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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/01/28 23:27:37 UTC
[GitHub] [tvm] junrushao1994 commented on a change in pull request #10055: [MetaSchedule][M4b] Testcases for TensorRT builder/runner
junrushao1994 commented on a change in pull request #10055:
URL: https://github.com/apache/tvm/pull/10055#discussion_r794951021
##########
File path: tests/python/unittest/test_meta_schedule_byoc_tensorrt.py
##########
@@ -0,0 +1,251 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+""" Test Meta Schedule Builder """
+
+
+import sys
+import pytest
+import itertools
+import tvm
+from tvm import relay
+from tvm.relay import testing
+from tvm.relay.op.contrib import tensorrt
+import numpy as np
+from typing import List, Tuple
+
+# from tvm import script
+# from tvm._ffi import register_func
+# from tvm.runtime import Module
+from tvm._ffi import register_func
+from tvm.relay.testing.init import Initializer
+from tvm.target import Target
+from tvm.runtime import Module
+from tvm.meta_schedule.arg_info import TensorInfo
+from tvm.meta_schedule.builder import BuilderInput, LocalBuilder, BuilderResult
+from tvm.meta_schedule.runner import (
+ EvaluatorConfig,
+ LocalRunner,
+ RunnerInput,
+)
+
+from tvm.tir import FloatImm
+from tvm.meta_schedule.testing import get_network
+
+has_tensorrt_codegen = pytest.mark.skipif(
+ not tvm.get_global_func("relay.ext.tensorrt", True), reason="TensorRT codegen not available"
+)
+has_tensorrt_runtime = pytest.mark.skipif(
+ not tensorrt.is_tensorrt_runtime_enabled(), reason="TensorRT runtime not available"
+)
+
+
+# conv2d+relu network
+def get_conv2d_relu(
+ data_shape,
+ out_channels,
+ kernel_size,
+ strides,
+ padding,
+ dilation,
+ groups,
+ data_layout,
+ kernel_layout,
+ dtype,
+):
+
+ data = relay.var("data", relay.TensorType(data_shape, dtype))
+ weight = relay.var("weight")
+
+ net = relay.nn.conv2d(
+ data=data,
+ weight=weight, # conv kernel
+ strides=strides,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ channels=out_channels,
+ kernel_size=kernel_size,
+ data_layout=data_layout,
+ kernel_layout=kernel_layout,
+ )
+ net = relay.add(net, net)
+ net = relay.nn.relu(net)
+
+ inputs = relay.analysis.free_vars(net)
+ return relay.Function(inputs, net)
+
+
+def verify_meta_schedule_with_tensorrt(
+ mod, params, data_shape, use_meta_sched: bool = True, use_trt: bool = True, mode: str = "vm"
+):
+ if use_meta_sched:
+ # With meta_schedule
+ dev = "nvidia/geforce-rtx-2080"
+
+ # Build
+ if use_trt:
+
+ def relay_build_with_tensorrt(
+ mod: Module,
+ target: Target,
+ params: dict,
+ ) -> List[BuilderResult]:
+ from tvm.relay.op.contrib.tensorrt import partition_for_tensorrt
+
+ mod, config = partition_for_tensorrt(mod, params)
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relay.ext.tensorrt.options": config}
+ ):
+ return tvm.relay.build_module._build_module_no_factory(
+ mod, "cuda", "llvm", params
+ )
Review comment:
maybe we should refactor these functions, put them under `python/tvm/meta_schedule/testing/byoc_trt.py`, so that others could conveniently reuse these cool stuff
##########
File path: tests/python/unittest/test_meta_schedule_byoc_tensorrt.py
##########
@@ -0,0 +1,251 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+""" Test Meta Schedule Builder """
+
+
+import sys
+import pytest
+import itertools
+import tvm
+from tvm import relay
+from tvm.relay import testing
+from tvm.relay.op.contrib import tensorrt
+import numpy as np
+from typing import List, Tuple
+
+# from tvm import script
+# from tvm._ffi import register_func
+# from tvm.runtime import Module
Review comment:
remove this?
