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“068bfbb817611c856acd8c535de2b33a6126786c”上不存在“paddle/legacy/gserver/layers/ValidationLayer.cpp”
提交 0968c7cd 编写于 作者: W wanghaox
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......@@ -28,4 +28,3 @@ cmake_install.cmake
paddle/.timestamp
python/paddlepaddle.egg-info/
paddle/pybind/pybind.h
python/paddle/v2/framework/tests/tmp/*
cmake/external/openblas.cmake
浏览文件 @ 0968c7cd
......@@ -98,7 +98,7 @@ IF(NOT ${CBLAS_FOUND})
ENDIF()
INSTALL(CODE "execute_process(
COMMAND ${CMAKE_COMMAND} -E copy_directory ${CBLAS_INSTALL_DIR}/lib
destination ${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR}
${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR}
)"
)
INSTALL(CODE "MESSAGE(STATUS \"Installing: \"
......
doc/design/ops/images/2_level_rnn.dot
浏览文件 @ 0968c7cd
digraph G {
rnn [label="1-th level RNN" shape=box]
rnn [label="1st level RNN" shape=box]
subgraph cluster0 {
label = "time step 0"
......@@ -8,7 +8,7 @@ digraph G {
sent0 [label="sentence"]
sent1 [label="sentence"]
rnn1 [label="2-th level RNN" shape=box]
rnn1 [label="2nd level RNN" shape=box]
sent0 -> rnn1
sent1 -> rnn1
......@@ -20,7 +20,7 @@ digraph G {
sent2 [label="sentence"]
sent3 [label="sentence"]
rnn2 [label="2-th level RNN" shape=box]
rnn2 [label="2nd level RNN" shape=box]
sent2 -> rnn2
sent3 -> rnn2
......@@ -32,7 +32,7 @@ digraph G {
sent4 [label="sentence"]
sent5 [label="sentence"]
rnn3 [label="2-th level RNN" shape=box]
rnn3 [label="2nd level RNN" shape=box]
sent4 -> rnn3
sent5 -> rnn3
......
doc/design/ops/rnn.md
浏览文件 @ 0968c7cd
# RNNOp design
This document is about an RNN operator which requires that instances in a mini-batch have the same length. We will have a more flexible RNN operator.
This document describes the RNN (Recurrent Neural Network) operator and how it is implemented in PaddlePaddle. The RNN op requires that all instances in a mini-batch have the same length. We will have a more flexible dynamic RNN operator in the future.
## RNN Algorithm Implementation
<p aligh="center">
<p align="center">
<img src="./images/rnn.jpg"/>
</p>
The above diagram shows an RNN unrolled into a full network.
There are several important concepts:
There are several important concepts here:
- *step-net*: the sub-graph to run at each step,
- *memory*, $h_t$, the state of the current step,
- *ex-memory*, $h_{t-1}$, the state of the previous step,
- *initial memory value*, the ex-memory of the first step.
- *step-net*: the sub-graph that runs at each step.
- *memory*, $h_t$, the state of the current step.
- *ex-memory*, $h_{t-1}$, the state of the previous step.
- *initial memory value*, the memory of the first (initial) step.
### Step-scope
There could be local variables defined in step-nets. PaddlePaddle runtime realizes these variables in *step-scopes* -- scopes created for each step.
There could be local variables defined in each step-net. PaddlePaddle runtime realizes these variables in *step-scopes* which are created for each step.
<p aligh="center">
<p align="center">
<img src="./images/rnn.png"/><br/>
Figure 2 the RNN's data flow
Figure 2 illustrates the RNN's data flow
</p>
Please be aware that all steps run the same step-net. Each step
Please be aware that every step runs the same step-net. Each step does the following:
1. creates the step-scope,
2. realizes local variables, including step-outputs, in the step-scope, and
3. runs the step-net, which could use these variables.
1. Creates the step-scope.
2. Initializes the local variables including step-outputs, in the step-scope.
3. Runs the step-net, which uses the above mentioned variables.
The RNN operator will compose its output from step outputs in step scopes.
The RNN operator will compose its output from step outputs in each of the step scopes.
### Memory and Ex-memory
Let's give more details about memory and ex-memory via a simply example:
Let's give more details about memory and ex-memory using a simple example:
$$
h_t = U h_{t-1} + W x_t
$$,
where $h_t$ and $h_{t-1}$ are the memory and ex-memory of step $t$'s respectively.
where $h_t$ and $h_{t-1}$ are the memory and ex-memory (previous memory) of step $t$ respectively.
In the implementation, we can make an ex-memory variable either "refers to" the memory variable of the previous step,
or copy the value of the previous memory value to the current ex-memory variable.
In the implementation, we can make an ex-memory variable either "refer to" the memory variable of the previous step,
or copy the memory value of the previous step to the current ex-memory variable.
### Usage in Python
For more information on Block, please refer to the [design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md).
We can define an RNN's step-net using Block:
We can define an RNN's step-net using a Block:
```python
import paddle as pd
X = some_op() # x is some operator's output, and is a LoDTensor
X = some_op() # x is some operator's output and is a LoDTensor
a = some_op()
# declare parameters
......@@ -68,7 +68,7 @@ with rnn.stepnet():
x = rnn.add_input(X)
# declare a memory (rnn's step)
h = rnn.add_memory(init=a)
# h.pre_state() means previous memory of rnn
# h.pre_state(), the previous memory of rnn
new_state = pd.add_two( pd.matmul(W, x) + pd.matmul(U, h.pre_state()))
# update current memory
h.update(new_state)
......@@ -80,19 +80,19 @@ out = rnn()
Python API functions in above example:
- `rnn.add_input` indicates the parameter is a variable that will be segmented into step-inputs.
- `rnn.add_memory` creates a variable used as the memory.
- `rnn.add_outputs` mark the variables that will be concatenated across steps into the RNN output.
- `rnn.add_input`: indicates that the parameter is a variable that will be segmented into step-inputs.
- `rnn.add_memory`: creates a variable used as the memory.
- `rnn.add_outputs`: marks the variables that will be concatenated across steps into the RNN output.
### Nested RNN and LoDTensor
An RNN whose step-net includes other RNN operators is known as an *nested RNN*.
For example, we could have a 2-level RNN, where the top level corresponds to paragraphs, and the lower level corresponds to sentences.
For example, we could have a 2-level RNN, where the top level corresponds to paragraphs, and the lower level corresponds to sentences. Each step of the higher level RNN also receives an input from the corresponding step of the lower level, and additionally the output from the previous time step at the same level.
The following figure illustrates the feeding of text into the lower level, one sentence each step, and the feeding of step outputs to the top level. The final top level output is about the whole text.
The following figure illustrates feeding in text into the lower level, one sentence at a step, and the feeding in step outputs to the top level. The final top level output is about the whole text.
<p aligh="center">
<p align="center">
<img src="./images/2_level_rnn.png"/>
</p>
......@@ -110,7 +110,7 @@ a = some_op()
# chapter_data is a set of 128-dim word vectors
# the first level of LoD is sentence
# the second level of LoD is chapter
# the second level of LoD is a chapter
chapter_data = pd.Variable(shape=[None, 128], type=pd.lod_tensor, level=2)
def lower_level_rnn(paragraph):
......@@ -138,14 +138,14 @@ with top_level_rnn.stepnet():
pd.matmul(W0, paragraph_data) + pd.matmul(U0, h.pre_state()))
top_level_rnn.add_outputs(h)
# just output the last step
# output the last step
chapter_out = top_level_rnn(output_all_steps=False)
```
in above example, the construction of the `top_level_rnn` calls `lower_level_rnn`. The input is a LoD Tensor. The top level RNN segments input text data into paragraphs, and the lower level RNN segments each paragraph into sentences.
In the above example, the construction of the `top_level_rnn` calls `lower_level_rnn`. The input is an LoD Tensor. The top level RNN segments input text data into paragraphs, and the lower level RNN segments each paragraph into sentences.
By default, the `RNNOp` will concatenate the outputs from all the time steps,
if the `output_all_steps` set to False, it will only output the final time step.
By default, the `RNNOp` will concatenate the outputs from all the time steps.
If the `output_all_steps` is set to False, it will only output the final time step.
<p align="center">
......
doc/design/ops/sequence_decoder.md
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# Design: Sequence Decoder Generating LoDTensors
In tasks such as machine translation and image to text,
a [sequence decoder](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md) is necessary to generate sequences.
In tasks such as machine translation and visual captioning,
a [sequence decoder](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md) is necessary to generate sequences, one word at a time.
This documentation describes how to implement the sequence decoder as an operator.
## Beam Search based Decoder
The [beam search algorithm](https://en.wikipedia.org/wiki/Beam_search) is necessary when generating sequences,
it is a heuristic search algorithm that explores the paths by expanding the most promising node in a limited set.
The [beam search algorithm](https://en.wikipedia.org/wiki/Beam_search) is necessary when generating sequences. It is a heuristic search algorithm that explores the paths by expanding the most promising node in a limited set.
In the old version of PaddlePaddle, a C++ class `RecurrentGradientMachine` implements the general sequence decoder based on beam search,
due to the complexity, the implementation relays on a lot of special data structures,
quite trivial and hard to be customized by users.
In the old version of PaddlePaddle, the C++ class `RecurrentGradientMachine` implements the general sequence decoder based on beam search, due to the complexity involved, the implementation relies on a lot of special data structures that are quite trivial and hard to be customized by users.
There are a lot of heuristic tricks in the sequence generation tasks,
so the flexibility of sequence decoder is very important to users.
There are a lot of heuristic tricks in the sequence generation tasks, so the flexibility of sequence decoder is very important to users.
During PaddlePaddle's refactoring work,
some new concept is proposed such as [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md) and [TensorArray](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/tensor_array.md) that can better support sequence usage,
and they can help to make the implementation of beam search based sequence decoder **more transparent and modular** .
During the refactoring of PaddlePaddle, some new concepts are proposed such as: [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md) and [TensorArray](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/tensor_array.md) that can better support the sequence usage, and they can also help make the implementation of beam search based sequence decoder **more transparent and modular** .
For example, the RNN sates, candidates IDs and probabilities of beam search can be represented as `LoDTensors`;
For example, the RNN states, candidates IDs and probabilities of beam search can be represented all as `LoDTensors`;
the selected candidate's IDs in each time step can be stored in a `TensorArray`, and `Packed` to the sentences translated.
## Changing LoD's absolute offset to relative offsets
The current `LoDTensor` is designed to store levels of variable-length sequences,
it stores several arrays of integers each represents a level.
The current `LoDTensor` is designed to store levels of variable-length sequences. It stores several arrays of integers where each represents a level.
The integers in each level represents the begin and end (not inclusive) offset of a sequence **in the underlying tensor**,
let's call this format the **absolute-offset LoD** for clear.
The integers in each level represent the begin and end (not inclusive) offset of a sequence **in the underlying tensor**,
let's call this format the **absolute-offset LoD** for clarity.
The relative-offset LoD can fast retrieve any sequence but fails to represent empty sequences, for example, a two-level LoD is as follows
The relative-offset LoD can retrieve any sequence very quickly but fails to represent empty sequences, for example, a two-level LoD is as follows
```python
[[0, 3, 9]
[0, 2, 3, 3, 3, 9]]
......@@ -41,8 +34,7 @@ The first level tells that there are two sequences:
while on the second level, there are several empty sequences that both begin and end at `3`.
It is impossible to tell how many empty second-level sequences exist in the first-level sequences.
There are many scenarios that relay on empty sequence representation,
such as machine translation or image to text, one instance has no translations or the empty candidate set for a prefix.
There are many scenarios that rely on empty sequence representation, for example in machine translation or visual captioning, one instance has no translation or the empty candidate set for a prefix.
So let's introduce another format of LoD,
it stores **the offsets of the lower level sequences** and is called **relative-offset** LoD.
......@@ -60,13 +52,12 @@ their offsets in the second-level LoD is `[0, 3)` and `[3, 5)`.
The second level is the same with the relative offset example because the lower level is a tensor.
It is easy to find out the second sequence in the first-level LoD has two empty sequences.
The following demos are based on relative-offset LoD.
The following examples are based on relative-offset LoD.
## Usage in a simple machine translation model
Let's start from a simple machine translation model that is simplified from [machine translation chapter](https://github.com/PaddlePaddle/book/tree/develop/08.machine_translation) to draw a simple blueprint of what a sequence decoder can do and how to use it.
Let's start from a simple machine translation model that is simplified from the [machine translation chapter](https://github.com/PaddlePaddle/book/tree/develop/08.machine_translation) to draw a blueprint of what a sequence decoder can do and how to use it.
The model has an encoder that learns the semantic vector from a sequence,
and a decoder which uses the sequence decoder to generate new sentences.
The model has an encoder that learns the semantic vector from a sequence, and a decoder which uses the sequence encoder to generate new sentences.
**Encoder**
```python
......@@ -154,34 +145,33 @@ def generate():
translation_ids, translation_scores = decoder()
```
The `decoder.beam_search` is a operator that given the candidates and the scores of translations including the candidates,
return the result of the beam search algorithm.
The `decoder.beam_search` is an operator that, given the candidates and the scores of translations including the candidates,
returns the result of the beam search algorithm.
In this way, users can customize anything on the inputs or outputs of beam search, for example, two ways to prune some translation prefixes
In this way, users can customize anything on the input or output of beam search, for example:
1. meke the correspondind elements in `topk_generated_scores` zero or some small values, beam_search will discard this candidate.
2. remove some specific candidate in `selected_ids`
3. get the final `translation_ids`, remove the translation sequence in it.
1. Make the corresponding elements in `topk_generated_scores` zero or some small values, beam_search will discard this candidate.
2. Remove some specific candidate in `selected_ids`.
3. Get the final `translation_ids`, remove the translation sequence in it.
The implementation of sequence decoder can reuse the C++ class [RNNAlgorithm](https://github.com/Superjom/Paddle/blob/68cac3c0f8451fe62a4cdf156747d6dc0ee000b3/paddle/operators/dynamic_recurrent_op.h#L30),
so the python syntax is quite similar to a [RNN](https://github.com/Superjom/Paddle/blob/68cac3c0f8451fe62a4cdf156747d6dc0ee000b3/doc/design/block.md#blocks-with-for-and-rnnop).
The implementation of sequence decoder can reuse the C++ class: [RNNAlgorithm](https://github.com/Superjom/Paddle/blob/68cac3c0f8451fe62a4cdf156747d6dc0ee000b3/paddle/operators/dynamic_recurrent_op.h#L30),
so the python syntax is quite similar to that of an [RNN](https://github.com/Superjom/Paddle/blob/68cac3c0f8451fe62a4cdf156747d6dc0ee000b3/doc/design/block.md#blocks-with-for-and-rnnop).
Both of them are two-level `LoDTensors`
Both of them are two-level `LoDTensors`:
- the first level represents `batch_size` of (source) sentences;
- the second level represents the candidate ID sets for translation prefix.
- The first level represents `batch_size` of (source) sentences.
- The second level represents the candidate ID sets for translation prefix.
for example, 3 source sentences to translate, and has 2, 3, 1 candidates.
For example, 3 source sentences to translate, and has 2, 3, 1 candidates.
Unlike an RNN, in sequence decoder, the previous state and the current state have different LoD and shape,
a `lod_expand` operator is used to expand the LoD of the previous state to fit the current state.
Unlike an RNN, in sequence decoder, the previous state and the current state have different LoD and shape, and an `lod_expand` operator is used to expand the LoD of the previous state to fit the current state.
For example, the previous state
For example, the previous state:
* LoD is `[0, 1, 3][0, 2, 5, 6]`
* content of tensor is `a1 a2 b1 b2 b3 c1`
the current state stored in `encoder_ctx_expanded`
the current state is stored in `encoder_ctx_expanded`:
* LoD is `[0, 2, 7][0 3 5 8 9 11 11]`
* the content is
......@@ -192,54 +182,48 @@ the current state stored in `encoder_ctx_expanded`
- b3 b3
- None (c1 has 0 candidates, so c1 is dropped)
Benefit from the relative offset LoD, empty candidate set can be represented naturally.
The benefit from the relative offset LoD is that the empty candidate set can be represented naturally.
the status in each time step can be stored in `TensorArray`, and `Pack`ed to a final LoDTensor, the corresponding syntax is
The status in each time step can be stored in `TensorArray`, and `Pack`ed to a final LoDTensor. The corresponding syntax is:
```python
decoder.output(selected_ids)
decoder.output(selected_generation_scores)
```
the `selected_ids` is the candidate ids for the prefixes,
it will be `Packed` by `TensorArray` to a two-level `LoDTensor`,
the first level represents the source sequences,
the second level represents generated sequences.
The `selected_ids` are the candidate ids for the prefixes, and will be `Packed` by `TensorArray` to a two-level `LoDTensor`, where the first level represents the source sequences and the second level represents generated sequences.
Pack the `selected_scores` will get a `LoDTensor` that stores scores of each candidate of translations.
Packing the `selected_scores` will get a `LoDTensor` that stores scores of each translation candidate.
Pack the `selected_generation_scores` will get a `LoDTensor`, and each tail is the probability of the translation.
Packing the `selected_generation_scores` will get a `LoDTensor`, and each tail is the probability of the translation.
## LoD and shape changes during decoding
<p align="center">
<img src="./images/LOD-and-shape-changes-during-decoding.jpg"/>
</p>
According the image above, the only phrase to change LoD is beam search.
According to the image above, the only phase that changes the LoD is beam search.
## Beam search design
The beam search algorthm will be implemented as one method of the sequence decoder, it has 3 inputs
The beam search algorithm will be implemented as one method of the sequence decoder and has 3 inputs:
1. `topk_ids`, top K candidate ids for each prefix.
1. `topk_ids`, the top K candidate ids for each prefix.
2. `topk_scores`, the corresponding scores for `topk_ids`
3. `generated_scores`, the score of the prefixes.
All of the are LoDTensors, so that the sequence affilication is clear.
Beam search will keep a beam for each prefix and select a smaller candidate set for each prefix.
All of these are LoDTensors, so that the sequence affiliation is clear. Beam search will keep a beam for each prefix and select a smaller candidate set for each prefix.
It will return three variables
It will return three variables:
1. `selected_ids`, the final candidate beam search function selected for the next step.
2. `selected_scores`, the scores for the candidates.
3. `generated_scores`, the updated scores for each prefixes (with the new candidates appended).
3. `generated_scores`, the updated scores for each prefix (with the new candidates appended).
## Introducing the LoD-based `Pack` and `Unpack` methods in `TensorArray`
The `selected_ids`, `selected_scores` and `generated_scores` are LoDTensors,
and they exist in each time step,
The `selected_ids`, `selected_scores` and `generated_scores` are LoDTensors that exist at each time step,
so it is natural to store them in arrays.
