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Fix qmm_t for unaligned cases (#923)

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Angelos Katharopoulos 2024-03-28 15:34:57 -07:00 committed by GitHub
parent 46caf0bef0
commit 5f9ba3019f
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2 changed files with 15 additions and 1 deletions
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6
mlx/backend/metal/kernels/quantized.metal
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@ -520,6 +520,7 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
const int K_g = K / group_size; const int K_g = K / group_size;
const int y_row = tid.y * BM; const int y_row = tid.y * BM;
const int y_col = tid.x * BN; const int y_col = tid.x * BN;
x += y_row * K; x += y_row * K;
w += y_col * K_w; w += y_col * K_w;
scales += y_col * K_g; scales += y_col * K_g;
@ -572,7 +573,10 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
const device uint32_t * w_local = w + offset_row * K_w + offset_col; const device uint32_t * w_local = w + offset_row * K_w + offset_col;
threadgroup T * Ws_local = Ws + offset_row * BK + offset_col * el_per_int; threadgroup T * Ws_local = Ws + offset_row * BK + offset_col * el_per_int;
if (y_row + offset_row < N) { // y_col corresponds to the row of the weight matrix and added to
// offset_row it should be less than the total number of rows
// otherwise skip.
if (y_col + offset_row < N) {
uint32_t wi = *w_local; uint32_t wi = *w_local;
T scale = scales_block[offset_row * groups_per_block + offset_col / (group_size / el_per_int)]; T scale = scales_block[offset_row * groups_per_block + offset_col / (group_size / el_per_int)];
T bias = biases_block[offset_row * groups_per_block + offset_col / (group_size / el_per_int)]; T bias = biases_block[offset_row * groups_per_block + offset_col / (group_size / el_per_int)];

10
python/tests/test_quantized.py
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@ -229,6 +229,16 @@ class TestQuantized(mlx_tests.MLXTestCase):
self.assertEqual(y_q.shape, y_hat.shape) self.assertEqual(y_q.shape, y_hat.shape)
self.assertLess((y_q - y_hat).abs().max(), 1e-3) self.assertLess((y_q - y_hat).abs().max(), 1e-3)
# Test with larger than 128 unaligned sizes
w = mx.random.normal(shape=(99, 256))
w_q, scales, biases = mx.quantize(w)
w_hat = mx.dequantize(w_q, scales, biases)
x = mx.random.normal(shape=(129, 256))
y_q = mx.quantized_matmul(x, w_q, scales, biases, transpose=True)
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__": if __name__ == "__main__":
unittest.main() unittest.main()