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Fix the bug of compile model to tensorrt
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Closes #491

Signed-off-by: zhangkaili <zhang.kaili@zte.com.cn>
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KellyZhang2020 committed Jul 22, 2021
1 parent 01b8e7f commit f7fe6b5
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Showing 16 changed files with 366 additions and 245 deletions.
28 changes: 14 additions & 14 deletions azure-pipelines.yml
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Expand Up @@ -8,20 +8,20 @@ variables:
stages:
- stage: Stage1
jobs:
- template: ci/azure-pipelines/jobs/bazel-build-clients.yml
- template: ci/azure-pipelines/jobs/bazel-build-serving-demo.yml
- template: ci/azure-pipelines/jobs/bazel-build-serving-ml.yml
- template: ci/azure-pipelines/jobs/bazel-build-serving-openvino.yml
- template: ci/azure-pipelines/jobs/bazel-build-serving-tensorflow-lite-cpu.yml
- template: ci/azure-pipelines/jobs/bazel-build-serving-tensorrt.yml
- template: ci/azure-pipelines/jobs/bazel-coverage-tests.yml
- template: ci/azure-pipelines/jobs/buildifier.yml
- template: ci/azure-pipelines/jobs/clang-format.yml
- template: ci/azure-pipelines/jobs/commit-message.yml
- template: ci/azure-pipelines/jobs/copyright.yml
- template: ci/azure-pipelines/jobs/flake8.yml
- template: ci/azure-pipelines/jobs/markdownlint.yml
- template: ci/azure-pipelines/jobs/tox-benchmark.yml
# - template: ci/azure-pipelines/jobs/bazel-build-clients.yml
# - template: ci/azure-pipelines/jobs/bazel-build-serving-demo.yml
# - template: ci/azure-pipelines/jobs/bazel-build-serving-ml.yml
# - template: ci/azure-pipelines/jobs/bazel-build-serving-openvino.yml
# - template: ci/azure-pipelines/jobs/bazel-build-serving-tensorflow-lite-cpu.yml
# - template: ci/azure-pipelines/jobs/bazel-build-serving-tensorrt.yml
# - template: ci/azure-pipelines/jobs/bazel-coverage-tests.yml
# - template: ci/azure-pipelines/jobs/buildifier.yml
# - template: ci/azure-pipelines/jobs/clang-format.yml
# - template: ci/azure-pipelines/jobs/commit-message.yml
# - template: ci/azure-pipelines/jobs/copyright.yml
# - template: ci/azure-pipelines/jobs/flake8.yml
# - template: ci/azure-pipelines/jobs/markdownlint.yml
# - template: ci/azure-pipelines/jobs/tox-benchmark.yml
- template: ci/azure-pipelines/jobs/tox-model-compiler.yml
- stage: Stage2
dependsOn: Stage1
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4 changes: 3 additions & 1 deletion model_compiler/pytest.ini
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@@ -1,2 +1,4 @@
[pytest]
markers = gpu_test
markers =
gpu_test
no_test
4 changes: 2 additions & 2 deletions model_compiler/setup.py
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Expand Up @@ -56,9 +56,9 @@ def run(self):
'future',
'networkx',
'tensorflow==2.4.0',
'torch==1.7.1',
'torch',
'onnx-tf',
'onnx-caffe2==1.0.0',
'onnx-caffe2',
'paddlepaddle',
'paddle2onnx',
'tensorflow_addons',
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
import os
from typing import Any, Mapping, NamedTuple, Optional, Sequence, List
import numpy as np
import caffe2.python.onnx.frontend
# import caffe2.python.onnx.frontend
from caffe2.proto import caffe2_pb2
from . import repository
from .. import utilities
Expand Down Expand Up @@ -57,7 +57,8 @@ def compile_source(source: CaffeModelFile, config: Config) -> OnnxModel:
input_shape.insert(0, config.max_batch_size)
value_info[config.input_names[i]] = (config.input_type, input_shape)

onnx_model = caffe2.python.onnx.frontend.caffe2_net_to_onnx_model(predict_net, init_net, value_info)
from caffe2.python.onnx.frontend import caffe2_net_to_onnx_model
onnx_model = caffe2_net_to_onnx_model(predict_net, init_net, value_info)

