MusicGen (#1020)

* Add MusicGen model

* add benchmarks

* change to from_pretrained

* symlinks

* add readme and requirements

* fix readme

* readme

encodec/README.md

@@ -33,13 +33,14 @@ An example using the model:
 
 ```python
 import mlx.core as mx
-from utils import load, load_audio, save_audio
+from encodec import EncodecModel
+from utils import load_audio, save_audio
 
 # Load the 48 KHz model and preprocessor.
-model, processor = load("mlx-community/encodec-48khz-float32")
+model, processor = EncodecModel.from_pretrained("mlx-community/encodec-48khz-float32")
 
 # Load an audio file
-audio = load_audio("path/to/aduio", model.sampling_rate, model.channels)
+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).

encodec/benchmarks/bench_mx.py

@@ -3,9 +3,10 @@
 import time
 
 import mlx.core as mx
-from utils import load
 
-model, processor = load("mlx-community/encodec-48khz-float32")
+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)

encodec/convert.py

@@ -10,7 +10,6 @@ from typing import Any, Dict, Union
 import mlx.core as mx
 import mlx.nn as nn
 from huggingface_hub import snapshot_download
-from mlx.utils import tree_flatten
 
 import encodec
 

encodec/encodec.py

@@ -1,7 +1,10 @@
 # Copyright © 2024 Apple Inc.
 
+import functools
+import json
 import math
-from dataclasses import dataclass
+from pathlib import Path
+from types import SimpleNamespace
 from typing import List, Optional, Tuple, Union
 
 import mlx.core as mx
@@ -669,3 +672,70 @@ class EncodecModel(nn.Module):
         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

@@ -1,10 +1,12 @@
 # Copyright © 2024 Apple Inc.
 
 import mlx.core as mx
-from utils import load, load_audio, save_audio
+from utils import load_audio, save_audio
+
+from encodec import EncodecModel
 
 # Load the 48 KHz model and preprocessor.
-model, processor = load("mlx-community/encodec-48khz-float32")
+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)

encodec/test.py

@@ -3,9 +3,10 @@
 import mlx.core as mx
 import numpy as np
 import torch
-from datasets import Audio, load_dataset
+from transformers import AutoProcessor
-from transformers import AutoProcessor, EncodecModel
+from transformers import EncodecModel as PTEncodecModel
-from utils import load, load_audio, preprocess_audio
+
+from encodec import EncodecModel, preprocess_audio
 
 
 def compare_processors():
@@ -30,8 +31,8 @@ def compare_processors():
 
 
 def compare_models():
-    pt_model = EncodecModel.from_pretrained("facebook/encodec_48khz")
+    pt_model = PTEncodecModel.from_pretrained("facebook/encodec_48khz")
-    mx_model, _ = load("mlx-community/encodec-48khz-float32")
+    mx_model, _ = EncodecModel.from_pretrained("mlx-community/encodec-48khz-float32")
 
     np.random.seed(0)
     audio_length = 190560

encodec/utils.py

@@ -1,16 +1,7 @@
 # Copyright © 2024 Apple Inc.
 
-import functools
-import json
-from pathlib import Path
-from types import SimpleNamespace
-from typing import List, Optional, Union
-
 import mlx.core as mx
 import numpy as np
-from huggingface_hub import snapshot_download
-
-import encodec
 
 
 def save_audio(file: str, audio: mx.array, sampling_rate: int):
@@ -59,71 +50,3 @@ def load_audio(file: str, sampling_rate: int, channels: int):
 
     out = mx.array(np.frombuffer(out, np.int16))
     return out.reshape(-1, channels).astype(mx.float32) / 32767.0
-
-
-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)
-
-
-def load(path_or_repo):
-    """
-    Load the model and audo preprocessor.
-    """
-    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 = encodec.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

musicgen/README.md

@@ -0,0 +1,31 @@
+# MusicGen
+
+An example of Meta's MusicGen model in MLX.[^1] MusicGen is used to generate
+music from text descriptions.
+
+### Setup
+
+Install the requirements:
+
+```
+pip install -r requirements.txt
+```
+
+### Example
+
+An example using the model:
+
+```python
+import mlx.core as mx
+from music_gen import MusicGen
