배열과 자료형, 통합 메모리가 만드는 차이

import mlx.core as mx

a = mx.array([1, 2, 3, 4])
b = mx.array([1.5, 2.5, 3.5, 4.5])
c = mx.zeros((3, 4))
d = mx.ones((2, 2), dtype=mx.float16)

print('a:', a, 'dtype:', a.dtype, 'shape:', a.shape, 'size:', a.size, 'ndim:', a.ndim)
print('b:', b, 'dtype:', b.dtype)
print('c shape:', c.shape, 'dtype:', c.dtype)
print('d:', d, 'dtype:', d.dtype)

# Verified output (2026-05-03):
#   a: array([1, 2, 3, 4], dtype=int32) dtype: mlx.core.int32 shape: (4,) size: 4 ndim: 1
#   b: array([1.5, 2.5, 3.5, 4.5], dtype=float32) dtype: mlx.core.float32
#   c shape: (3, 4) dtype: mlx.core.float32
#   d: array([[1, 1], [1, 1]], dtype=float16) dtype: mlx.core.float16

import mlx.core as mx

a = mx.array([1, 2, 3, 4])               # inferred → int32
print('a       :', a, a.dtype)

a_f = a.astype(mx.float32)
print('a.astype:', a_f, a_f.dtype)        # → float32

# Verified:
#   a       : array([1, 2, 3, 4], dtype=int32) mlx.core.int32
#   a.astype: array([1, 2, 3, 4], dtype=float32) mlx.core.float32

# Be explicit when you mean float — implicit promotion to float32
# happens in arithmetic anyway, but .astype documents your intent.

import mlx.core as mx

# The default 'device' is just a label about where compute runs next.
# All bytes live in the unified-memory pool either way.
print('default device:', mx.default_device())   # → Device(gpu, 0) on Apple Silicon

x = mx.array([1.0, 2.0, 3.0])
print('x lives in :', mx.default_device(), '— but the bytes are in unified memory.')