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top_k and min_p refactor

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This commit is contained in:
Neil Mehta 2025-03-08 10:12:28 -05:00
parent 58e912966a
commit 932b7c0510
2 changed files with 49 additions and 40 deletions
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20
llms/mlx_lm/sample_utils.py
View File

@ -88,7 +88,6 @@ def make_logits_processors(
def top_k_sampling(
logprobs: mx.array,
top_k: int,
temperature=1.0,
) -> mx.array:
"""
Sample from only the top K tokens ranked by probability.
@ -103,12 +102,11 @@ def top_k_sampling(
f"`top_k` has to be an integer in the (0, {vocab_size}] interval,"
f" but is {top_k}."
)
logprobs = logprobs * (1 / temperature)
mask_idx = mx.argpartition(-logprobs, kth=top_k - 1, axis=-1)[..., top_k:]
masked_logprobs = mx.put_along_axis(
logprobs, mask_idx, mx.array(-float("inf"), logprobs.dtype), axis=-1
)
return mx.random.categorical(masked_logprobs, axis=-1)
return masked_logprobs
@partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
@ -116,7 +114,6 @@ def min_p_sampling(
logprobs: mx.array,
min_p: float,
min_tokens_to_keep: int = 1,
temperature=1.0,
) -> mx.array:
"""
Apply min-p sampling to the logprobs.
@ -144,8 +141,6 @@ def min_p_sampling(
)
# reference implementation: https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py#L531-L605
logprobs = logprobs * (1 / temperature)
# Indices sorted in decreasing order
sorted_indices = mx.argsort(-logprobs, axis=-1)
sorted_logprobs = mx.take_along_axis(logprobs, sorted_indices, axis=-1)
@ -163,9 +158,16 @@ def min_p_sampling(
# Create pool of tokens with probability less than scaled min_p
selected_logprobs = mx.where(tokens_to_remove, -float("inf"), sorted_logprobs)
# Return sampled tokens
sorted_tokens = mx.random.categorical(selected_logprobs, axis=-1)[:, None]
return mx.take_along_axis(sorted_indices, sorted_tokens, axis=-1).squeeze(1)
# Create a mapping to rearrange back to original indices
# Use argsort of sorted_indices to get the inverse permutation
inverse_indices = mx.argsort(sorted_indices, axis=-1)
# Rearrange selected_logprobs back to original order
original_order_logprobs = mx.take_along_axis(
selected_logprobs, inverse_indices, axis=-1
)
return original_order_logprobs
@partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)

69
llms/tests/test_sample_utils.py
View File

@ -9,28 +9,28 @@ class TestSampleUtils(unittest.TestCase):
probs = mx.array([0.9, 0.0, 0.0, 0.1])[None]
logits = mx.log(probs)
actual_logits = top_p_sampling(logits, 0.3)
actual_probs = mx.softmax(actual_logits.squeeze())
new_logits = top_p_sampling(logits, 0.3)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertEqual(actual_probs.tolist(), [1.0, 0.0, 0.0, 0.0])
actual_logits = top_p_sampling(logits, 0.95)
actual_probs = mx.softmax(actual_logits.squeeze())
new_logits = top_p_sampling(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)
actual_logits = top_p_sampling(logits, 0.4)
actual_probs = mx.softmax(actual_logits.squeeze())
new_logits = top_p_sampling(logits, 0.4)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertEqual(actual_probs.tolist(), [0.0, 1.0, 0.0, 0.0])
actual_logits = top_p_sampling(logits, 0.6)
actual_probs = mx.softmax(actual_logits.squeeze())
new_logits = top_p_sampling(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]
)
actual_logits = top_p_sampling(logits, 0.95)
actual_probs = mx.softmax(actual_logits.squeeze())
new_logits = top_p_sampling(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]
self.assertEqual(actual_rounded, expected_rounded)
@ -39,8 +39,8 @@ 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)
actual_logits = top_p_sampling(logits, 0.5)
actual_probs = mx.softmax(actual_logits, axis=-1)
new_logits = top_p_sampling(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]]
)
@ -48,43 +48,50 @@ class TestSampleUtils(unittest.TestCase):
def test_min_p_sampling(self):
probs = mx.array([0.9, 0.0, 0.0, 0.1])[None]
logits = mx.log(probs)
temperature = 1.0
token = min_p_sampling(logits, 0.8)
self.assertEqual(token, 0)
new_logits = min_p_sampling(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)
temperature = 1.0
for _ in range(5):
token = min_p_sampling(logits, 0.05)
self.assertTrue(token in (0, 3))
new_logits = min_p_sampling(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)
tokens = min_p_sampling(logits, 0.7)
self.assertEqual(tokens.tolist(), [0, 1])
new_logits = min_p_sampling(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):
probs = mx.array([0.9, 0.0, 0.0, 0.1])[None]
logits = mx.log(probs)
token = top_k_sampling(logits, 1).item()
self.assertEqual(token, 0)
new_logits = top_k_sampling(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([0.5, 0.0, 0.0, 0.5])[None]
tokens = set()
for _ in range(100):
token = top_k_sampling(logits, 2)
tokens.add(token.item())
self.assertEqual(tokens, {0, 3})
probs = mx.array([0.6, 0.0, 0.1, 0.3])[None]
logits = mx.log(probs)
new_logits = top_k_sampling(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]
)
# 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)
tokens = top_k_sampling(logits, 1)
self.assertEqual(tokens.tolist(), [0, 1])
new_logits = top_k_sampling(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]]
)
if __name__ == "__main__":