Currently, PaddlePaddle has a module called `TensorArray` which can store an array of tensors,
the results of beam search are better to store in a `TensorArray`.
Currently, PaddlePaddle has a module called `TensorArray` which can store an array of tensors. It is better to store the results of beam search in a `TensorArray`.
The `Pack` and `UnPack` in `TensorArray` are used to package tensors in the array to a `LoDTensor` or split the `LoDTensor` to an array of tensors.
It needs some extensions to support pack or unpack an array of `LoDTensors`.
The `Pack` and `UnPack` in `TensorArray` are used to pack tensors in the array to an `LoDTensor` or split the `LoDTensor` to an array of tensors.
It needs some extensions to support the packing or unpacking an array of `LoDTensors`.
paddle/framework/CMakeLists.txt
浏览文件 @ 0968c7cd
......@@ -38,9 +38,9 @@ py_proto_compile(framework_py_proto SRCS framework.proto)
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
add_dependencies(framework_py_proto framework_py_proto_init)
add_custom_command(TARGET framework_py_proto POST_BUILD
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SOURCE_DIR}/python/paddle/v2/framework/proto
COMMAND cp *.py ${PADDLE_SOURCE_DIR}/python/paddle/v2/framework/proto/
COMMENT "Copy generated python proto into directory paddle/v2/framework/proto."
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SOURCE_DIR}/python/paddle/v2/fluid/proto
COMMAND cp *.py ${PADDLE_SOURCE_DIR}/python/paddle/v2/fluid/proto/
COMMENT "Copy generated python proto into directory paddle/v2/fluid/proto."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
cc_library(backward SRCS backward.cc DEPS net_op)
......
paddle/gserver/layers/ROIPoolLayer.cpp
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......@@ -100,8 +100,9 @@ void ROIPoolLayer::forward(PassType passType) {
size_t roiEndH = round(bottomROIs[4] * spatialScale_);
CHECK_GE(roiBatchIdx, 0UL);
CHECK_LT(roiBatchIdx, batchSize);
size_t roiHeight = std::max(roiEndH - roiStartH + 1, 1UL);
size_t roiWidth = std::max(roiEndW - roiStartW + 1, 1UL);
size_t roiHeight =
std::max(roiEndH - roiStartH + 1, static_cast<size_t>(1));
size_t roiWidth = std::max(roiEndW - roiStartW + 1, static_cast<size_t>(1));
real binSizeH =
static_cast<real>(roiHeight) / static_cast<real>(pooledHeight_);
real binSizeW =
......@@ -114,10 +115,14 @@ void ROIPoolLayer::forward(PassType passType) {
size_t wstart = static_cast<size_t>(std::floor(pw * binSizeW));
size_t hend = static_cast<size_t>(std::ceil((ph + 1) * binSizeH));
size_t wend = static_cast<size_t>(std::ceil((pw + 1) * binSizeW));
hstart = std::min(std::max(hstart + roiStartH, 0UL), height_);
wstart = std::min(std::max(wstart + roiStartW, 0UL), width_);
hend = std::min(std::max(hend + roiStartH, 0UL), height_);
wend = std::min(std::max(wend + roiStartW, 0UL), width_);
hstart = std::min(
std::max(hstart + roiStartH, static_cast<size_t>(0)), height_);
wstart = std::min(
std::max(wstart + roiStartW, static_cast<size_t>(0)), width_);
hend = std::min(std::max(hend + roiStartH, static_cast<size_t>(0)),
height_);
wend = std::min(std::max(wend + roiStartW, static_cast<size_t>(0)),
width_);
bool isEmpty = (hend <= hstart) || (wend <= wstart);
size_t poolIndex = ph * pooledWidth_ + pw;
......
paddle/operators/accuracy_op.cu
浏览文件 @ 0968c7cd
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <thrust/reduce.h>
#include "paddle/operators/accuracy_op.h"
#include "paddle/platform/cuda_helper.h"
#include "paddle/platform/gpu_info.h"
namespace paddle {
namespace operators {
......@@ -73,26 +74,28 @@ class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
int num_samples = static_cast<int>(inference->dims()[0]);
size_t infer_width = inference->dims()[1];
PADDLE_ENFORCE(cudaMemset(accuracy_data, 0, sizeof(float)));
// cudaMemset((void**)&correct_data, 0, sizeof(float));
auto stream = ctx.cuda_device_context().stream();
platform::GpuMemsetAsync(accuracy_data, 0, sizeof(float), stream);
if (num_samples == 0) {
return;
}
cudaMemcpy(total_data, &num_samples, sizeof(int), cudaMemcpyHostToDevice);
platform::GpuMemcpyAsync(total_data, &num_samples, sizeof(int),
cudaMemcpyHostToDevice, stream);
AccuracyCudaKernel<PADDLE_CUDA_NUM_THREADS><<<
1, PADDLE_CUDA_NUM_THREADS, 0, ctx.cuda_device_context().stream()>>>(
AccuracyCudaKernel<
PADDLE_CUDA_NUM_THREADS><<<1, PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
num_samples, infer_width, indices_data, label_data, correct_data,
accuracy_data);
int d_num_samples, d_num_correct;
float d_accuracy;
cudaMemcpy(&d_num_correct, correct_data, sizeof(int),
cudaMemcpyDeviceToHost);
cudaMemcpy(&d_num_samples, total_data, sizeof(int), cudaMemcpyDeviceToHost);
cudaMemcpy(&d_accuracy, accuracy_data, sizeof(float),
cudaMemcpyDeviceToHost);
platform::GpuMemcpyAsync(&d_num_correct, correct_data, sizeof(int),
cudaMemcpyDeviceToHost, stream);
platform::GpuMemcpyAsync(&d_num_samples, total_data, sizeof(int),
cudaMemcpyDeviceToHost, stream);
platform::GpuMemcpyAsync(&d_accuracy, accuracy_data, sizeof(float),
cudaMemcpyDeviceToHost, stream);
}
};
......
paddle/operators/beam_search_op.cc 0 → 100644
浏览文件 @ 0968c7cd
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#include "paddle/operators/beam_search_op.h"
#include <map>
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
framework::LoDTensor *selected_ids,
framework::LoDTensor *selected_scores) {
auto items = SelectTopBeamSizeItems();
auto selected_items = ToMap(items);
PruneEndidCandidates(pre_ids, &selected_items);
// calculate the output tensor's height
size_t num_instances = std::accumulate(
std::begin(items), std::end(items), 0,
[](size_t a, std::vector<Item> &b) { return a + b.size(); });
// the output tensor shape should be [num_instances, 1]
auto dims = framework::make_ddim(
std::vector<int64_t>({static_cast<int>(num_instances), 1}));
selected_ids->Resize(dims);
selected_scores->Resize(dims);
std::map<size_t /*offset*/, std::vector<Item>> hash;
framework::LoD new_lod;
auto *ids_data = selected_ids->mutable_data<int>(platform::CPUPlace());
auto *scores_data =
selected_scores->mutable_data<float>(platform::CPUPlace());
// fill in data
std::vector<size_t> low_level;
size_t low_offset = 0;
for (auto &items : selected_items) {
low_level.push_back(low_offset);
for (auto &item : items) {
ids_data[low_offset] = item.id;
scores_data[low_offset] = item.score;
low_offset++;
}
}
// fill lod
auto abs_lod = framework::ToAbsOffset(ids_->lod());
auto &high_level = abs_lod[lod_level_];
framework::LoD lod(2);
lod[0].assign(high_level.begin(), high_level.end());
lod[1].assign(low_level.begin(), low_level.end());
selected_ids->set_lod(lod);
selected_scores->set_lod(lod);
}
void BeamSearch::PruneEndidCandidates(const framework::LoDTensor &pre_ids,
std::vector<std::vector<Item>> *items) {
auto *pre_ids_data = pre_ids.data<int>();
for (size_t offset = 0; offset < items->size(); offset++) {
auto prefix_id = pre_ids_data[offset];
if (prefix_id == end_id_) {
items->at(offset).clear();
}
}
}
std::vector<std::vector<BeamSearch::Item>> BeamSearch::ToMap(
const std::vector<std::vector<Item>> &items) {
std::vector<std::vector<Item>> result;
for (auto &entries : items) {
for (const auto &item : entries) {
if (item.offset >= result.size()) {
result.resize(item.offset + 1);
}
result[item.offset].push_back(item);
}
}
return result;
}
std::vector<std::vector<BeamSearch::Item>>
BeamSearch::SelectTopBeamSizeItems() {
std::vector<std::vector<Item>> result;
std::vector<Item> items;
// for each source sentence, select the top beam_size items across all
// candidate sets.
while (NextItemSet(&items)) {
std::nth_element(std::begin(items), std::begin(items) + beam_size_,
std::end(items), [](const Item &a, const Item &b) {
// TODO(superjom) make score's comparation customizable.
// partial sort in descending order
return a.score > b.score;
});
// prune the top beam_size items.
if (items.size() > beam_size_) {
items.resize(beam_size_);
}
result.emplace_back(items);
}
return result;
}
// the candidates of a source
bool BeamSearch::NextItemSet(std::vector<BeamSearch::Item> *items) {
if (sent_offset_ >= ids_->NumElements(lod_level_)) {
return false;
}
// find the current candidates
auto ids = *ids_;
auto scores = *scores_;
auto source_abs_two_level_lod = framework::SliceInLevel(
ids.lod(), lod_level_, sent_offset_, sent_offset_ + 1);
source_abs_two_level_lod = framework::ToAbsOffset(source_abs_two_level_lod);
auto abs_lod = framework::ToAbsOffset(ids.lod());
PADDLE_ENFORCE_GE(source_abs_two_level_lod.size(), 2UL);
auto *ids_data = ids.data<int>();
auto *scores_data = scores.data<float>();
size_t instance_dim = 1;
for (int i = 1; i < ids.dims().size(); i++) {
instance_dim *= ids.dims()[i];
}
items->clear();
items->reserve(framework::product(ids.dims()));
for (size_t offset = abs_lod[lod_level_][sent_offset_];
offset < abs_lod[lod_level_][sent_offset_ + 1]; offset++) {
for (int d = 0; d < instance_dim; d++) {
const size_t dim_offset = offset * instance_dim + d;
items->emplace_back(offset, ids_data[dim_offset],
scores_data[dim_offset]);
}
}
sent_offset_++;
return true;
}
class BeamSearchProtoAndCheckerMaker
: public framework::OpProtoAndCheckerMaker {
public:
BeamSearchProtoAndCheckerMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
// inputs and outputs stored in proto
AddInput("pre_ids", "ids in previous step");
AddInput("ids", "a LoDTensor of shape of [None,k]");
AddInput("scores",
"a LoDTensor that has the same shape and LoD with `ids`");
AddOutput("selected_ids",
"a LoDTensor that stores the IDs selected by beam search");
AddOutput(
"selected_scores",
"a LoDTensor that has the same shape and LoD with `selected_ids`");
// Attributes stored in AttributeMap
AddAttr<int>("level", "the level of LoDTensor");
AddAttr<int>("beam_size", "beam size for beam search");
AddAttr<int>("end_id",
"the token id which indicates the end of a sequence");
AddComment(
"This is a beam search operator that help to generate sequences.");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_WITHOUT_GRADIENT(beam_search, paddle::operators::BeamSearchOp,
paddle::operators::BeamSearchProtoAndCheckerMaker);
paddle/operators/beam_search_op.h 0 → 100644
浏览文件 @ 0968c7cd
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#pragma once
#ifdef PADDLE_WITH_TESTING
#include "gtest/gtest.h"
#endif
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
/*
* This is an implementation of beam search.
*
* To explain the details, lets take machine translation task for example, in
* this task, one source sentence is translated to multiple target sentences,
* during this period, one sentence will be translated to multiple translation
* prefixes(target sentence that have not ended), in each time step a prefix
* will have some candidates, input the candidate ids and their corresponding
* scores (probabilities), it will sort and select the top beam_size candidates
* for each source sentence, and store the selected candidates's score and their
* corresponding ids to LoDTensors.
*
* A detailed example:
*
* Input
*
* ids:
* LoD (should have 2 levels)
* first level: [0, 1, 4]
* second level: [0, 1, 2, 3, 4]
*
* tensor's data
* [
* [4, 2, 5]
* [2, 1, 3]
* [3, 5, 2]
* [8, 2, 1]
* ]
*
* scores:
* LoD same as `ids`
* tensor's data
* [
* [0.5, 0.3, 0.2]
* [0.6, 0.3, 0.1]
* [0.9, 0.5, 0.1]
* [0.7, 0.5, 0.1]
* ]
*
* the inputs means that there are 2 source sentences to translate, and the
* first source has 1 prefix, the second source has 2 prefix.
*
* lets assume beam size is 2, and the beam search's output should be
* LoD
* first level:
* [0, 1, 2]
* second level:
* [0, 2, 4]
*
* tensor's data
* [[
* 0.5,
* 0.3,
* 0.9,
* 0.7
* ]]
*
* TODO all the prune operations should be in the beam search, so it is better
* to split the beam search algorithm into a sequence of smaller operators, and
* the prune operators can be inserted in this sequence.
*/
class BeamSearch {
public:
// TODO(superjom) make type customizable
using id_t = size_t;
using score_t = float;
/*
* Input the arguments that needed by this class.
*/
BeamSearch(const framework::LoDTensor& ids,
const framework::LoDTensor& scores, size_t level, size_t beam_size,
int end_id)
: beam_size_(beam_size),
ids_(&ids),
scores_(&scores),
lod_level_(level),
end_id_(end_id) {}
/*
* The main function of beam search.
*
* @selected_ids: a [None, 1]-shaped tensor with LoD.
* In a machine translation model, it might be the candidate term id sets,
* each set stored as a varience-length sequence.
* The format might be described with a two-level LoD
* - [[0 1]
* - [0 1 2]]
* - [[]
* - [0 1]]
* the first level of LoD tells that there are two source sentences. The
* second level describes the details of the candidate id set's offsets in
* the
* source sentences.
*
* @selected_scores: a LoD tensor with the same shape and LoD with
* selected_ids.
* It stores the corresponding scores of candidate ids in selected_ids.
*
* Return false if all the input tensor is empty, in machine translation task
* that means no candidates is provided, and the task will stop running.
*/
void operator()(const framework::LoDTensor& pre_ids,
framework::LoDTensor* selected_ids,
framework::LoDTensor* selected_scores);
protected:
/*
* The basic items help to sort.
*/
struct Item {
Item() {}
Item(size_t offset, size_t id, float score)
: offset(offset), id(id), score(score) {}
// offset in the lod_level_+1
size_t offset;
// the candidate id
id_t id;
// the corresponding score
score_t score;
};
void PruneEndidCandidates(const framework::LoDTensor& pre_ids,
std::vector<std::vector<Item>>* items);
/*
* Transform the items into a map whose key is offset, value is the items.
* NOTE low performance
*/
std::vector<std::vector<Item>> ToMap(
const std::vector<std::vector<Item>>& inputs);
/*
* For each source, select top beam_size records.
*/
std::vector<std::vector<Item>> SelectTopBeamSizeItems();
/*
* Get the items of next source sequence, return false if no remaining items.
*/
bool NextItemSet(std::vector<Item>* items);
private:
size_t beam_size_;
const framework::LoDTensor* ids_;
const framework::LoDTensor* scores_;
size_t lod_level_{0};
size_t sent_offset_{0};
int end_id_{0};
};
class BeamSearchOp : public framework::OperatorBase {
public:
BeamSearchOp(const std::string& type,
const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
BeamSearchOp(const BeamSearchOp& o)
: framework::OperatorBase(
static_cast<const framework::OperatorBase&>(o)) {
PADDLE_THROW("Not Implemented");
}
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
LOG(INFO) << "run beam search op";
auto ids_var = scope.FindVar(Input("ids"));
auto scores_var = scope.FindVar(Input("scores"));
auto pre_ids_var = scope.FindVar(Input("pre_ids"));
PADDLE_ENFORCE_NOT_NULL(ids_var);
PADDLE_ENFORCE_NOT_NULL(scores_var);
PADDLE_ENFORCE_NOT_NULL(pre_ids_var);
auto& ids = ids_var->Get<framework::LoDTensor>();
auto& scores = scores_var->Get<framework::LoDTensor>();
auto& pre_ids = pre_ids_var->Get<framework::LoDTensor>();
size_t level = Attr<int>("level");
size_t beam_size = Attr<int>("beam_size");
int end_id = Attr<int>("end_id");
LOG(INFO) << "init beam search";
BeamSearch alg(ids, scores, level, beam_size, end_id);
LOG(INFO) << "after beam search";
auto selected_ids_var = scope.FindVar(Output("selected_ids"));
auto selected_scores_var = scope.FindVar(Output("selected_scores"));
PADDLE_ENFORCE_NOT_NULL(selected_ids_var);
PADDLE_ENFORCE_NOT_NULL(selected_scores_var);
auto& selected_ids_tensor =
*selected_ids_var->GetMutable<framework::LoDTensor>();
auto& selected_scores_tensor =
*selected_scores_var->GetMutable<framework::LoDTensor>();
LOG(INFO) << "run beam search";
alg(pre_ids, &selected_ids_tensor, &selected_scores_tensor);
LOG(INFO) << "finish beam search";
}
};
} // namespace operators
} // namespace paddle
paddle/operators/conv_cudnn_op.cc
浏览文件 @ 0968c7cd
......@@ -22,8 +22,6 @@ class CudnnConvOpMaker : public Conv2DOpMaker {
CudnnConvOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: Conv2DOpMaker(proto, op_checker) {
AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
.SetDefault(std::vector<int>{1, 1});
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
......
paddle/operators/conv_op.cc
浏览文件 @ 0968c7cd
......@@ -30,6 +30,7 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
int groups = ctx->Attrs().Get<int>("groups");
std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
int input_channels = in_dims[1];
int output_channels = filter_dims[0];
......@@ -52,9 +53,15 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
"The number of output channels should be divided by groups.");
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < paddings.size(); ++i) {
for (size_t i = 0; i < strides.size(); ++i) {
PADDLE_ENFORCE(in_dims[i + 2] + 2 * paddings[i] -
(dilations[i] * (filter_dims[i + 2] - 1) + 1) >
0,
"Due to the settings of paddings, filter_dims and "
"dilations, the output size is less than 0, please check "
"again.");
output_shape.push_back(OutputSize(in_dims[i + 2], filter_dims[i + 2],
paddings[i], strides[i]));
dilations[i], paddings[i], strides[i]));
}
ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
}
......@@ -78,9 +85,15 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
AddOutput("Output",
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW.");
AddAttr<std::vector<int>>("strides", "strides of convolution operator.")
AddAttr<std::vector<int>>("strides",
"(vector<int> default:{1, 1}), the "
"strides(h_stride, w_stride) of "
"convolution operator.")
.SetDefault({1, 1});
AddAttr<std::vector<int>>("paddings", "paddings of convolution operator.")
