Update scripts to support torch.compile(). Make --results_file arg more consistent across benchmark/validate/inference. Fix #1570

benchmark.py

@@ -56,13 +56,7 @@ try:
 except ImportError as e:
     has_functorch = False
 
-try:
-    import torch._dynamo
-    has_dynamo = True
-except ImportError:
-    has_dynamo = False
-    pass
-
+has_compile = hasattr(torch, 'compile')
 
 if torch.cuda.is_available():
     torch.backends.cuda.matmul.allow_tf32 = True
@@ -81,8 +75,10 @@ parser.add_argument('--detail', action='store_true', default=False,
                     help='Provide train fwd/bwd/opt breakdown detail if True. Defaults to False')
 parser.add_argument('--no-retry', action='store_true', default=False,
                     help='Do not decay batch size and retry on error.')
-parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
+parser.add_argument('--results-file', default='', type=str,
                     help='Output csv file for validation results (summary)')
+parser.add_argument('--results-format', default='csv', type=str,
+                    help='Format for results file one of (csv, json) (default: csv).')
 parser.add_argument('--num-warm-iter', default=10, type=int,
                     metavar='N', help='Number of warmup iterations (default: 10)')
 parser.add_argument('--num-bench-iter', default=40, type=int,
@@ -113,8 +109,6 @@ parser.add_argument('--precision', default='float32', type=str,
                     help='Numeric precision. One of (amp, float32, float16, bfloat16, tf32)')
 parser.add_argument('--fuser', default='', type=str,
                     help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
-parser.add_argument('--dynamo-backend', default=None, type=str,
-                    help="Select dynamo backend. Default: None")
 parser.add_argument('--fast-norm', default=False, action='store_true',
                     help='enable experimental fast-norm')
 
@@ -122,10 +116,11 @@ parser.add_argument('--fast-norm', default=False, action='store_true',
 scripting_group = parser.add_mutually_exclusive_group()
 scripting_group.add_argument('--torchscript', dest='torchscript', action='store_true',
                              help='convert model torchscript for inference')
+scripting_group.add_argument('--torchcompile', nargs='?', type=str, default=None, const='inductor',
+                             help="Enable compilation w/ specified backend (default: inductor).")
 scripting_group.add_argument('--aot-autograd', default=False, action='store_true',
                              help="Enable AOT Autograd optimization.")
-scripting_group.add_argument('--dynamo', default=False, action='store_true',
-                             help="Enable Dynamo optimization.")
+
 
 # train optimizer parameters
 parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
@@ -218,9 +213,8 @@ class BenchmarkRunner:
             detail=False,
             device='cuda',
             torchscript=False,
+            torchcompile=None,
             aot_autograd=False,
-            dynamo=False,
-            dynamo_backend=None,
             precision='float32',
             fuser='',
             num_warm_iter=10,
@@ -259,20 +253,19 @@ class BenchmarkRunner:
         self.input_size = data_config['input_size']
         self.batch_size = kwargs.pop('batch_size', 256)
 
-        self.scripted = False
+        self.compiled = False
         if torchscript:
             self.model = torch.jit.script(self.model)
-            self.scripted = True
-        elif dynamo:
-            assert has_dynamo, "torch._dynamo is needed for --dynamo"
+            self.compiled = True
+        elif torchcompile:
+            assert has_compile, 'A version of torch w/ torch.compile() is required, possibly a nightly.'
             torch._dynamo.reset()
-            if dynamo_backend is not None:
-                self.model = torch._dynamo.optimize(dynamo_backend)(self.model)
-            else:
-                self.model = torch._dynamo.optimize()(self.model)
+            self.model = torch.compile(self.model, backend=torchcompile)
+            self.compiled = True
         elif aot_autograd:
             assert has_functorch, "functorch is needed for --aot-autograd"
             self.model = memory_efficient_fusion(self.model)
+            self.compiled = True
 
         self.example_inputs = None
         self.num_warm_iter = num_warm_iter
@@ -344,7 +337,7 @@ class InferenceBenchmarkRunner(BenchmarkRunner):
             param_count=round(self.param_count / 1e6, 2),
         )
 