##########
File path: tests/python/unittest/test_meta_schedule_byoc_tensorrt.py
##########
@@ -0,0 +1,251 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+""" Test Meta Schedule Builder """
+
+
+import sys
+import pytest
+import itertools
+import tvm
+from tvm import relay
+from tvm.relay import testing
+from tvm.relay.op.contrib import tensorrt
+import numpy as np
+from typing import List, Tuple
+
+# from tvm import script
+# from tvm._ffi import register_func
+# from tvm.runtime import Module
+from tvm._ffi import register_func
+from tvm.relay.testing.init import Initializer
+from tvm.target import Target
+from tvm.runtime import Module
+from tvm.meta_schedule.arg_info import TensorInfo
+from tvm.meta_schedule.builder import BuilderInput, LocalBuilder, BuilderResult
+from tvm.meta_schedule.runner import (
+ EvaluatorConfig,
+ LocalRunner,
+ RunnerInput,
+)
+
+from tvm.tir import FloatImm
+from tvm.meta_schedule.testing import get_network
+
+has_tensorrt_codegen = pytest.mark.skipif(
+ not tvm.get_global_func("relay.ext.tensorrt", True), reason="TensorRT codegen not available"
+)
+has_tensorrt_runtime = pytest.mark.skipif(
+ not tensorrt.is_tensorrt_runtime_enabled(), reason="TensorRT runtime not available"
+)
+
+
+# conv2d+relu network
+def get_conv2d_relu(
+ data_shape,
+ out_channels,
+ kernel_size,
+ strides,
+ padding,
+ dilation,
+ groups,
+ data_layout,
+ kernel_layout,
+ dtype,
+):
+
+ data = relay.var("data", relay.TensorType(data_shape, dtype))
+ weight = relay.var("weight")
+
+ net = relay.nn.conv2d(
+ data=data,
+ weight=weight, # conv kernel
+ strides=strides,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ channels=out_channels,
+ kernel_size=kernel_size,
+ data_layout=data_layout,
+ kernel_layout=kernel_layout,
+ )
+ net = relay.add(net, net)
+ net = relay.nn.relu(net)
+
+ inputs = relay.analysis.free_vars(net)
+ return relay.Function(inputs, net)
+
+
+def verify_meta_schedule_with_tensorrt(
+ mod, params, data_shape, use_meta_sched: bool = True, use_trt: bool = True, mode: str = "vm"
+):
+ if use_meta_sched:
+ # With meta_schedule
+ dev = "nvidia/geforce-rtx-2080"
+
+ # Build
+ if use_trt:
+
+ def relay_build_with_tensorrt(
+ mod: Module,
+ target: Target,
+ params: dict,
+ ) -> List[BuilderResult]:
+ from tvm.relay.op.contrib.tensorrt import partition_for_tensorrt
+
+ mod, config = partition_for_tensorrt(mod, params)
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relay.ext.tensorrt.options": config}
+ ):
+ return tvm.relay.build_module._build_module_no_factory(
+ mod, "cuda", "llvm", params
+ )
+
+ builder = LocalBuilder(f_build=relay_build_with_tensorrt)
+ else:
+
+ def relay_build_without_tensorrt(
+ mod: Module,
+ target: Target,
+ params: dict,
+ ) -> List[BuilderResult]:
+ # @Sung: Weird. Cannot pass keyword arg
+ return tvm.relay.build_module._build_module_no_factory(mod, "cuda", "llvm", params)
+
+ builder = LocalBuilder(f_build=relay_build_without_tensorrt)
+
+ builder_input = BuilderInput(mod, Target(dev, host="llvm"), params)
+
+ (builder_result,) = builder.build([builder_input])
+ assert builder_result.error_msg is None
+ assert builder_result.artifact_path is not None
+
+ # Run
+ evaluator_config = EvaluatorConfig(
+ number=5,
+ repeat=2,
+ min_repeat_ms=0,
+ enable_cpu_cache_flush=False,
+ )
+
+ runner_input = RunnerInput(