AddAttr<std::vector<int>>("paddings",
"(vector<int> default:{0, 0}), the "
"paddings(h_pad, w_pad) of "
"convolution operator.")
.SetDefault({0, 0});
AddAttr<int>(
"groups",
......@@ -90,15 +103,20 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
"first half of the input channels, while the second half of the filters "
"is only connected to the second half of the input channels.")
.SetDefault(1);
AddAttr<std::vector<int>>("dilations",
"(vector<int> default:{1, 1}), the "
"dilations(h_dilation, w_dilation) of "
"convolution operator.")
.SetDefault({1, 1});
AddComment(R"DOC(
Convolution Operator.
The convolution operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
and strides, paddings, groups, dilations parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCHW format. Where N is batch
size, C is the number of channels, H is the height of the feature, and W is
the width of the feature. Parameters(ksize, strides, paddings) are two elements.
the width of the feature. Parameters(ksize, strides, paddings, dilations) are two elements.
These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
......@@ -109,8 +127,8 @@ Example:
Output:
Output shape: (N, C_out, H_out, W_out)
where
H_out = (H_in - filter_size[0] + 2 * paddings[0]) / strides[0] + 1;
W_out = (W_in - filter_size[1] + 2 * paddings[1]) / strides[1] + 1;
H_out = (H_in + 2 * paddings[0] - (dilations[0]*(filter_size[0] - 1) + 1)) / strides[0] + 1;
W_out = (W_in + 2 * paddings[1] - (dilations[1]*(filter_size[1] - 1) + 1)) / strides[1] + 1;
)DOC");
}
......@@ -135,13 +153,15 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
AddOutput("Output",
"(Tensor) The output tensor of convolution operator."
"The format of output tensor is also NCDHW.");
AddAttr<std::vector<int>>(
"strides",
"(vector, default:{0, 0, 0}), the strides of convolution operator.")
AddAttr<std::vector<int>>("strides",
"(vector<int>, default:{1, 1, 1}), the "
"strides(d_stride, h_stride, w_stride) of "
"convolution operator.")
.SetDefault({1, 1, 1});
AddAttr<std::vector<int>>(
"paddings",
"(vector, default:{0, 0, 0}), the paddings of convolution operator.")
AddAttr<std::vector<int>>("paddings",
"(vector<int>, default:{0, 0, 0}), the "
"paddings(d_pad, h_pad, w_pad) of convolution "
"operator.")
.SetDefault({0, 0, 0});
AddAttr<int>(
"groups",
......@@ -151,6 +171,12 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
"first half of the input channels, while the second half of the filters "
"is only connected to the second half of the input channels.")
.SetDefault(1);
AddAttr<std::vector<int>>("dilations",
"(vector<int> default:{1, 1, 1}), the "
"dilations(d_dilation, h_dilation, w_dilation) of "
"convolution operator. Currently, conv3d doesn't "
"support dilation.")
.SetDefault({1, 1, 1});
AddComment(R"DOC(
Convolution3D Operator.
......
paddle/operators/conv_op.h
浏览文件 @ 0968c7cd
......@@ -27,11 +27,24 @@ using Tensor = framework::Tensor;
// Base convolution operator definations for other conv
// like operators to reuse the implementation.
inline int OutputSize(int input_size, int filter_size, int padding,
int stride) {
int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
inline int OutputSize(int input_size, int filter_size, int dilation,
int padding, int stride) {
const int dkernel = dilation * (filter_size - 1) + 1;
const int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
return output_size;
}
inline bool IsExpand(std::vector<int64_t>& filter_dim,
std::vector<int>& strides, std::vector<int>& paddings,
std::vector<int>& dilations) {
bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
for (size_t j = 0; j < strides.size(); ++j) {
filter_1 = filter_1 && (static_cast<int>(filter_dim[j]) == 1);
strides_1 = strides_1 && (strides[j] == 1);
padding_0 = padding_0 && (paddings[j] == 0);
dilation_1 = dilation_1 && (dilations[j] == 1);
}
return !(filter_1 && strides_1 && padding_0 && dilation_1);
}
// Define Op classes in .h file so that other conv
// operator implementations can reuse the code.
......@@ -50,14 +63,12 @@ class Conv3DOpMaker : public framework::OpProtoAndCheckerMaker {
class ConvOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
};
class ConvOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
};
......@@ -73,9 +84,10 @@ class GemmConvKernel : public framework::OpKernel<T> {
Tensor* output = context.Output<Tensor>("Output");
output->mutable_data<T>(context.GetPlace());
int groups = context.Attr<int>("groups");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
int groups = context.Attr<int>("groups");
std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
const int batch_size = static_cast<int>(input->dims()[0]);
......@@ -106,14 +118,17 @@ class GemmConvKernel : public framework::OpKernel<T> {
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, filter_shape_vec.size() + 1);
bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix;
if (is_expand) {
col.mutable_data<T>(col_shape, context.GetPlace());
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
......@@ -130,24 +145,30 @@ class GemmConvKernel : public framework::OpKernel<T> {
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<Place, T> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (filter_shape_vec.size() == 2) {
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (filter_shape_vec.size() == 2) {
// im2col
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
im2col(context.device_context(), in_slice, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
im2col(context.device_context(), in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (filter_shape_vec.size() == 3) {
// vol2col
math::Vol2ColFunctor<Place, T> vol2col;
vol2col(context.device_context(), in_slice, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
vol2col(context.device_context(), in_slice, dilations, strides,
paddings, &col);
}
// gemm
......@@ -178,9 +199,10 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
if (!input_grad && !filter_grad) return;
int groups = context.Attr<int>("groups");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
int groups = context.Attr<int>("groups");
std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
const int batch_size = static_cast<int>(input->dims()[0]);
......@@ -230,14 +252,17 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output_grad->dims()[1]) / groups;
bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix;
if (is_expand) {
col.mutable_data<T>(col_shape, context.GetPlace());
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
math::SetConstant<Place, T> set_zero;
......@@ -245,6 +270,9 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
input_grad->mutable_data<T>(context.GetPlace());
set_zero(context.device_context(), input_grad, static_cast<T>(0));
math::Col2VolFunctor<Place, T> col2vol;
math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
for (int i = 0; i < batch_size; i++) {
Tensor out_grad_batch =
output_grad->Slice(i, i + 1).Resize(output_matrix_shape);
......@@ -254,24 +282,26 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
Tensor out_grad_slice =
out_grad_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<Place, T>(context.device_context(), filter_slice, true,
out_grad_slice, false, T(1.0), &col_matrix,
T(0.0));
// col2im
Tensor in_grad_slice =
in_grad_batch.Slice(g * in_step, (g + 1) * in_step);
if (filter_shape_vec.size() == 2) {
math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
col2im(context.device_context(), in_grad_slice, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
if (!is_expand) {
col_matrix.ShareDataWith(in_grad_slice);
col_matrix.Resize(col_matrix_shape);
}
math::matmul<Place, T>(context.device_context(), filter_slice, true,
out_grad_slice, false, T(1.0), &col_matrix,
T(0.0));
} else if (filter_shape_vec.size() == 3) {
math::Col2VolFunctor<Place, T> col2vol;
col2vol(context.device_context(), in_grad_slice, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
if (is_expand && filter_shape_vec.size() == 2) {
col2im(context.device_context(), col, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&in_grad_slice);
} else if (is_expand && filter_shape_vec.size() == 3) {
col2vol(context.device_context(), col, dilations, strides, paddings,
&in_grad_slice);
}
}
}
......@@ -282,7 +312,8 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
Tensor filter_grad_ = *filter_grad;
filter_grad_.Resize(filter_matrix_shape);
set_zero(context.device_context(), filter_grad, static_cast<T>(0));
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
math::Vol2ColFunctor<Place, T> vol2col;
for (int i = 0; i < batch_size; i++) {
Tensor out_grad_batch =
output_grad->Slice(i, i + 1).Resize(output_matrix_shape);
......@@ -293,16 +324,18 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
out_grad_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (filter_shape_vec.size() == 2) {
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
im2col(context.device_context(), in_slice, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (filter_shape_vec.size() == 2) {
im2col(context.device_context(), in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (filter_shape_vec.size() == 3) {
math::Vol2ColFunctor<Place, T> vol2col;
vol2col(context.device_context(), in_slice, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
vol2col(context.device_context(), in_slice, dilations, strides,
paddings, &col);
}
// gemm
......
paddle/operators/conv_transpose_op.cc
浏览文件 @ 0968c7cd
......@@ -51,7 +51,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
"as the number of filters.");
std::vector<int64_t> output_shape({in_dims[0], filter_dims[1]});
for (size_t i = 0; i < paddings.size(); ++i) {
for (size_t i = 0; i < strides.size(); ++i) {
output_shape.push_back((in_dims[i + 2] - 1) * strides[i] +
filter_dims[i + 2]);
}
......@@ -79,11 +79,13 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
"The format of output tensor is also NCHW.");
AddAttr<std::vector<int>>(
"strides",
"(vector defalut:{1, 1}), strides of convolution transpose operator.")
"(vector<int> defalut:{1, 1}), the strides(h_stride, w_stride) of "
"convolution transpose operator.")
.SetDefault({1, 1});
AddAttr<std::vector<int>>(
"paddings",
"(vector defalut:{0, 0}), paddings of convolution transpose operator.")
"(vector<int> defalut:{0, 0}), the paddings(h_pad, w_pad) of convolution "
"transpose operator.")
.SetDefault({0, 0});
AddComment(R"DOC(
Convolution2D Transpose Operator.
......@@ -132,13 +134,14 @@ Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
"Where N is batch size, C is "
"the number of channels, D is the depth of the feature, H is the "
"height of the feature, and W is the width of the feature.");
AddAttr<std::vector<int>>(
"strides",
"(vector defalut:{1, 1, 1}), strides of convolution transpose operator.")
AddAttr<std::vector<int>>("strides",
"(vector<int> defalut:{1, 1, 1}), the "
"strides{d_stride, h_stride, w_stride} of "
"convolution transpose operator.")
.SetDefault({1, 1, 1});
AddAttr<std::vector<int>>(
"paddings",
"(vector defalut:{0, 0, 0}), paddings of convolution transpose operator.")
AddAttr<std::vector<int>>("paddings",
"(vector<int> defalut:{0, 0, 0}), paddings(d_pad, "
"h_pad, w_pad) of convolution transpose operator.")
.SetDefault({0, 0, 0});
AddComment(R"DOC(
Convolution3D Transpose Operator.
......
paddle/operators/conv_transpose_op.h
浏览文件 @ 0968c7cd
......@@ -43,16 +43,12 @@ class Conv3DTransposeOpMaker : public framework::OpProtoAndCheckerMaker {
class ConvTransposeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override;
};
class ConvTransposeOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override;
};
......@@ -66,6 +62,8 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
Tensor* output = context.Output<Tensor>("Output");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
// Actually, no paddings and groups allowed in conv transpose.
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
// TODO(Zhuoyuan): Paddings can be added in future.
// groups will alway be disabled in conv2dtranspose.
......@@ -120,6 +118,10 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
math::SetConstant<Place, T> set_zero;
set_zero(context.device_context(), output, static_cast<T>(0));
math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
math::Col2VolFunctor<Place, T> col2vol;
std::vector<int> dilations({1, 1, 1});
// convolution transpose: gemm + col2im or col2vol (similar to conv-backward
// on input)
for (int i = 0; i < batch_size; i++) {
......@@ -138,16 +140,16 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
if (filter_shape_vec.size() == 2) {
// col2im: col_matrix -> dy
// from (c * k_h * k_w, h * w) to (c, o_h, o_w)
math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
col2im(context.device_context(), output_batch, col, strides[0],
strides[1], 0, 0, 0, 0);
col2im(context.device_context(), col,
std::vector<int>{dilations[0], dilations[1]}, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&output_batch);
} else if (filter_shape_vec.size() == 3) {
// col2vol: col_matrix -> dy
// from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
math::Col2VolFunctor<Place, T> col2vol;
col2vol(context.device_context(), output_batch, col, strides[0],
strides[1], strides[2], 0, 0, 0);
col2vol(context.device_context(), col, dilations, strides,
std::vector<int>{0, 0, 0}, &output_batch);
}
}
}
......@@ -228,6 +230,10 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
Tensor filter_grad_;
math::SetConstant<Place, T> set_zero;
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
math::Vol2ColFunctor<Place, T> vol2col;
std::vector<int> dilations({1, 1, 1});
if (input_grad) {
input_grad->mutable_data<T>(context.GetPlace());
set_zero(context.device_context(), input_grad, static_cast<T>(0));
......@@ -247,17 +253,16 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
if (filter_shape_vec.size() == 2) {
// im2col: dy -> col matrix
// from (c, o_h, o_w) to (c * k_h * k_w, h * w)
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
im2col(context.device_context(), output_grad_batch, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
im2col(context.device_context(), output_grad_batch,
std::vector<int>{dilations[0], dilations[1]}, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (filter_shape_vec.size() == 3) {
// vol2col: dy -> col_matrix
// from (c, o_d, o_h, o_w) to (c * k_d * k_h * k_w, d * h * w)
math::Vol2ColFunctor<Place, T> vol2col;
vol2col(context.device_context(), output_grad_batch, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
vol2col(context.device_context(), output_grad_batch, dilations,
strides, paddings, &col);
}
if (input_grad) {
......
paddle/operators/elementwise_add_op.cu
浏览文件 @ 0968c7cd
......@@ -19,7 +19,13 @@ namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
elementwise_add,
ops::ElementwiseAddKernel<paddle::platform::GPUPlace, float>);
ops::ElementwiseAddKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseAddKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseAddKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseAddKernel<paddle::platform::GPUPlace, int64_t>);
REGISTER_OP_GPU_KERNEL(
elementwise_add_grad,
ops::ElementwiseAddGradKernel<paddle::platform::GPUPlace, float>);
ops::ElementwiseAddGradKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseAddGradKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseAddGradKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseAddGradKernel<paddle::platform::GPUPlace, int64_t>);
paddle/operators/elementwise_div_op.cu
浏览文件 @ 0968c7cd
......@@ -19,7 +19,13 @@ namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
elementwise_div,
ops::ElementwiseDivKernel<paddle::platform::GPUPlace, float>);
ops::ElementwiseDivKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseDivKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseDivKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseDivKernel<paddle::platform::GPUPlace, int64_t>);
REGISTER_OP_GPU_KERNEL(
elementwise_div_grad,
ops::ElementwiseDivGradKernel<paddle::platform::GPUPlace, float>);
ops::ElementwiseDivGradKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseDivGradKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseDivGradKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseDivGradKernel<paddle::platform::GPUPlace, int64_t>);
paddle/operators/elementwise_mul_op.cu
浏览文件 @ 0968c7cd
......@@ -20,8 +20,12 @@ namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
elementwise_mul,
ops::ElementwiseMulKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseMulKernel<paddle::platform::GPUPlace, double>);
ops::ElementwiseMulKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseMulKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseMulKernel<paddle::platform::GPUPlace, int64_t>);
REGISTER_OP_GPU_KERNEL(
elementwise_mul_grad,
ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, double>);
ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseMulGradKernel<paddle::platform::GPUPlace, int64_t>);
paddle/operators/elementwise_sub_op.cu
浏览文件 @ 0968c7cd
......@@ -19,7 +19,13 @@ namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
elementwise_sub,
ops::ElementwiseSubKernel<paddle::platform::GPUPlace, float>);
ops::ElementwiseSubKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseSubKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseSubKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseSubKernel<paddle::platform::GPUPlace, int64_t>);
REGISTER_OP_GPU_KERNEL(
elementwise_sub_grad,
ops::ElementwiseSubGradKernel<paddle::platform::GPUPlace, float>);
ops::ElementwiseSubGradKernel<paddle::platform::GPUPlace, float>,
ops::ElementwiseSubGradKernel<paddle::platform::GPUPlace, double>,
ops::ElementwiseSubGradKernel<paddle::platform::GPUPlace, int>,
ops::ElementwiseSubGradKernel<paddle::platform::GPUPlace, int64_t>);
paddle/operators/math/context_project.h
浏览文件 @ 0968c7cd
......@@ -85,13 +85,18 @@ template <typename Place, typename T>
class ContextProjectFunctor {
public:
void operator()(const platform::DeviceContext& context, const LoDTensor& in,
const Tensor& padding_data, Tensor& col,
bool padding_trainable, int context_start, int context_length,
int context_stride, int up_pad, int down_pad) {
const Tensor& padding_data, bool padding_trainable,
const int context_start, const int context_length,
const int context_stride, const int up_pad,
const int down_pad, Tensor* col) {
auto lod_level_0 = in.lod()[0];
math::Im2ColFunctor<math::ColFormat::kOCF, Place, float> im2col_ocf;
std::vector<int> dilation({1, 1});
std::vector<int> padding({up_pad, 0, down_pad, 0});
std::vector<int> stride({context_stride, 1});
int input_row_begin, input_row_end;
int sequence_height, sequence_width;
sequence_width = in.dims()[1];
......@@ -102,7 +107,7 @@ class ContextProjectFunctor {
: static_cast<int>(lod_level_0[i]);
input_row_end = static_cast<int>(lod_level_0[i + 1]);
Tensor out_t = col.Slice(static_cast<int>(lod_level_0[i]),
Tensor out_t = col->Slice(static_cast<int>(lod_level_0[i]),
static_cast<int>(lod_level_0[i + 1]));
sequence_height = static_cast<int>(out_t.dims()[0]);
......@@ -120,16 +125,13 @@ class ContextProjectFunctor {
{1, input_row_end - input_row_begin,
sequence_width}); // input_channels, input_height, input_width
in_t.Resize(framework::make_ddim(input_shape));
im2col_ocf(context, in_t, out_t,
/*stride_height*/ context_stride, /*stride_width*/ 1, up_pad,
down_pad, 0, 0);
im2col_ocf(context, in_t, dilation, stride, padding, &out_t);
out_t.Resize({sequence_height, context_length * sequence_width});
}
}
if (padding_trainable) {
for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
Tensor out_t = col.Slice(static_cast<int>(lod_level_0[i]),
Tensor out_t = col->Slice(static_cast<int>(lod_level_0[i]),
static_cast<int>(lod_level_0[i + 1]));
sequence_height = static_cast<int>(out_t.dims()[0]);
......@@ -189,14 +191,19 @@ class ContextProjectFunctor {
template <typename Place, typename T>
class ContextProjectGradFunctor {
public:
void operator()(const platform::DeviceContext& context, LoDTensor& in,
Tensor& padding_data, Tensor& col, bool padding_trainable,
int context_start, int context_length, int context_stride,
int up_pad, int down_pad, bool input_grad, bool pad_grad) {
void operator()(const platform::DeviceContext& context, const LoDTensor& in,
bool padding_trainable, const int context_start,
const int context_length, const int context_stride,
const int up_pad, const int down_pad, bool pad_grad,
bool input_grad, Tensor* padding_data, Tensor* col) {
auto lod_level_0 = in.lod()[0];
math::Col2ImFunctor<math::ColFormat::kOCF, Place, float> col2im_ocf;
std::vector<int> dilation({1, 1});
std::vector<int> padding({up_pad, 0, down_pad, 0});
std::vector<int> stride({context_stride, 1});
int input_row_begin, input_row_end;
int sequence_height, sequence_width;
sequence_width = in.dims()[1];
......@@ -208,7 +215,7 @@ class ContextProjectGradFunctor {
: static_cast<int>(lod_level_0[i]);
input_row_end = static_cast<int>(lod_level_0[i + 1]);
Tensor out_t = col.Slice(static_cast<int>(lod_level_0[i]),
Tensor out_t = col->Slice(static_cast<int>(lod_level_0[i]),
static_cast<int>(lod_level_0[i + 1]));
sequence_height = static_cast<int>(out_t.dims()[0]);
......@@ -227,9 +234,7 @@ class ContextProjectGradFunctor {
sequence_width}); // input_channels, input_height, input_width
in_t.Resize(framework::make_ddim(input_shape));
col2im_ocf(context, in_t, out_t,
/*stride_height*/ context_stride, /*stride_width*/ 1,
up_pad, down_pad, 0, 0);
col2im_ocf(context, out_t, dilation, stride, padding, &in_t);
out_t.Resize({sequence_height, context_length * sequence_width});
}
}
......@@ -237,7 +242,7 @@ class ContextProjectGradFunctor {
if (pad_grad) {
if (padding_trainable) {
for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
Tensor out_t = col.Slice(static_cast<int>(lod_level_0[i]),
Tensor out_t = col->Slice(static_cast<int>(lod_level_0[i]),
static_cast<int>(lod_level_0[i + 1]));
sequence_height = static_cast<int>(out_t.dims()[0]);
......@@ -252,7 +257,7 @@ class ContextProjectGradFunctor {
k + context_length < up_pad ? context_length : up_pad - k;
Tensor out_t_sub = out_t.Slice(k * context_length,
k * context_length + padding_size);
Tensor w_sub = padding_data.Slice(k, k + padding_size);
Tensor w_sub = padding_data->Slice(k, k + padding_size);
axpy<Place, T>(context, w_sub.numel(), static_cast<T>(1),
out_t_sub.data<T>(), w_sub.data<T>());
}
......@@ -283,7 +288,7 @@ class ContextProjectGradFunctor {
Tensor out_t_sub = out_t.Slice(
(down_pad_begin_row + t) * context_length - padding_size,
(down_pad_begin_row + t) * context_length);
Tensor w_sub = padding_data.Slice(
Tensor w_sub = padding_data->Slice(
up_pad + padding_idx, up_pad + padding_idx + padding_size);
axpy<Place, T>(context, w_sub.numel(), static_cast<T>(1),
out_t_sub.data<T>(), w_sub.data<T>());
......
