-        "--data",
-        type=str,
-        help="Directory with {train, valid, test}.jsonl files",
-    )
-    parser.add_argument(
-        "--lora-layers",
-        type=int,
-        help="Number of layers to fine-tune. Default is 16, use -1 for all.",
-    )
-    parser.add_argument("--batch-size", type=int, help="Minibatch size.")
-    parser.add_argument("--iters", type=int, help="Iterations to train for.")
-    parser.add_argument(
-        "--val-batches",
-        type=int,
-        help="Number of validation batches, -1 uses the entire validation set.",
-    )
-    parser.add_argument("--learning-rate", type=float, help="Adam learning rate.")
-    parser.add_argument(
-        "--steps-per-report",
-        type=int,
-        help="Number of training steps between loss reporting.",
-    )
-    parser.add_argument(
-        "--steps-per-eval",
-        type=int,
-        help="Number of training steps between validations.",
-    )
-    parser.add_argument(
-        "--resume-adapter-file",
-        type=str,
-        help="Load path to resume training with the given adapters.",
-    )
-    parser.add_argument(
-        "--adapter-path",
-        type=str,
-        help="Save/load path for the adapters.",
-    )
-    parser.add_argument(
-        "--save-every",
-        type=int,
-        help="Save the model every N iterations.",
-    )
-    parser.add_argument(
-        "--test",
-        action="store_true",
-        help="Evaluate on the test set after training",
-        default=None,
-    )
-    parser.add_argument(
-        "--test-batches",
-        type=int,
-        help="Number of test set batches, -1 uses the entire test set.",
-    )
-    parser.add_argument(
-        "--max-seq-length",
-        type=int,
-        help="Maximum sequence length.",
-    )
-    parser.add_argument(
-        "-c",
-        "--config",
-        default=None,
-        help="A YAML configuration file with the training options",
-    )
-    parser.add_argument(
-        "--grad-checkpoint",
-        action="store_true",
-        help="Use gradient checkpointing to reduce memory use.",
-        default=None,
-    )
-    parser.add_argument("--seed", type=int, default=None, help="The PRNG seed")
-    parser.add_argument(
-        "--use-dora", action="store_true", default=None, help="Use DoRA to finetune."
-    )
-    return parser
-
-
-def train_model(
-    args,
-    model: nn.Module,
-    tokenizer: TokenizerWrapper,
-    train_set,
-    valid_set,
-    training_callback: TrainingCallback = None,
-):
-    # Freeze all layers
-    model.freeze()
-
-    # Convert linear layers to lora layers and unfreeze in the process
-    linear_to_lora_layers(model, args.lora_layers, args.lora_parameters)
-
-    # Resume training the given adapters.
-    if args.resume_adapter_file is not None:
-        print(f"Loading pretrained adapters from {args.resume_adapter_file}")
-        model.load_weights(args.resume_adapter_file, strict=False)
-
-    print_trainable_parameters(model)
-
-    adapter_path = Path(args.adapter_path)
-    adapter_path.mkdir(parents=True, exist_ok=True)
-    adapter_file = adapter_path / "adapters.safetensors"
-    save_config(vars(args), adapter_path / "adapter_config.json")
-
-    # init training args
-    training_args = TrainingArgs(
-        batch_size=args.batch_size,
-        iters=args.iters,
-        val_batches=args.val_batches,
-        steps_per_report=args.steps_per_report,
-        steps_per_eval=args.steps_per_eval,
-        steps_per_save=args.save_every,
-        adapter_file=adapter_file,
-        max_seq_length=args.max_seq_length,
-        grad_checkpoint=args.grad_checkpoint,
-    )
-
-    model.train()
-    opt = optim.Adam(
-        learning_rate=(
-            build_schedule(args.lr_schedule) if args.lr_schedule else args.learning_rate
-        )
-    )
-    # Train model
-    train(
-        model=model,
-        tokenizer=tokenizer,
-        args=training_args,
-        optimizer=opt,
-        train_dataset=train_set,
-        val_dataset=valid_set,
-        training_callback=training_callback,
-    )
-
-
-def evaluate_model(args, model: nn.Module, tokenizer: TokenizerWrapper, test_set):
-    model.eval()
-
-    test_loss = evaluate(
-        model=model,
-        dataset=test_set,
-        tokenizer=tokenizer,
-        batch_size=args.batch_size,
-        num_batches=args.test_batches,
-        max_seq_length=args.max_seq_length,
-    )
-
-    test_ppl = math.exp(test_loss)
-
-    print(f"Test loss {test_loss:.3f}, Test ppl {test_ppl:.3f}.")
-
-
-def run(args, training_callback: TrainingCallback = None):
-    np.random.seed(args.seed)
-
-    print("Loading pretrained model")
-    model, tokenizer = load(args.model)
-
-    print("Loading datasets")
-    train_set, valid_set, test_set = load_dataset(args, tokenizer)
-
-    if args.test and not args.train:
-        # Allow testing without LoRA layers by providing empty path
-        if args.adapter_path != "":
-            apply_lora_layers(model, args.adapter_path)
-
-    elif args.train:
-        print("Training")
-        train_model(args, model, tokenizer, train_set, valid_set, training_callback)
-    else:
-        raise ValueError("Must provide at least one of --train or --test")
-
-    if args.test:
-        print("Testing")
-        evaluate_model(args, model, tokenizer, test_set)
-
-
-def main():
-    parser = build_parser()
-    args = parser.parse_args()
-    config = args.config
-    args = vars(args)
-    if config:
-        print("Loading configuration file", config)
-        with open(config, "r") as file:
-            config = yaml.load(file, yaml_loader)
-        # Prefer parameters from command-line arguments
-        for k, v in config.items():
-            if args.get(k, None) is None:
-                args[k] = v
-
-    # Update defaults for unspecified parameters
-    for k, v in CONFIG_DEFAULTS.items():
-        if args.get(k, None) is None:
-            args[k] = v
-    run(types.SimpleNamespace(**args))
-
-
-if __name__ == "__main__":
-    main()

llms/mlx_lm/manage.py

@@ -1,121 +0,0 @@
-import argparse
-from typing import List, Union
-
-from huggingface_hub import scan_cache_dir
-from transformers.commands.user import tabulate
-
-
-def ask_for_confirmation(message: str) -> bool:
-    y = ("y", "yes", "1")
-    n = ("n", "no", "0")
-    all_values = y + n + ("",)
-    full_message = f"{message} (Y/n) "
-    while True:
-        answer = input(full_message).lower()
-        if answer == "":
-            return False
-        if answer in y:
-            return True
-        if answer in n:
-            return False
-        print(f"Invalid input. Must be one of {all_values}")
-
-
-def main():
-    parser = argparse.ArgumentParser(description="MLX Model Cache.")
-    parser.add_argument(
-        "--scan",
-        action="store_true",
-        help="Scan Hugging Face cache for mlx models.",
-    )
-    parser.add_argument(
-        "--delete",
-        action="store_true",
-        help="Delete models matching the given pattern.",
-    )
-    parser.add_argument(
-        "--pattern",
-        type=str,
-        help="Model repos contain the pattern.",
-        default="mlx",
-    )
-
-    args = parser.parse_args()
-
-    if args.scan:
-        print(
-            "Scanning Hugging Face cache for models with" f'pattern "{args.pattern}".'
-        )
-        hf_cache_info = scan_cache_dir()
-        print(
-            tabulate(
-                rows=[
-                    [
-                        repo.repo_id,
-                        repo.repo_type,
-                        "{:>12}".format(repo.size_on_disk_str),
-                        repo.nb_files,
-                        repo.last_accessed_str,
-                        repo.last_modified_str,
-                        str(repo.repo_path),
-                    ]
-                    for repo in sorted(
-                        hf_cache_info.repos, key=lambda repo: repo.repo_path
-                    )
-                    if args.pattern in repo.repo_id
-                ],
-                headers=[
-                    "REPO ID",
-                    "REPO TYPE",
-                    "SIZE ON DISK",
-                    "NB FILES",
-                    "LAST_ACCESSED",
-                    "LAST_MODIFIED",
-                    "LOCAL PATH",
-                ],
-            )
-        )
-
-    if args.delete:
-        print(f'Deleting models matching pattern "{args.pattern}"')
-        hf_cache_info = scan_cache_dir()
-
-        repos = [
-            repo
-            for repo in sorted(hf_cache_info.repos, key=lambda repo: repo.repo_path)
-            if args.pattern in repo.repo_id
-        ]
-        if repos:
-            print(
-                tabulate(
-                    rows=[
-                        [
-                            repo.repo_id,
-                            str(repo.repo_path),
-                        ]
-                        for repo in repos
-                    ],
-                    headers=[
-                        "REPO ID",
-                        "LOCAL PATH",
-                    ],
-                )
-            )
-
-            confirmed = ask_for_confirmation(f"Confirm deletion ?")
-            if confirmed:
-                for model_info in repos:
-                    for revision in sorted(
-                        model_info.revisions, key=lambda revision: revision.commit_hash
-                    ):
-                        strategy = hf_cache_info.delete_revisions(revision.commit_hash)
-                        strategy.execute()
-                print("Model(s) deleted.")
-            else:
-                print("Deletion is cancelled. Do nothing.")
-        else:
-            print(f"No models found.")
-
-
-if __name__ == "__main__":
-    main()

llms/mlx_lm/merge.py

@@ -1,172 +0,0 @@
-# Copyright © 2023-2024 Apple Inc.
-
-import argparse
-import glob
-import shutil
-from pathlib import Path
-from typing import Optional
-
-import mlx.core as mx
-import mlx.nn as nn
-import numpy as np
-import yaml
-from mlx.utils import tree_flatten, tree_map
-
-from .utils import (
-    fetch_from_hub,
-    get_model_path,
-    save_config,
-    save_weights,
-    upload_to_hub,
-)
-
-
-def configure_parser() -> argparse.ArgumentParser:
-    """
-    Configures and returns the argument parser for the script.
-
-    Returns:
-        argparse.ArgumentParser: Configured argument parser.
-    """
-    parser = argparse.ArgumentParser(description="Merge multiple models.")
-
-    parser.add_argument("--config", type=str, help="Path to the YAML config.")
-    parser.add_argument(
-        "--mlx-path",
-        type=str,
-        default="mlx_merged_model",
-        help="Path to save the MLX model.",
-    )
-    parser.add_argument(
-        "--upload-repo",
-        help="The Hugging Face repo to upload the model to.",
-        type=str,
-        default=None,
-    )
-    return parser
-
-
-def slerp(t, w1, w2, eps=1e-5):
-    """
-    Spherical linear interpolation
-
-    Args:
-        t (float): Interpolation weight in [0.0, 1.0]
-        w1 (mx.array): First input
-        w2 (mx.array): Second input
-        eps (float): Constant for numerical stability
-    Returns:
-        mx.array: Interpolated result
-    """
-    t = float(t)
-    if t == 0:
-        return w1
-    elif t == 1:
-        return w2
-    # Normalize
-    v1 = w1 / mx.linalg.norm(w1)
-    v2 = w2 / mx.linalg.norm(w2)
-    # Angle
-    dot = mx.clip((v1 * v2).sum(), 0.0, 1.0)
-    theta = mx.arccos(dot)
-    sin_theta = mx.sin(theta + eps)
-    s1 = mx.sin(theta * (1 - t)) / sin_theta
-    s2 = mx.sin(theta * t) / sin_theta
-    return s1 * w1 + s2 * w2
-
-
-def merge_models(base_model: nn.Module, model: nn.Module, config: dict):
-    method = config.get("method", None)
-    if method != "slerp":
-        raise ValueError(f"Merge method {method} not supported")
-
-    num_layers = len(model.layers)
-
-    def unpack_values(vals):
-        if isinstance(vals, (int, float)):
-            return np.full(num_layers, vals)
-        bins = len(vals) - 1
-        sizes = [num_layers // bins] * bins
-        sizes[-1] = num_layers - sum(sizes[:-1])
-        return np.concatenate(
-            [np.linspace(v1, v2, s) for v1, v2, s in zip(vals[:-1], vals[1:], sizes)]
-        )
-
-    param_list = config["parameters"]["t"]
-    params = {}
-    filter_keys = set()
-    for pl in param_list[:-1]:
-        params[pl["filter"]] = unpack_values(pl["value"])
-        filter_keys.add(pl["filter"])
-    default = unpack_values(param_list[-1]["value"])
-
-    for e in range(num_layers):
-        bl = base_model.layers[e]
-        l = model.layers[e]
-        base_weights = bl.parameters()
-        weights = l.parameters()
-        for k, w1 in base_weights.items():
-            w2 = weights[k]
-            t = params.get(k, default)[e]
-            base_weights[k] = tree_map(lambda x, y: slerp(t, x, y), w1, w2)
-        base_model.update(base_weights)
-
-
-def merge(
-    config: str,
-    mlx_path: str = "mlx_model",
-    upload_repo: Optional[str] = None,
-):
-    with open(config, "r") as fid:
-        merge_conf = yaml.safe_load(fid)
-    print("[INFO] Loading")
-
-    model_paths = merge_conf.get("models", [])
-    if len(model_paths) < 2:
-        raise ValueError(f"Expected at least 2 models, got {len(model_paths)}.")
-
-    # Load all models
-    base_hf_path = model_paths[0]
-    base_path = get_model_path(base_hf_path)
-    base_model, base_config, tokenizer = fetch_from_hub(base_path, lazy=True)
-    models = []
-    for mp in model_paths[1:]:
-        model, model_config, _ = fetch_from_hub(get_model_path(mp), lazy=True)
-        base_type = base_config["model_type"]
-        model_type = model_config["model_type"]
-        if base_type != model_type:
-            raise ValueError(
-                f"Can only merge models of the same type,"
-                f" but got {base_type} and {model_type}."
-            )
-        models.append(model)
-
-    # Merge models into base model
-    for m in models:
-        merge_models(base_model, m, merge_conf)
-
-    # Save base model
-    mlx_path = Path(mlx_path)
-    weights = dict(tree_flatten(base_model.parameters()))
-    del models, base_model
-    save_weights(mlx_path, weights, donate_weights=True)
-    py_files = glob.glob(str(base_path / "*.py"))
-    for file in py_files:
-        shutil.copy(file, mlx_path)
-
-    tokenizer.save_pretrained(mlx_path)
-
-    save_config(config, config_path=mlx_path / "config.json")
-
-    if upload_repo is not None:
-        upload_to_hub(mlx_path, upload_repo, base_hf_path)
-
-
-def main():
-    parser = configure_parser()
-    args = parser.parse_args()
-    merge(**vars(args))
-
-
-if __name__ == "__main__":
-    main()

