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[DOCS] Add TensorFlow frontend docs (#4154) 

* Start to update TF frontend docs

* Add rst

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# Tensorflow Frontend
Tensorflow frontend helps in importing tensorflow released model into TVM.
This document helps few steps while importing various different models from
[tensorflow research/slim](https://github.com/tensorflow/models/tree/master/research/slim).
Current frontend is tested with all versions of below models
- Inception (V1/V2/V3/V4)
- Resnet (All)
- Mobilenet (V1/V2 All)
- Vgg (16/19)
Tensorflow frontend expects a freezed protobuf format as input.
Not all models are released as freezed protobuf. Some of them are checkpoints (.ckpt).
Please refer to [export](https://github.com/tensorflow/models/tree/master/research/slim#exporting-the-inference-graph)
and [freeze](https://github.com/tensorflow/models/tree/master/research/slim#freezing-the-exported-graph)
instructions to generate protobuf from checkpoint.
## General Instructions
### Add Shapes:
While freezing of protobuf add additional option ```add_shapes=True``` to embed output shapes of each node into graph.
You may use ```tvm.relay.testing.tf.AddShapesToGraphDef``` from nnvm for the same.
Please refer to [tensorflow tutorial](https://github.com/dmlc/tvm/blob/master/tutorials/nnvm/from_tensorflow.py).
### Explicit Shape:
There might be situations where the add_shapes=True may not provide sufficient information about shape.
You may pass explicit dictionary of input shapes argument for ```from_tensorflow```.
Please refer to [test cases](https://github.com/dmlc/tvm/blob/master/nnvm/tests/python/frontend/tensorflow/test_forward.py#L36).
### GPU:
Most of these tensorflow models are released for CPU with NHWC layout.
To compile for GPU we need to pass extra argument ```layout='NCHW'``` for from_tensorflow.
This option will do a layout conversion before and after for neural network ops.
Remaining nnvm build options for GPU compilation remain as it is.
docs/frontend/tensorflow.rst 0 → 100644
.. 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.
TensorFlow Frontend
===================
The TensorFlow frontend helps in importing TensorFlow models into TVM.
Supported versions:
- 1.12 and below
Tested models:
- Inception (V1/V2/V3/V4)
- Resnet (All)
- Mobilenet (V1/V2 All)
- Vgg (16/19)
- BERT (Base/3-layer)
Preparing a Model for Inference
-------------------------------
Remove Unneeded Nodes
~~~~~~~~~~~~~~~~~~~~~
The export process will remove many nodes that are not needed for inference, but unfortunately will leave some remaining. The nodes that should be manually removed are:
- Dropout, including `Dropout`_ and `DropoutWrapper`_
- `Assert`_
.. _Dropout: https://www.tensorflow.org/api_docs/python/tf/nn/dropout
.. _DropoutWrapper: https://www.tensorflow.org/versions/r1.12/api_docs/python/tf/nn/rnn_cell/DropoutWrapper?hl=hr
.. _Assert: https://www.tensorflow.org/api_docs/python/tf/debugging/Assert
Convert None Dimensions to Constants
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
TVM has minimal support for dynamic tensor shapes. Dimensions that are ``None`` should be replaced with constants. For example, a model may accept an input with shape ``(None,20)``. This should be converted to a shape like ``(1,20)``. The model should be modified accordingly to ensure that these shapes match throughout the graph.
Export
~~~~~~
TensorFlow frontend expects a frozen protobuf (.pb) or saved model as input. It currently does not support checkpoint (.ckpt). The graphdef needed by the TensorFlow frontend can be extracted from the active session, or by using the `TFParser`_ helper class.
.. _TFParser: https://github.com/dmlc/tvm/blob/master/python/tvm/relay/frontend/tensorflow_parser.py