paddle/operators/math/im2col.cc
浏览文件 @ 0968c7cd
......@@ -28,57 +28,55 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& im, framework::Tensor& col,
int stride_height, int stride_width, int padding_up,
int padding_down, int padding_left, int padding_right) {
const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
PADDLE_ENFORCE(col->dims().size() == 5);
int input_channels = im.dims()[0];
int input_height = im.dims()[1];
int input_width = im.dims()[2];
int filter_height = col.dims()[1];
int filter_width = col.dims()[2];
int output_height = col.dims()[3];
int output_width = col.dims()[4];
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int col_height = col->dims()[3];
int col_width = col->dims()[4];
PADDLE_ENFORCE_EQ(
(input_height + padding_up + padding_down - filter_height) /
stride_height +
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
((dilation[0] * (filter_height - 1) + 1))) /
stride[0] +
1,
output_height,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ(
(input_width + padding_left + padding_right - filter_width) /
stride_width +
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
((dilation[1] * (filter_width - 1) + 1))) /
stride[1] +
1,
output_width,
"output_width and padding(padding_left, padding_right) are "
col_width,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
int channels_col = input_channels * filter_height * filter_width;
int channels_col = im_channels * filter_height * filter_width;
const T* im_data = im.data<T>();
T* col_data = col.data<T>();
T* col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / filter_width / filter_height;
for (int h = 0; h < output_height; ++h) {
for (int w = 0; w < output_width; ++w) {
int im_row_idx = h * stride_height + h_offset - padding_up;
int im_col_idx = w * stride_width + w_offset - padding_left;
for (int h = 0; h < col_height; ++h) {
for (int w = 0; w < col_width; ++w) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * col_height + h) * col_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
if (im_row_idx < 0 || im_row_idx >= input_height || im_col_idx < 0 ||
im_col_idx >= input_width) {
col_data[(c * output_height + h) * output_width + w] = T(0);
} else {
im_row_idx += c_im * input_height;
col_data[(c * output_height + h) * output_width + w] =
im_data[im_row_idx * input_width + im_col_idx];
}
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
: im_data[im_idx];
}
}
}
......@@ -94,54 +92,55 @@ template <class T>
class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context, framework::Tensor& im,
const framework::Tensor& col, int stride_height,
int stride_width, int padding_up, int padding_down,
int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
void operator()(const platform::DeviceContext& context,
const framework::Tensor& col,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im) {
PADDLE_ENFORCE(im->dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
int input_height = im.dims()[1];
int input_width = im.dims()[2];
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[1];
int filter_width = col.dims()[2];
int output_height = col.dims()[3];
int output_width = col.dims()[4];
int col_height = col.dims()[3];
int col_width = col.dims()[4];
PADDLE_ENFORCE_EQ(
(input_height + padding_up + padding_down - filter_height) /
stride_height +
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
((dilation[0] * (filter_height - 1) + 1))) /
stride[0] +
1,
output_height,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ(
(input_width + padding_left + padding_right - filter_width) /
stride_width +
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
((dilation[1] * (filter_width - 1) + 1))) /
stride[1] +
1,
output_width,
"output_width and padding(padding_left, padding_right) are "
col_width,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
int channels_col = input_channels * filter_height * filter_width;
int channels_col = im_channels * filter_height * filter_width;
T* im_data = im.data<T>();
T* im_data = im->data<T>();
const T* col_data = col.data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / filter_width / filter_height;
for (int h = 0; h < output_height; ++h) {
for (int w = 0; w < output_width; ++w) {
int im_row_idx = h * stride_height + h_offset - padding_up;
int im_col_idx = w * stride_width + w_offset - padding_left;
for (int h = 0; h < col_height; ++h) {
for (int w = 0; w < col_width; ++w) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
if ((im_row_idx) >= 0 && (im_row_idx) < input_height &&
(im_col_idx) >= 0 && (im_col_idx) < input_width) {
im_row_idx += c_im * input_height;
im_data[im_row_idx * input_width + im_col_idx] +=
col_data[(c * output_height + h) * output_width + w];
if ((im_row_idx) >= 0 && (im_row_idx) < im_height &&
(im_col_idx) >= 0 && (im_col_idx) < im_width) {
im_row_idx += c_im * im_height;
im_data[im_row_idx * im_width + im_col_idx] +=
col_data[(c * col_height + h) * col_width + w];
}
}
}
......@@ -168,64 +167,59 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& im, framework::Tensor& col,
int stride_height, int stride_width, int padding_up,
int padding_down, int padding_left, int padding_right) {
const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
int input_height = im.dims()[1];
int input_width = im.dims()[2];
int filter_height = col.dims()[3];
int filter_width = col.dims()[4];
int output_height = col.dims()[0];
int output_width = col.dims()[1];
PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[3];
int filter_width = col->dims()[4];
int col_height = col->dims()[0];
int col_width = col->dims()[1];
PADDLE_ENFORCE_EQ(
(input_height + padding_up + padding_down - filter_height) /
stride_height +
1,
output_height,
(im_height + padding[0] + padding[2] - filter_height) / stride[0] + 1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ(
(input_width + padding_left + padding_right - filter_width) /
stride_width +
1,
output_width,
"output_width and padding(padding_left, padding_right) are "
(im_width + padding[1] + padding[3] - filter_width) / stride[1] + 1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
const T* im_data = im.data<T>();
T* col_data = col.data<T>();
T* col_data = col->data<T>();
for (int col_row_idx = 0; col_row_idx < output_height; ++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < output_width; ++col_col_idx) {
for (int channel = 0; channel < input_channels; ++channel) {
for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
for (int channel = 0; channel < im_channels; ++channel) {
for (int filter_row_idx = 0; filter_row_idx < filter_height;
++filter_row_idx) {
for (int filter_col_idx = 0; filter_col_idx < filter_width;
++filter_col_idx) {
int im_row_offset =
col_row_idx * stride_height + filter_row_idx - padding_up;
col_row_idx * stride[0] + filter_row_idx - padding[0];
int im_col_offset =
col_col_idx * stride_width + filter_col_idx - padding_left;
int col_offset = ((((col_row_idx)*output_width + col_col_idx) *
input_channels +
col_col_idx * stride[1] + filter_col_idx - padding[1];
int col_offset =
((((col_row_idx)*col_width + col_col_idx) * im_channels +
channel) *
filter_height +
filter_row_idx) *
filter_width +
filter_col_idx;
if (im_row_offset < 0 || im_row_offset >= input_height ||
im_col_offset < 0 || im_col_offset >= input_width) {
col_data[col_offset] = T(0);
} else {
int im_offset =
(channel * input_height + im_row_offset) * input_width +
int im_offset = (channel * im_height + im_row_offset) * im_width +
im_col_offset;
col_data[col_offset] = im_data[im_offset];
}
col_data[col_offset] =
(im_row_offset < 0 || im_row_offset >= im_height ||
im_col_offset < 0 || im_col_offset >= im_width)
? static_cast<T>(0)
: im_data[im_offset];
}
}
}
......@@ -243,60 +237,57 @@ template <class T>
class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context, framework::Tensor& im,
const framework::Tensor& col, int stride_height,
int stride_width, int padding_up, int padding_down,
int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
void operator()(const platform::DeviceContext& context,
const framework::Tensor& col,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im) {
PADDLE_ENFORCE(im->dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
int input_height = im.dims()[1];
int input_width = im.dims()[2];
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[3];
int filter_width = col.dims()[4];
int output_height = col.dims()[0];
int output_width = col.dims()[1];
int col_height = col.dims()[0];
int col_width = col.dims()[1];
PADDLE_ENFORCE_EQ(
(input_height + padding_up + padding_down - filter_height) /
stride_height +
1,
output_height,
(im_height + padding[0] + padding[2] - filter_height) / stride[0] + 1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ(
(input_width + padding_left + padding_right - filter_width) /
stride_width +
1,
output_width,
"output_width and padding(padding_left, padding_right) are "
(im_width + padding[1] + padding[3] - filter_width) / stride[1] + 1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
T* im_data = im.data<T>();
T* im_data = im->data<T>();
const T* col_data = col.data<T>();
for (int col_row_idx = 0; col_row_idx < output_height; ++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < output_width; ++col_col_idx) {
for (int channel = 0; channel < input_channels; ++channel) {
for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
for (int channel = 0; channel < im_channels; ++channel) {
for (int filter_row_idx = 0; filter_row_idx < filter_height;
++filter_row_idx) {
for (int filter_col_idx = 0; filter_col_idx < filter_width;
++filter_col_idx) {
int im_row_offset =
col_row_idx * stride_height + filter_row_idx - padding_up;
col_row_idx * stride[0] + filter_row_idx - padding[0];
int im_col_offset =
col_col_idx * stride_width + filter_col_idx - padding_left;
int col_offset = (((col_row_idx * output_width + col_col_idx) *
input_channels +
col_col_idx * stride[1] + filter_col_idx - padding[1];
int col_offset =
(((col_row_idx * col_width + col_col_idx) * im_channels +
channel) *
filter_height +
filter_row_idx) *
filter_width +
filter_col_idx;
if (im_row_offset >= 0 && im_row_offset < input_height &&
im_col_offset >= 0 && im_col_offset < input_width) {
if (im_row_offset >= 0 && im_row_offset < im_height &&
im_col_offset >= 0 && im_col_offset < im_width) {
int im_offset =
(channel * input_height + im_row_offset) * input_width +
(channel * im_height + im_row_offset) * im_width +
im_col_offset;
im_data[im_offset] += col_data[col_offset];
}
......
paddle/operators/math/im2col.cu
浏览文件 @ 0968c7cd
......@@ -20,36 +20,32 @@ namespace operators {
namespace math {
template <class T>
__global__ void im2col(const T* data_im, int num_outs, int height, int width,
__global__ void im2col(const T* data_im, int num_outs, int im_height,
int im_width, int dilation_h, int dilation_w,
int filter_height, int filter_width, int stride_height,
int stride_width, int padding_height, int padding_width,
int output_height, int output_width, T* data_col) {
int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
int col_height, int col_width, T* data_col) {
const int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < num_outs) {
int w_out = index % output_width;
index /= output_width;
int h_out = index % output_height;
int channel_in = index / output_height;
int w_out = index % col_width;
int h_out = (index / col_width) % col_height;
int channel_in = index / col_width / col_height;
int channel_out = channel_in * filter_height * filter_width;
int h_in = h_out * stride_height;
int w_in = w_out * stride_width;
int h_in = h_out * stride_height - padding_height;
int w_in = w_out * stride_width - padding_width;
data_col += (channel_out * output_height + h_out) * output_width + w_out;
data_col += (channel_out * col_height + h_out) * col_width + w_out;
data_im += (channel_in * im_height + h_in) * im_width + w_in;
for (int i = 0; i < filter_height; ++i) {
for (int j = 0; j < filter_width; ++j) {
int rIdx = int(h_in + i);
int cIdx = int(w_in + j);
if ((rIdx - (int)padding_height) >= (int)height ||
(rIdx - (int)padding_height) < 0 ||
(cIdx - (int)padding_width) >= (int)width ||
(cIdx - (int)padding_width) < 0) {
*data_col = 0;
} else {
rIdx = rIdx + channel_in * height - padding_height;
cIdx = cIdx - padding_width;
*data_col = data_im[rIdx * width + cIdx];
}
data_col += output_height * output_width;
int rIdx = h_in + i * dilation_h;
int cIdx = w_in + j * dilation_w;
*data_col =
(rIdx >= im_height || rIdx < 0 || cIdx >= im_width || cIdx < 0)
? 0
: data_im[i * dilation_h * im_width + j * dilation_w];
data_col += col_height * col_width;
}
}
}
......@@ -65,30 +61,36 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& im, framework::Tensor& col,
int stride_height, int stride_width, int padding_up,
int padding_down, int padding_left, int padding_right) {
const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
int input_height = im.dims()[1];
int input_width = im.dims()[2];
int filter_height = col.dims()[1];
int filter_width = col.dims()[2];
int output_height = col.dims()[3];
int output_width = col.dims()[4];
PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
stride_height +
1 ==
output_height);
PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
stride_width +
1 ==
output_width);
int num_outputs = input_channels * output_height * output_width;
PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int col_height = col->dims()[3];
int col_width = col->dims()[4];
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
(dilation[0] * (filter_height - 1) + 1)) /
stride[0] +
1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
(dilation[1] * (filter_width - 1) + 1)) /
stride[1] +
1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
int num_outputs = im_channels * col_height * col_width;
int blocks = (num_outputs + 1024 - 1) / 1024;
int block_x = 512;
int block_y = (blocks + 512 - 1) / 512;
......@@ -97,56 +99,57 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
im2col<T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
im.data<T>(), num_outputs, input_height, input_width, filter_height,
filter_width, stride_height, stride_width, padding_up, padding_left,
output_height, output_width, col.data<T>());
im.data<T>(), num_outputs, im_height, im_width, dilation[0],
dilation[1], filter_height, filter_width, stride[0], stride[1],
padding[0], padding[1], col_height, col_width, col->data<T>());
}
};
template <class T>
__global__ void col2im(size_t n, const T* data_col, size_t height, size_t width,
size_t channels, size_t filter_height,
size_t filter_width, size_t stride_height,
size_t stride_width, size_t padding_height,
size_t padding_width, size_t output_height,
size_t output_width, T* data_im) {
size_t index =
__global__ void col2im(int n, const T* data_col, int im_height, int im_width,
int dilation_h, int dilation_w, int filter_height,
int filter_width, int stride_height, int stride_width,
int padding_height, int padding_width, int col_height,
int col_width, T* data_im) {
const int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
const int d_filter_height = dilation_h * (filter_height - 1) + 1;
const int d_filter_width = dilation_w * (filter_width - 1) + 1;
if (index < n) {
T val = 0;
int w = int(index % width);
int h = int((index / width) % height);
int c = int(index / (width * height));
if ((w - (int)padding_width) >= 0 &&
(w - (int)padding_width) < (width - 2 * padding_width) &&
(h - (int)padding_height) >= 0 &&
(h - padding_height) < (height - 2 * padding_height)) {
int w = index % im_width;
int h = (index / im_width) % im_height;
int c = index / (im_width * im_height);
// compute the start and end of the output
int w_col_start = (w < (int)filter_width)
? 0
: (w - int(filter_width)) / (int)stride_width + 1;
int w_col_end =
min((int)(w / (int)stride_width + 1), (int)(output_width));
int h_col_start = (h < (int)filter_height)
? 0
: (h - (int)filter_height) / (int)stride_height + 1;
int h_col_end = min(int(h / stride_height + 1), int(output_height));
int w_col_start =
(w < d_filter_width) ? 0 : (w - d_filter_width) / stride_width + 1;
int w_col_end = min(w / stride_width + 1, col_width);
int h_col_start =
(h < d_filter_height) ? 0 : (h - d_filter_height) / stride_height + 1;
int h_col_end = min(h / stride_height + 1, col_height);
for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
// the col location: [c * width * height + h_out, w_out]
int c_col = int(c * filter_height * filter_width) +
(h - h_col * (int)stride_height) * (int)filter_width +
(w - w_col * (int)stride_width);
val +=
data_col[(c_col * output_height + h_col) * output_width + w_col];
int h_off = (h - h_col * stride_height);
int w_off = (w - w_col * stride_width);
if (h_off % dilation_h == 0 && w_off % dilation_w == 0) {
h_off /= dilation_h;
w_off /= dilation_w;
int data_col_index =
(((c * filter_height + h_off) * filter_width + w_off) *
col_height +
h_col) *
col_width +
w_col;
val += data_col[data_col_index];
}
}
h -= padding_height;
w -= padding_width;
data_im[c * ((width - 2 * padding_width) *
(height - 2 * padding_height)) +
h * (width - 2 * padding_width) + w] += val;
}
data_im[index] = val;
}
}
......@@ -159,33 +162,38 @@ template <class T>
class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context, framework::Tensor& im,
const framework::Tensor& col, int stride_height,
int stride_width, int padding_up, int padding_down,
int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
void operator()(const platform::DeviceContext& context,
const framework::Tensor& col,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im) {
PADDLE_ENFORCE(im->dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
int input_height = im.dims()[1];