+ builder_result.artifact_path, "cuda", [TensorInfo("float32", data_shape)]
+ )
+
+ def eval_func(rt_mod, device, evaluator_config, repeated_args):
+ rt_mod = tvm.contrib.graph_executor.GraphModule(rt_mod["default"](device))
+
+ eval = rt_mod.module.time_evaluator(
+ func_name="run",
+ dev=device,
+ number=evaluator_config.number,
+ repeat=evaluator_config.repeat,
+ min_repeat_ms=evaluator_config.min_repeat_ms,
+ f_preproc="cache_flush_cpu_non_first_arg"
+ if evaluator_config.enable_cpu_cache_flush
+ else "",
+ )
+ repeated_costs: List[List[float]] = []
+ for args in repeated_args:
+ profile_result = eval(*args)
+ repeated_costs.append(profile_result.results)
+
+ costs = [float(cost) for cost in itertools.chain.from_iterable(repeated_costs)]
+ return costs
+
+ runner = LocalRunner(
+ evaluator_config=evaluator_config,
+ f_run_evaluator=eval_func,
+ )
+
+ # Run the module
+ (runner_future,) = runner.run([runner_input])
+ runner_result = runner_future.result()
+ assert runner_result is not None
+ assert runner_result.run_secs is not None
+ assert runner_result.error_msg is None
+
+ for result in runner_result.run_secs:
+ if isinstance(result, FloatImm):
+ result = result.value
+ assert isinstance(result, float)
+ assert result >= 0.0
+
+ else:
+ # Without meta_schedule
+ if use_trt:
+ mod, config = tensorrt.partition_for_tensorrt(mod)
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relay.ext.tensorrt.options": config}
+ ):
+ func = relay.create_executor(
+ mode, mod=mod, device=tvm.cuda(0), target="cuda"
+ ).evaluate()
+ else:
+ with tvm.transform.PassContext(opt_level=3):
+ func = relay.create_executor(
+ mode, mod=mod, device=tvm.cuda(0), target="cuda", params=params
+ ).evaluate()
+
+
+@tvm.testing.requires_cuda
+@has_tensorrt_codegen
+@has_tensorrt_runtime
+def test_conv2d_relu():
+ data_shape = (1, 1280, 14, 14)
+ out_channels = 256
+ kernel_size, strides, padding, dilation, groups = (1, 1), (1, 1), (0, 0, 0, 0), (1, 1), 1
+ data_layout, kernel_layout = "NCHW", "OIHW"
+ dtype = "float32"
+
+ f = get_conv2d_relu(
+ data_shape,
+ out_channels,
+ kernel_size,
+ strides,
+ padding,
+ dilation,
+ groups,
+ data_layout,
+ kernel_layout,
+ dtype,
+ )
+
+ mod, params = testing.create_workload(f)
+ verify_meta_schedule_with_tensorrt(mod, params, data_shape)
+
+
+@tvm.testing.requires_cuda
+@has_tensorrt_codegen
+@has_tensorrt_runtime
+@pytest.mark.parametrize(
+ "model_name",
+ ["resnet-50", "mobilenet"],
+)
+@pytest.mark.parametrize("batch_size", [1, 8, 16])
+@pytest.mark.parametrize("use_meta_sched", [True])
+@pytest.mark.parametrize("use_trt", [True, False])
+def test_relay_model(model_name: str, batch_size: int, use_meta_sched: bool, use_trt: bool):
+
+ mod, params, input_shape, output_shape = get_network(name=model_name, batch_size=batch_size)
+ verify_meta_schedule_with_tensorrt(
+ mod, params, input_shape, use_meta_sched=use_meta_sched, use_trt=use_trt, mode="vm"
+ )
+
+
+# @sunggg: memory verification error at test_relay_model("resnet-50", 1, use_meta_sched=False, use_trt=True)
Review comment:
cannot reproduce this, so let's double confirm :-) If there is no problem, let's remove this line
##########
File path: tests/python/unittest/test_meta_schedule_byoc_tensorrt.py
##########
@@ -0,0 +1,251 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+""" Test Meta Schedule Builder """