llms/mlx_lm/models/base.py

@@ -1,56 +0,0 @@
-import inspect
-from dataclasses import dataclass
-
-import mlx.core as mx
-
-
-def create_additive_causal_mask(N: int, offset: int = 0):
-    rinds = mx.arange(offset + N)
-    linds = mx.arange(offset, offset + N) if offset else rinds
-    mask = linds[:, None] < rinds[None]
-    return mask * -1e9
-
-
-class KVCache:
-
-    def __init__(self, head_dim, n_kv_heads):
-        self.n_kv_heads = n_kv_heads
-        self.head_dim = head_dim
-        self.keys = None
-        self.values = None
-        self.offset = 0
-        self.step = 256
-
-    def update_and_fetch(self, keys, values):
-        prev = self.offset
-        if self.keys is None or (prev + keys.shape[2]) > self.keys.shape[2]:
-            n_steps = (self.step + keys.shape[2] - 1) // self.step
-            shape = (1, self.n_kv_heads, n_steps * self.step, self.head_dim)
-            new_k = mx.zeros(shape, keys.dtype)
-            new_v = mx.zeros(shape, values.dtype)
-            if self.keys is not None:
-                if prev % self.step != 0:
-                    self.keys = self.keys[..., :prev, :]
-                    self.values = self.values[..., :prev, :]
-                self.keys = mx.concatenate([self.keys, new_k], axis=2)
-                self.values = mx.concatenate([self.values, new_v], axis=2)
-            else:
-                self.keys, self.values = new_k, new_v
-
-        self.offset += keys.shape[2]
-        self.keys[..., prev : self.offset, :] = keys
-        self.values[..., prev : self.offset, :] = values
-        return self.keys[..., : self.offset, :], self.values[..., : self.offset, :]
-
-
-@dataclass
-class BaseModelArgs:
-    @classmethod
-    def from_dict(cls, params):
-        return cls(
-            **{
-                k: v
-                for k, v in params.items()
-                if k in inspect.signature(cls).parameters
-            }
-        )

llms/mlx_lm/models/cohere.py

@@ -1,201 +0,0 @@
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int = 8192
-    num_hidden_layers: int = 40
-    intermediate_size: int = 22528
-    num_attention_heads: int = 64
-    num_key_value_heads: int = 64
-    rope_theta: float = 8000000.0
-    vocab_size: int = 256000
-    layer_norm_eps: float = 1e-05
-    logit_scale: float = 0.0625
-    attention_bias: bool = False
-    layer_norm_bias: bool = False
-    use_qk_norm: bool = False
-
-
-class LayerNorm2D(nn.Module):
-
-    def __init__(self, d1, d2, eps):
-        super().__init__()
-        self.weight = mx.zeros((d1, d2))
-        self.eps = eps
-
-    def __call__(self, x):
-        return self.weight * mx.fast.layer_norm(x, None, None, self.eps)
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-
-        dim = args.hidden_size
-        self.n_heads = n_heads = args.num_attention_heads
-        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
-
-        head_dim = args.hidden_size // args.num_attention_heads
-        self.scale = head_dim**-0.5
-
-        attetion_bias = args.attention_bias
-
-        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=attetion_bias)
-        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attetion_bias)
-        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attetion_bias)
-        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=attetion_bias)
-
-        self.use_qk_norm = args.use_qk_norm
-        if self.use_qk_norm:
-            self.q_norm = LayerNorm2D(self.n_heads, head_dim, eps=args.layer_norm_eps)
-            self.k_norm = LayerNorm2D(
-                self.n_kv_heads, head_dim, eps=args.layer_norm_eps
-            )
-
-        self.rope = nn.RoPE(head_dim, traditional=True, base=args.rope_theta)
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
-
-        queries = queries.reshape(B, L, self.n_heads, -1)
-        keys = keys.reshape(B, L, self.n_kv_heads, -1)
-        if self.use_qk_norm:
-            queries = self.q_norm(queries)
-            keys = self.k_norm(keys)
-
-        queries = queries.transpose(0, 2, 1, 3)
-        keys = keys.transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.o_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, dim, hidden_dim):
-        super().__init__()
-        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
-        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
-        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
-
-    def __call__(self, x):
-        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.hidden_size = args.hidden_size
-        self.n_heads = args.num_attention_heads
-
-        self.self_attn = Attention(args)
-        self.mlp = MLP(args.hidden_size, args.intermediate_size)
-        self.input_layernorm = nn.LayerNorm(
-            args.hidden_size, eps=args.layer_norm_eps, bias=args.layer_norm_bias
-        )
-        self.args = args
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        h = self.input_layernorm(x)
-        attn_h = self.self_attn(h, mask, cache)
-        ff_h = self.mlp(h)
-        return attn_h + ff_h + x
-
-
-class CohereModel(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.vocab_size = args.vocab_size
-        self.num_hidden_layers = args.num_hidden_layers
-        assert self.vocab_size > 0
-        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [
-            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
-        ]
-        self.norm = nn.LayerNorm(
-            args.hidden_size, eps=args.layer_norm_eps, bias=args.layer_norm_bias
-        )
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.embed_tokens(inputs)
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.model_type = args.model_type
-        self.model = CohereModel(args)
-        self.args = args
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-        out = self.model.embed_tokens.as_linear(out)
-        out = out * self.model.args.logit_scale
-        return out
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/dbrx.py

@@ -1,261 +0,0 @@
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import mlx.core as mx
-import mlx.nn as nn
-import numpy as np
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    vocab_size: int
-    d_model: int
-    ffn_config: dict
-    attn_config: dict
-    n_layers: int
-    n_heads: int
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.num_heads = args.n_heads
-        self.d_model = args.d_model
-        self.head_dim = args.d_model // args.n_heads
-        self.num_key_value_heads = args.attn_config["kv_n_heads"]
-        self.clip_qkv = args.attn_config["clip_qkv"]
-        self.rope_theta = args.attn_config["rope_theta"]
-
-        self.scale = self.head_dim**-0.5
-
-        self.Wqkv = nn.Linear(
-            args.d_model,
-            (self.num_key_value_heads * 2 + self.num_heads) * self.head_dim,
-            bias=False,
-        )
-        self.out_proj = nn.Linear(args.d_model, args.d_model, bias=False)
-        self.rope = nn.RoPE(
-            self.head_dim,
-            traditional=False,
-            base=self.rope_theta,
-        )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-
-        qkv = self.Wqkv(x)
-        qkv = mx.clip(qkv, a_min=-self.clip_qkv, a_max=self.clip_qkv)
-        splits = [self.d_model, self.d_model + self.head_dim * self.num_key_value_heads]
-        queries, keys, values = mx.split(qkv, splits, axis=-1)
-
-        B, L, D = x.shape
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.num_key_value_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.num_key_value_heads, -1).transpose(
-            0, 2, 1, 3
-        )
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.out_proj(output)
-
-
-class NormAttnNorm(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.norm_1 = nn.LayerNorm(args.d_model, bias=False)
-        self.norm_2 = nn.LayerNorm(args.d_model, bias=False)
-        self.attn = Attention(args)
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        h = self.attn(self.norm_1(x), mask=mask, cache=cache)
-        x = h + x
-        return x, self.norm_2(x)
-
-
-class MLP(nn.Module):
-    def __init__(self, d_model: int, ffn_dim: int):
-        super().__init__()
-        self.v1 = nn.Linear(d_model, ffn_dim, bias=False)
-        self.w1 = nn.Linear(d_model, ffn_dim, bias=False)
-        self.w2 = nn.Linear(ffn_dim, d_model, bias=False)
-        self.act_fn = nn.silu
-
-    def __call__(self, x: mx.array) -> mx.array:
-        current_hidden_states = self.act_fn(self.w1(x)) * self.v1(x)
-        current_hidden_states = self.w2(current_hidden_states)
-        return current_hidden_states
-
-
-class Router(nn.Module):
-    def __init__(self, d_model: int, num_experts: int):
-        super().__init__()
-        self.layer = nn.Linear(d_model, num_experts, bias=False)
-
-    def __call__(self, x: mx.array):
-        return self.layer(x)
-
-
-class SparseMoeBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.d_model = args.d_model
-        self.ffn_dim = args.ffn_config["ffn_hidden_size"]
-        self.num_experts = args.ffn_config["moe_num_experts"]
-        self.num_experts_per_tok = args.ffn_config["moe_top_k"]
-
-        self.router = Router(self.d_model, self.num_experts)
-        self.experts = [
-            MLP(self.d_model, self.ffn_dim) for _ in range(self.num_experts)
-        ]
-
-    def __call__(self, x: mx.array) -> mx.array:
-        ne = self.num_experts_per_tok
-        orig_shape = x.shape
-        x = x.reshape(-1, x.shape[-1])
-
-        gates = self.router(x)
-        gates = mx.softmax(gates.astype(mx.float32), axis=-1)
-
-        inds = mx.stop_gradient(mx.argpartition(-gates, kth=ne - 1, axis=-1)[:, :ne])
-        scores = mx.take_along_axis(gates, inds, axis=-1)
-        scores = scores / mx.linalg.norm(scores, ord=1, axis=-1, keepdims=True)
-        scores = scores.astype(x.dtype)
-
-        if self.training:
-            inds = np.array(inds)
-            y = mx.zeros((x.shape[0], ne, x.shape[-1]), x.dtype)
-            for e, expert in enumerate(self.experts):
-                idx1, idx2 = map(mx.array, np.where(inds == e))
-                if idx1.size == 0:
-                    continue
-                y[idx1, idx2] = expert(x[idx1])
-
-            y = (y * scores[:, :, None]).sum(axis=1)
-        else:
-            y = []
-            for xt, st, it in zip(x, scores, inds.tolist()):
-                yt = mx.stack([self.experts[e](xt) for e in it], axis=-1)
-                yt = (yt * st).sum(axis=-1)
-                y.append(yt)
-            y = mx.stack(y, axis=0)
-
-        return y.reshape(orig_shape)
-
-
-class DecoderLayer(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.ffn = SparseMoeBlock(args)
-        self.norm_attn_norm = NormAttnNorm(args)
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r, h = self.norm_attn_norm(x, mask, cache)
-        out = self.ffn(h) + r
-        return out
-
-
-class DBRX(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.vocab_size = args.vocab_size
-        self.wte = nn.Embedding(args.vocab_size, args.d_model)
-        self.blocks = [DecoderLayer(args=args) for _ in range(args.n_layers)]
-        self.norm_f = nn.LayerNorm(args.d_model, bias=False)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.wte(inputs)
-
-        mask = None
-        T = h.shape[1]
-        if T > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(T)
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.blocks)
-
-        for layer, c in zip(self.blocks, cache):
-            h = layer(h, mask, c)
-
-        return self.norm_f(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.model_type = args.model_type
-        self.transformer = DBRX(args)
-        self.lm_head = nn.Linear(args.d_model, args.vocab_size, bias=False)
-        self.args = args
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.transformer(inputs, cache)
-        return self.lm_head(out)
-
-    @property
-    def layers(self):
-        return self.transformer.blocks
-
-    def sanitize(self, weights):
-        # Split experts into sub matrices
-        num_experts = self.args.ffn_config["moe_num_experts"]
-        dim = self.args.ffn_config["ffn_hidden_size"]
-
-        pattern = "experts.mlp"
-        new_weights = {k: v for k, v in weights.items() if pattern not in k}
-        for k, v in weights.items():
-            if pattern in k:
-                experts = [
-                    (k.replace(".mlp", f".{e}") + ".weight", sv)
-                    for e, sv in enumerate(mx.split(v, num_experts, axis=0))
-                ]
-                if k.endswith("w2"):
-                    experts = [(s, sv.T) for s, sv in experts]
-                new_weights.update(experts)
-        return new_weights
-
-    @property
-    def head_dim(self):
-        return self.args.d_model // self.args.n_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.attn_config["kv_n_heads"]

llms/mlx_lm/models/gemma.py

@@ -1,184 +0,0 @@
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int
-    num_hidden_layers: int
-    intermediate_size: int
-    num_attention_heads: int
-    head_dim: int
-    rms_norm_eps: float
-    vocab_size: int
-    num_key_value_heads: int
-    rope_theta: float = 10000
-    rope_traditional: bool = False
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dims: int, eps: float = 1e-5):
-        super().__init__()
-        self.weight = mx.ones((dims,))
-        self.eps = eps
-
-    def __call__(self, x):
-        return mx.fast.rms_norm(x, 1.0 + self.weight, self.eps)
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        dim = args.hidden_size
-        self.n_heads = n_heads = args.num_attention_heads
-        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
-        self.head_dim = head_dim = args.head_dim
-
-        self.scale = head_dim**-0.5
-
-        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
-        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
-        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
-        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
-
-        self.rope = nn.RoPE(
-            head_dim,
-            traditional=args.rope_traditional,
-            base=args.rope_theta,
-        )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.o_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, dim, hidden_dim):
-        super().__init__()
-        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
-        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
-        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
-
-    def __call__(self, x) -> mx.array:
-        return self.down_proj(nn.gelu(self.gate_proj(x)) * self.up_proj(x))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.num_attention_heads = args.num_attention_heads
-        self.hidden_size = args.hidden_size
-        self.self_attn = Attention(args)
-        self.mlp = MLP(args.hidden_size, args.intermediate_size)
-        self.input_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-        self.post_attention_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-        self.args = args
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.self_attn(self.input_layernorm(x), mask, cache)
-        h = x + r
-        r = self.mlp(self.post_attention_layernorm(h))
-        out = h + r
-        return out
-
-
-class GemmaModel(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.vocab_size = args.vocab_size
-        self.num_hidden_layers = args.num_hidden_layers
-        assert self.vocab_size > 0
-        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [
-            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
-        ]
-        self.norm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.embed_tokens(inputs)
-        h = h * (self.args.hidden_size**0.5)
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.model_type = args.model_type
-        self.model = GemmaModel(args)
-        self.args = args
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-        out = self.model.embed_tokens.as_linear(out)
-        return out
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.head_dim
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/gpt2.py

