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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/09/10 18:02:20 UTC
[GitHub] [incubator-tvm] comaniac commented on a change in pull request #6302: [tvmc] command line driver 'compile' (part 2/4)
comaniac commented on a change in pull request #6302:
URL: https://github.com/apache/incubator-tvm/pull/6302#discussion_r486517802
##########
File path: python/tvm/driver/tvmc/compiler.py
##########
@@ -0,0 +1,280 @@
+# 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.
+"""
+Provides support to compile networks both AOT and JIT.
+"""
+import logging
+import os.path
+import tarfile
+from pathlib import Path
+
+import tvm
+from tvm import autotvm
+from tvm import relay
+from tvm.contrib import cc
+from tvm.contrib import util
+
+from . import common, frontends
+from .main import register_parser
+
+
+@register_parser
+def add_compile_parser(subparsers):
+ """ Include parser for 'compile' subcommand """
+
+ parser = subparsers.add_parser("compile", help="compile a model")
+ parser.set_defaults(func=drive_compile)
+ parser.add_argument(
+ "--cross-compiler",
+ default="",
+ help="the cross compiler to generate target libraries, e.g. 'aarch64-linux-gnu-gcc'",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ parser.add_argument(
+ "--dump-code",
+ metavar="FORMAT",
+ default="",
+ help="comma separarated list of formats to export, e.g. 'asm,ll,relay' "
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ default="module.tar",
+ help="output the compiled module to an archive",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ default="",
+ help="path to an auto-tuning log file from AutoTVM"
Review comment:
```suggestion
help="path to an auto-tuning log file by AutoTVM. If not presented, the fallback/tophub configs will be used"
```
##########
File path: python/tvm/driver/tvmc/compiler.py
##########
@@ -0,0 +1,280 @@
+# 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.
+"""
+Provides support to compile networks both AOT and JIT.
+"""
+import logging
+import os.path
+import tarfile
+from pathlib import Path
+
+import tvm
+from tvm import autotvm
+from tvm import relay
+from tvm.contrib import cc
+from tvm.contrib import util
+
+from . import common, frontends
+from .main import register_parser
+
+
+@register_parser
+def add_compile_parser(subparsers):
+ """ Include parser for 'compile' subcommand """
+
+ parser = subparsers.add_parser("compile", help="compile a model")
+ parser.set_defaults(func=drive_compile)
+ parser.add_argument(
+ "--cross-compiler",
+ default="",
+ help="the cross compiler to generate target libraries, e.g. 'aarch64-linux-gnu-gcc'",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ parser.add_argument(
+ "--dump-code",
+ metavar="FORMAT",
+ default="",
+ help="comma separarated list of formats to export, e.g. 'asm,ll,relay' "
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ default="module.tar",
+ help="output the compiled module to an archive",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ default="",
+ help="path to an auto-tuning log file from AutoTVM"
+ )
+ parser.add_argument(
+ "-v", "--verbose", action="count", default=0, help="increase verbosity"
+ )
+ #TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_compile(args):
+ """ Invoke tvmc.compiler module with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ graph, lib, params, dumps = compile_model(
+ args.FILE,
+ args.target,
+ args.dump_code,
+ "",
+ args.model_format,
+ args.tuning_records,
+ args.tensor_layout,
+ )
+
+ if dumps:
+ save_dumps(args.output, dumps)
+
+ save_module(args.output, graph, lib, params, args.cross_compiler)
+ return 0
+
+
+def compile_model(
+ path,
+ target,
+ dump_code=None,
+ target_host=None,
+ model_format=None,
+ tuning_records=None,
+ alter_layout=None,
+):
+ """Compile a model from a supported framework into a TVM module.
+
+ This function takes a union of the arguments of both frontends.load_model
+ and compiler.compile_relay. The resulting TVM module can be executed using
+ the graph runtime.
+
+ Parameters
+ ----------
+ path: str
+ Path to a file
+ target : str
+ The target for which to compile. Can be a plain string or
+ a path.
+ dump_code : list, optional
+ Dump the generated code for the specified source types, on
+ the requested target.
+ target_host : str, optional
+ The target of the host machine if host-side code
+ needs to be generated.
+ model_format: str, optional
+ A string representing a name of a frontend to be used
+ tuning_records: str, optional
+ Name of the file produced by the tuning to be used during
+ compilation.
+ alter_layout: str, optional
+ The layout to convert the graph to. Note, the convert layout
+ pass doesn't currently guarantee the whole of the graph will
+ be converted to the chosen layout.
+
+ Returns
+ -------
+ graph : str
+ A JSON-serialized TVM execution graph.
+ lib : tvm.module.Module
+ A TVM module containing the compiled functions.
+ params : dict
+ The parameters (weights) for the TVM module.
+ dumps : dict
+ Dictionary containing the dumps specified.
+
+ """
+ dump_code = [x.strip() for x in dump_code.split(',')] if dump_code else None
+ mod, params = frontends.load_model(path, model_format)
+
+ if alter_layout:
+ mod = common.convert_graph_layout(mod, alter_layout)
+
+ if os.path.exists(str(target)):
Review comment:
1. You don't need `str()` as `target` is already a string.
2. Add comments saying this is to handle the target file in JSON format.
##########
File path: python/tvm/driver/tvmc/compiler.py
##########
@@ -0,0 +1,280 @@
+# 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.
+"""
+Provides support to compile networks both AOT and JIT.
+"""
+import logging
+import os.path
+import tarfile
+from pathlib import Path
+
+import tvm
+from tvm import autotvm
+from tvm import relay
+from tvm.contrib import cc
+from tvm.contrib import util
+
+from . import common, frontends
+from .main import register_parser
+
+
+@register_parser
+def add_compile_parser(subparsers):
+ """ Include parser for 'compile' subcommand """
+
+ parser = subparsers.add_parser("compile", help="compile a model")
+ parser.set_defaults(func=drive_compile)
+ parser.add_argument(
+ "--cross-compiler",
+ default="",
+ help="the cross compiler to generate target libraries, e.g. 'aarch64-linux-gnu-gcc'",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ parser.add_argument(
+ "--dump-code",
+ metavar="FORMAT",
+ default="",
+ help="comma separarated list of formats to export, e.g. 'asm,ll,relay' "
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ default="module.tar",
+ help="output the compiled module to an archive",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ default="",
+ help="path to an auto-tuning log file from AutoTVM"
+ )
+ parser.add_argument(
+ "-v", "--verbose", action="count", default=0, help="increase verbosity"
+ )
+ #TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_compile(args):
+ """ Invoke tvmc.compiler module with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ graph, lib, params, dumps = compile_model(
+ args.FILE,
+ args.target,
+ args.dump_code,
+ "",
Review comment:
```suggestion
None,
```
##########
File path: tests/python/driver/tvmc/test_frontends.py
##########
@@ -0,0 +1,196 @@
+# 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.