+
+
+import sys
+import pytest
+import itertools
+import tvm
+from tvm import relay
+from tvm.relay import testing
+from tvm.relay.op.contrib import tensorrt
+import numpy as np
+from typing import List, Tuple
+
+# from tvm import script
+# from tvm._ffi import register_func
+# from tvm.runtime import Module
+from tvm._ffi import register_func
+from tvm.relay.testing.init import Initializer
+from tvm.target import Target
+from tvm.runtime import Module
+from tvm.meta_schedule.arg_info import TensorInfo
+from tvm.meta_schedule.builder import BuilderInput, LocalBuilder, BuilderResult
+from tvm.meta_schedule.runner import (
+ EvaluatorConfig,
+ LocalRunner,
+ RunnerInput,
+)
+
+from tvm.tir import FloatImm
+from tvm.meta_schedule.testing import get_network
+
+has_tensorrt_codegen = pytest.mark.skipif(
+ not tvm.get_global_func("relay.ext.tensorrt", True), reason="TensorRT codegen not available"
+)
+has_tensorrt_runtime = pytest.mark.skipif(
+ not tensorrt.is_tensorrt_runtime_enabled(), reason="TensorRT runtime not available"
+)
+
+
+# conv2d+relu network
+def get_conv2d_relu(
+ data_shape,
+ out_channels,
+ kernel_size,
+ strides,
+ padding,
+ dilation,
+ groups,
+ data_layout,
+ kernel_layout,
+ dtype,
+):
+
+ data = relay.var("data", relay.TensorType(data_shape, dtype))
+ weight = relay.var("weight")
+
+ net = relay.nn.conv2d(
+ data=data,
+ weight=weight, # conv kernel
+ strides=strides,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ channels=out_channels,
+ kernel_size=kernel_size,
+ data_layout=data_layout,
+ kernel_layout=kernel_layout,
+ )
+ net = relay.add(net, net)
+ net = relay.nn.relu(net)
+
+ inputs = relay.analysis.free_vars(net)
+ return relay.Function(inputs, net)
+
+
+def verify_meta_schedule_with_tensorrt(
+ mod, params, data_shape, use_meta_sched: bool = True, use_trt: bool = True, mode: str = "vm"
+):
+ if use_meta_sched:
+ # With meta_schedule
+ dev = "nvidia/geforce-rtx-2080"
+
+ # Build
+ if use_trt:
+
+ def relay_build_with_tensorrt(
+ mod: Module,
+ target: Target,
+ params: dict,
+ ) -> List[BuilderResult]:
+ from tvm.relay.op.contrib.tensorrt import partition_for_tensorrt
+
+ mod, config = partition_for_tensorrt(mod, params)
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relay.ext.tensorrt.options": config}
+ ):
+ return tvm.relay.build_module._build_module_no_factory(
+ mod, "cuda", "llvm", params
+ )
+
+ builder = LocalBuilder(f_build=relay_build_with_tensorrt)
+ else:
+
+ def relay_build_without_tensorrt(
+ mod: Module,
+ target: Target,
+ params: dict,
+ ) -> List[BuilderResult]:
+ # @Sung: Weird. Cannot pass keyword arg
Review comment:
if you have time, you may add a proxy function to `_build_module_no_factory` to allow kwargs
```python
@register_func("tvm.relay.build")
def _build_module_no_factory_impl(mod, target, target_host, params, mod_name):
target, target_host = Target.check_and_update_host_consist(target, target_host)
return build(mod, target, params=params, mod_name=mod_name).module
def _build_module_no_factory(mod, target=None, target_host=None, params=None, mod_name="default"):
"""A wrapper around build which discards the Python GraphFactoryRuntime.
This wrapper is suitable to be used from other programming languages as
the runtime::Module can be freely passed between language boundaries.
