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Create sampler chain

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This commit is contained in:
Neil Mehta
2025-03-08 14:08:55 -05:00
parent 932b7c0510
commit 956da0ddc7
2 changed files with 38 additions and 27 deletions
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32
llms/tests/test_sample_utils.py
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@@ -1,35 +1,35 @@
import unittest
import mlx.core as mx
from mlx_lm.sample_utils import min_p_sampling, top_k_sampling, top_p_sampling
from mlx_lm.sample_utils import apply_min_p, apply_top_k, apply_top_p
class TestSampleUtils(unittest.TestCase):
def test_top_p_sampling(self):
def test_apply_top_p(self):
probs = mx.array([0.9, 0.0, 0.0, 0.1])[None]
logits = mx.log(probs)
new_logits = top_p_sampling(logits, 0.3)
new_logits = apply_top_p(logits, 0.3)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertEqual(actual_probs.tolist(), [1.0, 0.0, 0.0, 0.0])
new_logits = top_p_sampling(logits, 0.95)
new_logits = apply_top_p(logits, 0.95)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertEqual(probs.squeeze().tolist(), actual_probs.tolist())
probs = mx.array([0.0, 0.5, 0.4, 0.1])[None]
logits = mx.log(probs)
new_logits = top_p_sampling(logits, 0.4)
new_logits = apply_top_p(logits, 0.4)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertEqual(actual_probs.tolist(), [0.0, 1.0, 0.0, 0.0])
new_logits = top_p_sampling(logits, 0.6)
new_logits = apply_top_p(logits, 0.6)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertEqual(
[round(p, 4) for p in actual_probs.tolist()], [0.0, 0.5556, 0.4444, 0.0]
)
new_logits = top_p_sampling(logits, 0.95)
new_logits = apply_top_p(logits, 0.95)
actual_probs = mx.softmax(new_logits.squeeze())
actual_rounded = [round(p, 4) for p in actual_probs.tolist()]
expected_rounded = [0.0, 0.5, 0.4, 0.1]
@@ -39,45 +39,45 @@ class TestSampleUtils(unittest.TestCase):
# Batch mode works
probs = mx.array([[0.9, 0.0, 0.0, 0.1], [0.0, 0.8, 0.1, 0.1]])
logits = mx.log(probs)
new_logits = top_p_sampling(logits, 0.5)
new_logits = apply_top_p(logits, 0.5)
actual_probs = mx.softmax(new_logits, axis=-1)
self.assertEqual(
actual_probs.tolist(), [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]]
)
def test_min_p_sampling(self):
def test_apply_min_p(self):
probs = mx.array([0.9, 0.0, 0.0, 0.1])[None]
logits = mx.log(probs)
new_logits = min_p_sampling(logits, 0.8)
new_logits = apply_min_p(logits, 0.8)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertEqual(actual_probs.tolist(), [1.0, 0.0, 0.0, 0.0])
probs = mx.array([0.9, 0.0, 0.0, 0.1])[None]
logits = mx.log(probs)
new_logits = min_p_sampling(logits, 0.05)
new_logits = apply_min_p(logits, 0.05)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertEqual(actual_probs.tolist(), mx.squeeze(probs).tolist())
# Batch mode works
probs = mx.array([[0.9, 0.0, 0.0, 0.1], [0.0, 0.8, 0.0, 0.1]])
logits = mx.log(probs)
new_logits = min_p_sampling(logits, 0.7)
new_logits = apply_min_p(logits, 0.7)
actual_probs = mx.softmax(new_logits, axis=-1)
self.assertEqual(
actual_probs.tolist(), [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]]
)
def test_top_k_sampling(self):
def test_apply_top_k(self):
probs = mx.array([0.9, 0.0, 0.0, 0.1])[None]
logits = mx.log(probs)
new_logits = top_k_sampling(logits, 1)
new_logits = apply_top_k(logits, 1)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertEqual(actual_probs.tolist(), [1.0, 0.0, 0.0, 0.0])
probs = mx.array([0.6, 0.0, 0.1, 0.3])[None]
logits = mx.log(probs)
new_logits = top_k_sampling(logits, 2)
new_logits = apply_top_k(logits, 2)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertEqual(
[round(p, 4) for p in actual_probs.tolist()], [0.6667, 0.0, 0.0, 0.3333]
@@ -87,7 +87,7 @@ class TestSampleUtils(unittest.TestCase):
probs = mx.array([[0.9, 0.0, 0.0, 0.1], [0.0, 0.8, 0.0, 0.1]])
logits = mx.log(probs)
new_logits = top_k_sampling(logits, 1)
new_logits = apply_top_k(logits, 1)
actual_probs = mx.softmax(new_logits, axis=-1)
self.assertEqual(
actual_probs.tolist(), [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]]