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This will allow us to reuse the log_extract.py tools in torchbench Pull Request resolved: https://github.com/pytorch/pytorch/pull/75436 Approved by: https://github.com/eellison
105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
import argparse
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import functools
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import traceback
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from torch.utils.jit.log_extract import extract_ir, load_graph_and_inputs, run_baseline_no_fusion, run_nnc, run_nvfuser
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from typing import List, Tuple, Callable, Optional
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'''
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Usage:
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1. Run your script and pipe into a log file
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PYTORCH_JIT_LOG_LEVEL=">>graph_fuser" python3 my_test.py &> log.txt
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2. Run log_extract:
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log_extract.py log.txt --nvfuser --nnc-dynamic --nnc-static
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You can also extract the list of extracted IR:
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log_extract.py log.txt --output
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Passing in --graphs 0 2 will only run graphs 0 and 2
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'''
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def test_runners(graphs: List[str], runners: List[Tuple[str, Callable]], graph_set: Optional[List[int]]):
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for i, ir in enumerate(graphs):
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_, inputs = load_graph_and_inputs(ir)
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if graph_set and i not in graph_set:
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continue
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print(f"Running Graph {i}")
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prev_result = None
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prev_runner_name = None
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for runner in runners:
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runner_name, runner_fn = runner
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try:
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result = runner_fn(ir, inputs)
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if prev_result:
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improvement = (prev_result / result - 1) * 100
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print(f"{runner_name} : {result:.6f} ms improvement over {prev_runner_name}: improvement: {improvement:.2f}%")
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else:
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print(f"{runner_name} : {result:.6f} ms")
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prev_result = result
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prev_runner_name = runner_name
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except RuntimeError:
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print(f" Graph {i} failed for {runner_name} :", traceback.format_exc())
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def run():
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parser = argparse.ArgumentParser(
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description="Extracts torchscript IR from log files and, optionally, benchmarks it or outputs the IR"
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)
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parser.add_argument("filename", help="Filename of log file")
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parser.add_argument("--nvfuser", dest="nvfuser", action="store_true", help="benchmark nvfuser")
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parser.add_argument("--no-nvfuser", dest="nvfuser", action="store_false", help="DON'T benchmark nvfuser")
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parser.set_defaults(nvfuser=False)
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parser.add_argument("--nnc-static", dest="nnc_static", action="store_true", help="benchmark nnc static")
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parser.add_argument("--no-nnc-static", dest="nnc_static", action="store_false", help="DON'T benchmark nnc static")
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parser.set_defaults(nnc_static=False)
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parser.add_argument("--nnc-dynamic", dest="nnc_dynamic", action="store_true", help="nnc with dynamic shapes")
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parser.add_argument(
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"--no-nnc-dynamic",
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dest="nnc_dynamic",
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action="store_false",
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help="DONT't benchmark nnc with dynamic shapes")
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parser.set_defaults(nnc_dynamic=False)
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parser.add_argument("--baseline", dest="baseline", action="store_true", help="benchmark baseline")
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parser.add_argument("--no-baseline", dest="baseline", action="store_false", help="DON'T benchmark baseline")
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parser.set_defaults(baseline=False)
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parser.add_argument("--output", dest="output", action="store_true", help="Output graph IR")
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parser.add_argument("--no-output", dest="output", action="store_false", help="DON'T output graph IR")
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parser.set_defaults(output=False)
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parser.add_argument('--graphs', nargs="+", type=int, help="Run only specified graph indices")
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args = parser.parse_args()
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graphs = extract_ir(args.filename)
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graph_set = args.graphs
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graph_set = graph_set if graph_set else None
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options = []
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if args.baseline:
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options.append(("Baseline no fusion", run_baseline_no_fusion))
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if args.nnc_dynamic:
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options.append(("NNC Dynamic", functools.partial(run_nnc, dynamic=True)))
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if args.nnc_static:
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options.append(("NNC Static", functools.partial(run_nnc, dynamic=False)))
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if args.nvfuser:
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options.append(("NVFuser", run_nvfuser))
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test_runners(graphs, options, graph_set)
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if args.output:
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quoted = []
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for i, ir in enumerate(graphs):
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if graph_set and i not in graph_set:
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continue
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quoted.append("\"\"\"" + ir + "\"\"\"")
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print("[" + ", ".join(quoted) + "]")
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if __name__ == "__main__":
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run()
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