-        retries = 0 if self.scripted else 2  # skip profiling if model is scripted
+        retries = 0 if self.compiled else 2  # skip profiling if model is scripted
         while retries:
             retries -= 1
             try:
@@ -642,7 +635,6 @@ def main():
         model_cfgs = [(n, None) for n in model_names]
 
     if len(model_cfgs):
-        results_file = args.results_file or './benchmark.csv'
         _logger.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))
         results = []
         try:
@@ -663,22 +655,30 @@ def main():
             sort_key = 'infer_gmacs'
         results = filter(lambda x: sort_key in x, results)
         results = sorted(results, key=lambda x: x[sort_key], reverse=True)
-        if len(results):
-            write_results(results_file, results)
     else:
         results = benchmark(args)
 
+    if args.results_file:
+        write_results(args.results_file, results, format=args.results_format)
+
     # output results in JSON to stdout w/ delimiter for runner script
     print(f'--result\n{json.dumps(results, indent=4)}')
 
 
-def write_results(results_file, results):
+def write_results(results_file, results, format='csv'):
     with open(results_file, mode='w') as cf:
-        dw = csv.DictWriter(cf, fieldnames=results[0].keys())
-        dw.writeheader()
-        for r in results:
-            dw.writerow(r)
-        cf.flush()
+        if format == 'json':
+            json.dump(results, cf, indent=4)
+        else:
+            if not isinstance(results, (list, tuple)):
+                results = [results]
+            if not results:
+                return
+            dw = csv.DictWriter(cf, fieldnames=results[0].keys())
+            dw.writeheader()
+            for r in results:
+                dw.writerow(r)
+            cf.flush()
 
 
 if __name__ == '__main__':

inference.py

@@ -8,6 +8,7 @@ Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
 import os
 import time
 import argparse
+import json
 import logging
 from contextlib import suppress
 from functools import partial
@@ -41,11 +42,7 @@ try:
 except ImportError as e:
     has_functorch = False
 
-try:
-    import torch._dynamo
-    has_dynamo = True
-except ImportError:
-    has_dynamo = False
+has_compile = hasattr(torch, 'compile')
 
 
 _FMT_EXT = {
@@ -60,14 +57,16 @@ _logger = logging.getLogger('inference')
 
 
 parser = argparse.ArgumentParser(description='PyTorch ImageNet Inference')
-parser.add_argument('data', metavar='DIR',
-                    help='path to dataset')
-parser.add_argument('--dataset', '-d', metavar='NAME', default='',
-                    help='dataset type (default: ImageFolder/ImageTar if empty)')
+parser.add_argument('data', nargs='?', metavar='DIR', const=None,
+                    help='path to dataset (*deprecated*, use --data-dir)')
+parser.add_argument('--data-dir', metavar='DIR',
+                    help='path to dataset (root dir)')
+parser.add_argument('--dataset', metavar='NAME', default='',
+                    help='dataset type + name ("<type>/<name>") (default: ImageFolder or ImageTar if empty)')
 parser.add_argument('--split', metavar='NAME', default='validation',
                     help='dataset split (default: validation)')
-parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
-                    help='model architecture (default: dpn92)')
+parser.add_argument('--model', '-m', metavar='MODEL', default='resnet50',
+                    help='model architecture (default: resnet50)')
 parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
                     help='number of data loading workers (default: 2)')
 parser.add_argument('-b', '--batch-size', default=256, type=int,
@@ -112,16 +111,14 @@ parser.add_argument('--amp-dtype', default='float16', type=str,
                     help='lower precision AMP dtype (default: float16)')
 parser.add_argument('--fuser', default='', type=str,
                     help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
-parser.add_argument('--dynamo-backend', default=None, type=str,
-                    help="Select dynamo backend. Default: None")
 
 scripting_group = parser.add_mutually_exclusive_group()
 scripting_group.add_argument('--torchscript', default=False, action='store_true',
                              help='torch.jit.script the full model')
+scripting_group.add_argument('--torchcompile', nargs='?', type=str, default=None, const='inductor',
+                             help="Enable compilation w/ specified backend (default: inductor).")
 scripting_group.add_argument('--aot-autograd', default=False, action='store_true',
                              help="Enable AOT Autograd support.")
-scripting_group.add_argument('--dynamo', default=False, action='store_true',
-                             help="Enable Dynamo optimization.")
 