+from utils import save_audio
+
+model = MusicGen.from_pretrained("facebook/musicgen-medium")
+
+audio = model.generate("happy rock")
+
+save_audio("out.wav", audio, model.sampling_rate)
+```
+
+[^1]: Refer to the [arXiv paper](https://arxiv.org/abs/2306.05284) and
+  [code](https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN.md) for more details.

musicgen/benchmarks/bench_mx.py

@@ -0,0 +1,28 @@
+# Copyright © 2024 Apple Inc.
+
+import sys
+import time
+from pathlib import Path
+
+import mlx.core as mx
+
+cur_path = Path(__file__).parents[1].resolve()
+sys.path.append(str(cur_path))
+
+from musicgen import MusicGen
+
+text = "folk ballad"
+model = MusicGen.from_pretrained("facebook/musicgen-medium")
+
+max_steps = 100
+
+audio = model.generate(text, max_steps=10)
+mx.eval(audio)
+
+tic = time.time()
+audio = model.generate(text, max_steps=max_steps)
+mx.eval(audio)
+toc = time.time()
+
+ms = 1000 * (toc - tic) / max_steps
+print(f"Time (ms) per step: {ms:.3f}")

musicgen/benchmarks/bench_pt.py

@@ -0,0 +1,31 @@
+# Copyright © 2024 Apple Inc.
+
+import time
+
+import torch
+from transformers import AutoProcessor, MusicgenForConditionalGeneration
+
+model_name = "facebook/musicgen-medium"
+processor = AutoProcessor.from_pretrained(model_name)
+model = MusicgenForConditionalGeneration.from_pretrained(model_name).to("mps")
+
+inputs = processor(
+    text=["folk ballad"],
+    padding=True,
+    return_tensors="pt",
+)
+inputs["input_ids"] = inputs["input_ids"].to("mps")
+inputs["attention_mask"] = inputs["attention_mask"].to("mps")
+
+# warmup
+audio_values = model.generate(**inputs, max_new_tokens=10)
+torch.mps.synchronize()
+
+max_steps = 100
+tic = time.time()
+audio_values = model.generate(**inputs, max_new_tokens=max_steps)
+torch.mps.synchronize()
+toc = time.time()
+
+ms = 1000 * (toc - tic) / max_steps
+print(f"Time (ms) per step: {ms:.3f}")

musicgen/encodec.py

@@ -0,0 +1 @@
+../encodec/encodec.py

musicgen/generate.py

@@ -0,0 +1,23 @@
+# Copyright © 2024 Apple Inc.
+
+import argparse
+
+from utils import save_audio
+
+from musicgen import MusicGen
+
+
+def main(text: str, output_path: str, model_name: str, max_steps: int):
+    model = MusicGen.from_pretrained(model_name)
+    audio = model.generate(text, max_steps=max_steps)
+    save_audio(output_path, audio, model.sampling_rate)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", required=False, default="facebook/musicgen-medium")
+    parser.add_argument("--text", required=False, default="happy rock")
+    parser.add_argument("--output-path", required=False, default="0.wav")
+    parser.add_argument("--max-steps", required=False, default=500, type=int)
+    args = parser.parse_args()
+    main(args.text, args.output_path, args.model, args.max_steps)

musicgen/musicgen.py

@@ -0,0 +1,358 @@
+# Copyright © 2024 Apple Inc.
+
+import json
+from functools import partial
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+from tqdm import tqdm
+
+from encodec import EncodecModel
+from t5 import T5
+
+
+class TextConditioner(nn.Module):
+    def __init__(self, t5_name, input_dim, output_dim):
+        super().__init__()
+        self._t5, self.tokenizer = T5.from_pretrained(t5_name)
+        self.output_proj = nn.Linear(input_dim, output_dim)
+
+    def __call__(self, text):
+        x = self.tokenizer.encode(text)
+        x = self._t5.encode(x)
+        return self.output_proj(x)
+
+
+class KVCache:
+    def __init__(self, head_dim, n_kv_heads):
+        self.n_kv_heads = n_kv_heads
+        if isinstance(head_dim, int):
+            self.k_head_dim = self.v_head_dim = head_dim
+        elif isinstance(head_dim, tuple) and len(head_dim) == 2:
+            self.k_head_dim, self.v_head_dim = head_dim
+        else:
+            raise ValueError("head_dim must be an int or a tuple of two ints")
+        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]:
+            B = keys.shape[0]
+            n_steps = (self.step + keys.shape[2] - 1) // self.step