"""
return _build_module_no_factory_impl(mod, target, target_host, params, mod_name)
```
##########
File path: tests/python/unittest/test_meta_schedule_byoc_tensorrt.py
##########
@@ -0,0 +1,251 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+""" Test Meta Schedule Builder """
+
+
+import sys
+import pytest
+import itertools
+import tvm
+from tvm import relay
+from tvm.relay import testing
+from tvm.relay.op.contrib import tensorrt
+import numpy as np
+from typing import List, Tuple
+
+# from tvm import script
+# from tvm._ffi import register_func
+# from tvm.runtime import Module
+from tvm._ffi import register_func
+from tvm.relay.testing.init import Initializer
+from tvm.target import Target
+from tvm.runtime import Module
+from tvm.meta_schedule.arg_info import TensorInfo
+from tvm.meta_schedule.builder import BuilderInput, LocalBuilder, BuilderResult
+from tvm.meta_schedule.runner import (
+ EvaluatorConfig,
+ LocalRunner,
+ RunnerInput,
+)
+
+from tvm.tir import FloatImm
+from tvm.meta_schedule.testing import get_network
+
+has_tensorrt_codegen = pytest.mark.skipif(
+ not tvm.get_global_func("relay.ext.tensorrt", True), reason="TensorRT codegen not available"
+)
+has_tensorrt_runtime = pytest.mark.skipif(
+ not tensorrt.is_tensorrt_runtime_enabled(), reason="TensorRT runtime not available"
+)
+
+
+# conv2d+relu network
+def get_conv2d_relu(
+ data_shape,
+ out_channels,
+ kernel_size,
+ strides,
+ padding,
+ dilation,
+ groups,
+ data_layout,
+ kernel_layout,
+ dtype,
+):
+
+ data = relay.var("data", relay.TensorType(data_shape, dtype))
+ weight = relay.var("weight")
+
+ net = relay.nn.conv2d(
+ data=data,
+ weight=weight, # conv kernel
+ strides=strides,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ channels=out_channels,
+ kernel_size=kernel_size,
+ data_layout=data_layout,
+ kernel_layout=kernel_layout,
+ )
+ net = relay.add(net, net)
+ net = relay.nn.relu(net)
+
+ inputs = relay.analysis.free_vars(net)
+ return relay.Function(inputs, net)
+
+
+def verify_meta_schedule_with_tensorrt(
+ mod, params, data_shape, use_meta_sched: bool = True, use_trt: bool = True, mode: str = "vm"
+):
+ if use_meta_sched:
+ # With meta_schedule
+ dev = "nvidia/geforce-rtx-2080"
Review comment:
```suggestion
dev = "cuda"
```
##########
File path: tests/python/unittest/test_meta_schedule_byoc_tensorrt.py
##########
@@ -0,0 +1,251 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+""" Test Meta Schedule Builder """
+
+
+import sys
+import pytest
+import itertools
+import tvm
+from tvm import relay
+from tvm.relay import testing
+from tvm.relay.op.contrib import tensorrt
+import numpy as np
+from typing import List, Tuple
+
+# from tvm import script
+# from tvm._ffi import register_func
+# from tvm.runtime import Module
+from tvm._ffi import register_func
+from tvm.relay.testing.init import Initializer
+from tvm.target import Target
+from tvm.runtime import Module
+from tvm.meta_schedule.arg_info import TensorInfo
+from tvm.meta_schedule.builder import BuilderInput, LocalBuilder, BuilderResult
+from tvm.meta_schedule.runner import (
+ EvaluatorConfig,
+ LocalRunner,
+ RunnerInput,
+)
+
+from tvm.tir import FloatImm
+from tvm.meta_schedule.testing import get_network
+
+has_tensorrt_codegen = pytest.mark.skipif(
+ not tvm.get_global_func("relay.ext.tensorrt", True), reason="TensorRT codegen not available"
+)
+has_tensorrt_runtime = pytest.mark.skipif(
+ not tensorrt.is_tensorrt_runtime_enabled(), reason="TensorRT runtime not available"
+)
+
+
+# conv2d+relu network
+def get_conv2d_relu(
+ data_shape,
+ out_channels,
+ kernel_size,
+ strides,
+ padding,
+ dilation,
+ groups,