 parser.add_argument('--results-dir',type=str, default=None,
                     help='folder for output results')
@@ -160,7 +157,6 @@ def main():
     device = torch.device(args.device)
 
     # resolve AMP arguments based on PyTorch / Apex availability
-    use_amp = None
     amp_autocast = suppress
     if args.amp:
         assert has_native_amp, 'Please update PyTorch to a version with native AMP (or use APEX).'
@@ -201,22 +197,20 @@ def main():
 
     if args.torchscript:
         model = torch.jit.script(model)
+    elif args.torchcompile:
+        assert has_compile, 'A version of torch w/ torch.compile() is required for --compile, possibly a nightly.'
+        torch._dynamo.reset()
+        model = torch.compile(model, backend=args.torchcompile)
     elif args.aot_autograd:
         assert has_functorch, "functorch is needed for --aot-autograd"
         model = memory_efficient_fusion(model)
-    elif args.dynamo:
-        assert has_dynamo, "torch._dynamo is needed for --dynamo"
-        torch._dynamo.reset()
-        if args.dynamo_backend is not None:
-            model = torch._dynamo.optimize(args.dynamo_backend)(model)
-        else:
-            model = torch._dynamo.optimize()(model)
 
     if args.num_gpu > 1:
         model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu)))
 
+    root_dir = args.data or args.data_dir
     dataset = create_dataset(
-        root=args.data,
+        root=root_dir,
         name=args.dataset,
         split=args.split,
         class_map=args.class_map,
@@ -304,6 +298,9 @@ def main():
     for fmt in args.results_format:
         save_results(df, results_filename, fmt)
 
+    print(f'--result')
+    print(json.dumps(dict(filename=results_filename)))
+
 
 def save_results(df, results_filename, results_format='csv', filename_col='filename'):
     results_filename += _FMT_EXT[results_format]

train.py

@@ -66,12 +66,7 @@ try:
 except ImportError as e:
     has_functorch = False
 
-try:
-    import torch._dynamo
-    has_dynamo = True
-except ImportError:
-    has_dynamo = False
-    pass
+has_compile = hasattr(torch, 'compile')
 
 
 _logger = logging.getLogger('train')
@@ -88,10 +83,12 @@ parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
 # Dataset parameters
 group = parser.add_argument_group('Dataset parameters')
 # Keep this argument outside of the dataset group because it is positional.
-parser.add_argument('data_dir', metavar='DIR',
-                    help='path to dataset')
-group.add_argument('--dataset', '-d', metavar='NAME', default='',
-                    help='dataset type (default: ImageFolder/ImageTar if empty)')
+parser.add_argument('data', nargs='?', metavar='DIR', const=None,
+                    help='path to dataset (positional is *deprecated*, use --data-dir)')
+parser.add_argument('--data-dir', metavar='DIR',
+                    help='path to dataset (root dir)')
+parser.add_argument('--dataset', metavar='NAME', default='',
+                    help='dataset type + name ("<type>/<name>") (default: ImageFolder or ImageTar if empty)')
 group.add_argument('--train-split', metavar='NAME', default='train',
                     help='dataset train split (default: train)')
 group.add_argument('--val-split', metavar='NAME', default='validation',
@@ -143,16 +140,14 @@ group.add_argument('--grad-checkpointing', action='store_true', default=False,
                     help='Enable gradient checkpointing through model blocks/stages')
 group.add_argument('--fast-norm', default=False, action='store_true',
                     help='enable experimental fast-norm')
-parser.add_argument('--dynamo-backend', default=None, type=str,
-                    help="Select dynamo backend. Default: None")
 