The model should be exported with a number of transformations to prepare the model for inference. It is also important to set ```add_shapes=True```, as this will embed the output shapes of each node into the graph. Here is one function to export a model as a protobuf given a session:
.. code:: python
import tensorflow as tf
from tensorflow.tools.graph_transforms import TransformGraph
def export_pb(session):
with tf.gfile.GFile("myexportedmodel.pb", "wb") as f:
inputs = ["myinput1", "myinput2"] # replace with your input names
outputs = ["myoutput1"] # replace with your output names
graph_def = session.graph.as_graph_def(add_shapes=True)
graph_def = tf.graph.util.convert_variables_to_constants(session, graph_def, outputs)
graph_def = TransformGraph(
graph_def,
inputs,
outputs,
[
"remove_nodes(op=Identity, op=CheckNumerics, op=StopGradient)",
"sort_by_execution_order", # sort by execution order after each transform to ensure correct node ordering
"remove_device",
"sort_by_execution_order",
"fold_batch_norms",
"sort_by_execution_order",
"fold_old_batch_norms",
"sort_by_execution_order"
]
)
f.write(graph_def.SerializeToString())
Another method is to `export and freeze the graph <https://github.com/tensorflow/models/tree/master/research/slim#exporting-the-inference-graph>`_.
Import the Model
----------------
Explicit Shape:
~~~~~~~~~~~~~~~
To ensure shapes can be known throughout the entire graph, pass the ```shape``` argument to ```from_tensorflow```. This dictionary maps input names to input shapes. Please refer to these `test cases <https://github.com/dmlc/tvm/blob/master/nnvm/tests/python/frontend/tensorflow/test_forward.py#L36>`_ for examples.
Data Layout
~~~~~~~~~~~
Most TensorFlow models are released with NHWC layout. NCHW layout often provides better performance, especially on GPU. The TensorFlow frontend can automatically convert the model's data layout by passing the argument ```layout='NCHW'``` to ```from_tensorflow```.
Best Practices
--------------
- Use static tensor shapes instead of dynamic shapes (remove ```None``` dimensions).
- Use static RNN instead of dynamic RNN, as ```TensorArray``` isn't supported yet.
Supported Ops
-------------
- Abs
- Add
- All
- ArgMax
- ArgMin
- AvgPool
- BatchMatMul
- BatchMatMulV2
- BatchNormWithGlobalNormalization
- BatchToSpaceND
- BiasAdd
- BroadcastTo
- Cast
- Ceil
- CheckNumerics
- ClipByValue
- Concat
- ConcatV2
- Conv2D
- Cos
- CropAndResize
- DecodeJpeg
- DepthwiseConv2dNative
- DepthToSpace
- Equal
- Elu
- Enter
- Erf
- Exit
- Exp
- ExpandDims
- Fill
- Floor
- FloorDiv
- FusedBatchNorm
- FusedBatchNormV2
- Gather
- GatherNd
- GatherV2
- Greater
- GreaterEqual
- Identity
- LeakyRelu
- LeftShift
- Less
- LessEqual
- Log
- Log1p
- LoopCond
- LogicalAnd
- LogicalOr
- LogicalNot
- LogSoftmax
- LRN
- LSTMBlockCell
- MatMul
- Max
- MaxPool
- Maximum
- Mean
- Merge
- Min
- Minimum
- MirrorPad
- Mod
- Mul
- Neg
- NextIteration
- NotEqual
- OneHot
- Pack
- Pad
- PadV2
- Pow
- Prod
- Range
- Rank
- RealDiv
- Relu
- Relu6
- Reshape
- ResizeBilinear
- ResizeBicubic
- ResizeNearestNeighbor
- ReverseV2
- RightShift
- Round
- Rsqrt
- Select
- Selu
- Shape
- Sigmoid
- Sign
- Sin
- Size
- Slice
- Softmax
- Softplus
- SpaceToBatchND
- SpaceToDepth,
- Split
- SplitV
- Sqrt
- Square
- SquareDifference
- Squeeze
- StridedSlice
- Sub
- Sum
- Switch
- Tanh
- TensorArrayV3
- TensorArrayScatterV3
- TensorArrayGatherV3
- TensorArraySizeV3
- TensorArrayWriteV3
- TensorArrayReadV3
- TensorArraySplitV3
- TensorArrayConcatV3
- Tile
- TopKV2
- Transpose
- TruncateMod
- Unpack
- Where
- ZerosLike
docs/index.rst
......@@ -47,6 +47,12 @@ Developer Guide
dev/index
nnvm_top
Frontends
----------------
.. toctree::
:maxdepth: 1
frontend/tensorflow
Index
-----
......
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