@@ -1,207 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-import numpy as np
-
-from .base import BaseModelArgs, create_additive_causal_mask
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    n_ctx: int
-    n_embd: int
-    n_head: int
-    n_layer: int
-    n_positions: int
-    layer_norm_epsilon: float
-    vocab_size: int
-    num_key_value_heads: int = None
-
-    def __post_init__(self):
-        if self.num_key_value_heads is None:
-            self.num_key_value_heads = self.n_head
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        assert args.n_embd % args.n_head == 0, "n_embd must be divisible by n_head"
-
-        self.n_embd = args.n_embd
-        self.n_head = args.n_head
-        self.head_dim = self.n_embd // self.n_head
-
-        self.scale = self.head_dim**-0.5
-
-        self.c_attn = nn.Linear(self.n_embd, 3 * self.n_embd, bias=True)
-        self.c_proj = nn.Linear(self.n_embd, self.n_embd, bias=True)
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        qkv = self.c_attn(x)
-        queries, keys, values = mx.split(qkv, 3, axis=-1)
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_head, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_head, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_head, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            keys, values = cache.update_and_fetch(keys, values)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.c_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        self.n_embd = args.n_embd
-        self.c_fc = nn.Linear(self.n_embd, 4 * self.n_embd)
-        self.c_proj = nn.Linear(4 * self.n_embd, self.n_embd)
-
-    def __call__(self, x) -> mx.array:
-        return self.c_proj(nn.gelu_approx(self.c_fc(x)))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        self.n_head = args.n_head
-        self.n_embd = args.n_embd
-        self.layer_norm_epsilon = args.layer_norm_epsilon
-        self.attn = Attention(args)
-        self.mlp = MLP(args)
-        self.ln_1 = nn.LayerNorm(
-            self.n_embd,
-            eps=self.layer_norm_epsilon,
-        )
-        self.ln_2 = nn.LayerNorm(self.n_embd, eps=self.layer_norm_epsilon)
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.attn(self.ln_1(x), mask, cache)
-        h = x + r
-        r = self.mlp(self.ln_2(h))
-        out = h + r
-        return out
-
-
-class GPT2Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.n_embd = args.n_embd
-        self.n_positions = args.n_positions
-        self.vocab_size = args.vocab_size
-        self.n_layer = args.n_layer
-        self.layer_norm_epsilon = args.layer_norm_epsilon
-        assert self.vocab_size > 0
-        self.wte = nn.Embedding(self.vocab_size, self.n_embd)
-        self.wpe = nn.Embedding(self.n_positions, self.n_embd)
-        self.h = [TransformerBlock(args=args) for _ in range(self.n_layer)]
-        self.ln_f = nn.LayerNorm(self.n_embd, eps=self.layer_norm_epsilon)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        _, L = inputs.shape
-
-        hidden_states = self.wte(inputs)
-
-        mask = None
-        if hidden_states.shape[1] > 1:
-
-            position_ids = mx.array(np.arange(L))
-            hidden_states += self.wpe(position_ids)
-
-            mask = create_additive_causal_mask(
-                hidden_states.shape[1], cache[0].offset if cache is not None else 0
-            )
-            mask = mask.astype(hidden_states.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.h)
-
-        for layer, c in zip(self.h, cache):
-            hidden_states = layer(hidden_states, mask, cache=c)
-
-        return self.ln_f(hidden_states)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.model_type = args.model_type
-        self.model = GPT2Model(args)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-        out = self.model.wte.as_linear(out)
-        return out
-
-    def sanitize(self, weights):
-        new_weights = {}
-        for i in range(self.args.n_layer):
-            if f"h.{i}.attn.bias" in weights:
-                del weights[f"h.{i}.attn.bias"]
-            if f"h.{i}.attn.c_attn.weight" in weights:
-                weights[f"h.{i}.attn.c_attn.weight"] = weights[
-                    f"h.{i}.attn.c_attn.weight"
-                ].transpose(1, 0)
-            if f"h.{i}.attn.c_proj.weight" in weights:
-                weights[f"h.{i}.attn.c_proj.weight"] = weights[
-                    f"h.{i}.attn.c_proj.weight"
-                ].transpose(1, 0)
-            if f"h.{i}.mlp.c_fc.weight" in weights:
-                weights[f"h.{i}.mlp.c_fc.weight"] = weights[
-                    f"h.{i}.mlp.c_fc.weight"
-                ].transpose(1, 0)
-            if f"h.{i}.mlp.c_proj.weight" in weights:
-                weights[f"h.{i}.mlp.c_proj.weight"] = weights[
-                    f"h.{i}.mlp.c_proj.weight"
-                ].transpose(1, 0)
-        for weight in weights:
-            if not weight.startswith("model."):
-                new_weights[f"model.{weight}"] = weights[weight]
-            else:
-                new_weights[weight] = weights[weight]
-        return new_weights
-
-    @property
-    def layers(self):
-        return self.model.h
-
-    @property
-    def head_dim(self):
-        return self.args.n_embd // self.args.n_head
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/gpt_bigcode.py

@@ -1,195 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-import numpy as np
-
-from .base import BaseModelArgs, create_additive_causal_mask
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    n_embd: int
-    n_layer: int
-    n_inner: int
-    n_head: int
-    n_positions: int
-    layer_norm_epsilon: float
-    vocab_size: int
-    num_key_value_heads: int = None
-    multi_query: bool = True
-    attention_bias: bool = True
-    mlp_bias: bool = True
-    tie_word_embeddings: bool = True
-
-    def __post_init__(self):
-        if self.num_key_value_heads is None:
-            self.num_key_value_heads = 1 if self.multi_query else self.n_head
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        self.dim = dim = args.n_embd
-        self.n_heads = n_heads = args.n_head
-        self.n_kv_heads = n_kv_heads = 1 if args.multi_query else args.n_head
-
-        self.head_dim = head_dim = dim // n_heads
-
-        self.kv_dim = n_kv_heads * head_dim
-
-        self.scale = head_dim**-0.5
-
-        if hasattr(args, "attention_bias"):
-            attention_bias = args.attention_bias
-        else:
-            attention_bias = False
-
-        self.c_attn = nn.Linear(dim, dim + 2 * self.kv_dim, bias=attention_bias)
-        self.c_proj = nn.Linear(dim, dim, bias=attention_bias)
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        qkv = self.c_attn(x)
-        queries, keys, values = mx.split(
-            qkv, [self.dim, self.dim + self.kv_dim], axis=-1
-        )
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            keys, values = cache.update_and_fetch(keys, values)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.c_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        dim = args.n_embd
-        hidden_dim = args.n_inner
-        if hasattr(args, "mlp_bias"):
-            mlp_bias = args.mlp_bias
-        else:
-            mlp_bias = False
-
-        self.c_fc = nn.Linear(dim, hidden_dim, bias=mlp_bias)
-        self.c_proj = nn.Linear(hidden_dim, dim, bias=mlp_bias)
-
-    def __call__(self, x) -> mx.array:
-        return self.c_proj(nn.gelu(self.c_fc(x)))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.n_head = args.n_head
-        self.n_embd = args.n_embd
-        self.attn = Attention(args)
-        self.mlp = MLP(args)
-        self.ln_1 = nn.LayerNorm(args.n_embd, eps=args.layer_norm_epsilon)
-        self.ln_2 = nn.LayerNorm(args.n_embd, eps=args.layer_norm_epsilon)
-        self.args = args
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.attn(self.ln_1(x), mask, cache)
-        h = x + r
-        r = self.mlp(self.ln_2(h))
-        out = h + r
-        return out
-
-
-class GPTBigCodeModel(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.vocab_size = args.vocab_size
-        assert self.vocab_size > 0
-        self.wte = nn.Embedding(args.vocab_size, args.n_embd)
-        self.wpe = nn.Embedding(args.n_positions, args.n_embd)
-        self.h = [TransformerBlock(args=args) for _ in range(args.n_layer)]
-        self.ln_f = nn.LayerNorm(args.n_embd, eps=args.layer_norm_epsilon)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        B, L = inputs.shape
-
-        hidden_states = self.wte(inputs)
-
-        mask = None
-        if hidden_states.shape[1] > 1:
-
-            position_ids = mx.array(np.arange(L))
-            hidden_states += self.wpe(position_ids)
-
-            mask = create_additive_causal_mask(
-                hidden_states.shape[1], cache[0].offset if cache is not None else 0
-            )
-            mask = mask.astype(hidden_states.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.h)
-
-        for layer, c in zip(self.h, cache):
-            hidden_states = layer(hidden_states, mask, cache=c)
-
-        return self.ln_f(hidden_states)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.model_type = args.model_type
-        self.transformer = GPTBigCodeModel(args)
-        if not args.tie_word_embeddings:
-            self.lm_head = nn.Linear(args.n_embd, args.vocab_size, bias=False)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.transformer(inputs, cache)
-        if self.args.tie_word_embeddings:
-            out = self.transformer.wte.as_linear(out)
-        else:
-            out = self.lm_head(out)
-        return out
-
-    @property
-    def layers(self):
-        return self.transformer.h
-
-    @property
-    def head_dim(self):
-        return self.args.n_embd // self.args.n_head
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/internlm2.py

@@ -1,198 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int
-    num_hidden_layers: int
-    intermediate_size: int
-    num_attention_heads: int
-    rms_norm_eps: float
-    vocab_size: int
-    bias: bool = True
-    num_key_value_heads: int = None
-    rope_theta: float = 10000
-    rope_traditional: bool = False
-    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
-    tie_word_embeddings: bool = False
-
-    def __post_init__(self):
-        if self.num_key_value_heads is None:
-            self.num_key_value_heads = self.num_attention_heads
-
-        if self.rope_scaling:
-            required_keys = {"factor", "type"}
-            if not all(key in self.rope_scaling for key in required_keys):
-                raise ValueError(f"rope_scaling must contain keys {required_keys}")
-
-            if self.rope_scaling["type"] != "linear":
-                raise ValueError("rope_scaling 'type' currently only supports 'linear'")
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        dim = args.hidden_size
-        self.n_heads = n_heads = args.num_attention_heads
-        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
-        self.n_kv_groups = n_heads // args.num_key_value_heads
-
-        self.head_dim = head_dim = args.hidden_size // n_heads
-        self.scale = head_dim**-0.5
-
-        self.wqkv = nn.Linear(
-            dim, (n_heads + 2 * n_kv_heads) * head_dim, bias=args.bias
-        )
-        self.wo = nn.Linear(n_heads * head_dim, dim, bias=args.bias)
-
-        rope_scale = (
-            1 / args.rope_scaling["factor"]
-            if args.rope_scaling is not None and args.rope_scaling["type"] == "linear"
-            else 1
-        )
-        self.rope = nn.RoPE(
-            head_dim,
-            traditional=args.rope_traditional,
-            base=args.rope_theta,
-            scale=rope_scale,
-        )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        qkv_states = self.wqkv(x)
-        qkv_states = qkv_states.reshape(B, L, -1, 2 + self.n_kv_groups, self.head_dim)
-
-        queries = qkv_states[..., : self.n_kv_groups, :]
-        queries = queries.reshape(B, L, -1, self.head_dim)
-        keys = qkv_states[..., -2, :]
-        values = qkv_states[..., -1, :]
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.wo(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, dim, hidden_dim):
-        super().__init__()
-        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
-        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
-        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
-
-    def __call__(self, x) -> mx.array:
-        return self.w2(nn.silu(self.w1(x)) * self.w3(x))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.attention = Attention(args)
-        self.feed_forward = MLP(args.hidden_size, args.intermediate_size)
-        self.attention_norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-        self.ffn_norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.attention(self.attention_norm(x), mask, cache)
-        h = x + r
-        r = self.feed_forward(self.ffn_norm(h))
-        out = h + r
-        return out
-
-
-class InternLM2Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        assert args.vocab_size > 0
-        self.tok_embeddings = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [
-            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
-        ]
-        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.tok_embeddings(inputs)
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, cache=c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.model_type = args.model_type
-        self.model = InternLM2Model(args)
-        if not args.tie_word_embeddings:
-            self.output = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-        if self.args.tie_word_embeddings:
-            out = self.model.tok_embeddings.as_linear(out)
-        else:
-            out = self.output(out)
-        return out
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/llama.py

@@ -1,220 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs, KVCache, create_additive_causal_mask
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int
-    num_hidden_layers: int
-    intermediate_size: int
-    num_attention_heads: int
-    rms_norm_eps: float
-    vocab_size: int
-    num_key_value_heads: Optional[int] = None
-    attention_bias: bool = False
-    mlp_bias: bool = False
-    rope_theta: float = 10000
-    rope_traditional: bool = False
-    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
-    tie_word_embeddings: bool = True
-
-    def __post_init__(self):
-        if self.num_key_value_heads is None:
-            self.num_key_value_heads = self.num_attention_heads
-
-        if self.rope_scaling:
-            required_keys = {"factor", "type"}
-            if not all(key in self.rope_scaling for key in required_keys):
-                raise ValueError(f"rope_scaling must contain keys {required_keys}")
-
-            if self.rope_scaling["type"] != "linear":
-                raise ValueError("rope_scaling 'type' currently only supports 'linear'")
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        dim = args.hidden_size
-        self.n_heads = n_heads = args.num_attention_heads
-        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
-
-        head_dim = args.hidden_size // n_heads
-        self.scale = head_dim**-0.5
-        if hasattr(args, "attention_bias"):
-            attention_bias = args.attention_bias
-        else:
-            attention_bias = False
-
-        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=attention_bias)
-        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
-        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
-        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=attention_bias)
-
-        rope_scale = (
-            1 / args.rope_scaling["factor"]
-            if args.rope_scaling is not None and args.rope_scaling["type"] == "linear"
-            else 1
-        )
-        self.rope = nn.RoPE(
-            head_dim,
-            traditional=args.rope_traditional,
-            base=args.rope_theta,
-            scale=rope_scale,
-        )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[KVCache] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.o_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        dim = args.hidden_size
-        hidden_dim = args.intermediate_size
-        if hasattr(args, "mlp_bias"):
-            mlp_bias = args.mlp_bias
-        else:
-            mlp_bias = False
-
-        self.gate_proj = nn.Linear(dim, hidden_dim, bias=mlp_bias)
-        self.down_proj = nn.Linear(hidden_dim, dim, bias=mlp_bias)
-        self.up_proj = nn.Linear(dim, hidden_dim, bias=mlp_bias)
-
-    def __call__(self, x) -> mx.array:
-        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.num_attention_heads = args.num_attention_heads
-        self.hidden_size = args.hidden_size
-        self.self_attn = Attention(args)
-        self.mlp = MLP(args)
-        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-        self.post_attention_layernorm = nn.RMSNorm(
-            args.hidden_size, eps=args.rms_norm_eps
-        )
-        self.args = args
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[KVCache] = None,
-    ) -> mx.array:
-        r = self.self_attn(self.input_layernorm(x), mask, cache)
-        h = x + r
-        r = self.mlp(self.post_attention_layernorm(h))
-        out = h + r
-        return out
-
-
-class LlamaModel(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.vocab_size = args.vocab_size
-        self.num_hidden_layers = args.num_hidden_layers
-        assert self.vocab_size > 0
-        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [
-            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
-        ]
-        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.embed_tokens(inputs)
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = create_additive_causal_mask(
-                h.shape[1], cache[0].offset if cache is not None else 0
-            )
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, cache=c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.model_type = args.model_type
-        self.model = LlamaModel(args)
-        if not args.tie_word_embeddings:
-            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-        if self.args.tie_word_embeddings:
-            out = self.model.embed_tokens.as_linear(out)
-        else:
-            out = self.lm_head(out)
-        return out
-
-    def sanitize(self, weights):
-        # Remove unused precomputed rotary freqs
-        return {
-            k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
-        }
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/minicpm.py