+            k_shape = (B, self.n_kv_heads, n_steps * self.step, self.k_head_dim)
+            v_shape = (B, self.n_kv_heads, n_steps * self.step, self.v_head_dim)
+            new_k = mx.zeros(k_shape, keys.dtype)
+            new_v = mx.zeros(v_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, :]
+
+    @property
+    def state(self):
+        return self.keys, self.values
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, dim, n_heads):
+        super().__init__()
+
+        self.n_heads = n_heads
+
+        head_dim = dim // n_heads
+
+        self.scale = head_dim**-0.5
+
+        self.q_proj = nn.Linear(dim, dim, bias=False)
+        self.k_proj = nn.Linear(dim, dim, bias=False)
+        self.v_proj = nn.Linear(dim, dim, bias=False)
+        self.out_proj = nn.Linear(dim, dim, bias=False)
+
+    def __call__(
+        self,
+        queries: mx.array,
+        keys: mx.array,
+        values: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[KVCache] = None,
+    ) -> mx.array:
+        B, L_q, D = queries.shape
+        L_k = keys.shape[1]
+
+        queries, keys, values = (
+            self.q_proj(queries),
+            self.k_proj(keys),
+            self.v_proj(values),
+        )
+
+        # Prepare the queries, keys and values for the attention computation
+        queries = queries.reshape(B, L_q, self.n_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, L_k, self.n_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L_k, self.n_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_q, -1)
+        return self.out_proj(output)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.num_attention_heads = config.decoder.num_attention_heads
+        self.hidden_size = config.decoder.hidden_size
+        self.self_attn = MultiHeadAttention(self.hidden_size, self.num_attention_heads)
+        self.cross_attn = MultiHeadAttention(self.hidden_size, self.num_attention_heads)
+        self.linear1 = nn.Linear(self.hidden_size, config.decoder.ffn_dim, bias=False)
+        self.linear2 = nn.Linear(config.decoder.ffn_dim, self.hidden_size, bias=False)
+
+        self.norm1 = nn.LayerNorm(self.hidden_size, eps=1e-5)
+        self.norm_cross = nn.LayerNorm(self.hidden_size, eps=1e-5)
+        self.norm2 = nn.LayerNorm(self.hidden_size, eps=1e-5)
+
+    def __call__(
+        self,
+        x: mx.array,
+        conditioning: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[KVCache] = None,
+    ) -> mx.array:
+        xn = self.norm1(x)
+        x += self.self_attn(xn, xn, xn, mask, cache)
+        xn = self.norm_cross(x)
+        x += self.cross_attn(xn, conditioning, conditioning, mask)
+        xn = self.norm2(x)
+        x += self.linear2(nn.gelu(self.linear1(xn)))
+        return x
+
+
+@partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
+def top_k_sampling(
+    logits: mx.array, top_k: float, temperature: float, axis: int = -1
+) -> mx.array:
+    """
+    Apply top-k sampling to logits.
+
+    Args:
+        logits: The logits from the model's output.
+        top_k: Sample from the top k logits.
+        temperature: Temperature parameter for softmax distribution reshaping.
+        axis: Axis along which to sample.
+    Returns:
+        token selected based on the top-k criterion.
+    """
+    # referenced implementation from https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py#L449-L460
+    probs = mx.softmax(logits * (1 / temperature), axis=axis)
+
+    # sort probs in ascending order
+    sorted_indices = mx.argsort(probs, axis=axis)
+    sorted_probs = mx.take_along_axis(probs, sorted_indices, axis=axis)
+    prob_threshold = mx.take(sorted_probs, mx.array(-top_k), axis=axis)
+
+    # select the top K tokens in probability
+    top_probs = mx.where(
+        sorted_probs > prob_threshold,
+        sorted_probs,
+        0,
+    )
+
+    sorted_token = mx.random.categorical(mx.log(top_probs), axis=axis)
+    token = mx.take_along_axis(
+        sorted_indices, mx.expand_dims(sorted_token, axis), axis=axis