+import os
+import tarfile
+
+import pytest
+
+from tvm.ir.module import IRModule
+
+from tvm.driver import tvmc
+from tvm.driver.tvmc.common import TVMCException
+
+
+def test_get_frontend_names_is_list():
+ sut = tvmc.frontends.get_frontend_names()
+ assert type(sut) is list
+
+
+def test_get_frontends_contains_only_strings():
+ sut = tvmc.frontends.get_frontend_names()
+ assert all([type(x) is str for x in sut]) is True
+
+
+def test_get_frontend_by_name_valid():
+ # some CI environments wont offer TensorFlow/Keras, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ sut = tvmc.frontends.get_frontend_by_name("keras")
+ assert type(sut) is tvmc.frontends.KerasFrontend
+
+
+def test_get_frontend_by_name_invalid():
+ with pytest.raises(TVMCException) as e:
+ def f():
+ tvmc.frontends.get_frontend_by_name("unsupported_thing")
+ f()
+ assert 'unrecognized frontend' in str(e.value)
+
+
+def test_guess_frontend_tflite():
+ # some CI environments wont offer TFLite, so skip in case it is not present
+ pytest.importorskip('tflite')
+
+ sut = tvmc.frontends.guess_frontend("a_model.tflite")
+ assert type(sut) is tvmc.frontends.TFLiteFrontend
+
+
+def test_guess_frontend_onnx():
+ # some CI environments wont offer onnx, so skip in case it is not present
+ pytest.importorskip('onnx')
+
+ sut = tvmc.frontends.guess_frontend("a_model.onnx")
+ assert type(sut) is tvmc.frontends.OnnxFrontend
+
+
+def test_guess_frontend_pytorch():
+ # some CI environments wont offer pytorch, so skip in case it is not present
+ pytest.importorskip('torch')
+
+ sut = tvmc.frontends.guess_frontend("a_model.pth")
+ assert type(sut) is tvmc.frontends.PyTorchFrontend
+
+
+def test_guess_frontend_keras():
+ # some CI environments wont offer TensorFlow/Keras, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ sut = tvmc.frontends.guess_frontend("a_model.h5")
+ assert type(sut) is tvmc.frontends.KerasFrontend
+
+
+def test_guess_frontend_tensorflow():
+ # some CI environments wont offer TensorFlow, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ sut = tvmc.frontends.guess_frontend("a_model.pb")
+ assert type(sut) is tvmc.frontends.TensorflowFrontend
+
+
+def test_guess_frontend_invalid():
+ with pytest.raises(TVMCException):
+ tvmc.frontends.guess_frontend("not/a/file.txt")
+
+
+def test_load_model__invalid_path__no_language():
+ # some CI environments wont offer TFLite, so skip in case it is not present
+ pytest.importorskip('tflite')
+
+ with pytest.raises(FileNotFoundError):
+ tvmc.frontends.load_model("not/a/file.tflite")
+
+
+def test_load_model__invalid_path__with_language():
+ # some CI environments wont offer onnx, so skip in case it is not present
+ pytest.importorskip('onnx')
+
+ with pytest.raises(FileNotFoundError):
+ tvmc.frontends.load_model("not/a/file.txt", model_format="onnx")
+
+
+def test_load_model__tflite(tflite_mobilenet_v1_1_quant):
+ # some CI environments wont offer TFLite, so skip in case it is not present
+ pytest.importorskip('tflite')
+
+ mod, params = tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant)
+ assert type(mod) is IRModule
+ assert type(params) is dict
+ # check whether one known value is part of the params dict
+ assert '_param_1' in params.keys()
+
+
+def test_load_model__keras(keras_resnet50):
+ # some CI environments wont offer TensorFlow/Keras, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ mod, params = tvmc.frontends.load_model(keras_resnet50)
+ assert type(mod) is IRModule
+ assert type(params) is dict
+ ## check whether one known value is part of the params dict
+ assert '_param_1' in params.keys()
+
+
+def test_load_model__onnx(onnx_resnet50):
+ # some CI environments wont offer onnx, so skip in case it is not present
+ pytest.importorskip('onnx')
+
+ mod, params = tvmc.frontends.load_model(onnx_resnet50)
+ assert type(mod) is IRModule
+ assert type(params) is dict
+ ## check whether one known value is part of the params dict
+ assert 'resnetv24_batchnorm0_gamma' in params.keys()
+
+
+def test_load_model__pb(pb_mobilenet_v1_1_quant):
+ # some CI environments wont offer TensorFlow, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ mod, params = tvmc.frontends.load_model(pb_mobilenet_v1_1_quant)
+ assert type(mod) is IRModule
+ assert type(params) is dict
+ # check whether one known value is part of the params dict
+ assert 'MobilenetV1/Conv2d_0/weights' in params.keys()
+
+
+def test_load_model___wrong_language__to_keras(tflite_mobilenet_v1_1_quant):
+ # some CI environments wont offer TensorFlow/Keras, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ with pytest.raises(OSError):
+ tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant, model_format="keras")
+
+
+def test_load_model___wrong_language__to_tflite(keras_resnet50):
+ # some CI environments wont offer TFLite, so skip in case it is not present
+ pytest.importorskip('tflite')
+
+ with pytest.raises(TVMCException) as e:
+ def f():
+ tvmc.frontends.load_model(keras_resnet50, model_format="tflite")
+ f()
+ assert 'input file not tflite' in str(e.value)
+
+
+def test_load_model___wrong_language__to_onnx(tflite_mobilenet_v1_1_quant):
+ # some CI environments wont offer onnx, so skip in case it is not present
+ pytest.importorskip('onnx')
+
+ from google.protobuf.message import DecodeError
+
+ with pytest.raises(DecodeError):
+ tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant, model_format="onnx")
+
+
+def test_load_model___wrong_language__to_pytorch(tflite_mobilenet_v1_1_quant):
+ # some CI environments wont offer pytorch, so skip in case it is not present
+ pytest.importorskip('torch')
+
+ with pytest.raises(RuntimeError) as e:
+ def f():
+ tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant, model_format="pytorch")
+ f()
+ assert 'PytorchStreamReader' in str(e.value)
Review comment:
ditto.
##########
File path: python/tvm/driver/tvmc/frontends.py
##########
@@ -0,0 +1,417 @@
+# 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.
+"""
+Provides support to parse models from different frameworks into Relay networks.
+
+Frontend classes do lazy-loading of modules on purpose, to reduce time spent on
+loading the tool.
+"""
+import logging
+import os
+import sys
+from abc import ABC
+from abc import abstractmethod
+from pathlib import Path
+
+import numpy as np
+
+from tvm import relay
+from tvm.driver.tvmc.common import TVMCException
+
+
+class Frontend(ABC):
+ """Abstract class for frontend"""
Review comment:
Add more descriptions to clarify this frontend, as we already have Relay frontends. It seems to me that this frontend is used to preprocess models from each framework.
##########
File path: python/tvm/driver/tvmc/compiler.py
##########
@@ -0,0 +1,280 @@
+# 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.
+"""
+Provides support to compile networks both AOT and JIT.