@@ -1,216 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-import numpy as np
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int
-    dim_model_base: int
-    num_hidden_layers: int
-    intermediate_size: int
-    num_attention_heads: int
-    rms_norm_eps: float
-    vocab_size: int
-    num_key_value_heads: int
-    scale_depth: float
-    scale_emb: float
-    rope_theta: float = 1000000.0
-    rope_traditional: bool = False
-    rope_scaling: Optional[Dict[str, Union[str, float]]] = None
-    tie_word_embeddings: bool = False
-
-
-class MLP(nn.Module):
-    def __init__(self, args):
-        super().__init__()
-        self.gate_proj = nn.Linear(args.hidden_size, args.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(args.hidden_size, args.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(args.intermediate_size, args.hidden_size, bias=False)
-
-    def __call__(self, x):
-        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-
-        self.hidden_size = args.hidden_size
-        self.num_heads = n_heads = args.num_attention_heads
-        self.rope_theta = args.rope_theta
-
-        self.head_dim = head_dim = args.hidden_size // n_heads
-        self.scale = head_dim**-0.5
-
-        self.num_key_value_heads = args.num_key_value_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-
-        self.q_proj = nn.Linear(
-            self.hidden_size, self.num_heads * self.head_dim, bias=False
-        )
-        self.k_proj = nn.Linear(
-            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
-        )
-        self.v_proj = nn.Linear(
-            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
-        )
-        self.o_proj = nn.Linear(
-            self.num_heads * self.head_dim, self.hidden_size, bias=False
-        )
-
-        rope_scale = (
-            1 / args.rope_scaling["factor"]
-            if args.rope_scaling is not None and args.rope_scaling["type"] == "linear"
-            else 1
-        )
-
-        self.rope = nn.RoPE(
-            dims=self.head_dim,
-            traditional=args.rope_traditional,
-            base=self.rope_theta,
-            scale=rope_scale,
-        )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ):
-        B, L, _ = x.shape
-
-        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
-
-        queries = queries.reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.num_key_value_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.num_key_value_heads, -1).transpose(
-            0, 2, 1, 3
-        )
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        attn_output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-
-        attn_output = attn_output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-
-        return self.o_proj(attn_output)
-
-
-class DecoderLayer(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.hidden_size = args.hidden_size
-        self.num_hidden_layers = args.num_hidden_layers
-
-        self.self_attn = Attention(args)
-        self.mlp = MLP(args)
-        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-        self.post_attention_layernorm = nn.RMSNorm(
-            args.hidden_size, eps=args.rms_norm_eps
-        )
-
-        self.scale_depth = args.scale_depth
-        self.num_hidden_layers = args.num_hidden_layers
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.self_attn(self.input_layernorm(x), mask, cache)
-        h = x + r * (self.scale_depth / np.sqrt(self.num_hidden_layers))
-        r = self.mlp(self.post_attention_layernorm(h))
-        out = h + r * (self.scale_depth / np.sqrt(self.num_hidden_layers))
-        return out
-
-
-class MiniCPMModel(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.vocab_size = args.vocab_size
-        assert self.vocab_size > 0
-
-        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [DecoderLayer(args) for _ in range(args.num_hidden_layers)]
-        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.embed_tokens(inputs) * self.args.scale_emb
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.model_type = args.model_type
-        self.model = MiniCPMModel(args)
-
-        if not self.args.tie_word_embeddings:
-            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-
-        if not self.args.tie_word_embeddings:
-            out = self.lm_head(out / (self.args.hidden_size / self.args.dim_model_base))
-        else:
-            out = out @ self.model.embed_tokens.weight.T
-
-        return out
-
-    def sanitize(self, weights):
-        if "lm_head.weight" not in weights:
-            weights["lm_head.weight"] = weights["model.embed_tokens.weight"]
-        return weights
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/mixtral.py

@@ -1,227 +0,0 @@
-import math
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-from .switch_layers import SwitchGLU
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    vocab_size: int = 32000
-    hidden_size: int = 4096
-    intermediate_size: int = 14336
-    num_hidden_layers: int = 32
-    num_attention_heads: int = 32
-    num_experts_per_tok: int = 2
-    num_key_value_heads: int = 8
-    num_local_experts: int = 8
-    rms_norm_eps: float = 1e-5
-    rope_theta: float = 1e6
-    rope_traditional: bool = False
-    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
-
-    def __post_init__(self):
-        if self.num_key_value_heads is None:
-            self.num_key_value_heads = self.num_attention_heads
-
-
-class MixtralAttention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.hidden_size = args.hidden_size
-        self.num_heads = args.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.num_key_value_heads = args.num_key_value_heads
-        self.rope_theta = args.rope_theta
-
-        self.scale = self.head_dim**-0.5
-
-        self.q_proj = nn.Linear(
-            self.hidden_size, self.num_heads * self.head_dim, bias=False
-        )
-        self.k_proj = nn.Linear(
-            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
-        )
-        self.v_proj = nn.Linear(
-            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
-        )
-        self.o_proj = nn.Linear(
-            self.num_heads * self.head_dim, self.hidden_size, bias=False
-        )
-
-        self.rope = nn.RoPE(
-            self.head_dim,
-            traditional=args.rope_traditional,
-            base=args.rope_theta,
-        )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.num_key_value_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.num_key_value_heads, -1).transpose(
-            0, 2, 1, 3
-        )
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.o_proj(output)
-
-
-class MixtralSparseMoeBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.hidden_dim = args.hidden_size
-        self.ffn_dim = args.intermediate_size
-        self.num_experts = args.num_local_experts
-        self.num_experts_per_tok = args.num_experts_per_tok
-
-        # gating
-        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
-
-        self.switch_mlp = SwitchGLU(self.hidden_dim, self.ffn_dim, self.num_experts)
-
-    def __call__(self, x: mx.array) -> mx.array:
-        gates = self.gate(x)
-
-        k = self.num_experts_per_tok
-        inds = mx.stop_gradient(mx.argpartition(-gates, kth=k - 1, axis=-1)[..., :k])
-        scores = mx.take_along_axis(gates, inds, axis=-1)
-        scores = mx.softmax(scores, axis=-1, precise=True)
-
-        y = self.switch_mlp(x, inds)
-        y = (y * scores[..., None]).sum(axis=-2)
-
-        return y
-
-
-class MixtralDecoderLayer(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.hidden_size = args.hidden_size
-
-        self.self_attn = MixtralAttention(args)
-
-        self.block_sparse_moe = MixtralSparseMoeBlock(args)
-        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-        self.post_attention_layernorm = nn.RMSNorm(
-            args.hidden_size, eps=args.rms_norm_eps
-        )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.self_attn(self.input_layernorm(x), mask, cache)
-        h = x + r
-        r = self.block_sparse_moe(self.post_attention_layernorm(h))
-        out = h + r
-        return out
-
-
-class MixtralModel(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.vocab_size = args.vocab_size
-        self.num_hidden_layers = args.num_hidden_layers
-
-        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [
-            MixtralDecoderLayer(args=args) for _ in range(args.num_hidden_layers)
-        ]
-        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.embed_tokens(inputs)
-
-        mask = None
-        T = h.shape[1]
-        if T > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(T)
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.model_type = args.model_type
-        self.model = MixtralModel(args)
-        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
-        self.args = args
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-        return self.lm_head(out)
-
-    def sanitize(self, weights):
-        if "model.layers.0.block_sparse_moe.experts.0.w1.weight" not in weights:
-            return weights
-        for l in range(self.args.num_hidden_layers):
-            prefix = f"model.layers.{l}"
-            for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]:
-                for k in ["weight", "scales", "biases"]:
-                    if f"{prefix}.block_sparse_moe.experts.0.{n}.{k}" in weights:
-                        to_join = [
-                            weights.pop(
-                                f"{prefix}.block_sparse_moe.experts.{e}.{n}.{k}"
-                            )
-                            for e in range(self.args.num_local_experts)
-                        ]
-                        weights[f"{prefix}.block_sparse_moe.switch_mlp.{m}.{k}"] = (
-                            mx.stack(to_join)
-                        )
-        return weights
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/olmo.py

@@ -1,185 +0,0 @@
-from dataclasses import dataclass
-from sys import exit
-from typing import Optional, Tuple
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-
-try:
-    import hf_olmo
-except ImportError:
-    print("To run olmo install ai2-olmo: pip install ai2-olmo")
-    exit(1)
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    d_model: int
-    n_layers: int
-    mlp_hidden_size: int
-    n_heads: int
-    vocab_size: int
-    embedding_size: int
-    rope_theta: float = 10000
-    rope_traditional: bool = False
-    mlp_ratio: int = 4
-    weight_tying: bool = False
-
-    def __post_init__(self):
-        self.mlp_hidden_size = (
-            self.mlp_hidden_size
-            if self.mlp_hidden_size is not None
-            else self.mlp_ratio * self.d_model
-        )
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.n_heads = args.n_heads
-        dim = args.d_model
-
-        self.ff_proj = nn.Linear(dim, args.mlp_hidden_size, bias=False)
-        self.ff_out = nn.Linear(args.mlp_hidden_size // 2, dim, bias=False)
-
-        self.att_norm = nn.LayerNorm(dim, affine=False)
-        self.ff_norm = nn.LayerNorm(dim, affine=False)
-
-        head_dim = dim // self.n_heads
-        self.scale = head_dim**-0.5
-
-        self.att_proj = nn.Linear(dim, 3 * dim, bias=False)
-        self.attn_out = nn.Linear(dim, dim, bias=False)
-
-        self.rope = nn.RoPE(
-            head_dim,
-            traditional=args.rope_traditional,
-            base=args.rope_theta,
-        )
-
-        self.args = args
-
-    def attend(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        queries, keys, values = mx.split(self.att_proj(x), 3, axis=-1)
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        scores = (queries * self.scale) @ keys.transpose(0, 1, 3, 2)
-        if mask is not None:
-            scores += mask
-        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
-        output = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.attn_out(output)
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.attend(self.att_norm(x), mask, cache)
-        h = x + r
-
-        x1, x2 = mx.split(self.ff_proj(self.ff_norm(h)), 2, axis=-1)
-
-        out = h + self.ff_out(nn.silu(x2) * x1)
-        return out
-
-
-class Transformer(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.n_layers = args.n_layers
-        self.weight_tying = args.weight_tying
-
-        self.wte = nn.Embedding(args.embedding_size, args.d_model)
-        self.blocks = [TransformerBlock(args=args) for _ in range(args.n_layers)]
-        if not self.weight_tying:
-            self.ff_out = nn.Linear(args.d_model, args.embedding_size, bias=False)
-        self.norm = nn.LayerNorm(args.d_model, affine=False)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.wte(inputs)
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.blocks)
-
-        for block, c in zip(self.blocks, cache):
-            h = block(h, mask, c)
-
-        h = self.norm(h)
-
-        if self.weight_tying:
-            return self.wte.as_linear(h), cache
-
-        return self.ff_out(h)
-
-
-class OlmoModel(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.transformer = Transformer(args)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        return self.transformer(inputs, cache)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.model_type = args.model_type
-        self.model = OlmoModel(args)
-        self.args = args
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        return self.model(inputs, cache)
-
-    @property
-    def layers(self):
-        return self.model.transformer.blocks
-
-    @property
-    def head_dim(self):
-        return self.args.d_model // self.args.n_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.n_heads