int input_width = im.dims()[2];
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[1];
int filter_width = col.dims()[2];
int output_height = col.dims()[3];
int output_width = col.dims()[4];
PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
stride_height +
1 ==
output_height);
PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
stride_width +
1 ==
output_width);
size_t num_kernels = input_channels *
(input_height + padding_up + padding_down) *
(input_width + padding_left + padding_right);
int col_height = col.dims()[3];
int col_width = col.dims()[4];
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
(dilation[0] * (filter_height - 1) + 1)) /
stride[0] +
1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
(dilation[1] * (filter_width - 1) + 1)) /
stride[1] +
1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
size_t num_kernels = im_channels * im_height * im_width;
size_t blocks = (num_kernels + 1024 - 1) / 1024;
size_t block_x = 512;
......@@ -198,10 +206,9 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
col2im<T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
num_kernels, col.data<T>(), input_height + padding_up + padding_down,
input_width + padding_left + padding_left, input_channels,
filter_height, filter_width, stride_height, stride_width, padding_up,
padding_left, output_height, output_width, im.data<T>());
num_kernels, col.data<T>(), im_height, im_width, dilation[0],
dilation[1], filter_height, filter_width, stride[0], stride[1],
padding[0], padding[2], col_height, col_width, im->data<T>());
}
};
......@@ -215,33 +222,32 @@ template class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
platform::GPUPlace, double>;
template <class T>
__global__ void im2colOCF(const T* im_data, T* col_data, int input_channels,
int input_height, int input_width, int filter_height,
int filter_width, int stride_height, int stride_width,
int padding_height, int padding_width,
int output_height, int output_width) {
__global__ void im2colOCF(const T* im_data, int im_channels, int im_height,
int im_width, int filter_height, int filter_width,
int stride_height, int stride_width,
int padding_height, int padding_width, int col_height,
int col_width, T* col_data) {
int swid = blockIdx.x;
int shid = blockIdx.y;
for (int channelid = threadIdx.z; channelid < input_channels;
for (int channelid = threadIdx.z; channelid < im_channels;
channelid += blockDim.z) {
for (int idy = threadIdx.y; idy < filter_height; idy += blockDim.y) {
for (int idx = threadIdx.x; idx < filter_width; idx += blockDim.x) {
int width_offset = idx + swid * stride_width - padding_width;
int height_offset = idy + shid * stride_height - padding_height;
int im_offset = width_offset + height_offset * input_width +
channelid * input_height * input_width;
int im_offset = width_offset + height_offset * im_width +
channelid * im_height * im_width;
int col_offset = idx + idy * filter_width +
channelid * filter_height * filter_width +
(shid * output_width + swid) *
(input_channels * filter_height * filter_width);
if (height_offset >= input_height || height_offset < 0 ||
width_offset >= input_width || width_offset < 0) {
col_data[col_offset] = T(0);
} else {
col_data[col_offset] = im_data[im_offset];
}
(shid * col_width + swid) *
(im_channels * filter_height * filter_width);
col_data[col_offset] =
(height_offset >= im_height || height_offset < 0 ||
width_offset >= im_width || width_offset < 0)
? T(0)
: im_data[im_offset];
}
}
}
......@@ -257,27 +263,33 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& im, framework::Tensor& col,
int stride_height, int stride_width, int padding_up,
int padding_down, int padding_left, int padding_right) {
const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
int input_height = im.dims()[1];
int input_width = im.dims()[2];
int filter_height = col.dims()[3];
int filter_width = col.dims()[4];
int output_height = col.dims()[0];
int output_width = col.dims()[1];
PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
stride_height +
1 ==
output_height);
PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
stride_width +
1 ==
output_width);
PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[3];
int filter_width = col->dims()[4];
int col_height = col->dims()[0];
int col_width = col->dims()[1];
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
(dilation[0] * (filter_height - 1) + 1)) /
stride[0] +
1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
(dilation[1] * (filter_width - 1) + 1)) /
stride[1] +
1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
int block_dim_x = 0;
int block_dim_y = 0;
......@@ -296,42 +308,41 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
}
int block_dim_z = 1024 / block_dim_x / block_dim_y;
dim3 threads(block_dim_x, block_dim_y,
std::min(block_dim_z, input_channels));
dim3 grid(output_width, output_height);
dim3 threads(block_dim_x, block_dim_y, std::min(block_dim_z, im_channels));
dim3 grid(col_width, col_height);
im2colOCF<T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
im.data<T>(), col.data<T>(), input_channels, input_height, input_width,
filter_height, filter_width, stride_height, stride_width, padding_up,
padding_left, output_height, output_width);
im.data<T>(), im_channels, im_height, im_width, filter_height,
filter_width, stride[0], stride[1], padding[0], padding[1], col_height,
col_width, col->data<T>());
}
};
template <class T>
__global__ void col2imOCF(T* im_data, const T* col_data, int input_channels,
int input_height, int input_width, int filter_height,
int filter_width, int stride_height, int stride_width,
int padding_height, int padding_width,
int output_height, int output_width) {
__global__ void col2imOCF(const T* col_data, int im_channels, int im_height,
int im_width, int filter_height, int filter_width,
int stride_height, int stride_width,
int padding_height, int padding_width, int col_height,
int col_width, T* im_data) {
int swid = blockIdx.x;
int shid = blockIdx.y;
for (int channelid = threadIdx.z; channelid < input_channels;
for (int channelid = threadIdx.z; channelid < im_channels;
channelid += blockDim.z) {
for (int idy = threadIdx.y; idy < filter_height; idy += blockDim.y) {
for (int idx = threadIdx.x; idx < filter_width; idx += blockDim.x) {
int width_offset = idx + swid * stride_width - padding_width;
int height_offset = idy + shid * stride_height - padding_height;
int im_offset = width_offset + height_offset * input_width +
channelid * input_height * input_width;
int im_offset = width_offset + height_offset * im_width +
channelid * im_height * im_width;
int col_offset = idx + idy * filter_width +
channelid * filter_height * filter_width +
(shid * output_width + swid) *
(input_channels * filter_height * filter_width);
(shid * col_width + swid) *
(im_channels * filter_height * filter_width);
if (height_offset >= 0 && height_offset < input_height &&
width_offset >= 0 && width_offset < input_width) {
if (height_offset >= 0 && height_offset < im_height &&
width_offset >= 0 && width_offset < im_width) {
paddle::platform::CudaAtomicAdd(im_data + im_offset,
col_data[col_offset]);
}
......@@ -349,28 +360,35 @@ template <class T>
class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context, framework::Tensor& im,
const framework::Tensor& col, int stride_height,
int stride_width, int padding_up, int padding_down,
int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
void operator()(const platform::DeviceContext& context,
const framework::Tensor& col,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im) {
PADDLE_ENFORCE(im->dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
int input_height = im.dims()[1];
int input_width = im.dims()[2];
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[3];
int filter_width = col.dims()[4];
int output_height = col.dims()[0];
int output_width = col.dims()[1];
PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
stride_height +
1 ==
output_height);
PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
stride_width +
1 ==
output_width);
int col_height = col.dims()[0];
int col_width = col.dims()[1];
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
(dilation[0] * (filter_height - 1) + 1)) /
stride[0] +
1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
(dilation[1] * (filter_width - 1) + 1)) /
stride[1] +
1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
int block_dim_x = 0;
int block_dim_y = 0;
......@@ -389,15 +407,14 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
}
int block_dim_z = 1024 / block_dim_x / block_dim_y;
dim3 threads(block_dim_x, block_dim_y,
std::min(block_dim_z, input_channels));
dim3 grid(output_width, output_height);
dim3 threads(block_dim_x, block_dim_y, std::min(block_dim_z, im_channels));
dim3 grid(col_width, col_height);
col2imOCF<T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
im.data<T>(), col.data<T>(), input_channels, input_height, input_width,
filter_height, filter_width, stride_height, stride_width, padding_up,
padding_left, output_height, output_width);
col.data<T>(), im_channels, im_height, im_width, filter_height,
filter_width, stride[0], stride[1], padding[0], padding[1], col_height,
col_width, im->data<T>());
}
};
......
paddle/operators/math/im2col.h
浏览文件 @ 0968c7cd
......@@ -35,6 +35,15 @@ enum class ColFormat { kCFO = 0, kOCF = 1 };
* \param colData Column data.
* \param colShape The shape of colData.
*
* \param dilations dilation data.
* \param 2-dimension [dilation_height, dilation_width].
*
* \param strides stride data.
* \param 2-dimension [stride_height, stride_width].
*
* \param paddings padding data.
* \param 4-dimension [up_pad, left_pad, down_pad, right_pad].
*
* If the template argument Format is kCFO, the shape of colData is:
* [input_channels, filter_height, filter_width, output_height, output_width]
* So, it is easy to reshape into a convolution matrix for convolution
......@@ -73,18 +82,19 @@ template <ColFormat Format, typename Place, typename T>
class Im2ColFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& im, framework::Tensor& col,
int stride_height, int stride_width, int padding_up,
int padding_down, int padding_left, int padding_right);
const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col);
};
template <ColFormat Format, typename Place, typename T>
class Col2ImFunctor {
public:
void operator()(const platform::DeviceContext& context, framework::Tensor& im,
const framework::Tensor& col, int stride_height,
int stride_width, int padding_up, int padding_down,
int padding_left, int padding_right);
void operator()(const platform::DeviceContext& context,
const framework::Tensor& col,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im);
};
} // namespace math
......
paddle/operators/math/im2col_test.cc
浏览文件 @ 0968c7cd
......@@ -45,10 +45,14 @@ void testIm2col() {
int input_height = 2;
int input_width = 3;
int filter_size = 2;
int stride = 1;
int padding = 0;
int output_height = (input_height - filter_size + 2 * padding) / stride + 1;
int output_width = (input_width - filter_size + 2 * padding) / stride + 1;
std::vector<int> stride({1, 1}); // stride_y, stride_x
std::vector<int> padding(
{0, 0, 0, 0}); // up_pad, left_pad, down_pad, right_pad
std::vector<int> dilation({1, 1}); // dilation_y, dilation_x
int output_height =
(input_height - filter_size + padding[0] + padding[1]) / stride[0] + 1;
int output_width =
(input_width - filter_size + padding[2] + padding[3]) / stride[1] + 1;
float* input_ptr = input_tmp.mutable_data<float>(
{1, input_height, input_width}, paddle::platform::CPUPlace());
float arr[6] = {0, 1, 2, 3, 4, 5};
......@@ -85,10 +89,8 @@ void testIm2col() {
paddle::operators::math::ColFormat::kOCF, Place, float>
im2col_ocf;
im2col(*context, input, output_cfo, stride, stride, padding, padding, padding,
padding);
im2col_ocf(*context, input, output_ocf, stride, stride, padding, padding,
padding, padding);
im2col(*context, input, dilation, stride, padding, &output_cfo);
im2col_ocf(*context, input, dilation, stride, padding, &output_ocf);
float out_cfo_data[] = {0, 1, 1, 2, 3, 4, 4, 5};
float out_ocf_data[] = {0, 1, 3, 4, 1, 2, 4, 5};
......@@ -131,8 +133,7 @@ void testIm2col() {
input.CopyFrom(input_tmp, *place, *context);
}
col2im(*context, input, output_cfo, stride, stride, padding, padding, padding,
padding);
col2im(*context, output_cfo, dilation, stride, padding, &input);
float* in_ptr;
if (paddle::platform::is_cpu_place(*place)) {
......@@ -153,8 +154,7 @@ void testIm2col() {
input.CopyFrom(input_tmp, *place, *context);
}
col2im_ocf(*context, input, output_ocf, stride, stride, padding, padding,
padding, padding);
col2im_ocf(*context, output_ocf, dilation, stride, padding, &input);
if (paddle::platform::is_cpu_place(*place)) {
in_ptr = input.data<float>();
......
paddle/operators/math/vol2col.cc
浏览文件 @ 0968c7cd
......@@ -28,28 +28,51 @@ template <class T>
class Vol2ColFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& vol, framework::Tensor& col,
int stride_depth, int stride_height, int stride_width,
int padding_depth, int padding_height,
int padding_width) const {
const framework::Tensor& vol,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* col) const {
PADDLE_ENFORCE(vol.dims().size() == 4);
PADDLE_ENFORCE(col.dims().size() == 7);
PADDLE_ENFORCE(col->dims().size() == 7);
int input_channels = vol.dims()[0];
int input_depth = vol.dims()[1];
int input_height = vol.dims()[2];
int input_width = vol.dims()[3];
int filter_depth = col.dims()[1];
int filter_height = col.dims()[2];
int filter_width = col.dims()[3];
int output_depth = col.dims()[4];
int output_height = col.dims()[5];
int output_width = col.dims()[6];
int filter_depth = col->dims()[1];
int filter_height = col->dims()[2];
int filter_width = col->dims()[3];
int output_depth = col->dims()[4];
int output_height = col->dims()[5];
int output_width = col->dims()[6];
int channels_col =
input_channels * filter_depth * filter_height * filter_width;
PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1,
output_width,
"input_width and output_width are "
"mismatching.");
const T* vol_data = vol.data<T>();
T* col_data = col.data<T>();
T* col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
......@@ -57,24 +80,23 @@ class Vol2ColFunctor<platform::CPUPlace, T> {
int d_offset = (c / filter_width / filter_height) % filter_depth;
int c_in = c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) {
int d_pad = d * stride_depth - padding_depth + d_offset;
int d_pad = d * strides[0] - paddings[0] + d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) {
int h_pad = h * stride_height - padding_height + h_offset;
int h_pad = h * strides[1] - paddings[1] + h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) {
int w_pad = w * stride_width - padding_width + w_offset;
int w_pad = w * strides[2] - paddings[2] + w_offset * dilations[2];
int col_idx =
((c * output_depth + d) * output_height + h) * output_width + w;
if (h_pad < 0 || h_pad >= input_height || w_pad < 0 ||
w_pad >= input_width || d_pad < 0 || d_pad >= input_depth) {
col_data[col_idx] = static_cast<T>(0);
} else {
int vol_idx =
((c_in * input_depth + d_pad) * input_height + h_pad) *
input_width +
w_pad;
col_data[col_idx] = vol_data[vol_idx];
}
col_data[col_idx] =
(h_pad < 0 || h_pad >= input_height || w_pad < 0 ||
w_pad >= input_width || d_pad < 0 || d_pad >= input_depth)
? static_cast<T>(0)
: vol_data[vol_idx];
}
}
}
......@@ -92,17 +114,18 @@ template <class T>
class Col2VolFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
framework::Tensor& vol, const framework::Tensor& col,
int stride_depth, int stride_height, int stride_width,
int padding_depth, int padding_height,
int padding_width) const {
PADDLE_ENFORCE(vol.dims().size() == 4);
const framework::Tensor& col,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* vol) const {
PADDLE_ENFORCE(vol->dims().size() == 4);
PADDLE_ENFORCE(col.dims().size() == 7);
int input_channels = vol.dims()[0];
int input_depth = vol.dims()[1];
int input_height = vol.dims()[2];
int input_width = vol.dims()[3];
int input_channels = vol->dims()[0];
int input_depth = vol->dims()[1];
int input_height = vol->dims()[2];
int input_width = vol->dims()[3];
int filter_depth = col.dims()[1];
int filter_height = col.dims()[2];
int filter_width = col.dims()[3];
......@@ -112,7 +135,28 @@ class Col2VolFunctor<platform::CPUPlace, T> {
int channels_col =
input_channels * filter_depth * filter_height * filter_width;
T* vol_data = vol.data<T>();
PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1,
output_width,
"input_width and output_width are "
"mismatching.");
T* vol_data = vol->data<T>();
const T* col_data = col.data<T>();
for (int c = 0; c < channels_col; ++c) {
......@@ -121,11 +165,11 @@ class Col2VolFunctor<platform::CPUPlace, T> {
int d_offset = (c / filter_width / filter_height) % filter_depth;
int cIm = c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) {
int d_pad = d * stride_depth - padding_depth + d_offset;
int d_pad = d * strides[0] - paddings[0] + d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) {
int h_pad = h * stride_height - padding_height + h_offset;
int h_pad = h * strides[1] - paddings[1] + h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) {
int w_pad = w * stride_width - padding_width + w_offset;
int w_pad = w * strides[2] - paddings[2] + w_offset * dilations[2];
if (h_pad >= 0 && h_pad < input_height && w_pad >= 0 &&
w_pad < input_width && d_pad >= 0 && d_pad < input_depth) {
......@@ -133,6 +177,7 @@ class Col2VolFunctor<platform::CPUPlace, T> {
((cIm * input_depth + d_pad) * input_height + h_pad) *
input_width +
w_pad;
int col_idx =
((c * output_depth + d) * output_height + h) * output_width +
w;
......
