diff options
Diffstat (limited to 'src/mlia/backend/vela/performance.py')
| -rw-r--r-- | src/mlia/backend/vela/performance.py | 85 |
1 files changed, 37 insertions, 48 deletions
diff --git a/src/mlia/backend/vela/performance.py b/src/mlia/backend/vela/performance.py index 72a8ceb..2cf945d 100644 --- a/src/mlia/backend/vela/performance.py +++ b/src/mlia/backend/vela/performance.py @@ -10,15 +10,12 @@ from collections import Counter from dataclasses import dataclass from dataclasses import fields from pathlib import Path -from pydoc import locate import numpy as np -from ethosu.vela.npu_performance import PassCycles -from ethosu.vela.tensor import MemArea -from mlia.backend.vela.compiler import OptimizedModel from mlia.backend.vela.compiler import VelaCompiler from mlia.backend.vela.compiler import VelaCompilerOptions +from mlia.backend.vela.compiler import VelaSummary logger = logging.getLogger(__name__) @@ -37,11 +34,10 @@ class PerformanceMetrics: # pylint: disable=too-many-instance-attributes batch_inference_time: float inferences_per_second: float batch_size: int - unknown_memory_area_size: int - sram_memory_area_size: int - dram_memory_area_size: int - on_chip_flash_memory_area_size: int - off_chip_flash_memory_area_size: int + sram_memory_area_size: float + dram_memory_area_size: float + on_chip_flash_memory_area_size: float + off_chip_flash_memory_area_size: float layerwise_performance_info: LayerwisePerfInfo @@ -145,19 +141,19 @@ def parse_layerwise_perf_csv( # pylint: disable=too-many-locals if row == headers_to_check_cpu_ops: continue try: + # pylint: disable=eval-used key_types = { - field.name: locate(str(field.type)) + field.name: eval(field.type) # type: ignore # nosec for field in fields(LayerPerfInfo) } + # pylint: enable=eval-used ids_to_metrics = {} for key, title, _ in metrics: try: - ids_to_metrics[key] = key_types[key]( # type: ignore - row_as_dict[title] - ) + ids_to_metrics[key] = key_types[key](row_as_dict[title]) except ValueError as err: if "invalid literal for int() with base 10" in str(err): - ids_to_metrics[key] = key_types[key]( # type: ignore + ids_to_metrics[key] = key_types[key]( float(row_as_dict[title]) ) else: @@ -180,17 +176,20 @@ def estimate_performance( model_path, compiler_options.accelerator_config, ) - vela_compiler = VelaCompiler(compiler_options) - - initial_model = vela_compiler.read_model(model_path) - if initial_model.optimized: - raise ValueError( - "Unable to estimate performance for the given optimized model." - ) - - optimized_model = vela_compiler.compile_model(initial_model) - output_dir = optimized_model.compiler_options.output_dir + if Path( + Path(compiler_options.output_dir).as_posix() + + "/" + + model_path.stem + + "_summary_" + + compiler_options.system_config + + ".csv" + ).is_file(): + summary_data, _ = vela_compiler.compile_model(model_path, True) + else: + summary_data, _ = vela_compiler.compile_model(model_path) + + output_dir = compiler_options.output_dir csv_paths = [entry for entry in os.listdir(output_dir) if "per-layer.csv" in entry] model_name = str(model_path.stem) csv_file_found = None @@ -204,41 +203,31 @@ def estimate_performance( vela_csv_file=csv_path, metrics=layer_metrics ) - return _performance_metrics(layerwise_performance_info, optimized_model) + return _performance_metrics(layerwise_performance_info, summary_data) def _performance_metrics( - layerwise_performance_info: LayerwisePerfInfo, optimized_model: OptimizedModel + layerwise_performance_info: LayerwisePerfInfo, summary_data: VelaSummary ) -> PerformanceMetrics: """Return performance metrics for optimized model.""" - cycles = optimized_model.nng.cycles - - def memory_usage(mem_area: MemArea) -> int: - """Get memory usage for the proviced memory area type.""" - memory_used: dict[MemArea, int] = optimized_model.nng.memory_used - bandwidths = optimized_model.nng.bandwidths - - return memory_used.get(mem_area, 0) if np.sum(bandwidths[mem_area]) > 0 else 0 - midpoint_fps = np.nan - midpoint_inference_time = cycles[PassCycles.Total] / optimized_model.arch.core_clock + midpoint_inference_time = summary_data.cycles_total / summary_data.core_clock if midpoint_inference_time > 0: midpoint_fps = 1 / midpoint_inference_time return PerformanceMetrics( - npu_cycles=int(cycles[PassCycles.Npu]), - sram_access_cycles=int(cycles[PassCycles.SramAccess]), - dram_access_cycles=int(cycles[PassCycles.DramAccess]), - on_chip_flash_access_cycles=int(cycles[PassCycles.OnChipFlashAccess]), - off_chip_flash_access_cycles=int(cycles[PassCycles.OffChipFlashAccess]), - total_cycles=int(cycles[PassCycles.Total]), + npu_cycles=int(summary_data.cycles_npu), + sram_access_cycles=int(summary_data.cycles_sram_access), + dram_access_cycles=int(summary_data.cycles_dram_access), + on_chip_flash_access_cycles=int(summary_data.cycles_on_chip_flash_access), + off_chip_flash_access_cycles=int(summary_data.cycles_off_chip_flash_access), + total_cycles=int(summary_data.cycles_total), batch_inference_time=midpoint_inference_time * 1000, inferences_per_second=midpoint_fps, - batch_size=optimized_model.nng.batch_size, - unknown_memory_area_size=memory_usage(MemArea.Unknown), - sram_memory_area_size=memory_usage(MemArea.Sram), - dram_memory_area_size=memory_usage(MemArea.Dram), - on_chip_flash_memory_area_size=memory_usage(MemArea.OnChipFlash), - off_chip_flash_memory_area_size=memory_usage(MemArea.OffChipFlash), + batch_size=summary_data.batch_size, + sram_memory_area_size=float(summary_data.sram_memory_used), + dram_memory_area_size=float(summary_data.dram_memory_used), + on_chip_flash_memory_area_size=float(summary_data.on_chip_flash_memory_used), + off_chip_flash_memory_area_size=float(summary_data.off_chip_flash_memory_used), layerwise_performance_info=layerwise_performance_info, ) |