add dense matmul benchmark #256
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JosseVanDelm
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Sep 25, 2024
| from util.snax_benchmark import SNAXBenchmark | ||
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| def create_tiled_matrix_multiply(k, m, n): |
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This is not a tiled_matrix_multiply anymore :)
| return module | ||
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| def write_module_to_file(module, file): |
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I want to move this to the tools folder at some point
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| def create_test_data(n, m, k): | ||
| print(f"Creating test data with n={n}, m={m}, k={k}") | ||
| # Reset random seed for reproducible behavior | ||
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| np.random.seed(0) | ||
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| A_size = [n, k] | ||
| B_size = [k, m] | ||
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| # C = A.B | ||
| low_bound = -128 | ||
| high_bound = 127 | ||
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| A = np.random.randint(low_bound, high_bound, size=A_size, dtype=np.dtype("int8")) | ||
| B = np.random.randint(low_bound, high_bound, size=B_size, dtype=np.dtype("int8")) | ||
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| # Make sure the product is possible! | ||
| assert A.shape[1] == B.shape[0] | ||
| C_golden = np.matmul(A.astype(np.dtype("int32")), B.astype(np.dtype("int32"))) | ||
| C = np.zeros(C_golden.shape, np.dtype("int32")) | ||
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| # Perform layout transformations before writing to memory | ||
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| # only thing necessary: transform B from row-major to column-major | ||
| B_new_layout = np.transpose(B) | ||
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| # C are just all zeros, so layout not important | ||
| sizes = { | ||
| "N_size": A.shape[0], | ||
| "K_size": A.shape[1], | ||
| "M_size": B.shape[1], | ||
| } | ||
| variables = { | ||
| "A": A, | ||
| "B": B_new_layout, | ||
| "C_golden": C_golden, | ||
| "C": C, | ||
| } | ||
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| create_header("data.h", sizes, variables) | ||
| create_data("data.c", variables) |
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We can probably move this to a more common folder :)
| } | ||
| } | ||
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| // insert mcycle to show fault in trace |
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What do you mean by this?
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just a quick way to check if an operation succeeds by looking at the trace. if there are 3 mcycles, it was successful. if there are 4, an error has occured
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This adds a benchmark that measures peak performance of the gemm accelerator measuring from the time it is launched until it is finished.
It is configured to run the benchmark on every push to main, and uploads the output report to github artifacts, sneek peak available here: https://github.com/KULeuven-MICAS/snax-mlir/actions/runs/11012799975