llms/mlx_lm/models/openelm.py

@@ -1,229 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    head_dim: int
-    num_transformer_layers: int
-    model_dim: int
-    vocab_size: int
-    ffn_dim_divisor: int
-    num_query_heads: List
-    num_kv_heads: List
-    ffn_multipliers: List
-    ffn_with_glu: bool = True
-    normalize_qk_projections: bool = True
-    share_input_output_layers: bool = True
-    rms_norm_eps: float = 1e-6
-    rope_freq_constant: float = 10000
-
-
-def make_divisible(
-    v: Union[float, int],
-    divisor: Optional[int] = 8,
-    min_value: Optional[Union[float, int]] = None,
-) -> Union[float, int]:
-    """
-    This function is taken from the original tf repo.
-    It ensures that all layers have a channel number that is divisible by the divisor
-    It can be seen at:
-    https://github.com/tensorflow/models/blob/2cfc99eff5e5eb729c6793d2f3d03aa1c9be2b15/research/slim/nets/mobilenet/mobilenet.py#L62
-    Args:
-        v: input value
-        divisor: default to 8
-        min_value: minimum divisor value
-    Returns:
-        new_v: new divisible value
-    """
-    if min_value is None:
-        min_value = divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    # Make sure that round down does not go down by more than 10%.
-    if new_v < 0.9 * v:
-        new_v += divisor
-    return new_v
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs, layer_id: int):
-        super().__init__()
-        self.head_dim = head_dim = args.head_dim
-        self.layer_id = layer_id
-        self.model_dim = model_dim = args.model_dim
-
-        self.n_heads = n_heads = args.num_query_heads[layer_id]
-        self.n_kv_heads = n_kv_heads = args.num_kv_heads[layer_id]
-        self.scale = head_dim**-0.5
-
-        op_size = (n_heads + (n_kv_heads * 2)) * head_dim
-        self.qkv_proj = nn.Linear(model_dim, op_size, bias=False)
-        self.out_proj = nn.Linear(n_heads * head_dim, model_dim, bias=False)
-
-        self.normalize_qk_projections = args.normalize_qk_projections
-
-        if self.normalize_qk_projections:
-            self.q_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps)
-            self.k_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps)
-
-        self.rope = nn.RoPE(head_dim, traditional=False, base=args.rope_freq_constant)
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        qkv = self.qkv_proj(x)
-
-        qkv = qkv.reshape(
-            B, L, self.n_heads + (self.n_kv_heads * 2), self.head_dim
-        ).transpose(0, 2, 1, 3)
-
-        queries, keys, values = mx.split(
-            qkv, [self.n_heads, self.n_heads + self.n_kv_heads], axis=1
-        )
-
-        # Prepare the queries, keys and values for the attention computation
-        if self.normalize_qk_projections:
-            queries = self.q_norm(queries)
-            keys = self.k_norm(keys)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-
-        return self.out_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, args: ModelArgs, layer_id: int):
-        super().__init__()
-        self.args = args
-        dim = args.model_dim
-        ffn_multiplier = args.ffn_multipliers[layer_id]
-
-        intermediate_dim = int(
-            make_divisible(
-                ffn_multiplier * args.model_dim,
-                divisor=args.ffn_dim_divisor,
-            )
-        )
-
-        self.proj_1 = nn.Linear(dim, 2 * intermediate_dim, bias=False)
-        self.proj_2 = nn.Linear(intermediate_dim, dim, bias=False)
-
-    def __call__(self, x) -> mx.array:
-        x = self.proj_1(x)
-        gate, x = mx.split(x, 2, axis=-1)
-        return self.proj_2(nn.silu(gate) * x)
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs, layer_id: int):
-        super().__init__()
-        dim = args.model_dim
-        self.attn = Attention(args, layer_id=layer_id)
-        self.ffn = MLP(args, layer_id=layer_id)
-        self.ffn_norm = nn.RMSNorm(dim, eps=args.rms_norm_eps)
-        self.attn_norm = nn.RMSNorm(dim, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.attn(self.attn_norm(x), mask, cache)
-        h = x + r
-        r = self.ffn(self.ffn_norm(h))
-        out = h + r
-        return out
-
-
-class OpenELMModel(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.vocab_size = args.vocab_size
-        self.num_transformer_layers = args.num_transformer_layers
-        assert self.vocab_size > 0
-        self.token_embeddings = nn.Embedding(args.vocab_size, args.model_dim)
-        self.layers = [
-            TransformerBlock(args, layer_id=layer_id)
-            for layer_id in range(self.num_transformer_layers)
-        ]
-        self.norm = nn.RMSNorm(args.model_dim, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.token_embeddings(inputs)
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, cache=c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.model_type = args.model_type
-        self.transformer = OpenELMModel(args)
-        if not args.share_input_output_layers:
-            self.lm_head = nn.Linear(args.model_dim, args.vocab_size, bias=False)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.transformer(inputs, cache)
-        if self.args.share_input_output_layers:
-            out = self.transformer.token_embeddings.as_linear(out)
-        else:
-            out = self.lm_head(out)
-
-        return out
-
-    @property
-    def layers(self):
-        return self.transformer.layers
-
-    @property
-    def head_dim(self):
-        return self.args.head_dim
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_kv_heads

llms/mlx_lm/models/phi.py

@@ -1,180 +0,0 @@
-import math
-from dataclasses import dataclass
-from typing import Tuple
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str = "phi"
-    max_position_embeddings: int = 2048
-    vocab_size: int = 51200
-    hidden_size: int = 2560
-    num_attention_heads: int = 32
-    num_hidden_layers: int = 32
-    num_key_value_heads: int = 32
-    partial_rotary_factor: float = 0.4
-    intermediate_size: int = 10240
-    layer_norm_eps: float = 1e-5
-    rope_theta: float = 10000.0
-
-    def __post_init__(self):
-        if self.num_key_value_heads is None:
-            self.num_key_value_heads = self.num_attention_heads
-
-
-class PhiAttention(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.num_key_value_heads = config.num_key_value_heads
-        self.repeats = self.num_heads // self.num_key_value_heads
-        self.rope_theta = config.rope_theta
-        self.partial_rotary_factor = config.partial_rotary_factor
-
-        if (self.head_dim * self.num_heads) != self.hidden_size:
-            raise ValueError(
-                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
-                f" and `num_heads`: {self.num_heads})."
-            )
-
-        self.q_proj = nn.Linear(
-            self.hidden_size, self.num_heads * self.head_dim, bias=True
-        )
-        self.k_proj = nn.Linear(
-            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True
-        )
-        self.v_proj = nn.Linear(
-            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True
-        )
-        self.dense = nn.Linear(
-            self.num_heads * self.head_dim, self.hidden_size, bias=True
-        )
-
-        self.rope = nn.RoPE(
-            int(self.partial_rotary_factor * self.head_dim),
-            traditional=False,
-            base=self.rope_theta,
-        )
-
-    def __call__(self, x, mask=None, cache=None):
-        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
-
-        # Extract some shapes
-        B, L, D = queries.shape
-        n_heads, n_kv_heads = self.num_heads, self.num_key_value_heads
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(
-            B,
-            L,
-            n_heads,
-            -1,
-        ).moveaxis(1, 2)
-        keys = keys.reshape(B, L, n_kv_heads, -1).moveaxis(1, 2)
-        values = values.reshape(B, L, n_kv_heads, -1).moveaxis(1, 2)
-
-        # Add RoPE to the queries and keys and combine them with the cache
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        scale = math.sqrt(1 / queries.shape[-1])
-        output = mx.fast.scaled_dot_product_attention(
-            queries.astype(mx.float32), keys, values, scale=scale, mask=mask
-        ).astype(values.dtype)
-
-        output = output.moveaxis(2, 1).reshape(B, L, -1)
-
-        return self.dense(output)
-
-
-class PhiMLP(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.act = nn.GELU(approx="precise")
-
-    def __call__(self, x) -> mx.array:
-        return self.fc2(self.act(self.fc1(x)))
-
-
-class PhiDecoderLayer(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.self_attn = PhiAttention(config=config)
-        self.input_layernorm = nn.LayerNorm(
-            config.hidden_size, eps=config.layer_norm_eps
-        )
-        self.mlp = PhiMLP(config)
-
-    def __call__(self, x, mask, cache):
-        h = self.input_layernorm(x)
-        attn_h = self.self_attn(h, mask, cache)
-        ff_h = self.mlp(h)
-        return attn_h + ff_h + x
-
-
-class PhiModel(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
-        self.layers = [PhiDecoderLayer(config) for i in range(config.num_hidden_layers)]
-        self.final_layernorm = nn.LayerNorm(
-            config.hidden_size, eps=config.layer_norm_eps
-        )
-
-    def __call__(self, x, cache):
-        x = self.embed_tokens(x)
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        mask = None
-        if x.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
-            mask = mask.astype(x.dtype)
-
-        for layer, c in zip(self.layers, cache):
-            x = layer(x, mask, c)
-        return self.final_layernorm(x)
-
-
-class Model(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.model_type = config.model_type
-        self.model = PhiModel(config)
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)
-        self.args = config
-
-    def __call__(
-        self,
-        x: mx.array,
-        cache: mx.array = None,
-    ) -> Tuple[mx.array, mx.array]:
-        y = self.model(x, cache)
-        return self.lm_head(y)
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/phi3.py

@@ -1,213 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-from .su_rope import SuScaledRotaryEmbedding
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int
-    num_hidden_layers: int
-    intermediate_size: int
-    num_attention_heads: int
-    rms_norm_eps: float
-    vocab_size: int
-    num_key_value_heads: int = None
-    rope_theta: float = 10000
-    rope_traditional: bool = False
-    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
-    max_position_embeddings: int = 131072
-    original_max_position_embeddings: int = 4096
-
-    def __post_init__(self):
-        if self.num_key_value_heads is None:
-            self.num_key_value_heads = self.num_attention_heads
-
-        if self.rope_scaling:
-            required_keys = {"long_factor", "type"}
-            if not all(key in self.rope_scaling for key in required_keys):
-                raise ValueError(f"rope_scaling must contain keys {required_keys}")
-
-            if self.rope_scaling["type"] not in ["su", "linear"]:
-                print(
-                    "[WARNING] rope_scaling 'type' currently only supports 'linear' and 'su'; setting rope scaling to false."
-                )
-                self.rope_scaling = None
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        dim = args.hidden_size
-        self.n_heads = n_heads = args.num_attention_heads
-        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
-        self.num_hidden_layers = args.num_hidden_layers
-
-        self.head_dim = head_dim = args.hidden_size // n_heads
-        self.scale = head_dim**-0.5
-
-        op_size = n_heads * head_dim + 2 * (n_kv_heads * head_dim)
-        self.qkv_proj = nn.Linear(dim, op_size, bias=False)
-        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
-
-        rope_scale = 1.0
-        if args.rope_scaling and args.rope_scaling["type"] == "su":
-            self.rope = SuScaledRotaryEmbedding(
-                head_dim,
-                traditional=False,
-                base=args.rope_theta,
-                scale=rope_scale,
-                max_position_embeddings=args.max_position_embeddings,
-                original_max_position_embeddings=args.original_max_position_embeddings,
-                short_factor=args.rope_scaling["short_factor"],
-                long_factor=args.rope_scaling["long_factor"],
-            )
-        else:
-            if args.rope_scaling and args.rope_scaling["type"] == "linear":
-                rope_scale = 1 / args.rope_scaling["factor"]
-            self.rope = nn.RoPE(
-                head_dim,
-                traditional=args.rope_traditional,
-                base=args.rope_theta,
-                scale=rope_scale,
-            )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        qkv = self.qkv_proj(x)
-        query_pos = self.n_heads * self.head_dim
-        queries, keys, values = mx.split(
-            qkv, [query_pos, query_pos + self.n_kv_heads * self.head_dim], axis=-1
-        )
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.o_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, dim, hidden_dim):
-        super().__init__()
-        self.gate_up_proj = nn.Linear(dim, 2 * hidden_dim, bias=False)
-        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
-
-    def __call__(self, x) -> mx.array:
-        x = self.gate_up_proj(x)
-        gate, x = mx.split(x, 2, axis=-1)
-        return self.down_proj(nn.silu(gate) * x)
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.num_attention_heads = args.num_attention_heads
-        self.hidden_size = args.hidden_size
-        self.self_attn = Attention(args)
-        self.mlp = MLP(args.hidden_size, args.intermediate_size)
-        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-        self.post_attention_layernorm = nn.RMSNorm(
-            args.hidden_size, eps=args.rms_norm_eps
-        )
-        self.args = args
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.self_attn(self.input_layernorm(x), mask, cache)
-        h = x + r
-        r = self.mlp(self.post_attention_layernorm(h))
-        out = h + r
-        return out
-
-
-class Phi3Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.vocab_size = args.vocab_size
-        self.num_hidden_layers = args.num_hidden_layers
-        assert self.vocab_size > 0
-        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [
-            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
-        ]
-        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.embed_tokens(inputs)
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.model_type = args.model_type
-        self.model = Phi3Model(args)
-        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
-        self.args = args
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-        return self.lm_head(out)
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/phi3small.py

