diff options
Diffstat (limited to 'ethosu/vela/stats_writer.py')
-rw-r--r-- | ethosu/vela/stats_writer.py | 92 |
1 files changed, 33 insertions, 59 deletions
diff --git a/ethosu/vela/stats_writer.py b/ethosu/vela/stats_writer.py index 494b25e7..02d95d81 100644 --- a/ethosu/vela/stats_writer.py +++ b/ethosu/vela/stats_writer.py @@ -22,7 +22,6 @@ import numpy as np from .nn_graph import PassPlacement from .npu_performance import BandwidthDirection -from .npu_performance import MacCount from .npu_performance import PassCycles from .numeric_util import round_up_to_int from .operation import Op @@ -70,7 +69,7 @@ def write_summary_metrics_csv(nng, summary_filename, arch): mem_area.identifier_name() + "_total_bytes", ] - labels += ["nn_macs", "hardware_macs", "nn_tops", "hardware_tops"] + labels += ["nn_macs", "nn_tops"] labels += ["cycles_" + kind.identifier_name() for kind in PassCycles.all()] @@ -128,10 +127,8 @@ def write_summary_metrics_csv(nng, summary_filename, arch): ] data_items += [ - nng.macs[MacCount.NeuralNetworkMacs], - nng.macs[MacCount.HardwareMacs], - nng.macs[MacCount.NeuralNetworkMacs] * 2 * midpoint_fps / 1e12, - nng.macs[MacCount.HardwareMacs] * 2 * midpoint_fps / 1e12, + nng.macs, + nng.macs * 2 * midpoint_fps / 1e12, ] data_items += [nng.cycles[kind] for kind in PassCycles.all()] @@ -164,7 +161,6 @@ def write_pass_metrics_csv(nng, pass_filename): bandwidth_names.append(label) bandwidth_indices.append((mem_area, purpose_candidates, direction_candidates)) - all_macs = MacCount.all() all_cycles = ( PassCycles.Total, PassCycles.Npu, @@ -183,10 +179,9 @@ def write_pass_metrics_csv(nng, pass_filename): "block_config_width", "block_config_input_channels", "block_config_output_channels", - "n_blocks_in_pass", ] + ["cycles_" + v.identifier_name() for v in all_cycles] - + [v.identifier_name() for v in all_macs] + + ["nn_macs"] + bandwidth_names + ["sram_used"] ) @@ -205,9 +200,8 @@ def write_pass_metrics_csv(nng, pass_filename): stats += [ps.placement.name] stats += [cps.strategy.name] stats += list(ps.block_config) - stats += [ps.n_blocks] stats += [round_up_to_int(ps.cycles[v]) for v in all_cycles] - stats += [round_up_to_int(ps.macs[v]) for v in all_macs] + stats += [round_up_to_int(ps.macs)] for indices in bandwidth_indices: res = 0 i = indices[0] @@ -256,17 +250,16 @@ def print_performance_metrics_for_strat( if name: print("", file=f) - print("Network summary for", name, file=f) - print("Accelerator configuration {:>20}".format(arch.accelerator_config.name), file=f) - print("System configuration {:>20}".format(arch.system_config), file=f) - print("Memory mode {:>20}".format(arch.memory_mode), file=f) - print("Accelerator clock {:12d} MHz".format(int(arch.core_clock / 1e6)), file=f) + print(f"Network summary for {name}", file=f) + print(f"Accelerator configuration {arch.accelerator_config.name:>20}", file=f) + print(f"System configuration {arch.system_config:>20}", file=f) + print(f"Memory mode {arch.memory_mode:>20}", file=f) + print(f"Accelerator clock {int(arch.core_clock / 1e6):12d} MHz", file=f) for mem_area, label in mem_area_labels: + label += " bandwidth" + bandwidth = arch.memory_bandwidths_per_second[mem_area] / 1000.0 / 1000 / 1000 print( - "Design peak {:25} {:12.2f} GB/s".format( - label + " bandwidth", arch.memory_bandwidths_per_second[mem_area] / 1000.0 / 1000 / 1000 - ), - file=f, + f"Design peak {label:25} {bandwidth:12.2f} GB/s", file=f, ) print(file=f) for mem_area, label in mem_area_labels: @@ -277,12 +270,12 @@ def print_performance_metrics_for_strat( extra = "" if (mem_area == MemArea.OnChipFlash or mem_area == MemArea.OffChipFlash) and bits_per_element is not None: - extra = " ({:.2f} bits per element)".format(bits_per_element[mem_area]) + extra = f" ({bits_per_element[mem_area]:.2f} bits per element)" - print("Total {:25} {:12.2f} KiB{}".format(aug_label, memory_used[mem_area] / 