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Merge branch 'develop' into refine_relu_test

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fix_gru_py
Luo Tao 7 years ago
parent 89dcb0bd15 22ab14c572
commit 0ae97e8a5b
13 changed files with 96 additions and 16 deletions
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1
doc/fluid/build_and_install/paddleci.png
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@ -0,0 +1 @@
../../v2/build_and_install/paddleci.png

6
doc/fluid/design/motivation/refactorization.md
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@ -125,12 +125,12 @@ Compile Time -> IR -> Runtime
## Operator/OpWithKernel/OpKernel
![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/49caf1fb70820fb4a6c217634317c9306f361f36/op_op_with_kern_class_diagram.dot)
![class_diagram](https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/op_op_with_kern_class_diagram.dot)
---
## Operator
![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot)
![class_diagram](https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/op.dot)
* `Operator` is the fundamental building block of the user interface.
* Operator stores input/output variable names and attributes.
@ -141,7 +141,7 @@ Compile Time -> IR -> Runtime
## OpWithKernel/Kernel
![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/9d7f4eba185cf41c8e2fbfb40ae21890dbddcd39/op_with_kernel.dot)
![class_diagram](https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/op_with_kernel.dot)
* `OpWithKernel` inherits `Operator`.
* `OpWithKernel` contains a Kernel map.

4
doc/fluid/images/op.dot
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@ -0,0 +1,4 @@
digraph sample {
graph [rankdir=TD]; node [shape=record];
op [label="{Operator| InferShape()=0\lRun()=0\l | map<string, string[]> inputs_\lmap<string, string[]> outputs_ \l AttributeMap attrs_\l}"];
}

38
doc/fluid/images/op_op_with_kern_class_diagram.dot
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@ -0,0 +1,38 @@
digraph sample {
graph [rankdir=TD]; node [shape=record];
op [label="{Operator| InferShape()=0\lRun()=0\l | map<string, string[]> inputs_\lmap<string, string[]> outputs_ \l AttributeMap attrs_\l}"];
op_with_kern [label="{OpWithKernel | InferShape()=0\lRun()\l | map<OpKernelKey,OpKernel>kernels_ }"]
op_kernel [label="{OpKernel | Compute()=0}"]
op_kernel_key [label="{OpKernelKey| Place place\n...}"]
op -> op_with_kern [dir=back, arrowtail=onormal]
op_with_kern -> op_kernel [arrowhead=vee, label="contains many"]
{
rank=same;
op_with_kern
op_kernel
}
op_kernel -> op_kernel_key [style=invis]
{
rank=same;
op_kernel
op_kernel_key
}
op_with_kern -> op_kernel_key [arrowhead=vee, label ="\nas map key"]
mul_op [label="MulOp"]
op_with_kern -> mul_op [dir=back, arrowtail=onormal]
mul_kernel [label="template <typename Place>\lclass MulOpKernel\l"]
op_kernel -> mul_kernel [dir=back, arrowtail=onormal]
mul_op -> mul_kernel [arrowhead=vee, label="register many"]
{
rank=same;
mul_op;
mul_kernel;
}
}

26
doc/fluid/images/op_with_kernel.dot
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@ -0,0 +1,26 @@
digraph sample {
graph [rankdir=TD]; node [shape=record];
op [label="{Operator}"];
op_with_kern [label="{OpWithKernel | InferShape()=0\lRun()\l | map<OpKernelKey,OpKernel>kernels_ }"]
op_kernel [label="{OpKernel | Compute()=0}"]
op_kernel_key [label="{OpKernelKey| Place place\n...}"]
op -> op_with_kern [dir=back, arrowtail=onormal]
op_with_kern -> op_kernel [arrowhead=vee, label="contains many"]
{
rank=same;
op_with_kern
op_kernel
}
op_kernel -> op_kernel_key [style=invis]
{
rank=same;
op_kernel
op_kernel_key
}
op_with_kern -> op_kernel_key [arrowhead=vee, label ="\nas map key"]
}

11
doc/v2/api/config/layer.rst
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@ -142,7 +142,7 @@ gated_unit
-----------
.. autoclass:: paddle.v2.layer.gated_unit
:noindex:
Recurrent Layer Group
=====================
@ -354,7 +354,7 @@ dropout
--------
.. autoclass:: paddle.v2.layer.dropout
:noindex:
dot_prod
---------
.. autoclass:: paddle.v2.layer.dot_prod
@ -460,6 +460,11 @@ multi_binary_label_cross_entropy_cost
.. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost
:noindex:
classification_cost
-------------------
.. autoclass:: paddle.v2.layer.classification_cost
:noindex:
huber_regression_cost
-------------------------
.. autoclass:: paddle.v2.layer.huber_regression_cost
@ -534,7 +539,7 @@ detection_output
----------------
.. autoclass:: paddle.v2.layer.detection_output
:noindex:
Check Layer
============