+    )
+
+    return token
+
+
+def create_sin_embedding(positions: mx.array, dim: int, max_period: float = 10000):
+    assert dim % 2 == 0
+    half_dim = dim // 2
+    adim = mx.arange(half_dim).reshape(1, 1, -1)
+    phase = positions / (max_period ** (adim / (half_dim - 1)))
+    return mx.concatenate([mx.cos(phase), mx.sin(phase)], axis=-1)
+
+
+class MusicGen(nn.Module):
+    def __init__(self, config):
+        self.num_codebooks = config.decoder.num_codebooks
+        self.codebook_size = config.audio_encoder.codebook_size
+        self.bos_token_id = config.decoder.bos_token_id
+        self.hidden_size = config.decoder.hidden_size
+        self.num_attention_heads = config.decoder.num_attention_heads
+        self.sampling_rate = config.audio_encoder.sampling_rate
+
+        self.text_conditioner = TextConditioner(
+            config.text_encoder._name_or_path,
+            config.text_encoder.d_model,
+            self.hidden_size,
+        )
+        self.emb = [
+            nn.Embedding(self.codebook_size + 1, self.hidden_size)
+            for _ in range(self.num_codebooks)
+        ]
+        self.layers = [
+            TransformerBlock(config) for _ in range(config.decoder.num_hidden_layers)
+        ]
+        self.out_norm = nn.LayerNorm(self.hidden_size, eps=1e-5)
+        self.linears = [
+            nn.Linear(self.hidden_size, self.codebook_size, bias=False)
+            for _ in range(self.num_codebooks)
+        ]
+        encodec_name = config.audio_encoder._name_or_path.split("/")[-1]
+        encodec_name = encodec_name.replace("_", "-")
+        self._audio_decoder, _ = EncodecModel.from_pretrained(
+            f"mlx-community/{encodec_name}-float32"
+        )
+
+    def __call__(
+        self,
+        audio_tokens: mx.array,
+        conditioning: mx.array,
+        cache: list[KVCache] = None,
+    ):
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        x = sum([self.emb[k](audio_tokens[..., k]) for k in range(self.num_codebooks)])
+
+        offset = cache[0].offset if cache[0] is not None else 0
+        pos_emb = create_sin_embedding(offset, self.hidden_size)
+        x += pos_emb.astype(x.dtype)
+
+        for layer, c in zip(self.layers, cache):
+            x = layer(x, conditioning, cache=c)
+
+        x = self.out_norm(x)
+        x = mx.stack([self.linears[k](x) for k in range(self.num_codebooks)], axis=-1)
+        return x
+
+    def generate(
+        self,
+        text: str,
+        max_steps: int = 200,
+        top_k: int = 250,
+        temp: float = 1.0,
+        guidance_coef: float = 3.0,
+    ) -> mx.array:
+        """
+        Generates a waveform conditioned on `text`.
+
+        Args:
+            text (str): The text to condition generation on.
+            max_steps (int): Max steps to generate.
+            top_k (int): Top k used in sampling.
+            temp (float): Sampling softmax temperature.
+            guidance_coef (float): Classifier free guidance coefficent.
+                Used to combine conditional and unconditional logits.
+
+        Returns:
+            An mx.array of audio samples of shape ``(num_samples,)``.
+        """
+        # Assuming no audio prompt we start with all bos token for the codebooks
+        audio_shape = (1, max_steps + 1, self.num_codebooks)
+        audio_seq = mx.full(audio_shape, self.bos_token_id)
+
+        text_tokens = self.text_conditioner(text)
+        # Compute conditional and unconditional logits in one batch
+        text_tokens = mx.concatenate([text_tokens, mx.zeros_like(text_tokens)], axis=0)
+
+        head_dim = self.hidden_size // self.num_attention_heads
+        cache = [
+            KVCache(head_dim, self.num_attention_heads) for _ in range(len(self.layers))
+        ]
+        for offset in tqdm(range(max_steps)):
+            audio_input = mx.tile(audio_seq[:, offset : offset + 1], [2, 1, 1])
+            audio_logits = self(audio_input, text_tokens, cache)
