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Remove obsolete cmake script and test data (#4944)

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* Remove obsolete cmake script

* Removed obsolete models

* Removed obsolete generators

* Remove obsolete markdowns
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Ilya Churaev 2021-03-25 07:45:47 +03:00 committed by GitHub
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ngraph/ABOUT.md
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About nGraph Compiler stack
===========================
## Bridge
Starting from the top of the stack, nGraph receives a computational graph
from a deep learning framework such as TensorFlow or MXNet. The
computational graph is converted to an nGraph internal representation
by a bridge created for the corresponding framework.
An nGraph bridge examines the whole graph to pattern match subgraphs
which nGraph knows how to execute, and these subgraphs are encapsulated.
Parts of the graph that are not encapsulated will default to framework
implementation when executed.
## nGraph Core
nGraph uses a strongly-typed and platform-neutral
`Intermediate Representation (IR)` to construct a "stateless"
computational graph. Each node, or op, in the graph corresponds to
one `step` in a computation, where each step produces zero or
more tensor outputs from zero or more tensor inputs.
This allows nGraph to apply its state of the art optimizations instead
of having to follow how a particular framework implements op execution,
memory management, data layouts, etc.
In addition, using nGraph IR allows faster optimization delivery
for many of the supported frameworks. For example, if nGraph optimizes
ResNet for TensorFlow, the same optimization can be readily applied
to MXNet* or ONNX* implementations of ResNet.
Features
--------
nGraph performs a combination of device-specific and
non-device-specific optimizations:
- **Fusion** -- Fuse multiple ops to to decrease memory usage.
- **Data layout abstraction** -- Make abstraction easier and faster
with nGraph translating element order to work best for a given or
available device.
- **Data reuse** -- Save results and reuse for subgraphs with the
same input.