2
paddle/fluid/inference/tests/book/CMakeLists.txt
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@ -36,5 +36,5 @@ inference_test(label_semantic_roles)
inference_test(recognize_digits ARGS mlp conv)
inference_test(recommender_system)
#inference_test(rnn_encoder_decoder)
inference_test(understand_sentiment ARGS conv)
#inference_test(understand_sentiment ARGS conv)
inference_test(word2vec)

10
paddle/fluid/operators/conv_op.h
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@ -187,7 +187,8 @@ class GemmConvKernel : public framework::OpKernel<T> {
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
blas.MatMul(filter_slice, col_matrix, &out_slice);
blas.MatMul(filter_slice, false, col_matrix, false, T(1.0), &out_slice,
T(0.0));
}
}
}
@ -304,7 +305,8 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
col_matrix.ShareDataWith(in_grad_slice);
col_matrix.Resize(col_matrix_shape);
}
blas.MatMul(filter_slice, true, out_grad_slice, false, &col_matrix);
blas.MatMul(filter_slice, true, out_grad_slice, false, T(1.0),
&col_matrix, T(0.0));
if (is_expand && data_dim == 2U) {
col2im(dev_ctx, col, dilations, strides,
@ -351,8 +353,8 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
// gemm
Tensor filter_grad_slice =
filter_grad_.Slice(g * out_step, (g + 1) * out_step);
blas.MatMul(out_grad_slice, false, col_matrix, true,
&filter_grad_slice);
blas.MatMul(out_grad_slice, false, col_matrix, true, T(1.0),
&filter_grad_slice, T(1.0));
}
}
}

9
paddle/fluid/operators/conv_transpose_op.h
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@ -135,7 +135,8 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
// col_matrix = filter * input_batch
// of shape (c * k_h * k_w, h * w) or (c * k_d * k_h * k_w, d * h * w)
blas.MatMul(filter, true, input_batch, false, &col_matrix);
blas.MatMul(filter, true, input_batch, false, static_cast<T>(1.0),
&col_matrix, static_cast<T>(0.0));
if (data_dim == 2U) {
// col2im: col_matrix -> dy
@ -267,7 +268,8 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// or
// (m, c * k_d * k_h * k_w) * (c * k_d * k_h * k_w, d * h * w) -> (m,
// d, h, w)
blas.MatMul(filter, false, col_matrix, false, &input_grad_batch);
blas.MatMul(filter, false, col_matrix, false, static_cast<T>(1.0),
&input_grad_batch, static_cast<T>(0.0));
}
if (filter_grad) {
// input batch
@ -277,7 +279,8 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// or
// (m, d * h * w) * (d * h * w, c * k_d * k_h * k_w) -> (m, c * k_d *
// k_h * k_w)
blas.MatMul(in_batch, false, col_matrix, true, &filter_grad_);
blas.MatMul(in_batch, false, col_matrix, true, static_cast<T>(1.0),
&filter_grad_, static_cast<T>(1.0));
}
}
}

1
paddle/fluid/platform/cuda_device_function.h
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@ -63,6 +63,7 @@ __device__ T reduceSum(T val, int tid, int len) {
val += platform::CudaShuffleDownSync(mask, val, offset);
if (tid < warpSize) shm[tid] = 0;
__syncthreads();
if (tid % warpSize == 0) {
shm[tid / warpSize] = val;

0
python/paddle/fluid/tests/book/test_understand_sentiment.py → python/paddle/fluid/tests/book/notest_understand_sentiment.py
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2
python/paddle/fluid/tests/unittests/test_memory_optimization_transpiler.py
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@ -18,7 +18,7 @@ import unittest
import paddle.fluid.layers as layers
import paddle.fluid.optimizer as optimizer
from paddle.fluid.framework import Program, program_guard
from paddle.fluid.memory_optimization_transpiler import memory_optimize
from paddle.fluid.transpiler import memory_optimize
class TestControlFlowGraph(unittest.TestCase):

2
python/paddle/fluid/tests/unittests/test_split_var.py
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@ -14,7 +14,7 @@
import math
import unittest
from paddle.fluid.distribute_transpiler import split_dense_variable
from paddle.fluid.transpiler.distribute_transpiler import split_dense_variable
import paddle.fluid as fluid
import paddle.fluid.core as core
import random

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