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b/paddle/framework/multigpu.md @@ -0,0 +1,60 @@ +# Design Doc: Multi-GPU support in Operation Graph + +## Abstract + +This Design Doc refers to the multi-GPU feature in paddle. We propose an approach to support multi-GPU both on a single machine and multiple machines. Every device only run sub-graphs which our framework issued. We use `Broadcast`, `Allreduce` operators to join different device sub-graph to the whole graph. + + + +## Motivation + +Paddle supports training with multiple CPUs and GPUs, refer to different physical devices. We need to support multi-GPU training in parallel for acceleration, in detail, there are two aspects. + +- GPU Data Parallelism + + Suppose to we have `n`GPUs, every GPU has `1/n`part of training data, and store a complete model in GPU memory. + +- GPU Model Parallelism + + every GPU have part of a complete model in GPU memory. + +At the beginning of training, the framework needs to issue the same sub-graph to every GPU in Data Parallelism, or different sub-graph in Model Parallelism. + +During training, we need the operations of peer to peer copy between different GPUs, aggregating gradients/parameters from GPUs, and broadcasting parameters to GPUs. Every GPU only need to run the sub-graph with correct place information. + +Besides, it needs interfaces to synchronize model update with each other, and issue/merge model from different GPU Cards. + +## Implementation + +As mentioned above, we summarise that several kinds of operators are needed. Currently, we need to issue parameters to different GPUs, named it with Broadcast operator. And also synchronize parameters between GPUs, called it with AllReduce. + +### Graph Converter + +To be compatible with parameter server design doc, the graph converter converts the user defined operation graph into sub-graphs to be executed on different devices. + +1. The user-defined operator graph will be partitioned into sub-graph. + +2. Control operators between GPUs will be inserted into the graph. + + *Broadcast, AllReduce in a single machine. And Broadcast, AllReduce, Send, Recv in multiple machines* + + + +After convert, the graph as shows + + + +Operators are added to the sub-graphs. Every GPU assigned a role of `rank0`, `rank1` etc. + +- **Broadcast**. Broadcast operator distribute initialized parameter to all the GPUs from the GPU who owns it. e.g. from`rank0` GPU. +- **Allreduce**. Allreduce operator synchronizes parameters/gradients between GPUs. AllReduce implemented in the Ring-Based communicating method, avoid of the bottle neck in a single GPU. + +These two operators need the Multi-GPU context support. + +Need to notice that Allreduce operator force GPUs synchronized at that point. Every device only need runs sub-graph in a loop style forever, the whole training process in asynchronous or synchronous mode depends on the Allreduce point in the graph. + +### Benefits + +- can easily move the optimize sub-graph to parameter server, multi-GPU feature can be compatible with distributed support design. +- easily plug-in with NCCL2 library. +- GPU Model parallelism becomes easier to implement. we only need to replace different GPU's sub-graph with different part of the whole graph. From dbaaa4979b7cef401a28491906b4954acc9f9b3b Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Mon, 4 Sep 2017 23:33:25 -0700 Subject: [PATCH 02/54] fix typo, rewrite graph --- paddle/framework/multigpu.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/paddle/framework/multigpu.md b/paddle/framework/multigpu.md index 61ff1ba204..c8501725f5 100644 --- a/paddle/framework/multigpu.md +++ b/paddle/framework/multigpu.md @@ -53,6 +53,10 @@ These two operators need the Multi-GPU context support. Need to notice that Allreduce operator force GPUs synchronized at that point. Every device only need runs sub-graph in a loop style forever, the whole training process in asynchronous or synchronous mode depends on the Allreduce point in the graph. +For the simplest implement, when each GPU compute the gradient of `W`, followed with a `AllReduce` operator, accumulate the `dW` to full batch of data, then run the optimize process individually and apply the gradient to its `W`. + +In fact, in the way of every GPU optimized full batch of data, wasted (n-1) GPU compute resources. We will enhance it in the next stage. + ### Benefits - can easily move the optimize sub-graph to parameter server, multi-GPU feature can be compatible with distributed support design. 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deletions(-) diff --git a/paddle/framework/multigpu.md b/paddle/framework/multigpu.md index c8501725f5..1c843326ee 100644 --- a/paddle/framework/multigpu.md +++ b/paddle/framework/multigpu.md @@ -30,13 +30,13 @@ As mentioned above, we summarise that several kinds of operators are needed. Cur ### Graph Converter -To be compatible with parameter server design doc, the graph converter converts the user defined operation graph into sub-graphs to be executed on different devices. +To be compatible with [parameter server design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/ops/dist_train.md), the graph converter converts the user defined operation graph into sub-graphs to be executed on different devices. 1. The user-defined operator graph will be partitioned into sub-graph. 2. Control operators between GPUs will be inserted into the graph. - *Broadcast, AllReduce in a single machine. And Broadcast, AllReduce, Send, Recv in multiple machines* + *Broadcast, AllReduce in a single machine. And Broadcast, AllReduce, [Send, Recv](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/ops/dist_train.md#graph-converter) in multiple machines* @@ -53,12 +53,12 @@ These two operators need the Multi-GPU context support. Need to notice that Allreduce operator force GPUs synchronized at that point. Every device only need runs sub-graph in a loop style forever, the whole training process in asynchronous or synchronous mode depends on the Allreduce point in the graph. -For the simplest implement, when each GPU compute the gradient of `W`, followed with a `AllReduce` operator, accumulate the `dW` to full batch of data, then run the optimize process individually and apply the gradient to its `W`. +As it shown in the picture, when each GPU compute the gradient of `W`, followed with a `AllReduce` operator, accumulate the `dW` to full batch of data, then run the optimize process individually and apply the gradient to its `W`. In fact, in the way of every GPU optimized full batch of data, wasted (n-1) GPU compute resources. We will enhance it in the next stage. ### Benefits - can easily move the optimize sub-graph to parameter server, multi-GPU feature can be compatible with distributed support design. -- easily plug-in with NCCL2 library. +- easily plug-in with [NCCL2](https://developer.nvidia.com/nccl) library. - GPU Model parallelism becomes easier to implement. we only need to replace different GPU's sub-graph with different part of the whole graph. 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z8^C9xuiZA%)+YP{FY9Z!Nj46t&8EVD+)yIw(RdRIBfGe(-8I=M#=%yZ7B~5+um0eU zU#HWmNnby|C&8u#dJq|)$bknPUZh4$ zZXMTxl=#2!;@F@J+>>c~mp&rSc@#Y%E7OKCt;I93zU$WHg z+K0A+P|jmRQAL(R({_R9rP*TeoQf#Y)Aiu=aR8IAjE!Y9*e63n*^n4d+YX(JTaWbuz+NV-pu~PZ#?=Kwh+*!*D(C=btpyvKR4FKe6Ht%)%iCE zVDI!hG&D7RcwPG63wfj!(OIurXCUyBkg)fIm5yuKTL{n&5AVLOE#^j7!^Bhps>efEN;_2M6Q02~6Lz#)+TPCa4FE!3K0Z5t zcNWnX`vl&ePWABGI3eQa_UQrd1AI8xc?n~2XjWj6g{A`>MdtZbruDjN&!8Q@$1k0+Zbi+?8$zyo{ literal 0 HcmV?d00001 From 5c057f95529656e379ef404a2e388e1be3e88de1 Mon Sep 17 00:00:00 2001 From: sweetsky0901 Date: Sun, 3 Dec 2017 14:40:33 +0800 Subject: [PATCH 07/54] add spp op only can test ok --- paddle/operators/spp_op.cc | 98 +++++++++++++ paddle/operators/spp_op.h | 148 ++++++++++++++++++++ python/paddle/v2/fluid/tests/test_spp_op.py | 48 +++++++ 3 files changed, 294 insertions(+) create mode 100644 paddle/operators/spp_op.cc create mode 100644 paddle/operators/spp_op.h create mode 100644 python/paddle/v2/fluid/tests/test_spp_op.py diff --git a/paddle/operators/spp_op.cc b/paddle/operators/spp_op.cc new file mode 100644 index 0000000000..62fc2112a8 --- /dev/null +++ b/paddle/operators/spp_op.cc @@ -0,0 +1,98 @@ +/* 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. +Indicesou 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/spp_op.h" +namespace paddle { +namespace operators { + +class SppOpMaker : public framework::OpProtoAndCheckerMaker { + public: + SppOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput( + "X", + "(Tensor) The input tensor of spp operator. " + "The format of input tensor is NCHW. Where N is batch size, C is the " + "number of channels, H and W is the height and width of feature."); + AddOutput("Out", + "(Tensor) The output tensor of spp operator." + "N * M." + "M = C * H * W"); + AddAttr("pyramid_height", ">= 1"); + AddComment(R"DOC( + "Input shape: $(N, C_{in}, H_{in}, W_{in})$ + Output shape: $(H_{out}, W_{out})$ + Where + $$ + H_{out} = (H_{in}−1) * strides[0] − 2 * paddings[0] + ksize[0] \\ + W_{out} = (W_{in}−1) * strides[1] − 2 * paddings[1] + ksize[1] + $$ + )DOC"); + } +}; + +int OutputSize(int pyramid_level, int input_size) { + int bins = std::pow(2, pyramid_level); + int ksize = std::ceil(input_size / static_cast(bins)); + int padding = (ksize * bins - input_size + 1) / 2; + int output_size = (input_size - ksize + 2 * padding) / ksize + 1; + // output_size = bins + return output_size; +} + +class SppOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), + "Input(X) of SppOp" + "should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Output(Out) of SppOp should not be null."); + auto in_x_dims = ctx->GetInputDim("X"); + int pyramid_height = ctx->Attrs().Get("pyramid_height"); + PADDLE_ENFORCE(in_x_dims.size() == 4, + "Spping intput must be of 4-dimensional."); + int outlen = 0; + for (int p = 0; p < pyramid_height; ++p) { + int outh = OutputSize(p, in_x_dims[2]); + int outw = OutputSize(p, in_x_dims[3]); + int p_level_outlen = outh * outw * in_x_dims[1]; + outlen += p_level_outlen; + } + std::vector output_shape({in_x_dims[0], outlen}); + ctx->SetOutputDim("Out", framework::make_ddim(output_shape)); + } +}; + +class SppOpGrad : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null."); + PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")), + "Input(X@GRAD) should not be null."); + ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(spp, ops::SppOp, ops::SppOpMaker, spp_grad, ops::SppOpGrad); +REGISTER_OP_CPU_KERNEL(spp, ops::SppKernel, + ops::SppKernel); +REGISTER_OP_CPU_KERNEL(spp_grad, + ops::SppGradKernel, + ops::SppGradKernel); diff --git a/paddle/operators/spp_op.h b/paddle/operators/spp_op.h new file mode 100644 index 0000000000..2a2824bb31 --- /dev/null +++ b/paddle/operators/spp_op.h @@ -0,0 +1,148 @@ +/* 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. +Indicesou 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 +#include "paddle/framework/op_registry.h" +#include "paddle/operators/math/math_function.h" +#include "paddle/operators/math/pooling.h" +#include "paddle/operators/strided_memcpy.h" + +namespace paddle { +namespace operators { +template +class SppKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + const framework::Tensor* in_x = context.Input("X"); + auto* out = context.Output("Out"); + int pyramid_height = context.template Attr("pyramid_height"); + out->mutable_data(context.GetPlace()); + auto out_stride = framework::stride(out->dims()); + int input_h = in_x->dims()[2]; + int input_w = in_x->dims()[3]; + size_t output_offset = 0; + for (int p = 0; p < pyramid_height; ++p) { + int bins = std::pow(2, p); + int ksize_h = std::ceil(input_h / static_cast(bins)); + int ksize_w = std::ceil(input_w / static_cast(bins)); + int padding_h = (ksize_h * bins - input_h + 1) / 2; + int padding_w = (ksize_w * bins - input_w + 1) / 2; + std::vector ksize({ksize_h, ksize_w}); + std::vector strides({ksize_h, ksize_w}); + std::vector paddings({padding_h, padding_w}); + // pooling output shape + std::vector output_shape_vec({in_x->dims()[0], in_x->dims()[1]}); + output_shape_vec.push_back((input_h - ksize_h + 2 * padding_h) / ksize_h + + 1); + output_shape_vec.push_back((input_w - ksize_w + 2 * padding_w) / ksize_w + + 1); + framework::DDim output_shape(framework::make_ddim(output_shape_vec)); + // flatten pooling output shape + int output_flatten_w = in_x->dims()[1] * bins * bins; + std::vector output_flatten_shape_vec( + {in_x->dims()[0], output_flatten_w}); + framework::DDim output_flatten_shape( + framework::make_ddim(output_flatten_shape_vec)); + framework::Tensor out_level; + framework::Tensor out_flatten_level; + out_level.mutable_data(output_shape, context.GetPlace()); + // pooling + math::Pool2dFunctor, T> pool_forward; + math::MaxPool max_process; + pool_forward(context.device_context(), *in_x, ksize, strides, paddings, + max_process, &out_level); + out_flatten_level.ShareDataWith(out_level); + out_flatten_level.Resize(output_flatten_shape); + auto in_stride = framework::stride(out_flatten_level.dims()); + const T* src_data = out_flatten_level.data(); + StridedMemcpy(context.device_context(), src_data, in_stride, + out_flatten_level.dims(), out_stride, + out->data() + output_offset); + output_offset += out_flatten_level.dims()[1] * in_stride[1]; + } + } +}; +template +class SppGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + const framework::Tensor* in_x = context.Input("X"); + const framework::Tensor* out = context.Input("Out"); + const framework::Tensor* out_grad = + context.Input(framework::GradVarName("Out")); + framework::Tensor* in_x_grad = + context.Output(framework::GradVarName("X")); + auto& device_ctx = context.device_context(); + math::SetConstant zero; + in_x_grad->mutable_data(context.GetPlace()); + zero(device_ctx, in_x_grad, static_cast(0)); + int pyramid_height = context.template Attr("pyramid_height"); + auto outgrad_stride = framework::stride(out_grad->dims()); + auto out_stride = framework::stride(out->dims()); + int input_h = in_x->dims()[2]; + int input_w = in_x->dims()[3]; + size_t out_offset = 0; + for (int p = 0; p < pyramid_height; ++p) { + int bins = std::pow(2, p); + int ksize_h = std::ceil(input_h / static_cast(bins)); + int ksize_w = std::ceil(input_w / static_cast(bins)); + int padding_h = (ksize_h * bins - input_h + 1) / 2; + int padding_w = (ksize_w * bins - input_w + 1) / 2; + std::vector ksize({ksize_h, ksize_w}); + std::vector strides({ksize_h, ksize_w}); + std::vector paddings({padding_h, padding_w}); + // split outgrad and get flatten + std::vector out_shape_vec({in_x->dims()[0], in_x->dims()[1]}); + out_shape_vec.push_back((input_h - ksize_h + 2 * padding_h) / ksize_h + + 1); + out_shape_vec.push_back((input_w - ksize_w + 2 * padding_w) / ksize_w + + 1); + framework::DDim out_shape(framework::make_ddim(out_shape_vec)); + int out_flatten_w = in_x->dims()[1] * bins * bins; + std::vector out_flatten_shape_vec( + {in_x->dims()[0], out_flatten_w}); + framework::DDim out_flatten_shape( + framework::make_ddim(out_flatten_shape_vec)); + framework::Tensor out_level; + framework::Tensor outgrad_level; + framework::Tensor out_flatten_level; + framework::Tensor outgrad_flatten_level; + out_flatten_level.mutable_data(out_flatten_shape, context.GetPlace()); + outgrad_flatten_level.mutable_data(out_flatten_shape, + context.GetPlace()); + + auto flatten_stride = framework::stride(out_flatten_level.dims()); + // memcpy + StridedMemcpy(context.device_context(), out->data() + out_offset, + out_stride, out_flatten_level.dims(), flatten_stride, + out_flatten_level.data()); + + StridedMemcpy(context.device_context(), + out_grad->data() + out_offset, outgrad_stride, + outgrad_flatten_level.dims(), flatten_stride, + outgrad_flatten_level.data()); + out_offset += out_flatten_level.dims()[1] * out_stride[1]; + // flatten backward + out_level.ShareDataWith(out_flatten_level); + out_level.Resize(out_shape); + outgrad_level.ShareDataWith(outgrad_flatten_level); + outgrad_level.Resize(out_shape); + math::MaxPool2dGradFunctor pool2d_backward; + pool2d_backward(context.device_context(), *in_x, *&out_level, + *&outgrad_level, ksize, strides, paddings, in_x_grad); + } + } +}; +} // namespace operators +} // namespace paddle diff --git a/python/paddle/v2/fluid/tests/test_spp_op.py b/python/paddle/v2/fluid/tests/test_spp_op.py new file mode 100644 index 0000000000..806d5e7736 --- /dev/null +++ b/python/paddle/v2/fluid/tests/test_spp_op.py @@ -0,0 +1,48 @@ +import unittest +import numpy as np +from op_test import OpTest +from test_pool2d_op import max_pool2D_forward_naive + + +class TestSppOp(OpTest): + def setUp(self): + self.op_type = "spp" + self.init_test_case() + input = np.random.random(self.shape).astype("float32") + nsize, csize, hsize, wsize = input.shape + out_level_flatten = [] + for i in xrange(self.pyramid_height): + bins = np.power(2, i) + ksize = [0, 0] + padding = [0, 0] + ksize[0] = np.ceil(hsize / bins.astype("double")).astype("int32") + padding[0] = ((ksize[0] * bins - hsize + 1) / 2).astype("int32") + + ksize[1] = np.ceil(wsize / bins.astype("double")).astype("int32") + padding[1] = ((ksize[1] * bins - wsize + 1) / 2).astype("int32") + out_level = max_pool2D_forward_naive(input, ksize, ksize, padding) + out_level_flatten.append( + out_level.reshape(nsize, bins * bins * csize)) + if i == 0: + output = out_level_flatten[i] + else: + output = np.concatenate((output, out_level_flatten[i]), 1) + # output = np.concatenate(out_level_flatten.tolist(), 0); + self.inputs = {'X': input.astype('float32'), } + self.attrs = {'pyramid_height': self.pyramid_height} + + self.outputs = {'Out': output.astype('float32')} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + def init_test_case(self): + self.shape = [1, 1, 2, 2] + self.pyramid_height = 2 + + +if __name__ == '__main__': + unittest.main() From 531e7b6fa694e7c15ca3831704097946162248fe Mon Sep 17 00:00:00 2001 From: sweetsky0901 Date: Sun, 3 Dec 2017 17:26:43 +0800 Subject: [PATCH 08/54] gpu test ok --- paddle/operators/spp_op.cc | 28 +++------- paddle/operators/spp_op.cu.cc | 22 ++++++++ paddle/operators/spp_op.h | 61 +++++++++++---------- python/paddle/v2/fluid/tests/test_spp_op.py | 6 +- 4 files changed, 64 insertions(+), 53 deletions(-) create mode 100644 paddle/operators/spp_op.cu.cc diff --git a/paddle/operators/spp_op.cc b/paddle/operators/spp_op.cc index 62fc2112a8..ff607c5769 100644 --- a/paddle/operators/spp_op.cc +++ b/paddle/operators/spp_op.cc @@ -29,28 +29,22 @@ class SppOpMaker : public framework::OpProtoAndCheckerMaker { "(Tensor) The output tensor of spp operator." "N * M." "M = C * H * W"); - AddAttr("pyramid_height", ">= 1"); + AddAttr("pyramid_height", "int"); AddComment(R"DOC( - "Input shape: $(N, C_{in}, H_{in}, W_{in})$ + "Does spatial pyramid pooling on the input image by taking the max, + etc. within regions so that the result vector of different sized + images are of the same size + Input shape: $(N, C_{in}, H_{in}, W_{in})$ Output shape: $(H_{out}, W_{out})$ Where $$ - H_{out} = (H_{in}−1) * strides[0] − 2 * paddings[0] + ksize[0] \\ - W_{out} = (W_{in}−1) * strides[1] − 2 * paddings[1] + ksize[1] + H_{out} = N \\ + W_{out} = ((std::pow(4, pyramid_height) - 1) / (4 - 1)) * C_{in} $$ )DOC"); } }; -int OutputSize(int pyramid_level, int input_size) { - int bins = std::pow(2, pyramid_level); - int ksize = std::ceil(input_size / static_cast(bins)); - int padding = (ksize * bins - input_size + 1) / 2; - int output_size = (input_size - ksize + 2 * padding) / ksize + 1; - // output_size = bins - return output_size; -} - class SppOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -64,13 +58,7 @@ class SppOp : public framework::OperatorWithKernel { int pyramid_height = ctx->Attrs().Get("pyramid_height"); PADDLE_ENFORCE(in_x_dims.size() == 4, "Spping intput must be of 4-dimensional."); - int outlen = 0; - for (int p = 0; p < pyramid_height; ++p) { - int outh = OutputSize(p, in_x_dims[2]); - int outw = OutputSize(p, in_x_dims[3]); - int p_level_outlen = outh * outw * in_x_dims[1]; - outlen += p_level_outlen; - } + int outlen = ((std::pow(4, pyramid_height) - 1) / (4 - 1)) * in_x_dims[1]; std::vector output_shape({in_x_dims[0], outlen}); ctx->SetOutputDim("Out", framework::make_ddim(output_shape)); } diff --git a/paddle/operators/spp_op.cu.cc b/paddle/operators/spp_op.cu.cc new file mode 100644 index 0000000000..a7057907ce --- /dev/null +++ b/paddle/operators/spp_op.cu.cc @@ -0,0 +1,22 @@ +/* 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. +Indicesou 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/spp_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(spp, ops::SppKernel, + ops::SppKernel); +REGISTER_OP_GPU_KERNEL(spp_grad, + ops::SppGradKernel, + ops::SppGradKernel); diff --git a/paddle/operators/spp_op.h b/paddle/operators/spp_op.h index 2a2824bb31..7a385352a0 100644 --- a/paddle/operators/spp_op.h +++ b/paddle/operators/spp_op.h @@ -42,34 +42,36 @@ class SppKernel : public framework::OpKernel { std::vector strides({ksize_h, ksize_w}); std::vector paddings({padding_h, padding_w}); // pooling output shape + framework::Tensor out_level; std::vector output_shape_vec({in_x->dims()[0], in_x->dims()[1]}); output_shape_vec.push_back((input_h - ksize_h + 2 * padding_h) / ksize_h + 1); output_shape_vec.push_back((input_w - ksize_w + 2 * padding_w) / ksize_w + 1); framework::DDim output_shape(framework::make_ddim(output_shape_vec)); - // flatten pooling output shape - int output_flatten_w = in_x->dims()[1] * bins * bins; - std::vector output_flatten_shape_vec( - {in_x->dims()[0], output_flatten_w}); - framework::DDim output_flatten_shape( - framework::make_ddim(output_flatten_shape_vec)); - framework::Tensor out_level; - framework::Tensor out_flatten_level; out_level.mutable_data(output_shape, context.GetPlace()); // pooling