+"""
+import logging
+import os.path
+import tarfile
+from pathlib import Path
+
+import tvm
+from tvm import autotvm
+from tvm import relay
+from tvm.contrib import cc
+from tvm.contrib import util
+
+from . import common, frontends
+from .main import register_parser
+
+
+@register_parser
+def add_compile_parser(subparsers):
+ """ Include parser for 'compile' subcommand """
+
+ parser = subparsers.add_parser("compile", help="compile a model")
+ parser.set_defaults(func=drive_compile)
+ parser.add_argument(
+ "--cross-compiler",
+ default="",
+ help="the cross compiler to generate target libraries, e.g. 'aarch64-linux-gnu-gcc'",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ parser.add_argument(
+ "--dump-code",
+ metavar="FORMAT",
+ default="",
+ help="comma separarated list of formats to export, e.g. 'asm,ll,relay' "
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ default="module.tar",
+ help="output the compiled module to an archive",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ default="",
+ help="path to an auto-tuning log file from AutoTVM"
+ )
+ parser.add_argument(
+ "-v", "--verbose", action="count", default=0, help="increase verbosity"
+ )
+ #TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_compile(args):
+ """ Invoke tvmc.compiler module with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ graph, lib, params, dumps = compile_model(
+ args.FILE,
+ args.target,
+ args.dump_code,
+ "",
+ args.model_format,
+ args.tuning_records,
+ args.tensor_layout,
+ )
+
+ if dumps:
+ save_dumps(args.output, dumps)
+
+ save_module(args.output, graph, lib, params, args.cross_compiler)
+ return 0
+
+
+def compile_model(
+ path,
+ target,
+ dump_code=None,
+ target_host=None,
+ model_format=None,
+ tuning_records=None,
+ alter_layout=None,
+):
+ """Compile a model from a supported framework into a TVM module.
+
+ This function takes a union of the arguments of both frontends.load_model
+ and compiler.compile_relay. The resulting TVM module can be executed using
+ the graph runtime.
+
+ Parameters
+ ----------
+ path: str
+ Path to a file
+ target : str
+ The target for which to compile. Can be a plain string or
+ a path.
+ dump_code : list, optional
+ Dump the generated code for the specified source types, on
+ the requested target.
+ target_host : str, optional
+ The target of the host machine if host-side code
+ needs to be generated.
+ model_format: str, optional
+ A string representing a name of a frontend to be used
+ tuning_records: str, optional
+ Name of the file produced by the tuning to be used during
+ compilation.
+ alter_layout: str, optional
+ The layout to convert the graph to. Note, the convert layout
+ pass doesn't currently guarantee the whole of the graph will
+ be converted to the chosen layout.
+
+ Returns
+ -------
+ graph : str
+ A JSON-serialized TVM execution graph.
+ lib : tvm.module.Module
+ A TVM module containing the compiled functions.
+ params : dict
+ The parameters (weights) for the TVM module.
+ dumps : dict
+ Dictionary containing the dumps specified.
+
+ """
+ dump_code = [x.strip() for x in dump_code.split(',')] if dump_code else None
+ mod, params = frontends.load_model(path, model_format)
+
+ if alter_layout:
+ mod = common.convert_graph_layout(mod, alter_layout)
+
+ if os.path.exists(str(target)):
+ with open(target) as target_file:
+ logging.info("using target input from file: %s", target)
+ target = "".join(target_file.readlines())
+
+ # TODO(@leandron) We don't have an API to collect a list of supported
+ # targets yet
+ logging.debug("creating target from input: %s", target)
+ tvm_target = tvm.target.create(target)
Review comment:
```suggestion
tvm_target = tvm.target.Target(target)
```
##########
File path: python/setup.py
##########
@@ -162,6 +162,12 @@ def get_package_data_files():
'attrs',
'psutil',
'typed_ast',
+ 'tensorflow==2.1.0',
+ 'tflite==2.1.0',
+ 'onnx==1.6.0',
+ 'onnxruntime==1.0.0',
+ 'torch==1.4.0',
+ 'torchvision==0.5.0'
Review comment:
I'm not familiar with the version requirements for other frameworks. @masahi, @tqchen would you mind to help checking?
##########
File path: python/tvm/driver/tvmc/compiler.py
##########
@@ -0,0 +1,280 @@
+# 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.
+"""
+Provides support to compile networks both AOT and JIT.
+"""
+import logging
+import os.path
+import tarfile
+from pathlib import Path
+
+import tvm
+from tvm import autotvm
+from tvm import relay
+from tvm.contrib import cc
+from tvm.contrib import util
+
+from . import common, frontends
+from .main import register_parser
+
+
+@register_parser
+def add_compile_parser(subparsers):
+ """ Include parser for 'compile' subcommand """
+
+ parser = subparsers.add_parser("compile", help="compile a model")
+ parser.set_defaults(func=drive_compile)
+ parser.add_argument(
+ "--cross-compiler",
+ default="",
+ help="the cross compiler to generate target libraries, e.g. 'aarch64-linux-gnu-gcc'",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ parser.add_argument(
+ "--dump-code",
+ metavar="FORMAT",
+ default="",
+ help="comma separarated list of formats to export, e.g. 'asm,ll,relay' "
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ default="module.tar",
+ help="output the compiled module to an archive",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ default="",
+ help="path to an auto-tuning log file from AutoTVM"
+ )
+ parser.add_argument(
+ "-v", "--verbose", action="count", default=0, help="increase verbosity"
+ )
+ #TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_compile(args):
+ """ Invoke tvmc.compiler module with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ graph, lib, params, dumps = compile_model(
+ args.FILE,
+ args.target,
+ args.dump_code,
+ "",
+ args.model_format,
+ args.tuning_records,
+ args.tensor_layout,
+ )
+
+ if dumps:
+ save_dumps(args.output, dumps)
+
+ save_module(args.output, graph, lib, params, args.cross_compiler)
+ return 0
+
+
+def compile_model(
+ path,
+ target,
+ dump_code=None,
+ target_host=None,
+ model_format=None,
+ tuning_records=None,
+ alter_layout=None,
+):
+ """Compile a model from a supported framework into a TVM module.
+
+ This function takes a union of the arguments of both frontends.load_model
+ and compiler.compile_relay. The resulting TVM module can be executed using
+ the graph runtime.
+
+ Parameters
+ ----------
+ path: str
+ Path to a file
+ target : str
+ The target for which to compile. Can be a plain string or
+ a path.
+ dump_code : list, optional
+ Dump the generated code for the specified source types, on
+ the requested target.
+ target_host : str, optional
+ The target of the host machine if host-side code
+ needs to be generated.
+ model_format: str, optional
+ A string representing a name of a frontend to be used
+ tuning_records: str, optional
+ Name of the file produced by the tuning to be used during
Review comment:
Path?
##########
File path: tests/python/driver/tvmc/test_frontends.py
##########
@@ -0,0 +1,196 @@
+# 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.