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ngraph/changes.md
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# API Changes
## Deprecation Notice
<table>
<tr>
<td><strong>Deprecation Begins</strong></td>
<td>June 1, 2020</td>
</tr>
<tr>
<td><strong>Removal Date</strong></td>
<td>December 1, 2020</td>
</tr>
</table>
*Starting with the OpenVINO™ toolkit 2020.2 release, all of the features previously available through nGraph have been merged into the OpenVINO™ toolkit. As a result, all the features previously available through ONNX RT Execution Provider for nGraph have been merged with ONNX RT Execution Provider for OpenVINO™ toolkit.*
*Therefore, ONNX RT Execution Provider for nGraph will be deprecated starting June 1, 2020 and will be completely removed on December 1, 2020. Users are recommended to migrate to the ONNX RT Execution Provider for OpenVINO™ toolkit as the unified solution for all AI inferencing on Intel® hardware.*
## Op Definition
* Every Op class must declare a `static constexpr NodeTypeInfo type_info{name, version}` in the class definition and define it in the .cpp file. See any op definition for an example.
* The boolean function `is_type<T>` is for testing if a node is the op `T`.
* `T as_type_ptr<T>()` and `T as_type<T>()` will upcast `Node` to an explicit op class if it is of class `T`, or `nullptr` if it is not.
## Backend library interface
* Each backend `BACKEND` needs to define the macro `${BACKEND}_API` appropriately to import symbols
referenced from outside the library and to export them from within the library. See any
of the `${backend}_backend_visibility.hpp` files for an example.
* The `CMakeLists.txt` file for a backend defines `${BACKEND}_BACKEND_DLL_EXPORTS`.
`target_compile_definitions(${backend}_backend PRIVATE ${BACKEND}_BACKEND_DLL_EXPORTS)`
* Each backend must define a function `ngraph_register_${backend}_backend` that registers a
backend constructor function and ensures that initializations are performed.
`ngraph/src/runtime/cpu/cpu_backend.cpp` has an example that includes initializations.
Remove the old backend constructor code.
## Passes
* `LikeReplacement` pass must be run by all transformers.
* `ngraph::pass::FusionType` is now an enum class. Constant values defined by `FusionType` are created for backward compatibility and will be removed in future releases.
## Nodes, Parameters
* `Nodes` is now `NodeVector`
* `Parameters` is now `ParameterVector`
* `NodeVector`, `ParameterVector`, `AxisVector`, `AxisSet`, `Shape`, `Stride`, `Coordinate`, and `CoordinateDiff` are now classes, not type aliases.
* `PrimaryTensorView` is now `TensorView` (and will merge into `Tensor`)
## Changes to ops
* The namespace `ngraph::op` is only for actual ops. Helpers have been moved into
`ngraph::op::util`:
+ `BinaryElementwiseArithmetic`
+ `BinaryElementwiseComparison`
+ `BinaryElementwise`
+ `RequiresTensorViewArgs`
+ `UnaryElementwiseArithmetic`
+ `UnaryElementwise`
Ops defined outside of nGraph core will need to get the base class from `ngraph::op::util` and
change the include file to `#include "ngraph/ops/util/requires_tensor_view_args.hpp"`, etc.
See any of the core ops for an example.
## Changes to convolution and pooling ops
* Backprop ops have been added for convolution ops.
* The convolution and pooling ops have had several methods/fields renamed, to reflect a shift
in terminology from "images" to "data". Generally this just means that you will have to
`s/image_batch/data_batch/` and `s/image_dilation_strides/data_dilation_strides/`.
* The following functions have been removed:
+ `AvgPool`: `get_channel_count get_input_image_physical_shape get_input_image_virtual_shape get_output_image_shape get_batch_size get_image_dimension_count`
+ `MaxPool`: `get_channel_count get_input_image_shape get_output_image_shape get_batch_size get_image_dimension_count`
+ `Convolution`: `get_input_channel_count get_output_channel_count get_input_image_physical_shape get_input_image_virtual_shape get_output_image_shape get_window_physical_shape get_window_virtual_shape get_batch_size get_image_dimension_count`
All of the above information can be inferred from the shapes and parameters of the op.
* The `AvgPool` operator has a new attribute governing whether or not padding-region values
are considered when computing a given window's average: `include_padding_in_avg_computation`.
One of the class constructors adds this to the parameter list, and the others use a default
value of `false` which matches the old behavior.
## Negative convolution padding
`Convolution` now allows negative padding. This means that the `padding_below` and `padding_above`
arguments now take type `CoordinateDiff` instead of `Shape`. `CoordinateDiff` is an alias for
`std::vector<std::ptrdiff_t>`, which "is like `size_t` but is allowed to be negative". Callers may
need to be adapted.
## Changes to Concat op
* `get_concatenation_axis` was renamed to `get_axis`. In order to provide backward compatibility `get_concatenation_axis` is now alis of `get_axis` method
* `set_concatenation_axis` was renamed to `set_axis`. In order to provide backward compatibility `set_concatenation_axis` is now alis of `set_axis` method
## `Parameter` and `Function` no longer take a type argument.
## Changes to Tensor read and write methods
The `read` and `write` methods on ngraph::runtime::Tensor which take a `tensor_offset` as the
second of three arguments have been deprecated. The replacement `read` and `write` methods take
two arguments, the buffer pointer and the size. For any references to the deprecated methods
remove the second argument, the tensor offset, to update to the new API. These old read/write
methods have been decorated with deprecated warnings.
To update, remove the passed argument. For example,
```C++
// Old
make_shared<Parameter>(make_shared<descriptor::TensorViewType>(element::f32, Shape{2, 4}));
// New (remove TensorViewType)
make_shared<Parameter>(element::f32, Shape{2, 4});
// Old
make_shared<Function>(results, result_type, parameters);
// New
make_shared<Function>(results, parameters);
```
The runtime::Tensor methods to get_tensor<> and write<T>(std::vector&) have been removed
to the unit test directory under utils/test_tool.hpp read_vector and write_vector.
## Changes to reshape op utils
Utility functions from `src/ngraph/op/util/reshape.hpp`, placed at namespace `ngraph::op::util`:
- `reshape`
- `reorder_axes`
- `transpose`
- `flatten`
Are moved to new location: `src/ngraph/builder/reshape.hpp` to namespace `ngraph::builder`.

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ngraph/cmake/Modules/fix_mode.cmake
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# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#
function(MODE_APPLY_FILE PATH)
execute_process(COMMAND git update-index --add --chmod=-x ${PATH}
OUTPUT_VARIABLE RESULT
ERROR_QUIET)
endfunction()
set(DIRECTORIES_OF_INTEREST
src
doc
test
python/pyngraph
)
foreach(DIRECTORY ${DIRECTORIES_OF_INTEREST})
set(DIR "${NGRAPH_SOURCE_DIR}/${DIRECTORY}/*.?pp")
file(GLOB_RECURSE XPP_FILES ${DIR})
foreach(FILE ${XPP_FILES})
mode_apply_file(${FILE})
endforeach(FILE)
endforeach(DIRECTORY)