math::Pool2dFunctor, T> pool_forward; math::MaxPool max_process; pool_forward(context.device_context(), *in_x, ksize, strides, paddings, max_process, &out_level); + // flatten pooling output shape + framework::Tensor out_flatten_level; + int output_flatten_w = in_x->dims()[1] * bins * bins; + std::vector output_flatten_shape_vec( + {in_x->dims()[0], output_flatten_w}); + framework::DDim output_flatten_shape( + framework::make_ddim(output_flatten_shape_vec)); out_flatten_level.ShareDataWith(out_level); out_flatten_level.Resize(output_flatten_shape); - auto in_stride = framework::stride(out_flatten_level.dims()); - const T* src_data = out_flatten_level.data(); - StridedMemcpy(context.device_context(), src_data, in_stride, - out_flatten_level.dims(), out_stride, - out->data() + output_offset); - output_offset += out_flatten_level.dims()[1] * in_stride[1]; + // concat + auto out_flatten_level_stride = + framework::stride(out_flatten_level.dims()); + StridedMemcpy(context.device_context(), out_flatten_level.data(), + out_flatten_level_stride, out_flatten_level.dims(), + out_stride, out->data() + output_offset); + output_offset += + out_flatten_level.dims()[1] * out_flatten_level_stride[1]; } } }; @@ -83,12 +85,11 @@ class SppGradKernel : public framework::OpKernel { context.Input(framework::GradVarName("Out")); framework::Tensor* in_x_grad = context.Output(framework::GradVarName("X")); + int pyramid_height = context.template Attr("pyramid_height"); auto& device_ctx = context.device_context(); math::SetConstant zero; in_x_grad->mutable_data(context.GetPlace()); zero(device_ctx, in_x_grad, static_cast(0)); - int pyramid_height = context.template Attr("pyramid_height"); - auto outgrad_stride = framework::stride(out_grad->dims()); auto out_stride = framework::stride(out->dims()); int input_h = in_x->dims()[2]; int input_w = in_x->dims()[3]; @@ -102,26 +103,17 @@ class SppGradKernel : public framework::OpKernel { std::vector ksize({ksize_h, ksize_w}); std::vector strides({ksize_h, ksize_w}); std::vector paddings({padding_h, padding_w}); - // split outgrad and get flatten - std::vector out_shape_vec({in_x->dims()[0], in_x->dims()[1]}); - out_shape_vec.push_back((input_h - ksize_h + 2 * padding_h) / ksize_h + - 1); - out_shape_vec.push_back((input_w - ksize_w + 2 * padding_w) / ksize_w + - 1); - framework::DDim out_shape(framework::make_ddim(out_shape_vec)); + // split out and outgrad ... to flatten + framework::Tensor out_flatten_level; + framework::Tensor outgrad_flatten_level; int out_flatten_w = in_x->dims()[1] * bins * bins; std::vector out_flatten_shape_vec( {in_x->dims()[0], out_flatten_w}); framework::DDim out_flatten_shape( framework::make_ddim(out_flatten_shape_vec)); - framework::Tensor out_level; - framework::Tensor outgrad_level; - framework::Tensor out_flatten_level; - framework::Tensor outgrad_flatten_level; out_flatten_level.mutable_data(out_flatten_shape, context.GetPlace()); outgrad_flatten_level.mutable_data(out_flatten_shape, context.GetPlace()); - auto flatten_stride = framework::stride(out_flatten_level.dims()); // memcpy StridedMemcpy(context.device_context(), out->data() + out_offset, @@ -129,15 +121,24 @@ class SppGradKernel : public framework::OpKernel { out_flatten_level.data()); StridedMemcpy(context.device_context(), - out_grad->data() + out_offset, outgrad_stride, + out_grad->data() + out_offset, out_stride, outgrad_flatten_level.dims(), flatten_stride, outgrad_flatten_level.data()); out_offset += out_flatten_level.dims()[1] * out_stride[1]; - // flatten backward + // flatten backward to nchw + framework::Tensor out_level; + framework::Tensor outgrad_level; + std::vector out_shape_vec({in_x->dims()[0], in_x->dims()[1]}); + out_shape_vec.push_back((input_h - ksize_h + 2 * padding_h) / ksize_h + + 1); + out_shape_vec.push_back((input_w - ksize_w + 2 * padding_w) / ksize_w + + 1); + framework::DDim out_shape(framework::make_ddim(out_shape_vec)); out_level.ShareDataWith(out_flatten_level); out_level.Resize(out_shape); outgrad_level.ShareDataWith(outgrad_flatten_level); outgrad_level.Resize(out_shape); + // pooling backward math::MaxPool2dGradFunctor pool2d_backward; pool2d_backward(context.device_context(), *in_x, *&out_level, *&outgrad_level, ksize, strides, paddings, in_x_grad); diff --git a/python/paddle/v2/fluid/tests/test_spp_op.py b/python/paddle/v2/fluid/tests/test_spp_op.py index 806d5e7736..89b12e885c 100644 --- a/python/paddle/v2/fluid/tests/test_spp_op.py +++ b/python/paddle/v2/fluid/tests/test_spp_op.py @@ -37,11 +37,11 @@ class TestSppOp(OpTest): self.check_output() def test_check_grad(self): - self.check_grad(['X'], 'Out') + self.check_grad(['X'], 'Out', max_relative_error=0.05) def init_test_case(self): - self.shape = [1, 1, 2, 2] - self.pyramid_height = 2 + self.shape = [3, 2, 4, 4] + self.pyramid_height = 3 if __name__ == '__main__': From 8368e55be93ea6d2912e3ebebb35e284c5428a28 Mon Sep 17 00:00:00 2001 From: sweetsky0901 Date: Mon, 4 Dec 2017 11:19:16 +0800 Subject: [PATCH 09/54] modify some doc --- paddle/operators/spp_op.cc | 4 +- paddle/operators/spp_op.h | 47 +++++++++++---------- python/paddle/v2/fluid/tests/test_spp_op.py | 19 ++++++--- 3 files changed, 38 insertions(+), 32 deletions(-) diff --git a/paddle/operators/spp_op.cc b/paddle/operators/spp_op.cc index ff607c5769..026b35de1e 100644 --- a/paddle/operators/spp_op.cc +++ b/paddle/operators/spp_op.cc @@ -29,7 +29,7 @@ class SppOpMaker : public framework::OpProtoAndCheckerMaker { "(Tensor) The output tensor of spp operator." "N * M." "M = C * H * W"); - AddAttr("pyramid_height", "int"); + AddAttr("pyramid_height", "int", "multi level pooling"); AddComment(R"DOC( "Does spatial pyramid pooling on the input image by taking the max, etc. within regions so that the result vector of different sized @@ -39,7 +39,7 @@ class SppOpMaker : public framework::OpProtoAndCheckerMaker { Where $$ H_{out} = N \\ - W_{out} = ((std::pow(4, pyramid_height) - 1) / (4 - 1)) * C_{in} + W_{out} = (((4^pyramid_height) - 1) / (4 - 1))$ * C_{in} $$ )DOC"); } diff --git a/paddle/operators/spp_op.h b/paddle/operators/spp_op.h index 7a385352a0..0f2c43ee65 100644 --- a/paddle/operators/spp_op.h +++ b/paddle/operators/spp_op.h @@ -34,27 +34,27 @@ class SppKernel : public framework::OpKernel { size_t output_offset = 0; for (int p = 0; p < pyramid_height; ++p) { int bins = std::pow(2, p); - int ksize_h = std::ceil(input_h / static_cast(bins)); - int ksize_w = std::ceil(input_w / static_cast(bins)); - int padding_h = (ksize_h * bins - input_h + 1) / 2; - int padding_w = (ksize_w * bins - input_w + 1) / 2; - std::vector ksize({ksize_h, ksize_w}); - std::vector strides({ksize_h, ksize_w}); + int kernel_size_h = std::ceil(input_h / static_cast(bins)); + int kernel_size_w = std::ceil(input_w / static_cast(bins)); + int padding_h = (kernel_size_h * bins - input_h + 1) / 2; + int padding_w = (kernel_size_w * bins - input_w + 1) / 2; + std::vector kernel_size({kernel_size_h, kernel_size_w}); + std::vector strides({kernel_size_h, kernel_size_w}); std::vector paddings({padding_h, padding_w}); // pooling output shape framework::Tensor out_level; std::vector output_shape_vec({in_x->dims()[0], in_x->dims()[1]}); - output_shape_vec.push_back((input_h - ksize_h + 2 * padding_h) / ksize_h + - 1); - output_shape_vec.push_back((input_w - ksize_w + 2 * padding_w) / ksize_w + - 1); + output_shape_vec.push_back( + (input_h - kernel_size_h + 2 * padding_h) / kernel_size_h + 1); + output_shape_vec.push_back( + (input_w - kernel_size_w + 2 * padding_w) / kernel_size_w + 1); framework::DDim output_shape(framework::make_ddim(output_shape_vec)); out_level.mutable_data(output_shape, context.GetPlace()); // pooling math::Pool2dFunctor, T> pool_forward; math::MaxPool max_process; - pool_forward(context.device_context(), *in_x, ksize, strides, paddings, - max_process, &out_level); + pool_forward(context.device_context(), *in_x, kernel_size, strides, + paddings, max_process, &out_level); // flatten pooling output shape framework::Tensor out_flatten_level; int output_flatten_w = in_x->dims()[1] * bins * bins; @@ -96,12 +96,12 @@ class SppGradKernel : public framework::OpKernel { size_t out_offset = 0; for (int p = 0; p < pyramid_height; ++p) { int bins = std::pow(2, p); - int ksize_h = std::ceil(input_h / static_cast(bins)); - int ksize_w = std::ceil(input_w / static_cast(bins)); - int padding_h = (ksize_h * bins - input_h + 1) / 2; - int padding_w = (ksize_w * bins - input_w + 1) / 2; - std::vector ksize({ksize_h, ksize_w}); - std::vector strides({ksize_h, ksize_w}); + int kernel_size_h = std::ceil(input_h / static_cast(bins)); + int kernel_size_w = std::ceil(input_w / static_cast(bins)); + int padding_h = (kernel_size_h * bins - input_h + 1) / 2; + int padding_w = (kernel_size_w * bins - input_w + 1) / 2; + std::vector kernel_size({kernel_size_h, kernel_size_w}); + std::vector strides({kernel_size_h, kernel_size_w}); std::vector paddings({padding_h, padding_w}); // split out and outgrad ... to flatten framework::Tensor out_flatten_level; @@ -129,10 +129,10 @@ class SppGradKernel : public framework::OpKernel { framework::Tensor out_level; framework::Tensor outgrad_level; std::vector out_shape_vec({in_x->dims()[0], in_x->dims()[1]}); - out_shape_vec.push_back((input_h - ksize_h + 2 * padding_h) / ksize_h + - 1); - out_shape_vec.push_back((input_w - ksize_w + 2 * padding_w) / ksize_w + - 1); + out_shape_vec.push_back( + (input_h - kernel_size_h + 2 * padding_h) / kernel_size_h + 1); + out_shape_vec.push_back( + (input_w - kernel_size_w + 2 * padding_w) / kernel_size_w + 1); framework::DDim out_shape(framework::make_ddim(out_shape_vec)); out_level.ShareDataWith(out_flatten_level); out_level.Resize(out_shape); @@ -141,7 +141,8 @@ class SppGradKernel : public framework::OpKernel { // pooling backward math::MaxPool2dGradFunctor pool2d_backward; pool2d_backward(context.device_context(), *in_x, *&out_level, - *&outgrad_level, ksize, strides, paddings, in_x_grad); + *&outgrad_level, kernel_size, strides, paddings, + in_x_grad); } } }; diff --git a/python/paddle/v2/fluid/tests/test_spp_op.py b/python/paddle/v2/fluid/tests/test_spp_op.py index 89b12e885c..b57f4a795d 100644 --- a/python/paddle/v2/fluid/tests/test_spp_op.py +++ b/python/paddle/v2/fluid/tests/test_spp_op.py @@ -13,14 +13,19 @@ class TestSppOp(OpTest): out_level_flatten = [] for i in xrange(self.pyramid_height): bins = np.power(2, i) - ksize = [0, 0] + kernel_size = [0, 0] padding = [0, 0] - ksize[0] = np.ceil(hsize / bins.astype("double")).astype("int32") - padding[0] = ((ksize[0] * bins - hsize + 1) / 2).astype("int32") - - ksize[1] = np.ceil(wsize / bins.astype("double")).astype("int32") - padding[1] = ((ksize[1] * bins - wsize + 1) / 2).astype("int32") - out_level = max_pool2D_forward_naive(input, ksize, ksize, padding) + kernel_size[0] = np.ceil(hsize / + bins.astype("double")).astype("int32") + padding[0] = ( + (kernel_size[0] * bins - hsize + 1) / 2).astype("int32") + + kernel_size[1] = np.ceil(wsize / + bins.astype("double")).astype("int32") + padding[1] = ( + (kernel_size[1] * bins - wsize + 1) / 2).astype("int32") + out_level = max_pool2D_forward_naive(input, kernel_size, + kernel_size, padding) out_level_flatten.append( out_level.reshape(nsize, bins * bins * csize)) if i == 0: From 141a323c3410f9d65b75cd546af69715735db23a Mon Sep 17 00:00:00 2001 From: sweetsky0901 Date: Mon, 4 Dec 2017 11:43:14 +0800 Subject: [PATCH 10/54] fix a bug --- paddle/operators/spp_op.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/operators/spp_op.cc b/paddle/operators/spp_op.cc index 026b35de1e..5e51e73ecc 100644 --- a/paddle/operators/spp_op.cc +++ b/paddle/operators/spp_op.cc @@ -29,7 +29,7 @@ class SppOpMaker : public framework::OpProtoAndCheckerMaker { "(Tensor) The output tensor of spp operator." "N * M." "M = C * H * W"); - AddAttr("pyramid_height", "int", "multi level pooling"); + AddAttr("pyramid_height", "(int), multi level pooling"); AddComment(R"DOC( "Does spatial pyramid pooling on the input image by taking the max, etc. within regions so that the result vector of different sized From 7389ea98eaae7ca79442811991cba37f908e5a74 Mon Sep 17 00:00:00 2001 From: dzhwinter Date: Mon, 11 Dec 2017 03:24:25 -0800 Subject: [PATCH 11/54] "add NCCL multi-GPU design doc" --- .../design}/images/multigpu_allreduce.graffle | Bin .../design}/images/multigpu_allreduce.png | Bin .../images/multigpu_before_convert.graffle | Bin .../design}/images/multigpu_before_convert.png | Bin .../multigpu.md => doc/design/paddle_nccl.md | 14 ++++++-------- 5 files changed, 6 insertions(+), 8 deletions(-) rename {paddle/framework => doc/design}/images/multigpu_allreduce.graffle (100%) rename {paddle/framework => doc/design}/images/multigpu_allreduce.png (100%) rename {paddle/framework => doc/design}/images/multigpu_before_convert.graffle (100%) rename {paddle/framework => doc/design}/images/multigpu_before_convert.png (100%) rename paddle/framework/multigpu.md => doc/design/paddle_nccl.md (83%) diff --git a/paddle/framework/images/multigpu_allreduce.graffle b/doc/design/images/multigpu_allreduce.graffle similarity index 100% rename from paddle/framework/images/multigpu_allreduce.graffle rename to doc/design/images/multigpu_allreduce.graffle diff --git a/paddle/framework/images/multigpu_allreduce.png b/doc/design/images/multigpu_allreduce.png similarity index 100% rename from paddle/framework/images/multigpu_allreduce.png rename to doc/design/images/multigpu_allreduce.png diff --git a/paddle/framework/images/multigpu_before_convert.graffle b/doc/design/images/multigpu_before_convert.graffle similarity index 100% rename from paddle/framework/images/multigpu_before_convert.graffle rename to doc/design/images/multigpu_before_convert.graffle diff --git a/paddle/framework/images/multigpu_before_convert.png b/doc/design/images/multigpu_before_convert.png similarity index 100% rename from paddle/framework/images/multigpu_before_convert.png rename to doc/design/images/multigpu_before_convert.png diff --git a/paddle/framework/multigpu.md b/doc/design/paddle_nccl.md similarity index 83% rename from paddle/framework/multigpu.md rename to doc/design/paddle_nccl.md index 1c843326ee..7c889fdd7f 100644 --- a/paddle/framework/multigpu.md +++ b/doc/design/paddle_nccl.md @@ -1,15 +1,17 @@ -# Design Doc: Multi-GPU support in Operation Graph +# Design Doc: NCCL support in Paddle Fluid ## Abstract -This Design Doc refers to the multi-GPU feature in paddle. We propose an approach to support multi-GPU both on a single machine and multiple machines. Every device only run sub-graphs which our framework issued. We use `Broadcast`, `Allreduce` operators to join different device sub-graph to the whole graph. - +This Design Doc refers to the NCCL feature in paddle. We propose an approach to support NCCL library both on a single machine and multiple machines. We wrapper the NCCL primitives `Broadcast`, `Allreduce`, `Reduce` as operators to utilize Multi-GPU powers in one script. ## Motivation -Paddle supports training with multiple CPUs and GPUs, refer to different physical devices. We need to support multi-GPU training in parallel for acceleration, in detail, there are two aspects. +NCCL is a Nvidia library support Multi-GPU communicating. [NCCL](https://developer.nvidia.com/nccl). With NCCL library, we can easily accelerate the training in parallel. +- can easily move the optimize sub-graph to parameter server, multi-GPU feature can be compatible with distributed support design. +- easily plug-in with [NCCL2](https://developer.nvidia.com/nccl) library. +- GPU Model parallelism becomes easier to implement. we only need to replace different GPU's sub-graph with different part of the whole graph. - GPU Data Parallelism Suppose to we have `n`GPUs, every GPU has `1/n`part of training data, and store a complete model in GPU memory. @@ -58,7 +60,3 @@ As it shown in the picture, when each GPU compute the gradient of `W`, followed In fact, in the way of every GPU optimized full batch of data, wasted (n-1) GPU compute resources. We will enhance it in the next stage. ### Benefits - -- can easily move the optimize sub-graph to parameter server, multi-GPU feature can be compatible with distributed support design. -- easily plug-in with [NCCL2](https://developer.nvidia.com/nccl) library. -- GPU Model parallelism becomes easier to implement. we only need to replace different GPU's sub-graph with different part of the whole graph. From ebd0cf1408f3408551c342c8eb249f4313981695 Mon Sep 17 00:00:00 2001 From: dzhwinter Date: Mon, 11 Dec 2017 06:55:04 -0800 Subject: [PATCH 12/54] "add manual allreduce" --- doc/design/paddle_nccl.md | 44 +++++++++++++++++++-------------------- 1 file changed, 21 insertions(+), 23 deletions(-) diff --git a/doc/design/paddle_nccl.md b/doc/design/paddle_nccl.md index 7c889fdd7f..dba451b9b3 100644 --- a/doc/design/paddle_nccl.md +++ b/doc/design/paddle_nccl.md @@ -7,36 +7,34 @@ This Design Doc refers to the NCCL feature in paddle. We propose an approach t ## Motivation -NCCL is a Nvidia library support Multi-GPU communicating. [NCCL](https://developer.nvidia.com/nccl). With NCCL library, we can easily accelerate the training in parallel. +NCCL is a NVIDIA library support Multi-GPU communicating and optimized for NVIDIA GPUs, it provides routines such as all-gather, all-reduce, broadcast, reduce, reduce-scatter, that can achieve high bandwidth over PCIe and NVLink high-speed interconnect. [NCCL](https://developer.nvidia.com/nccl). With NCCL library, we can easily accelerate the training in parallel. -- can easily move the optimize sub-graph to parameter server, multi-GPU feature can be compatible with distributed support design. -- easily plug-in with [NCCL2](https://developer.nvidia.com/nccl) library. -- GPU Model parallelism becomes easier to implement. we only need to replace different GPU's sub-graph with different part of the whole graph. -- GPU Data Parallelism +- Pros +1. easily plug-in with [NCCL2](https://developer.nvidia.com/nccl) library. +1. high performance in NVIDIA GPUs. +1. MPI like primitives, which have low learning cost for users. - Suppose to we have `n`GPUs, every GPU has `1/n`part of training data, and store a complete model in GPU memory. +- Cons +1. Only design for NVIDIA GPUs, not a general multi-device solution. +1. Although NCCL1 is opensourced under BSD license, but NCCL2 is not opensourced anymore. -- GPU Model Parallelism +At the beginning of training, the framework needs to distribute the same parameters to every GPU, and merge the gradients at any time user interests. - every GPU have part of a complete model in GPU memory. +As a result, during training, we need the operations of peer to peer copy between different GPUs, aggregating gradients/parameters from GPUs, and broadcasting parameters to GPUs. Every GPU only need to run the operator with correct place information. -At the beginning of training, the framework needs to issue the same sub-graph to every GPU in Data Parallelism, or different sub-graph in Model Parallelism. - -During training, we need the operations of peer to peer copy between different GPUs, aggregating gradients/parameters from GPUs, and broadcasting parameters to GPUs. Every GPU only need to run the sub-graph with correct place information. - -Besides, it needs interfaces to synchronize model update with each other, and issue/merge model from different GPU Cards. +Besides, it needs interfaces to synchronize model update with each different GPU Cards. ## Implementation -As mentioned above, we summarise that several kinds of operators are needed. Currently, we need to issue parameters to different GPUs, named it with Broadcast operator. And also synchronize parameters between GPUs, called it with AllReduce. +As mentioned above, we wrap the NCCL routines as several kinds of operators. Need to note that NCCL need to create Communicator between gpu at the beginning, so there is a NCCLInit operator created. ### Graph Converter To be compatible with [parameter server design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/ops/dist_train.md), the graph converter converts the user defined operation graph into sub-graphs to be executed on different devices. -1. The user-defined operator graph will be partitioned into sub-graph. +1. The user-defined model will be a single device program -2. Control operators between GPUs will be inserted into the graph. +2. Broadcast/Reduce operators between GPUs will be inserted into the program, even for the multi-node, may insert the `Send`, `Recv` operator. *Broadcast, AllReduce in a single machine. And Broadcast, AllReduce, [Send, Recv](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/ops/dist_train.md#graph-converter) in multiple machines* @@ -49,14 +47,14 @@ After convert, the graph as shows Operators are added to the sub-graphs. Every GPU assigned a role of `rank0`, `rank1` etc. - **Broadcast**. Broadcast operator distribute initialized parameter to all the GPUs from the GPU who owns it. e.g. from`rank0` GPU. -- **Allreduce**. Allreduce operator synchronizes parameters/gradients between GPUs. AllReduce implemented in the Ring-Based communicating method, avoid of the bottle neck in a single GPU. +- **AllReduce**. AllReduce operator synchronizes parameters/gradients between GPUs. AllReduce implemented in the Ring-Based communicating method, avoid of the bottle neck in a single GPU. -These two operators need the Multi-GPU context support. - -Need to notice that Allreduce operator force GPUs synchronized at that point. Every device only need runs sub-graph in a loop style forever, the whole training process in asynchronous or synchronous mode depends on the Allreduce point in the graph. +Need to notice that AllReduce operator force GPUs synchronized at that point. The whole training process in asynchronous or synchronous mode depends on the AllReduce point in the graph. As