[Docs][Opeartor] Add more pytorch operator bindings and docs

docs/source/python_api/data_types.rst

@@ -0,0 +1,16 @@
+hidet.dtypes
+============
+
+.. data:: hidet.uint8
+.. data:: hidet.uint16
+.. data:: hidet.uint32
+.. data:: hidet.uint64
+.. data:: hidet.int8
+.. data:: hidet.int16
+.. data:: hidet.int32
+.. data:: hidet.int64
+.. data:: hidet.float16
+.. data:: hidet.float32
+.. data:: hidet.float64
+.. data:: hidet.bfloat16
+.. data:: hidet.tfloat32

docs/source/python_api/index.rst

@@ -15,6 +15,7 @@ Python API
    driver
    cuda
    tensor
+   data_types
    ops/index
    ir/index
    graph/index

docs/source/python_api/ir/type.rst

@@ -1,6 +1,6 @@
 hidet.ir.type
 =============
 
-.. automodule:: hidet.ir.type
-  :members:
-  :autosummary:
+.. autoclass:: hidet.ir.type.DataType
+
+.. autofunction:: hidet.ir.type.data_type

python/hidet/cli/bench/__init__.py

@@ -1,5 +1,5 @@
 from hidet.graph.frontend.torch import availability as torch_availability
-from .root import bench_group
+from .bench import bench_group
 
 if not torch_availability.dynamo_available():
     raise RuntimeError(

python/hidet/cli/bench/bench.py

@@ -0,0 +1,42 @@
+import click
+from hidet.utils import initialize
+from . import vision
+from . import nlp
+from .model import BenchModel
+from .bench_common import bench_common
+from .bench_all import bench_all
+
+
+@click.group(name='bench', help='Benchmark models.')
+@click.option(
+    '--space',
+    default='0',
+    show_default=True,
+    type=click.Choice(['0', '1', '2']),
+    help='Schedule space. 0: default schedule. 1: small schedule space. 2: large schedule space.',
+)
+@click.option(
+    '--torch-tf32',
+    default=False,
+    show_default=True,
+    type=bool,
+    help='Enable torch.backends.cuda.matmul.allow_tf32 and torch.backends.cudnn.allow_tf32.',
+)
+def bench_group(space: str, torch_tf32: bool):
+    BenchModel.search_space = int(space)
+    BenchModel.allow_tf32 = torch_tf32
+
+
+@initialize()
+def register_commands():
+    for command in [
+        bench_common,
+        bench_all,
+        vision.bench_resnet,
+        vision.bench_resnext,
+        vision.bench_inception_v3,
+        vision.bench_mobilenet_v2,
+        nlp.bench_nlp,
+    ]:
+        assert isinstance(command, click.Command)
+        bench_group.add_command(command)

python/hidet/cli/bench/bench_all.py

@@ -0,0 +1,12 @@
+import click
+from tabulate import tabulate
+from hidet.cli.bench.model import BenchModel, all_registered_models
+
+
+@click.command(name='all')
+def bench_all():
+    header = BenchModel.headers()
+    result = [bench_model.benchmark() for bench_model in all_registered_models]
+
+    click.echo(tabulate(result, headers=header, tablefmt='github', floatfmt='.3f', numalign='right', stralign='left'))
+    click.echo('(PyTorch backend: allow_tf32={})'.format(BenchModel.allow_tf32))

python/hidet/cli/bench/bench_common.py

@@ -0,0 +1,12 @@
+import click
+from tabulate import tabulate
+from hidet.cli.bench.model import BenchModel, commonly_used_models
+
+
+@click.command(name='common')
+def bench_common():
+    header = BenchModel.headers()
+    result = [bench_model.benchmark() for bench_model in commonly_used_models]
+
+    click.echo(tabulate(result, headers=header, tablefmt='github', floatfmt='.3f', numalign='right', stralign='left'))
+    click.echo('(PyTorch backend: allow_tf32={})'.format(BenchModel.allow_tf32))

python/hidet/cli/bench/models/model.py → python/hidet/cli/bench/model.py

@@ -3,13 +3,11 @@
 from hidet.testing import benchmark_func
 import hidet
 
-if hidet.torch.dynamo_available():
-    import torch._dynamo as dynamo
-else:
-    dynamo = None
-
 
 class BenchModel:
+    search_space = 0
+    allow_tf32 = False
+
     def __str__(self):
         raise NotImplementedError()
 
