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fix: always jit the transform function #6

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merged 1 commit into from
Mar 13, 2024
Merged

fix: always jit the transform function #6

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20 changes: 16 additions & 4 deletions tsnex/tsne.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
from functools import partial
from typing import Callable
import jax
import jax.numpy as jnp
Expand Down Expand Up @@ -40,7 +41,7 @@ def _binary_search_perplexity(distances, target, tol=1e-5, max_iter=200):
sigma = 1.0

def cond_fun(val):
(sigma, perplexity, i, sigma_min, sigma_max) = val
(_, perplexity, i, _, _) = val
return (jnp.abs(perplexity - target) > tol) & (i < max_iter)

def body_fun(val):
Expand All @@ -59,6 +60,19 @@ def body_fun(val):
return sigma


@partial(
jax.jit,
static_argnames=[
"n_components",
"perplexity",
"learning_rate",
"init",
"seed",
"n_iter",
"metric_fn",
"early_exageration",
],
)
def transform(
X: jax.Array,
*,
Expand All @@ -68,7 +82,7 @@ def transform(
init: str = "pca",
seed: int = 0,
n_iter: int = 1000,
metric_fn: Callable = None,
metric_fn: Callable = euclidean_distance,
early_exageration: float = 12.0,
) -> jax.Array:
"""
Expand Down Expand Up @@ -96,8 +110,6 @@ def transform(
else:
raise ValueError(f"Unknown init_method: {init}")

if metric_fn is None:
metric_fn = euclidean_distance
metric_fn = jax.vmap(jax.vmap(metric_fn, in_axes=(0, None)), in_axes=(None, 0))

# Compute the probability of neighbours on the original embedding.
Expand Down
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