 scripting_group = group.add_mutually_exclusive_group()
 scripting_group.add_argument('--torchscript', dest='torchscript', action='store_true',
                              help='torch.jit.script the full model')
+scripting_group.add_argument('--torchcompile', nargs='?', type=str, default=None, const='inductor',
+                             help="Enable compilation w/ specified backend (default: inductor).")
 scripting_group.add_argument('--aot-autograd', default=False, action='store_true',
                              help="Enable AOT Autograd support.")
-scripting_group.add_argument('--dynamo', default=False, action='store_true',
-                             help="Enable Dynamo optimization.")
 
 # Optimizer parameters
 group = parser.add_argument_group('Optimizer parameters')
@@ -377,6 +372,8 @@ def main():
         torch.backends.cuda.matmul.allow_tf32 = True
         torch.backends.cudnn.benchmark = True
 
+    if args.data and not args.data_dir:
+        args.data_dir = args.data
     args.prefetcher = not args.no_prefetcher
     device = utils.init_distributed_device(args)
     if args.distributed:
@@ -485,18 +482,16 @@ def main():
         assert not use_amp == 'apex', 'Cannot use APEX AMP with torchscripted model'
         assert not args.sync_bn, 'Cannot use SyncBatchNorm with torchscripted model'
         model = torch.jit.script(model)
+    elif args.torchcompile:
+        # FIXME dynamo might need move below DDP wrapping? TBD
+        assert has_compile, 'A version of torch w/ torch.compile() is required for --compile, possibly a nightly.'
+        torch._dynamo.reset()
+        model = torch.compile(model, backend=args.torchcompile)
     elif args.aot_autograd:
         assert has_functorch, "functorch is needed for --aot-autograd"
         model = memory_efficient_fusion(model)
-    elif args.dynamo:
-        # FIXME dynamo might need move below DDP wrapping? TBD
-        assert has_dynamo, "torch._dynamo is needed for --dynamo"
-        if args.dynamo_backend is not None:
-            model = torch._dynamo.optimize(args.dynamo_backend)(model)
-        else:
-            model = torch._dynamo.optimize()(model)
 
-    if args.lr is None:
+    if not args.lr:
         global_batch_size = args.batch_size * args.world_size
         batch_ratio = global_batch_size / args.lr_base_size
         if not args.lr_base_scale:

validate.py

@@ -26,12 +26,11 @@ from timm.data import create_dataset, create_loader, resolve_data_config, RealLa
 from timm.utils import accuracy, AverageMeter, natural_key, setup_default_logging, set_jit_fuser,\
     decay_batch_step, check_batch_size_retry
 
-has_apex = False
 try:
     from apex import amp
     has_apex = True
 except ImportError:
-    pass
+    has_apex = False
 
 has_native_amp = False
 try:
@@ -46,21 +45,18 @@ try:
 except ImportError as e:
     has_functorch = False
 
-try:
-    import torch._dynamo
-    has_dynamo = True
-except ImportError:
-    has_dynamo = False
-    pass
+has_compile = hasattr(torch, 'compile')
 
 _logger = logging.getLogger('validate')
 
 
 parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
-parser.add_argument('data', metavar='DIR',
-                    help='path to dataset')
-parser.add_argument('--dataset', '-d', metavar='NAME', default='',
-                    help='dataset type (default: ImageFolder/ImageTar if empty)')
+parser.add_argument('data', nargs='?', metavar='DIR', const=None,
+                    help='path to dataset (*deprecated*, use --data-dir)')
+parser.add_argument('--data-dir', metavar='DIR',
+                    help='path to dataset (root dir)')
+parser.add_argument('--dataset', metavar='NAME', default='',
+                    help='dataset type + name ("<type>/<name>") (default: ImageFolder or ImageTar if empty)')
 parser.add_argument('--split', metavar='NAME', default='validation',
                     help='dataset split (default: validation)')
 parser.add_argument('--dataset-download', action='store_true', default=False,
@@ -125,19 +121,19 @@ parser.add_argument('--fuser', default='', type=str,
                     help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
 parser.add_argument('--fast-norm', default=False, action='store_true',
                     help='enable experimental fast-norm')
-parser.add_argument('--dynamo-backend', default=None, type=str,
-                    help="Select dynamo backend. Default: None")
 