+import os
+import tarfile
+
+import pytest
+
+from tvm.ir.module import IRModule
+
+from tvm.driver import tvmc
+from tvm.driver.tvmc.common import TVMCException
+
+
+def test_get_frontend_names_is_list():
+ sut = tvmc.frontends.get_frontend_names()
+ assert type(sut) is list
+
+
+def test_get_frontends_contains_only_strings():
+ sut = tvmc.frontends.get_frontend_names()
+ assert all([type(x) is str for x in sut]) is True
+
+
+def test_get_frontend_by_name_valid():
+ # some CI environments wont offer TensorFlow/Keras, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ sut = tvmc.frontends.get_frontend_by_name("keras")
+ assert type(sut) is tvmc.frontends.KerasFrontend
+
+
+def test_get_frontend_by_name_invalid():
+ with pytest.raises(TVMCException) as e:
+ def f():
+ tvmc.frontends.get_frontend_by_name("unsupported_thing")
+ f()
+ assert 'unrecognized frontend' in str(e.value)
+
+
+def test_guess_frontend_tflite():
+ # some CI environments wont offer TFLite, so skip in case it is not present
+ pytest.importorskip('tflite')
+
+ sut = tvmc.frontends.guess_frontend("a_model.tflite")
+ assert type(sut) is tvmc.frontends.TFLiteFrontend
+
+
+def test_guess_frontend_onnx():
+ # some CI environments wont offer onnx, so skip in case it is not present
+ pytest.importorskip('onnx')
+
+ sut = tvmc.frontends.guess_frontend("a_model.onnx")
+ assert type(sut) is tvmc.frontends.OnnxFrontend
+
+
+def test_guess_frontend_pytorch():
+ # some CI environments wont offer pytorch, so skip in case it is not present
+ pytest.importorskip('torch')
+
+ sut = tvmc.frontends.guess_frontend("a_model.pth")
+ assert type(sut) is tvmc.frontends.PyTorchFrontend
+
+
+def test_guess_frontend_keras():
+ # some CI environments wont offer TensorFlow/Keras, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ sut = tvmc.frontends.guess_frontend("a_model.h5")
+ assert type(sut) is tvmc.frontends.KerasFrontend
+
+
+def test_guess_frontend_tensorflow():
+ # some CI environments wont offer TensorFlow, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ sut = tvmc.frontends.guess_frontend("a_model.pb")
+ assert type(sut) is tvmc.frontends.TensorflowFrontend
+
+
+def test_guess_frontend_invalid():
+ with pytest.raises(TVMCException):
+ tvmc.frontends.guess_frontend("not/a/file.txt")
+
+
+def test_load_model__invalid_path__no_language():
+ # some CI environments wont offer TFLite, so skip in case it is not present
+ pytest.importorskip('tflite')
+
+ with pytest.raises(FileNotFoundError):
+ tvmc.frontends.load_model("not/a/file.tflite")
+
+
+def test_load_model__invalid_path__with_language():
+ # some CI environments wont offer onnx, so skip in case it is not present
+ pytest.importorskip('onnx')
+
+ with pytest.raises(FileNotFoundError):
+ tvmc.frontends.load_model("not/a/file.txt", model_format="onnx")
+
+
+def test_load_model__tflite(tflite_mobilenet_v1_1_quant):
+ # some CI environments wont offer TFLite, so skip in case it is not present
+ pytest.importorskip('tflite')
+
+ mod, params = tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant)
+ assert type(mod) is IRModule
+ assert type(params) is dict
+ # check whether one known value is part of the params dict
+ assert '_param_1' in params.keys()
+
+
+def test_load_model__keras(keras_resnet50):
+ # some CI environments wont offer TensorFlow/Keras, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ mod, params = tvmc.frontends.load_model(keras_resnet50)
+ assert type(mod) is IRModule
+ assert type(params) is dict
+ ## check whether one known value is part of the params dict
+ assert '_param_1' in params.keys()
+
+
+def test_load_model__onnx(onnx_resnet50):
+ # some CI environments wont offer onnx, so skip in case it is not present
+ pytest.importorskip('onnx')
+
+ mod, params = tvmc.frontends.load_model(onnx_resnet50)
+ assert type(mod) is IRModule
+ assert type(params) is dict
+ ## check whether one known value is part of the params dict
+ assert 'resnetv24_batchnorm0_gamma' in params.keys()
+
+
+def test_load_model__pb(pb_mobilenet_v1_1_quant):
+ # some CI environments wont offer TensorFlow, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ mod, params = tvmc.frontends.load_model(pb_mobilenet_v1_1_quant)
+ assert type(mod) is IRModule
+ assert type(params) is dict
+ # check whether one known value is part of the params dict
+ assert 'MobilenetV1/Conv2d_0/weights' in params.keys()
+
+
+def test_load_model___wrong_language__to_keras(tflite_mobilenet_v1_1_quant):
+ # some CI environments wont offer TensorFlow/Keras, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ with pytest.raises(OSError):
+ tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant, model_format="keras")
+
+
+def test_load_model___wrong_language__to_tflite(keras_resnet50):
+ # some CI environments wont offer TFLite, so skip in case it is not present
+ pytest.importorskip('tflite')
+
+ with pytest.raises(TVMCException) as e:
+ def f():
+ tvmc.frontends.load_model(keras_resnet50, model_format="tflite")
+ f()
+ assert 'input file not tflite' in str(e.value)
Review comment:
ditto.
##########
File path: python/tvm/driver/tvmc/compiler.py
##########
@@ -0,0 +1,280 @@
+# 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.
+"""
+Provides support to compile networks both AOT and JIT.
+"""
+import logging
+import os.path
+import tarfile
+from pathlib import Path
+
+import tvm
+from tvm import autotvm
+from tvm import relay
+from tvm.contrib import cc
+from tvm.contrib import util
+
+from . import common, frontends
+from .main import register_parser
+
+
+@register_parser
+def add_compile_parser(subparsers):
+ """ Include parser for 'compile' subcommand """
+
+ parser = subparsers.add_parser("compile", help="compile a model")
+ parser.set_defaults(func=drive_compile)
+ parser.add_argument(
+ "--cross-compiler",
+ default="",
+ help="the cross compiler to generate target libraries, e.g. 'aarch64-linux-gnu-gcc'",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ parser.add_argument(
+ "--dump-code",
+ metavar="FORMAT",
+ default="",
+ help="comma separarated list of formats to export, e.g. 'asm,ll,relay' "
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ default="module.tar",
+ help="output the compiled module to an archive",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ default="",
+ help="path to an auto-tuning log file from AutoTVM"
+ )
+ parser.add_argument(
+ "-v", "--verbose", action="count", default=0, help="increase verbosity"
+ )
+ #TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_compile(args):
+ """ Invoke tvmc.compiler module with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ graph, lib, params, dumps = compile_model(
+ args.FILE,
+ args.target,
+ args.dump_code,
+ "",
+ args.model_format,
+ args.tuning_records,
+ args.tensor_layout,
+ )
+
+ if dumps:
+ save_dumps(args.output, dumps)
+
+ save_module(args.output, graph, lib, params, args.cross_compiler)
+ return 0
+
+
+def compile_model(
+ path,
+ target,
+ dump_code=None,
+ target_host=None,
+ model_format=None,
+ tuning_records=None,
+ alter_layout=None,
+):
+ """Compile a model from a supported framework into a TVM module.
+
+ This function takes a union of the arguments of both frontends.load_model
+ and compiler.compile_relay. The resulting TVM module can be executed using
+ the graph runtime.
+
+ Parameters
+ ----------
+ path: str
+ Path to a file
+ target : str
+ The target for which to compile. Can be a plain string or
+ a path.
+ dump_code : list, optional
+ Dump the generated code for the specified source types, on
+ the requested target.
+ target_host : str, optional
+ The target of the host machine if host-side code
+ needs to be generated.