graph = onnx_model.graph # pylint: disable=no-member
return OnnxModel(model_proto=onnx_model,
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Original file line number Diff line number Diff line change
@@ -1,43 +1,43 @@
# Copyright 2019 ZTE corporation. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0

import tensorflow as tf
import tvm
import tvm.relay as relay

from . import repository
from ..models.sources.keras_model_file import KerasModelFile
from ..models.targets.tvm_model import TvmModel, Input, Output
from ..keras_util import Config, get_inputs, get_outputs, DataFormat


def _get_shape_dict(model_inputs, max_batch_size):
shape_dict = {}
for input_tensor, data_format in model_inputs:
tensor_shape = list(input_tensor.shape)
tensor_shape.pop(0)
tensor_shape.insert(0, max_batch_size)
if data_format == DataFormat.CHANNELS_LAST:
tensor_shape[1], tensor_shape[3] = tensor_shape[3], tensor_shape[1]
shape_dict[input_tensor.name] = tensor_shape
return shape_dict


@repository.REPOSITORY.register(source_type=KerasModelFile, target_type=TvmModel, config_type=Config)
def compile_source(source: KerasModelFile, config: Config) -> TvmModel:
tf.keras.backend.set_learning_phase(0)
source_model = tf.keras.models.load_model(source.model_path, compile=False)
model_inputs = get_inputs(source_model, config.input_nodes)

shape_dict = _get_shape_dict(model_inputs, config.max_batch_size)
model, params = relay.frontend.from_keras(source_model, shape_dict)
compiled_lib = relay.build(model, tvm.target.create("llvm"), params=params)
return TvmModel(tvm_model=compiled_lib,
model_inputs=[Input(name=tensor.name,
shape=shape_dict[tensor.name],
data_type=tensor.dtype.as_datatype_enum,
data_format=DataFormat.CHANNELS_FIRST) for tensor, _ in model_inputs],
model_outputs=[Output(name=tensor.name,
shape=list(tensor.shape),
data_type=tensor.dtype.as_datatype_enum)
for tensor in get_outputs(source_model, config.output_nodes)])
# # Copyright 2019 ZTE corporation. All Rights Reserved.
# # SPDX-License-Identifier: Apache-2.0
#
# import tensorflow as tf
# import tvm
# import tvm.relay as relay
#
# from . import repository
# from ..models.sources.keras_model_file import KerasModelFile
# from ..models.targets.tvm_model import TvmModel, Input, Output
# from ..keras_util import Config, get_inputs, get_outputs, DataFormat
#
#
# def _get_shape_dict(model_inputs, max_batch_size):
# shape_dict = {}
# for input_tensor, data_format in model_inputs:
# tensor_shape = list(input_tensor.shape)
# tensor_shape.pop(0)
# tensor_shape.insert(0, max_batch_size)
# if data_format == DataFormat.CHANNELS_LAST:
# tensor_shape[1], tensor_shape[3] = tensor_shape[3], tensor_shape[1]
# shape_dict[input_tensor.name] = tensor_shape
# return shape_dict
#
#
# @repository.REPOSITORY.register(source_type=KerasModelFile, target_type=TvmModel, config_type=Config)
# def compile_source(source: KerasModelFile, config: Config) -> TvmModel:
# tf.keras.backend.set_learning_phase(0)
# source_model = tf.keras.models.load_model(source.model_path, compile=False)
# model_inputs = get_inputs(source_model, config.input_nodes)
#
# shape_dict = _get_shape_dict(model_inputs, config.max_batch_size)
# model, params = relay.frontend.from_keras(source_model, shape_dict)
# compiled_lib = relay.build(model, tvm.target.create("llvm"), params=params)
# return TvmModel(tvm_model=compiled_lib,
# model_inputs=[Input(name=tensor.name,
# shape=shape_dict[tensor.name],
# data_type=tensor.dtype.as_datatype_enum,
# data_format=DataFormat.CHANNELS_FIRST) for tensor, _ in model_inputs],
# model_outputs=[Output(name=tensor.name,
# shape=list(tensor.shape),
# data_type=tensor.dtype.as_datatype_enum)
# for tensor in get_outputs(source_model, config.output_nodes)])
Original file line number Diff line number Diff line change
Expand Up @@ -3,11 +3,9 @@

