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MLX Tiled Matmul
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| import mlx.core as mx | |
| # Possible tile size for tensor cores | |
| TS = 32 | |
| # Matrix dimension (M = N = K = D) | |
| D = 2048 | |
| A = mx.random.uniform(shape=(D, D)) | |
| B = mx.random.uniform(shape=(D, D)) | |
| # Reshape and transpose so a tile is in the last two dimensions | |
| A_tiled = A.reshape((2048 // TS, TS, D // TS, TS)).swapaxes(1, 2) | |
| B_tiled = B.reshape((2048 // TS, TS, D // TS, TS)).swapaxes(1, 2) | |
| # Each thread group computes one tile of the output: | |
| i = 1 | |
| j = 1 | |
| C_ij = sum(A_tiled[i, k] @ B_tiled[k, j] for k in range(D // TS)) | |
| C = A @ B | |
| # Get the `i, j` tile of the output | |
| C_ij_expected = C[i * TS:(i+1) * TS, j * TS: (j+1) * TS] | |
| assert mx.allclose(C_ij, C_ij_expected) |
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