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๐Ÿ“Œ JAX์˜ ํ•ต์‹ฌ ๊ธฐ๋Šฅ - ์ž๋™ ๋ฏธ๋ถ„๊ณผ JIT ์ปดํŒŒ์ผ๋กœ ์„ฑ๋Šฅ ๊ทน๋Œ€ํ™”


๐Ÿš€ JAX์˜ ํ•ต์‹ฌ ๊ธฐ๋Šฅ ๋‘˜๋Ÿฌ๋ณด๊ธฐ

JAX๋Š” ๋‹ค์–‘ํ•œ ๊ธฐ๋Šฅ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ํŠนํžˆ **์ž๋™ ๋ฏธ๋ถ„(Automatic Differentiation)**๊ณผ **JIT ์ปดํŒŒ์ผ(Just-In-Time Compilation)**์ด ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์š”์†Œ์ž…๋‹ˆ๋‹ค.
์ด ๋‘ ๊ฐ€์ง€ ๊ธฐ๋Šฅ์„ ๊นŠ์ด ์ดํ•ดํ•˜๋ฉด JAX๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ณ ์„ฑ๋Šฅ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.


๐Ÿ’ก 1. ์ž๋™ ๋ฏธ๋ถ„ (Automatic Differentiation)

์ž๋™ ๋ฏธ๋ถ„์€ ์ˆ˜ํ•™ ํ•จ์ˆ˜์˜ ๋ฏธ๋ถ„์„ ๊ธฐ๊ณ„์ ์œผ๋กœ ๊ณ„์‚ฐํ•˜๋Š” ๊ธฐ๋ฒ•์œผ๋กœ,
๊ธฐ๊ณ„ ํ•™์Šต ๋ชจ๋ธ์˜ ํ•™์Šต ๋‹จ๊ณ„์—์„œ ํ•„์ˆ˜์ ์ธ **๊ธฐ์šธ๊ธฐ ๊ณ„์‚ฐ(Gradient Calculation)**์— ์‚ฌ์šฉ๋ฉ๋‹ˆ๋‹ค.

โœ… ์ž๋™ ๋ฏธ๋ถ„์˜ ์žฅ์ 

  1. ์ˆ˜ํ•™์  ์œ ๋„ ๋ถˆํ•„์š”: ๋ณต์žกํ•œ ๋ฏธ๋ถ„ ๊ณต์‹์„ ์ง์ ‘ ๊ณ„์‚ฐํ•  ํ•„์š”๊ฐ€ ์—†์Šต๋‹ˆ๋‹ค.
  2. ์„ฑ๋Šฅ ์ตœ์ ํ™”: GPU๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋น ๋ฅด๊ฒŒ ๊ณ„์‚ฐํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
  3. ๋ณต์žกํ•œ ํ•จ์ˆ˜๋„ ๋ฌธ์ œ์—†์Œ: ๊ณ ์ฐจ ๋ฏธ๋ถ„๋„ ์‰ฝ๊ฒŒ ๊ณ„์‚ฐํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

๐Ÿ“ ์ž๋™ ๋ฏธ๋ถ„ ๊ธฐ์ดˆ ์‚ฌ์šฉ๋ฒ•

JAX์—์„œ ๋ฏธ๋ถ„์„ ๊ณ„์‚ฐํ•˜๋Š” ๊ธฐ๋ณธ ํ•จ์ˆ˜๋Š” jax.grad()์ž…๋‹ˆ๋‹ค.
๋‹ค์Œ์€ ๊ฐ„๋‹จํ•œ ์˜ˆ์ œ์ž…๋‹ˆ๋‹ค:

import jax.numpy as jnp
from jax import grad

# ํ•จ์ˆ˜ ์ •์˜
def loss_fn(x):
    return jnp.sum(x ** 2)

# ๊ธฐ์šธ๊ธฐ ๊ณ„์‚ฐ
x = jnp.array([1.0, 2.0, 3.0])
gradient = grad(loss_fn)(x)
print(f"Gradient: {gradient}")

์ถœ๋ ฅ:

Gradient: [2. 4. 6.]

๐Ÿ’ก Tip:
grad() ํ•จ์ˆ˜๋Š” ์Šค์นผ๋ผ ๊ฐ’์„ ๋ฐ˜ํ™˜ํ•˜๋Š” ํ•จ์ˆ˜์—์„œ๋งŒ ๋™์ž‘ํ•ฉ๋‹ˆ๋‹ค.
๋งŒ์•ฝ ๋ฒกํ„ฐ๋ฅผ ๋ฐ˜ํ™˜ํ•˜๋Š” ํ•จ์ˆ˜๋ผ๋ฉด jax.jacrev() ๋˜๋Š” jax.jacfwd()๋ฅผ ์‚ฌ์šฉํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.