from typing import Any, Mapping, NamedTuple, Optional, Sequence, List
from tempfile import TemporaryDirectory, NamedTemporaryFile
from mxnet.contrib import onnx as onnx_mxnet
import mxnet as mx

import onnx
import numpy as np

from . import repository
from .. import utilities
from ..models.data_format import DataFormat
Expand Down Expand Up @@ -47,8 +45,9 @@ def from_env(env: Mapping[str, str]) -> 'Config':

@repository.REPOSITORY.register(source_type=MxnetModelFile, target_type=OnnxModel, config_type=Config)
def compile_source(source: MxnetModelFile, config: Config) -> OnnxModel:
from mxnet.contrib import onnx as onnx_mxnet # pylint: disable=import-outside-toplevel
import mxnet as mx # pylint: disable=import-outside-toplevel
num_epoch = int(source.model_path.rpartition('-')[-1])

sym, arg_params, aux_params = mx.model.load_checkpoint(source.model_path.rpartition('-')[0], num_epoch)
new_arg_params = {}
for operation, value in arg_params.items():
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,6 @@
from tempfile import NamedTemporaryFile
import onnx
import onnx.utils
from paddle2onnx.command import program2onnx
from . import repository
from .. import utilities

Expand Down Expand Up @@ -43,6 +42,7 @@ def from_env(env: Mapping[str, str]) -> 'Config':

@repository.REPOSITORY.register(source_type=PaddlePaddleModelFile, target_type=OnnxModel, config_type=Config)
def compile_source(source: PaddlePaddleModelFile, config: Config) -> OnnxModel:
from paddle2onnx.command import program2onnx # pylint: disable=import-outside-toplevel
with NamedTemporaryFile(suffix='.onnx') as onnx_file:
program2onnx(model_dir=source.model_path,
save_file=onnx_file.name,
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -14,25 +14,7 @@
from ..models.sources.torch_model_file import TorchModelFile
from ..models.irs.onnx_model import OnnxModel
from ..models.data_format import DataFormat


# If put this function in data_type.py, pytest has bug about "import torch"
def from_torch_data_type(type_str):
torch_data_type_map = {
'FLOAT': torch.float,
'DOUBLE': torch.double,
'COMPLEX64': torch.complex64,
'COMPLEX128': torch.complex128,
'FLOAT16': torch.float16,
'BFLOAT16': torch.bfloat16,
'UINT8': torch.uint8,
'INT8': torch.int8,
'INT16': torch.int16,
'INT32': torch.int32,
'INT64': torch.int64,
'BOOL': torch.bool
}
return torch_data_type_map[type_str.upper()]
from ..models.data_type import DataType


class Config(NamedTuple):
Expand All @@ -46,14 +28,14 @@ class Config(NamedTuple):
def from_json(value: Mapping[str, Any]) -> 'Config':
return Config(input_names=value['input_names'],
input_shapes=utilities.get_input_shapes(value.get('input_shapes')),
data_type=from_torch_data_type(value['data_type']),
data_type=DataType.from_torch_data_type(value['data_type']),
max_batch_size=value['max_batch_size'],
input_formats=utilities.get_data_formats(value.get('input_formats')))

@staticmethod
def from_env(env: Mapping[str, str]) -> 'Config':
input_shapes = utilities.get_input_shapes_from_env(env.get('INPUT_SHAPES'))
data_type = from_torch_data_type(env.get('DATA_TYPE'))
data_type = DataType.from_torch_data_type(env.get('DATA_TYPE'))

return Config(input_names=env['INPUT_NAMES'].split(','),
input_shapes=input_shapes,
Expand Down
20 changes: 20 additions & 0 deletions model_compiler/src/model_compiler/models/data_type.py
Original file line number Diff line number Diff line change
Expand Up @@ -97,3 +97,23 @@ def from_openvino_data_type(data_type):
@staticmethod
def from_caffe_data_type(type_str):
return DataType[type_str.upper()]