@@ -1,318 +0,0 @@
-from dataclasses import dataclass
-from functools import partial
-from typing import Dict, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int
-    dense_attention_every_n_layers: int
-    ff_intermediate_size: int
-    gegelu_limit: float
-    num_hidden_layers: int
-    num_attention_heads: int
-    layer_norm_epsilon: float
-    vocab_size: int
-    num_key_value_heads: int = None
-    mup_attn_multiplier: float = 1.0
-    mup_use_scaling: bool = True
-    mup_embedding_multiplier: float = 10.0
-    mup_width_multiplier: float = 8.0
-    rope_embedding_base: float = 1000000
-    rope_position_scale: float = 1.0
-    blocksparse_block_size: int = (64,)
-    blocksparse_num_local_blocks: int = 16
-    blocksparse_vert_stride: int = 8
-
-
-@partial(mx.compile, shapeless=True)
-def gegelu_impl(a_gelu, a_linear, limit):
-    a_gelu = mx.where(
-        mx.isinf(a_gelu),
-        a_gelu,
-        mx.clip(a_gelu, a_min=None, a_max=limit),
-    )
-    a_linear = mx.where(
-        mx.isinf(a_linear),
-        a_linear,
-        mx.clip(a_linear, a_min=-limit, a_max=limit),
-    )
-    out_gelu = a_gelu * mx.sigmoid(1.702 * a_gelu)
-    return out_gelu * (a_linear + 1.0)
-
-
-def gegelu(x, limit):
-    a_gelu, a_linear = x[..., ::2], x[..., 1::2]
-    return gegelu_impl(a_gelu, a_linear, limit)
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs, layer_idx):
-        super().__init__()
-
-        dim = args.hidden_size
-        self.n_heads = n_heads = args.num_attention_heads
-        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
-        self.n_q_per_kv = n_heads // n_kv_heads
-
-        self.head_dim = head_dim = args.hidden_size // n_heads
-
-        self.query_key_value = nn.Linear(
-            dim, (self.n_heads + 2 * self.n_kv_heads) * head_dim
-        )
-        self.dense = nn.Linear(dim, dim)
-
-        if args.mup_use_scaling:
-            norm_factor = head_dim / args.mup_attn_multiplier
-        else:
-            norm_factor = math.sqrt(head_dim)
-        self.scale = 1.0 / norm_factor
-
-        self.rope = nn.RoPE(
-            head_dim,
-            traditional=False,
-            base=args.rope_embedding_base,
-            scale=args.rope_position_scale,
-        )
-
-        if layer_idx % args.dense_attention_every_n_layers == 0:
-            self.block_sparse = True
-            self.blocksparse_block_size = args.blocksparse_block_size
-            if self.blocksparse_block_size not in (32, 64):
-                raise ValueError(
-                    f"Unsupported block size {self.blocksparse_block_size}"
-                )
-            self.blocksparse_num_local_blocks = args.blocksparse_num_local_blocks
-            self.blocksparse_vert_stride = args.blocksparse_vert_stride
-        else:
-            self.block_sparse = False
-
-    def _block_sparse_mask(self, q_len, kv_len):
-        vert_stride = self.blocksparse_vert_stride
-        local_blocks = self.blocksparse_num_local_blocks
-        block_size = self.blocksparse_block_size
-        n_heads = self.n_heads
-
-        kv_blocks = (kv_len + block_size - 1) // block_size
-        q_blocks = (q_len + block_size - 1) // block_size
-        q_pos = mx.arange(kv_blocks - q_blocks, kv_blocks)[None, :, None]
-        k_pos = mx.arange(kv_blocks)[None, None]
-
-        mask_vert_strided = (
-            mx.arange(kv_blocks)[None, :] + mx.arange(1, n_heads + 1)[:, None]
-        ) % vert_stride
-        mask_vert_strided = (mask_vert_strided == 0)[:, None, :]
-
-        block_mask = (q_pos >= k_pos) & (
-            (q_pos - k_pos < local_blocks) | mask_vert_strided
-        )
-        block_mask = block_mask.reshape(
-            self.n_kv_heads, self.n_q_per_kv, *block_mask.shape[-2:]
-        )
-        dense_mask = mx.repeat(
-            mx.repeat(block_mask, block_size, axis=-1), block_size, axis=-2
-        )
-        return block_mask, dense_mask[..., -q_len:, :kv_len]
-
-    def _block_sparse_attention(self, queries, keys, values, scale, mask):
-        queries = scale * queries
-        B = queries.shape[0]
-        L = queries.shape[2]
-        queries = mx.reshape(queries, (B, self.n_kv_heads, self.n_q_per_kv, L, -1))
-        keys = mx.expand_dims(keys, 2)
-        values = mx.expand_dims(values, 2)
-
-        # TODO get rid of dense mask if we have a fill value
-        block_mask, dense_mask = self._block_sparse_mask(L, keys.shape[-2])
-        scores = queries @ mx.swapaxes(keys, -1, -2)
-        # TODO, uncomment when faster
-        # scores = mx.block_masked_mm(
-        #   queries,
-        #   mx.swapaxes(keys, -1, -2),
-        #   mask_out=block_mask,
-        #   block_size=self.blocksparse_block_size,
-        # )
-
-        if mask is not None:
-            scores = scores + mask
-        scores = scores + mx.where(
-            dense_mask, mx.array(0, scores.dtype), mx.array(-float("inf"), scores.dtype)
-        )
-        scores = mx.softmax(scores, axis=-1, precise=True)
-
-        output = scores @ values
-        # TODO, uncomment when faster
-        # output = mx.block_masked_mm(
-        #    scores, values, mask_lhs=block_mask, block_size=self.blocksparse_block_size
-        # )
-        return mx.reshape(output, (B, self.n_heads, L, -1))
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        qkv = self.query_key_value(x)
-        qkv = qkv.reshape(B, L, -1, self.n_q_per_kv + 2, self.head_dim)
-        queries = qkv[..., :-2, :].flatten(-3, -2)
-        keys = qkv[..., -2, :]
-        values = qkv[..., -1, :]
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.transpose(0, 2, 1, 3)
-        keys = keys.transpose(0, 2, 1, 3)
-        values = values.transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        if self.block_sparse:
-            output = self._block_sparse_attention(
-                queries, keys, values, scale=self.scale, mask=mask
-            )
-        else:
-            output = mx.fast.scaled_dot_product_attention(
-                queries, keys, values, scale=self.scale, mask=mask
-            )
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.dense(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, args):
-        super().__init__()
-        dim = args.hidden_size
-        hidden_dim = args.ff_intermediate_size
-        self.gegelu_limit = args.gegelu_limit
-        self.up_proj = nn.Linear(dim, 2 * hidden_dim)
-        self.down_proj = nn.Linear(hidden_dim, dim)
-
-    def __call__(self, x) -> mx.array:
-        x = self.up_proj(x)
-        return self.down_proj(gegelu(x, self.gegelu_limit))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs, layer_idx):
-        super().__init__()
-        self.num_attention_heads = args.num_attention_heads
-        self.hidden_size = args.hidden_size
-        self.self_attn = Attention(args, layer_idx)
-        self.mlp = MLP(args)
-        self.input_layernorm = nn.LayerNorm(
-            args.hidden_size, eps=args.layer_norm_epsilon
-        )
-        self.post_attention_layernorm = nn.LayerNorm(
-            args.hidden_size,
-            eps=args.layer_norm_epsilon,
-        )
-        self.args = args
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.self_attn(self.input_layernorm(x), mask, cache)
-        h = x + r
-        r = self.mlp(self.post_attention_layernorm(h))
-        out = h + r
-        return out
-
-
-class Phi3Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.vocab_size = args.vocab_size
-        self.num_hidden_layers = args.num_hidden_layers
-        assert self.vocab_size > 0
-        self.mup_embedding_multiplier = args.mup_embedding_multiplier
-        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [
-            TransformerBlock(args=args, layer_idx=l)
-            for l in range(args.num_hidden_layers)
-        ]
-        self.final_layernorm = nn.LayerNorm(
-            args.hidden_size, eps=args.layer_norm_epsilon
-        )
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.embed_tokens(inputs)
-        if self.mup_embedding_multiplier:
-            h = self.mup_embedding_multiplier * h
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, c)
-
-        return self.final_layernorm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.model_type = args.model_type
-        self.model = Phi3Model(args)
-        self.args = args
-        self.mup_width_multiplier = args.mup_width_multiplier
-        self._dummy_tokenizer_ids = mx.array(
-            [100256, 100258, 100259, 100260, 100264, 100265]
-            + list(range(100267, 100352))
-        )
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-        out = self.model.embed_tokens.as_linear(out)
-        if self.mup_width_multiplier:
-            out = out / self.mup_width_multiplier
-        out[self._dummy_tokenizer_ids] = -float("inf")
-        return out
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    def sanitize(self, weights):
-        # Remove unused precomputed rotary freqs
-        return {
-            k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
-        }
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/phixtral.py

@@ -1,203 +0,0 @@
-import inspect
-import math
-from dataclasses import dataclass
-from typing import Tuple
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .switch_layers import SwitchMLP
-
-
-@dataclass
-class ModelArgs:
-    model_type: str
-    num_vocab: int = 51200
-    model_dim: int = 2560
-    num_heads: int = 32
-    num_layers: int = 32
-    rotary_dim: int = 32
-    num_experts_per_tok: int = 2
-    num_local_experts: int = 4
-
-    @classmethod
-    def from_dict(cls, params):
-        return cls(
-            **{
-                k: v
-                for k, v in params.items()
-                if k in inspect.signature(cls).parameters
-            }
-        )
-
-
-class RoPEAttention(nn.Module):
-    def __init__(self, dims: int, num_heads: int, rotary_dim: int):
-        super().__init__()
-
-        self.num_heads = num_heads
-
-        self.rope = nn.RoPE(rotary_dim, traditional=False)
-        self.Wqkv = nn.Linear(dims, 3 * dims)
-        self.out_proj = nn.Linear(dims, dims)
-
-    def __call__(self, x, mask=None, cache=None):
-        qkv = self.Wqkv(x)
-        queries, keys, values = mx.split(qkv, 3, axis=-1)
-
-        # Extract some shapes
-        num_heads = self.num_heads
-        B, L, D = queries.shape
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
-
-        # Add RoPE to the queries and keys and combine them with the cache
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        queries = queries.astype(mx.float32)
-
-        # Finally perform the attention computation
-        scale = math.sqrt(1 / queries.shape[-1])
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries.astype(mx.float32), keys, values, scale=scale, mask=mask
-        ).astype(values.dtype)
-        output = output.moveaxis(2, 1).reshape(B, L, -1)
-
-        return self.out_proj(output)
-
-
-class MOE(nn.Module):
-    def __init__(self, args: ModelArgs, dim: int, hidden_dim: int):
-        super().__init__()
-        self.dim = dim
-        self.hidden_dim = hidden_dim
-        self.num_experts = args.num_local_experts
-        self.num_experts_per_tok = args.num_experts_per_tok
-        self.switch_mlp = SwitchMLP(
-            self.dim, self.hidden_dim, self.num_experts, bias=True
-        )
-        self.gate = nn.Linear(args.model_dim, self.num_experts, bias=False)
-
-    def __call__(self, x: mx.array) -> mx.array:
-        gates = self.gate(x)
-
-        k = self.num_experts_per_tok
-        inds = mx.stop_gradient(mx.argpartition(-gates, kth=k - 1, axis=-1))[..., :k]
-        scores = mx.take_along_axis(gates, inds, axis=-1)
-        scores = mx.softmax(scores, axis=-1, precise=True)
-
-        y = self.switch_mlp(x, inds)
-        y = (y * scores[..., None]).sum(axis=-2)
-
-        return y
-
-
-class ParallelBlock(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        dims = config.model_dim
-        mlp_dims = dims * 4
-        self.mixer = RoPEAttention(dims, config.num_heads, config.rotary_dim)
-        self.ln = nn.LayerNorm(dims)
-        self.moe = MOE(config, dims, mlp_dims)
-
-    def __call__(self, x, mask, cache):
-        h = self.ln(x)
-        attn_h = self.mixer(h, mask, cache)
-        ff_h = self.moe(h)
-        return attn_h + ff_h + x
-
-
-class TransformerDecoder(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.embd = Embd(config)
-        self.h = [ParallelBlock(config) for i in range(config.num_layers)]
-
-    def __call__(self, x, mask, cache):
-        x = self.embd(x)
-        if cache is None:
-            cache = [None] * len(self.h)
-
-        for layer, c in zip(self.h, cache):
-            x = layer(x, mask, c)
-        return x
-
-
-class Embd(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.wte = nn.Embedding(config.num_vocab, config.model_dim)
-
-    def __call__(self, x):
-        return self.wte(x)
-
-
-class OutputHead(nn.Module):
-    def __init__(self, config: ModelArgs) -> None:
-        super().__init__()
-        self.ln = nn.LayerNorm(config.model_dim)
-        self.linear = nn.Linear(config.model_dim, config.num_vocab)
-
-    def __call__(self, inputs):
-        return self.linear(self.ln(inputs))
-
-
-class Model(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.model_type = config.model_type
-        self.transformer = TransformerDecoder(config)
-        self.lm_head = OutputHead(config)
-        self.args = config
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: mx.array = None,
-        cache: mx.array = None,
-    ) -> Tuple[mx.array, mx.array]:
-        mask = None
-        if x.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
-            mask = mask.astype(x.dtype)
-
-        y = self.transformer(x, mask, cache)
-        return self.lm_head(y)
-
-    def sanitize(self, weights):
-        if "transformer.h.0.moe.mlp.0.fc1.weight" not in weights:
-            return weights
-        for l in range(self.args.num_layers):
-            prefix = f"transformer.h.{l}"
-            for n in ["fc1", "fc2"]:
-                for k in ["weight", "scales", "biases", "bias"]:
-                    if f"{prefix}.moe.mlp.0.{n}.{k}" in weights:
-                        to_join = [
-                            weights.pop(f"{prefix}.moe.mlp.{e}.{n}.{k}")
-                            for e in range(self.args.num_local_experts)
-                        ]
-                        weights[f"{prefix}.moe.switch_mlp.{n}.{k}"] = mx.stack(to_join)
-        return weights
-
-    @property
-    def layers(self):
-        return self.transformer.h
-
-    @property
-    def head_dim(self):
-        return self.args.model_dim // self.args.num_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_heads

llms/mlx_lm/models/plamo.py

@@ -1,216 +0,0 @@
-from dataclasses import dataclass
-from typing import Any, List, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-import numpy as np
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int
-    num_hidden_layers: int
-    intermediate_size: int
-    num_attention_heads: int
-    rms_norm_eps: float
-    vocab_size: int
-    n_shared_head: int = 8
-    rope_theta: float = 10000
-    rope_traditional: bool = False
-
-
-class Attention(nn.Module):
-    def __init__(self, config: ModelArgs) -> None:
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        head_dim = self.hidden_size // config.num_attention_heads
-
-        self.q_num_heads = config.num_attention_heads
-        self.qk_dim = self.v_dim = head_dim
-        self.k_num_heads = self.v_num_heads = int(
-            np.ceil(self.q_num_heads / config.n_shared_head)
-        )
-
-        self.scale = head_dim**-0.5
-
-        self.q_proj = nn.Linear(
-            self.hidden_size, self.q_num_heads * self.qk_dim, bias=False
-        )
-        self.k_proj = nn.Linear(
-            self.hidden_size, self.k_num_heads * self.qk_dim, bias=False
-        )
-        self.v_proj = nn.Linear(
-            self.hidden_size, self.v_num_heads * self.v_dim, bias=False
-        )
-        self.o_proj = nn.Linear(
-            self.q_num_heads * self.v_dim, self.hidden_size, bias=False
-        )
-        self.rotary_emb = nn.RoPE(
-            head_dim,
-            traditional=config.rope_traditional,
-            base=config.rope_theta,
-            scale=1.0,
-        )
-
-    def __call__(
-        self,
-        hidden_states: mx.array,
-        attention_mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> Tuple[mx.array, Tuple[mx.array, mx.array]]:
-        bsz, q_len, _ = hidden_states.shape
-
-        queries = self.q_proj(hidden_states)
-        keys = self.k_proj(hidden_states)
-        values = self.v_proj(hidden_states)
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(bsz, q_len, self.q_num_heads, self.qk_dim).transpose(
-            0, 2, 1, 3
-        )
-        keys = keys.reshape(bsz, q_len, self.k_num_heads, self.qk_dim).transpose(
-            0, 2, 1, 3
-        )
-        values = values.reshape(bsz, q_len, self.v_num_heads, self.v_dim).transpose(
-            0, 2, 1, 3
-        )
-
-        if cache is not None:
-            queries = self.rotary_emb(queries, offset=cache.offset)
-            keys = self.rotary_emb(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rotary_emb(queries)
-            keys = self.rotary_emb(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries,
-            keys,
-            values,
-            scale=self.scale,
-            mask=attention_mask,
-        )
-        output = output.transpose(0, 2, 1, 3).reshape(bsz, q_len, -1)
-        return self.o_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, config: ModelArgs) -> None:
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = config.intermediate_size
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
-
-    def __call__(self, x: mx.array) -> mx.array:
-        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))  # type: ignore
-
-
-class PlamoDecoderLayer(nn.Module):
-    def __init__(self, config: ModelArgs) -> None:
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.self_attn = Attention(config)
-        self.mlp = MLP(config)
-        self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def __call__(
-        self,
-        hidden_states: mx.array,
-        attention_mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> Tuple[Any, ...]:
-        # from LlamaDecoder
-        residual = hidden_states
-
-        hidden_states = self.norm(hidden_states)
-
-        # Self Attention
-        hidden_states_sa = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            cache=cache,
-        )
-
-        # Fully Connected
-        hidden_states_mlp = self.mlp(hidden_states)
-
-        hidden_states = residual + hidden_states_sa + hidden_states_mlp
-        return hidden_states
-
-
-class PlamoDecoder(nn.Module):
-    def __init__(self, config: ModelArgs) -> None:
-        super().__init__()
-        self.layers = [
-            PlamoDecoderLayer(config) for _ in range(config.num_hidden_layers)
-        ]
-
-
-class PlamoModel(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.config = config
-        self.vocab_size = config.vocab_size
-
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
-        self.layers = PlamoDecoder(config)  # type: ignore
-        self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache: Optional[List[Union[Tuple[mx.array, mx.array], None]]] = None,
-    ) -> Tuple[mx.array, Optional[List[Union[Tuple[mx.array, mx.array], None]]]]:
-        h = self.embed_tokens(inputs)
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(self.embed_tokens.weight.dtype)
-
-        if cache is None:
-            cache = [None for _ in range(len(self.layers.layers))]
-
-        for layer, c in zip(self.layers.layers, cache):
-            h = layer(h, mask, cache=c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs) -> None:
-        super().__init__()
-        self.model_type = args.model_type
-        self.model = PlamoModel(args)
-        self.lm_head: nn.Module = nn.Linear(
-            args.hidden_size, args.vocab_size, bias=False
-        )
-        self.args = args
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache: Optional[List[Tuple[mx.array, mx.array]]] = None,
-    ) -> Tuple[mx.array, mx.array]:
-        out = self.model(inputs, cache)
-        return self.lm_head(out)
-
-    @property
-    def layers(self):
-        return self.model.layers.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_attention_heads // self.args.n_shared_head