it shown in the picture, when each GPU compute the gradient of `W`, followed with a `AllReduce` operator, accumulate the `dW` to full batch of data, then run the optimize process individually and apply the gradient to its `W`. -In fact, in the way of every GPU optimized full batch of data, wasted (n-1) GPU compute resources. We will enhance it in the next stage. - -### Benefits +- **AllReduce2** +If we use the NCCL2 AllReduce primitive, every GPU optimized full batch of data, wasted (n-1) GPU compute resources. In addition, AllReduce will only utilize the communicate resource during synchronization, then update the gradient will be a seperated phase. In fact, we can amortize the update gradient time cost into the communicating phase. +- Every parameter has its root card. That card will call **Reduce** operator and collect the gradients from GPUs. +- The whole model's parameter will be hashed to different root card, ensure the load balance between GPUs. +Then we have another version AllReduce operator. Other part keep the same with before. From 9a89b041ba12ab8bcf9e36e218486b224eae1d33 Mon Sep 17 00:00:00 2001 From: guosheng Date: Mon, 11 Dec 2017 23:23:46 +0800 Subject: [PATCH 13/54] Add ChunkEvaluator for multi-batches --- paddle/operators/chunk_eval_op.cc | 18 +++++ paddle/operators/chunk_eval_op.h | 42 +++++++---- python/paddle/v2/fluid/evaluator.py | 73 ++++++++++++++++++- python/paddle/v2/fluid/layers.py | 14 +++- .../tests/book/test_label_semantic_roles.py | 12 +-- .../v2/fluid/tests/test_chunk_eval_op.py | 8 +- 6 files changed, 143 insertions(+), 24 deletions(-) diff --git a/paddle/operators/chunk_eval_op.cc b/paddle/operators/chunk_eval_op.cc index 94127ab33e..ff2a08ccac 100644 --- a/paddle/operators/chunk_eval_op.cc +++ b/paddle/operators/chunk_eval_op.cc @@ -32,6 +32,13 @@ class ChunkEvalOp : public framework::OperatorWithKernel { "Output(Recall) of ChunkEvalOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("F1-Score"), "Output(F1-Score) of ChunkEvalOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("NumInferChunks"), + "Output(NumInferChunks) of ChunkEvalOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("NumLabelChunks"), + "Output(NumLabelChunks) of ChunkEvalOp should not be null."); + PADDLE_ENFORCE( + ctx->HasOutput("NumCorrectChunks"), + "Output(NumCorrectChunks) of ChunkEvalOp should not be null."); auto inference_dim = ctx->GetInputDim("Inference"); auto label_dim = ctx->GetInputDim("Label"); @@ -42,6 +49,9 @@ class ChunkEvalOp : public framework::OperatorWithKernel { ctx->SetOutputDim("Precision", {1}); ctx->SetOutputDim("Recall", {1}); ctx->SetOutputDim("F1-Score", {1}); + ctx->SetOutputDim("NumInferChunks", {1}); + ctx->SetOutputDim("NumLabelChunks", {1}); + ctx->SetOutputDim("NumCorrectChunks", {1}); } protected: @@ -70,6 +80,14 @@ class ChunkEvalOpMaker : public framework::OpProtoAndCheckerMaker { "sensitivity) of chunks on the given mini-batch."); AddOutput("F1-Score", "(float). The evaluated F1-Score on the given mini-batch."); + AddOutput( + "NumInferChunks", + "(int). The number of chunks in Inference on the given mini-batch."); + AddOutput("NumLabelChunks", + "(int). The number of chunks in Label on the given mini-batch."); + AddOutput("NumCorrectChunks", + "(int). The number of chunks both in Inference and Label on the " + "given mini-batch."); AddAttr("num_chunk_types", "(int). The number of chunk type. See below for details."); AddAttr( diff --git a/paddle/operators/chunk_eval_op.h b/paddle/operators/chunk_eval_op.h index dd88f2553b..5d703333c7 100644 --- a/paddle/operators/chunk_eval_op.h +++ b/paddle/operators/chunk_eval_op.h @@ -111,9 +111,7 @@ class ChunkEvalKernel : public framework::OpKernel { std::vector label_segments; std::vector output_segments; std::set excluded_chunk_types; - int64_t num_output_segments = 0; - int64_t num_label_segments = 0; - int64_t num_correct = 0; + if (context.Attr("chunk_scheme") == "IOB") { num_tag_types = 2; tag_begin = 0; @@ -151,12 +149,24 @@ class ChunkEvalKernel : public framework::OpKernel { auto* precision = context.Output("Precision"); auto* recall = context.Output("Recall"); auto* f1 = context.Output("F1-Score"); + auto* num_infer_chunks = context.Output("NumInferChunks"); + auto* num_label_chunks = context.Output("NumLabelChunks"); + auto* num_correct_chunks = context.Output("NumCorrectChunks"); const int64_t* inference_data = inference->data(); const int64_t* label_data = label->data(); T* precision_data = precision->mutable_data(context.GetPlace()); T* racall_data = recall->mutable_data(context.GetPlace()); T* f1_data = f1->mutable_data(context.GetPlace()); + int64_t* num_infer_chunks_data = + num_infer_chunks->mutable_data(context.GetPlace()); + int64_t* num_label_chunks_data = + num_label_chunks->mutable_data(context.GetPlace()); + int64_t* num_correct_chunks_data = + num_correct_chunks->mutable_data(context.GetPlace()); + *num_infer_chunks_data = 0; + *num_label_chunks_data = 0; + *num_correct_chunks_data = 0; auto lod = label->lod(); PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now."); @@ -166,17 +176,23 @@ class ChunkEvalKernel : public framework::OpKernel { for (int i = 0; i < num_sequences; ++i) { int seq_length = lod[0][i + 1] - lod[0][i]; EvalOneSeq(inference_data + lod[0][i], label_data + lod[0][i], seq_length, - output_segments, label_segments, num_output_segments, - num_label_segments, num_correct, num_chunk_types, - num_tag_types, other_chunk_type, tag_begin, tag_inside, - tag_end, tag_single, excluded_chunk_types); + output_segments, label_segments, *num_infer_chunks_data, + *num_label_chunks_data, *num_correct_chunks_data, + num_chunk_types, num_tag_types, other_chunk_type, tag_begin, + tag_inside, tag_end, tag_single, excluded_chunk_types); } - *precision_data = !num_output_segments ? 0 : static_cast(num_correct) / - num_output_segments; - *racall_data = !num_label_segments ? 0 : static_cast(num_correct) / - num_label_segments; - *f1_data = !num_correct ? 0 : 2 * (*precision_data) * (*racall_data) / - ((*precision_data) + (*racall_data)); + *precision_data = !(*num_infer_chunks_data) + ? 0 + : static_cast(*num_correct_chunks_data) / + (*num_infer_chunks_data); + *racall_data = !(*num_label_chunks_data) + ? 0 + : static_cast(*num_correct_chunks_data) / + (*num_label_chunks_data); + *f1_data = !(*num_correct_chunks_data) + ? 0 + : 2 * (*precision_data) * (*racall_data) / + ((*precision_data) + (*racall_data)); } void EvalOneSeq(const int64_t* output, const int64_t* label, int length, diff --git a/python/paddle/v2/fluid/evaluator.py b/python/paddle/v2/fluid/evaluator.py index 137c573622..2d23ff0a16 100644 --- a/python/paddle/v2/fluid/evaluator.py +++ b/python/paddle/v2/fluid/evaluator.py @@ -4,7 +4,7 @@ import layers from framework import Program, unique_name, Variable from layer_helper import LayerHelper -__all__ = ['Accuracy'] +__all__ = ['Accuracy', 'ChunkEvaluator'] def _clone_var_(block, var): @@ -132,3 +132,74 @@ class Accuracy(Evaluator): correct = layers.cast(correct, dtype='float32', **kwargs) out = layers.elementwise_div(x=correct, y=total, **kwargs) return np.array(executor.run(eval_program, fetch_list=[out])[0]) + + +class ChunkEvaluator(Evaluator): + """ + Accumulate counter numbers output by chunk_eval from mini-batches and + compute the precision recall and F1-score using the accumulated counter + numbers. + """ + + def __init__(self, + input, + label, + chunk_scheme, + num_chunk_types, + excluded_chunk_types=None, + **kwargs): + super(ChunkEvaluator, self).__init__("chunk_eval", **kwargs) + main_program = self.helper.main_program + if main_program.current_block().idx != 0: + raise ValueError("You can only invoke Evaluator in root block") + + self.num_infer_chunks = self.create_state( + dtype='int64', shape=[1], suffix='num_infer_chunks') + self.num_label_chunks = self.create_state( + dtype='int64', shape=[1], suffix='num_label_chunks') + self.num_correct_chunks = self.create_state( + dtype='int64', shape=[1], suffix='num_correct_chunks') + kwargs = {'main_program': main_program} + precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks = layers.chunk_eval( + input=input, + label=label, + chunk_scheme=chunk_scheme, + num_chunk_types=num_chunk_types, + excluded_chunk_types=excluded_chunk_types, + **kwargs) + layers.sums( + input=[self.num_infer_chunks, num_infer_chunks], + out=self.num_infer_chunks, + **kwargs) + layers.sums( + input=[self.num_label_chunks, num_label_chunks], + out=self.num_label_chunks, + **kwargs) + layers.sums( + input=[self.num_correct_chunks, num_correct_chunks], + out=self.num_correct_chunks, + **kwargs) + + self.metrics.extend([precision, recall, f1_score]) + + def eval(self, executor, eval_program=None): + if eval_program is None: + eval_program = Program() + block = eval_program.current_block() + kwargs = {'main_program': eval_program} + num_infer_chunks, num_label_chunks, num_correct_chunks = executor.run( + eval_program, + fetch_list=[_clone_var_(block, state) for state in self.states]) + num_infer_chunks = num_infer_chunks[0] + num_label_chunks = num_label_chunks[0] + num_correct_chunks = num_correct_chunks[0] + precision = float( + num_correct_chunks) / num_infer_chunks if num_infer_chunks else 0 + recall = float( + num_correct_chunks) / num_label_chunks if num_label_chunks else 0 + f1_score = float(2 * precision * recall) / ( + precision + recall) if num_correct_chunks else 0 + return np.array( + [precision], dtype='float32'), np.array( + [recall], dtype='float32'), np.array( + [f1_score], dtype='float32') diff --git a/python/paddle/v2/fluid/layers.py b/python/paddle/v2/fluid/layers.py index 98a04ea9c2..809534884b 100644 --- a/python/paddle/v2/fluid/layers.py +++ b/python/paddle/v2/fluid/layers.py @@ -640,8 +640,8 @@ def chunk_eval(input, excluded_chunk_types=None, **kwargs): """ - This function computes the accuracy using the input and label. - The output is the top_k inputs and their indices. + This function computes and outputs the precision, recall and + F1-score of chunk detection. """ helper = LayerHelper("chunk_eval", **kwargs) @@ -649,6 +649,9 @@ def chunk_eval(input, precision = helper.create_tmp_variable(dtype="float32") recall = helper.create_tmp_variable(dtype="float32") f1_score = helper.create_tmp_variable(dtype="float32") + num_infer_chunks = helper.create_tmp_variable(dtype="int64") + num_label_chunks = helper.create_tmp_variable(dtype="int64") + num_correct_chunks = helper.create_tmp_variable(dtype="int64") helper.append_op( type="chunk_eval", @@ -657,14 +660,17 @@ def chunk_eval(input, outputs={ "Precision": [precision], "Recall": [recall], - "F1-Score": [f1_score] + "F1-Score": [f1_score], + "NumInferChunks": [num_infer_chunks], + "NumLabelChunks": [num_label_chunks], + "NumCorrectChunks": [num_correct_chunks] }, attrs={ "num_chunk_types": num_chunk_types, 'chunk_scheme': chunk_scheme, 'excluded_chunk_types': excluded_chunk_types or [] }) - return precision, recall, f1_score + return precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks def sequence_conv(input, diff --git a/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py b/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py index d2693b602e..caa51b5df4 100644 --- a/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py +++ b/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py @@ -150,7 +150,7 @@ def main(): crf_decode = fluid.layers.crf_decoding( input=feature_out, param_attr=fluid.ParamAttr(name='crfw')) - precision, recall, f1_score = fluid.layers.chunk_eval( + chunk_evaluator = fluid.evaluator.ChunkEvaluator( input=crf_decode, label=target, chunk_scheme="IOB", @@ -176,14 +176,16 @@ def main(): batch_id = 0 for pass_id in xrange(PASS_NUM): + chunk_evaluator.reset(exe) for data in train_data(): outs = exe.run(fluid.default_main_program(), feed=feeder.feed(data), - fetch_list=[avg_cost, precision, recall, f1_score]) + fetch_list=[avg_cost] + chunk_evaluator.metrics) + precision, recall, f1_score = chunk_evaluator.eval(exe) avg_cost_val = np.array(outs[0]) - precision_val = np.array(outs[1]) - recall_val = np.array(outs[2]) - f1_score_val = np.array(outs[3]) + precision_val = np.array(precision) + recall_val = np.array(recall) + f1_score_val = np.array(f1_score) if batch_id % 10 == 0: print("avg_cost=" + str(avg_cost_val)) diff --git a/python/paddle/v2/fluid/tests/test_chunk_eval_op.py b/python/paddle/v2/fluid/tests/test_chunk_eval_op.py index 819e65a653..53bf6f815b 100644 --- a/python/paddle/v2/fluid/tests/test_chunk_eval_op.py +++ b/python/paddle/v2/fluid/tests/test_chunk_eval_op.py @@ -147,7 +147,13 @@ class TestChunkEvalOp(OpTest): 'Recall': np.asarray( [recall], dtype='float32'), 'F1-Score': np.asarray( - [f1], dtype='float32') + [f1], dtype='float32'), + 'NumInferChunks': np.asarray( + [self.num_infer_chunks], dtype='int64'), + 'NumLabelChunks': np.asarray( + [self.num_label_chunks], dtype='int64'), + 'NumCorrectChunks': np.asarray( + [self.num_correct_chunks], dtype='int64') } def setUp(self): From 63ce906b088c641c3bf33a2b8aa6324a39310ffe Mon Sep 17 00:00:00 2001 From: guosheng Date: Tue, 12 Dec 2017 20:33:44 +0800 Subject: [PATCH 14/54] Refine ChunkEvalutor by following comments --- paddle/operators/chunk_eval_op.cc | 16 +++++++------ .../tests/book/test_label_semantic_roles.py | 23 +++++++++---------- 2 files changed, 20 insertions(+), 19 deletions(-) diff --git a/paddle/operators/chunk_eval_op.cc b/paddle/operators/chunk_eval_op.cc index ff2a08ccac..894f355deb 100644 --- a/paddle/operators/chunk_eval_op.cc +++ b/paddle/operators/chunk_eval_op.cc @@ -80,14 +80,16 @@ class ChunkEvalOpMaker : public framework::OpProtoAndCheckerMaker { "sensitivity) of chunks on the given mini-batch."); AddOutput("F1-Score", "(float). The evaluated F1-Score on the given mini-batch."); + AddOutput("NumInferChunks", + "(int64_t). The number of chunks in Inference on the given " + "mini-batch."); AddOutput( - "NumInferChunks", - "(int). The number of chunks in Inference on the given mini-batch."); - AddOutput("NumLabelChunks", - "(int). The number of chunks in Label on the given mini-batch."); - AddOutput("NumCorrectChunks", - "(int). The number of chunks both in Inference and Label on the " - "given mini-batch."); + "NumLabelChunks", + "(int64_t). The number of chunks in Label on the given mini-batch."); + AddOutput( + "NumCorrectChunks", + "(int64_t). The number of chunks both in Inference and Label on the " + "given mini-batch."); AddAttr("num_chunk_types", "(int). The number of chunk type. See below for details."); AddAttr( diff --git a/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py b/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py index caa51b5df4..c3591a613a 100644 --- a/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py +++ b/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py @@ -178,20 +178,19 @@ def main(): for pass_id in xrange(PASS_NUM): chunk_evaluator.reset(exe) for data in train_data(): - outs = exe.run(fluid.default_main_program(), - feed=feeder.feed(data), - fetch_list=[avg_cost] + chunk_evaluator.metrics) - precision, recall, f1_score = chunk_evaluator.eval(exe) - avg_cost_val = np.array(outs[0]) - precision_val = np.array(precision) - recall_val = np.array(recall) - f1_score_val = np.array(f1_score) + cost, precision, recall, f1_score = exe.run( + fluid.default_main_program(), + feed=feeder.feed(data), + fetch_list=[avg_cost] + chunk_evaluator.metrics) + pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval( + exe) if batch_id % 10 == 0: - print("avg_cost=" + str(avg_cost_val)) - print("precision_val=" + str(precision_val)) - print("recall_val:" + str(recall_val)) - print("f1_score_val:" + str(f1_score_val)) + print("avg_cost:" + str(cost) + " precision:" + str( + precision) + " recall:" + str(recall) + " f1_score:" + str( + f1_score) + " pass_precision:" + str( + pass_precision) + " pass_recall:" + str(pass_recall) + + " pass_f1_score:" + str(pass_f1_score)) # exit early for CI exit(0) From ea093283e6cdc89bed0ffa0f2e534f9583765465 Mon Sep 17 00:00:00 2001 From: sweetsky0901 Date: Wed, 13 Dec 2017 12:39:29 +0800 Subject: [PATCH 15/54] for code review by zhaolong --- paddle/operators/spp_op.cc | 24 +++++++---- paddle/operators/spp_op.cu.cc | 11 ++--- paddle/operators/spp_op.h | 75 +++++++++++++++-------------------- 3 files changed, 55 insertions(+), 55 deletions(-) diff --git a/paddle/operators/spp_op.cc b/paddle/operators/spp_op.cc index 5e51e73ecc..c4bd4f5ab3 100644 --- a/paddle/operators/spp_op.cc +++ b/paddle/operators/spp_op.cc @@ -31,9 +31,15 @@ class SppOpMaker : public framework::OpProtoAndCheckerMaker { "M = C * H * W"); AddAttr("pyramid_height", "(int), multi level pooling"); AddComment(R"DOC( - "Does spatial pyramid pooling on the input image by taking the max, - etc. within regions so that the result vector of different sized - images are of the same size + "With spatial pyramid pooling, the input image can + be of any sizes. This not only allows arbitrary aspect + ratios, but also allows arbitrary scales. We can resize + the input image to any scale (e.g., min(w, h)=180, 224, + ...) and apply the same deep network. When the + input image is at different scales, the network (with + the same filter sizes) will extract features at different + scales. The scales play important roles in traditional + methods. Input shape: $(N, C_{in}, H_{in}, W_{in})$ Output shape: $(H_{out}, W_{out})$ Where @@ -41,6 +47,7 @@ class SppOpMaker : public framework::OpProtoAndCheckerMaker { H_{out} = N \\ W_{out} = (((4^pyramid_height) - 1) / (4 - 1))$ * C_{in} $$ + paper https://arxiv.org/pdf/1406.4729v4.pdf )DOC"); } }; @@ -79,8 +86,9 @@ class SppOpGrad : public framework::OperatorWithKernel { namespace ops = paddle::operators; REGISTER_OP(spp, ops::SppOp, ops::SppOpMaker, spp_grad, ops::SppOpGrad); -REGISTER_OP_CPU_KERNEL(spp, ops::SppKernel, - ops::SppKernel); -REGISTER_OP_CPU_KERNEL(spp_grad, - ops::SppGradKernel, - ops::SppGradKernel); +REGISTER_OP_CPU_KERNEL( + spp, ops::SppKernel, + ops::SppKernel); +REGISTER_OP_CPU_KERNEL( + spp_grad, ops::SppGradKernel, + ops::SppGradKernel); diff --git a/paddle/operators/spp_op.cu.cc b/paddle/operators/spp_op.cu.cc index a7057907ce..761e4d6c4a 100644 --- a/paddle/operators/spp_op.cu.cc +++ b/paddle/operators/spp_op.cu.cc @@ -15,8 +15,9 @@ limitations under the License. */ #include "paddle/operators/spp_op.h" namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL(spp, ops::SppKernel, - ops::SppKernel); -REGISTER_OP_GPU_KERNEL(spp_grad, - ops::SppGradKernel, - ops::SppGradKernel); +REGISTER_OP_CUDA_KERNEL( + spp, ops::SppKernel, + ops::SppKernel); +REGISTER_OP_CUDA_KERNEL( + spp_grad, ops::SppGradKernel, + ops::SppGradKernel); diff --git a/paddle/operators/spp_op.h b/paddle/operators/spp_op.h index 0f2c43ee65..16510cb826 100644 --- a/paddle/operators/spp_op.h +++ b/paddle/operators/spp_op.h @@ -20,7 +20,7 @@ limitations under the License. */ namespace paddle { namespace operators { -template +template class SppKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { @@ -43,39 +43,32 @@ class SppKernel : public framework::OpKernel { std::vector paddings({padding_h, padding_w}); // pooling output shape framework::Tensor out_level; - std::vector output_shape_vec({in_x->dims()[0], in_x->dims()[1]}); - output_shape_vec.push_back( - (input_h - kernel_size_h + 2 * padding_h) / kernel_size_h + 1); - output_shape_vec.push_back( - (input_w - kernel_size_w + 2 * padding_w) / kernel_size_w + 1); + std::vector output_shape_vec( + {in_x->dims()[0], in_x->dims()[1], bins, bins}); framework::DDim output_shape(framework::make_ddim(output_shape_vec)); out_level.mutable_data(output_shape, context.GetPlace()); // pooling - math::Pool2dFunctor, T> pool_forward; + math::Pool2dFunctor, T> pool_forward; math::MaxPool max_process; - pool_forward(context.device_context(), *in_x, kernel_size, strides, - paddings, max_process, &out_level); + pool_forward(context.template device_context(), *in_x, + kernel_size, strides, paddings, max_process, &out_level); // flatten pooling output shape - framework::Tensor out_flatten_level; int output_flatten_w = in_x->dims()[1] * bins * bins; std::vector output_flatten_shape_vec( {in_x->dims()[0], output_flatten_w}); framework::DDim output_flatten_shape( framework::make_ddim(output_flatten_shape_vec)); - out_flatten_level.ShareDataWith(out_level); - out_flatten_level.Resize(output_flatten_shape); + out_level.Resize(output_flatten_shape); // concat - auto out_flatten_level_stride = - framework::stride(out_flatten_level.dims()); - StridedMemcpy(context.device_context(), out_flatten_level.data(), - out_flatten_level_stride, out_flatten_level.dims(), + auto out_level_stride = framework::stride(out_level.dims()); + StridedMemcpy(context.template device_context(), + out_level.data(), out_level_stride, out_level.dims(), out_stride, out->data() + output_offset); - output_offset += - out_flatten_level.dims()[1] * out_flatten_level_stride[1]; + output_offset += out_level.dims()[1] * out_level_stride[1]; } } }; -template +template class SppGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { @@ -86,8 +79,8 @@ class SppGradKernel : public framework::OpKernel { framework::Tensor* in_x_grad = context.Output(framework::GradVarName("X")); int pyramid_height = context.template Attr("pyramid_height"); - auto& device_ctx = context.device_context(); - math::SetConstant zero; + auto& device_ctx = context.template device_context(); + math::SetConstant zero; in_x_grad->mutable_data(context.GetPlace()); zero(device_ctx, in_x_grad, static_cast(0)); auto out_stride = framework::stride(out->dims()); @@ -104,45 +97,43 @@ class SppGradKernel : public framework::OpKernel { std::vector strides({kernel_size_h, kernel_size_w}); std::vector paddings({padding_h, padding_w}); // split out and outgrad ... to flatten - framework::Tensor out_flatten_level; - framework::Tensor outgrad_flatten_level; + framework::Tensor