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Add KQuant quantization mode #3588

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Add KQuant quantization mode #3588

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33 changes: 24 additions & 9 deletions benchmarks/python/gather_qmm_bench.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,12 +40,21 @@ def gather_mm_simulate(x, w, indices):
return x


def time_gather_qmm():
def time_gather_qmm(mode="affine", kquant_type=""):
label = kquant_type if mode == "kquant" else mode
print(f"\n--- gather_qmm ({label}) ---")

quantize_kwargs = {"mode": mode}
qmm_kwargs = {"mode": mode}
if mode == "kquant":
quantize_kwargs["kquant_type"] = kquant_type
qmm_kwargs["kquant_type"] = kquant_type

x = mx.random.normal((N, 1, 1, D)) / 1024**0.5
w1 = mx.random.normal((E, M, D)) / 1024**0.5
w2 = mx.random.normal((E, D, M)) / 1024**0.5
w1 = mx.quantize(w1)
w2 = mx.quantize(w2)
w1 = mx.quantize(w1, **quantize_kwargs)
w2 = mx.quantize(w2, **quantize_kwargs)
indices = (mx.random.uniform(shape=(N, I)) * E).astype(mx.uint32)
sorted_indices = mx.sort(indices.flatten()).reshape(N, I)
mx.eval(x, w1, w2, indices, sorted_indices)
Expand All @@ -55,8 +64,12 @@ def gather_mm(x, w1, w2, indices, sort):
inv_order = None
if sort:
x, idx, inv_order = gather_sort(x, indices)
x = mx.gather_qmm(x, *w1, transpose=True, rhs_indices=idx, sorted_indices=sort)
x = mx.gather_qmm(x, *w2, transpose=True, rhs_indices=idx, sorted_indices=sort)
x = mx.gather_qmm(
x, *w1, transpose=True, rhs_indices=idx, sorted_indices=sort, **qmm_kwargs
)
x = mx.gather_qmm(
x, *w2, transpose=True, rhs_indices=idx, sorted_indices=sort, **qmm_kwargs
)
if sort:
x = scatter_unsort(x, inv_order, indices.shape)
return x
Expand All @@ -68,17 +81,19 @@ def gather_mm(x, w1, w2, indices, sort):
x = mx.random.normal((N * I, D)) / 1024**0.5
w1 = mx.random.normal((M, D)) / 1024**0.5
w2 = mx.random.normal((D, M)) / 1024**0.5
w1 = mx.quantize(w1)
w2 = mx.quantize(w2)
w1 = mx.quantize(w1, **quantize_kwargs)
w2 = mx.quantize(w2, **quantize_kwargs)
mx.eval(x, w1, w2)

def equivalent_matmul(x, w1, w2):
x = mx.quantized_matmul(x, *w1, transpose=True)
x = mx.quantized_matmul(x, *w2, transpose=True)
x = mx.quantized_matmul(x, *w1, transpose=True, **qmm_kwargs)
x = mx.quantized_matmul(x, *w2, transpose=True, **qmm_kwargs)
return x

time_fn(equivalent_matmul, x, w1, w2)


if __name__ == "__main__":
time_gather_qmm()
for codec in ("q8_0", "q4_k", "q6_k"):
time_gather_qmm(mode="kquant", kquant_type=codec)
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