MusicGen (#1020)

* Add MusicGen model

* add benchmarks

* change to from_pretrained

* symlinks

* add readme and requirements

* fix readme

* readme

musicgen/README.md

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+# 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

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+# 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

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+# 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

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+../encodec/encodec.py

musicgen/generate.py

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+# 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

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+# 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

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+mlx>=0.18
+numpy
+huggingface_hub
+torch
+transformers
+scipy

musicgen/t5.py

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+../t5/t5.py

musicgen/utils.py

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+# 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))