Created
August 26, 2024 21:44
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| import time | |
| import numpy as np | |
| import torch | |
| bmnks = [(2, 8192, 6144, 4096), (2, 8192, 4096, 4096), (2, 8192, 14336, 4096), (2, 8192, 4096, 14336)] | |
| problems = {True: {}, False: {}} | |
| reference = {True: {}, False: {}} | |
| for _ in range(3): | |
| for (b, m, n, k) in bmnks: | |
| x = torch.ones((b, m, k), dtype=torch.bfloat16, device='cuda') | |
| w = torch.ones((k, n), dtype=torch.bfloat16, device='cuda') | |
| out = torch.ones((b, m, n), dtype=torch.bfloat16, device='cuda') | |
| for tunable_op_enabled in [False, True]: | |
| torch.cuda.tunable.enable(tunable_op_enabled) | |
| permutations = [("fw_x_b", x, w), ("bw_dx_b", out, w.mT), ("bw_dx_f", out.reshape(-1, out.shape[-1]), w.mT), ("bw_dw_f", out.reshape(-1, out.shape[-1]).mT, x.reshape(-1, x.shape[-1]))] | |
| for (tag, x1, w1) in permutations: | |
| for _ in range(5): | |
| z = torch.matmul(x1, w1) | |
| torch.cuda.synchronize() | |
| times = [] | |
| for _ in range(10): | |
| start = time.time() | |
| z = torch.matmul(x1, w1) | |
| torch.cuda.synchronize() | |
| times.append(time.time() - start) | |
| problem = f"{b}x{m}x{n}x{k}_" + tag | |
| s = sum(times) / len(times) | |
| stddev = np.std(np.array(times) / 1e-6) | |
| us = s / 1e-6 | |
| problems[tunable_op_enabled][problem] = us | |
| reference[tunable_op_enabled][problem] = z | |
| if tunable_op_enabled: | |
| assert(torch.allclose(reference[False][problem], reference[True][problem])) | |
| speedup = problems[False][problem] / problems[True][problem] | |
| power = torch.cuda.power_draw() / 1000.0 | |
| tflops = 2 * b * m * n * k / s / 1e12 | |
| print(f'{problem}: \t {us:.2f} (+-{stddev:.2f}) tflops: {tflops:.2f} power: {power:.2f} speedup: {speedup if tunable_op_enabled else 0}') |
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