+ model_format: str, optional
+ A string representing a name of a frontend to be used
+ tuning_records: str, optional
+ Name of the file produced by the tuning to be used during
+ compilation.
+ alter_layout: str, optional
+ The layout to convert the graph to. Note, the convert layout
+ pass doesn't currently guarantee the whole of the graph will
+ be converted to the chosen layout.
+
+ Returns
+ -------
+ graph : str
+ A JSON-serialized TVM execution graph.
+ lib : tvm.module.Module
+ A TVM module containing the compiled functions.
+ params : dict
+ The parameters (weights) for the TVM module.
+ dumps : dict
+ Dictionary containing the dumps specified.
+
+ """
+ dump_code = [x.strip() for x in dump_code.split(',')] if dump_code else None
+ mod, params = frontends.load_model(path, model_format)
+
+ if alter_layout:
+ mod = common.convert_graph_layout(mod, alter_layout)
+
+ if os.path.exists(str(target)):
+ with open(target) as target_file:
+ logging.info("using target input from file: %s", target)
+ target = "".join(target_file.readlines())
+
+ # TODO(@leandron) We don't have an API to collect a list of supported
+ # targets yet
+ logging.debug("creating target from input: %s", target)
+ tvm_target = tvm.target.create(target)
+ target_host = target_host or ""
+
+ if tuning_records:
Review comment:
Need `os.path.exists(tuning_records)`
##########
File path: python/tvm/driver/tvmc/compiler.py
##########
@@ -0,0 +1,305 @@
+# 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.
+"""
+Provides support to compile networks both AOT and JIT.
+"""
+import logging
+import os.path
+import tarfile
+from pathlib import Path
+
+import tvm
+from tvm import autotvm
+from tvm import relay
+from tvm.contrib import cc
+from tvm.contrib import util
+
+from . import common, frontends
+from .main import register_parser
+
+
+@register_parser
+def add_compile_parser(subparsers):
+ """ Include parser for 'compile' subcommand """
+
+ parser = subparsers.add_parser("compile", help="compile a model")
+ parser.set_defaults(func=drive_compile)
+ parser.add_argument(
+ "--cross-compiler",
+ default="",
+ help="the cross compiler to generate target libraries, e.g. 'aarch64-linux-gnu-gcc'",
+ )
+ parser.add_argument(
+ "--dump-code",
+ metavar="FORMAT",
+ default="",
+ help="comma separarated list of formats to export, e.g. 'asm,ll,relay' "
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontends(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--input-shape",
+ type=common.parse_input_shapes,
+ metavar="INPUT_SHAPE,[INPUT_SHAPE]...",
+ help="for pytorch, e.g. '(1,3,224,224)'",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ default="module.tar",
+ help="output the compiled module to an archive",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ default="",
+ help="path to an auto-tuning log file from AutoTVM"
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ parser.add_argument(
+ "-v", "--verbose", action="count", default=0, help="increase verbosity"
+ )
+ parser.add_argument("FILE")
+
+
+def drive_compile(args):
+ """ Invoke tvmc.compiler module with command line arguments """
+
+ graph, lib, params, dumps = compile_model(
+ args.FILE,
+ args.target,
+ args.dump_code,
+ "",
+ args.model_format,
+ args.input_shape,
+ args.tuning_records,
+ args.tensor_layout,
+ )
+
+ if dumps:
+ save_dumps(args.output, dumps)
+
+ save_module(args.output, graph, lib, params, args.cross_compiler)
+ return 0
+
+
+def compile_model(
+ path,
+ target,
+ dump_sources=None,
+ target_host=None,
+ model_format=None,
+ shapes=None,
+ tuning_records=None,
+ alter_layout=None,
+):
+ """Compile a model from a supported framework into a TVM module.
+
+ This function takes a union of the arguments of both frontends.load_model
+ and compiler.compile_relay. The resulting TVM module can be executed using
+ the graph runtime.
+
+ Returns
+ -------
+ graph : str
+ A JSON-serialized TVM execution graph.
+ lib : tvm.module.Module
+ A TVM module containing the compiled functions.
+ params : dict
+ The parameters (weights) for the TVM module.
+ dumps : dict
+ Dictionary containing the dumps specified.
+
+ """
+ dump_sources = [x.strip() for x in dump_sources.split(',')] if dump_sources else None
+ mod, params = frontends.load_model(path, model_format, shapes)
+
+ return compile_relay(
+ mod,
+ params,
+ target,
+ dump_sources=dump_sources,
+ target_host=target_host,
+ tuning_records=tuning_records,
+ alter_layout=alter_layout,
+ )
+
+
+def compile_relay(
+ mod,
+ params,
+ target,
+ dump_sources=None,
+ target_host=None,
+ tuning_records=None,
+ alter_layout=None,
+):
+ """Compile a relay module to a TVM module for the graph runtime.
+
+ Parameters
+ ----------
+ mod : tvm.relay.Module
+ The relay module to compile.
+ params : dict
+ The parameters (weights) for the relay module.
+ target : str
+ The target for which to compile. Can be a plain string or
+ a path.
+ dump_sources : list, optional
+ Dump the generated code for the specified source types, on
+ the requested target.
+ target_host : Union[str, tvm.target.Target], optional
+ The target of the host machine if host-side code
+ needs to be generated.
+ tuning_records: str, optional
+ Name of the file produced by the tuning to be used during
+ compilation.
+ alter_layout: str, optional
+ The layout to convert the graph to. Note, the convert layout
+ pass doesn't currently guarantee the whole of the graph will
+ be converted to the chosen layout.
+
+ Returns
+ -------
+ graph : str
+ A JSON-serialized TVM execution graph.
+ lib : tvm.module.Module
+ A TVM module containing the compiled functions.
+ params : dict
+ The parameters (weights) for the TVM module.
+ dumps : dict
+ Dictionary containing the dumps specified.
+
+ """
+
+ if alter_layout:
+ mod = common.convert_graph_layout(mod, alter_layout)
+
+ if os.path.exists(str(target)):
+ with open(target) as target_file:
+ logging.info("using target input from file: %s", target)
+ target = "".join(target_file.readlines())
+
+ # TODO: We don't have an API to collect a list of supported
+ # targets yet. (@leandron)
+ logging.debug("creating target from input: %s", target)
+ tvm_target = tvm.target.create(target)
+ target_host = target_host or ""
+
+ if tuning_records:
+ logging.debug("tuning records file provided: %s", tuning_records)
+ with autotvm.apply_history_best(tuning_records):
+ with tvm.transform.PassContext(opt_level=3):
+ logging.debug("building relay graph with tuning records")
+ graph_module = relay.build(mod, tvm_target, params=params, target_host=tvm_target)
Review comment:
The TODO doesn't really catch my point tho. When the graph tuner log is provided, we should use `ApplyGraphBest` instead of `ApplyHistoryBest`.
##########
File path: tests/python/driver/tvmc/test_frontends.py
##########
@@ -0,0 +1,196 @@
+# 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.
