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Adds C++ and nn quantization utilities (#230)

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* Add C++ de-/quantize ops
* Add quantize functions to the docs and tests
* Add a QuantizedLinear module
This commit is contained in:
Angelos Katharopoulos
2023-12-20 14:17:38 -08:00
committed by GitHub
parent 4912ff3ec2
commit 57fe918cf8
12 changed files with 451 additions and 68 deletions
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80
python/tests/test_quantized.py
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@@ -6,48 +6,14 @@ import mlx.core as mx
import mlx_tests
def select_bits(w, width, start):
shift_left = 32 - (start + width)
shift_right = shift_left + start
return (w * (2**shift_left)) // (2**shift_right)
def dequantize(w, scales, biases, width):
w_full = mx.concatenate(
[select_bits(w, width, i)[..., None] for i in range(0, 32, width)], axis=-1
)
w_full = w_full.reshape(len(w), scales.shape[-1], -1)
w_full = scales[..., None] * w_full + biases[..., None]
w_full = w_full.reshape(len(w), -1)
return w_full
def quantize(w, width, groups):
w = w.reshape(len(w), -1, groups)
w_max = w.max(-1, keepdims=True)
w_min = w.min(-1, keepdims=True)
delta = (w_max - w_min) / (2**width - 1)
w_int = mx.round((w - w_min) / delta).astype(mx.uint32)
scales = delta.squeeze(-1)
biases = w_min.squeeze(-1)
shifts = mx.array([2**i for i in range(0, 32, width)], dtype=mx.uint32)
w_int = w_int.reshape(len(w), -1, 32 // width)
w_int = w_int * shifts[None, None]
packed_w = w_int.sum(-1)
return packed_w, scales, biases
class TestQuantized(mlx_tests.MLXTestCase):
def test_quantize_dequantize(self):
w = mx.random.normal(shape=(128, 128))
w_q, scales, biases = quantize(w, 4, 64)
w_hat = dequantize(w_q, scales, biases, 4)
w_hat2 = dequantize(*quantize(w_hat, 4, 64), 4)
self.assertLess((w_hat - w_hat2).abs().max(), 1e-6)
for b in [2, 4, 8]:
w_q, scales, biases = mx.quantize(w, 64, b)
w_hat = mx.dequantize(w_q, scales, biases, 64, b)
errors = (w - w_hat).abs().reshape(*scales.shape, -1)
self.assertTrue((errors <= scales[..., None] / 2).all())
def test_qmm(self):
key = mx.random.key(0)
@@ -62,14 +28,16 @@ class TestQuantized(mlx_tests.MLXTestCase):
):
x = mx.random.normal(shape=(M, K), key=k1)
w = mx.random.normal(shape=(N, K), key=k2)
w_q, scales, biases = quantize(w, width, groups)
w_hat = dequantize(w_q, scales, biases, width)
w_q, scales, biases = mx.quantize(w, groups, width)
w_hat = mx.dequantize(
w_q, scales, biases, groups, width
)
y_q = mx.quantized_matmul(
x, w_q.T, scales, biases, width=width, groups=groups
)
y_hat = x @ w_hat.T
self.assertEqual(y_q.shape, y_hat.shape)
self.assertLess((y_q - y_hat).abs().max(), 0.1)
self.assertLess((y_q - y_hat).abs().max(), 1e-3)
def test_qmm_shapes(self):
key = mx.random.key(0)
@@ -77,8 +45,8 @@ class TestQuantized(mlx_tests.MLXTestCase):
groups = 64
width = 4
w = mx.random.normal(shape=(32, 128), key=k2)
w_q, scales, biases = quantize(w, width, groups)
w_hat = dequantize(w_q, scales, biases, width)
w_q, scales, biases = mx.quantize(w, groups, width)
w_hat = mx.dequantize(w_q, scales, biases, groups, width)
for s in [(3, 128), (2, 1, 7, 128)]:
x = mx.random.normal(shape=(3, 128), key=k1)
y_q = mx.quantized_matmul(
@@ -86,7 +54,7 @@ class TestQuantized(mlx_tests.MLXTestCase):
)
y_hat = x @ w_hat.T
self.assertEqual(y_q.shape, y_hat.shape)
self.assertLess((y_q - y_hat).abs().max(), 0.1)
self.assertLess((y_q - y_hat).abs().max(), 1e-3)
def test_qmv(self):
key = mx.random.key(0)
@@ -95,17 +63,17 @@ class TestQuantized(mlx_tests.MLXTestCase):
for width in [2, 4, 8]:
for M in [512, 1024]:
for N in [512, 1024]:
# with self.subTest(shape=(M, N), groups=groups, width=width):
x = mx.random.normal(shape=(1, N), key=k1)
w = mx.random.normal(shape=(M, N), key=k2)
w_q, scales, biases = quantize(w, width, groups)
w_hat = dequantize(w_q, scales, biases, width)
y_q = mx.quantized_matmul(
x, w_q.T, scales, biases, width=width, groups=groups
)
y_hat = x @ w_hat.T
self.assertEqual(y_q.shape, y_hat.shape)
self.assertLess((y_q - y_hat).abs().max(), 0.1)
with self.subTest(shape=(M, N), groups=groups, width=width):
x = mx.random.normal(shape=(1, N), key=k1)
w = mx.random.normal(shape=(M, N), key=k2)
w_q, scales, biases = mx.quantize(w, groups, width)
w_hat = mx.dequantize(w_q, scales, biases, groups, width)
y_q = mx.quantized_matmul(
x, w_q.T, scales, biases, width=width, groups=groups
)
y_hat = x @ w_hat.T
self.assertEqual(y_q.shape, y_hat.shape)
self.assertLess((y_q - y_hat).abs().max(), 1e-3)
if __name__ == "__main__":