MusicGen (#1020)

* Add MusicGen model

* add benchmarks

* change to from_pretrained

* symlinks

* add readme and requirements

* fix readme

* readme

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

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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}")