+            cond_logits, uncond_logits = audio_logits[:1], audio_logits[1:2]
+            audio_logits = uncond_logits + (cond_logits - uncond_logits) * guidance_coef
+            audio_tokens = top_k_sampling(audio_logits, top_k, temp, axis=-2)
+            # "delay" pattern
+            audio_tokens[..., offset + 1 :] = self.bos_token_id
+            audio_tokens[..., : -max_steps + offset] = self.bos_token_id
+            audio_seq[:, offset + 1 : offset + 2] = audio_tokens
+            mx.eval(audio_seq)
+
+        # Undo delay
+        for i in range(self.num_codebooks):
+            audio_seq[:, : -self.num_codebooks, i] = audio_seq[
+                :, i : -self.num_codebooks + i, i
+            ]
+        audio_seq = audio_seq[:, 1 : -self.num_codebooks + 1]
+
+        audio_seq = mx.swapaxes(audio_seq, -1, -2)[:, mx.newaxis]
+        audio = self._audio_decoder.decode(audio_seq, audio_scales=[None])
+        return audio[0]
+
+    @classmethod
+    def sanitize(cls, weights):
+        out_weights = {}
+        for k, arr in weights.items():
+            if k.startswith("transformer."):
+                k = k[len("transformer.") :]
+
+            if "cross_attention" in k:
+                k = k.replace("cross_attention", "cross_attn")
+
+            if "condition_provider" in k:
+                k = k.replace(
+                    "condition_provider.conditioners.description", "text_conditioner"
+                )
+
+            if "in_proj_weight" in k:
+                dim = arr.shape[0] // 3
+                name = "in_proj_weight"
+                out_weights[k.replace(name, "q_proj.weight")] = arr[:dim]
+                out_weights[k.replace(name, "k_proj.weight")] = arr[dim : dim * 2]
+                out_weights[k.replace(name, "v_proj.weight")] = arr[dim * 2 :]
+                continue
+
+            out_weights[k] = arr
+        return out_weights
+
+    @classmethod
+    def from_pretrained(cls, path_or_repo: str):
+        import torch
+        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", "state_dict.bin"],
+                )
+            )
+
+        with open(path / "config.json", "r") as f:
+            config = SimpleNamespace(**json.load(f))
+            config.text_encoder = SimpleNamespace(**config.text_encoder)
+            config.audio_encoder = SimpleNamespace(**config.audio_encoder)
+            config.decoder = SimpleNamespace(**config.decoder)
+
+        weights = torch.load(path / "state_dict.bin", weights_only=True)["best_state"]
+        weights = {k: mx.array(v) for k, v in weights.items()}
+        weights = cls.sanitize(weights)
+
+        model = MusicGen(config)
+        model.load_weights(list(weights.items()))
+        return model

musicgen/requirements.txt

@@ -0,0 +1,6 @@
+mlx>=0.18
+numpy
+huggingface_hub
+torch
+transformers
+scipy

musicgen/t5.py

@@ -0,0 +1 @@
+../t5/t5.py

musicgen/utils.py

@@ -0,0 +1,15 @@
+# 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 = mx.clip(audio, -1, 1)
+    audio = (audio * 32767).astype(mx.int16)
+    write(file, sampling_rate, np.array(audio))

t5/README.md

@@ -7,31 +7,6 @@ tasks by prepending task-specific prefixes to the input, e.g.:
 
 This example also supports the FLAN-T5 models variants.[^2]
 
-## Setup
-
-Download and convert the model:
-
-```sh
-python convert.py --model <model>
-```
-
-This will make the `<model>.npz` file which MLX can read.
-
-The `<model>` can be any of the following:
-
-| Model Name | Model Size  |
-| ---------- | ----------
-| t5-small   | 60 million  |
-| t5-base    | 220 million |
-| t5-large   | 770 million |
-| t5-3b      | 3 billion   |
-| t5-11b     | 11 billion  |
-
-The FLAN variants can be specified with `google/flan-t5-small`,
-`google/flan-t5-base`, etc. See the [Hugging Face
-page](https://huggingface.co/docs/transformers/model_doc/flan-t5) for a
-complete list of models.
-
 ## Generate
 
 Generate text with:
@@ -48,6 +23,21 @@ To see a list of options run:
 python t5.py --help
 ```
 