paddle/operators/math/vol2col.cu
浏览文件 @ 0968c7cd
......@@ -21,11 +21,12 @@ namespace math {
template <class T>
__global__ void vol2col(int num_kernels, const T* data_vol, int depth,
int height, int width, int filter_depth,
int filter_height, int filter_width, int stride_depth,
int stride_height, int stride_width, int padding_depth,
int padding_height, int padding_width, int output_detph,
int output_height, int output_width, T* data_col) {
int height, int width, int dilation_d, int dilation_h,
int dilation_w, int filter_depth, int filter_height,
int filter_width, int stride_depth, int stride_height,
int stride_width, int padding_depth, int padding_height,
int padding_width, int output_detph, int output_height,
int output_width, T* data_col) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels;
index += blockDim.x * gridDim.x) {
int w_out = index % output_width;
......@@ -44,12 +45,14 @@ __global__ void vol2col(int num_kernels, const T* data_vol, int depth,
for (int k = 0; k < filter_depth; ++k) {
for (int i = 0; i < filter_height; ++i) {
for (int j = 0; j < filter_width; ++j) {
int d = d_in + k;
int h = h_in + i;
int w = w_in + j;
int d = d_in + k * dilation_d;
int h = h_in + i * dilation_h;
int w = w_in + j * dilation_w;
int col_idx = (k * dilation_d * height + i * dilation_h) * width +
j * dilation_w;
*data_col = (d >= 0 && d < depth && h >= 0 && h < height && w >= 0 &&
w < width)
? data_vol[(k * height + i) * width + j]
? data_vol[col_idx]
: 0;
data_col += output_detph * output_height * output_width;
}
......@@ -68,23 +71,46 @@ template <class T>
class Vol2ColFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& vol, framework::Tensor& col,
int stride_depth, int stride_height, int stride_width,
int padding_depth, int padding_height,
int padding_width) const {
const framework::Tensor& vol,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* col) const {
PADDLE_ENFORCE(vol.dims().size() == 4);
PADDLE_ENFORCE(col.dims().size() == 7);
PADDLE_ENFORCE(col->dims().size() == 7);
int input_channels = vol.dims()[0];
int input_depth = vol.dims()[1];
int input_height = vol.dims()[2];
int input_width = vol.dims()[3];
int filter_depth = col.dims()[1];
int filter_height = col.dims()[2];
int filter_width = col.dims()[3];
int output_depth = col.dims()[4];
int output_height = col.dims()[5];
int output_width = col.dims()[6];
int filter_depth = col->dims()[1];
int filter_height = col->dims()[2];
int filter_width = col->dims()[3];
int output_depth = col->dims()[4];
int output_height = col->dims()[5];
int output_width = col->dims()[6];
PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"Mismatching.");
PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"Mismatching.");
PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1,
output_width,
"input_width and output_width are "
"Mismatching.");
int num_outputs =
input_channels * output_depth * output_height * output_width;
......@@ -95,19 +121,25 @@ class Vol2ColFunctor<platform::GPUPlace, T> {
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
num_outputs, vol.data<T>(), input_depth, input_height, input_width,
filter_depth, filter_height, filter_width, stride_depth, stride_height,
stride_width, padding_depth, padding_height, padding_width,
output_depth, output_height, output_width, col.data<T>());
dilations[0], dilations[1], dilations[2], filter_depth, filter_height,
filter_width, strides[0], strides[1], strides[2], paddings[0],
paddings[1], paddings[2], output_depth, output_height, output_width,
col->data<T>());
}
};
template <class T>
__global__ void col2vol(int num_kernels, const T* data_col, int depth,
int height, int width, int filter_depth,
int filter_height, int filter_width, int stride_depth,
int stride_height, int stride_width, int padding_depth,
int padding_height, int padding_width, int output_detph,
int output_height, int output_width, T* data_vol) {
int height, int width, int dilation_d, int dilation_h,
int dilation_w, int filter_depth, int filter_height,
int filter_width, int stride_depth, int stride_height,
int stride_width, int padding_depth, int padding_height,
int padding_width, int output_detph, int output_height,
int output_width, T* data_vol) {
const int d_filter_depth = dilation_d * (filter_depth - 1) + 1;
const int d_filter_height = dilation_h * (filter_height - 1) + 1;
const int d_filter_width = dilation_w * (filter_width - 1) + 1;
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels;
index += blockDim.x * gridDim.x) {
T src_val = 0;
......@@ -115,35 +147,41 @@ __global__ void col2vol(int num_kernels, const T* data_col, int depth,
int h = (index / width) % height + padding_height;
int d = (index / width / height) % depth + padding_depth;
int c = index / width / height / depth;
// compute the start and end of the output
int w_col_start =
(w < filter_width) ? 0 : (w - filter_width) / stride_width + 1;
(w < d_filter_width) ? 0 : (w - d_filter_width) / stride_width + 1;
int w_col_end = min(w / stride_width + 1, output_width);
int h_col_start =
(h < filter_height) ? 0 : (h - filter_height) / stride_height + 1;
(h < d_filter_height) ? 0 : (h - d_filter_height) / stride_height + 1;
int h_col_end = min(h / stride_height + 1, output_height);
int d_col_start =
(d < filter_depth) ? 0 : (d - filter_depth) / stride_depth + 1;
(d < d_filter_depth) ? 0 : (d - d_filter_depth) / stride_depth + 1;
int d_col_end = min(d / stride_depth + 1, output_detph);
int offset = (c * filter_depth * filter_height * filter_width +
d * filter_width * filter_height + h * filter_width + w) *
output_detph * output_height * output_width;
int coeff_d_col =
(1 - stride_depth * filter_width * filter_height * output_detph) *
output_height * output_width;
int coeff_h_col =
(1 - stride_height * filter_width * output_detph * output_height) *
output_width;
int coeff_w_col =
(1 - stride_width * output_detph * output_height * output_width);
for (int d_col = d_col_start; d_col < d_col_end; ++d_col) {
for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
src_val += data_col[offset + d_col * coeff_d_col +
h_col * coeff_h_col + w_col * coeff_w_col];
int d_off = (d - d_col * stride_depth);
int h_off = (h - h_col * stride_height);
int w_off = (w - w_col * stride_width);
if (d_off % dilation_d == 0 && h_off % dilation_h == 0 &&
w_off % dilation_w == 0) {
d_off /= dilation_d;
h_off /= dilation_h;
w_off /= dilation_w;
int data_col_index =
(((((c * filter_depth + d_off) * filter_height + h_off) *
filter_width +
w_off)));
data_col_index =
((data_col_index * output_detph + d_col) * output_height +
h_col) *
output_width +
w_col;
src_val += data_col[data_col_index];
}
}
}
}
......@@ -161,17 +199,18 @@ template <class T>
class Col2VolFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
framework::Tensor& vol, const framework::Tensor& col,
int stride_depth, int stride_height, int stride_width,
int padding_depth, int padding_height,
int padding_width) const {
PADDLE_ENFORCE(vol.dims().size() == 4);
const framework::Tensor& col,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* vol) const {
PADDLE_ENFORCE(vol->dims().size() == 4);
PADDLE_ENFORCE(col.dims().size() == 7);
int input_channels = vol.dims()[0];
int input_depth = vol.dims()[1];
int input_height = vol.dims()[2];
int input_width = vol.dims()[3];
int input_channels = vol->dims()[0];
int input_depth = vol->dims()[1];
int input_height = vol->dims()[2];
int input_width = vol->dims()[3];
int filter_depth = col.dims()[1];
int filter_height = col.dims()[2];
int filter_width = col.dims()[3];
......@@ -179,6 +218,28 @@ class Col2VolFunctor<platform::GPUPlace, T> {
int output_height = col.dims()[5];
int output_width = col.dims()[6];
PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"Mismatching.");
PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"Mismatching.");
PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1,
output_width,
"input_width and output_width are "
"Mismatching.");
int num_kernels = input_channels * input_depth * input_height * input_width;
const int threads = 1024;
......@@ -188,9 +249,10 @@ class Col2VolFunctor<platform::GPUPlace, T> {
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
num_kernels, col.data<T>(), input_depth, input_height, input_width,
filter_depth, filter_height, filter_width, stride_depth, stride_height,
stride_width, padding_depth, padding_height, padding_width,
output_depth, output_height, output_width, vol.data<T>());
dilations[0], dilations[1], dilations[2], filter_depth, filter_height,
filter_width, strides[0], strides[1], strides[2], paddings[0],
paddings[1], paddings[2], output_depth, output_height, output_width,
vol->data<T>());
}
};
......
paddle/operators/math/vol2col.h
浏览文件 @ 0968c7cd
......@@ -31,6 +31,15 @@ namespace math {
* \param colData Column data.
* \param colShape The shape of colData.
*
* \param dilations dilation data.
* \param 3-dimension [dilation_depth, dilation_height, dilation_width].
*
* \param strides stride data.
* \param 3-dimension [stride_depth, stride_height, stride_width].
*
* \param paddings padding data.
* \param 3-dimension [d_pad, h_pad, w_pad].
*
* The shape of colData is:
* [input_channels, filter_depth, filter_height, filter_width, output_depth,
* output_height, output_width]
......@@ -57,20 +66,22 @@ template <typename Place, typename T>
class Vol2ColFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& vol, framework::Tensor& col,
int stride_depth, int stride_height, int stride_width,
int padding_depth, int padding_height,
int padding_width) const;
const framework::Tensor& vol,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* col) const;
};
template <typename Place, typename T>
class Col2VolFunctor {
public:
void operator()(const platform::DeviceContext& context,
framework::Tensor& vol, const framework::Tensor& col,
int stride_depth, int stride_height, int stride_width,
int padding_depth, int padding_height,
int padding_width) const;
const framework::Tensor& col,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* vol) const;
};
} // namespace math
......
paddle/operators/math/vol2col_test.cc
浏览文件 @ 0968c7cd
......@@ -62,11 +62,15 @@ void testVol2col() {
int input_height = 2;
int input_width = 3;
int filter_size = 2;
int stride = 1;
int padding = 0;
int output_depth = (input_depth - filter_size + 2 * padding) / stride + 1;
int output_height = (input_height - filter_size + 2 * padding) / stride + 1;
int output_width = (input_width - filter_size + 2 * padding) / stride + 1;
std::vector<int> strides({1, 1, 1});
std::vector<int> paddings({0, 0, 0});
std::vector<int> dilations({1, 1, 1});
int output_depth =
(input_depth - filter_size + 2 * paddings[0]) / strides[0] + 1;
int output_height =
(input_height - filter_size + 2 * paddings[1]) / strides[1] + 1;
int output_width =
(input_width - filter_size + 2 * paddings[2]) / strides[2] + 1;
// Vol2Col test
float* input_ptr =
......@@ -85,8 +89,7 @@ void testVol2col() {
*place);
paddle::operators::math::Vol2ColFunctor<Place, float> vol2col;
vol2col(*context, input, output, stride, stride, stride, padding, padding,
padding);
vol2col(*context, input, dilations, strides, paddings, &output);
float vol_2_col[] = {0, 1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 10, 11};
float* out_cfo_ptr;
......@@ -111,8 +114,7 @@ void testVol2col() {
}
paddle::operators::math::Col2VolFunctor<Place, float> col2vol;
col2vol(*context, input, output, stride, stride, stride, padding, padding,
padding);
col2vol(*context, output, dilations, strides, paddings, &input);
float* in_ptr;
if (paddle::platform::is_cpu_place(*place)) {
......
paddle/operators/sequence_conv_op.h
浏览文件 @ 0968c7cd
......@@ -61,9 +61,9 @@ class SequenceConvKernel : public framework::OpKernel<T> {
math::ContextProjectFunctor<Place, T> seq_project_functor;
seq_project_functor(context.device_context(), *in, *padding_data, col,
seq_project_functor(context.device_context(), *in, *padding_data,
padding_trainable, context_start, context_length,
context_stride, up_pad, down_pad);
context_stride, up_pad, down_pad, &col);
math::matmul<Place, T>(context.device_context(), col, false, filter, false,
static_cast<T>(1.0), out, static_cast<T>(0.0));
......@@ -116,10 +116,10 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
in_g->set_lod(in->lod());
set_zero(context.device_context(), in_g, static_cast<T>(0));
seq_project_grad_functor(context.device_context(), *in_g, *padding_data_g,
col, padding_trainable, context_start,
context_length, context_stride, up_pad, down_pad,
true, false);
seq_project_grad_functor(context.device_context(), *in_g,
padding_trainable, context_start, context_length,
context_stride, up_pad, down_pad, false, true,
padding_data_g, &col);
}
if (padding_trainable && padding_data_g) {
......@@ -128,9 +128,9 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
LoDTensor* input = const_cast<LoDTensor*>(in);
seq_project_grad_functor(context.device_context(), *input,
*padding_data_g, col, padding_trainable,
context_start, context_length, context_stride,
up_pad, down_pad, false, true);
padding_trainable, context_start, context_length,
context_stride, up_pad, down_pad, true, false,
padding_data_g, &col);
}
if (filter_g) {
......@@ -145,9 +145,9 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
padding_data = context.Input<Tensor>("PaddingData");
}
seq_project_functor(context.device_context(), *in, *padding_data, col,
seq_project_functor(context.device_context(), *in, *padding_data,
padding_trainable, context_start, context_length,
context_stride, up_pad, down_pad);
context_stride, up_pad, down_pad, &col);
math::matmul<Place, T>(context.device_context(), col, true, out_grad,
false, T(1.0), &filter_grad, T(1.0));
......
paddle/platform/gpu_info.cc
浏览文件 @ 0968c7cd
......@@ -109,5 +109,10 @@ void GpuMemcpyPeer(void *dst, int dst_device, const void *src, int src_device,
cudaMemcpyPeerAsync(dst, dst_device, src, src_device, count, stream),
"cudaMemcpyPeerAsync failed in paddle::platform::GpuMemcpyPeer");
}
void GpuMemsetAsync(void *dst, int value, size_t count, cudaStream_t stream) {
PADDLE_ENFORCE(cudaMemsetAsync(dst, value, count, stream),
"cudaMemsetAsync failed in paddle::platform::GpuMemsetAsync");
}
} // namespace platform
} // namespace paddle
paddle/platform/gpu_info.h
浏览文件 @ 0968c7cd
......@@ -60,6 +60,9 @@ void GpuMemcpySync(void *dst, const void *src, size_t count,
void GpuMemcpyPeer(void *dst, int dst_device, const void *src, int src_device,
size_t count, cudaStream_t stream);
//! Set memory dst with value count size asynchronously
void GpuMemsetAsync(void *dst, int value, size_t count, cudaStream_t stream);
} // namespace platform
} // namespace paddle
......
paddle/scripts/deb/postinst 已删除 100644 → 0
浏览文件 @ d9a305cb
#!/bin/bash
set -e
echo "Post install paddle debian package."
echo "Install some python package used for paddle. You can run "
echo " pip install /usr/opt/paddle/share/wheels/*.whl to install them."
find /usr/ -name '*paddle*.whl' | xargs pip install
paddle/scripts/docker/README.md
浏览文件 @ 0968c7cd
......@@ -2,178 +2,198 @@
## Goals
We want the building procedure generates Docker images so that we can run PaddlePaddle applications on Kubernetes clusters.
We want to make the building procedures:
We want to build .deb packages so that enterprise users can run PaddlePaddle applications without Docker.
1. Static, can reproduce easily.
1. Generate python `whl` packages that can be widely use cross many distributions.
1. Build different binaries per release to satisfy different environments:
- Binaries for different CUDA and CUDNN versions, like CUDA 7.5, 8.0, 9.0
- Binaries containing only capi
- Binaries for python with wide unicode support or not.
1. Build docker images with PaddlePaddle pre-installed, so that we can run
PaddlePaddle applications directly in docker or on Kubernetes clusters.
We want to minimize the size of generated Docker images and .deb packages so to reduce the download time.
To achieve this, we created a repo: https://github.com/PaddlePaddle/buildtools
which gives several docker images that are `manylinux1` sufficient. Then we
can build PaddlePaddle using these images to generate corresponding `whl`
binaries.
We want to encapsulate building tools and dependencies in a *development* Docker image so to ease the tools installation for developers.
## Run The Build
Developers use various editors (emacs, vim, Eclipse, Jupyter Notebook), so the development Docker image contains only building tools, not editing tools, and developers are supposed to git clone source code into their development computers and map the code into the development container.
### Build Evironments
We want the procedure and tools also work with testing, continuous integration, and releasing.
The pre-built build environment images are:
| Image | Tag |
| ----- | --- |
| paddlepaddle/paddle_manylinux_devel | cuda7.5_cudnn5 |
| paddlepaddle/paddle_manylinux_devel | cuda8.0_cudnn5 |
| paddlepaddle/paddle_manylinux_devel | cuda7.5_cudnn7 |
| paddlepaddle/paddle_manylinux_devel | cuda9.0_cudnn7 |
## Docker Images
So we need two Docker images for each version of PaddlePaddle:
1. `paddle:<version>-dev`
This a development image contains only the development tools and standardizes the building procedure. Users include:
### Start Build
- developers -- no longer need to install development tools on the host, and can build their current work on the host (development computer).
- release engineers -- use this to build the official release from certain branch/tag on Github.com.
- document writers / Website developers -- Our documents are in the source repo in the form of .md/.rst files and comments in source code. We need tools to extract the information, typeset, and generate Web pages.
Choose one docker image that suit your environment and run the following
command to start a build:
Of course, developers can install building tools on their development computers. But different versions of PaddlePaddle might require different set or version of building tools. Also, it makes collaborative debugging easier if all developers use a unified development environment.
The development image should include the following tools:
- gcc/clang
- nvcc
- Python
- sphinx
- woboq
- sshd
```bash
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
docker run --rm -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_AVX=ON" -e "WITH_TESTING=OFF" -e "RUN_TEST=OFF" -e "PYTHON_ABI=cp27-cp27mu" paddlepaddle/paddle_manylinux_devel /paddle/paddle/scripts/docker/build.sh
```
Many developers work on a remote computer with GPU; they could ssh into the computer and `docker exec` into the development container. However, running `sshd` in the container allows developers to ssh into the container directly.
After the build finishes, you can get output `whl` package under
`build/python/dist`.
1. `paddle:<version>`
This command mounts the source directory on the host into `/paddle` in the container, then run the build script `/paddle/paddle/scripts/docker/build.sh`
in the container. When it writes to `/paddle/build` in the container, it writes to `$PWD/build` on the host indeed.
This is the production image, generated using the development image. This image might have multiple variants:
### Build Options
- GPU/AVX `paddle:<version>-gpu`
- GPU/no-AVX `paddle:<version>-gpu-noavx`
- no-GPU/AVX `paddle:<version>`
- no-GPU/no-AVX `paddle:<version>-noavx`
Users can specify the following Docker build arguments with either "ON" or "OFF" value:
We allow users to choose between GPU and no-GPU because the GPU version image is much larger than then the no-GPU version.
| Option | Default | Description |
| ------ | -------- | ----------- |
| `WITH_GPU` | OFF | Generates NVIDIA CUDA GPU code and relies on CUDA libraries. |
| `WITH_AVX` | OFF | Set to "ON" to enable AVX support. |
| `WITH_TESTING` | ON | Build unit tests binaries. |
| `WITH_MKLDNN` | ON | Build with [Intel® MKL DNN](https://github.com/01org/mkl-dnn) support. |
| `WITH_MKLML` | ON | Build with [Intel® MKL](https://software.intel.com/en-us/mkl) support. |
| `WITH_GOLANG` | ON | Build fault-tolerant parameter server written in go. |
| `WITH_SWIG_PY` | ON | Build with SWIG python API support. |
| `WITH_C_API` | OFF | Build capi libraries for inference. |
| `WITH_PYTHON` | ON | Build with python support. Turn this off if build is only for capi. |
| `WITH_STYLE_CHECK` | ON | Check the code style when building. |
| `PYTHON_ABI` | "" | Build for different python ABI support, can be cp27-cp27m or cp27-cp27mu |
| `RUN_TEST` | OFF | Run unit test immediently after the build. |
| `WITH_DOC` | OFF | Build docs after build binaries. |
| `WOBOQ` | OFF | Generate WOBOQ code viewer under `build/woboq_out` |
We allow users the choice between AVX and no-AVX, because some cloud providers don't provide AVX-enabled VMs.
## Docker Images
## Development Environment
You can get the latest PaddlePaddle docker images by
`docker pull paddlepaddle/paddle:<version>` or build one by yourself.
Here we describe how to use above two images. We start from considering our daily development environment.
