diff --git a/develop/doc/_sources/design/tensor_array.md.txt b/develop/doc/_sources/design/tensor_array.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0419ec002159893b035fae1300fce489e68936a --- /dev/null +++ b/develop/doc/_sources/design/tensor_array.md.txt @@ -0,0 +1,73 @@ +# Design for TensorArray +TensorArray as a new concept is borrowed from TensorFlow, +it is meant to be used with dynamic iteration primitives such as `while_loop` and `map_fn`. + +This concept can be used to support our new design of dynamic operations, and help to refactor some existing variant-sentence-related layers, +such as `RecurrentGradientMachine`. + +In [our design for dynamic RNN](https://github.com/PaddlePaddle/Paddle/pull/4401), +`TensorArray` is used to segment inputs and store states in all time steps. +By providing some methods similar to a C++ array, +the definition of some state-based dynamic models such as RNN could be more natural and highly flexible. + +## Dynamic-Related Methods +Some basic methods should be proposed as follows: + +### stack() +Pack the values in a `TensorArray` into a tensor with rank one higher than each tensor in `values`. +### unstack(axis=0) +Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors. +### concat() +Return the values in the `TensorArray` as a concatenated Tensor. +### write(index, value, data_shared=true) +Write value into index of the TensorArray. +### read(index) +Read the value at location `index` in the `TensorArray`. +### size() +Return the number of values. + +## LoDTensor-related Supports +The `RecurrentGradientMachine` in Paddle serves as a flexible RNN layer; it takes variant length sequences as input, +because each step of RNN could only take a tensor-represented batch of data as input, +some preprocess should be taken on the inputs such as sorting the sentences by their length in descending order and cut each word and pack to new batches. + +Such cut-like operations can be embedded into `TensorArray` as general methods called `unpack` and `pack`. + +With these two methods, a variant-sentence-RNN can be implemented like + +```c++ +// input is the varient-length data +LodTensor sentence_input(xxx); +TensorArray ta; +Tensor indice_map; +Tensor boot_state = xxx; // to initialize rnn's first state +TensorArray::unpack(input, 1/*level*/, true/*sort_by_length*/, &ta, &indice_map); +TessorArray step_outputs; +TensorArray states; + +for (int step = 0; step = ta.size(); step++) { + auto state = states.read(step); + // rnnstep is a function which acts like a step of RNN + auto step_input = ta.read(step); + auto step_output = rnnstep(step_input, state); + step_outputs.write(step_output, true/*data_shared*/); +} + +// rnn_output is the final output of an rnn +LoDTensor rnn_output = ta.pack(ta, indice_map); +``` +the code above shows that by embedding the LoDTensor-related preprocess operations into `TensorArray`, +the implementation of a RNN that supports varient-length sentences is far more concise than `RecurrentGradientMachine` because the latter mixes all the codes together, hard to read and extend. + + +some details are as follows. + +### unpack(level, sort_by_length) +Split LodTensor in some `level` and generate batches, if set `sort_by_length`, will sort by length. + +Returns: + +- a new `TensorArray`, whose values are LodTensors and represents batches of data. +- an int32 Tensor, which stores the map from the new batch's indices to original LoDTensor +### pack(level, indices_map) +Recover the original LoD-arranged LoDTensor with the values in a `TensorArray` and `level` and `indices_map`. diff --git a/develop/doc/design/tensor_array.html b/develop/doc/design/tensor_array.html new file mode 100644 index 0000000000000000000000000000000000000000..63d905ed349c98f3d47a28fb5ada68c249c7793c --- /dev/null +++ b/develop/doc/design/tensor_array.html @@ -0,0 +1,321 @@ + + + + + + + + + + + Design for TensorArray — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ Fork me on Github +
+ + English + + + + +
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  • Design for TensorArray
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+

Design for TensorArray

+

TensorArray as a new concept is borrowed from TensorFlow, +it is meant to be used with dynamic iteration primitives such as while_loop and map_fn.

+

This concept can be used to support our new design of dynamic operations, and help to refactor some existing variant-sentence-related layers, +such as RecurrentGradientMachine.

+

In our design for dynamic RNN, +TensorArray is used to segment inputs and store states in all time steps. +By providing some methods similar to a C++ array, +the definition of some state-based dynamic models such as RNN could be more natural and highly flexible.