 scripting_group = parser.add_mutually_exclusive_group()
 scripting_group.add_argument('--torchscript', default=False, action='store_true',
                              help='torch.jit.script the full model')
+scripting_group.add_argument('--torchcompile', nargs='?', type=str, default=None, const='inductor',
+                             help="Enable compilation w/ specified backend (default: inductor).")
 scripting_group.add_argument('--aot-autograd', default=False, action='store_true',
                              help="Enable AOT Autograd support.")
-scripting_group.add_argument('--dynamo', default=False, action='store_true',
-                             help="Enable Dynamo optimization.")
 
 parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
                     help='Output csv file for validation results (summary)')
+parser.add_argument('--results-format', default='csv', type=str,
+                    help='Format for results file one of (csv, json) (default: csv).')
 parser.add_argument('--real-labels', default='', type=str, metavar='FILENAME',
                     help='Real labels JSON file for imagenet evaluation')
 parser.add_argument('--valid-labels', default='', type=str, metavar='FILENAME',
@@ -218,16 +214,13 @@ def validate(args):
     if args.torchscript:
         assert not use_amp == 'apex', 'Cannot use APEX AMP with torchscripted model'
         model = torch.jit.script(model)
+    elif args.torchcompile:
+        assert has_compile, 'A version of torch w/ torch.compile() is required for --compile, possibly a nightly.'
+        torch._dynamo.reset()
+        model = torch.compile(model, backend=args.torchcompile)
     elif args.aot_autograd:
         assert has_functorch, "functorch is needed for --aot-autograd"
         model = memory_efficient_fusion(model)
-    elif args.dynamo:
-        assert has_dynamo, "torch._dynamo is needed for --dynamo"
-        torch._dynamo.reset()
-        if args.dynamo_backend is not None:
-            model = torch._dynamo.optimize(args.dynamo_backend)(model)
-        else:
-            model = torch._dynamo.optimize()(model)
 
     if use_amp == 'apex':
         model = amp.initialize(model, opt_level='O1')
@@ -407,7 +400,6 @@ def main():
             model_cfgs = [(n, None) for n in model_names if n]
 
     if len(model_cfgs):
-        results_file = args.results_file or './results-all.csv'
         _logger.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))
         results = []
         try:
@@ -424,24 +416,34 @@ def main():
         except KeyboardInterrupt as e:
             pass
         results = sorted(results, key=lambda x: x['top1'], reverse=True)
-        if len(results):
-            write_results(results_file, results)
     else:
         if args.retry:
             results = _try_run(args, args.batch_size)
         else:
             results = validate(args)
+
+    if args.results_file:
+        write_results(args.results_file, results, format=args.results_format)
+
     # output results in JSON to stdout w/ delimiter for runner script
     print(f'--result\n{json.dumps(results, indent=4)}')
 
 
-def write_results(results_file, results):
+def write_results(results_file, results, format='csv'):
     with open(results_file, mode='w') as cf:
-        dw = csv.DictWriter(cf, fieldnames=results[0].keys())
-        dw.writeheader()
-        for r in results:
-            dw.writerow(r)
-        cf.flush()
+        if format == 'json':
+            json.dump(results, cf, indent=4)
+        else:
+            if not isinstance(results, (list, tuple)):
+                results = [results]
+            if not results:
+                return
+            dw = csv.DictWriter(cf, fieldnames=results[0].keys())
+            dw.writeheader()
+            for r in results:
+                dw.writerow(r)
+            cf.flush()
+
 
 
 if __name__ == '__main__':