### Official Docker Releases
Developers work on a computer, which is usually a laptop or desktop:
Official docker images at
[here](https://hub.docker.com/r/paddlepaddle/paddle/tags/),
you can choose either latest or images with a release tag like `0.10.0`,
Currently available tags are:
<img src="doc/paddle-development-environment.png" width=500 />
| Tag | Description |
| ------ | --------------------- |
| latest | latest CPU only image |
| latest-gpu | latest binary with GPU support |
| 0.10.0 | release 0.10.0 CPU only binary image |
| 0.10.0-gpu | release 0.10.0 with GPU support |
or, they might rely on a more sophisticated box (like with GPUs):
### Build Your Own Image
<img src="doc/paddle-development-environment-gpu.png" width=500 />
Build PaddlePaddle docker images are quite simple since PaddlePaddle can
be installed by just running `pip install`. A sample `Dockerfile` is:
A principle here is that source code lies on the development computer (host) so that editors like Eclipse can parse the source code to support auto-completion.
```dockerfile
FROM nvidia/cuda:7.5-cudnn5-runtime-centos6
RUN yum install -y centos-release-SCL
RUN yum install -y python27
# This whl package is generated by previous build steps.
ADD python/dist/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl /
RUN pip install /paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl && rm -f /*.whl
```
Then build the image by running `docker build -t [REPO]/paddle:[TAG] .` under
the directory containing your own `Dockerfile`.
## Usages
- NOTE: note that you can choose different base images for your environment, you can find all the versions [here](https://hub.docker.com/r/nvidia/cuda/).
### Build the Development Docker Image
### Use Docker Images
The following commands check out the source code to the host and build the development image `paddle:dev`:
Suppose that you have written an application program `train.py` using
PaddlePaddle, we can test and run it using docker:
```bash
git clone https://github.com/PaddlePaddle/Paddle paddle
cd paddle
docker build -t paddle:dev .
docker run --rm -it -v $PWD:/work paddlepaddle/paddle /work/a.py
```
The `docker build` command assumes that `Dockerfile` is in the root source tree. Note that in this design, this `Dockerfile` is this only one in our repo.
Users can specify a Ubuntu mirror server for faster downloading:
```bash
docker build -t paddle:dev --build-arg UBUNTU_MIRROR=mirror://mirrors.ubuntu.com/mirrors.txt .
```
But this works only if all dependencies of `train.py` are in the production image. If this is not the case, we need to build a new Docker image from the production image and with more dependencies installs.
### Build PaddlePaddle from Source Code
### Run PaddlePaddle Book In Docker
Given the development image `paddle:dev`, the following command builds PaddlePaddle from the source tree on the development computer (host):
Our [book repo](https://github.com/paddlepaddle/book) also provide a docker
image to start a jupiter notebook inside docker so that you can run this book
using docker:
```bash
docker run --rm -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_AVX=ON" -e "WITH_TESTING=OFF" -e "RUN_TEST=OFF" paddle:dev
docker run -d -p 8888:8888 paddlepaddle/book
```
This command mounts the source directory on the host into `/paddle` in the container, so the default entry point of `paddle:dev`, `build.sh`, could build the source code with possible local changes. When it writes to `/paddle/build` in the container, it writes to `$PWD/build` on the host indeed.
`build.sh` builds the following:
- PaddlePaddle binaries,
- `$PWD/build/paddle-<version>.deb` for production installation, and
- `$PWD/build/Dockerfile`, which builds the production Docker image.
Please refer to https://github.com/paddlepaddle/book if you want to build this
docker image by your self.
Users can specify the following Docker build arguments with either "ON" or "OFF" value:
- `WITH_GPU`: ***Required***. Generates NVIDIA CUDA GPU code and relies on CUDA libraries.
- `WITH_AVX`: ***Required***. Set to "OFF" prevents from generating AVX instructions. If you don't know what is AVX, you might want to set "ON".
- `WITH_TEST`: ***Optional, default OFF***. Build unit tests binaries. Once you've built the unit tests, you can run these test manually by the following command:
```bash
docker run --rm -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_AVX=ON" paddle:dev sh -c "cd /paddle/build; make coverall"
```
- `RUN_TEST`: ***Optional, default OFF***. Run unit tests after building. You can't run unit tests without building it.
### Run Distributed Applications
### Build the Production Docker Image
In our [API design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/api.md#distributed-training), we proposed an API that starts a distributed training job on a cluster. This API need to build a PaddlePaddle application into a Docker image as above and calls kubectl to run it on the cluster. This API might need to generate a Dockerfile look like above and call `docker build`.
The following command builds the production image:
Of course, we can manually build an application image and launch the job using the kubectl tool:
```bash
docker build -t paddle -f build/Dockerfile ./build
docker build -f some/Dockerfile -t myapp .
docker tag myapp me/myapp
docker push
kubectl ...
```
This production image is minimal -- it includes binary `paddle`, the shared library `libpaddle.so`, and Python runtime.
## Docker Images for Developers
### Run PaddlePaddle Applications
We have a special docker image for developers:
`paddlepaddle/paddle:<version>-dev`. This image is also generated from
https://github.com/PaddlePaddle/buildtools
Again the development happens on the host. Suppose that we have a simple application program in `a.py`, we can test and run it using the production image:
This a development image contains only the
development tools and standardizes the building procedure. Users include:
```bash
docker run --rm -it -v $PWD:/work paddle /work/a.py
```
- developers -- no longer need to install development tools on the host, and can build their current work on the host (development computer).
- release engineers -- use this to build the official release from certain branch/tag on Github.com.
- document writers / Website developers -- Our documents are in the source repo in the form of .md/.rst files and comments in source code. We need tools to extract the information, typeset, and generate Web pages.
But this works only if all dependencies of `a.py` are in the production image. If this is not the case, we need to build a new Docker image from the production image and with more dependencies installs.
Of course, developers can install building tools on their development computers. But different versions of PaddlePaddle might require different set or version of building tools. Also, it makes collaborative debugging easier if all developers use a unified development environment.
### Build and Run PaddlePaddle Applications
The development image contains the following tools:
We need a Dockerfile in https://github.com/paddlepaddle/book that builds Docker image `paddlepaddle/book:<version>`, basing on the PaddlePaddle production image:
- gcc/clang
- nvcc
- Python
- sphinx
- woboq
- sshd
```
FROM paddlepaddle/paddle:<version>
RUN pip install -U matplotlib jupyter ...
COPY . /book
EXPOSE 8080
CMD ["jupyter"]
```
Many developers work on a remote computer with GPU; they could ssh into the computer and `docker exec` into the development container. However, running `sshd` in the container allows developers to ssh into the container directly.
The book image is an example of PaddlePaddle application image. We can build it
```bash
git clone https://github.com/paddlepaddle/book
cd book
docker build -t book .
```
### Development Workflow
### Build and Run Distributed Applications
Here we describe how the workflow goes on. We start from considering our daily development environment.
In our [API design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/api.md#distributed-training), we proposed an API that starts a distributed training job on a cluster. This API need to build a PaddlePaddle application into a Docker image as above and calls kubectl to run it on the cluster. This API might need to generate a Dockerfile look like above and call `docker build`.
Developers work on a computer, which is usually a laptop or desktop:
Of course, we can manually build an application image and launch the job using the kubectl tool:
<img src="doc/paddle-development-environment.png" width=500 />
```bash
docker build -f some/Dockerfile -t myapp .
docker tag myapp me/myapp
docker push
kubectl ...
```
or, they might rely on a more sophisticated box (like with GPUs):
<img src="doc/paddle-development-environment-gpu.png" width=500 />
A principle here is that source code lies on the development computer (host) so that editors like Eclipse can parse the source code to support auto-completion.
### Reading source code with woboq codebrowser
For developers who are interested in the C++ source code, please use -e "WOBOQ=ON" to enable the building of C++ source code into HTML pages using [Woboq codebrowser](https://github.com/woboq/woboq_codebrowser).
- The following command builds PaddlePaddle, generates HTML pages from C++ source code, and writes HTML pages into `$HOME/woboq_out` on the host:
```bash
docker run -v $PWD:/paddle -v $HOME/woboq_out:/woboq_out -e "WITH_GPU=OFF" -e "WITH_AVX=ON" -e "WITH_TEST=ON" -e "WOBOQ=ON" paddle:dev
docker run -v $PWD:/paddle -v $HOME/woboq_out:/woboq_out -e "WITH_GPU=OFF" -e "WITH_AVX=ON" -e "WITH_TEST=ON" -e "WOBOQ=ON" paddlepaddle/paddle:latest-dev
```
- You can open the generated HTML files in your Web browser. Or, if you want to run a Nginx container to serve them for a wider audience, you can run:
......
paddle/scripts/docker/build.sh
浏览文件 @ 0968c7cd
#!/bin/bash
set -xe
function cmake_gen() {
# Set BASE_IMAGE according to env variables
if [[ ${WITH_GPU} == "ON" ]]; then
BASE_IMAGE="nvidia/cuda:8.0-cudnn5-runtime-ubuntu16.04"
else
BASE_IMAGE="ubuntu:16.04"
fi
DOCKERFILE_GPU_ENV=""
DOCKERFILE_CUDNN_DSO=""
if [[ ${WITH_GPU:-OFF} == 'ON' ]]; then
DOCKERFILE_GPU_ENV="ENV LD_LIBRARY_PATH /usr/lib/x86_64-linux-gnu:${LD_LIBRARY_PATH}"
DOCKERFILE_CUDNN_DSO="RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.5 /usr/lib/x86_64-linux-gnu/libcudnn.so"
fi
mkdir -p /paddle/build
cd /paddle/build
......@@ -26,10 +9,29 @@ function cmake_gen() {
# delete previous built whl packages
rm -rf /paddle/paddle/dist 2>/dev/null || true
# Support build for all python versions, currently
# including cp27-cp27m and cp27-cp27mu.
PYTHON_FLAGS=""
if [ "$1" != "" ]; then
echo "using python abi: $1"
if [ "$1" == "cp27-cp27m" ]; then
export LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH#/opt/_internal/cpython-2.7.11-ucs4/lib:}
PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/python/cp27-cp27m/bin/python
-DPYTHON_INCLUDE_DIR:PATH=/opt/python/cp27-cp27m/include/python2.7
-DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-2.7.11-ucs2/lib/libpython2.7.so"
elif [ "$1" == "cp27-cp27mu" ]; then
export LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH#/opt/_internal/cpython-2.7.11-ucs2/lib:}
PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/python/cp27-cp27mu/bin/python
-DPYTHON_INCLUDE_DIR:PATH=/opt/python/cp27-cp27mu/include/python2.7
-DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-2.7.11-ucs4/lib/libpython2.7.so"
fi
fi
cat <<EOF
========================================
Configuring cmake in /paddle/build ...
-DCMAKE_BUILD_TYPE=Release
${PYTHON_FLAGS}
-DWITH_DOC=OFF
-DWITH_GPU=${WITH_GPU:-OFF}
-DWITH_MKLDNN=${WITH_MKLDNN:-ON}
......@@ -46,12 +48,12 @@ function cmake_gen() {
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
========================================
EOF
# Disable UNITTEST_USE_VIRTUALENV in docker because
# docker environment is fully controlled by this script.
# See /Paddle/CMakeLists.txt, UNITTEST_USE_VIRTUALENV option.
cmake .. \
-DCMAKE_BUILD_TYPE=Release \
${PYTHON_FLAGS} \
-DWITH_DOC=OFF \
-DWITH_GPU=${WITH_GPU:-OFF} \
-DWITH_MKLDNN=${WITH_MKLDNN:-ON} \
......@@ -134,6 +136,19 @@ EOF
function gen_dockerfile() {
# Set BASE_IMAGE according to env variables
if [[ ${WITH_GPU} == "ON" ]]; then
BASE_IMAGE="nvidia/cuda:8.0-cudnn5-runtime-ubuntu16.04"
else
BASE_IMAGE="ubuntu:16.04"
fi
DOCKERFILE_GPU_ENV=""
DOCKERFILE_CUDNN_DSO=""
if [[ ${WITH_GPU:-OFF} == 'ON' ]]; then
DOCKERFILE_GPU_ENV="ENV LD_LIBRARY_PATH /usr/lib/x86_64-linux-gnu:${LD_LIBRARY_PATH}"
DOCKERFILE_CUDNN_DSO="RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.5 /usr/lib/x86_64-linux-gnu/libcudnn.so"
fi
cat <<EOF
========================================
......@@ -168,13 +183,14 @@ EOF
${DOCKERFILE_GPU_ENV}
ADD go/cmd/pserver/pserver /usr/bin/
ADD go/cmd/master/master /usr/bin/
ADD paddle/pybind/print_operators_doc /usr/bin/
# default command shows the paddle version and exit
CMD ["paddle", "version"]
EOF
}
cmake_gen
set -xe
cmake_gen ${PYTHON_ABI:-""}
run_build
run_test
gen_docs
......
paddle/scripts/docker/build_android.sh
浏览文件 @ 0968c7cd
......@@ -44,7 +44,7 @@ if [ $ANDROID_ABI == "armeabi-v7a" ]; then
-DHOST_C_COMPILER=/usr/bin/gcc \
-DHOST_CXX_COMPILER=/usr/bin/g++ \
-DCMAKE_INSTALL_PREFIX=$DEST_ROOT \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_BUILD_TYPE=MinSizeRel \
-DUSE_EIGEN_FOR_BLAS=ON \
-DWITH_C_API=ON \
-DWITH_SWIG_PY=OFF \
......@@ -58,7 +58,7 @@ elif [ $ANDROID_ABI == "arm64-v8a" ]; then
-DHOST_C_COMPILER=/usr/bin/gcc \
-DHOST_CXX_COMPILER=/usr/bin/g++ \
-DCMAKE_INSTALL_PREFIX=$DEST_ROOT \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_BUILD_TYPE=MinSizeRel \
-DUSE_EIGEN_FOR_BLAS=OFF \
-DWITH_C_API=ON \
-DWITH_SWIG_PY=OFF \
......@@ -72,7 +72,7 @@ elif [ $ANDROID_ABI == "armeabi" ]; then
-DHOST_C_COMPILER=/usr/bin/gcc \
-DHOST_CXX_COMPILER=/usr/bin/g++ \
-DCMAKE_INSTALL_PREFIX=$DEST_ROOT \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_BUILD_TYPE=MinSizeRel \
-DWITH_C_API=ON \
-DWITH_SWIG_PY=OFF \
-DWITH_STYLE_CHECK=OFF \
......
paddle/trainer/MergeModel.cpp
浏览文件 @ 0968c7cd
......@@ -27,6 +27,9 @@ using namespace paddle; // NOLINT
using namespace std; // NOLINT
int main(int argc, char** argv) {
initMain(argc, argv);
initPython(argc, argv);
if (FLAGS_model_dir.empty() || FLAGS_config_file.empty() ||
FLAGS_model_file.empty()) {
LOG(INFO) << "Usage: ./paddle_merge_model --model_dir=pass-00000 "
......@@ -34,9 +37,6 @@ int main(int argc, char** argv) {
return 0;
}
initMain(argc, argv);
initPython(argc, argv);
string confFile = FLAGS_config_file;
#ifndef PADDLE_WITH_CUDA
FLAGS_use_gpu = false;
......
python/paddle/v2/__init__.py
浏览文件 @ 0968c7cd
......@@ -33,7 +33,6 @@ import networks
import minibatch
import plot
import image
import model
import paddle.trainer.config_parser as cp
__all__ = [
......@@ -58,7 +57,6 @@ __all__ = [
'evaluator',
'image',
'master',
'model',
]
cp.begin_parse()
......
python/paddle/v2/fluid/io.py
浏览文件 @ 0968c7cd
......@@ -165,7 +165,7 @@ def save_inference_model(dirname,
:param target_vars: Variables from which we can get inference results.
:param executor: executor that save inference model
:param main_program: original program, which will be pruned to build the inference model.
Default g_program.
Default g_main_program.
:return: None
"""
......@@ -234,3 +234,35 @@ def load_inference_model(dirname, executor):
fetch_vars = [program.global_block().var(name) for name in fetch_var_names]
return [program, feed_var_names, fetch_vars]
def get_parameter_value(para, executor):
"""
Get the LoDTensor for the parameter
:param executor: executor for retrieving the value
:param para: the given parameter
:return: the LoDTensor for the parameter
"""
assert is_parameter(para)
get_program = Program()
block = get_program.global_block()
new_var = _clone_var_in_block_(block, para)
return executor.run(get_program, feed={}, fetch_list=[new_var])[0]
def get_parameter_value_by_name(name, executor, program=None):
"""
Get the LoDTensor for paramter with the given name
:param executor: executor for retrieving the value
:param name: the name of the parameter
:param program: the program where the variable is found
Default g_main_program.
:return: the LoDTensor for the variable
"""
if program is None:
program = g_main_program
var = program.global_block().var(name)
return get_parameter_value(var, executor)
python/paddle/v2/fluid/layer_helper.py
浏览文件 @ 0968c7cd
......@@ -72,7 +72,7 @@ class LayerHelper(object):
@property
def bias_attr(self):
default = {'name': None, 'initializer': XavierInitializer()}
default = {'name': None, 'initializer': ConstantInitializer()}
bias_attr = self.kwargs.get('bias_attr', None)
if bias_attr is None:
bias_attr = default
......@@ -149,24 +149,19 @@ class LayerHelper(object):
persistable=True,
initializer=initializer)
def append_bias_op(self, input_var, num_flatten_dims=None):
def append_bias_op(self, input_var, dim_start=1, dim_end=None):
"""
Append bias operator and return its output. If the user does not set
bias_attr, append_bias_op will return input_var
:param input_var: the input variable. The len(input_var.shape) is larger
or equal than 2.
:param num_flatten_dims: The input tensor will be flatten as a matrix
when adding bias.