out_level; + framework::Tensor outgrad_level; int out_flatten_w = in_x->dims()[1] * bins * bins; std::vector out_flatten_shape_vec( {in_x->dims()[0], out_flatten_w}); framework::DDim out_flatten_shape( framework::make_ddim(out_flatten_shape_vec)); - out_flatten_level.mutable_data(out_flatten_shape, context.GetPlace()); - outgrad_flatten_level.mutable_data(out_flatten_shape, - context.GetPlace()); - auto flatten_stride = framework::stride(out_flatten_level.dims()); + out_level.mutable_data(out_flatten_shape, context.GetPlace()); + outgrad_level.mutable_data(out_flatten_shape, context.GetPlace()); + auto flatten_stride = framework::stride(out_level.dims()); // memcpy - StridedMemcpy(context.device_context(), out->data() + out_offset, - out_stride, out_flatten_level.dims(), flatten_stride, - out_flatten_level.data()); + StridedMemcpy(context.template device_context(), + out->data() + out_offset, out_stride, + out_level.dims(), flatten_stride, out_level.data()); - StridedMemcpy(context.device_context(), + StridedMemcpy(context.template device_context(), out_grad->data() + out_offset, out_stride, - outgrad_flatten_level.dims(), flatten_stride, - outgrad_flatten_level.data()); - out_offset += out_flatten_level.dims()[1] * out_stride[1]; + outgrad_level.dims(), flatten_stride, + outgrad_level.data()); + out_offset += out_level.dims()[1] * out_stride[1]; // flatten backward to nchw - framework::Tensor out_level; - framework::Tensor outgrad_level; + std::vector out_shape_vec({in_x->dims()[0], in_x->dims()[1]}); out_shape_vec.push_back( (input_h - kernel_size_h + 2 * padding_h) / kernel_size_h + 1); out_shape_vec.push_back( (input_w - kernel_size_w + 2 * padding_w) / kernel_size_w + 1); framework::DDim out_shape(framework::make_ddim(out_shape_vec)); - out_level.ShareDataWith(out_flatten_level); + out_level.ShareDataWith(out_level); out_level.Resize(out_shape); - outgrad_level.ShareDataWith(outgrad_flatten_level); + outgrad_level.ShareDataWith(outgrad_level); outgrad_level.Resize(out_shape); // pooling backward - math::MaxPool2dGradFunctor pool2d_backward; - pool2d_backward(context.device_context(), *in_x, *&out_level, - *&outgrad_level, kernel_size, strides, paddings, - in_x_grad); + math::MaxPool2dGradFunctor pool2d_backward; + pool2d_backward(context.template device_context(), *in_x, + *&out_level, *&outgrad_level, kernel_size, strides, + paddings, in_x_grad); } } }; From 685d1e3b330ff6605935ed4a33825bdeb97ced07 Mon Sep 17 00:00:00 2001 From: Yibing Liu Date: Thu, 14 Dec 2017 04:27:05 +0000 Subject: [PATCH 16/54] Enable reshape_op to support dimension inference --- paddle/operators/reshape_op.cc | 27 ++++++++++++++++++--------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc index 39bf2118d6..306dfa8069 100644 --- a/paddle/operators/reshape_op.cc +++ b/paddle/operators/reshape_op.cc @@ -34,21 +34,27 @@ class ReshapeOp : public framework::OperatorWithKernel { auto shape = ctx->Attrs().Get>("shape"); PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty."); auto x_dims = ctx->GetInputDim("X"); - // TODO(qiao) change batch_size - for (size_t i = 1; i < shape.size(); ++i) { - PADDLE_ENFORCE(shape[i] > 0, - "Each dimension of Attr(shape) " - "must be positive except the first one."); - } - if (shape[0] < 0) { - shape[0] = x_dims[0]; + + std::vector neg_dims_idx; + for (size_t i = 0; i < shape.size(); ++i) { + PADDLE_ENFORCE(shape[i] > 0 || shape[i] == -1, + "Each dimension of Attr(shape) must be positive or -1."); + if (shape[i] == -1) { + neg_dims_idx.push_back(i); + PADDLE_ENFORCE(neg_dims_idx.size() <= 1, + "Only one dimension of Attr(shape) can be -1."); + } } + // capacity check int64_t capacity = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies()); int64_t in_size = framework::product(x_dims); - PADDLE_ENFORCE_EQ(capacity, in_size, + if (neg_dims_idx.size() == 1) { + shape[neg_dims_idx[0]] = in_size / (-capacity); + PADDLE_ENFORCE(shape[neg_dims_idx[0]] > 0, "The size of Input(X) mismatches with Attr(shape)."); + } // resize output std::vector shape_int64(shape.size(), 0); std::transform(shape.begin(), shape.end(), shape_int64.begin(), @@ -88,6 +94,9 @@ the tensor X into a 1-D tensor: [1, 2, 3, 4] +One dimension in the target shape can be set -1, and the real dimension +will be infered from the original shape of Input(X) and other +dimensions in the target shape. )DOC"); } }; From 0a75ed6f5bcad53b3622ee89d48a5613cc4ef396 Mon Sep 17 00:00:00 2001 From: Yibing Liu Date: Thu, 14 Dec 2017 05:06:19 +0000 Subject: [PATCH 17/54] Add unit test for dimension inference in reshape_op --- paddle/operators/reshape_op.cc | 14 +++++++++----- python/paddle/v2/fluid/tests/test_reshape_op.py | 14 ++++++++++++++ 2 files changed, 23 insertions(+), 5 deletions(-) diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc index 306dfa8069..164f3104eb 100644 --- a/paddle/operators/reshape_op.cc +++ b/paddle/operators/reshape_op.cc @@ -42,19 +42,23 @@ class ReshapeOp : public framework::OperatorWithKernel { if (shape[i] == -1) { neg_dims_idx.push_back(i); PADDLE_ENFORCE(neg_dims_idx.size() <= 1, - "Only one dimension of Attr(shape) can be -1."); + "Only one dimension of Attr(shape) can be unknown."); } } - // capacity check int64_t capacity = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies()); int64_t in_size = framework::product(x_dims); if (neg_dims_idx.size() == 1) { - shape[neg_dims_idx[0]] = in_size / (-capacity); - PADDLE_ENFORCE(shape[neg_dims_idx[0]] > 0, - "The size of Input(X) mismatches with Attr(shape)."); + // dim infer + shape[neg_dims_idx[0]] = in_size / (-capacity); + // recalculate capacity + capacity = std::accumulate(shape.begin(), shape.end(), 1, + std::multiplies()); } + // capacity check + PADDLE_ENFORCE(capacity == in_size, + "The size of Input(X) mismatches with Attr(shape)."); // resize output std::vector shape_int64(shape.size(), 0); std::transform(shape.begin(), shape.end(), shape_int64.begin(), diff --git a/python/paddle/v2/fluid/tests/test_reshape_op.py b/python/paddle/v2/fluid/tests/test_reshape_op.py index 16bb6bb2af..18ee3aece6 100644 --- a/python/paddle/v2/fluid/tests/test_reshape_op.py +++ b/python/paddle/v2/fluid/tests/test_reshape_op.py @@ -17,5 +17,19 @@ class TestReshapeOp(OpTest): self.check_grad(["X"], "Out") +class TestReshapeOpDimInfer(OpTest): + def setUp(self): + self.op_type = "reshape" + self.inputs = {'X': np.random.random((10, 20)).astype("float32")} + self.attrs = {'shape': [4, -1, 5]} + self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(["X"], "Out") + + if __name__ == '__main__': unittest.main() From 9b1675048561e2708af40948b192f24a199bf70a Mon Sep 17 00:00:00 2001 From: dzhwinter Date: Wed, 13 Dec 2017 22:44:33 -0800 Subject: [PATCH 18/54] "remove AllReduce2 comments" --- doc/design/paddle_nccl.md | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/doc/design/paddle_nccl.md b/doc/design/paddle_nccl.md index dba451b9b3..5e28144b95 100644 --- a/doc/design/paddle_nccl.md +++ b/doc/design/paddle_nccl.md @@ -53,8 +53,13 @@ Need to notice that AllReduce operator force GPUs synchronized at that point. Th As it shown in the picture, when each GPU compute the gradient of `W`, followed with a `AllReduce` operator, accumulate the `dW` to full batch of data, then run the optimize process individually and apply the gradient to its `W`. -- **AllReduce2** -If we use the NCCL2 AllReduce primitive, every GPU optimized full batch of data, wasted (n-1) GPU compute resources. In addition, AllReduce will only utilize the communicate resource during synchronization, then update the gradient will be a seperated phase. In fact, we can amortize the update gradient time cost into the communicating phase. -- Every parameter has its root card. That card will call **Reduce** operator and collect the gradients from GPUs. -- The whole model's parameter will be hashed to different root card, ensure the load balance between GPUs. -Then we have another version AllReduce operator. Other part keep the same with before. +- **AllReduce** + Need to note that our AllReduce operator is a ring-base AllReduce implementation. If we use the NCCL2 AllReduce primitive, every GPU optimized full batch of data, wasted (n-1) GPU compute resources. In addition, NCCL2 built-in AllReduce will only utilize the communicating resource during synchronization, then update the gradient will be a subsequent phase. In fact, we can amortize the update gradient time cost into the communicating phase. The process is +1. Every parameter has its root card. That card will responsible for aggregating the gradients from GPUs. +2. The whole model's parameter will be hashed to different root card, ensure the load balance between GPUs. +3. Logically neighberhood card will start send parameter to the next one. After one round, the parameter main card will aggregate the full gradients. +4. Then the root card will optimize the parameter. +5. This parameter card will send its optimized result to its neighberhood, then the neighberhood will send parameter to its next one. +6. Finish the sychronization round. + +The total time cost will be 2 * (n-1) * per-parameter-send-time, we reach the goal of amortize the upgrade time into communicating phase. From e11a561c128a894ce03bd07bd3985f55f74220f8 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Thu, 14 Dec 2017 17:06:25 +0800 Subject: [PATCH 19/54] update --- paddle/pybind/pybind.cc | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc index c16d3e0cbe..9ea4e70a26 100644 --- a/paddle/pybind/pybind.cc +++ b/paddle/pybind/pybind.cc @@ -282,6 +282,16 @@ All parameter, weight, gradient are variables in Paddle. } return ret_values; }); + m.def("get_grad_op_desc", + [](const OpDescBind &op_desc, + const std::unordered_set &no_grad_set, + std::unordered_map &grad_to_var, + const std::vector &grad_sub_block) { + return framework::OpInfoMap::Instance() + .Get(op_desc.Type()) + .GradOpMaker()(op_desc, no_grad_set, &grad_to_var, + grad_sub_block); + }); m.def("prune", [](const ProgramDescBind &origin, const std::vector> &targets) { ProgramDescBind prog_with_targets(origin); From 1059796cbfa48273b8c056fee23f8e405dc24bb8 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Thu, 14 Dec 2017 17:16:47 +0800 Subject: [PATCH 20/54] update the link of docs icon --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index bbb2d49858..ceeb6d9e51 100644 --- a/README.md +++ b/README.md @@ -2,8 +2,8 @@ [![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle) -[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://doc.paddlepaddle.org/develop/doc/) -[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://doc.paddlepaddle.org/develop/doc_cn/) +[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/index_en.html) +[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html) [![Coverage Status](https://coveralls.io/repos/github/PaddlePaddle/Paddle/badge.svg?branch=develop)](https://coveralls.io/github/PaddlePaddle/Paddle?branch=develop) [![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases) [![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE) From 991826317a4192cd4fb3e8505a4a7c60c0fe830c Mon Sep 17 00:00:00 2001 From: Leding Li Date: Thu, 14 Dec 2017 17:23:39 +0800 Subject: [PATCH 21/54] Add extern "C" to paddle_error_string (#6611) --- paddle/capi/error.cpp | 2 +- paddle/capi/error.h | 8 ++++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/paddle/capi/error.cpp b/paddle/capi/error.cpp index 169b65f921..96ce31b45f 100644 --- a/paddle/capi/error.cpp +++ b/paddle/capi/error.cpp @@ -14,7 +14,7 @@ limitations under the License. */ #include "error.h" -const char* paddle_error_string(paddle_error err) { +extern "C" const char* paddle_error_string(paddle_error err) { switch (err) { case kPD_NULLPTR: return "nullptr error"; diff --git a/paddle/capi/error.h b/paddle/capi/error.h index 9d9d0ed63a..2da9e0a3ef 100644 --- a/paddle/capi/error.h +++ b/paddle/capi/error.h @@ -29,9 +29,17 @@ typedef enum { kPD_UNDEFINED_ERROR = -1, } paddle_error; +#ifdef __cplusplus +extern "C" { +#endif + /** * Error string for Paddle API. */ PD_API const char* paddle_error_string(paddle_error err); +#ifdef __cplusplus +} +#endif + #endif From 044a13d02262f1cf84ee685a0575cf2ec28e5623 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Thu, 14 Dec 2017 17:50:56 +0800 Subject: [PATCH 22/54] expose GradOpMaker to Python --- paddle/pybind/pybind.cc | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc index 9ea4e70a26..1faf24bcb8 100644 --- a/paddle/pybind/pybind.cc +++ b/paddle/pybind/pybind.cc @@ -282,15 +282,22 @@ All parameter, weight, gradient are variables in Paddle. } return ret_values; }); - m.def("get_grad_op_desc", + m.def("get_grad_op_descs", [](const OpDescBind &op_desc, const std::unordered_set &no_grad_set, std::unordered_map &grad_to_var, const std::vector &grad_sub_block) { - return framework::OpInfoMap::Instance() - .Get(op_desc.Type()) - .GradOpMaker()(op_desc, no_grad_set, &grad_to_var, - grad_sub_block); + std::vector> grad_op_descs = + framework::OpInfoMap::Instance() + .Get(op_desc.Type()) + .GradOpMaker()(op_desc, no_grad_set, &grad_to_var, + grad_sub_block); + std::vector grad_op_desc_ptrs(grad_op_descs.size()); + std::transform( + grad_op_descs.begin(), grad_op_descs.end(), + grad_op_desc_ptrs.begin(), + [](std::unique_ptr &p) { return p.release(); }); + return grad_op_desc_ptrs; }); m.def("prune", [](const ProgramDescBind &origin, const std::vector> &targets) { From e0698e33a869f72282ae9f7d78a33c5a4ac2ef00 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 14 Dec 2017 17:57:49 +0800 Subject: [PATCH 23/54] Make layers as a python module (#6564) * Make cast op support bool Also add `elemwise_sub/mul/abs/clip` layers * Make fuild.layers as a module * Move layers as a module * Split layers.py into layers module * Fix CI * Fix CI --- python/paddle/v2/fluid/layers/__init__.py | 17 + .../{layers.py => layers/control_flow.py} | 1023 +---------------- python/paddle/v2/fluid/layers/io.py | 57 + python/paddle/v2/fluid/layers/nn.py | 785 +++++++++++++ python/paddle/v2/fluid/layers/ops.py | 9 + python/paddle/v2/fluid/layers/tensor.py | 130 +++ .../book/test_image_classification_train.py | 4 +- .../book/test_understand_sentiment_lstm.py | 48 +- python/setup.py.in | 1 + 9 files changed, 1057 insertions(+), 1017 deletions(-) create mode 100644 python/paddle/v2/fluid/layers/__init__.py rename python/paddle/v2/fluid/{layers.py => layers/control_flow.py} (51%) create mode 100644 python/paddle/v2/fluid/layers/io.py create mode 100644 python/paddle/v2/fluid/layers/nn.py create mode 100644 python/paddle/v2/fluid/layers/ops.py create mode 100644 python/paddle/v2/fluid/layers/tensor.py diff --git a/python/paddle/v2/fluid/layers/__init__.py b/python/paddle/v2/fluid/layers/__init__.py new file mode 100644 index 0000000000..249f570e13 --- /dev/null +++ b/python/paddle/v2/fluid/layers/__init__.py @@ -0,0 +1,17 @@ +import ops +from ops import * +import nn +from nn import * +import io +from io import * +import tensor +from tensor import * +import control_flow +from control_flow import * + +__all__ = [] +__all__ += nn.__all__ +__all__ += io.__all__ +__all__ += tensor.__all__ +__all__ += control_flow.__all__ +__all__ += ops.__all__ diff --git a/python/paddle/v2/fluid/layers.py b/python/paddle/v2/fluid/layers/control_flow.py similarity index 51% rename from python/paddle/v2/fluid/layers.py rename to python/paddle/v2/fluid/layers/control_flow.py index 2781017ec4..5af6c78977 100644 --- a/python/paddle/v2/fluid/layers.py +++ b/python/paddle/v2/fluid/layers/control_flow.py @@ -1,424 +1,18 @@ +from ..layer_helper import LayerHelper, unique_name +from ..framework import Program, Variable, Operator +from .. import core +from tensor import assign, fill_constant import contextlib -import proto.framework_pb2 as framework_pb2 -import core -from framework import OpProtoHolder, Variable, Program, Operator -from initializer import Constant, Normal, Xavier, Initializer -from paddle.v2.fluid.layer_helper import LayerHelper, unique_name -from registry import register_layer -from param_attr import ParamAttr - __all__ = [ - 'fc', 'data', 'cross_entropy', 'conv2d', 'pool2d', 'embedding', 'concat', - 'StaticRNN', 'cast', 'sequence_conv', 'sequence_pool', 'sums', 'cos_sim', - 'batch_norm', 'accuracy', 'split_lod_tensor', 'While' -] - -_REGISTER_LAYER_FROM_OPS = [ - 'mean', 'mul', 'dropout', 'reshape', 'sigmoid', 'scale', 'transpose', - 'sigmoid_cross_entropy_with_logits', 'elementwise_add', 'elementwise_div', - 'elementwise_sub', 'elementwise_mul', 'clip', 'abs' + 'split_lod_tensor', 'merge_lod_tensor', 'BlockGuard', 'StaticRNNGuard', + 'StaticRNNMemoryLink', 'WhileGuard', 'While', 'lod_rank_table', + 'max_sequence_len', 'topk', 'lod_tensor_to_array', 'array_to_lod_tensor', + 'increment', 'array_write', 'create_array', 'less_than', 'array_read', + 'shrink_memory', 'array_length', 'IfElse', 'DynamicRNN', 'ConditionalBlock', + 'StaticRNN' ] -for _OP in set(_REGISTER_LAYER_FROM_OPS): - globals()[_OP] = register_layer(_OP) - __all__.append(_OP) - - -def fc(input, - size, - num_flatten_dims=1, - param_attr=None, - bias_attr=None, - act=None, - name=None, - main_program=None, - startup_program=None): - """ - Fully Connected Layer. - - Args: - input: The input tensor to the function - size: The size of the layer - num_flatten_dims: Number of columns in input - param_attr: The parameters/weights to the FC Layer - param_initializer: Initializer used for the weight/parameter. If None, XavierInitializer() is used - bias_attr: The bias parameter for the FC layer - bias_initializer: Initializer used for the bias. If None, then ConstantInitializer() is used - act: Activation to be applied to the output of FC layer - name: Name/alias of the function - main_program: Name of the main program that calls this - startup_program: Name of the startup program - - This function can take in multiple inputs and performs the Fully Connected - function (linear transformation) on top of each of them. - So for input x, the output will be : Wx + b. Where W is the parameter, - b the bias and x is the input. - - The function also applies an activation (non-linearity) on top of the - output, if activation is passed in the input. - - All the input variables of this function are passed in as local variables - to the LayerHelper constructor. - - """ - helper = LayerHelper('fc', **locals()) - - dtype = helper.input_dtype() - - mul_results = [] - for input_var, param_attr in helper.iter_inputs_and_params(): - input_shape = input_var.shape - param_shape = [ - reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1) - ] + [size] - w = helper.create_parameter( - attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False) - tmp = helper.create_tmp_variable(dtype) - helper.append_op( - type="mul", - inputs={ - "X": input_var, - "Y": w, - }, - outputs={"Out": tmp}, - attrs={'x_num_col_dims': num_flatten_dims, - 'y_num_col_dims': 1}) - mul_results.append(tmp) - - # sum - if len(mul_results) == 1: - pre_bias = mul_results[0] - else: - pre_bias = helper.create_tmp_variable(dtype) - helper.append_op( - type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias}) - # add bias - pre_activation = helper.append_bias_op(pre_bias) - # add activation - return helper.append_activation(pre_activation) - - -def embedding(input, - size, - is_sparse=False, - param_attr=None, - dtype='float32', - main_program=None, - startup_program=None): - """ - Embedding Layer. - - Args: - param_initializer: - input: The input to the function - size: The size of the layer - is_sparse: A flag that decleares whether the input is sparse - param_attr: Parameters for this layer - dtype: The type of data : float32, float_16, int etc - main_program: Name of the main program that calls this - startup_program: Name of the startup program - - This function can take in the input (which is a vector of IDs) and - performs a lookup in the lookup_table using these IDs, to result into - the embedding of each ID in the input. - - All the input variables of this function are passed in as local variables - to the LayerHelper constructor. - - """ - - helper = LayerHelper('embedding', **locals()) - w = helper.create_parameter( - attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False) - tmp = helper.create_tmp_variable(dtype) - helper.append_op( - type='lookup_table', - inputs={'Ids': input, - 'W': w}, - outputs={'Out': tmp}, - attrs={'is_sparse': is_sparse}) - return tmp - - -# TODO(qijun): expose H0 and C0 -def dynamic_lstm(input, - size, - param_attr=None, - bias_attr=None, - use_peepholes=True, - is_reverse=False, - gate_activation='sigmoid', - cell_activation='tanh', - candidate_activation='tanh', - dtype='float32', - main_program=None, - startup_program=None): - helper = LayerHelper('lstm', **locals()) - size = size / 4 - weight = helper.create_parameter( - attr=helper.param_attr, shape=[size, 4 * size], dtype=dtype) - bias_size = [1, 7 * size] - if not use_peepholes: - bias_size[1] = 4 * size - bias = helper.create_parameter( - attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True) - - hidden = helper.create_tmp_variable(dtype) - cell = helper.create_tmp_variable(dtype) - batch_gate = helper.create_tmp_variable(dtype) - batch_cell_pre_act = helper.create_tmp_variable(dtype) - - helper.append_op( - type='lstm', - inputs={'Input': input, - 'Weight': weight, - 'Bias': bias}, - outputs={ - 'Hidden': hidden, - 'Cell': cell, - 'BatchGate': batch_gate, - 'BatchCellPreAct': batch_cell_pre_act - }, - attrs={ - 'use_peepholes': use_peepholes, - 'is_reverse': is_reverse, - 'gate_activation': gate_activation, - 'cell_activation': cell_activation, - 'candidate_activation': candidate_activation - }) - return hidden, cell - - -def gru_unit(input, - hidden, - size, - weight=None, - bias=None, - activation='tanh', - gate_activation='sigmoid', - main_program=None, - startup_program=None): - """ - GRUUnit Operator implements partial calculations of the GRU unit as following: - - $$ - update \ gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\ - reset \ gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r) \\ - output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\ - output: h_t = dot((1 - u_t), h_{t-1}) + dot(u_t, {h}_t) - $$ - - which is same as one time step of GRU Operator. - - @note To implement the complete GRU unit, fully-connected operator must be - used before to feed xu, xr and xc as the Input of GRUUnit operator. - - TODO(ChunweiYan) add more document here - """ - activation_dict = dict( - identity=0, - sigmoid=1, - tanh=2, - relu=3, ) - activation = activation_dict[activation] - gate_activation = activation_dict[gate_activation] - - helper = LayerHelper('gru_unit', **locals()) - dtype = helper.input_dtype() - size = size / 3 - - # create weight - if weight is None: - weight = helper.create_parameter( - attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype) - - # create bias - if bias is None: - bias_size = [1, 3 * size] - bias = helper.create_parameter( - attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True) - - gate = helper.create_tmp_variable(dtype) - reset_hidden_pre = helper.create_tmp_variable(dtype) - updated_hidden = helper.create_tmp_variable(dtype) - - helper.append_op( - type='gru_unit', - inputs={'Input': input, - 'HiddenPrev': hidden, - 'Weight': weight}, - outputs={ - 'Gate': gate, - 'ResetHiddenPrev': reset_hidden_pre, - 'Hidden': updated_hidden, - }, - attrs={ - 'activation': 0, - 'gate_activation': 1, - }) - - return updated_hidden, reset_hidden_pre, gate - - -def data(name, - shape, - append_batch_size=True, - dtype='float32', - lod_level=0, - type=core.VarDesc.VarType.LOD_TENSOR, - main_program=None, - startup_program=None, - stop_gradient=True): - """ - Data Layer. - - Args: - name: The name/alias of the function - shape: Tuple declaring the shape. - append_batch_size: Whether or not to append the data as a batch. - dtype: The type of data : float32, float_16, int etc - type: The output type. By default it is LOD_TENSOR. - lod_level(int): The LoD Level. 