+The `<model>` can be any of the following:
+
+| Model Name | Model Size  |
+| ---------- | ----------
+| t5-small   | 60 million  |
+| t5-base    | 220 million |
+| t5-large   | 770 million |
+| t5-3b      | 3 billion   |
+| t5-11b     | 11 billion  |
+
+The FLAN variants can be specified with `google/flan-t5-small`,
+`google/flan-t5-base`, etc. See the [Hugging Face
+page](https://huggingface.co/docs/transformers/model_doc/flan-t5) for a
+complete list of models.
+
 [^1]: For more information on T5 see the [original paper](https://arxiv.org/abs/1910.10683)
    or the [Hugging Face page](https://huggingface.co/docs/transformers/model_doc/t5).
 [^2]: For more information on FLAN-T5 see the [original paper](https://arxiv.org/abs/2210.11416).

t5/convert.py

@@ -1,75 +0,0 @@
-import numpy as np
-from transformers import T5ForConditionalGeneration
-
-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."),
-]
-
-DECODER_REPLACEMENT_PATTERNS = [
-    (".layer.0.SelfAttention.", ".self_attention."),
-    (".layer.1.EncDecAttention.", ".cross_attention."),
-    (".layer.2.DenseReluDense.", ".dense."),
-]
-
-
-def replace_key(key: str) -> str:
-    for old, new in SHARED_REPLACEMENT_PATTERNS:
-        key = key.replace(old, new)
-    if key.startswith("encoder."):
-        for old, new in ENCODER_REPLACEMENT_PATTERNS:
-            key = key.replace(old, new)
-    elif key.startswith("decoder."):
-        for old, new in DECODER_REPLACEMENT_PATTERNS:
-            key = key.replace(old, new)
-    return key
-
-
-def convert(model_name, dtype):
-    dtype = getattr(np, dtype)
-    model = T5ForConditionalGeneration.from_pretrained(model_name, torch_dtype="auto")
-    weights = {
-        replace_key(k): v.numpy().astype(dtype) for k, v in model.state_dict().items()
-    }
-    file_name = model_name.replace("/", "-")
-    print(f"Saving weights to {file_name}.npz")
-    np.savez(f"{file_name}.npz", **weights)
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser(description="Convert T5 weights to MLX")
-    parser.add_argument(
-        "--model",
-        type=str,
-        help="Name of the T5 model.",
-        default="t5-small",
-    )
-    parser.add_argument(
-        "--dtype",
-        help="The model data type.",
-        type=str,
-        choices=["float16", "float32"],
-        default="float32",
-    )
-    args = parser.parse_args()
-    convert(args.model, args.dtype)

t5/t5.py

@@ -1,12 +1,45 @@
 import argparse
+import json
+from pathlib import Path
 from time import perf_counter_ns
+from types import SimpleNamespace
 from typing import List, Optional, Tuple
 
 import mlx.core as mx
 import mlx.nn as nn
 import numpy as np
-from mlx.utils import tree_map, tree_unflatten
+from transformers import AutoTokenizer
-from transformers import AutoTokenizer, T5Config
+
+
+class Tokenizer:
+    def __init__(self, config, model_name):
+        self._decoder_start_id = config.decoder_start_token_id
+        self._tokenizer = AutoTokenizer.from_pretrained(
+            model_name,
+            legacy=False,
+            model_max_length=getattr(config, "n_positions", 512),
+        )
+
+    @property
+    def eos_id(self) -> int:
+        return self._tokenizer.eos_token_id
+
+    @property
+    def decoder_start_id(self) -> int:
+        return self._decoder_start_id
+
+    def encode(self, s: str) -> mx.array:
+        return mx.array(
+            self._tokenizer(
+                s,
+                return_tensors="np",
+                return_attention_mask=False,
+            )["input_ids"]
+        )
+
+    def decode(self, t: List[int], with_sep: bool = True) -> str:
+        tokens = self._tokenizer.convert_ids_to_tokens(t)
+        return "".join(t.replace("▁", " " if with_sep else "") for t in tokens)
 