llms/mlx_lm/models/qwen.py

@@ -1,169 +0,0 @@
-from dataclasses import dataclass
-from typing import Tuple
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int = 2048
-    num_attention_heads: int = 16
-    num_hidden_layers: int = 24
-    kv_channels: int = 128
-    max_position_embeddings: int = 8192
-    layer_norm_epsilon: float = 1e-6
-    intermediate_size: int = 11008
-    no_bias: bool = True
-    vocab_size: int = 151936
-    num_key_value_heads = None
-
-    def __post_init__(self):
-        if self.num_key_value_heads is None:
-            self.num_key_value_heads = self.num_attention_heads
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        hidden_size = args.hidden_size
-        self.num_attention_heads = args.num_attention_heads
-
-        hidden_size_per_attention_head = hidden_size // self.num_attention_heads
-
-        self.rotary_emb = nn.RoPE(hidden_size_per_attention_head, traditional=False)
-
-        proj_size = args.kv_channels * self.num_attention_heads
-
-        self.c_attn = nn.Linear(hidden_size, proj_size * 3, bias=True)
-        self.c_proj = nn.Linear(hidden_size, proj_size, bias=not args.no_bias)
-
-        self.scale = hidden_size_per_attention_head**-0.5
-
-    def __call__(self, x, mask=None, cache=None):
-        qkv = self.c_attn(x)
-
-        q, k, v = mx.split(qkv, 3, axis=-1)
-
-        B, L, _ = q.shape
-
-        queries = q.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3)
-        keys = k.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3)
-        values = v.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rotary_emb(queries, offset=cache.offset)
-            keys = self.rotary_emb(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rotary_emb(queries)
-            keys = self.rotary_emb(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-
-        return self.c_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        self.w1 = nn.Linear(
-            args.hidden_size, args.intermediate_size // 2, bias=not args.no_bias
-        )
-        self.w2 = nn.Linear(
-            args.hidden_size, args.intermediate_size // 2, bias=not args.no_bias
-        )
-        self.c_proj = nn.Linear(
-            args.intermediate_size // 2, args.hidden_size, bias=not args.no_bias
-        )
-
-    def __call__(self, x):
-        a1 = self.w1(x)
-        a2 = self.w2(x)
-        return self.c_proj(a1 * nn.silu(a2))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        self.ln_1 = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
-        self.attn = Attention(args)
-        self.ln_2 = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
-        self.mlp = MLP(args)
-
-    def __call__(self, x, mask=None, cache=None):
-        residual = x
-        x = self.ln_1(x)
-        x = self.attn(x, mask=mask, cache=cache)
-        residual = x + residual
-        x = self.ln_2(residual)
-        x = self.mlp(x)
-        x = x + residual
-
-        return x
-
-
-class QwenModel(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.wte = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.h = [TransformerBlock(args) for _ in range(args.num_hidden_layers)]
-        self.ln_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
-
-    def __call__(self, inputs, mask=None, cache=None):
-        x = self.wte(inputs)
-
-        mask = None
-        T = x.shape[1]
-        if T > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(T)
-            mask = mask.astype(x.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.h)
-
-        for layer, c in zip(self.h, cache):
-            x = layer(x, mask, c)
-
-        return self.ln_f(x)
-
-
-class Model(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.model_type = config.model_type
-        self.transformer = QwenModel(config)
-        self.lm_head = nn.Linear(
-            config.hidden_size, config.vocab_size, bias=not config.no_bias
-        )
-        self.args = config
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: mx.array = None,
-        cache: mx.array = None,
-    ) -> Tuple[mx.array, mx.array]:
-        y = self.transformer(x, mask, cache)
-        return self.lm_head(y)
-
-    @property
-    def layers(self):
-        return self.transformer.h
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_attention_heads

llms/mlx_lm/models/qwen2.py

@@ -1,206 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int
-    num_hidden_layers: int
-    intermediate_size: int
-    num_attention_heads: int
-    rms_norm_eps: float
-    vocab_size: int
-    num_key_value_heads: int = None
-    rope_theta: float = 1000000
-    rope_traditional: bool = False
-    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
-    tie_word_embeddings: bool = True
-
-    def __post_init__(self):
-        if self.num_key_value_heads is None:
-            self.num_key_value_heads = self.num_attention_heads
-
-        if self.rope_scaling:
-            required_keys = {"factor", "type"}
-            if not all(key in self.rope_scaling for key in required_keys):
-                raise ValueError(f"rope_scaling must contain keys {required_keys}")
-
-            if self.rope_scaling["type"] != "linear":
-                raise ValueError("rope_scaling 'type' currently only supports 'linear'")
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        dim = args.hidden_size
-        self.n_heads = n_heads = args.num_attention_heads
-        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
-
-        head_dim = args.hidden_size // n_heads
-        self.scale = head_dim**-0.5
-
-        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=True)
-        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
-        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
-        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
-
-        rope_scale = (
-            1 / args.rope_scaling["factor"]
-            if args.rope_scaling is not None and args.rope_scaling["type"] == "linear"
-            else 1
-        )
-        self.rope = nn.RoPE(
-            head_dim,
-            traditional=args.rope_traditional,
-            base=args.rope_theta,
-            scale=rope_scale,
-        )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.o_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, dim, hidden_dim):
-        super().__init__()
-        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
-        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
-        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
-
-    def __call__(self, x) -> mx.array:
-        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.num_attention_heads = args.num_attention_heads
-        self.hidden_size = args.hidden_size
-        self.self_attn = Attention(args)
-        self.mlp = MLP(args.hidden_size, args.intermediate_size)
-        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-        self.post_attention_layernorm = nn.RMSNorm(
-            args.hidden_size, eps=args.rms_norm_eps
-        )
-        self.args = args
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.self_attn(self.input_layernorm(x), mask, cache)
-        h = x + r
-        r = self.mlp(self.post_attention_layernorm(h))
-        out = h + r
-        return out
-
-
-class Qwen2Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.vocab_size = args.vocab_size
-        self.num_hidden_layers = args.num_hidden_layers
-        assert self.vocab_size > 0
-        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [
-            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
-        ]
-        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.embed_tokens(inputs)
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.model_type = args.model_type
-        self.model = Qwen2Model(args)
-        if not args.tie_word_embeddings:
-            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-        if self.args.tie_word_embeddings:
-            out = self.model.embed_tokens.as_linear(out)
-        else:
-            out = self.lm_head(out)
-        return out
-
-    def sanitize(self, weights):
-        if self.args.tie_word_embeddings:
-            weights.pop("lm_head.weight", None)
-        # Remove unused precomputed rotary freqs
-        return {
-            k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
-        }
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/qwen2_moe.py

@@ -1,246 +0,0 @@
-import math
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-from .switch_layers import SwitchGLU
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int
-    num_hidden_layers: int
-    intermediate_size: int
-    num_attention_heads: int
-    num_experts_per_tok: int
-    num_experts: int
-    moe_intermediate_size: int
-    shared_expert_intermediate_size: int
-    rms_norm_eps: float
-    vocab_size: int
-    num_key_value_heads: int = None
-    rope_theta: float = 1000000
-    rope_traditional: bool = False
-    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
-    tie_word_embeddings: bool = False
-
-    def __post_init__(self):
-        if self.num_key_value_heads is None:
-            self.num_key_value_heads = self.num_attention_heads
-
-        if self.rope_scaling:
-            required_keys = {"factor", "type"}
-            if not all(key in self.rope_scaling for key in required_keys):
-                raise ValueError(f"rope_scaling must contain keys {required_keys}")
-
-            if self.rope_scaling["type"] != "linear":
-                raise ValueError("rope_scaling 'type' currently only supports 'linear'")
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        dim = args.hidden_size
-        self.n_heads = n_heads = args.num_attention_heads
-        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
-
-        head_dim = args.hidden_size // n_heads
-        self.scale = head_dim**-0.5
-
-        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=True)
-        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
-        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
-        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
-
-        self.rope = nn.RoPE(
-            head_dim,
-            traditional=args.rope_traditional,
-            base=args.rope_theta,
-        )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.o_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, dim, hidden_dim):
-        super().__init__()
-        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
-        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
-        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
-
-    def __call__(self, x) -> mx.array:
-        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
-
-
-class Qwen2MoeSparseMoeBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        dim = args.hidden_size
-        intermediate_size = args.moe_intermediate_size
-        shared_expert_intermediate_size = args.shared_expert_intermediate_size
-
-        self.num_experts = num_experts = args.num_experts
-        self.top_k = args.num_experts_per_tok
-
-        self.gate = nn.Linear(dim, num_experts, bias=False)
-        self.switch_mlp = SwitchGLU(dim, intermediate_size, num_experts)
-
-        self.shared_expert = MLP(dim, shared_expert_intermediate_size)
-        self.shared_expert_gate = nn.Linear(dim, 1, bias=False)
-
-    def __call__(
-        self,
-        x: mx.array,
-    ):
-        gates = self.gate(x)
-        gates = mx.softmax(gates, axis=-1, precise=True)
-
-        k = self.top_k
-        inds = mx.stop_gradient(mx.argpartition(-gates, kth=k - 1, axis=-1)[..., :k])
-        scores = mx.take_along_axis(gates, inds, axis=-1)
-
-        y = self.switch_mlp(x, inds)
-        y = (y * scores[..., None]).sum(axis=-2)
-
-        shared_expert_output = self.shared_expert(x)
-        shared_expert_output = (
-            mx.sigmoid(self.shared_expert_gate(x)) * shared_expert_output
-        )
-
-        return y + shared_expert_output
-
-
-class Qwen2MoeDecoderLayer(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.hidden_size = args.hidden_size
-        self.self_attn = Attention(args)
-        self.mlp = Qwen2MoeSparseMoeBlock(args)
-
-        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-        self.post_attention_layernorm = nn.RMSNorm(
-            args.hidden_size, eps=args.rms_norm_eps
-        )
-        self.args = args
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.self_attn(self.input_layernorm(x), mask, cache)
-        h = x + r
-        r = self.mlp(self.post_attention_layernorm(h))
-        out = h + r
-        return out
-
-
-class Qwen2MoeModel(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.vocab_size = args.vocab_size
-        self.num_hidden_layers = args.num_hidden_layers
-        assert self.vocab_size > 0
-        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [
-            Qwen2MoeDecoderLayer(args=args) for _ in range(args.num_hidden_layers)
-        ]
-        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.embed_tokens(inputs)
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.model_type = args.model_type
-        self.model = Qwen2MoeModel(args)
-        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-        return self.lm_head(out)
-
-    def sanitize(self, weights):
-        if "model.layers.0.mlp.experts.0.up_proj.weight" not in weights:
-            return weights
-        for l in range(self.args.num_hidden_layers):
-            prefix = f"model.layers.{l}"
-            for n in ["up_proj", "down_proj", "gate_proj"]:
-                for k in ["weight", "scales", "biases"]:
-                    if f"{prefix}.mlp.experts.0.{n}.{k}" in weights:
-                        to_join = [
-                            weights.pop(f"{prefix}.mlp.experts.{e}.{n}.{k}")
-                            for e in range(self.args.num_experts)
-                        ]
-                        weights[f"{prefix}.mlp.switch_mlp.{n}.{k}"] = mx.stack(to_join)
-        return weights
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/stablelm.py