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ngraph/test/models/conv_bias.json
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[{
"name" : "Function_4",
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"name" : "Reshape_678",
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},
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"op" : "Reshape",
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"op" : "Broadcast",
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11101
ngraph/test/models/mxnet/10_bucket_LSTM.json
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613
ngraph/test/models/mxnet/bn_bprop.json
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@ -1,613 +0,0 @@
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289
ngraph/test/models/mxnet/bn_fprop.json
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@ -1,289 +0,0 @@
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ngraph/test/models/tensorflow/mnist_cnn_maxpool/tf_function_cluster_17[_XlaCompiledKernel=true,_XlaNumConstantArgs=1,_XlaNumResourceArgs=0].v170.json
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ngraph/test/models/tensorflow/mnist_mlp/tf_function_cluster_4[_XlaCompiledKernel=true,_XlaNumConstantArgs=1,_XlaNumResourceArgs=0].v82.json
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185
ngraph/test/models/tensorflow/resnet8/tf_function_cluster_12[_XlaCompiledKernel=true,_XlaNumConstantArgs=3,_XlaNumResourceArgs=0].v23.json
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311
ngraph/test/models/tensorflow/resnet8/tf_function_cluster_16[_XlaCompiledKernel=true,_XlaNumConstantArgs=6,_XlaNumResourceArgs=0].v42.json
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185
ngraph/test/models/tensorflow/resnet8/tf_function_cluster_20[_XlaCompiledKernel=true,_XlaNumConstantArgs=3,_XlaNumResourceArgs=0].v23.json
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200
ngraph/test/models/tensorflow/resnet8/tf_function_cluster_22[_XlaCompiledKernel=true,_XlaNumConstantArgs=4,_XlaNumResourceArgs=0].v24.json
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1707
ngraph/test/models/tensorflow/resnet8/tf_function_cluster_23[_XlaCompiledKernel=true,_XlaNumConstantArgs=1,_XlaNumResourceArgs=0].v296.json
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158
ngraph/test/models/tensorflow/resnet8/tf_function_cluster_28[_XlaCompiledKernel=true,_XlaNumConstantArgs=0,_XlaNumResourceArgs=0].v13.json
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594
ngraph/test/models/tensorflow/resnet8/tf_function_cluster_34[_XlaCompiledKernel=true,_XlaNumConstantArgs=7,_XlaNumResourceArgs=0].v73.json
View File

@ -1,594 +0,0 @@
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104
ngraph/test/models/tensorflow/resnet8/tf_function_cluster_4[_XlaCompiledKernel=true,_XlaNumConstantArgs=1,_XlaNumResourceArgs=0].v14.json
View File