@@ -71,7 +69,16 @@ def inputs_str(self) -> str:
         return ', '.join(items)
 
     def bench_with_backend(self, backend: str, mode=None, passes=None, warmup=3, number=10, repeat=10):
-        import torch
+        import torch.backends.cudnn
+        import torch.backends.cuda
+
+        if not hidet.torch.dynamo_available():
+            raise RuntimeError('Torch Dynamo is not available, please install pytorch 2.0 or higher.')
+        import torch._dynamo as dynamo
+
+        hidet.torch.register_dynamo_backends()
+        torch.backends.cudnn.allow_tf32 = self.allow_tf32
+        torch.backends.cuda.matmul.allow_tf32 = self.allow_tf32
 
         model, (args, kwargs) = self.model(), self.example_inputs()
         model = model.cuda().eval()
@@ -86,31 +93,41 @@ def bench_with_backend(self, backend: str, mode=None, passes=None, warmup=3, num
         return latency
 
     def bench_eager(self) -> float:
+        print('Benchmarking {} with backend {}...'.format(self, 'eager'))
         return self.bench_with_backend('eager')
 
-    def bench_inductor(self) -> float:
-        return self.bench_with_backend('inductor', mode='max-autotune')
+    def bench_inductor(self, mode: str) -> float:
+        print('Benchmarking {} with backend {}...'.format(self, 'inductor(mode={})'.format(mode)))
+        return self.bench_with_backend('inductor', mode=mode)
 
-    def bench_hidet(self, use_cuda_graph=True, use_fp16=False, use_fp16_reduction=False, space=2) -> float:
+    def bench_hidet(self, use_cuda_graph=True, use_fp16=False, use_fp16_reduction=False) -> float:
+        print('Benchmarking {} with backend {}...'.format(self, 'hidet(space={})'.format(self.search_space)))
         config = hidet.torch.dynamo_config
-        config.search_space(space)
+        config.search_space(self.search_space)
         config.use_cuda_graph(use_cuda_graph)
         config.use_fp16(use_fp16)
         config.use_fp16_reduction(use_fp16_reduction)
         return self.bench_with_backend('hidet')
 
     @staticmethod
     def headers() -> List[str]:
-        return ['model', 'inputs', 'eager', 'inductor', 'hidet', 'hidet_f16']
+        return [
+            'model',
+            'inputs',
+            'eager',
+            'inductor(mode=reduce-overhead)',
+            # 'inductor(mode=max-autotune)'
+            'hidet(space={})'.format(BenchModel.search_space),
+        ]
 
     def benchmark(self) -> List[Any]:
         return [
             str(self),
             self.inputs_str(),
             self.bench_eager(),
-            self.bench_inductor(),
+            self.bench_inductor('reduce-overhead'),
+            # self.bench_inductor('max-autotune'),
             self.bench_hidet(),
-            self.bench_hidet(use_fp16=True),
         ]
 
 

python/hidet/cli/bench/nlp/__init__.py

@@ -0,0 +1 @@
+from .models import bench_nlp

python/hidet/cli/bench/nlp/models.py

@@ -0,0 +1,51 @@
+import click
+from tabulate import tabulate
+from hidet.cli.bench.model import commonly_used_models, all_registered_models
+from hidet.utils import initialize
+from .nlp_model import NLPModel, BenchModel
+
+
+available_models = ['bert-base-uncased', 'bert-large-uncased', 'bert-base-cased', 'bert-large-cased', 'gpt2']
+
+commonly_used_models.extend(
+    [
+        NLPModel('huggingface/pytorch-transformers', 'model', 'bert-base-uncased', 1, 128),
+        NLPModel('huggingface/pytorch-transformers', 'model', 'gpt2', 1, 128),
+    ]
+)
+
+
+@initialize()
+def initialize_models():
+    for model in available_models:
+        for batch_size in [1, 8]:
+            for seq_length in [128, 512]:
+                all_registered_models.append(
+                    NLPModel('huggingface/pytorch-transformers', 'model', model, batch_size, seq_length)
+                )
+
+
+@click.command(name='nlp')
+@click.option(
+    '--models',
+    type=str,
+    default='bert-base-uncased',
+    show_default=True,
+    help='Comma seperated models to benchmark. Available models: {}'.format(', '.join(available_models)),
+)
+@click.option('-n', '--batch-size', default=1, show_default=True, help='Batch size')
+@click.option('-q', '--seq-length', default=128, show_default=True, help='Sequence length')
+def bench_nlp(models: str, batch_size: int, seq_length: int):
+    models = [model.strip() for model in models.split(',')]
+    for model in models:
+        if model not in available_models:
+            raise ValueError('Unknown model: {}, candidates: {}'.format(model, list(available_models)))
+
+    bench_models = [
+        NLPModel('huggingface/pytorch-transformers', 'model', model, batch_size, seq_length) for model in models
+    ]
+    header = BenchModel.headers()
+    result = [bench_model.benchmark() for bench_model in bench_models]
+
+    click.echo(tabulate(result, headers=header, tablefmt='github', floatfmt='.3f', numalign='right', stralign='left'))
+    click.echo('(PyTorch backend: allow_tf32={})'.format(BenchModel.allow_tf32))