1024.0, extra), file=f) + print(f"Total {aug_label:25} {memory_used[mem_area] / 1024.0:12.2f} KiB{extra}", file=f) print(file=f) - print("{:d} passes fused into {:d}".format(num_passes, num_cascaded_passes), file=f) + print(f"{num_passes:d} passes fused into {num_cascaded_passes:d}", file=f) if cpu_operations is None: cpu_operations = [] @@ -290,9 +283,8 @@ def print_performance_metrics_for_strat( n_cpu_operations = len(cpu_operations) if n_operations > 0: print( - "{:d}/{:d} ({:4.1%}) operations falling back to the CPU".format( - n_cpu_operations, n_operations, n_cpu_operations / n_operations * 100 - ), + f"{n_cpu_operations:d}/{n_operations:d}" + f" ({n_cpu_operations / n_operations * 100:4.1%}) operations falling back to the CPU", file=f, ) @@ -303,9 +295,8 @@ def print_performance_metrics_for_strat( return " ".join(str(list(tens.shape)) for tens in lst) print( - "CPU operation: {} inputs {}, outputs {}".format( - op.type, format_tens_list(op.inputs), format_tens_list(op.outputs) - ), + f"CPU operation: {op.type}" + f" inputs {format_tens_list(op.inputs)}, outputs {format_tens_list(op.outputs)}", file=f, ) @@ -318,60 +309,43 @@ def print_performance_metrics_for_strat( fm_bws = bws[TensorPurpose.FeatureMap] aug_label = label + " bandwidth" print( - "Average {:25} {:12.2f} GB/s".format(aug_label, total_bw * midpoint_fps / 1000.0 / 1000.0 / 1000.0), - file=f, + f"Average {aug_label:25} {total_bw * midpoint_fps / 1000.0 / 1000.0 / 1000.0:12.2f} GB/s", file=f, ) print( - "Input {:25} {:12.2f} MB/batch".format( - aug_label, np.sum(fm_bws[BandwidthDirection.Read]) / 1000.0 / 1000.0 - ), + f"Input {aug_label:25} {np.sum(fm_bws[BandwidthDirection.Read]) / 1000.0 / 1000.0:12.2f} MB/batch", file=f, ) - print("Weight {:25} {:12.2f} MB/batch".format(aug_label, np.sum(weight_bws) / 1000.0 / 1000.0), file=f) + print(f"Weight {aug_label:25} {np.sum(weight_bws) / 1000.0 / 1000.0:12.2f} MB/batch", file=f) print( - "Output {:25} {:12.2f} MB/batch".format( - aug_label, np.sum(fm_bws[BandwidthDirection.Write]) / 1000.0 / 1000.0 - ), + f"Output {aug_label:25} " + f"{np.sum(fm_bws[BandwidthDirection.Write]) / 1000.0 / 1000.0:12.2f} MB/batch", file=f, ) - print("Total {:25} {:12.2f} MB/batch".format(aug_label, total_bw / 1000.0 / 1000.0), file=f) + print(f"Total {aug_label:25} {total_bw / 1000.0 / 1000.0:12.2f} MB/batch", file=f) print( - "Total {:25} per input {:9.2f} MB/inference (batch size {:d})".format( - aug_label, total_bw / 1000.0 / 1000.0 / batch_size, batch_size - ), + f"Total {aug_label:25} per input " + f"{total_bw / 1000.0 / 1000.0 / batch_size:9.2f} MB/inference (batch size {batch_size:d})", file=f, ) print(file=f) print( - "Neural network macs {:12d} MACs/batch".format(int(macs[MacCount.NeuralNetworkMacs])), - file=f, + f"Neural network macs {int(macs):12d} MACs/batch", file=f, ) - print("Hardware macs {:12d} MACs/batch".format(int(macs[MacCount.HardwareMacs])), file=f) print( - "Network Tops/s {:12.2f} Tops/s".format( - macs[MacCount.NeuralNetworkMacs] * 2 * midpoint_fps / 1e12 - ), - file=f, - ) - print( - "Hardware Tops/s {:12.2f} Tops/s".format( - macs[MacCount.HardwareMacs] * 2 * midpoint_fps / 1e12 - ), - file=f, + f"Network Tops/s {macs * 2 * midpoint_fps / 1e12:12.2f} Tops/s", file=f, ) print(file=f) for kind in PassCycles.all(): aug_label = kind.display_name() + " cycles" cyc = cycles[kind] - print("{:30} {:12d} cycles/batch".format(aug_label, int(cyc)), file=f) + print(f"{aug_label:30} {int(cyc):12d} cycles/batch", file=f) print(file=f) print( - "Batch Inference time {:7.2f} ms, {:7.2f} inferences/s (batch size {:d})".format( - midpoint_inference_time * 1000, midpoint_fps, batch_size - ), + f"Batch Inference time {midpoint_inference_time * 1000:7.2f} ms," + f" {midpoint_fps:7.2f} inferences/s (batch size {batch_size:d})", file=f, ) print(file=f) |