@ -1,104 +0,0 @@
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299
ngraph/test/models/tensorflow/resnet8/tf_function_cluster_8[_XlaCompiledKernel=true,_XlaNumConstantArgs=2,_XlaNumResourceArgs=0].v28.json
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587
ngraph/test/models/tensorflow/rnn/vanilla_rnn_3_time_step.json
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332
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"shape" : [ 3, 3, 3, 3 ]
},
{
"cacheable" : false,
"element_type" : "float",
"inputs" : [],
"name" : "Parameter_4",
"op" : "Parameter",
"outputs" : ["Parameter_4_0"],
"shape" : [3]
},
{
"cacheable" : false,
"element_type" : "float",
"inputs" : [],
"name" : "Parameter_3",
"op" : "Parameter",
"outputs" : ["Parameter_3_0"],
"shape" : [3]
},
{
"cacheable" : false,
"element_type" : "float",
"inputs" : [],
"name" : "Parameter_2",
"op" : "Parameter",
"outputs" : ["Parameter_2_0"],
"shape" : [3]
},
{
"cacheable" : false,
"element_type" : "float",
"inputs" : [],
"name" : "Parameter_1",
"op" : "Parameter",
"outputs" : ["Parameter_1_0"],
"shape" : [3]
},
{
"cacheable" : false,
"element_type" : "float",
"inputs" : [],
"name" : "Parameter_0",
"op" : "Parameter",
"outputs" : ["Parameter_0_0"],
"shape" : [ 3, 3, 3, 3 ]
},
{
"input_order" : [ 0, 3, 1, 2 ],
"inputs" : ["Parameter_12"],
"name" : "Reshape_13",
"op" : "Reshape",
"output_shape" : [ 2, 3, 224, 224 ],
"outputs" : ["Reshape_13_0"]
},
{
"axes" : [0],
"inputs" : ["Parameter_11"],
"name" : "Broadcast_36",
"op" : "Broadcast",
"outputs" : ["Broadcast_36_0"],
"shape" : [ 2, 10 ]
},
{
"input_order" : [ 3, 2, 0, 1 ],
"inputs" : ["Parameter_5"],
"name" : "Reshape_22",
"op" : "Reshape",
"output_shape" : [ 3, 3, 3, 3 ],
"outputs" : ["Reshape_22_0"]
},
{
"input_order" : [ 3, 2, 0, 1 ],
"inputs" : ["Parameter_0"],
"name" : "Reshape_14",
"op" : "Reshape",
"output_shape" : [ 3, 3, 3, 3 ],
"outputs" : ["Reshape_14_0"]
},
{
"data_dilation_strides" : [ 1, 1 ],
"inputs" : [ "Reshape_13", "Reshape_14" ],
"name" : "Convolution_15",
"op" : "Convolution",
"outputs" : ["Convolution_15_0"],
"padding_above" : [ 1, 1 ],
"padding_below" : [ 1, 1 ],
"window_dilation_strides" : [ 1, 1 ],
"window_movement_strides" : [ 1, 1 ]
},
{
"input_order" : [ 0, 2, 3, 1 ],
"inputs" : ["Convolution_15"],
"name" : "Reshape_16",
"op" : "Reshape",
"output_shape" : [ 2, 224, 224, 3 ],
"outputs" : ["Reshape_16_0"]
},
{
"input_order" : [ 0, 3, 1, 2 ],
"inputs" : ["Reshape_16"],
"name" : "Reshape_17",
"op" : "Reshape",
"output_shape" : [ 2, 3, 224, 224 ],
"outputs" : ["Reshape_17_0"]
},
{
"eps" : 1.0009999641624745e-05,
"inputs" : [
"Parameter_1", "Parameter_2", "Reshape_17", "Parameter_3",
"Parameter_4"
],
"name" : "BatchNormInference_18",
"op" : "BatchNormInference",
"outputs" : ["BatchNormInference_18_0"],
"training" : false
},
{
"input_order" : [ 0, 2, 3, 1 ],
"inputs" : ["BatchNormInference_18"],
"name" : "Reshape_19",
"op" : "Reshape",
"output_shape" : [ 2, 224, 224, 3 ],
"outputs" : ["Reshape_19_0"]
},
{
"inputs" : ["Reshape_19"],
"name" : "Relu_20",
"op" : "Relu",
"outputs" : ["Relu_20_0"]
},
{
"input_order" : [ 0, 3, 1, 2 ],
"inputs" : ["Relu_20"],
"name" : "Reshape_21",
"op" : "Reshape",
"output_shape" : [ 2, 3, 224, 224 ],
"outputs" : ["Reshape_21_0"]
},
{
"data_dilation_strides" : [ 1, 1 ],
"inputs" : [ "Reshape_21", "Reshape_22" ],
"name" : "Convolution_23",
"op" : "Convolution",
"outputs" : ["Convolution_23_0"],
"padding_above" : [ 1, 1 ],
"padding_below" : [ 1, 1 ],
"window_dilation_strides" : [ 1, 1 ],
"window_movement_strides" : [ 1, 1 ]
},
{
"input_order" : [ 0, 2, 3, 1 ],
"inputs" : ["Convolution_23"],
"name" : "Reshape_24",
"op" : "Reshape",
"output_shape" : [ 2, 224, 224, 3 ],
"outputs" : ["Reshape_24_0"]
},
{
"input_order" : [ 0, 3, 1, 2 ],
"inputs" : ["Reshape_24"],
"name" : "Reshape_25",
"op" : "Reshape",
"output_shape" : [ 2, 3, 224, 224 ],
"outputs" : ["Reshape_25_0"]
},
{
"eps" : 1.0009999641624745e-05,
"inputs" : [
"Parameter_6", "Parameter_7", "Reshape_25", "Parameter_8",
"Parameter_9"
],
"name" : "BatchNormInference_26",
"op" : "BatchNormInference",
"outputs" : ["BatchNormInference_26_0"],
"training" : false
},
{
"input_order" : [ 0, 2, 3, 1 ],
"inputs" : ["BatchNormInference_26"],
"name" : "Reshape_27",
"op" : "Reshape",
"output_shape" : [ 2, 224, 224, 3 ],
"outputs" : ["Reshape_27_0"]
},
{
"inputs" : [ "Reshape_27", "Parameter_12" ],
"name" : "Add_28",
"op" : "Add",
"outputs" : ["Add_28_0"]
},
{
"inputs" : ["Add_28"],
"name" : "Relu_29",
"op" : "Relu",
"outputs" : ["Relu_29_0"]
},
{
"input_order" : [ 0, 3, 1, 2 ],
"inputs" : ["Relu_29"],
"name" : "Reshape_30",
"op" : "Reshape",
"output_shape" : [ 2, 3, 224, 224 ],
"outputs" : ["Reshape_30_0"]
},
{
"include_padding_in_avg_computation" : false,
"inputs" : ["Reshape_30"],
"name" : "AvgPool_31",
"op" : "AvgPool",
"outputs" : ["AvgPool_31_0"],
"padding_above" : [ 0, 0 ],
"padding_below" : [ 0, 0 ],
"window_movement_strides" : [ 2, 2 ],
"window_shape" : [ 2, 2 ]
},
{
"input_order" : [ 0, 2, 3, 1 ],
"inputs" : ["AvgPool_31"],
"name" : "Reshape_32",
"op" : "Reshape",
"output_shape" : [ 2, 112, 112, 3 ],
"outputs" : ["Reshape_32_0"]
},
{
"input_order" : [ 0, 1, 2, 3 ],
"inputs" : ["Reshape_32"],
"name" : "Reshape_34",
"op" : "Reshape",
"output_shape" : [ 2, 37632 ],
"outputs" : ["Reshape_34_0"]
},
{
"inputs" : [ "Reshape_34", "Parameter_10" ],
"name" : "Dot_35",
"op" : "Dot",
"outputs" : ["Dot_35_0"],
"reduction_axes_count" : 1
},
{
"inputs" : [ "Dot_35", "Broadcast_36" ],
"name" : "Add_37",
"op" : "Add",
"outputs" : ["Add_37_0"]
},
{
"inputs" : ["Add_37"],
"name" : "Result_38",
"op" : "Result",
"outputs" : ["Result_38_0"]
}
],
"parameters" : [
"Parameter_0", "Parameter_1", "Parameter_2", "Parameter_3", "Parameter_4",
"Parameter_5", "Parameter_6", "Parameter_7", "Parameter_8", "Parameter_9",
"Parameter_10", "Parameter_11", "Parameter_12"
],
"result" : ["Result_38"]
}]