+ data_layout,
+ kernel_layout,
+ dtype,
+):
+
+ data = relay.var("data", relay.TensorType(data_shape, dtype))
+ weight = relay.var("weight")
+
+ net = relay.nn.conv2d(
+ data=data,
+ weight=weight, # conv kernel
+ strides=strides,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ channels=out_channels,
+ kernel_size=kernel_size,
+ data_layout=data_layout,
+ kernel_layout=kernel_layout,
+ )
+ net = relay.add(net, net)
+ net = relay.nn.relu(net)
+
+ inputs = relay.analysis.free_vars(net)
+ return relay.Function(inputs, net)
+
+
+def verify_meta_schedule_with_tensorrt(
+ mod, params, data_shape, use_meta_sched: bool = True, use_trt: bool = True, mode: str = "vm"
+):
+ if use_meta_sched:
+ # With meta_schedule
+ dev = "nvidia/geforce-rtx-2080"
+
+ # Build
+ if use_trt:
+
+ def relay_build_with_tensorrt(
+ mod: Module,
+ target: Target,
+ params: dict,
+ ) -> List[BuilderResult]:
+ from tvm.relay.op.contrib.tensorrt import partition_for_tensorrt
+
+ mod, config = partition_for_tensorrt(mod, params)
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relay.ext.tensorrt.options": config}
+ ):
+ return tvm.relay.build_module._build_module_no_factory(
+ mod, "cuda", "llvm", params
+ )
Review comment:
example: https://github.com/junrushao1994/tvm/blob/meta-schedule/python/tvm/meta_schedule/testing/byoc_trt.py
##########
File path: tests/python/unittest/test_meta_schedule_byoc_tensorrt.py
##########
@@ -0,0 +1,251 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+""" Test Meta Schedule Builder """
+
+
+import sys
+import pytest
+import itertools
+import tvm
+from tvm import relay
+from tvm.relay import testing
+from tvm.relay.op.contrib import tensorrt
+import numpy as np
+from typing import List, Tuple
+
+# from tvm import script
+# from tvm._ffi import register_func
+# from tvm.runtime import Module
+from tvm._ffi import register_func
+from tvm.relay.testing.init import Initializer
+from tvm.target import Target
+from tvm.runtime import Module
+from tvm.meta_schedule.arg_info import TensorInfo
+from tvm.meta_schedule.builder import BuilderInput, LocalBuilder, BuilderResult
+from tvm.meta_schedule.runner import (
+ EvaluatorConfig,
+ LocalRunner,
+ RunnerInput,
+)
+
+from tvm.tir import FloatImm
+from tvm.meta_schedule.testing import get_network
+
+has_tensorrt_codegen = pytest.mark.skipif(
+ not tvm.get_global_func("relay.ext.tensorrt", True), reason="TensorRT codegen not available"
+)
+has_tensorrt_runtime = pytest.mark.skipif(
+ not tensorrt.is_tensorrt_runtime_enabled(), reason="TensorRT runtime not available"
+)
+
+
+# conv2d+relu network
+def get_conv2d_relu(
+ data_shape,
+ out_channels,
+ kernel_size,
+ strides,
+ padding,
+ dilation,
+ groups,
+ data_layout,
+ kernel_layout,
+ dtype,
+):
+
+ data = relay.var("data", relay.TensorType(data_shape, dtype))
+ weight = relay.var("weight")
+
+ net = relay.nn.conv2d(
+ data=data,
+ weight=weight, # conv kernel
+ strides=strides,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ channels=out_channels,
+ kernel_size=kernel_size,
+ data_layout=data_layout,
+ kernel_layout=kernel_layout,
+ )
+ net = relay.add(net, net)
+ net = relay.nn.relu(net)
+
+ inputs = relay.analysis.free_vars(net)
+ return relay.Function(inputs, net)
+
+
+def verify_meta_schedule_with_tensorrt(
+ mod, params, data_shape, use_meta_sched: bool = True, use_trt: bool = True, mode: str = "vm"
+):
+ if use_meta_sched:
+ # With meta_schedule
+ dev = "nvidia/geforce-rtx-2080"
+
+ # Build
+ if use_trt:
+
+ def relay_build_with_tensorrt(
+ mod: Module,
+ target: Target,
+ params: dict,
+ ) -> List[BuilderResult]:
+ from tvm.relay.op.contrib.tensorrt import partition_for_tensorrt