๐ŸŒŸ ๊ณ ์ฐจ ๋ฏธ๋ถ„ ๊ณ„์‚ฐ

JAX๋Š” ๊ณ ์ฐจ ๋ฏธ๋ถ„๋„ ์‰ฝ๊ฒŒ ์ง€์›ํ•ฉ๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, 2์ฐจ ๋ฏธ๋ถ„์„ ๊ณ„์‚ฐํ•˜๋ ค๋ฉด grad() ํ•จ์ˆ˜๋ฅผ ์ค‘์ฒฉํ•ด์„œ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค:

# 2์ฐจ ๋ฏธ๋ถ„ ๊ณ„์‚ฐ
second_derivative = grad(grad(loss_fn))(x)
print(f"Second Gradient: {second_derivative}")

์ถœ๋ ฅ:

Second Gradient: [2. 2. 2.]

โšก 2. JIT ์ปดํŒŒ์ผ (Just-In-Time Compilation)

JIT ์ปดํŒŒ์ผ์€ Python ์ฝ”๋“œ๋ฅผ GPU์—์„œ ๋น ๋ฅด๊ฒŒ ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ตœ์ ํ™”ํ•˜๋Š” ๊ธฐ๋Šฅ์ž…๋‹ˆ๋‹ค.
JAX์—์„œ๋Š” jax.jit() ๋ฐ์ฝ”๋ ˆ์ดํ„ฐ๋ฅผ ํ†ตํ•ด JIT ์ปดํŒŒ์ผ์„ ์ ์šฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

โœ… JIT์˜ ์žฅ์ 

  1. ์„ฑ๋Šฅ ํ–ฅ์ƒ: CPU ๋Œ€๋น„ ์ˆ˜์‹ญ ๋ฐฐ ๋น ๋ฅธ ์—ฐ์‚ฐ ์†๋„๋ฅผ ์ž๋ž‘ํ•ฉ๋‹ˆ๋‹ค.
  2. ์ฝ”๋“œ ์ตœ์ ํ™”: ๋ฐ˜๋ณต ๊ณ„์‚ฐ์ด ๋งŽ์€ ์‹ ๊ฒฝ๋ง ํ•™์Šต์— ์ ํ•ฉํ•ฉ๋‹ˆ๋‹ค.
  3. GPU ํ™œ์šฉ ๊ทน๋Œ€ํ™”: CUDA๋ฅผ ํ†ตํ•ด ๋Œ€๊ทœ๋ชจ ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ์— ์œ ๋ฆฌํ•ฉ๋‹ˆ๋‹ค.

๐Ÿ“ JIT ๊ธฐ์ดˆ ์‚ฌ์šฉ๋ฒ•

๋ฐ˜์‘ํ˜•

JIT์„ ์‚ฌ์šฉํ•˜์—ฌ ํ–‰๋ ฌ ๊ณฑ์…ˆ ์„ฑ๋Šฅ์„ ๋น„๊ตํ•ด๋ด…์‹œ๋‹ค:

from jax import jit
import time

# ํ–‰๋ ฌ ๊ณฑ์…ˆ ํ•จ์ˆ˜
def matmul(x, y):
    return jnp.dot(x, y)

# JIT ์ ์šฉ
jit_matmul = jit(matmul)

# ์ž…๋ ฅ ๋ฐ์ดํ„ฐ
x = jnp.ones((1000, 1000))
y = jnp.ones((1000, 1000))

# ์„ฑ๋Šฅ ๋น„๊ต
start = time.time()
result = matmul(x, y)
print(f"์ผ๋ฐ˜ ๊ณ„์‚ฐ ์‹œ๊ฐ„: {time.time() - start:.6f}์ดˆ")

start = time.time()
result_jit = jit_matmul(x, y)
print(f"JIT ๊ณ„์‚ฐ ์‹œ๊ฐ„: {time.time() - start:.6f}์ดˆ")

๐Ÿ’ก JIT์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฒฝ์šฐ

  • ๋Œ€๊ทœ๋ชจ ํ–‰๋ ฌ ๊ณ„์‚ฐ
  • CNN๊ณผ ๊ฐ™์€ ๋ณต์žกํ•œ ์‹ ๊ฒฝ๋ง ํ•™์Šต
  • ๋ฐ˜๋ณต์ ์ธ ์ˆ˜์น˜ ์ตœ์ ํ™” ๋ฌธ์ œ
  • ์‹ค์‹œ๊ฐ„ ์‘๋‹ต์ด ์ค‘์š”ํ•œ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜

๐ŸŒŸ JAX์˜ ์ž๋™ ๋ฏธ๋ถ„๊ณผ JIT์„ ํ™œ์šฉํ•œ ์‹ค์ „ ๋ชจ๋ธ

๐Ÿ“Š ์„ ํ˜• ํšŒ๊ท€ ๋ชจ๋ธ ํ•™์Šต

์ž๋™ ๋ฏธ๋ถ„๊ณผ JIT์„ ์ด์šฉํ•˜์—ฌ ์„ ํ˜• ํšŒ๊ท€ ๋ชจ๋ธ์„ ํ•™์Šตํ•ด๋ด…์‹œ๋‹ค:

# ๋ชจ๋ธ ์ •์˜
def model(w, x):
    return w[0] * x + w[1]

# ์†์‹ค ํ•จ์ˆ˜
def loss_fn(w, x, y):
    pred = model(w, x)
    return jnp.mean((pred - y) ** 2)

# ๋ฐ์ดํ„ฐ ์ƒ์„ฑ
x_data = jnp.array([1.0, 2.0, 3.0, 4.0])
y_data = jnp.array([2.0, 4.0, 6.0, 8.0])

# ์ดˆ๊ธฐ ๊ฐ€์ค‘์น˜
w = jnp.array([0.0, 0.0])

# ๊ธฐ์šธ๊ธฐ ๊ณ„์‚ฐ ํ•จ์ˆ˜
grad_fn = jit(grad(loss_fn))

# ํ•™์Šต ๋ฃจํ”„
learning_rate = 0.01
for epoch in range(100):
    gradient = grad_fn(w, x_data, y_data)
    w = w - learning_rate * gradient
    if epoch % 10 == 0:
        loss = loss_fn(w, x_data, y_data)
        print(f"Epoch {epoch}, Loss: {loss:.4f}")

๐Ÿš€ JAX์˜ ํ™œ์šฉ์„ฑ ์ •๋ฆฌ

  1. ์ž๋™ ๋ฏธ๋ถ„์˜ ํšจ์œจ์„ฑ
    • ๊ธฐ์šธ๊ธฐ ๊ณ„์‚ฐ์„ ํ†ตํ•ด ์‹ ๊ฒฝ๋ง ํ•™์Šต์— ์ตœ์ ํ™”๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค.
  2. JIT ์ปดํŒŒ์ผ์˜ ๊ณ ์† ์ฒ˜๋ฆฌ
    • GPU๋ฅผ ์ตœ๋Œ€ํ•œ ํ™œ์šฉํ•˜์—ฌ ๋ฐ˜๋ณต ์—ฐ์‚ฐ ์†๋„๋ฅผ ํฌ๊ฒŒ ๊ฐœ์„ ํ•ฉ๋‹ˆ๋‹ค.
  3. ์‹ค์ „ ํ”„๋กœ์ ํŠธ ํ™œ์šฉ ๊ฐ€๋Šฅ
    • CNN, RNN๊ณผ ๊ฐ™์€ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ ํ•™์Šต
    • ๊ฐ•ํ™” ํ•™์Šต ์•Œ๊ณ ๋ฆฌ์ฆ˜ ํ•™์Šต
    • ํ™•๋ฅ ์  ๊ทธ๋ž˜ํ”„ ๋ชจ๋ธ ๊ตฌํ˜„

๐Ÿ“Œ ๋‹ค์Œ ๊ธ€ ์˜ˆ๊ณ : JAX๋ฅผ ํ™œ์šฉํ•œ ์‹ฌํ™” ๋ชจ๋ธ ๊ตฌ์ถ•

๋‹ค์Œ ๊ธ€์—์„œ๋Š” JAX๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ CNN๊ณผ ๊ฐ•ํ™” ํ•™์Šต ๋ชจ๋ธ์„ ๊ตฌํ˜„ํ•˜์—ฌ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•ด๋ณด๊ฒ ์Šต๋‹ˆ๋‹ค.
์‹ค์ œ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ชจ๋ธ์„ ํ•™์Šตํ•˜๋Š” ๊ณผ์ •์„ ๋‹จ๊ณ„๋ณ„๋กœ ์„ค๋ช…ํ•  ์˜ˆ์ •์ž…๋‹ˆ๋‹ค.


 

JAX, ์ž๋™ ๋ฏธ๋ถ„, JIT ์ปดํŒŒ์ผ, GPU ์—ฐ์‚ฐ, ์„ ํ˜• ํšŒ๊ท€, ๋ชจ๋ธ ํ•™์Šต, ๊ณ ์† ์ฒ˜๋ฆฌ, ์‹ค์ „ ํ”„๋กœ์ ํŠธ, Python, ๊ณ ์„ฑ๋Šฅ ๊ณ„์‚ฐ, ๋”ฅ๋Ÿฌ๋‹, ๊ธฐ์šธ๊ธฐ ๊ณ„์‚ฐ, ์„ฑ๋Šฅ ์ตœ์ ํ™”, ๊ณ ์ฐจ ๋ฏธ๋ถ„, ํ•จ์ˆ˜ ๋ฒกํ„ฐํ™”

โ€ป ์ด ํฌ์ŠคํŒ…์€ ์ฟ ํŒก ํŒŒํŠธ๋„ˆ์Šค ํ™œ๋™์˜ ์ผํ™˜์œผ๋กœ, ์ด์— ๋”ฐ๋ฅธ ์ผ์ •์•ก์˜ ์ˆ˜์ˆ˜๋ฃŒ๋ฅผ ์ œ๊ณต๋ฐ›์Šต๋‹ˆ๋‹ค.
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