+import os
+import tarfile
+
+import pytest
+
+from tvm.ir.module import IRModule
+
+from tvm.driver import tvmc
+from tvm.driver.tvmc.common import TVMCException
+
+
+def test_get_frontend_names_is_list():
+ sut = tvmc.frontends.get_frontend_names()
+ assert type(sut) is list
+
+
+def test_get_frontends_contains_only_strings():
+ sut = tvmc.frontends.get_frontend_names()
+ assert all([type(x) is str for x in sut]) is True
+
+
+def test_get_frontend_by_name_valid():
+ # some CI environments wont offer TensorFlow/Keras, so skip in case it is not present
+ pytest.importorskip('tensorflow')
+
+ sut = tvmc.frontends.get_frontend_by_name("keras")
+ assert type(sut) is tvmc.frontends.KerasFrontend
+
+
+def test_get_frontend_by_name_invalid():
+ with pytest.raises(TVMCException) as e:
+ def f():
+ tvmc.frontends.get_frontend_by_name("unsupported_thing")
+ f()
+ assert 'unrecognized frontend' in str(e.value)
Review comment:
Should be able to simplify to the following. If it raises an exception than this test is passed so we don't care what it printed; otherwise we will see a pytest error of not raising an expected exception.
```suggestion
tvmc.frontends.get_frontend_by_name("unsupported_thing")
```
##########
File path: tests/python/driver/tvmc/test_frontends.py
##########
@@ -0,0 +1,196 @@
+# 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.
+import os
+import tarfile
+
+import pytest
+
+from tvm.ir.module import IRModule
+
+from tvm.driver import tvmc
+from tvm.driver.tvmc.common import TVMCException
+
+
+def test_get_frontend_names_is_list():
+ sut = tvmc.frontends.get_frontend_names()
+ assert type(sut) is list
Review comment:
This test seems not necessary as this function is used by multiple tests.
##########
File path: python/tvm/driver/tvmc/frontends.py
##########
@@ -0,0 +1,417 @@
+# 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.
+"""
+Provides support to parse models from different frameworks into Relay networks.
+
+Frontend classes do lazy-loading of modules on purpose, to reduce time spent on
+loading the tool.
+"""
+import logging
+import os
+import sys
+from abc import ABC
+from abc import abstractmethod
+from pathlib import Path
+
+import numpy as np
+
+from tvm import relay
+from tvm.driver.tvmc.common import TVMCException
+
+
+class Frontend(ABC):
+ """Abstract class for frontend"""
+
+ @staticmethod
+ @abstractmethod
+ def name():
+ """Frontend name"""
+
+ @staticmethod
+ @abstractmethod
+ def suffixes():
+ """File suffixes (extensions) used by this frontend"""
+
+ @abstractmethod
+ def load(self, path):
+ """Load a model from a given path.
+
+ Parameters
+ ----------
+ path: str
+ Path to a file
+
+ Returns
+ -------
+ mod : tvm.relay.Module
+ The produced relay module.
+ params : dict
+ The parameters (weights) for the relay module.
+
+ """
+
+
+def import_keras():
+ """ Lazy import function for Keras"""
+ # Keras writes the message "Using TensorFlow backend." to stderr
+ # Redirect stderr during the import to disable this
+ stderr = sys.stderr
+ sys.stderr = open(os.devnull, "w")
+ try:
+ # pylint: disable=C0415
+ import tensorflow as tf
+ from tensorflow import keras
+
+ return tf, keras
+ finally:
+ sys.stderr = stderr
+
+
+class KerasFrontend(Frontend):
+ """ Keras frontend for TVMC """
+
+ @staticmethod
+ def name():
+ return "keras"
+
+ @staticmethod
+ def suffixes():
+ return ["h5"]
+
+ def load(self, path):
+ # pylint: disable=C0103
+ tf, keras = import_keras()
+
+ # tvm build currently imports keras directly instead of tensorflow.keras
+ try:
+ model = keras.models.load_model(path)
+ except ValueError as err:
+ raise TVMCException(str(err))
+
+ # There are two flavours of keras model, sequential and
+ # functional, TVM expects a functional model, so convert
+ # if required:
+ if self.is_sequential_p(model):
+ model = self.sequential_to_functional(model)
+
+ in_shapes = []
+ for layer in model._input_layers:
+ if tf.executing_eagerly():
+ in_shapes.append(
+ tuple(dim if dim is not None else 1 for dim in layer.input.shape)
+ )
+ else:
+ in_shapes.append(
+ tuple(
+ dim.value if dim.value is not None else 1
+ for dim in layer.input.shape
+ )
+ )
+
+ inputs = [
+ np.random.uniform(size=shape, low=-1.0, high=1.0) for shape in in_shapes
+ ]
+ shape_dict = {name: x.shape for (name, x) in zip(model.input_names, inputs)}
+ return relay.frontend.from_keras(model, shape_dict, layout="NHWC")
+
+ def is_sequential_p(self, model):
+ _, keras = import_keras()
+ return isinstance(model, keras.models.Sequential)
+
+ def sequential_to_functional(self, model):
+ _, keras = import_keras()
+ assert self.is_sequential_p(model)
+ input_layer = keras.layers.Input(batch_shape=model.layers[0].input_shape)
+ prev_layer = input_layer
+ for layer in model.layers:
+ prev_layer = layer(prev_layer)
+ model = keras.models.Model([input_layer], [prev_layer])
+ return model
+
+
+class OnnxFrontend(Frontend):
+ """ ONNX frontend for TVMC """
+
+ @staticmethod
+ def name():
+ return "onnx"
+
+ @staticmethod
+ def suffixes():
+ return ["onnx"]
+
+ def load(self, path):
+ # pylint: disable=C0415
+ import onnx
+
+ model = onnx.load(path)
+
+ # pylint: disable=E1101
+ name = model.graph.input[0].name
+
+ # pylint: disable=E1101
+ proto_shape = model.graph.input[0].type.tensor_type.shape.dim
+ shape = [d.dim_value for d in proto_shape]
+
+ shape_dict = {name: shape}
+
+ return relay.frontend.from_onnx(model, shape_dict)
+
+
+class TensorflowFrontend(Frontend):