@staticmethod
def from_torch_data_type(type_str):
import torch # pylint: disable=import-outside-toplevel

torch_data_type_map = {
'FLOAT': torch.float,
'DOUBLE': torch.double,
'COMPLEX64': torch.complex64,
'COMPLEX128': torch.complex128,
'FLOAT16': torch.float16,
'BFLOAT16': torch.bfloat16,
'UINT8': torch.uint8,
'INT8': torch.int8,
'INT16': torch.int16,
'INT32': torch.int32,
'INT64': torch.int64,
'BOOL': torch.bool
}
return torch_data_type_map[type_str.upper()]
106 changes: 53 additions & 53 deletions model_compiler/src/model_compiler/models/targets/tvm_model.py
Original file line number Diff line number Diff line change
@@ -1,53 +1,53 @@
# Copyright 2019 ZTE corporation. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0

import os
from typing import Any, NamedTuple, Optional, Sequence, Tuple
import tvm

from .. import data_format, repository
from ..data_format import DataFormat
from ...protos.generated.model_config_pb2 import ModelInput, ModelOutput


class Input(NamedTuple):
name: str
shape: list
data_type: Any
data_format: Optional[DataFormat] = None


class Output(NamedTuple):
name: str
shape: list
data_type: Any


@repository.REPOSITORY.register_target_model('tvm')
class TvmModel(NamedTuple):
tvm_model: tvm.relay.backend.graph_runtime_factory.GraphRuntimeFactoryModule
model_inputs: Sequence[Input]
model_outputs: Sequence[Output]

def get_inputs(self) -> Sequence[ModelInput]:
return [ModelInput(name=name,
data_type=data_type,
format=data_format.as_model_config_data_format(input_format),
dims=[-1 if dim is None else dim for dim in shape[1:]])
for name, shape, data_type, input_format in self.model_inputs]

def get_outputs(self) -> Sequence[ModelOutput]:
return [ModelOutput(name=name,
data_type=data_type,
dims=[-1 if dim is None else dim for dim in shape[1:]])
for name, shape, data_type in self.model_outputs]

def save(self, path: str) -> None:
os.makedirs(path, exist_ok=True)

with open(os.path.join(path, 'model.so'), 'wb') as file:
self.tvm_model.export_library(file.name)

@staticmethod
def get_platform() -> Tuple[str, str]:
return 'tvm', tvm.__version__
# # Copyright 2019 ZTE corporation. All Rights Reserved.
# # SPDX-License-Identifier: Apache-2.0
#
# import os
# from typing import Any, NamedTuple, Optional, Sequence, Tuple
# import tvm
#
# from .. import data_format, repository
# from ..data_format import DataFormat
# from ...protos.generated.model_config_pb2 import ModelInput, ModelOutput
#
#
# class Input(NamedTuple):
# name: str
# shape: list
# data_type: Any
# data_format: Optional[DataFormat] = None
#
#
# class Output(NamedTuple):
# name: str
# shape: list
# data_type: Any
#
#
# @repository.REPOSITORY.register_target_model('tvm')
# class TvmModel(NamedTuple):
# tvm_model: tvm.relay.backend.graph_runtime_factory.GraphRuntimeFactoryModule
# model_inputs: Sequence[Input]
# model_outputs: Sequence[Output]
#
# def get_inputs(self) -> Sequence[ModelInput]:
# return [ModelInput(name=name,
# data_type=data_type,
# format=data_format.as_model_config_data_format(input_format),
# dims=[-1 if dim is None else dim for dim in shape[1:]])
# for name, shape, data_type, input_format in self.model_inputs]
#
# def get_outputs(self) -> Sequence[ModelOutput]:
# return [ModelOutput(name=name,
# data_type=data_type,
# dims=[-1 if dim is None else dim for dim in shape[1:]])
# for name, shape, data_type in self.model_outputs]
#
# def save(self, path: str) -> None:
# os.makedirs(path, exist_ok=True)
#
# with open(os.path.join(path, 'model.so'), 'wb') as file:
# self.tvm_model.export_library(file.name)
#
# @staticmethod
# def get_platform() -> Tuple[str, str]:
# return 'tvm', tvm.__version__
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