python/hidet/cli/bench/nlp/nlp_model.py

@@ -0,0 +1,27 @@
+from hidet.cli.bench.model import BenchModel
+
+
+class NLPModel(BenchModel):
+    def __init__(self, repo_name, model_name, label, batch_size: int, sequence_length: int):
+        self.repo_name = repo_name
+        self.model_name = model_name
+        self.label = label
+        self.batch_size = batch_size
+        self.sequence_length = sequence_length
+
+    def __str__(self):
+        return '{}/{}'.format(self.model_name, self.label)
+
+    def model(self):
+        import torch
+
+        return torch.hub.load(self.repo_name, self.model_name, self.label)
+
+    def example_inputs(self):
+        import torch
+
+        tokens_tensor = torch.zeros((self.batch_size, self.sequence_length), dtype=torch.long, device='cuda')
+        segments_tensors = torch.zeros((self.batch_size, self.sequence_length), dtype=torch.long, device='cuda')
+        args = (tokens_tensor,)
+        kwargs = {'token_type_ids': segments_tensors}
+        return args, kwargs

python/hidet/cli/bench/vision/__init__.py

@@ -0,0 +1,4 @@
+from .inception_v3 import bench_inception_v3
+from .mobilenet_v2 import bench_mobilenet_v2
+from .resnet import bench_resnet
+from .resnext import bench_resnext

python/hidet/cli/bench/vision/inception_v3.py

@@ -0,0 +1,25 @@
+import click
+from tabulate import tabulate
+from hidet.cli.bench.model import BenchModel, all_registered_models, commonly_used_models
+from .vision_model import VisionModel
+
+
+inception_models = {'inception_v3': VisionModel('inception_v3', 1, 3, 299, 299)}
+
+
+all_registered_models.extend(inception_models.values())
+commonly_used_models.append(inception_models['inception_v3'])
+
+
+@click.command(name='inception-v3')
+@click.option('-n', '--batch-size', default=1, show_default=True, help='Batch size')
+@click.option('-c', '--channels', default=3, show_default=True, help='Input channels')
+@click.option('-h', '--height', default=224, show_default=True, help='Input image height')
+@click.option('-w', '--width', default=224, show_default=True, help='Input image width')
+def bench_inception_v3(batch_size: int, channels: int, height: int, width: int):
+    bench_models = [VisionModel('inception_v3', batch_size, channels, height, width)]
+    header = BenchModel.headers()
+    result = [bench_model.benchmark() for bench_model in bench_models]
+
+    click.echo(tabulate(result, headers=header, tablefmt='github', floatfmt='.3f', numalign='right', stralign='left'))
+    click.echo('(PyTorch backend: allow_tf32={})'.format(BenchModel.allow_tf32))

python/hidet/cli/bench/vision/mobilenet_v2.py

@@ -0,0 +1,25 @@
+import click
+from tabulate import tabulate
+from hidet.cli.bench.model import BenchModel, all_registered_models, commonly_used_models
+from .vision_model import VisionModel
+
+
+inception_models = {'mobilenet_v2': VisionModel('mobilenet_v2', 1, 3, 224, 224)}
+
+
+all_registered_models.extend(inception_models.values())
+commonly_used_models.append(inception_models['mobilenet_v2'])
+
+
+@click.command(name='mobilenet-v2')
+@click.option('-n', '--batch-size', default=1, show_default=True, help='Batch size')
+@click.option('-c', '--channels', default=3, show_default=True, help='Input channels')
+@click.option('-h', '--height', default=224, show_default=True, help='Input image height')
+@click.option('-w', '--width', default=224, show_default=True, help='Input image width')
+def bench_mobilenet_v2(batch_size: int, channels: int, height: int, width: int):
+    bench_models = [VisionModel('mobilenet_v2', batch_size, channels, height, width)]
+    header = BenchModel.headers()
+    result = [bench_model.benchmark() for bench_model in bench_models]
+
+    click.echo(tabulate(result, headers=header, tablefmt='github', floatfmt='.3f', numalign='right', stralign='left'))
+    click.echo('(PyTorch backend: allow_tf32={})'.format(BenchModel.allow_tf32))