+ """ TensorFlow frontend for TVMC """
+
+ @staticmethod
+ def name():
+ return "pb"
+
+ @staticmethod
+ def suffixes():
+ return ["pb"]
+
+ def load(self, path):
+ # pylint: disable=C0415
+ import tensorflow as tf
+ import tvm.relay.testing.tf as tf_testing
+
+ with tf.io.gfile.GFile(path, "rb") as tf_graph:
+ content = tf_graph.read()
+
+ graph_def = tf.compat.v1.GraphDef()
+ graph_def.ParseFromString(content)
+ graph_def = tf_testing.ProcessGraphDefParam(graph_def)
+
+ logging.debug("relay.frontend.from_tensorflow")
+ return relay.frontend.from_tensorflow(graph_def)
+
+
+class TFLiteFrontend(Frontend):
+ """ TFLite frontend for TVMC """
+
+ _tflite_m = {
+ 0: "float32",
+ 1: "float16",
+ 2: "int32",
+ 3: "uint8",
+ 4: "int64",
+ 5: "string",
+ 6: "bool",
+ 7: "int16",
+ 8: "complex64",
+ 9: "int8",
+ }
+
+ @staticmethod
+ def name():
+ return "tflite"
+
+ @staticmethod
+ def suffixes():
+ return ["tflite"]
+
+ def load(self, path):
+ # pylint: disable=C0415
+ import tflite.Model as model
+
+ with open(path, "rb") as tf_graph:
+ content = tf_graph.read()
+
+ # tflite.Model.Model is tflite.Model in 1.14 and 2.1.0
+ try:
+ tflite_model = model.Model.GetRootAsModel(content, 0)
+ except AttributeError:
+ tflite_model = model.GetRootAsModel(content, 0)
+
+ try:
+ version = tflite_model.Version()
+ logging.debug("tflite version %s", version)
+ except Exception:
+ raise TVMCException("input file not tflite")
+
+ if version != 3:
+ raise TVMCException("input file not tflite version 3")
+
+ logging.debug("tflite_input_type")
+ shape_dict, dtype_dict = TFLiteFrontend._input_type(tflite_model)
+
+ # parse TFLite model and convert into Relay computation graph
+ logging.debug("relay.frontend.from_tflite")
+ mod, params = relay.frontend.from_tflite(
+ tflite_model, shape_dict=shape_dict, dtype_dict=dtype_dict
+ )
+ return mod, params
+
+ @staticmethod
+ def _decode_type(n):
+ return TFLiteFrontend._tflite_m[n]
+
+ @staticmethod
+ def _input_type(model):
+ subgraph_count = model.SubgraphsLength()
+ assert subgraph_count > 0
+ shape_dict = {}
+ dtype_dict = {}
+ for subgraph_index in range(subgraph_count):
+ subgraph = model.Subgraphs(subgraph_index)
+ inputs_count = subgraph.InputsLength()
+ assert inputs_count >= 1
+ for input_index in range(inputs_count):
+ input_ = subgraph.Inputs(input_index)
+ assert subgraph.TensorsLength() > input_
+ tensor = subgraph.Tensors(input_)
+ input_shape = tuple(tensor.ShapeAsNumpy())
+ tensor_type = tensor.Type()
+ input_name = tensor.Name().decode("utf8")
+ shape_dict[input_name] = input_shape
+ dtype_dict[input_name] = TFLiteFrontend._decode_type(tensor_type)
+
+ return shape_dict, dtype_dict
+
+
+class PyTorchFrontend(Frontend):
+ """ PyTorch frontend for TVMC """
+
+ @staticmethod
+ def name():
+ return "pytorch"
+
+ @staticmethod
+ def suffixes():
+ # Torch Script is a zip file, but can be named pth
+ return ["pth", "zip"]
+
+ def load(self, path):
+ # pylint: disable=C0415
+ import torch
+
+ traced_model = torch.jit.load(path)
+
+ inputs = list(traced_model.graph.inputs())[1:]
+ input_shapes = [inp.type().sizes() for inp in inputs]
+
+ traced_model.eval() # Switch to inference mode
+ input_shapes = [
+ ("input{}".format(idx), shape) for idx, shape in enumerate(shapes)
+ ]
+ logging.debug("relay.frontend.from_pytorch")
+ return relay.frontend.from_pytorch(traced_model, input_shapes)
+
+
+ALL_FRONTENDS = [
+ KerasFrontend,
+ OnnxFrontend,
+ TensorflowFrontend,
+ TFLiteFrontend,
+ PyTorchFrontend,
+]
+
+
+def get_frontend_names():
+ """Return the names of all supported frontends
+
+ Returns
+ -------
+ list : list of str
+ A list of frontend names as strings
+
+ """
+ return [frontend.name() for frontend in ALL_FRONTENDS]
+
+
+def get_frontend_by_name(name):
+ """
+ This function will try to get a frontend instance, based
+ on the name provided.
+
+ Parameters
+ ----------
+ name : str
+ the name of a given frontend
+
+ Returns
+ -------
+ frontend : tvm.driver.tvmc.Frontend
+ An instance of the frontend that matches with
+ the file extension provided in `path`.
+
+ """
+
+ for frontend in ALL_FRONTENDS:
+ if name == frontend.name():
+ return frontend()
+
+ raise TVMCException("unrecognized frontend")
Review comment:
Also display the given name. It would be even better to provide a list of available frontends by calling `get_frontend_names` here.
##########
File path: python/tvm/driver/tvmc/frontends.py
##########
@@ -0,0 +1,417 @@
+# 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.
+"""
+Provides support to parse models from different frameworks into Relay networks.
+
+Frontend classes do lazy-loading of modules on purpose, to reduce time spent on
+loading the tool.
+"""
+import logging
+import os
+import sys
+from abc import ABC
+from abc import abstractmethod
+from pathlib import Path
+
+import numpy as np
+
+from tvm import relay
+from tvm.driver.tvmc.common import TVMCException
+
+
+class Frontend(ABC):
+ """Abstract class for frontend"""
+
+ @staticmethod
+ @abstractmethod
+ def name():
+ """Frontend name"""
+
+ @staticmethod
+ @abstractmethod
+ def suffixes():
+ """File suffixes (extensions) used by this frontend"""
+
+ @abstractmethod
+ def load(self, path):
+ """Load a model from a given path.
+
+ Parameters
+ ----------
+ path: str
+ Path to a file
+
+ Returns
+ -------
+ mod : tvm.relay.Module
+ The produced relay module.
+ params : dict
+ The parameters (weights) for the relay module.