+
+ mod, config = partition_for_tensorrt(mod, params)
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relay.ext.tensorrt.options": config}
+ ):
+ return tvm.relay.build_module._build_module_no_factory(
+ mod, "cuda", "llvm", params
+ )
+
+ builder = LocalBuilder(f_build=relay_build_with_tensorrt)
+ else:
+
+ def relay_build_without_tensorrt(
+ mod: Module,
+ target: Target,
+ params: dict,
+ ) -> List[BuilderResult]:
+ # @Sung: Weird. Cannot pass keyword arg
+ return tvm.relay.build_module._build_module_no_factory(mod, "cuda", "llvm", params)
+
+ builder = LocalBuilder(f_build=relay_build_without_tensorrt)
+
+ builder_input = BuilderInput(mod, Target(dev, host="llvm"), params)
+
+ (builder_result,) = builder.build([builder_input])
+ assert builder_result.error_msg is None
+ assert builder_result.artifact_path is not None
+
+ # Run
+ evaluator_config = EvaluatorConfig(
+ number=5,
+ repeat=2,
+ min_repeat_ms=0,
+ enable_cpu_cache_flush=False,
+ )
+
+ runner_input = RunnerInput(
+ builder_result.artifact_path, "cuda", [TensorInfo("float32", data_shape)]
+ )
+
+ def eval_func(rt_mod, device, evaluator_config, repeated_args):
+ rt_mod = tvm.contrib.graph_executor.GraphModule(rt_mod["default"](device))
+
+ eval = rt_mod.module.time_evaluator(
+ func_name="run",
+ dev=device,
+ number=evaluator_config.number,
+ repeat=evaluator_config.repeat,
+ min_repeat_ms=evaluator_config.min_repeat_ms,
+ f_preproc="cache_flush_cpu_non_first_arg"
+ if evaluator_config.enable_cpu_cache_flush
+ else "",
+ )
+ repeated_costs: List[List[float]] = []
+ for args in repeated_args:
+ profile_result = eval(*args)
+ repeated_costs.append(profile_result.results)
+
+ costs = [float(cost) for cost in itertools.chain.from_iterable(repeated_costs)]
+ return costs
+
+ runner = LocalRunner(
+ evaluator_config=evaluator_config,
+ f_run_evaluator=eval_func,
+ )
+
+ # Run the module
+ (runner_future,) = runner.run([runner_input])
+ runner_result = runner_future.result()
+ assert runner_result is not None
+ assert runner_result.run_secs is not None
+ assert runner_result.error_msg is None
+
+ for result in runner_result.run_secs:
+ if isinstance(result, FloatImm):
+ result = result.value
+ assert isinstance(result, float)
+ assert result >= 0.0
+
+ else:
+ # Without meta_schedule
+ if use_trt:
+ mod, config = tensorrt.partition_for_tensorrt(mod)
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relay.ext.tensorrt.options": config}
+ ):
+ func = relay.create_executor(
+ mode, mod=mod, device=tvm.cuda(0), target="cuda"
+ ).evaluate()
+ else:
+ with tvm.transform.PassContext(opt_level=3):
+ func = relay.create_executor(
+ mode, mod=mod, device=tvm.cuda(0), target="cuda", params=params
+ ).evaluate()
+
+
+@tvm.testing.requires_cuda
+@has_tensorrt_codegen
+@has_tensorrt_runtime
+def test_conv2d_relu():
+ data_shape = (1, 1280, 14, 14)
+ out_channels = 256
+ kernel_size, strides, padding, dilation, groups = (1, 1), (1, 1), (0, 0, 0, 0), (1, 1), 1
+ data_layout, kernel_layout = "NCHW", "OIHW"
+ dtype = "float32"
+
+ f = get_conv2d_relu(
+ data_shape,
+ out_channels,
+ kernel_size,
+ strides,
+ padding,
+ dilation,
+ groups,
+ data_layout,
+ kernel_layout,
+ dtype,
+ )
+
+ mod, params = testing.create_workload(f)
+ verify_meta_schedule_with_tensorrt(mod, params, data_shape)
+
+
+@tvm.testing.requires_cuda
+@has_tensorrt_codegen
+@has_tensorrt_runtime
+@pytest.mark.parametrize(
+ "model_name",
+ ["resnet-50", "mobilenet"],
+)
+@pytest.mark.parametrize("batch_size", [1, 8, 16])
Review comment:
```suggestion
@pytest.mark.parametrize("batch_size", [1])
```
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