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			85 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			85 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
# Copyright © 2025 Apple Inc.
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import mlx.core as mx
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from time_utils import time_fn
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N = 1024
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D = 1024
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M = 1024
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E = 32
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I = 4
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def gather_sort(x, indices):
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    N, M = indices.shape
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    indices = indices.flatten()
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    order = mx.argsort(indices)
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    inv_order = mx.argsort(order)
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    return x.flatten(0, -3)[order // M], indices[order], inv_order
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def scatter_unsort(x, inv_order, shape=None):
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    x = x[inv_order]
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    if shape is not None:
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        x = mx.unflatten(x, 0, shape)
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    return x
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def gather_mm_simulate(x, w, indices):
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    x, idx, inv_order = gather_sort(x, indices)
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    for i in range(2):
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        y = mx.concatenate(
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            [
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                mx.quantized_matmul(x[i], w[0][j], w[1][j], w[2][j], transpose=True)
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                for i, j in enumerate(idx.tolist())
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            ],
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            axis=0,
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        )
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        x = y[:, None]
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    x = scatter_unsort(x, inv_order, indices.shape)
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    return x
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def time_gather_qmm():
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    x = mx.random.normal((N, 1, 1, D)) / 1024**0.5
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    w1 = mx.random.normal((E, M, D)) / 1024**0.5
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    w2 = mx.random.normal((E, D, M)) / 1024**0.5
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    w1 = mx.quantize(w1)
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    w2 = mx.quantize(w2)
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    indices = (mx.random.uniform(shape=(N, I)) * E).astype(mx.uint32)
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    sorted_indices = mx.sort(indices.flatten()).reshape(N, I)
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    mx.eval(x, w1, w2, indices, sorted_indices)
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    def gather_mm(x, w1, w2, indices, sort):
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        idx = indices
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        inv_order = None
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        if sort:
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            x, idx, inv_order = gather_sort(x, indices)
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        x = mx.gather_qmm(x, *w1, transpose=True, rhs_indices=idx, sorted_indices=sort)
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        x = mx.gather_qmm(x, *w2, transpose=True, rhs_indices=idx, sorted_indices=sort)
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        if sort:
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            x = scatter_unsort(x, inv_order, indices.shape)
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        return x
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    time_fn(gather_mm, x, w1, w2, indices, False)
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    time_fn(gather_mm, x, w1, w2, sorted_indices, False)
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    time_fn(gather_mm, x, w1, w2, indices, True)
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    x = mx.random.normal((N * I, D)) / 1024**0.5
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    w1 = mx.random.normal((M, D)) / 1024**0.5
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    w2 = mx.random.normal((D, M)) / 1024**0.5
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    w1 = mx.quantize(w1)
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    w2 = mx.quantize(w2)
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    mx.eval(x, w1, w2)
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    def equivalent_matmul(x, w1, w2):
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        x = mx.quantized_matmul(x, *w1, transpose=True)
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        x = mx.quantized_matmul(x, *w2, transpose=True)
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        return x
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    time_fn(equivalent_matmul, x, w1, w2)
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if __name__ == "__main__":
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    time_gather_qmm()
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