+
+ """
+
+
+def import_keras():
+ """ Lazy import function for Keras"""
+ # Keras writes the message "Using TensorFlow backend." to stderr
+ # Redirect stderr during the import to disable this
+ stderr = sys.stderr
+ sys.stderr = open(os.devnull, "w")
+ try:
+ # pylint: disable=C0415
+ import tensorflow as tf
+ from tensorflow import keras
+
+ return tf, keras
+ finally:
+ sys.stderr = stderr
+
+
+class KerasFrontend(Frontend):
+ """ Keras frontend for TVMC """
+
+ @staticmethod
+ def name():
+ return "keras"
+
+ @staticmethod
+ def suffixes():
+ return ["h5"]
+
+ def load(self, path):
+ # pylint: disable=C0103
+ tf, keras = import_keras()
+
+ # tvm build currently imports keras directly instead of tensorflow.keras
+ try:
+ model = keras.models.load_model(path)
+ except ValueError as err:
+ raise TVMCException(str(err))
+
+ # There are two flavours of keras model, sequential and
+ # functional, TVM expects a functional model, so convert
+ # if required:
+ if self.is_sequential_p(model):
+ model = self.sequential_to_functional(model)
+
+ in_shapes = []
+ for layer in model._input_layers:
+ if tf.executing_eagerly():
+ in_shapes.append(
+ tuple(dim if dim is not None else 1 for dim in layer.input.shape)
+ )
+ else:
+ in_shapes.append(
+ tuple(
+ dim.value if dim.value is not None else 1
+ for dim in layer.input.shape
+ )
+ )
+
+ inputs = [
+ np.random.uniform(size=shape, low=-1.0, high=1.0) for shape in in_shapes
+ ]
+ shape_dict = {name: x.shape for (name, x) in zip(model.input_names, inputs)}
+ return relay.frontend.from_keras(model, shape_dict, layout="NHWC")
+
+ def is_sequential_p(self, model):
+ _, keras = import_keras()
+ return isinstance(model, keras.models.Sequential)
+
+ def sequential_to_functional(self, model):
+ _, keras = import_keras()
+ assert self.is_sequential_p(model)
+ input_layer = keras.layers.Input(batch_shape=model.layers[0].input_shape)
+ prev_layer = input_layer
+ for layer in model.layers:
+ prev_layer = layer(prev_layer)
+ model = keras.models.Model([input_layer], [prev_layer])
+ return model
+
+
+class OnnxFrontend(Frontend):
+ """ ONNX frontend for TVMC """
+
+ @staticmethod
+ def name():
+ return "onnx"
+
+ @staticmethod
+ def suffixes():
+ return ["onnx"]
+
+ def load(self, path):
+ # pylint: disable=C0415
+ import onnx
+
+ model = onnx.load(path)
+
+ # pylint: disable=E1101
+ name = model.graph.input[0].name
+
+ # pylint: disable=E1101
+ proto_shape = model.graph.input[0].type.tensor_type.shape.dim
+ shape = [d.dim_value for d in proto_shape]
+
+ shape_dict = {name: shape}
+
+ return relay.frontend.from_onnx(model, shape_dict)
+
+
+class TensorflowFrontend(Frontend):
+ """ TensorFlow frontend for TVMC """
+
+ @staticmethod
+ def name():
+ return "pb"
+
+ @staticmethod
+ def suffixes():
+ return ["pb"]
+
+ def load(self, path):
+ # pylint: disable=C0415
+ import tensorflow as tf
+ import tvm.relay.testing.tf as tf_testing
+
+ with tf.io.gfile.GFile(path, "rb") as tf_graph:
+ content = tf_graph.read()
+
+ graph_def = tf.compat.v1.GraphDef()
+ graph_def.ParseFromString(content)
+ graph_def = tf_testing.ProcessGraphDefParam(graph_def)
+
+ logging.debug("relay.frontend.from_tensorflow")
+ return relay.frontend.from_tensorflow(graph_def)
+
+
+class TFLiteFrontend(Frontend):
+ """ TFLite frontend for TVMC """
+
+ _tflite_m = {
+ 0: "float32",
+ 1: "float16",
+ 2: "int32",
+ 3: "uint8",
+ 4: "int64",
+ 5: "string",
+ 6: "bool",
+ 7: "int16",
+ 8: "complex64",
+ 9: "int8",
+ }
+
+ @staticmethod
+ def name():
+ return "tflite"
+
+ @staticmethod
+ def suffixes():
+ return ["tflite"]
+
+ def load(self, path):
+ # pylint: disable=C0415
+ import tflite.Model as model
+
+ with open(path, "rb") as tf_graph:
+ content = tf_graph.read()
+
+ # tflite.Model.Model is tflite.Model in 1.14 and 2.1.0
+ try:
+ tflite_model = model.Model.GetRootAsModel(content, 0)
+ except AttributeError:
+ tflite_model = model.GetRootAsModel(content, 0)
+
+ try:
+ version = tflite_model.Version()
+ logging.debug("tflite version %s", version)
+ except Exception:
+ raise TVMCException("input file not tflite")
+
+ if version != 3:
+ raise TVMCException("input file not tflite version 3")
+
+ logging.debug("tflite_input_type")
+ shape_dict, dtype_dict = TFLiteFrontend._input_type(tflite_model)
+
+ # parse TFLite model and convert into Relay computation graph
+ logging.debug("relay.frontend.from_tflite")
+ mod, params = relay.frontend.from_tflite(
+ tflite_model, shape_dict=shape_dict, dtype_dict=dtype_dict
+ )
+ return mod, params
+
+ @staticmethod
+ def _decode_type(n):
+ return TFLiteFrontend._tflite_m[n]
+
+ @staticmethod
+ def _input_type(model):
+ subgraph_count = model.SubgraphsLength()
+ assert subgraph_count > 0
+ shape_dict = {}
+ dtype_dict = {}
+ for subgraph_index in range(subgraph_count):
+ subgraph = model.Subgraphs(subgraph_index)
+ inputs_count = subgraph.InputsLength()
+ assert inputs_count >= 1
+ for input_index in range(inputs_count):
+ input_ = subgraph.Inputs(input_index)
+ assert subgraph.TensorsLength() > input_
+ tensor = subgraph.Tensors(input_)
+ input_shape = tuple(tensor.ShapeAsNumpy())
+ tensor_type = tensor.Type()
+ input_name = tensor.Name().decode("utf8")
+ shape_dict[input_name] = input_shape
+ dtype_dict[input_name] = TFLiteFrontend._decode_type(tensor_type)
+
+ return shape_dict, dtype_dict
+
+
+class PyTorchFrontend(Frontend):
+ """ PyTorch frontend for TVMC """
+
+ @staticmethod
+ def name():
+ return "pytorch"
+
+ @staticmethod
+ def suffixes():
+ # Torch Script is a zip file, but can be named pth
+ return ["pth", "zip"]
+
+ def load(self, path):
+ # pylint: disable=C0415
+ import torch
+
+ traced_model = torch.jit.load(path)
+
+ inputs = list(traced_model.graph.inputs())[1:]
+ input_shapes = [inp.type().sizes() for inp in inputs]
+
+ traced_model.eval() # Switch to inference mode
+ input_shapes = [
+ ("input{}".format(idx), shape) for idx, shape in enumerate(shapes)
+ ]
+ logging.debug("relay.frontend.from_pytorch")
+ return relay.frontend.from_pytorch(traced_model, input_shapes)
+
+
+ALL_FRONTENDS = [
+ KerasFrontend,
+ OnnxFrontend,
+ TensorflowFrontend,
+ TFLiteFrontend,
+ PyTorchFrontend,
+]
+
+
+def get_frontend_names():
+ """Return the names of all supported frontends
+
+ Returns
+ -------
+ list : list of str
+ A list of frontend names as strings
+
+ """
+ return [frontend.name() for frontend in ALL_FRONTENDS]
+
+
+def get_frontend_by_name(name):
+ """
+ This function will try to get a frontend instance, based
+ on the name provided.
+
+ Parameters
+ ----------
+ name : str
+ the name of a given frontend
+
+ Returns
+ -------
+ frontend : tvm.driver.tvmc.Frontend
+ An instance of the frontend that matches with
+ the file extension provided in `path`.
+
+ """
+
+ for frontend in ALL_FRONTENDS:
+ if name == frontend.name():
+ return frontend()
+
+ raise TVMCException("unrecognized frontend")
+
+
+def guess_frontend(path):
+ """
+ This function will try to imply which framework is being used,
+ based on the extension of the file provided in the path parameter.
+
+ Parameters
+ ----------
+ path : str
+ The path to the model file.
+
+ Returns
+ -------
+ frontend : tvm.driver.tvmc.Frontend
+ An instance of the frontend that matches with
+ the file extension provided in `path`.
+
+ """
+
+ suffix = Path(path).suffix.lower()
+ if suffix.startswith("."):
+ suffix = suffix[1:]
+
+ for frontend in ALL_FRONTENDS:
+ if suffix in frontend.suffixes():
+ return frontend()
+
+ raise TVMCException("cannot guess model format")
Review comment:
```suggestion
raise TVMCException("failed to infer the model format. Please specify --model-format")
```
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