@@ -1,219 +0,0 @@
-import math
-from dataclasses import dataclass
-from typing import Tuple
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    vocab_size: int
-    hidden_size: int
-    num_attention_heads: int
-    num_hidden_layers: int
-    num_key_value_heads: int
-    intermediate_size: int
-    rope_theta: float
-    use_qkv_bias: bool
-    partial_rotary_factor: float
-    layer_norm_eps: float
-    use_parallel_residual: bool = False
-    qk_layernorm: bool = False
-
-
-class LayerNormPerHead(nn.Module):
-
-    def __init__(self, head_dim, num_heads, eps):
-        super().__init__()
-        self.norms = [
-            nn.LayerNorm(head_dim, eps=eps, bias=False) for _ in range(num_heads)
-        ]
-        self.eps = eps
-
-    def __call__(self, x):
-        w = mx.stack([n.weight for n in self.norms])
-        return w * mx.fast.layer_norm(x, None, None, self.eps)
-
-
-class Attention(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.num_key_value_heads = config.num_key_value_heads
-        self.rope_theta = config.rope_theta
-        self.partial_rotary_factor = config.partial_rotary_factor
-
-        if (self.head_dim * self.num_heads) != self.hidden_size:
-            raise ValueError(
-                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
-                f" and `num_heads`: {self.num_heads})."
-            )
-
-        self.q_proj = nn.Linear(
-            self.hidden_size, self.num_heads * self.head_dim, bias=config.use_qkv_bias
-        )
-        self.k_proj = nn.Linear(
-            self.hidden_size,
-            self.num_key_value_heads * self.head_dim,
-            bias=config.use_qkv_bias,
-        )
-        self.v_proj = nn.Linear(
-            self.hidden_size,
-            self.num_key_value_heads * self.head_dim,
-            bias=config.use_qkv_bias,
-        )
-        self.o_proj = nn.Linear(
-            self.num_heads * self.head_dim, self.hidden_size, bias=False
-        )
-
-        self.rope = nn.RoPE(
-            int(self.partial_rotary_factor * self.head_dim),
-            traditional=False,
-            base=self.rope_theta,
-        )
-
-        self.qk_layernorm = config.qk_layernorm
-        if self.qk_layernorm:
-            self.q_layernorm = LayerNormPerHead(
-                self.head_dim, self.num_heads, eps=config.layer_norm_eps
-            )
-            self.k_layernorm = LayerNormPerHead(
-                self.head_dim, self.num_key_value_heads, eps=config.layer_norm_eps
-            )
-
-    def __call__(self, x, mask=None, cache=None):
-        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
-
-        # Extract some shapes
-        B, L, D = queries.shape
-
-        queries = queries.reshape(B, L, self.num_heads, -1)
-        keys = keys.reshape(B, L, self.num_key_value_heads, -1)
-        if self.qk_layernorm:
-            queries = self.q_layernorm(queries)
-            keys = self.k_layernorm(keys)
-        queries = queries.transpose(0, 2, 1, 3)
-        keys = keys.transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.num_key_value_heads, -1).transpose(
-            0, 2, 1, 3
-        )
-
-        # Add RoPE to the queries and keys and combine them with the cache
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        queries = queries.astype(mx.float32)
-        keys = keys.astype(mx.float32)
-
-        # Finally perform the attention computation
-        scale = math.sqrt(1 / queries.shape[-1])
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=scale, mask=mask
-        ).astype(values.dtype)
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.o_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, dim, hidden_dim):
-        super().__init__()
-        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
-        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
-        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
-
-    def __call__(self, x) -> mx.array:
-        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
-
-
-class DecoderLayer(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.self_attn = Attention(config=config)
-        self.mlp = MLP(config.hidden_size, config.intermediate_size)
-        self.input_layernorm = nn.LayerNorm(
-            config.hidden_size,
-            eps=config.layer_norm_eps,
-        )
-        self.use_parallel_residual = config.use_parallel_residual
-        if not self.use_parallel_residual:
-            self.post_attention_layernorm = nn.LayerNorm(
-                config.hidden_size,
-                eps=config.layer_norm_eps,
-            )
-
-    def __call__(self, x, mask, cache):
-        h = self.input_layernorm(x)
-        r = self.self_attn(h, mask, cache)
-
-        if self.use_parallel_residual:
-            out = x + r + self.mlp(h)
-        else:
-            h = x + r
-            r = self.mlp(self.post_attention_layernorm(h))
-            out = h + r
-        return out
-
-
-class StableLM(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
-        self.layers = [DecoderLayer(config) for i in range(config.num_hidden_layers)]
-        self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def __call__(self, x, mask, cache):
-        x = self.embed_tokens(x)
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            x = layer(x, mask, cache=c)
-
-        return self.norm(x)
-
-
-class Model(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        self.model_type = config.model_type
-        self.model = StableLM(config)
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.args = config
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: mx.array = None,
-        cache: mx.array = None,
-    ) -> Tuple[mx.array, mx.array]:
-        mask = None
-        if x.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
-            mask = mask.astype(x.dtype)
-
-        y = self.model(x, mask, cache)
-        return self.lm_head(y)
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/starcoder2.py

@@ -1,175 +0,0 @@
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int
-    num_hidden_layers: int
-    intermediate_size: int
-    num_attention_heads: int
-    num_key_value_heads: int
-    norm_epsilon: float = 1e-5
-    vocab_size: int = 49152
-    rope_theta: float = 100000
-    tie_word_embeddings: bool = True
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-
-        dim = args.hidden_size
-        self.n_heads = n_heads = args.num_attention_heads
-        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
-
-        head_dim = args.hidden_size // args.num_attention_heads
-        self.scale = head_dim**-0.5
-
-        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=True)
-        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
-        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
-        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=True)
-        self.rope = nn.RoPE(head_dim, traditional=False, base=args.rope_theta)
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = mx.fast.scaled_dot_product_attention(
-            queries, keys, values, scale=self.scale, mask=mask
-        )
-
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.o_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, dim, hidden_dim):
-        super().__init__()
-        self.c_fc = nn.Linear(dim, hidden_dim, bias=True)
-        self.c_proj = nn.Linear(hidden_dim, dim, bias=True)
-
-    def __call__(self, x):
-        return self.c_proj(nn.gelu(self.c_fc(x)))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.hidden_size = args.hidden_size
-        self.n_heads = args.num_attention_heads
-
-        self.self_attn = Attention(args)
-        self.mlp = MLP(args.hidden_size, args.intermediate_size)
-        self.input_layernorm = nn.LayerNorm(args.hidden_size, eps=args.norm_epsilon)
-        self.post_attention_layernorm = nn.LayerNorm(
-            args.hidden_size, eps=args.norm_epsilon
-        )
-        self.args = args
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ) -> mx.array:
-        r = self.self_attn(self.input_layernorm(x), mask, cache)
-        h = x + r
-        r = self.mlp(self.post_attention_layernorm(h))
-        out = h + r
-        return out
-
-
-class Starcoder2Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.vocab_size = args.vocab_size
-        self.num_hidden_layers = args.num_hidden_layers
-        assert self.vocab_size > 0
-        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [
-            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
-        ]
-        self.norm = nn.LayerNorm(args.hidden_size, eps=args.norm_epsilon)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.embed_tokens(inputs)
-
-        mask = None
-        if h.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
-            mask = mask.astype(h.dtype)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.model_type = args.model_type
-        self.model = Starcoder2Model(args)
-        if not args.tie_word_embeddings:
-            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache=None,
-    ):
-        out = self.model(inputs, cache)
-        if self.args.tie_word_embeddings:
-            out = self.model.embed_tokens.as_linear(out)
-        else:
-            out = self.lm_head(out)
-        return out
-
-    @property
-    def layers(self):
-        return self.model.layers
-
-    @property
-    def head_dim(self):
-        return self.args.hidden_size // self.args.num_attention_heads
-
-    @property
-    def n_kv_heads(self):
-        return self.args.num_key_value_heads

llms/mlx_lm/models/su_rope.py

@@ -1,79 +0,0 @@
-import math
-from typing import List, Union
-
-import mlx.core as mx
-
-
-class SuScaledRotaryEmbedding:
-    def __init__(
-        self,
-        dims: int,
-        traditional: bool = False,
-        base: float = 10000.0,
-        scale: float = 1.0,
-        max_position_embeddings: int = 131072,
-        original_max_position_embeddings: int = 4096,
-        short_factor: Union[List[float], float] = 1.0,
-        long_factor: Union[List[float], float] = 1.0,
-    ):
-        """
-        Phi3Su Scaled Rotary Embedding layer for Phi-3 models.
-
-        Args:
-            dims (int): The feature dimensions to be rotated.
-            traditional (bool, optional): Unused. Default: ``False``.
-            base (int, optional): Base for the exponential scaling.
-            scale (float, optional): The scale used to scale the positions.
-              Default: ``1.0``.
-            max_position_embeddings (int, optional): The maximum sequence
-              length that this model was trained with. This is used to determine
-              the size of the original RoPE embeddings when using long scaling.
-              Default: ``131072``.
-            original_max_position_embeddings (int, optional): The maximum
-              sequence length that this model was trained with. This is used to
-              determine the size of the original RoPE embeddings when using long
-              scaling. Default: ``4096``.
-            short_factor (float or list[float], optional): List of scaling
-              factors for sequences of length lesser than
-              ``original_max_position_embeddings``. Default: ``1.0``.
-            long_factor (float or list[float], optional): List of scaling
-              factors for sequences of length greater than
-              ``original_max_position_embeddings``.  Default: ``1.0``.
-        """
-        self.inv_freq_short = 1.0 / (
-            mx.array(short_factor, dtype=mx.float32)
-            * base ** (mx.arange(0, dims, 2, dtype=mx.float32) / dims)
-        )
-        self.inv_freq_long = 1.0 / (
-            scale
-            * mx.array(long_factor, dtype=mx.float32)
-            * base ** (mx.arange(0, dims, 2, dtype=mx.float32) / dims)
-        )
-        self.original_max_position_embeddings = original_max_position_embeddings
-        self.scaling_factor = math.sqrt(
-            1
-            + math.log(max_position_embeddings / original_max_position_embeddings)
-            / math.log(original_max_position_embeddings)
-        )
-
-    def _get_cos_sin(self, offset, L):
-        position_ids = mx.arange(offset, offset + L, dtype=mx.float32)
-        inv_freq = (
-            self.inv_freq_long
-            if (offset + L) > self.original_max_position_embeddings
-            else self.inv_freq_short
-        )
-        freqs = position_ids[:, None] * inv_freq[None, :]
-        emb = mx.concatenate([freqs, freqs], axis=-1)
-        cos = mx.cos(emb) * self.scaling_factor
-        sin = mx.sin(emb) * self.scaling_factor
-        return cos, sin
-
-    def __call__(self, x, offset: int = 0):
-        def _rotate_half(_x):
-            midpoint = _x.shape[-1] // 2
-            x1, x2 = _x[..., :midpoint], _x[..., midpoint:]
-            return mx.concatenate([-x2, x1], axis=-1)
-
-        cos, sin = self._get_cos_sin(offset, x.shape[2])
-        return (x * cos) + (_rotate_half(x) * sin)

llms/mlx_lm/models/switch_layers.py

@@ -1,165 +0,0 @@
-import math
-
-import mlx.core as mx
-import mlx.nn as nn
-
-
-class QuantizedSwitchLinear(nn.Module):
-    def __init__(
-        self,
-        input_dims: int,
-        output_dims: int,
-        num_experts: int,
-        bias: bool = True,
-        group_size: int = 64,
-        bits: int = 4,
-    ):
-        super().__init__()
-
-        scale = math.sqrt(1 / input_dims)
-        self.weight, self.scales, self.biases = mx.quantize(
-            mx.random.uniform(
-                low=-scale,
-                high=scale,
-                shape=(num_experts, output_dims, input_dims),
-            ),
-            group_size=group_size,
-            bits=bits,
-        )
-
-        if bias:
-            self.bias = mx.zeros((num_experts, output_dims))
-
-        self.group_size = group_size
-        self.bits = bits
-
-        # Freeze this model's parameters
-        self.freeze()
-
-    def unfreeze(self, *args, **kwargs):
-        """Wrap unfreeze so that we unfreeze any layers we might contain but
-        our parameters will remain frozen."""
-        super().unfreeze(*args, **kwargs)
-        self.freeze(recurse=False)
-
-    @property
-    def input_dims(self):
-        return self.scales.shape[2] * self.group_size
-
-    @property
-    def output_dims(self):
-        return self.weight.shape[1]
-
-    @property
-    def num_experts(self):
-        return self.weight.shape[0]
-
-    def __call__(self, x, indices):
-        x = mx.gather_qmm(
-            x,
-            self["weight"],
-            self["scales"],
-            self["biases"],
-            rhs_indices=indices,
-            transpose=True,
-            group_size=self.group_size,
-            bits=self.bits,
-        )
-        if "bias" in self:
-            x = x + mx.expand_dims(self["bias"][indices], -2)
-        return x
-
-
-class SwitchLinear(nn.Module):
-    def __init__(
-        self, input_dims: int, output_dims: int, num_experts: int, bias: bool = True
-    ):
-        super().__init__()
-        scale = math.sqrt(1 / input_dims)
-        self.weight = mx.random.uniform(
-            low=-scale,
-            high=scale,
-            shape=(num_experts, output_dims, input_dims),
-        )
-
-        if bias:
-            self.bias = mx.zeros((num_experts, output_dims))
-
-    @property
-    def input_dims(self):
-        return self.weight.shape[2]
-
-    @property
-    def output_dims(self):
-        return self.weight.shape[1]
-
-    @property
-    def num_experts(self):
-        return self.weight.shape[0]
-
-    def __call__(self, x, indices):
-        x = mx.gather_mm(x, self["weight"].swapaxes(-1, -2), rhs_indices=indices)
-        if "bias" in self:
-            x = x + mx.expand_dims(self["bias"][indices], -2)
-        return x
-
-    def to_quantized(self, group_size: int = 64, bits: int = 4):
-        num_experts, output_dims, input_dims = self.weight.shape
-        ql = QuantizedSwitchLinear(
-            input_dims, output_dims, num_experts, False, group_size, bits
-        )
-        ql.weight, ql.scales, ql.biases = mx.quantize(self.weight, group_size, bits)
-        if "bias" in self:
-            ql.bias = self.bias
-        return ql
-
-
-class SwitchGLU(nn.Module):
-    def __init__(
-        self,
-        input_dims: int,
-        hidden_dims: int,
-        num_experts: int,
-        activation=nn.silu,
-        bias: bool = False,
-    ):
-        super().__init__()
-
-        self.gate_proj = SwitchLinear(input_dims, hidden_dims, num_experts, bias=bias)
-        self.up_proj = SwitchLinear(input_dims, hidden_dims, num_experts, bias=bias)
-        self.down_proj = SwitchLinear(hidden_dims, input_dims, num_experts, bias=bias)
-        self.activation = activation
-
-    def __call__(self, x, indices) -> mx.array:
-        x = mx.expand_dims(x, (-2, -3))
-
-        x_up = self.up_proj(x, indices)
-        x_gate = self.gate_proj(x, indices)
-        x = self.down_proj(self.activation(x_gate) * x_up, indices)
-
-        return x.squeeze(-2)
-
-
-class SwitchMLP(nn.Module):
-    def __init__(
-        self,
-        input_dims: int,
-        hidden_dims: int,
-        num_experts: int,
-        activation=nn.gelu_approx,
-        bias: bool = False,
-    ):
-        super().__init__()
-
-        self.fc1 = SwitchLinear(input_dims, hidden_dims, num_experts, bias=bias)
-        self.fc2 = SwitchLinear(hidden_dims, input_dims, num_experts, bias=bias)
-        self.activation = activation
-
-    def __call__(self, x, indices) -> mx.array:
-        x = mx.expand_dims(x, (-2, -3))
-
-        x = self.fc1(x, indices)
-        x = self.activation(x)
-        x = self.fc2(x, indices)
-
-        return x.squeeze(-2)