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Fused Affine Quantize/Dequantize ops (#1282)

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* Add fast affine dequantize

* add full quantize kernel

* fused kernel with scale/bias computation

* fix docstring

* fix no jit error

* fix test

* test fix

* reduce fast api to only affine_quantize
This commit is contained in:
Alex Barron
2024-07-29 15:11:38 -07:00
committed by GitHub
parent aa1d6cadad
commit c52d1600f0
11 changed files with 655 additions and 400 deletions
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11
python/tests/test_quantized.py
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@@ -12,11 +12,12 @@ class TestQuantized(mlx_tests.MLXTestCase):
w = mx.random.normal(shape=(128, 512))
for gs in [32, 64, 128]:
for b in [2, 4, 8]:
w_q, scales, biases = mx.quantize(w, gs, b)
w_hat = mx.dequantize(w_q, scales, biases, gs, b)
errors = (w - w_hat).abs().reshape(*scales.shape, -1)
eps = 1e-6
self.assertTrue((errors <= (scales[..., None] + eps).abs()).all())
with self.subTest(gs=gs, b=b):
w_q, scales, biases = mx.quantize(w, group_size=gs, bits=b)
w_hat = mx.dequantize(w_q, scales, biases, gs, b)
errors = (w - w_hat).abs().reshape(*scales.shape, -1)
eps = 1e-6
self.assertTrue((errors <= (scales[..., None] + eps).abs()).all())
# test quantize/dequantize 0s
a = mx.zeros((256, 512))