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+ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc/objects.inv b/develop/doc/objects.inv index 6cd69a80cd03110ef59d6794cbca4c5181cf4a86..aa35e827e5d0dc0d910a40c0b193b04ad3d69109 100644 Binary files a/develop/doc/objects.inv and b/develop/doc/objects.inv differ diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js index 9025ee868a2c5f0fda7a1af48f44631436b2adeb..fb24b492fad3775e6baa54dae28d730b8cc26bbe 100644 --- a/develop/doc/searchindex.js +++ b/develop/doc/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/tensor_array.md.txt b/develop/doc_cn/_sources/design/tensor_array.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0419ec002159893b035fae1300fce489e68936a --- /dev/null +++ b/develop/doc_cn/_sources/design/tensor_array.md.txt @@ -0,0 +1,73 @@ +# Design for TensorArray +TensorArray as a new concept is borrowed from TensorFlow, +it is meant to be used with dynamic iteration primitives such as `while_loop` and `map_fn`. + +This concept can be used to support our new design of dynamic operations, and help to refactor some existing variant-sentence-related layers, +such as `RecurrentGradientMachine`. + +In [our design for dynamic RNN](https://github.com/PaddlePaddle/Paddle/pull/4401), +`TensorArray` is used to segment inputs and store states in all time steps. +By providing some methods similar to a C++ array, +the definition of some state-based dynamic models such as RNN could be more natural and highly flexible. + +## Dynamic-Related Methods +Some basic methods should be proposed as follows: + +### stack() +Pack the values in a `TensorArray` into a tensor with rank one higher than each tensor in `values`. +### unstack(axis=0) +Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors. +### concat() +Return the values in the `TensorArray` as a concatenated Tensor. +### write(index, value, data_shared=true) +Write value into index of the TensorArray. +### read(index) +Read the value at location `index` in the `TensorArray`. +### size() +Return the number of values. + +## LoDTensor-related Supports +The `RecurrentGradientMachine` in Paddle serves as a flexible RNN layer; it takes variant length sequences as input, +because each step of RNN could only take a tensor-represented batch of data as input, +some preprocess should be taken on the inputs such as sorting the sentences by their length in descending order and cut each word and pack to new batches. + +Such cut-like operations can be embedded into `TensorArray` as general methods called `unpack` and `pack`. + +With these two methods, a variant-sentence-RNN can be implemented like + +```c++ +// input is the varient-length data +LodTensor sentence_input(xxx); +TensorArray ta; +Tensor indice_map; +Tensor boot_state = xxx; // to initialize rnn's first state +TensorArray::unpack(input, 1/*level*/, true/*sort_by_length*/, &ta, &indice_map); +TessorArray step_outputs; +TensorArray states; + +for (int step = 0; step = ta.size(); step++) { + auto state = states.read(step); + // rnnstep is a function which acts like a step of RNN + auto step_input = ta.read(step); + auto step_output = rnnstep(step_input, state); + step_outputs.write(step_output, true/*data_shared*/); +} + +// rnn_output is the final output of an rnn +LoDTensor rnn_output = ta.pack(ta, indice_map); +``` +the code above shows that by embedding the LoDTensor-related preprocess operations into `TensorArray`, +the implementation of a RNN that supports varient-length sentences is far more concise than `RecurrentGradientMachine` because the latter mixes all the codes together, hard to read and extend. + + +some details are as follows. + +### unpack(level, sort_by_length) +Split LodTensor in some `level` and generate batches, if set `sort_by_length`, will sort by length. + +Returns: + +- a new `TensorArray`, whose values are LodTensors and represents batches of data. +- an int32 Tensor, which stores the map from the new batch's indices to original LoDTensor +### pack(level, indices_map) +Recover the original LoD-arranged LoDTensor with the values in a `TensorArray` and `level` and `indices_map`. diff --git a/develop/doc_cn/design/tensor_array.html b/develop/doc_cn/design/tensor_array.html new file mode 100644 index 0000000000000000000000000000000000000000..bede072208f03c4207230766a46d5694fe6769bd --- /dev/null +++ b/develop/doc_cn/design/tensor_array.html @@ -0,0 +1,336 @@ + + + + + + + + + + + Design for TensorArray — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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  • Design for TensorArray
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Design for TensorArray

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TensorArray as a new concept is borrowed from TensorFlow, +it is meant to be used with dynamic iteration primitives such as while_loop and map_fn.

+

This concept can be used to support our new design of dynamic operations, and help to refactor some existing variant-sentence-related layers, +such as RecurrentGradientMachine.

+

In our design for dynamic RNN, +TensorArray is used to segment inputs and store states in all time steps. +By providing some methods similar to a C++ array, +the definition of some state-based dynamic models such as RNN could be more natural and highly flexible.

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