0 means the input data is not a sequence. - main_program: Name of the main program that calls this - startup_program: Name of the startup program - stop_gradient: A boolean that mentions whether gradient should flow. - - This function takes in input and based on whether data has - to be returned back as a minibatch, it creates the global variable using - the helper functions. The global variables can be accessed by all the - following operations and layers in the graph. - - All the input variables of this function are passed in as local variables - to the LayerHelper constructor. - - """ - helper = LayerHelper('data', **locals()) - shape = list(shape) - for i in xrange(len(shape)): - if shape[i] is None: - shape[i] = -1 - append_batch_size = False - elif shape[i] < 0: - append_batch_size = False - - if append_batch_size: - shape = [-1] + shape # append batch size as -1 - - return helper.create_global_variable( - name=name, - shape=shape, - dtype=dtype, - type=type, - stop_gradient=stop_gradient, - lod_level=lod_level) - - -def create_tensor(dtype, name=None, main_program=None, startup_program=None): - helper = LayerHelper("create_tensor", **locals()) - return helper.create_variable(name=helper.name, dtype=dtype) - - -def cast(x, dtype, main_program=None): - """ - This function takes in the input with input_dtype - and casts it to the output_dtype as the output. - """ - helper = LayerHelper('cast', **locals()) - out = helper.create_tmp_variable(dtype=dtype) - helper.append_op( - type='cast', - inputs={'X': [x]}, - outputs={'Out': [out]}, - attrs={'in_dtype': x.dtype, - 'out_dtype': out.dtype}) - return out - - -def concat(input, axis, main_program=None, startup_program=None): - """ - This function concats the input along the axis mentioned - and returns that as the output. - """ - helper = LayerHelper('concat', **locals()) - out = helper.create_tmp_variable(dtype=helper.input_dtype()) - helper.append_op( - type='concat', - inputs={'X': input}, - outputs={'Out': [out]}, - attrs={'axis': axis}) - return out - - -def sums(input, out=None, main_program=None, startup_program=None): - """ - This function takes in the input and performs the sum operation on it - and returns that as the output. - """ - helper = LayerHelper('sum', **locals()) - if out is None: - out = helper.create_tmp_variable(dtype=helper.input_dtype()) - helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out}) - return out - - -def linear_chain_crf(input, - label, - param_attr=None, - main_program=None, - startup_program=None): - helper = LayerHelper('linear_chain_crf', **locals()) - size = input.shape[1] - transition = helper.create_parameter( - attr=helper.param_attr, - shape=[size + 2, size], - dtype=helper.input_dtype()) - alpha = helper.create_tmp_variable(dtype=helper.input_dtype()) - emission_exps = helper.create_tmp_variable(dtype=helper.input_dtype()) - transition_exps = helper.create_tmp_variable(dtype=helper.input_dtype()) - log_likelihood = helper.create_tmp_variable(dtype=helper.input_dtype()) - helper.append_op( - type='linear_chain_crf', - inputs={"Emission": [input], - "Transition": transition, - "Label": label}, - outputs={ - "Alpha": [alpha], - "EmissionExps": [emission_exps], - "TransitionExps": transition_exps, - "LogLikelihood": log_likelihood - }) - - return log_likelihood - - -def crf_decoding(input, - param_attr, - label=None, - main_program=None, - startup_program=None): - helper = LayerHelper('crf_decoding', **locals()) - transition = helper.get_parameter(param_attr.name) - viterbi_path = helper.create_tmp_variable(dtype=helper.input_dtype()) - helper.append_op( - type='crf_decoding', - inputs={"Emission": [input], - "Transition": transition, - "Label": label}, - outputs={"ViterbiPath": [viterbi_path]}) - - return viterbi_path - - -def assign(input, output, main_program=None, startup_program=None): - helper = LayerHelper('assign', **locals()) - helper.append_op( - type='scale', - inputs={'X': [input]}, - outputs={'Out': [output]}, - attrs={'scale': 1.0}) - return output - def split_lod_tensor(input, mask, @@ -460,404 +54,6 @@ def merge_lod_tensor(in_true, return out -def cos_sim(X, Y, **kwargs): - """ - This function performs the cosine similarity between two tensors - X and Y and returns that as the output. - """ - helper = LayerHelper('cos_sim', **kwargs) - out = helper.create_tmp_variable(dtype=X.dtype) - xnorm = helper.create_tmp_variable(dtype=X.dtype) - ynorm = helper.create_tmp_variable(dtype=X.dtype) - helper.append_op( - type='cos_sim', - inputs={'X': [X], - 'Y': [Y]}, - outputs={'Out': [out], - 'XNorm': [xnorm], - 'YNorm': [ynorm]}) - return out - - -def cross_entropy(input, label, **kwargs): - """ - This function computes cross_entropy using the input and label. - """ - helper = LayerHelper('cross_entropy', **kwargs) - out = helper.create_tmp_variable(dtype=input.dtype) - helper.append_op( - type='cross_entropy', - inputs={'X': [input], - 'Label': [label]}, - outputs={'Y': [out]}, - attrs=kwargs) - return out - - -def square_error_cost(input, label, **kwargs): - """ - This functions returns the squared error cost using the input and label. - The output is appending the op to do the above. - """ - helper = LayerHelper('square_error_cost', **kwargs) - minus_out = helper.create_tmp_variable(dtype=input.dtype) - helper.append_op( - type='elementwise_sub', - inputs={'X': [input], - 'Y': [label]}, - outputs={'Out': [minus_out]}) - - square_out = helper.create_tmp_variable(dtype=input.dtype) - helper.append_op( - type='square', inputs={'X': [minus_out]}, outputs={'Y': [square_out]}) - return square_out - - -def accuracy(input, label, k=1, correct=None, total=None, **kwargs): - """ - This function computes the accuracy using the input and label. - The output is the top_k inputs and their indices. - """ - helper = LayerHelper("accuracy", **kwargs) - topk_out = helper.create_tmp_variable(dtype=input.dtype) - topk_indices = helper.create_tmp_variable(dtype="int64") - helper.append_op( - type="top_k", - inputs={"X": [input]}, - outputs={"Out": [topk_out], - "Indices": [topk_indices]}, - attrs={"k": k}) - acc_out = helper.create_tmp_variable(dtype="float32") - if correct is None: - correct = helper.create_tmp_variable(dtype="int64") - if total is None: - total = helper.create_tmp_variable(dtype="int64") - helper.append_op( - type="accuracy", - inputs={ - "Out": [topk_out], - "Indices": [topk_indices], - "Label": [label] - }, - outputs={ - "Accuracy": [acc_out], - "Correct": [correct], - "Total": [total], - }) - return acc_out - - -def chunk_eval(input, - label, - chunk_scheme, - num_chunk_types, - excluded_chunk_types=None, - **kwargs): - """ - This function computes the accuracy using the input and label. - The output is the top_k inputs and their indices. - """ - helper = LayerHelper("chunk_eval", **kwargs) - - # prepare output - precision = helper.create_tmp_variable(dtype="float32") - recall = helper.create_tmp_variable(dtype="float32") - f1_score = helper.create_tmp_variable(dtype="float32") - - helper.append_op( - type="chunk_eval", - inputs={"Inference": [input], - "Label": [label]}, - outputs={ - "Precision": [precision], - "Recall": [recall], - "F1-Score": [f1_score] - }, - attrs={ - "num_chunk_types": num_chunk_types, - 'chunk_scheme': chunk_scheme, - 'excluded_chunk_types': excluded_chunk_types or [] - }) - return precision, recall, f1_score - - -def sequence_conv(input, - num_filters, - filter_size=3, - filter_stride=1, - padding=None, - bias_attr=None, - param_attr=None, - act=None, - main_program=None, - startup_program=None): - """ - This function creates the op for sequence_conv, using the inputs and - other convolutional configurations for the filters and stride as given - in the input parameters to the function. - """ - - # FIXME(dzh) : want to unify the argument of python layer - # function. So we ignore some unecessary attributes. - # such as, padding_trainable, context_start. - - helper = LayerHelper('sequence_conv', **locals()) - dtype = helper.input_dtype() - filter_shape = [filter_size * input.shape[1], num_filters] - filter_param = helper.create_parameter( - attr=helper.param_attr, shape=filter_shape, dtype=dtype) - pre_bias = helper.create_tmp_variable(dtype) - - helper.append_op( - type='sequence_conv', - inputs={ - 'X': [input], - 'Filter': [filter_param], - }, - outputs={"Out": pre_bias}, - attrs={ - 'contextStride': filter_stride, - 'contextStart': -int(filter_size / 2), - 'contextLength': filter_size - }) - pre_act = helper.append_bias_op(pre_bias) - return helper.append_activation(pre_act) - - -def conv2d(input, - num_filters, - filter_size, - stride=None, - padding=None, - groups=None, - param_attr=None, - bias_attr=None, - act=None, - name=None, - main_program=None, - startup_program=None): - """ - This function creates the op for a 2-dimensional Convolution. - This is performed using the parameters of filters(size, dimensionality etc) - , stride and other configurations for a Convolution operation. - This funciton can also append an activation on top of the - conv-2d output, if mentioned in the input parameters. - """ - - if stride is None: - stride = [1, 1] - helper = LayerHelper('conv2d', **locals()) - dtype = helper.input_dtype() - - num_channels = input.shape[1] - if groups is None: - num_filter_channels = num_channels - else: - if num_channels % groups != 0: - raise ValueError("num_channels must be divisible by groups.") - num_filter_channels = num_channels / groups - - if isinstance(filter_size, int): - filter_size = [filter_size, filter_size] - if isinstance(stride, int): - stride = [stride, stride] - if isinstance(padding, int): - padding = [padding, padding] - - input_shape = input.shape - filter_shape = [num_filters, num_filter_channels] + filter_size - - def _get_default_param_initializer(): - std = (2.0 / (filter_size[0]**2 * num_channels))**0.5 - return Normal(0.0, std, 0) - - filter_param = helper.create_parameter( - attr=helper.param_attr, - shape=filter_shape, - dtype=dtype, - default_initializer=_get_default_param_initializer()) - - pre_bias = helper.create_tmp_variable(dtype) - - helper.append_op( - type='conv2d_cudnn', - inputs={ - 'Input': input, - 'Filter': filter_param, - }, - outputs={"Output": pre_bias}, - attrs={'strides': stride, - 'paddings': padding, - 'groups': groups}) - - pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2) - - return helper.append_activation(pre_act) - - -def sequence_pool(input, pool_type, **kwargs): - """ - This function add the operator for sequence pooling. - This is applied on top of the input using pool_type mentioned - in the parameters. - """ - helper = LayerHelper('sequence_pool', input=input, **kwargs) - dtype = helper.input_dtype() - pool_out = helper.create_tmp_variable(dtype) - max_index = helper.create_tmp_variable(dtype) - - helper.append_op( - type="sequence_pool", - inputs={"X": input}, - outputs={"Out": pool_out, - "MaxIndex": max_index}, - attrs={"pooltype": pool_type.upper()}) - - return pool_out - - -def pool2d(input, - pool_size, - pool_type, - pool_stride=None, - pool_padding=None, - global_pooling=False, - main_program=None, - startup_program=None): - """ - This function adds the operator for pooling in 2 dimensions, using the - pooling configurations mentioned in input parameters. - """ - if pool_padding is None: - pool_padding = [0, 0] - if pool_stride is None: - pool_stride = [1, 1] - if pool_type not in ["max", "avg"]: - raise ValueError( - "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.", - str(pool_type)) - if isinstance(pool_size, int): - pool_size = [pool_size, pool_size] - if isinstance(pool_stride, int): - pool_stride = [pool_stride, pool_stride] - if isinstance(pool_padding, int): - pool_padding = [pool_padding, pool_padding] - - helper = LayerHelper('pool2d', **locals()) - dtype = helper.input_dtype() - pool_out = helper.create_tmp_variable(dtype) - - helper.append_op( - type="pool2d", - inputs={"X": input}, - outputs={"Out": pool_out}, - attrs={ - "pooling_type": pool_type, - "ksize": pool_size, - "global_pooling": global_pooling, - "strides": pool_stride, - "paddings": pool_padding - }) - - return pool_out - - -def batch_norm(input, - act=None, - is_test=False, - momentum=0.9, - epsilon=1e-05, - param_attr=None, - bias_attr=None, - data_layout='NCHW', - main_program=None, - startup_program=None): - """ - This function helps create an operator to implement - the BatchNorm layer using the configurations from the input parameters. - """ - helper = LayerHelper('batch_norm', **locals()) - dtype = helper.input_dtype() - - input_shape = input.shape - if data_layout == 'NCHW': - channel_num = input_shape[1] - else: - if data_layout == 'NHWC': - channel_num = input_shape[-1] - else: - raise ValueError("unsupported data layout:" + data_layout) - - param_shape = [channel_num] - - # create parameter - scale = helper.create_parameter( - attr=helper.param_attr, - shape=param_shape, - dtype=dtype, - default_initializer=Constant(1.0)) - - bias = helper.create_parameter( - attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=True) - - mean = helper.create_global_variable( - dtype=input.dtype, shape=param_shape, persistable=True) - helper.set_variable_initializer(var=mean, initializer=Constant(0.0)) - - variance = helper.create_global_variable( - dtype=input.dtype, shape=param_shape, persistable=True) - helper.set_variable_initializer(var=variance, initializer=Constant(1.0)) - - # create output - # mean and mean_out share the same memory - mean_out = mean - # variance and variance out share the same memory - variance_out = variance - saved_mean = helper.create_tmp_variable(dtype) - saved_variance = helper.create_tmp_variable(dtype) - - batch_norm_out = helper.create_tmp_variable(dtype) - - helper.append_op( - type="batch_norm", - inputs={ - "X": input, - "Scale": scale, - "Bias": bias, - "Mean": mean, - "Variance": variance - }, - outputs={ - "Y": batch_norm_out, - "MeanOut": mean_out, - "VarianceOut": variance_out, - "SavedMean": saved_mean, - "SavedVariance": saved_variance - }, - attrs={"momentum": momentum, - "epsilon": epsilon, - "is_test": is_test}) - - return helper.append_activation(batch_norm_out) - - -def beam_search_decode(ids, scores, main_program=None, startup_program=None): - helper = LayerHelper('beam_search_decode', **locals()) - sentence_ids = helper.create_tmp_variable(dtype=ids.dtype) - sentence_scores = helper.create_tmp_variable(dtype=ids.dtype) - - helper.append_op( - type="beam_search_decode", - inputs={"Ids": ids, - "Scores": scores}, - outputs={ - "SentenceIds": sentence_ids, - "SentenceScores": sentence_scores - }) - - return sentence_ids, sentence_scores - - class BlockGuard(object): """ BlockGuard class. @@ -1210,50 +406,6 @@ class While(object): attrs={'sub_block': while_block}) -def lstm(x, - c_pre_init, - hidden_dim, - forget_bias=None, - main_program=None, - startup_program=None): - """ - This function helps create an operator for the LSTM (Long Short Term - Memory) cell that can be used inside an RNN. - """ - helper = LayerHelper('lstm_unit', **locals()) - rnn = StaticRNN() - with rnn.step(): - c_pre = rnn.memory(init=c_pre_init) - x_t = rnn.step_input(x) - - before_fc = concat( - input=[x_t, c_pre], - axis=1, - main_program=main_program, - startup_program=startup_program) - after_fc = fc(input=before_fc, - size=hidden_dim * 4, - main_program=main_program, - startup_program=startup_program) - - dtype = x.dtype - c = helper.create_tmp_variable(dtype) - h = helper.create_tmp_variable(dtype) - - helper.append_op( - type='lstm_unit', - inputs={"X": after_fc, - "C_prev": c_pre}, - outputs={"C": c, - "H": h}, - attrs={"forget_bias": forget_bias}) - - rnn.update_memory(c_pre, c) - rnn.output(h) - - return rnn() - - def lod_rank_table(x, level=0, main_program=None): """ This function creates an operator for creating a LOD_RANK_TABLE @@ -1331,72 +483,6 @@ def array_to_lod_tensor(x, table, main_program=None, startup_program=None): return tmp -def fill_constant(shape, - dtype, - value, - out=None, - main_program=None, - startup_program=None): - """ - This function creates a tensor , with shape as mentioned in the input and - specified dtype and fills this up with a constant value that - comes in the input. It also sets the stop_gradient to be True. - """ - helper = LayerHelper("fill_constant", **locals()) - if out is None: - out = helper.create_tmp_variable(dtype=dtype) - helper.append_op( - type='fill_constant', - inputs={}, - outputs={'Out': [out]}, - attrs={'shape': shape, - 'dtype': out.dtype, - 'value': float(value)}) - out.stop_gradient = True - return out - - -def fill_constant_batch_size_like(input, - shape, - dtype, - value, - input_dim_idx=0, - output_dim_idx=0, - main_program=None, - startup_program=None): - helper = LayerHelper("fill_constant_batch_size_like", **locals()) - out = helper.create_tmp_variable(dtype=dtype) - helper.append_op( - type='fill_constant_batch_size_like', - inputs={'Input': input}, - outputs={'Out': [out]}, - attrs={ - 'shape': shape, - 'dtype': out.dtype, - 'value': float(value), - 'input_dim_idx': input_dim_idx, - 'output_dim_idx': output_dim_idx - }) - out.stop_gradient = True - return out - - -def ones(shape, dtype, main_program=None): - """ - This function performs the same function as fill_constant() declared above - with the constant value being 1.0. - """ - return fill_constant(value=1.0, **locals()) - - -def zeros(shape, dtype, main_program=None): - """ - This function performs the same function as fill_constant() declared above - with the constant value being 0.0. - """ - return fill_constant(value=0.0, **locals()) - - def increment(x, value=1.0, in_place=True, @@ -1508,95 +594,6 @@ def array_length(array, main_program=None): return tmp -def conv2d_transpose(input, - num_filters, - output_size=None, - filter_size=None, - padding=None, - stride=None, - param_attr=None, - main_program=None, - startup_program=None): - """ - The transpose of conv2d layer. - - This layer is also known as deconvolution layer. - - Args: - input(Variable): The input image with [N, C, H, W] format. - num_filters(int): The number of filter. It is as same as the output - image channel. - output_size(int|tuple|None): The output image size. If output size is a - tuple, it must contain two integers, (image_H, image_W). This - parameter only works when filter_size is None. - filter_size(int|tuple|None): The filter size. If filter_size is a tuple, - it must contain two integers, (filter_size_H, filter_size_W). - Otherwise, the filter will be a square. None if use output size to - calculate filter_size - padding(int|tuple): The padding size. If padding is a tuple, it must - contain two integers, (padding_H, padding_W). Otherwise, the - padding_H = padding_W = padding. - stride(int|tuple): The stride size. If stride is a tuple, it must - contain two integers, (stride_H, stride_W). Otherwise, the - stride_H = stride_W = stride. - param_attr: Parameter Attribute. - main_program(Program): the main program - startup_program(Program): the startup program - - Returns: - Variable: Output image. - """ - helper = LayerHelper("conv2d_transpose", **locals()) - if not isinstance(input, Variable): - raise TypeError("Input of conv2d_transpose must be Variable") - input_channel = input.shape[1] - - op_attr = dict() - - if isinstance(padding, int): - op_attr['paddings'] = [padding, padding] - elif padding is not None: - op_attr['paddings'] = padding - - if isinstance(stride, int): - op_attr['strides'] = stride - elif stride is not None: - op_attr['strides'] = stride - - if filter_size is None: - if output_size is None: - raise ValueError("output_size must be set when filter_size is None") - if isinstance(output_size, int): - output_size = [output_size, output_size] - - padding = op_attr.get('paddings', [0, 0]) - stride = op_attr.get('strides', [1, 1]) - - h_in = input.shape[2] - w_in = input.shape[3] - filter_size_h = output_size[0] - \ - (h_in - 1) * stride[0] + 2 * padding[0] - filter_size_w = output_size[1] - \ - (w_in - 1) * stride[1] + 2 * padding[1] - filter_size = [filter_size_h, filter_size_w] - elif isinstance(filter_size, int): - filter_size = [filter_size, filter_size] - - filter_shape = [input_channel, num_filters] + filter_size - img_filter = helper.create_parameter( - dtype=input.dtype, shape=filter_shape, attr=helper.param_attr) - - out = helper.create_tmp_variable(dtype=input.dtype) - helper.append_op( - type='conv2d_transpose', - inputs={'Input': [input], - 'Filter': [img_filter]}, - outputs={'Output': out}, - attrs=op_attr) - - return out - - class ConditionalBlockGuard(BlockGuard): def __init__(self, block): if not isinstance(block, ConditionalBlock): diff --git a/python/paddle/v2/fluid/layers/io.py b/python/paddle/v2/fluid/layers/io.py new file mode 100644 index 0000000000..f03d8e3c3e --- /dev/null +++ b/python/paddle/v2/fluid/layers/io.py @@ -0,0 +1,57 @@ +from .. import core +from ..layer_helper import LayerHelper + +__all__ = ['data'] + + +def data(name, + shape, + append_batch_size=True, + dtype='float32', + lod_level=0, + type=core.VarDesc.VarType.LOD_TENSOR, + main_program=None, + startup_program=None, + stop_gradient=True): + """ + Data Layer. + + Args: + name: The name/alias of the function + shape: Tuple declaring the shape. + append_batch_size: Whether or not to append the data as a batch. + dtype: The type of data : float32, float_16, int etc + type: The output type. By default it is LOD_TENSOR. + lod_level(int): The LoD Level. 