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make_sampler creates sampler chain with all sampling parameters (#1330)

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* top_p refactor

* top_k and min_p refactor

* Create sampler chain

* Remove unnecessary mx.where

* Use mx.allclose
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Neil Mehta 2025-03-11 16:37:35 -04:00 committed by GitHub
parent d2e02b3aae
commit 4c3df00162
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2 changed files with 107 additions and 70 deletions
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67
llms/mlx_lm/sample_utils.py
View File

@ -35,14 +35,25 @@ def make_sampler(
"""
if temp == 0:
return lambda x: mx.argmax(x, axis=-1)
elif top_p > 0 and top_p < 1.0:
return lambda x: top_p_sampling(x, top_p, temp)
elif min_p != 0.0:
return lambda x: min_p_sampling(x, min_p, min_tokens_to_keep, temp)
elif top_k > 0:
return lambda x: top_k_sampling(x, top_k, temp)
else:
return lambda x: categorical_sampling(x, temp)
# Create sampler chain
sampling_methods = []
if top_k > 0:
sampling_methods.append(lambda x: apply_top_k(x, top_k))
if top_p > 0 and top_p < 1.0:
sampling_methods.append(lambda x: apply_top_p(x, top_p))
if min_p != 0.0:
sampling_methods.append(lambda x: apply_min_p(x, min_p, min_tokens_to_keep))
# Apply the sampling methods
def sampler(logits):
for method in sampling_methods:
logits = method(logits)
# Return the sampled token
return categorical_sampling(logits, temp)
return sampler
def make_logits_processors(
@ -85,10 +96,9 @@ def make_logits_processors(
@partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
def top_k_sampling(
def apply_top_k(
logprobs: mx.array,
top_k: int,
temperature=1.0,
) -> mx.array:
"""
Sample from only the top K tokens ranked by probability.
@ -103,20 +113,18 @@ 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)
def min_p_sampling(
def apply_min_p(
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 +152,6 @@ 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)
@ -163,25 +169,31 @@ 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)
def top_p_sampling(logits: mx.array, top_p: float, temperature: float) -> mx.array:
def apply_top_p(logits: mx.array, top_p: float) -> mx.array:
"""
Apply top-p (nucleus) sampling to logits.
Args:
logits: The logits from the model's output.
top_p: The cumulative probability threshold for top-p filtering.
temperature: Temperature parameter for softmax distribution reshaping.
Returns:
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 * (1 / temperature), axis=-1)
probs = mx.softmax(logits, axis=-1)
# sort probs in ascending order
sorted_indices = mx.argsort(probs, axis=-1)
@ -196,8 +208,15 @@ def top_p_sampling(logits: mx.array, top_p: float, temperature: float) -> mx.arr
0,
)
sorted_tokens = mx.random.categorical(mx.log(top_probs), 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 top_probs back to original order
original_order_probs = mx.take_along_axis(top_probs, inverse_indices, axis=-1)
# Convert back to logits and return
return mx.log(original_order_probs)
@partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)

110
llms/tests/test_sample_utils.py
View File

@ -1,79 +1,97 @@
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)
temperature = 1.0
token = top_p_sampling(logits, 0.3, temperature).item()
self.assertEqual(token, 0)
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])
token = top_p_sampling(logits, 0.95, temperature).item()
self.assertTrue(token in (0, 3))
new_logits = apply_top_p(logits, 0.95)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertTrue(mx.allclose(probs.squeeze(), actual_probs))
probs = mx.array([0.0, 0.5, 0.4, 0.1])[None]
logits = mx.log(probs)
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])
token = top_p_sampling(logits, 0.4, temperature).item()
self.assertEqual(token, 1)
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]
)
token = top_p_sampling(logits, 0.6, temperature).item()
self.assertTrue(token in (1, 2))
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]
self.assertEqual(actual_rounded, expected_rounded)
self.assertAlmostEqual(sum(actual_probs.tolist()), 1.0)
token = top_p_sampling(logits, 0.95, temperature).item()
self.assertTrue(token in (1, 2, 3))
# 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 = 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_apply_min_p(self):
probs = mx.array([0.9, 0.0, 0.0, 0.1])[None]
logits = mx.log(probs)
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 = apply_min_p(logits, 0.05)
actual_probs = mx.softmax(new_logits.squeeze())
self.assertTrue(mx.allclose(actual_probs, mx.squeeze(probs)))
# 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_p_sampling(logits, 0.5, temperature)
self.assertEqual(tokens.tolist(), [0, 1])
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_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)
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))
# 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])
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)
token = top_k_sampling(logits, 1).item()
self.assertEqual(token, 0)
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([0.5, 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 = 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]
)
# 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 = 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]]
)
if __name__ == "__main__":