`matrix.shape = product(input_var.shape[0:num_flatten_dims]), product(
input_var.shape[num_flatten_dims:])`
:param dim_start:
:param dim_end: the shape of the bias will be
input_var.shape[dim_start:dim_end]. The bias is broadcasted to other
dimensions and added to input_var to get the output
"""
if num_flatten_dims is None:
num_flatten_dims = self.kwargs.get('num_flatten_dims', None)
if num_flatten_dims is None:
num_flatten_dims = 1
size = list(input_var.shape[num_flatten_dims:])
size = list(input_var.shape[dim_start:dim_end])
bias_attr = self.bias_attr
if not bias_attr:
return input_var
......@@ -178,7 +173,8 @@ class LayerHelper(object):
type='elementwise_add',
inputs={'X': [input_var],
'Y': [b]},
outputs={'Out': [tmp]})
outputs={'Out': [tmp]},
attrs={'axis': dim_start})
return tmp
def append_activation(self, input_var):
......
python/paddle/v2/fluid/layers.py
浏览文件 @ 0968c7cd
......@@ -694,7 +694,7 @@ def conv2d(input,
'paddings': padding,
'groups': groups})
pre_act = helper.append_bias_op(pre_bias, 1)
pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
return helper.append_activation(pre_act)
......
python/paddle/v2/fluid/optimizer.py
浏览文件 @ 0968c7cd
......@@ -9,7 +9,7 @@ from paddle.v2.fluid.layer_helper import LayerHelper
__all__ = [
'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
'AdamaxOptimizer'
'AdamaxOptimizer', 'DecayedAdagradOptimizer'
]
......@@ -85,7 +85,7 @@ class Optimizer(object):
"""
if (name in self._accumulators and
param.name in self._accumulators[name]):
raise Exception("Accumulator {} already exists for parmeter {}".
raise Exception("Accumulator {} already exists for parameter {}".
format(name, param.name))
assert isinstance(self.helper, LayerHelper)
......@@ -307,7 +307,7 @@ class AdagradOptimizer(Optimizer):
moment_acc = self._get_accumulator(self._moment_acc_str,
param_and_grad[0])
# create the adagrad optimizer op
# Create the adagrad optimizer op
adagrad_op = block.append_op(
type=self.type,
inputs={
......@@ -510,3 +510,51 @@ class AdamaxOptimizer(Optimizer):
attrs={"scale": self._beta1})
return [scale_beta1]
class DecayedAdagradOptimizer(Optimizer):
"""Simple Decayed Adagrad optimizer with moment state
"""
_moment_acc_str = "moment"
def __init__(self,
learning_rate,
decay=0.95,
epsilon=1.0e-6,
global_step=None):
assert learning_rate is not None
assert decay is not None
assert epsilon is not None
super(DecayedAdagradOptimizer, self).__init__(global_step)
self.type = "decayed_adagrad"
self._learning_rate = learning_rate
self._decay = decay
self._epsilon = epsilon
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
for p in parameters:
self._add_accumulator(self._moment_acc_str, p)
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
moment_acc = self._get_accumulator(self._moment_acc_str,
param_and_grad[0])
# Create the decayed adagrad optimizer op
decayed_adagrad_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"Moment": moment_acc,
"LearningRate": self._create_param_lr(param_and_grad)
},
outputs={"ParamOut": param_and_grad[0],
"MomentOut": moment_acc},
attrs={"epsilon": self._epsilon})
return decayed_adagrad_op
python/paddle/v2/fluid/tests/test_conv2d_op.py
浏览文件 @ 0968c7cd
......@@ -10,23 +10,33 @@ def conv2d_forward_naive(input, filter, group, conv_param):
assert np.mod(out_c, group) == 0
sub_out_c = out_c / group
stride, pad = conv_param['stride'], conv_param['pad']
out_h = 1 + (in_h + 2 * pad[0] - f_h) / stride[0]
out_w = 1 + (in_w + 2 * pad[1] - f_w) / stride[1]
stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[
'dilation']
out_h = 1 + (in_h + 2 * pad[0] - (dilation[0] * (f_h - 1) + 1)) / stride[0]
out_w = 1 + (in_w + 2 * pad[1] - (dilation[1] * (f_w - 1) + 1)) / stride[1]
out = np.zeros((in_n, out_c, out_h, out_w))
d_bolck_w = (dilation[0] * (f_h - 1) + 1)
d_bolck_h = (dilation[1] * (f_w - 1) + 1)
input_pad = np.pad(input, ((0, ), (0, ), (pad[0], ), (pad[1], )),
mode='constant',
constant_values=0)
filter_dilation = np.zeros((out_c, f_c, d_bolck_h, d_bolck_w))
filter_dilation[:, :, 0:d_bolck_h:dilation[0], 0:d_bolck_w:dilation[
1]] = filter
for i in range(out_h):
for j in range(out_w):
for g in range(group):
input_pad_masked = \
input_pad[:, g * f_c:(g + 1) * f_c,
i * stride[0]:i * stride[0] + f_h,
j * stride[1]:j * stride[1] + f_w]
i * stride[0]:i * stride[0] + d_bolck_h,
j * stride[1]:j * stride[1] + d_bolck_w]
f_sub = filter[g * sub_out_c:(g + 1) * sub_out_c, :, :, :]
f_sub = filter_dilation[g * sub_out_c:(g + 1) *
sub_out_c, :, :, :]
for k in range(sub_out_c):
out[:, g * sub_out_c + k, i, j] = \
np.sum(input_pad_masked * f_sub[k, :, :, :],
......@@ -39,9 +49,14 @@ class TestConv2dOp(OpTest):
def setUp(self):
self.init_op_type()
self.init_group()
self.init_dilation()
self.init_test_case()
conv2d_param = {'stride': self.stride, 'pad': self.pad}
conv2d_param = {
'stride': self.stride,
'pad': self.pad,
'dilation': self.dilations
}
input = np.random.random(self.input_size).astype("float32")
filter = np.random.random(self.filter_size).astype("float32")
output = conv2d_forward_naive(input, filter, self.groups,
......@@ -80,12 +95,14 @@ class TestConv2dOp(OpTest):
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.dilations = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] / self.groups
self.filter_size = [6, f_c, 3, 3]
def init_dilation(self):
self.dilations = [1, 1]
def init_group(self):
self.groups = 1
......@@ -101,24 +118,66 @@ class TestWithGroup(TestConv2dOp):
self.op_type = "conv2d"
#----------------Conv2dCudnn----------------
class TestWith1x1(TestConv2dOp):
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] / self.groups
self.filter_size = [6, f_c, 1, 1]
def init_dilation(self):
self.dilations = [1, 1]
class TestCudnn(TestConv2dOp):
def init_group(self):
self.groups = 1
self.groups = 3
def init_op_type(self):
self.op_type = "conv_cudnn"
self.op_type = "conv2d"
class TestCudnnWithGroup(TestConv2dOp):
class TestWithDilation(TestConv2dOp):
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.input_size = [2, 3, 10, 10] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] / self.groups
self.filter_size = [6, f_c, 3, 3]
def init_dilation(self):
self.dilations = [2, 2]
def init_group(self):
self.groups = 3
def init_op_type(self):
self.op_type = "conv2d"
#----------------Conv2dCudnn----------------
class TestCudnn(TestConv2dOp):
def init_op_type(self):
self.op_type = "conv_cudnn"
class TestCudnnWithGroup(TestWithGroup):
def init_op_type(self):
self.op_type = "conv_cudnn"
class TestCudnnWith1x1(TestWith1x1):
def init_op_type(self):
self.op_type = "conv_cudnn"
# cudnn v5 does not support dilation conv.
# class TestCudnnWithDilation(TestWithDilation):
# def init_op_type(self):
# self.op_type = "conv_cudnn"
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_conv3d_op.py
浏览文件 @ 0968c7cd
......@@ -10,26 +10,39 @@ def conv3d_forward_naive(input, filter, group, conv_param):
assert np.mod(out_c, group) == 0
sub_out_c = out_c / group
stride, pad = conv_param['stride'], conv_param['pad']
out_d = 1 + (in_d + 2 * pad[0] - f_h) / stride[0]
out_h = 1 + (in_h + 2 * pad[1] - f_h) / stride[1]
out_w = 1 + (in_w + 2 * pad[2] - f_w) / stride[2]
stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[
'dilations']
out_d = 1 + (in_d + 2 * pad[0] - (dilation[0] * (f_d - 1) + 1)) / stride[0]
out_h = 1 + (in_h + 2 * pad[1] - (dilation[1] * (f_h - 1) + 1)) / stride[1]
out_w = 1 + (in_w + 2 * pad[2] - (dilation[2] * (f_w - 1) + 1)) / stride[2]
out = np.zeros((in_n, out_c, out_d, out_h, out_w))
d_bolck_d = (dilation[0] * (f_d - 1) + 1)
d_bolck_h = (dilation[1] * (f_h - 1) + 1)
d_bolck_w = (dilation[2] * (f_w - 1) + 1)
input_pad = np.pad(input, ((0, ), (0, ), (pad[0], ), (pad[1], ),
(pad[2], )),
mode='constant',
constant_values=0)
filter_dilation = np.zeros((out_c, f_c, d_bolck_d, d_bolck_h, d_bolck_w))
filter_dilation[:, :, 0:d_bolck_d:dilation[0], 0:d_bolck_h:dilation[1], 0:
d_bolck_w:dilation[2]] = filter
for d in range(out_d):
for i in range(out_h):
for j in range(out_w):
for g in range(group):
input_pad_masked = \
input_pad[:, g * f_c:(g + 1) * f_c,
d * stride[0]:d * stride[0] + f_d,
i * stride[1]:i * stride[1] + f_h,
j * stride[2]:j * stride[2] + f_w]
f_sub = filter[g * sub_out_c:(g + 1) *
d * stride[0]:d * stride[0] + d_bolck_d,
i * stride[1]:i * stride[1] + d_bolck_h,
j * stride[2]:j * stride[2] + d_bolck_w]
f_sub = filter_dilation[g * sub_out_c:(g + 1) *
sub_out_c, :, :, :, :]
for k in range(sub_out_c):
out[:, g * sub_out_c + k, d, i, j] = \
......@@ -43,9 +56,14 @@ class TestConv3dOp(OpTest):
def setUp(self):
self.init_group()
self.init_op_type()
self.init_dilation()
self.init_test_case()
conv3d_param = {'stride': self.stride, 'pad': self.pad}
conv3d_param = {
'stride': self.stride,
'pad': self.pad,
'dilations': self.dilations
}
input = np.random.random(self.input_size).astype("float32")
filter = np.random.random(self.filter_size).astype("float32")
output = conv3d_forward_naive(input, filter, self.groups,
......@@ -55,7 +73,8 @@ class TestConv3dOp(OpTest):
self.attrs = {
'strides': self.stride,
'paddings': self.pad,
'groups': self.groups
'groups': self.groups,
'dilations': self.dilations
}
self.outputs = {'Output': output}
......@@ -88,6 +107,9 @@ class TestConv3dOp(OpTest):
f_c = self.input_size[1] / self.groups
self.filter_size = [6, f_c, 3, 3, 3]
def init_dilation(self):
self.dilations = [1, 1, 1]
def init_group(self):
self.groups = 1
......@@ -104,27 +126,47 @@ class TestCase1(TestConv3dOp):
f_c = self.input_size[1] / self.groups
self.filter_size = [6, f_c, 3, 3, 3]
def init_group(self):
self.groups = 1
def init_op_type(self):
self.op_type = "conv3d"
class TestWithGroup1(TestConv3dOp):
def init_group(self):
self.groups = 3
class TestWithGroup1(TestConv3dOp):
class TestWithGroup2(TestCase1):
def init_group(self):
self.groups = 3
def init_op_type(self):
self.op_type = "conv3d"
class TestWith1x1(TestConv3dOp):
def init_test_case(self):
self.pad = [0, 0, 0]
self.stride = [1, 1, 1]
self.input_size = [2, 3, 4, 4, 4] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] / self.groups
self.filter_size = [6, f_c, 1, 1, 1]
def init_dilation(self):
self.dilations = [1, 1, 1]
class TestWithGroup2(TestCase1):
def init_group(self):
self.groups = 3
def init_op_type(self):
self.op_type = "conv3d"
class TestWithDilation(TestConv3dOp):
def init_test_case(self):
self.pad = [0, 0, 0]
self.stride = [1, 1, 1]
self.input_size = [2, 3, 6, 6, 6] # NCDHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] / self.groups
self.filter_size = [6, f_c, 2, 2, 2]
def init_dilation(self):
self.dilations = [2, 2, 2]
def init_group(self):
self.groups = 3
if __name__ == '__main__':
......
python/paddle/v2/fluid/tests/test_optimizer.py
浏览文件 @ 0968c7cd
......@@ -198,7 +198,7 @@ class TestAdagradOptimizer(unittest.TestCase):
adagrad_op = opts[0]
self.assertEqual(adagrad_op.type, "adagrad")
# check accumulators
# Check accumulators
accumulators = adagrad_optimizer.get_accumulators()
self.assertEqual(len(accumulators), 1)
self.assertTrue(adagrad_optimizer.get_moment_str() in accumulators)
......@@ -331,5 +331,59 @@ class TestAdamaxOptimizer(unittest.TestCase):
self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
class TestDecayedAdagradOptimizer(unittest.TestCase):
class MockDecayedAdagrad(optimizer.DecayedAdagradOptimizer):
def get_accumulators(self):
return self._accumulators
def get_moment_str(self):
return self._moment_acc_str
def test_decayed_adagrad_optimizer(self):
init_program = framework.Program()
program = framework.Program()
block = program.global_block()
mul_x = block.create_parameter(
dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
mul_y = block.create_var(
dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
mul_out = block.create_var(
dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
block.append_op(
type="mul",
inputs={"X": mul_x,
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
learning_rate = 0.01
decayed_adagrad_optimizer = self.MockDecayedAdagrad(
learning_rate=learning_rate, decay=0.95, epsilon=1.0e-6)
params_grads = append_backward_ops(mul_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(decayed_adagrad_optimizer.get_accumulators()), 0)
opts = decayed_adagrad_optimizer.create_optimization_pass(
params_grads, mul_out, init_program)
self.assertEqual(len(opts), 1)
decayed_adagrad_op = opts[0]
self.assertEqual(decayed_adagrad_op.type, "decayed_adagrad")
# Check accumulators
accumulators = decayed_adagrad_optimizer.get_accumulators()
self.assertEqual(len(accumulators), 1)
self.assertTrue(
decayed_adagrad_optimizer.get_moment_str() in accumulators)
moment_acc = accumulators[decayed_adagrad_optimizer.get_moment_str()]
self.assertEqual(len(moment_acc), 1)
self.assertTrue(mul_x.name in moment_acc)
# Check init_program
init_ops = init_program.global_block().ops
self.assertEqual(len(init_ops), 2)
self.assertEqual(init_ops[0].type, "fill_constant")
self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
self.assertEqual(init_ops[1].type, "fill_constant")
self.assertAlmostEqual(init_ops[1].attr('value'), 0.0)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_parameter.py
浏览文件 @ 0968c7cd
import unittest
from paddle.v2.fluid.framework import g_main_program
import paddle.v2.fluid.core as core
from paddle.v2.fluid.executor import Executor
import paddle.v2.fluid.io as io
from paddle.v2.fluid.initializer import ConstantInitializer
import numpy as np
class TestParameter(unittest.TestCase):
def test_param(self):
b = g_main_program.create_block()
shape = [784, 100]
val = 1.0625
b = g_main_program.global_block()
param = b.create_parameter(
name='fc.w',
shape=[784, 100],
shape=shape,
dtype='float32',
initialize_attr={
'type': 'uniform_random',
'seed': 13,
'min': -5.0,
'max': 5.0
})
initializer=ConstantInitializer(val))
self.assertIsNotNone(param)
self.assertEqual('fc.w', param.name)
self.assertEqual((784, 100), param.shape)
self.assertEqual(core.DataType.FP32, param.data_type)
self.assertEqual(0, param.block.idx)
exe = Executor(core.CPUPlace())
p = exe.run(g_main_program, fetch_list=[param])[0]
self.assertTrue(np.allclose(np.array(p), np.ones(shape) * val))
p = io.get_parameter_value_by_name('fc.w', exe, g_main_program)
self.assertTrue(np.allclose(np.array(p), np.ones(shape) * val))
if __name__ == '__main__':
......
python/paddle/v2/framework/math_ops.py 已删除 100644 → 0
浏览文件 @ d9a305cb
import paddle.v2.framework.core as core
from paddle.v2.framework.framework import OpProtoHolder, Variable, Program, \
Operator
python/paddle/v2/framework/tests/test_beam_search_op.py 0 → 100644
浏览文件 @ 0968c7cd
import logging
from paddle.v2.framework.op import Operator, DynamicRecurrentOp
import paddle.v2.framework.core as core
import unittest
import numpy as np
def create_tensor(scope, name, np_data):
tensor = scope.var(name).get_tensor()
tensor.set(np_data, core.CPUPlace())
return tensor
class BeamSearchOpTester(unittest.TestCase):
def setUp(self):
self.scope = core.Scope()
self.ctx = core.DeviceContext.create(core.CPUPlace())
self._create_ids()
self._create_scores()
self._create_pre_ids()
self.scope.var('selected_ids')
self.scope.var('selected_scores')
def test_run(self):
op = Operator(
'beam_search',
pre_ids="pre_ids",
ids='ids',
scores='scores',
selected_ids='selected_ids',
selected_scores='selected_scores',
level=0,
beam_size=2,
end_id=0, )
op.run(self.scope, self.ctx)
selected_ids = self.scope.find_var("selected_ids").get_tensor()
print 'selected_ids', np.array(selected_ids)
print 'lod', selected_ids.lod()
def _create_pre_ids(self):
np_data = np.array([[1, 2, 3, 4]], dtype='int32')
tensor = create_tensor(self.scope, "pre_ids", np_data)
def _create_ids(self):
self.lod = [[0, 1, 4], [0, 1, 2, 3, 4]]
np_data = np.array(
[[4, 2, 5], [2, 1, 3], [3, 5, 2], [8, 2, 1]], dtype='int32')
tensor = create_tensor(self.scope, "ids", np_data)
tensor.set_lod(self.lod)
def _create_scores(self):
np_data = np.array(
[
[0.5, 0.3, 0.2],
[0.6, 0.3, 0.1],
[0.9, 0.5, 0.1],
[0.7, 0.5, 0.1],
],
dtype='float32')
tensor = create_tensor(self.scope, "scores", np_data)
tensor.set_lod(self.lod)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/model.py 已删除 100644 → 0
浏览文件 @ d9a305cb
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# 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 os
import errno
import uuid
import paddle.v2.master
__all__ = ["save_model", "load_model"]
trainer_id = str(uuid.uuid4())
def mkdir_p(path):
try:
os.makedirs(path)
except OSError as exc:
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
def save_model(parameters, path):
need_request = "KUBERNETES_SERVICE_HOST" in os.environ.keys()
if need_request:
# TODO(helin): figure out how MPI trains, since MPI only save
# model when trainer_id == "0", we can consolidate the logic
# here.
# TODO(helin): change this environment variable name from
# MASTER_IP to ETCD_IP
etcd_name = "MASTER_IP"
if etcd_name not in os.environ.keys():
raise Exception('not find ' + etcd_name +
' in environment variable.')
etcd_ip = os.environ.get(etcd_name)
client = paddle.v2.master.client("http://" + etcd_ip + ":2379", 5, 0)
r = client.request_save_model(trainer_id, 5000)
if r == 0:
# do not need to save
return
elif r < 0:
# error
return
else:
# save model
path = os.path.join(path, trainer_id)
path = os.path.join(path, "model.tar")
mkdir_p(path)
with open(path, 'wb') as f:
parameters.to_tar(f)
def load_model(parameters, path):
with open(path, 'rb') as f:
parameters.from_tar(f)
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