 
 def _relative_position_bucket(
@@ -60,10 +93,10 @@ def _relative_position_bucket(
 
 
 class RelativePositionBias(nn.Module):
-    def __init__(self, config: T5Config, bidirectional: bool):
+    def __init__(self, config, bidirectional: bool):
         self.bidirectional = bidirectional
         self.num_buckets = config.relative_attention_num_buckets
-        self.max_distance = config.relative_attention_max_distance
+        self.max_distance = getattr(config, "relative_attention_max_distance", 128)
         self.n_heads = config.num_heads
         self.embeddings = nn.Embedding(
             config.relative_attention_num_buckets, config.num_heads
@@ -91,7 +124,7 @@ class RelativePositionBias(nn.Module):
 
 
 class MultiHeadAttention(nn.Module):
-    def __init__(self, config: T5Config):
+    def __init__(self, config):
         super().__init__()
         inner_dim = config.d_kv * config.num_heads
         self.num_heads = config.num_heads
@@ -135,17 +168,21 @@ class MultiHeadAttention(nn.Module):
 
 
 class DenseActivation(nn.Module):
-    def __init__(self, config: T5Config):
+    def __init__(self, config):
         super().__init__()
         mlp_dims = config.d_ff or config.d_model * 4
-        self.gated = config.feed_forward_proj.startswith("gated")
+        self.gated = hasattr(config, "feed_forward_proj")
+        activation = (
+            "relu"
+            if not self.gated
+            else config.feed_forward_proj.removeprefix("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":
@@ -166,7 +203,7 @@ class DenseActivation(nn.Module):
 
 
 class TransformerEncoderLayer(nn.Module):
-    def __init__(self, config: T5Config):
+    def __init__(self, config):
         super().__init__()
         self.attention = MultiHeadAttention(config)
         self.ln1 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
@@ -184,7 +221,7 @@ class TransformerEncoderLayer(nn.Module):
 
 
 class TransformerEncoder(nn.Module):
-    def __init__(self, config: T5Config):
+    def __init__(self, config):
         super().__init__()
         self.layers = [
             TransformerEncoderLayer(config) for i in range(config.num_layers)
@@ -200,7 +237,7 @@ class TransformerEncoder(nn.Module):
 
 
 class TransformerDecoderLayer(nn.Module):
-    def __init__(self, config: T5Config):
+    def __init__(self, config):
         super().__init__()
         self.self_attention = MultiHeadAttention(config)
         self.cross_attention = MultiHeadAttention(config)
@@ -233,7 +270,7 @@ class TransformerDecoderLayer(nn.Module):
 
 
 class TransformerDecoder(nn.Module):
-    def __init__(self, config: T5Config):
+    def __init__(self, config):
         super().__init__()
         n_layers = getattr(config, "num_decoder_layers", config.num_layers)
         self.layers = [TransformerDecoderLayer(config) for i in range(n_layers)]
@@ -262,7 +299,7 @@ class TransformerDecoder(nn.Module):
 
 
 class OutputHead(nn.Module):
-    def __init__(self, config: T5Config):
+    def __init__(self, config):
         self.linear = nn.Linear(config.d_model, config.vocab_size, bias=False)
 
     def __call__(self, inputs):
@@ -270,11 +307,11 @@ class OutputHead(nn.Module):
 
 
 class T5(nn.Module):
-    def __init__(self, config: T5Config):
+    def __init__(self, config):
         self.wte = nn.Embedding(config.vocab_size, config.d_model)
         self.encoder = TransformerEncoder(config)
         self.decoder = TransformerDecoder(config)
-        self.tie_word_embeddings = config.tie_word_embeddings
+        self.tie_word_embeddings = getattr(config, "tie_word_embeddings", True)
         if not self.tie_word_embeddings:
             self.lm_head = OutputHead(config)
         self.model_dim = config.d_model
@@ -313,36 +350,82 @@ class T5(nn.Module):
     ):
         return self.decode(decoder_inputs, self.encode(inputs))[0]
 