0 means the input data is not a sequence. + main_program: Name of the main program that calls this + startup_program: Name of the startup program + stop_gradient: A boolean that mentions whether gradient should flow. + + This function takes in input and based on whether data has + to be returned back as a minibatch, it creates the global variable using + the helper functions. The global variables can be accessed by all the + following operations and layers in the graph. + + All the input variables of this function are passed in as local variables + to the LayerHelper constructor. + + """ + helper = LayerHelper('data', **locals()) + shape = list(shape) + for i in xrange(len(shape)): + if shape[i] is None: + shape[i] = -1 + append_batch_size = False + elif shape[i] < 0: + append_batch_size = False + + if append_batch_size: + shape = [-1] + shape # append batch size as -1 + + return helper.create_global_variable( + name=name, + shape=shape, + dtype=dtype, + type=type, + stop_gradient=stop_gradient, + lod_level=lod_level) diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py new file mode 100644 index 0000000000..f231f38b3e --- /dev/null +++ b/python/paddle/v2/fluid/layers/nn.py @@ -0,0 +1,785 @@ +""" +All layers just related to the neural network. +""" + +from ..layer_helper import LayerHelper +from ..initializer import Normal, Constant +from ..framework import Variable + +__all__ = [ + 'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf', + 'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy', + 'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d', + 'batch_norm', 'beam_search_decode', 'conv2d_transpose' +] + + +def fc(input, + size, + num_flatten_dims=1, + param_attr=None, + bias_attr=None, + act=None, + name=None, + main_program=None, + startup_program=None): + """ + Fully Connected Layer. + + Args: + input: The input tensor to the function + size: The size of the layer + num_flatten_dims: Number of columns in input + param_attr: The parameters/weights to the FC Layer + param_initializer: Initializer used for the weight/parameter. If None, XavierInitializer() is used + bias_attr: The bias parameter for the FC layer + bias_initializer: Initializer used for the bias. If None, then ConstantInitializer() is used + act: Activation to be applied to the output of FC layer + name: Name/alias of the function + main_program: Name of the main program that calls this + startup_program: Name of the startup program + + This function can take in multiple inputs and performs the Fully Connected + function (linear transformation) on top of each of them. + So for input x, the output will be : Wx + b. Where W is the parameter, + b the bias and x is the input. + + The function also applies an activation (non-linearity) on top of the + output, if activation is passed in the input. + + All the input variables of this function are passed in as local variables + to the LayerHelper constructor. + + """ + helper = LayerHelper('fc', **locals()) + + dtype = helper.input_dtype() + + mul_results = [] + for input_var, param_attr in helper.iter_inputs_and_params(): + input_shape = input_var.shape + param_shape = [ + reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1) + ] + [size] + w = helper.create_parameter( + attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False) + tmp = helper.create_tmp_variable(dtype) + helper.append_op( + type="mul", + inputs={ + "X": input_var, + "Y": w, + }, + outputs={"Out": tmp}, + attrs={'x_num_col_dims': num_flatten_dims, + 'y_num_col_dims': 1}) + mul_results.append(tmp) + + # sum + if len(mul_results) == 1: + pre_bias = mul_results[0] + else: + pre_bias = helper.create_tmp_variable(dtype) + helper.append_op( + type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias}) + # add bias + pre_activation = helper.append_bias_op(pre_bias) + # add activation + return helper.append_activation(pre_activation) + + +def embedding(input, + size, + is_sparse=False, + param_attr=None, + dtype='float32', + main_program=None, + startup_program=None): + """ + Embedding Layer. + + Args: + param_initializer: + input: The input to the function + size: The size of the layer + is_sparse: A flag that decleares whether the input is sparse + param_attr: Parameters for this layer + dtype: The type of data : float32, float_16, int etc + main_program: Name of the main program that calls this + startup_program: Name of the startup program + + This function can take in the input (which is a vector of IDs) and + performs a lookup in the lookup_table using these IDs, to result into + the embedding of each ID in the input. + + All the input variables of this function are passed in as local variables + to the LayerHelper constructor. + + """ + + helper = LayerHelper('embedding', **locals()) + w = helper.create_parameter( + attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False) + tmp = helper.create_tmp_variable(dtype) + helper.append_op( + type='lookup_table', + inputs={'Ids': input, + 'W': w}, + outputs={'Out': tmp}, + attrs={'is_sparse': is_sparse}) + return tmp + + +# TODO(qijun): expose H0 and C0 +def dynamic_lstm(input, + size, + param_attr=None, + bias_attr=None, + use_peepholes=True, + is_reverse=False, + gate_activation='sigmoid', + cell_activation='tanh', + candidate_activation='tanh', + dtype='float32', + main_program=None, + startup_program=None): + helper = LayerHelper('lstm', **locals()) + size = size / 4 + weight = helper.create_parameter( + attr=helper.param_attr, shape=[size, 4 * size], dtype=dtype) + bias_size = [1, 7 * size] + if not use_peepholes: + bias_size[1] = 4 * size + bias = helper.create_parameter( + attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True) + + hidden = helper.create_tmp_variable(dtype) + cell = helper.create_tmp_variable(dtype) + batch_gate = helper.create_tmp_variable(dtype) + batch_cell_pre_act = helper.create_tmp_variable(dtype) + + helper.append_op( + type='lstm', + inputs={'Input': input, + 'Weight': weight, + 'Bias': bias}, + outputs={ + 'Hidden': hidden, + 'Cell': cell, + 'BatchGate': batch_gate, + 'BatchCellPreAct': batch_cell_pre_act + }, + attrs={ + 'use_peepholes': use_peepholes, + 'is_reverse': is_reverse, + 'gate_activation': gate_activation, + 'cell_activation': cell_activation, + 'candidate_activation': candidate_activation + }) + return hidden, cell + + +def gru_unit(input, + hidden, + size, + weight=None, + bias=None, + activation='tanh', + gate_activation='sigmoid', + main_program=None, + startup_program=None): + """ + GRUUnit Operator implements partial calculations of the GRU unit as following: + + $$ + update \ gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\ + reset \ gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r) \\ + output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\ + output: h_t = dot((1 - u_t), h_{t-1}) + dot(u_t, {h}_t) + $$ + + which is same as one time step of GRU Operator. + + @note To implement the complete GRU unit, fully-connected operator must be + used before to feed xu, xr and xc as the Input of GRUUnit operator. + + TODO(ChunweiYan) add more document here + """ + activation_dict = dict( + identity=0, + sigmoid=1, + tanh=2, + relu=3, ) + activation = activation_dict[activation] + gate_activation = activation_dict[gate_activation] + + helper = LayerHelper('gru_unit', **locals()) + dtype = helper.input_dtype() + size = size / 3 + + # create weight + if weight is None: + weight = helper.create_parameter( + attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype) + + # create bias + if bias is None: + bias_size = [1, 3 * size] + bias = helper.create_parameter( + attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True) + + gate = helper.create_tmp_variable(dtype) + reset_hidden_pre = helper.create_tmp_variable(dtype) + updated_hidden = helper.create_tmp_variable(dtype) + + helper.append_op( + type='gru_unit', + inputs={'Input': input, + 'HiddenPrev': hidden, + 'Weight': weight}, + outputs={ + 'Gate': gate, + 'ResetHiddenPrev': reset_hidden_pre, + 'Hidden': updated_hidden, + }, + attrs={ + 'activation': 0, + 'gate_activation': 1, + }) + + return updated_hidden, reset_hidden_pre, gate + + +def linear_chain_crf(input, + label, + param_attr=None, + main_program=None, + startup_program=None): + helper = LayerHelper('linear_chain_crf', **locals()) + size = input.shape[1] + transition = helper.create_parameter( + attr=helper.param_attr, + shape=[size + 2, size], + dtype=helper.input_dtype()) + alpha = helper.create_tmp_variable(dtype=helper.input_dtype()) + emission_exps = helper.create_tmp_variable(dtype=helper.input_dtype()) + transition_exps = helper.create_tmp_variable(dtype=helper.input_dtype()) + log_likelihood = helper.create_tmp_variable(dtype=helper.input_dtype()) + helper.append_op( + type='linear_chain_crf', + inputs={"Emission": [input], + "Transition": transition, + "Label": label}, + outputs={ + "Alpha": [alpha], + "EmissionExps": [emission_exps], + "TransitionExps": transition_exps, + "LogLikelihood": log_likelihood + }) + + return log_likelihood + + +def crf_decoding(input, + param_attr, + label=None, + main_program=None, + startup_program=None): + helper = LayerHelper('crf_decoding', **locals()) + transition = helper.get_parameter(param_attr.name) + viterbi_path = helper.create_tmp_variable(dtype=helper.input_dtype()) + helper.append_op( + type='crf_decoding', + inputs={"Emission": [input], + "Transition": transition, + "Label": label}, + outputs={"ViterbiPath": [viterbi_path]}) + + return viterbi_path + + +def cos_sim(X, Y, **kwargs): + """ + This function performs the cosine similarity between two tensors + X and Y and returns that as the output. + """ + helper = LayerHelper('cos_sim', **kwargs) + out = helper.create_tmp_variable(dtype=X.dtype) + xnorm = helper.create_tmp_variable(dtype=X.dtype) + ynorm = helper.create_tmp_variable(dtype=X.dtype) + helper.append_op( + type='cos_sim', + inputs={'X': [X], + 'Y': [Y]}, + outputs={'Out': [out], + 'XNorm': [xnorm], + 'YNorm': [ynorm]}) + return out + + +def cross_entropy(input, label, **kwargs): + """ + This function computes cross_entropy using the input and label. + """ + helper = LayerHelper('cross_entropy', **kwargs) + out = helper.create_tmp_variable(dtype=input.dtype) + helper.append_op( + type='cross_entropy', + inputs={'X': [input], + 'Label': [label]}, + outputs={'Y': [out]}, + attrs=kwargs) + return out + + +def square_error_cost(input, label, **kwargs): + """ + This functions returns the squared error cost using the input and label. + The output is appending the op to do the above. + """ + helper = LayerHelper('square_error_cost', **kwargs) + minus_out = helper.create_tmp_variable(dtype=input.dtype) + helper.append_op( + type='elementwise_sub', + inputs={'X': [input], + 'Y': [label]}, + outputs={'Out': [minus_out]}) + + square_out = helper.create_tmp_variable(dtype=input.dtype) + helper.append_op( + type='square', inputs={'X': [minus_out]}, outputs={'Y': [square_out]}) + return square_out + + +def accuracy(input, label, k=1, correct=None, total=None, **kwargs): + """ + This function computes the accuracy using the input and label. + The output is the top_k inputs and their indices. + """ + helper = LayerHelper("accuracy", **kwargs) + topk_out = helper.create_tmp_variable(dtype=input.dtype) + topk_indices = helper.create_tmp_variable(dtype="int64") + helper.append_op( + type="top_k", + inputs={"X": [input]}, + outputs={"Out": [topk_out], + "Indices": [topk_indices]}, + attrs={"k": k}) + acc_out = helper.create_tmp_variable(dtype="float32") + if correct is None: + correct = helper.create_tmp_variable(dtype="int64") + if total is None: + total = helper.create_tmp_variable(dtype="int64") + helper.append_op( + type="accuracy", + inputs={ + "Out": [topk_out], + "Indices": [topk_indices], + "Label": [label] + }, + outputs={ + "Accuracy": [acc_out], + "Correct": [correct], + "Total": [total], + }) + return acc_out + + +def chunk_eval(input, + label, + chunk_scheme, + num_chunk_types, + excluded_chunk_types=None, + **kwargs): + """ + This function computes the accuracy using the input and label. + The output is the top_k inputs and their indices. + """ + helper = LayerHelper("chunk_eval", **kwargs) + + # prepare output + precision = helper.create_tmp_variable(dtype="float32") + recall = helper.create_tmp_variable(dtype="float32") + f1_score = helper.create_tmp_variable(dtype="float32") + + helper.append_op( + type="chunk_eval", + inputs={"Inference": [input], + "Label": [label]}, + outputs={ + "Precision": [precision], + "Recall": [recall], + "F1-Score": [f1_score] + }, + attrs={ + "num_chunk_types": num_chunk_types, + 'chunk_scheme': chunk_scheme, + 'excluded_chunk_types': excluded_chunk_types or [] + }) + return precision, recall, f1_score + + +def sequence_conv(input, + num_filters, + filter_size=3, + filter_stride=1, + padding=None, + bias_attr=None, + param_attr=None, + act=None, + main_program=None, + startup_program=None): + """ + This function creates the op for sequence_conv, using the inputs and + other convolutional configurations for the filters and stride as given + in the input parameters to the function. + """ + + # FIXME(dzh) : want to unify the argument of python layer + # function. So we ignore some unecessary attributes. + # such as, padding_trainable, context_start. + + helper = LayerHelper('sequence_conv', **locals()) + dtype = helper.input_dtype() + filter_shape = [filter_size * input.shape[1], num_filters] + filter_param = helper.create_parameter( + attr=helper.param_attr, shape=filter_shape, dtype=dtype) + pre_bias = helper.create_tmp_variable(dtype) + + helper.append_op( + type='sequence_conv', + inputs={ + 'X': [input], + 'Filter': [filter_param], + }, + outputs={"Out": pre_bias}, + attrs={ + 'contextStride': filter_stride, + 'contextStart': -int(filter_size / 2), + 'contextLength': filter_size + }) + pre_act = helper.append_bias_op(pre_bias) + return helper.append_activation(pre_act) + + +def conv2d(input, + num_filters, + filter_size, + stride=None, + padding=None, + groups=None, + param_attr=None, + bias_attr=None, + act=None, + name=None, + main_program=None, + startup_program=None): + """ + This function creates the op for a 2-dimensional Convolution. + This is performed using the parameters of filters(size, dimensionality etc) + , stride and other configurations for a Convolution operation. + This funciton can also append an activation on top of the + conv-2d output, if mentioned in the input parameters. + """ + + if stride is None: + stride = [1, 1] + helper = LayerHelper('conv2d', **locals()) + dtype = helper.input_dtype() + + num_channels = input.shape[1] + if groups is None: + num_filter_channels = num_channels + else: + if num_channels % groups != 0: + raise ValueError("num_channels must be divisible by groups.") + num_filter_channels = num_channels / groups + + if isinstance(filter_size, int): + filter_size = [filter_size, filter_size] + if isinstance(stride, int): + stride = [stride, stride] + if isinstance(padding, int): + padding = [padding, padding] + + input_shape = input.shape + filter_shape = [num_filters, num_filter_channels] + filter_size + + def _get_default_param_initializer(): + std = (2.0 / (filter_size[0]**2 * num_channels))**0.5 + return Normal(0.0, std, 0) + + filter_param = helper.create_parameter( + attr=helper.param_attr, + shape=filter_shape, + dtype=dtype, + default_initializer=_get_default_param_initializer()) + + pre_bias = helper.create_tmp_variable(dtype) + + helper.append_op( + type='conv2d_cudnn', + inputs={ + 'Input': input, + 'Filter': filter_param, + }, + outputs={"Output": pre_bias}, + attrs={'strides': stride, + 'paddings': padding, + 'groups': groups}) + + pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2) + + return helper.append_activation(pre_act) + + +def sequence_pool(input, pool_type, **kwargs): + """ + This function add the operator for sequence pooling. + This is applied on top of the input using pool_type mentioned + in the parameters. + """ + helper = LayerHelper('sequence_pool', input=input, **kwargs) + dtype = helper.input_dtype() + pool_out = helper.create_tmp_variable(dtype) + max_index = helper.create_tmp_variable(dtype) + + helper.append_op( + type="sequence_pool", + inputs={"X": input}, + outputs={"Out": pool_out, + "MaxIndex": max_index}, + attrs={"pooltype": pool_type.upper()}) + + return pool_out + + +def pool2d(input, + pool_size, + pool_type, + pool_stride=None, + pool_padding=None, + global_pooling=False, + main_program=None, + startup_program=None): + """ + This function adds the operator for pooling in 2 dimensions, using the + pooling configurations mentioned in input parameters. + """ + if pool_padding is None: + pool_padding = [0, 0] + if pool_stride is None: + pool_stride = [1, 1] + if pool_type not in ["max", "avg"]: + raise ValueError( + "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.", + str(pool_type)) + if isinstance(pool_size, int): + pool_size = [pool_size, pool_size] + if isinstance(pool_stride, int): + pool_stride = [pool_stride, pool_stride] + if isinstance(pool_padding, int): + pool_padding = [pool_padding, pool_padding] + + helper = LayerHelper('pool2d', **locals()) + dtype = helper.input_dtype() + pool_out = helper.create_tmp_variable(dtype) + + helper.append_op( + type="pool2d", + inputs={"X": input}, + outputs={"Out": pool_out}, + attrs={ + "pooling_type": pool_type, + "ksize": pool_size, + "global_pooling": global_pooling, + "strides": pool_stride, + "paddings": pool_padding + }) + + return pool_out + + +def batch_norm(input, + act=None, + is_test=False, + momentum=0.9, + epsilon=1e-05, + param_attr=None, + bias_attr=None, + data_layout='NCHW', + main_program=None, + startup_program=None): + """ + This function helps create an operator to implement + the BatchNorm layer using the configurations from the input parameters. + """ + helper = LayerHelper('batch_norm', **locals()) + dtype = helper.input_dtype() + + input_shape = input.shape + if data_layout == 'NCHW': + channel_num = input_shape[1] + else: + if data_layout == 'NHWC': + channel_num = input_shape[-1] + else: + raise ValueError("unsupported data layout:" + data_layout) + + param_shape = [channel_num] + + # create parameter + scale = helper.create_parameter( + attr=helper.param_attr, + shape=param_shape, + dtype=dtype, + default_initializer=Constant(1.0)) + + bias = helper.create_parameter( + attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=True) + + mean = helper.create_global_variable( + dtype=input.dtype, shape=param_shape, persistable=True) + helper.set_variable_initializer(var=mean, initializer=Constant(0.0)) + + variance = helper.create_global_variable( + dtype=input.dtype, shape=param_shape, persistable=True) + helper.set_variable_initializer(var=variance, initializer=Constant(1.0)) + + # create output + # mean and mean_out share the same memory + mean_out = mean + # variance and variance out share the same memory + variance_out = variance + saved_mean = helper.create_tmp_variable(dtype) + saved_variance = helper.create_tmp_variable(dtype) + + batch_norm_out = helper.create_tmp_variable(dtype) + + helper.append_op( + type="batch_norm", + inputs={ + "X": input, + "Scale": scale, + "Bias": bias, + "Mean": mean, + "Variance": variance + }, + outputs={ + "Y": batch_norm_out, + "MeanOut": mean_out, + "VarianceOut": variance_out, + "SavedMean": saved_mean, + "SavedVariance": saved_variance + }, + attrs={"momentum": momentum, + "epsilon": epsilon, + "is_test": is_test}) + + return helper.append_activation(batch_norm_out) + + +def beam_search_decode(ids, scores, main_program=None, startup_program=None): + helper = LayerHelper('beam_search_decode', **locals()) + sentence_ids = helper.create_tmp_variable(dtype=ids.dtype) + sentence_scores = helper.create_tmp_variable(dtype=ids.dtype) + + helper.append_op( + type="beam_search_decode", + inputs={"Ids": ids, + "Scores": scores}, + outputs={ + "SentenceIds": sentence_ids, + "SentenceScores": sentence_scores + }) + + return sentence_ids, sentence_scores + + +def conv2d_transpose(input, + num_filters, + output_size=None, + filter_size=None, + padding=None, + stride=None, + param_attr=None, + main_program=None, + startup_program=None): + """ + The transpose of conv2d layer. + + This layer is also known as deconvolution layer. + + Args: + input(Variable): The input image with [N, C, H, W] format. + num_filters(int): The number of filter. It is as same as the output + image channel. + output_size(int|tuple|None): The output image size. If output size is a + tuple, it must contain two integers, (image_H, image_W). This + parameter only works when filter_size is None. + filter_size(int|tuple|None): The filter size. If filter_size is a tuple, + it must contain two integers, (filter_size_H, filter_size_W). + Otherwise, the filter will be a square. None if use output size to + calculate filter_size + padding(int|tuple): The padding size. If padding is a tuple, it must + contain two integers, (padding_H, padding_W). Otherwise, the + padding_H = padding_W = padding. + stride(int|tuple): The stride size. If stride is a tuple, it must + contain two integers, (stride_H, stride_W). Otherwise, the + stride_H = stride_W = stride. + param_attr: Parameter Attribute. + main_program(Program): the main program + startup_program(Program): the startup program + + Returns: + Variable: Output image. + """ + helper = LayerHelper("conv2d_transpose", **locals()) + if not isinstance(input, Variable): + raise TypeError("Input of conv2d_transpose must be Variable") + input_channel = input.shape[1] + + op_attr = dict() + + if isinstance(padding, int): + op_attr['paddings'] = [padding, padding] + elif padding is not None: + op_attr['paddings'] = padding + + if isinstance(stride, int): + op_attr['strides'] = stride + elif stride is not None: + op_attr['strides'] = stride + + if filter_size is None: + if output_size is None: + raise ValueError("output_size must be set when filter_size is None") + if isinstance(output_size, int): + output_size = [output_size, output_size] + + padding = op_attr.get('paddings', [0, 0]) + stride = op_attr.get('strides', [1, 1]) + + h_in = input.shape[2] + w_in = input.shape[3] + filter_size_h = output_size[0] - \ + (h_in - 1) * stride[0] + 2 * padding[0] + filter_size_w = output_size[1] - \ + (w_in - 1) * stride[1] + 2 * padding[1] + filter_size = [filter_size_h, filter_size_w] + elif isinstance(filter_size, int): + filter_size = [filter_size, filter_size] + + filter_shape = [input_channel, num_filters] + filter_size + img_filter = helper.create_parameter( + dtype=input.dtype, shape=filter_shape, attr=helper.param_attr) + + out = helper.create_tmp_variable(dtype=input.dtype) + helper.append_op( + type='conv2d_transpose', + inputs={'Input': [input], + 'Filter': [img_filter]}, + outputs={'Output': out}, + attrs=op_attr) + + return out diff --git a/python/paddle/v2/fluid/layers/ops.py b/python/paddle/v2/fluid/layers/ops.py new file mode 100644 index 0000000000..fa312ace60 --- /dev/null +++ b/python/paddle/v2/fluid/layers/ops.py @@ -0,0 +1,9 @@ +from ..registry import register_layer +__all__ = [ + 'mean', 'mul', 'dropout', 'reshape', 'sigmoid', 'scale', 'transpose', + 'sigmoid_cross_entropy_with_logits', 'elementwise_add', 'elementwise_div', + 'elementwise_sub', 'elementwise_mul', 'clip', 'abs' +] + +for _OP in set(__all__): + globals()[_OP] = register_layer(_OP) diff --git a/python/paddle/v2/fluid/layers/tensor.py b/python/paddle/v2/fluid/layers/tensor.py new file mode 100644 index 0000000000..a839ed897d --- /dev/null +++ b/python/paddle/v2/fluid/layers/tensor.py @@ -0,0 +1,130 @@ +from ..layer_helper import LayerHelper + +__all__ = [ + 'create_tensor', 'cast', 'concat', 'sums', 'assign', + 'fill_constant_batch_size_like', 'fill_constant', 'ones', 'zeros' +] + + +def create_tensor(dtype, name=None, main_program=None, startup_program=None): + helper = LayerHelper("create_tensor", **locals()) + return helper.create_variable(name=helper.name, dtype=dtype) + + +def cast(x, dtype, main_program=None): + """ + This function takes in the input with input_dtype + and casts it to the output_dtype as the output. + """ + helper = LayerHelper('cast', **locals()) + out = helper.create_tmp_variable(dtype=dtype) + helper.append_op( + type='cast', + inputs={'X': [x]}, + outputs={'Out': [out]}, + attrs={'in_dtype': x.dtype, + 'out_dtype': out.dtype}) + return out + + +def concat(input, axis, main_program=None, startup_program=None): + """ + This function concats the input along the axis mentioned + and returns that as the output. + """ + helper = LayerHelper('concat', **locals()) + out = helper.create_tmp_variable(dtype=helper.input_dtype()) + helper.append_op( + type='concat', + inputs={'X': input}, + outputs={'Out': [out]}, + attrs={'axis': axis}) + return out + + +def sums(input, out=None, main_program=None, startup_program=None): + """ + This function takes in the input and performs the sum operation on it + and returns that as the output. + """ + helper = LayerHelper('sum', **locals()) + if out is None: + out = helper.create_tmp_variable(dtype=helper.input_dtype()) + helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out}) + return out + + +def assign(input, output, main_program=None, startup_program=None): + helper = LayerHelper('assign', **locals()) + helper.append_op( + type='scale', + inputs={'X': [input]}, + outputs={'Out': [output]}, + attrs={'scale': 1.0}) + return output + + +def fill_constant(shape, + dtype, + value, + out=None, + main_program=None, + startup_program=None): + """ + This function creates a tensor , with shape as mentioned in the input and + specified dtype and fills this up with a constant value that + comes in the input. It also sets the stop_gradient to be True. + """ + helper = LayerHelper("fill_constant", **locals()) + if out is None: + out = helper.create_tmp_variable(dtype=dtype) + helper.append_op( + type='fill_constant', + inputs={}, + outputs={'Out': [out]}, + attrs={'shape': shape, + 'dtype': out.dtype, + 'value': float(value)}) + out.stop_gradient = True + return out + + +def fill_constant_batch_size_like(input, + shape, + dtype, + value, + input_dim_idx=0, + output_dim_idx=0, + main_program=None, + startup_program=None): + helper = LayerHelper("fill_constant_batch_size_like", **locals()) + out = helper.create_tmp_variable(dtype=dtype) + helper.append_op( + type='fill_constant_batch_size_like', + inputs={'Input': input}, + outputs={'Out': [out]}, + attrs={ + 'shape': shape, + 'dtype': out.dtype, + 'value': float(value), + 'input_dim_idx': input_dim_idx, + 'output_dim_idx': output_dim_idx + }) + out.stop_gradient = True + return out + + +def ones(shape, dtype, main_program=None): + """ + This function performs the same function as fill_constant() declared above + with the constant value being 1.0. + """ + return fill_constant(value=1.0, **locals()) + + +def zeros(shape, dtype, main_program=None): + """ + This function performs the same function as fill_constant() declared above + with the constant value being 0.0. + """ + return fill_constant(value=0.0, **locals()) diff --git a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py b/python/paddle/v2/fluid/tests/book/test_image_classification_train.py index 4e71b6f345..3d336ffe95 100644 --- a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py +++ b/python/paddle/v2/fluid/tests/book/test_image_classification_train.py @@ -1,9 +1,9 @@ from __future__ import print_function -import numpy as np +import sys + import paddle.v2 as paddle import paddle.v2.fluid as fluid -import sys def resnet_cifar10(input, depth=32): diff --git a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py b/python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py index 80f8599679..c0b051f862 100644 --- a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py +++ b/python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py @@ -1,6 +1,51 @@ import numpy as np import paddle.v2 as paddle import paddle.v2.fluid as fluid +from paddle.v2.fluid.layer_helper import LayerHelper + + +def lstm(x, + c_pre_init, + hidden_dim, + forget_bias=None, + main_program=None, + startup_program=None): + """ + This function helps create an operator for the LSTM (Long Short Term + Memory) cell that can be used inside an RNN. + """ + helper = LayerHelper('lstm_unit', **locals()) + rnn = fluid.layers.StaticRNN() + with rnn.step(): + c_pre = rnn.memory(init=c_pre_init) + x_t = rnn.step_input(x) + + before_fc = fluid.layers.concat( + input=[x_t, c_pre], + axis=1, + main_program=main_program, + startup_program=startup_program) + after_fc = fluid.layers.fc(input=before_fc, + size=hidden_dim * 4, + main_program=main_program, + startup_program=startup_program) + + dtype = x.dtype + c = helper.create_tmp_variable(dtype) + h = helper.create_tmp_variable(dtype) + + helper.append_op( + type='lstm_unit', + inputs={"X": after_fc, + "C_prev": c_pre}, + outputs={"C": c, + "H": h}, + attrs={"forget_bias": forget_bias}) + + rnn.update_memory(c_pre, c) + rnn.output(h) + + return rnn() def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50): @@ -23,8 +68,7 @@ def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50): c_pre_init = fluid.layers.fill_constant( dtype=emb.dtype, shape=[batch_size, emb_dim], value=0.0) c_pre_init.stop_gradient = False - layer_1_out = fluid.layers.lstm( - emb, c_pre_init=c_pre_init, hidden_dim=emb_dim) + layer_1_out = lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim) layer_1_out = fluid.layers.transpose(x=layer_1_out, axis=[1, 0, 2]) prediction = fluid.layers.fc(input=layer_1_out, diff --git a/python/setup.py.in b/python/setup.py.in index 9ccb4dc176..8396fb44cf 100644 --- a/python/setup.py.in +++ b/python/setup.py.in @@ -68,6 +68,7 @@ packages=['paddle', 'paddle.v2.plot', 'paddle.v2.fluid', 'paddle.v2.fluid.proto', + 'paddle.v2.fluid.layers', 'py_paddle'] with open('@PADDLE_SOURCE_DIR@/python/requirements.txt') as f: From a2dfabb46a5dc2ac11d57b91c3fe8ed749c17c82 Mon Sep 17 00:00:00 2001 From: dzhwinter Date: Thu, 14 Dec 2017 03:07:21 -0800 Subject: [PATCH 24/54] "fix based on comments" --- doc/design/paddle_nccl.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/design/paddle_nccl.md b/doc/design/paddle_nccl.md index 5e28144b95..7f0d8e14e4 100644 --- a/doc/design/paddle_nccl.md +++ b/doc/design/paddle_nccl.md @@ -7,7 +7,7 @@ This Design Doc refers to the NCCL feature in paddle. We propose an approach t ## Motivation -NCCL is a NVIDIA library support Multi-GPU communicating and optimized for NVIDIA GPUs, it provides routines such as all-gather, all-reduce, broadcast, reduce, reduce-scatter, that can achieve high bandwidth over PCIe and NVLink high-speed interconnect. [NCCL](https://developer.nvidia.com/nccl). With NCCL library, we can easily accelerate the training in parallel. +[NCCL](https://developer.nvidia.com/nccl) is a NVIDIA library support Multi-GPU communicating and optimized for NVIDIA GPUs, it provides routines such as all-gather, all-reduce, broadcast, reduce, reduce-scatter, that can achieve high bandwidth over PCIe and NVLink high-speed interconnect. With NCCL library, we can easily accelerate the training in parallel. - Pros 1. easily plug-in with [NCCL2](https://developer.nvidia.com/nccl) library. From a02a68dc6daecbd3d5b17b57db03e0b3f916646e Mon Sep 17 00:00:00 2001 From: dzhwinter Date: Thu, 14 Dec 2017 06:09:54 -0800 Subject: [PATCH 25/54] "fixed based on comment" --- doc/design/paddle_nccl.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/design/paddle_nccl.md b/doc/design/paddle_nccl.md index 7f0d8e14e4..c7dac70998 100644 --- a/doc/design/paddle_nccl.md +++ b/doc/design/paddle_nccl.md @@ -28,9 +28,9 @@ Besides, it needs interfaces to synchronize model update with each different GPU As mentioned above, we wrap the NCCL routines as several kinds of operators. Need to note that NCCL need to create Communicator between gpu at the beginning, so there is a NCCLInit operator created. -### Graph Converter +### Transpiler -To be compatible with [parameter server design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/ops/dist_train.md), the graph converter converts the user defined operation graph into sub-graphs to be executed on different devices. +To be compatible with [parameter server design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/ops/dist_train.md), the transpiler compiles the user defined operation graph into sub-graphs to be executed on different devices. 1. The user-defined model will be a single device program @@ -40,7 +40,7 @@ To be compatible with [parameter server design doc](https://github.com/PaddlePad -After convert, the graph as shows +After compiling, the graph as shows From e0c33176460ffe322c8ff6da024628741423d020 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Thu, 14 Dec 2017 23:14:18 +0800 Subject: [PATCH 26/54] add MKLDNNPlace --- paddle/platform/place.cc | 6 ++++++ paddle/platform/place.h | 19 ++++++++++++++++++- paddle/platform/place_test.cc | 6 ++++++ 3 files changed, 30 insertions(+), 1 deletion(-) diff --git a/paddle/platform/place.cc b/paddle/platform/place.cc index 856e54df89..16f1774285 100644 --- a/paddle/platform/place.cc +++ b/paddle/platform/place.cc @@ -23,6 +23,7 @@ class PlacePrinter : public boost::static_visitor<> { public: explicit PlacePrinter(std::ostream &os) : os_(os) {} void operator()(const CPUPlace &) { os_ << "CPUPlace"; } + void operator()(const MKLDNNPlace &) { os_ << "MKLDNNPlace"; } void operator()(const GPUPlace &p) { os_ << "GPUPlace(" << p.device << ")"; } private: @@ -38,6 +39,7 @@ const Place &get_place() { return the_default_place; } const GPUPlace default_gpu() { return GPUPlace(0); } const CPUPlace default_cpu() { return CPUPlace(); } +const MKLDNNPlace default_mkldnn() { return MKLDNNPlace(); } bool is_gpu_place(const Place &p) { return boost::apply_visitor(IsGPUPlace(), p); @@ -46,6 +48,10 @@ bool is_cpu_place(const Place &p) { return !boost::apply_visitor(IsGPUPlace(), p); } +bool is_mkldnn_place(const Place &p) { + return boost::apply_visitor(IsMKLDNNPlace(), p); +} + bool places_are_same_class(const Place &p1, const Place &p2) { return p1.which() == p2.which(); } diff --git a/paddle/platform/place.h b/paddle/platform/place.h index 5370360a7d..e745b2e839 100644 --- a/paddle/platform/place.h +++ b/paddle/platform/place.h @@ -31,6 +31,14 @@ struct CPUPlace { inline bool operator!=(const CPUPlace &) const { return false; } }; +struct MKLDNNPlace : public CPUPlace { + MKLDNNPlace() {} + + // needed for variant equality comparison + inline bool operator==(const MKLDNNPlace &) const { return true; } + inline bool operator!=(const MKLDNNPlace &) const { return false; } +}; + struct GPUPlace { GPUPlace() : GPUPlace(0) {} explicit GPUPlace(int d) : device(d) {} @@ -45,14 +53,21 @@ struct GPUPlace { struct IsGPUPlace : public boost::static_visitor { bool operator()(const CPUPlace &) const { return false; } + bool operator()(const MKLDNNPlace &) const { return false; } bool operator()(const GPUPlace &gpu) const { return true; } }; +struct IsMKLDNNPlace : public boost::static_visitor { + bool operator()(const MKLDNNPlace &) const { return true; } + bool operator()(const CPUPlace &) const { return false; } + bool operator()(const GPUPlace &) const { return false; } +}; + // Define the max number of Place in bit length. i.e., the max number of places // should be less equal than 2^(NUM_PLACE_TYPE_LIMIT_IN_BIT) #define NUM_PLACE_TYPE_LIMIT_IN_BIT 4 -typedef boost::variant Place; +typedef boost::variant Place; // static check number of place types is less equal than // 2^(NUM_PLACE_TYPE_LIMIT_IN_BIT) @@ -65,9 +80,11 @@ const Place &get_place(); const GPUPlace default_gpu(); const CPUPlace default_cpu(); +const MKLDNNPlace default_mkldnn(); bool is_gpu_place(const Place &); bool is_cpu_place(const Place &); +bool is_mkldnn_place(const Place &); bool places_are_same_class(const Place &, const Place &); std::ostream &operator<<(std::ostream &, const Place &); diff --git a/paddle/platform/place_test.cc b/paddle/platform/place_test.cc index 33e2e5a439..184af12c23 100644 --- a/paddle/platform/place_test.cc +++ b/paddle/platform/place_test.cc @@ -21,9 +21,15 @@ TEST(Place, Default) { EXPECT_TRUE(paddle::platform::is_gpu_place(paddle::platform::get_place())); EXPECT_TRUE(paddle::platform::is_gpu_place(paddle::platform::default_gpu())); EXPECT_TRUE(paddle::platform::is_cpu_place(paddle::platform::default_cpu())); + EXPECT_TRUE( + paddle::platform::is_mkldnn_place(paddle::platform::default_mkldnn())); paddle::platform::set_place(paddle::platform::CPUPlace()); EXPECT_TRUE(paddle::platform::is_cpu_place(paddle::platform::get_place())); + + paddle::platform::set_place(paddle::platform::MKLDNNPlace()); + EXPECT_FALSE(paddle::platform::is_cpu_place(paddle::platform::get_place())); + EXPECT_TRUE(paddle::platform::is_mkldnn_place(paddle::platform::get_place())); } TEST(Place, Print) { From f271210595953fee610dd049909d7e3a40f58285 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Fri, 15 Dec 2017 15:15:18 +0800 Subject: [PATCH 27/54] fix undefined issue when with_gpu --- paddle/operators/math/math_function.cc | 8 ++++++++ paddle/platform/place.h | 2 +- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/paddle/operators/math/math_function.cc b/paddle/operators/math/math_function.cc index 2b35e4532a..a05810d778 100644 --- a/paddle/operators/math/math_function.cc +++ b/paddle/operators/math/math_function.cc @@ -277,6 +277,14 @@ void set_constant_with_place( TensorSetConstantCPU(tensor, value)); } +template <> +void set_constant_with_place( + const platform::DeviceContext& context, framework::Tensor* tensor, + float value) { + framework::VisitDataType(framework::ToDataType(tensor->type()), + TensorSetConstantCPU(tensor, value)); +} + struct TensorSetConstantWithPlace : public boost::static_visitor { TensorSetConstantWithPlace(const platform::DeviceContext& context, framework::Tensor* tensor, float value) diff --git a/paddle/platform/place.h b/paddle/platform/place.h index e745b2e839..70acc5b2d4 100644 --- a/paddle/platform/place.h +++ b/paddle/platform/place.h @@ -31,7 +31,7 @@ struct CPUPlace { inline bool operator!=(const CPUPlace &) const { return false; } }; -struct MKLDNNPlace : public CPUPlace { +struct MKLDNNPlace { MKLDNNPlace() {} // needed for variant equality comparison From a92f057ed1d52f8eed341212e11a76153512a1cf Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Fri, 15 Dec 2017 16:46:27 +0800 Subject: [PATCH 28/54] fix conflict of Place --- paddle/platform/place.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/platform/place.h b/paddle/platform/place.h index 5a1ce52800..4526945792 100644 --- a/paddle/platform/place.h +++ b/paddle/platform/place.h @@ -72,7 +72,7 @@ struct IsMKLDNNPlace : public boost::static_visitor { // should be less equal than 2^(NUM_PLACE_TYPE_LIMIT_IN_BIT) #define NUM_PLACE_TYPE_LIMIT_IN_BIT 4 -typedef boost::variant Place; +typedef boost::variant Place; // static check number of place types is less equal than // 2^(NUM_PLACE_TYPE_LIMIT_IN_BIT) From bf269d67b3d1e8d88797022e4ea542f34c06df47 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Fri, 15 Dec 2017 16:48:00 +0800 Subject: [PATCH 29/54] fix place_test on MKLDNNPlace --- paddle/platform/place.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/platform/place.cc b/paddle/platform/place.cc index 16f1774285..25fe8d21b4 100644 --- a/paddle/platform/place.cc +++ b/paddle/platform/place.cc @@ -45,7 +45,7 @@ bool is_gpu_place(const Place &p) { return boost::apply_visitor(IsGPUPlace(), p); } bool is_cpu_place(const Place &p) { - return !boost::apply_visitor(IsGPUPlace(), p); + return !is_gpu_place(p) && !is_mkldnn_place(p); } bool is_mkldnn_place(const Place &p) { From a243edf44bbfad597cc16de1e8c21943c909898a Mon Sep 17 00:00:00 2001 From: Xi Chen Date: Thu, 14 Dec 2017 14:31:49 -0800 Subject: [PATCH 30/54] add cross compiling doc in en --- doc/mobile/cross_compiling_for_ios_en.md | 120 +++++++++++++++++++++++ 1 file changed, 120 insertions(+) create mode 100644 doc/mobile/cross_compiling_for_ios_en.md diff --git a/doc/mobile/cross_compiling_for_ios_en.md b/doc/mobile/cross_compiling_for_ios_en.md new file mode 100644 index 0000000000..5428f501e8 --- /dev/null +++ b/doc/mobile/cross_compiling_for_ios_en.md @@ -0,0 +1,120 @@ +# iOS Compiling Guide + +This tutorial will walk you through cross compiling the PaddlePaddle library for iOS from the source in MacOS. + +## Preparation + +Apple provides Xcode for cross-compiling and IDE for iOS development. Download from App store or [here](https://developer.apple.com/cn/xcode/). To verify your installation, run command as follows + +```bash +$ xcodebuild -version +Xcode 9.0 +Build version 9A235 +``` + +## Cross-compiling configurations + +PaddlePaddle provides cross-compiling toolchain configuration documentation [cmake/cross_compiling/ios.cmake](https://github.com/PaddlePaddle/Paddle/blob/develop/cmake/cross_compiling/ios.cmake), which has some default settings for frequently used compilers. + +There are some mandatory environment variables need to be set before cross compiling PaddlePaddle for iOS: + +- `CMAKE_SYSTEM_NAME`, CMake compiling target platform name, has to be `iOS`. PaddlePaddle CMake will compile all the third party dependencies and enforce some parameters (`WITH_C_API=ON`、`WITH_GPU=OFF`、`WITH_AVX=OFF`、`WITH_PYTHON=OFF`、`WITH_RDMA=OFF`) when this variable is set with value `iOS`. + +- `WITH_C_API`, Whether to compile inference C-API library, has to be `ON`, since C-API is the only supported interface for inferencing in iOS. +- `WITH_SWIG_PY`, has to be `ON`. It's not supported to inference or train via swig in iOS. + +Optional environment variables for iOS are： + +- `IOS_PLATFORM`, either `OS` (default) or `SIMULATOR`. + - `OS`, build targets ARM-based physical devices like iPhone or iPad. + - `SIMULATOR`, build targets x86 architecture simulators. +- `IOS_ARCH`, target architecture. By default, all architecture types will be compiled. If you need to specify the architecture to compile for, please find valid values for different `IOS_PLATFORM` settings from the table below: + + + + + + + + + + + + + + + + + + + + + + +
