Faster sampling with mx.compile (#937)

* faster sampling with compile

* fix test

llms/mlx_lm/sample_utils.py

@@ -1,6 +1,11 @@
+# Copyright © 2023-2024 Apple Inc.
+
+from functools import partial
+
 import mlx.core as mx
 
 
+@partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
 def top_p_sampling(logits: mx.array, top_p: float, temperature: float) -> mx.array:
     """
     Apply top-p (nucleus) sampling to logits.
@@ -13,7 +18,7 @@ def top_p_sampling(logits: mx.array, top_p: float, temperature: float) -> mx.arr
         token selected based on the top-p criterion.
     """
     # referenced implementation from https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py#L449-L460
-    probs = mx.softmax(logits / temperature, axis=-1)
+    probs = mx.softmax(logits * (1 / temperature), axis=-1)
 
     # sort probs in ascending order
     sorted_indices = mx.argsort(probs, axis=-1)
@@ -25,10 +30,15 @@ def top_p_sampling(logits: mx.array, top_p: float, temperature: float) -> mx.arr
     top_probs = mx.where(
         cumulative_probs > 1 - top_p,
         sorted_probs,
-        mx.zeros_like(sorted_probs),
+        0,
     )
 
     sorted_token = mx.random.categorical(mx.log(top_probs))
     token = sorted_indices.squeeze(0)[sorted_token]
 
     return token
+
+
+@partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
+def categorical_sampling(logits, temp):
+    return mx.random.categorical(logits * (1 / temp))

llms/mlx_lm/utils.py

@@ -20,7 +20,7 @@ from transformers import PreTrainedTokenizer
 
 # Local imports
 from .models.base import KVCache
-from .sample_utils import top_p_sampling
+from .sample_utils import categorical_sampling, top_p_sampling
 from .tokenizer_utils import TokenizerWrapper, load_tokenizer
 from .tuner.utils import apply_lora_layers
 from .tuner.utils import dequantize as dequantize_model
@@ -169,7 +169,7 @@ def generate_step(
             if top_p > 0 and top_p < 1.0:
                 token = top_p_sampling(logits, top_p, temp)
             else:
-                token = mx.random.categorical(logits * (1 / temp))
+                token = categorical_sampling(logits, temp)
 
         return token, logprobs
 

llms/tests/test_sample_utils.py

@@ -1,38 +1,32 @@
 import unittest
-from unittest.mock import patch
 
 import mlx.core as mx
 from mlx_lm.sample_utils import top_p_sampling
 
 
 class TestSamplingUtils(unittest.TestCase):
-    @patch("mlx.core.random.categorical")
+    def test_top_p_sampling(self):
-    def test_top_p_sampling(self, mock_categorical):
+        probs = mx.array([0.9, 0.0, 0.0, 0.1])[None]
-        logits = mx.array([[1.0, 2.0, 3.0, 4.0]])
+        logits = mx.log(probs)
-        top_p = 0.3
         temperature = 1.0
-        expected_token = mx.array([3])
-        mock_categorical.return_value = expected_token
 
-        token = top_p_sampling(logits, top_p, temperature)
+        token = top_p_sampling(logits, 0.3, temperature).item()
-        expected_top_probs = mx.array([[0.0, 0.0, 0.0, 0.643914]])
+        self.assertEqual(token, 0)
-        self.assertTrue(mx.allclose(token, expected_token))
-        args, _ = mock_categorical.call_args
-        self.assertTrue(args[0].shape == expected_top_probs.shape)
-        self.assertTrue(mx.allclose(args[0], mx.log(expected_top_probs)))
 
-        logits = mx.array([[1.0, 2.0, 3.0, 4.0]])
+        token = top_p_sampling(logits, 0.95, temperature).item()
-        top_p = 0.9
+        self.assertTrue(token in (0, 3))
-        temperature = 1.0
-        expected_token = mx.array([3])
-        mock_categorical.return_value = expected_token
 
-        token = top_p_sampling(logits, top_p, temperature)
+        probs = mx.array([0.0, 0.5, 0.4, 0.1])[None]
-        expected_top_probs = mx.array([[0.0, 0.0871443, 0.236883, 0.643914]])
+        logits = mx.log(probs)
-        self.assertTrue(mx.allclose(token, expected_token))
+
-        args, _ = mock_categorical.call_args
+        token = top_p_sampling(logits, 0.4, temperature).item()
-        self.assertTrue(args[0].shape == expected_top_probs.shape)
+        self.assertEqual(token, 1)
-        self.assertTrue(mx.allclose(args[0], mx.log(expected_top_probs)))
+
+        token = top_p_sampling(logits, 0.6, temperature).item()
+        self.assertTrue(token in (1, 2))
+
+        token = top_p_sampling(logits, 0.95, temperature).item()
+        self.assertTrue(token in (1, 2, 3))
 
 
 if __name__ == "__main__":