+    @classmethod
+    def sanitize(cls, weights):
+        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.",
+            ),
+        ]
 
-class Tokenizer:
+        encoder_replacement_patterns = [
-    def __init__(self, config: T5Config):
+            (".layer.0.SelfAttention.", ".attention."),
-        self._decoder_start_id = config.decoder_start_token_id
+            (".layer.1.DenseReluDense.", ".dense."),
-        self._tokenizer = AutoTokenizer.from_pretrained(
+        ]
-            args.model,
+
-            legacy=False,
+        decoder_replacement_patterns = [
-            model_max_length=getattr(config, "n_positions", 512),
+            (".layer.0.SelfAttention.", ".self_attention."),
+            (".layer.1.EncDecAttention.", ".cross_attention."),
+            (".layer.2.DenseReluDense.", ".dense."),
+        ]
+
+        ignored_keys = [
+            "decoder.layers.0.cross_attention.relative_attention_bias.weight"
+        ]
+
+        def replace_key(key: str) -> str:
+            for old, new in shared_replacement_patterns:
+                key = key.replace(old, new)
+            if key.startswith("encoder."):
+                for old, new in encoder_replacement_patterns:
+                    key = key.replace(old, new)
+            elif key.startswith("decoder."):
+                for old, new in decoder_replacement_patterns:
+                    key = key.replace(old, new)
+            return key
+
+        weights = {replace_key(k): v for k, v in weights.items()}
+        for key in ignored_keys:
+            if key in weights:
+                del weights[key]
+        return weights
+
+    @classmethod
+    def from_pretrained(
+        cls, path_or_repo: str, dtype: mx.Dtype = mx.bfloat16
+    ) -> tuple["T5", Tokenizer]:
+        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"],
+                )
             )
 
-    @property
+        with open(path / "config.json", "r") as f:
-    def eos_id(self) -> int:
+            config = SimpleNamespace(**json.load(f))
-        return self._tokenizer.eos_token_id
 
-    @property
+        model = T5(config)
-    def decoder_start_id(self) -> int:
+        weights = mx.load(str(path / "model.safetensors"))
-        return self._decoder_start_id
+        weights = cls.sanitize(weights)
-
+        weights = {k: v.astype(dtype) for k, v in weights.items()}
-    def encode(self, s: str) -> mx.array:
+        model.load_weights(list(weights.items()))
-        return mx.array(
+        return model, Tokenizer(config, "t5-base")
-            self._tokenizer(
-                s,
-                return_tensors="np",
-                return_attention_mask=False,
-            )["input_ids"]
-        )
-
-    def decode(self, t: List[int], with_sep: bool = True) -> str:
-        tokens = self._tokenizer.convert_ids_to_tokens(t)
-        return "".join(t.replace("▁", " " if with_sep else "") for t in tokens)
 
 
 def generate(prompt: str, model: T5, tokenizer: Tokenizer, temp: Optional[float] = 0.0):
@@ -363,19 +446,6 @@ def generate(prompt: str, model: T5, tokenizer: Tokenizer, temp: Optional[float]
         yield y.squeeze()
 
 
-def load_model(model_name: str, dtype: str = "float16"):
-    config = T5Config.from_pretrained(args.model)
-    dtype = getattr(mx, dtype)
-    model = T5(config)
-    file_name = model_name.replace("/", "-")
-    weights = mx.load(f"{file_name}.npz")
-    weights = tree_unflatten(list(weights.items()))
-    weights = tree_map(lambda p: p.astype(dtype), weights)
-    model.update(weights)
-    mx.eval(model.parameters())
-    return model, Tokenizer(config)
-
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="T5 Inference script")
     parser.add_argument(
@@ -421,7 +491,8 @@ if __name__ == "__main__":
 
     mx.random.seed(args.seed)
 
-    model, tokenizer = load_model(args.model, args.dtype)
+    dtype = getattr(mx, args.dtype)
+    model, tokenizer = T5.from_pretrained(args.model, dtype)
 
     if args.encode_only:
         print("[INFO] Encoding with T5...", flush=True)