| 26 | |
| 27 | @staticmethod |
| 28 | def run(log_file, percentile, enhance, enhance_ratio): |
| 29 | res = {} |
| 30 | tensor_id = {} |
| 31 | idx = 0 |
| 32 | tensor_ranges = {} |
| 33 | with open(log_file) as log: |
| 34 | for line in log: |
| 35 | if line.find("Tensor range @@") != -1: |
| 36 | tensor_name, minmax = line.split("@@")[1:] |
| 37 | min_val, max_val = [float(i) for i in |
| 38 | minmax.strip().split(",")] |
| 39 | if tensor_name not in tensor_ranges: |
| 40 | tensor_ranges[tensor_name] = ([], []) |
| 41 | tensor_ranges[tensor_name][0].append(min_val) |
| 42 | tensor_ranges[tensor_name][1].append(max_val) |
| 43 | if tensor_name not in tensor_id: |
| 44 | tensor_id[tensor_name] = idx |
| 45 | idx = idx + 1 |
| 46 | |
| 47 | for tensor_name in tensor_ranges: |
| 48 | samples = len(tensor_ranges[tensor_name][0]) |
| 49 | tensor_min = np.percentile(tensor_ranges[tensor_name][0], |
| 50 | percentile) |
| 51 | tensor_max = np.percentile(tensor_ranges[tensor_name][1], |
| 52 | 100 - percentile) |
| 53 | assert tensor_min < tensor_max, \ |
| 54 | "min should be < max, %s min:%f max:%f" % \ |
| 55 | (tensor_name, tensor_min, tensor_max) |
| 56 | if not enhance or samples <= 1: |
| 57 | res[tensor_name] = (tensor_min, tensor_max) |
| 58 | else: |
| 59 | """ |
| 60 | Enhancement mode: |
| 61 | This policy eliminates outliers that cause long-tail |
| 62 | statistical range. We try to reduce as much range as it could |
| 63 | while retaining more samples. d(range)/d(sample_quantile) is |
| 64 | used to measure this qualitatively. |
| 65 | """ |
| 66 | tensor_mins = np.sort(tensor_ranges[tensor_name][0]) |
| 67 | tensor_maxs = np.sort(tensor_ranges[tensor_name][1])[::-1] |
| 68 | cur_min_idx = 0 |
| 69 | cur_max_idx = 0 |
| 70 | cur_min = tensor_min |
| 71 | cur_max = tensor_max |
| 72 | for i in range(samples): |
| 73 | if tensor_mins[i] + 0.1 > cur_max: |
| 74 | break |
| 75 | |
| 76 | delta_range = (tensor_mins[i] - cur_min) / (cur_max - cur_min) # noqa |
| 77 | delta_quantile = float(i - cur_min_idx) / (samples - cur_min_idx) # noqa |
| 78 | if delta_quantile > 0 and delta_range / delta_quantile > enhance_ratio: # noqa |
| 79 | cur_min_idx = i |
| 80 | cur_min = tensor_mins[i] |
| 81 | |
| 82 | if cur_min + 0.1 > tensor_maxs[i]: |
| 83 | break |
| 84 | |
| 85 | delta_range = (cur_max - tensor_maxs[i]) / (cur_max - cur_min) # noqa |