47
ngraph/test/ref_generators/generate_lrn_across_axes.py
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@ -1,47 +0,0 @@
#!/usr/bin/env python
# ******************************************************************************
# Copyright 2017-2021 Intel Corporation
#
# Licensed 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 copy
import numpy as np
def LRN(input, size=3, bias=1.0, alpha=3.0, beta=0.5):
output = copy.deepcopy(input)
N = input.shape[0]
C = input.shape[1]
H = input.shape[2]
W = input.shape[3]
for n in range(N):
begin_n = max(0, n - (size-1)//2)
end_n = min(N, n + (size-1)//2 + 1)
for c in range(C):
begin_c = max(0, c - (size-1)//2)
end_c = min(C, c + (size-1)//2 + 1)
for h in range(H):
begin_h = max(0, h - (size-1)//2)
end_h = min(H, h + (size-1)//2 + 1)
for w in range(W):
begin_w = max(0, w - (size-1)//2)
end_w = min(W, w + (size-1)//2 + 1)
patch = input[n, c, begin_h:end_h, begin_w:end_w]
output[n, c, h, w] /= (
np.power(bias + (alpha/(size**2)) * np.sum(patch * patch), beta))
return output
input = np.arange(0, 12, 1).reshape(2, 3, 2, 1).astype(np.float32)
result = LRN(input)
for elem in np.nditer(result):
print("{:.7f}f,".format(elem))

27
ngraph/test/ref_generators/generate_normalize_l2_ref.py
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@ -1,27 +0,0 @@
#!/usr/bin/env python
# ******************************************************************************
# Copyright 2017-2021 Intel Corporation
#
# Licensed 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 numpy as np
input = np.arange(1, 25, 1).reshape(1, 2, 3, 4).astype(np.float32)
eps = np.array([1e-6]).astype(np.float32)
# across chw axes
norm = np.sqrt(np.sum(np.power(input, 2), axis=(1), keepdims=True) + eps)
result = input/norm
for elem in np.nditer(result):
print(str(round(elem, 8)) + 'f, ')