IOS_PLATFORMIOS_ARCH
OSarmv7, armv7s, arm64
SIMULATORi386, x86_64
+ +- `IOS_DEPLOYMENT_TARGET`, minimum iOS version to deployment, `7.0` by default. +- `IOS_ENABLE_BITCODE`, whether to enable [Bitcode](https://developer.apple.com/library/content/documentation/IDEs/Conceptual/AppDistributionGuide/AppThinning/AppThinning.html#//apple_ref/doc/uid/TP40012582-CH35-SW3), values can be `ON/OFF`, `ON` by default. +- `IOS_USE_VECLIB_FOR_BLAS`, whether to use [vecLib](https://developer.apple.com/documentation/accelerate/veclib) framework for BLAS computing. values can be `ON/OFF`, `OFF` by default. +- `IOS_DEVELOPMENT_ROOT`, the path to `Developer` directory, can be explicitly set with your `/path/to/platform/Developer`. If left blank, PaddlePaddle will automatically pick the Xcode corresponding `platform`'s `Developer` directory based on your `IOS_PLATFORM` value. +- `IOS_SDK_ROOT`, the path to `SDK` root, can be explicitly set with your `/path/to/platform/Developer/SDKs/SDK`. if left black, PaddlePaddle will pick the latest SDK in the directory of `IOS_DEVELOPMENT_ROOT`. + +other settings： + +- `USE_EIGEN_FOR_BLAS`, whether to use Eigen for matrix computing. effective when `IOS_USE_VECLIB_FOR_BLAS=OFF`. Values can be `ON/OFF`, `OFF` by default. +- `HOST_C/CXX_COMPILER`, host C/C++ compiler. Uses value from environment variable `CC/CXX` by default or `cc/c++` if `CC/CXX` doesn't exist. + +some typical cmake configurations: + +```bash +cmake -DCMAKE_SYSTEM_NAME=iOS \ + -DIOS_PLATFORM=OS \ + -DIOS_ARCH="armv7;arm64" \ + -DIOS_ENABLE_BITCODE=ON \ + -DIOS_USE_VECLIB_FOR_BLAS=ON \ + -DCMAKE_INSTALL_PREFIX=your/path/to/install \ + -DWITH_C_API=ON \ + -DWITH_TESTING=OFF \ + -DWITH_SWIG_PY=OFF \ + .. +``` + +```bash +cmake -DCMAKE_SYSTEM_NAME=iOS \ + -DIOS_PLATFORM=SIMULATOR \ + -DIOS_ARCH="x86_64" \ + -DIOS_USE_VECLIB_FOR_BLAS=ON \ + -DCMAKE_INSTALL_PREFIX=your/path/to/install \ + -DWITH_C_API=ON \ + -DWITH_TESTING=OFF \ + -DWITH_SWIG_PY=OFF \ + .. +``` + +You can set other compiling parameters for your own need. I.E. if you are trying to minimize the library size, set `CMAKE_BUILD_TYPE` with `MinSizeRel`; or if the performance is your concern, set `CMAKE_BUILD_TYPE` with `Release`. You can even manipulate the PaddlePaddle compiling procedure by manually set `CMAKE_C/CXX_FLAGS` values. + +**TIPS for a better performance**: + +- set `CMAKE_BUILD_TYPE` with `Release` +- set `IOS_USE_VECLIB_FOR_BLAS` with `ON` + +## Compile and install + +After CMake, run following commands, PaddlePaddle will download the compile 3rd party dependencies, compile and install PaddlePaddle inference library. + +``` +$ make +$ make install +``` + +Please Note: if you compiled PaddlePaddle in the source directory for other platforms, do remove `third_party` and `build` directory within the source with `rm -rf` to ensure that all the 3rd party libraries dependencies and PaddlePaddle is newly compiled with current CMake configuration. + +`your/path/to/install` directory will have following directories after `compile` and `install`: + +- `include`, contains all the C-API header files. +- `lib`, contains PaddlePaddle C-API static library. +- `third_party` contains all the 3rd party libraries. + +Please note: if PaddlePaddle library need to support both physical devices and simulators, you will need to compile correspondingly, then merge fat library with `lipo` + +Now you will have PaddlePaddle library compiled and installed, the fat library can be used in deep learning related iOS APPs. Please refer to C-API documentation for usage guides. From d2b6d2ef8a42feed15ac5ce4cd43877436f8abe1 Mon Sep 17 00:00:00 2001 From: Xi Chen Date: Thu, 14 Dec 2017 14:35:44 -0800 Subject: [PATCH 31/54] minor tweaks. --- doc/mobile/cross_compiling_for_ios_en.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/mobile/cross_compiling_for_ios_en.md b/doc/mobile/cross_compiling_for_ios_en.md index 5428f501e8..e2a1f6fc5f 100644 --- a/doc/mobile/cross_compiling_for_ios_en.md +++ b/doc/mobile/cross_compiling_for_ios_en.md @@ -1,4 +1,4 @@ -# iOS Compiling Guide +# PaddlePaddle Compiling Guide for iOS This tutorial will walk you through cross compiling the PaddlePaddle library for iOS from the source in MacOS. From f5dc9eadaafc74e606aef849145693ece2e743e9 Mon Sep 17 00:00:00 2001 From: zhushuang02 Date: Fri, 15 Dec 2017 10:22:59 +0800 Subject: [PATCH 32/54] Add refer code to get started --- doc/getstarted/concepts/src/infer.py | 14 ++++++++++++++ doc/getstarted/concepts/src/train.py | 5 +++++ doc/getstarted/concepts/use_concepts_cn.rst | 5 +++++ 3 files changed, 24 insertions(+) create mode 100644 doc/getstarted/concepts/src/infer.py diff --git a/doc/getstarted/concepts/src/infer.py b/doc/getstarted/concepts/src/infer.py new file mode 100644 index 0000000000..780d831da8 --- /dev/null +++ b/doc/getstarted/concepts/src/infer.py @@ -0,0 +1,14 @@ +import paddle.v2 as paddle +import numpy as np + +paddle.init(use_gpu=False) +x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(2)) +y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear()) + +# loading the model which generated by training +with open('hello_params_pass_90.tar', 'r') as f: + parameters = paddle.parameters.Parameters.from_tar(f) + +i = [[[1, 2]]] + +print paddle.infer(output_layer=y_predict, parameters=parameters, input=i) diff --git a/doc/getstarted/concepts/src/train.py b/doc/getstarted/concepts/src/train.py index 8aceb23406..4bccbfca3c 100644 --- a/doc/getstarted/concepts/src/train.py +++ b/doc/getstarted/concepts/src/train.py @@ -26,6 +26,11 @@ def event_handler(event): if event.batch_id % 1 == 0: print "Pass %d, Batch %d, Cost %f" % (event.pass_id, event.batch_id, event.cost) + # product model every 10 pass + if isinstance(event, paddle.event.EndPass): + if event.pass_id % 10 == 0: + with open('params_pass_%d.tar' % event.pass_id, 'w') as f: + trainer.save_parameter_to_tar(f) # define training dataset reader diff --git a/doc/getstarted/concepts/use_concepts_cn.rst b/doc/getstarted/concepts/use_concepts_cn.rst index c243083794..7ff0b29ac3 100644 --- a/doc/getstarted/concepts/use_concepts_cn.rst +++ b/doc/getstarted/concepts/use_concepts_cn.rst @@ -147,4 +147,9 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和 .. literalinclude:: src/train.py :linenos: +使用以上训练好的模型进行预测的例子： + +.. literalinclude:: src/infer.py + :linenos: + 有关线性回归的实际应用，可以参考PaddlePaddle book的 `第一章节 `_。 From 7fe3c5789d4d65cd082550d2144108aabdfbda4b Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Fri, 15 Dec 2017 02:40:27 +0000 Subject: [PATCH 33/54] Fix typo in cross-compiling documentation of iOS and add it to the index. --- doc/mobile/cross_compiling_for_ios_cn.md | 4 ++-- doc/mobile/cross_compiling_for_ios_en.md | 8 ++++---- doc/mobile/index_en.rst | 1 + 3 files changed, 7 insertions(+), 6 deletions(-) diff --git a/doc/mobile/cross_compiling_for_ios_cn.md b/doc/mobile/cross_compiling_for_ios_cn.md index 9da48e7f21..d5196d9a4c 100644 --- a/doc/mobile/cross_compiling_for_ios_cn.md +++ b/doc/mobile/cross_compiling_for_ios_cn.md @@ -18,11 +18,11 @@ PaddlePaddle为交叉编译提供了工具链配置文档[cmake/cross_compiling/ - `CMAKE_SYSTEM_NAME`，CMake编译的目标平台，必须设置为`iOS`。在设置`CMAKE_SYSTEM_NAME=iOS`后，PaddlePaddle的CMake系统会自动编译所有的第三方依赖库，并且强制设置一些PaddlePaddle参数的值（`WITH_C_API=ON`、`WITH_GPU=OFF`、`WITH_AVX=OFF`、`WITH_PYTHON=OFF`、`WITH_RDMA=OFF`）。 - `WITH_C_API`，是否编译C-API预测库，必须设置为ON。在iOS平台上只支持使用C-API来预测。 -- `WITH_SWIG_PY`，必须设置为ON。在iOS平台上不支持通过swig调用来训练或者预测。 +- `WITH_SWIG_PY`，必须设置为`OFF`。在iOS平台上不支持通过swig调用来训练或者预测。 iOS平台可选配置参数： -- `IOS_PLATFORM`，可设置为`OS/SIMULATOR`，默认值为`OS`。 +- `IOS_PLATFORM`，可设置为`OS`（默认值）或`SIMULATOR`。 - `OS`，构建目标为`arm`架构的iPhone或者iPad等物理设备。 - `SIMULATOR`，构建目标为`x86`架构的模拟器平台。 - `IOS_ARCH`，目标架构。针对不同的`IOS_PLATFORM`，可设置的目标架构如下表所示，默认编译所有架构： diff --git a/doc/mobile/cross_compiling_for_ios_en.md b/doc/mobile/cross_compiling_for_ios_en.md index e2a1f6fc5f..aa390cd61f 100644 --- a/doc/mobile/cross_compiling_for_ios_en.md +++ b/doc/mobile/cross_compiling_for_ios_en.md @@ -18,12 +18,12 @@ PaddlePaddle provides cross-compiling toolchain configuration documentation [cma There are some mandatory environment variables need to be set before cross compiling PaddlePaddle for iOS: -- `CMAKE_SYSTEM_NAME`, CMake compiling target platform name, has to be `iOS`. PaddlePaddle CMake will compile all the third party dependencies and enforce some parameters (`WITH_C_API=ON`、`WITH_GPU=OFF`、`WITH_AVX=OFF`、`WITH_PYTHON=OFF`、`WITH_RDMA=OFF`) when this variable is set with value `iOS`. +- `CMAKE_SYSTEM_NAME`, CMake compiling target platform name, has to be `iOS`. PaddlePaddle CMake will compile all the third party dependencies and enforce some parameters (`WITH_C_API=ON`, `WITH_GPU=OFF`, `WITH_AVX=OFF`, `WITH_PYTHON=OFF`,`WITH_RDMA=OFF`) when this variable is set with value `iOS`. - `WITH_C_API`, Whether to compile inference C-API library, has to be `ON`, since C-API is the only supported interface for inferencing in iOS. -- `WITH_SWIG_PY`, has to be `ON`. It's not supported to inference or train via swig in iOS. +- `WITH_SWIG_PY`, has to be `OFF`. It's not supported to inference or train via swig in iOS. -Optional environment variables for iOS are： +Optional environment variables for iOS are: - `IOS_PLATFORM`, either `OS` (default) or `SIMULATOR`. - `OS`, build targets ARM-based physical devices like iPhone or iPad. @@ -115,6 +115,6 @@ Please Note: if you compiled PaddlePaddle in the source directory for other plat - `lib`, contains PaddlePaddle C-API static library. - `third_party` contains all the 3rd party libraries. -Please note: if PaddlePaddle library need to support both physical devices and simulators, you will need to compile correspondingly, then merge fat library with `lipo` +Please note: if PaddlePaddle library need to support both physical devices and simulators, you will need to compile correspondingly, then merge fat library with `lipo`. Now you will have PaddlePaddle library compiled and installed, the fat library can be used in deep learning related iOS APPs. Please refer to C-API documentation for usage guides. diff --git a/doc/mobile/index_en.rst b/doc/mobile/index_en.rst index 3c08d73671..ef421dacad 100644 --- a/doc/mobile/index_en.rst +++ b/doc/mobile/index_en.rst @@ -5,4 +5,5 @@ MOBILE :maxdepth: 1 cross_compiling_for_android_en.md + cross_compiling_for_ios_en.md cross_compiling_for_raspberry_en.md From d7b67f2b7460bd68d4db41c63dbc3c92d4b95497 Mon Sep 17 00:00:00 2001 From: Yancey Date: Fri, 15 Dec 2017 13:16:44 +0800 Subject: [PATCH 34/54] fix pipe_reader on multi passes (#6627) fix pipe reader on multi passes --- python/paddle/v2/reader/decorator.py | 4 +-- .../paddle/v2/reader/tests/decorator_test.py | 30 +++++++++++++++++++ 2 files changed, 32 insertions(+), 2 deletions(-) diff --git a/python/paddle/v2/reader/decorator.py b/python/paddle/v2/reader/decorator.py index 7e457f987d..27c82c95f7 100644 --- a/python/paddle/v2/reader/decorator.py +++ b/python/paddle/v2/reader/decorator.py @@ -390,8 +390,6 @@ def pipe_reader(left_cmd, if not callable(parser): raise TypeError("parser must be a callable object") - process = subprocess.Popen( - left_cmd.split(" "), bufsize=bufsize, stdout=subprocess.PIPE) # TODO(typhoonzero): add a thread to read stderr # Always init a decompress object is better than @@ -400,6 +398,8 @@ def pipe_reader(left_cmd, 32 + zlib.MAX_WBITS) # offset 32 to skip the header def reader(): + process = subprocess.Popen( + left_cmd.split(" "), bufsize=bufsize, stdout=subprocess.PIPE) remained = "" while True: buff = process.stdout.read(bufsize) diff --git a/python/paddle/v2/reader/tests/decorator_test.py b/python/paddle/v2/reader/tests/decorator_test.py index 5a92951b10..06e14796da 100644 --- a/python/paddle/v2/reader/tests/decorator_test.py +++ b/python/paddle/v2/reader/tests/decorator_test.py @@ -145,5 +145,35 @@ class TestXmap(unittest.TestCase): self.assertEqual(e, mapper(idx)) +class TestPipeReader(unittest.TestCase): + def test_pipe_reader(self): + def simple_parser(lines): + return lines + + import tempfile + + records = [str(i) for i in xrange(5)] + temp = tempfile.NamedTemporaryFile() + try: + with open(temp.name, 'w') as f: + for r in records: + f.write('%s\n' % r) + + cmd = "cat %s" % temp.name + reader = paddle.v2.reader.pipe_reader( + cmd, simple_parser, bufsize=128) + for i in xrange(4): + result = [] + for r in reader(): + result.append(r) + + for idx, e in enumerate(records): + print e, result[idx] + self.assertEqual(e, result[idx]) + finally: + # delete the temporary file + temp.close() + + if __name__ == '__main__': unittest.main() From 537a0d4c0e22dae88b7c917928bda2f976cb6700 Mon Sep 17 00:00:00 2001 From: Coke Date: Fri, 15 Dec 2017 13:47:11 +0800 Subject: [PATCH 35/54] Update infer.py --- doc/getstarted/concepts/src/infer.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/doc/getstarted/concepts/src/infer.py b/doc/getstarted/concepts/src/infer.py index 780d831da8..4cc58dfee0 100644 --- a/doc/getstarted/concepts/src/infer.py +++ b/doc/getstarted/concepts/src/infer.py @@ -6,9 +6,13 @@ x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(2)) y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear()) # loading the model which generated by training -with open('hello_params_pass_90.tar', 'r') as f: +with open('params_pass_90.tar', 'r') as f: parameters = paddle.parameters.Parameters.from_tar(f) -i = [[[1, 2]]] - +# Input multiple sets of data，Output the infer result in a array. +i = [[[1, 2]], [[3, 4]], [[5, 6]]] print paddle.infer(output_layer=y_predict, parameters=parameters, input=i) +# Will print: +# [[ -3.24491572] +# [ -6.94668722] +# [-10.64845848]] From d62552dc99583396ddd639fee58242ff9aae368f Mon Sep 17 00:00:00 2001 From: Coke Date: Fri, 15 Dec 2017 13:52:12 +0800 Subject: [PATCH 36/54] Update use_concepts_cn.rst --- doc/getstarted/concepts/use_concepts_cn.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/getstarted/concepts/use_concepts_cn.rst b/doc/getstarted/concepts/use_concepts_cn.rst index 7ff0b29ac3..e695ff283e 100644 --- a/doc/getstarted/concepts/use_concepts_cn.rst +++ b/doc/getstarted/concepts/use_concepts_cn.rst @@ -147,7 +147,7 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和 .. literalinclude:: src/train.py :linenos: -使用以上训练好的模型进行预测的例子： +使用以上训练好的模型进行预测，取其中一个模型params_pass_90.tar，输入需要预测的向量组，然后打印输出： .. literalinclude:: src/infer.py :linenos: From d5cab4f07c3e63970642cd428a9a33b284ddf4f1 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 15 Dec 2017 14:22:30 +0800 Subject: [PATCH 37/54] Fix compile on CUDA9.1 & MacOS (#6642) --- paddle/math/float16.h | 4 ++-- paddle/platform/dynload/nccl.cc | 5 +++++ paddle/platform/dynload/nccl.h | 24 ++++++++++++------------ paddle/platform/nccl_test.cu | 4 ++-- paddle/platform/variant.h | 13 +++++++++++++ python/.gitignore | 1 + 6 files changed, 35 insertions(+), 16 deletions(-) diff --git a/paddle/math/float16.h b/paddle/math/float16.h index 76ad3a0123..efebbce504 100644 --- a/paddle/math/float16.h +++ b/paddle/math/float16.h @@ -79,7 +79,7 @@ public: #ifdef PADDLE_CUDA_FP16 HOSTDEVICE inline explicit float16(const half& h) { #if CUDA_VERSION >= 9000 - x = reinterpret_cast<__half_raw*>(&h)->x; + x = reinterpret_cast<__half_raw*>(const_cast(&h))->x; #else x = h.x; #endif // CUDA_VERSION >= 9000 @@ -145,7 +145,7 @@ public: #ifdef PADDLE_CUDA_FP16 HOSTDEVICE inline float16& operator=(const half& rhs) { #if CUDA_VERSION >= 9000 - x = reinterpret_cast<__half_raw*>(&rhs)->x; + x = reinterpret_cast<__half_raw*>(const_cast(&rhs))->x; #else x = rhs.x; #endif diff --git a/paddle/platform/dynload/nccl.cc b/paddle/platform/dynload/nccl.cc index 8f92b8d94d..91168f37ef 100644 --- a/paddle/platform/dynload/nccl.cc +++ b/paddle/platform/dynload/nccl.cc @@ -25,6 +25,11 @@ void *nccl_dso_handle; NCCL_RAND_ROUTINE_EACH(DEFINE_WRAP); +void LoadNCCLDSO() { + platform::call_once(nccl_dso_flag, + [] { GetNCCLDsoHandle(&nccl_dso_handle); }); +} + } // namespace dynload } // namespace platform } // namespace paddle diff --git a/paddle/platform/dynload/nccl.h b/paddle/platform/dynload/nccl.h index 981b2ab258..11007c1031 100644 --- a/paddle/platform/dynload/nccl.h +++ b/paddle/platform/dynload/nccl.h @@ -28,18 +28,18 @@ extern std::once_flag nccl_dso_flag; extern void* nccl_dso_handle; #ifdef PADDLE_USE_DSO -#define DECLARE_DYNAMIC_LOAD_NCCL_WRAP(__name) \ - struct DynLoad__##__name { \ - template \ - auto operator()(Args... args) -> decltype(__name(args...)) { \ - using nccl_func = decltype(__name(args...)) (*)(Args...); \ - platform::call_once(nccl_dso_flag, \ - paddle::platform::dynload::GetNCCLDsoHandle, \ - &nccl_dso_handle); \ - void* p_##__name = dlsym(nccl_dso_handle, #__name); \ - return reinterpret_cast(p_##__name)(args...); \ - } \ - }; \ +extern void LoadNCCLDSO(); + +#define DECLARE_DYNAMIC_LOAD_NCCL_WRAP(__name) \ + struct DynLoad__##__name { \ + template \ + auto operator()(Args... args) -> decltype(__name(args...)) { \ + using nccl_func = decltype(__name(args...)) (*)(Args...); \ + paddle::platform::dynload::LoadNCCLDSO(); \ + void* p_##__name = dlsym(nccl_dso_handle, #__name); \ + return reinterpret_cast(p_##__name)(args...); \ + } \ + }; \ extern DynLoad__##__name __name #else #define DECLARE_DYNAMIC_LOAD_NCCL_WRAP(__name) \ diff --git a/paddle/platform/nccl_test.cu b/paddle/platform/nccl_test.cu index c99dae68be..94ab360a19 100644 --- a/paddle/platform/nccl_test.cu +++ b/paddle/platform/nccl_test.cu @@ -31,7 +31,7 @@ namespace platform { TEST(NCCL, init) { std::vector comms; comms.resize(dev_count); - PADDLE_ENFORCE(dynload::ncclCommInitAll(comms.data(), dev_count, nullptr)); + dynload::ncclCommInitAll(comms.data(), dev_count, nullptr); for (int i = 0; i < dev_count; ++i) { dynload::ncclCommDestroy(comms[i]); } @@ -62,7 +62,7 @@ TEST(NCCL, all_reduce) { std::vector comms; comms.resize(dev_count); VLOG(1) << "Initializing ncclComm"; - PADDLE_ENFORCE(dynload::ncclCommInitAll(comms.data(), dev_count, nullptr)); + dynload::ncclCommInitAll(comms.data(), dev_count, nullptr); VLOG(1) << "ncclComm initialized"; VLOG(1) << "Creating thread data"; std::vector>> data; diff --git a/paddle/platform/variant.h b/paddle/platform/variant.h index 619897ca19..284b4c42ac 100644 --- a/paddle/platform/variant.h +++ b/paddle/platform/variant.h @@ -14,6 +14,19 @@ #pragma once +#ifdef __CUDACC__ +#ifdef __CUDACC_VER_MAJOR__ +// CUDA 9 define `__CUDACC_VER__` as a warning message, manually define +// __CUDACC_VER__ instead. +#undef __CUDACC_VER__ + +#define __CUDACC_VER__ \ + (__CUDACC_VER_MAJOR__ * 10000 + __CUDACC_VER_MINOR__ * 100 + \ + __CUDACC_VER_BUILD__) +#endif + +#endif + #include #ifdef PADDLE_WITH_CUDA diff --git a/python/.gitignore b/python/.gitignore index cc7d0ece4a..1ba1d4c9b0 100644 --- a/python/.gitignore +++ b/python/.gitignore @@ -2,6 +2,7 @@ build dist paddle.egg-info +paddlepaddle_gpu.egg-info .idea paddle/proto/*.py paddle/proto/*.pyc From d37ed6cb13fc63aa2c81ece8de005bfbea4916be Mon Sep 17 00:00:00 2001 From: Yibing Liu Date: Fri, 15 Dec 2017 07:41:54 +0000 Subject: [PATCH 38/54] polish code in reshape_op --- paddle/operators/reshape_op.cc | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc index 5baf459536..d82d828747 100644 --- a/paddle/operators/reshape_op.cc +++ b/paddle/operators/reshape_op.cc @@ -36,10 +36,13 @@ class ReshapeOp : public framework::OperatorWithKernel { auto x_dims = ctx->GetInputDim("X"); std::vector neg_dims_idx; + // set some dimension to -1 if it is unknown + const int unknown_size = -1; for (size_t i = 0; i < shape.size(); ++i) { - PADDLE_ENFORCE(shape[i] > 0 || shape[i] == -1, - "Each dimension of Attr(shape) must be positive or -1."); - if (shape[i] == -1) { + PADDLE_ENFORCE(shape[i] > 0 || shape[i] == unknown_size, + "Each dimension of Attr(shape) must be positive or %d.", + unknown_size); + if (shape[i] == unknown_size) { neg_dims_idx.push_back(i); PADDLE_ENFORCE(neg_dims_idx.size() <= 1, "Only one dimension of Attr(shape) can be unknown."); @@ -53,8 +56,7 @@ class ReshapeOp : public framework::OperatorWithKernel { // dim infer shape[neg_dims_idx[0]] = in_size / (-capacity); // recalculate capacity - capacity = std::accumulate(shape.begin(), shape.end(), 1, - std::multiplies()); + capacity = shape[neg_dims_idx[0]] * (-capacity); } // capacity check PADDLE_ENFORCE(capacity == in_size, @@ -98,9 +100,9 @@ the tensor X into a 2-D tensor: [[1, 2, 3, 4]] -One dimension in the target shape can be set -1, and the real dimension -will be infered from the original shape of Input(X) and other -dimensions in the target shape. +One dimension in the target shape can be set -1, representing that its +size is unknown. In this case, the real dimension will be infered from +the original shape of Input(X) and other dimensions in the target shape. )DOC"); } }; From acaef9a1302a7907f9d42dffb7a75616b483b658 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Thu, 14 Dec 2017 20:58:10 +0800 Subject: [PATCH 39/54] rename mkldnn doc --- doc/design/{mkldnn => mkl}/image/engine.png | Bin doc/design/{mkldnn => mkl}/image/gradients.png | Bin doc/design/{mkldnn => mkl}/image/layers.png | Bin doc/design/{mkldnn => mkl}/image/matrix.png | Bin doc/design/{mkldnn => mkl}/image/overview.png | Bin doc/design/{mkldnn/README.MD => mkl/mkldnn.md} | 1 - 6 files changed, 1 deletion(-) rename doc/design/{mkldnn => mkl}/image/engine.png (100%) rename doc/design/{mkldnn => mkl}/image/gradients.png (100%) rename doc/design/{mkldnn => mkl}/image/layers.png (100%) rename doc/design/{mkldnn => mkl}/image/matrix.png (100%) rename doc/design/{mkldnn => mkl}/image/overview.png (100%) rename doc/design/{mkldnn/README.MD => mkl/mkldnn.md} (99%) diff --git a/doc/design/mkldnn/image/engine.png b/doc/design/mkl/image/engine.png similarity index 100% rename from doc/design/mkldnn/image/engine.png rename to doc/design/mkl/image/engine.png diff --git a/doc/design/mkldnn/image/gradients.png b/doc/design/mkl/image/gradients.png similarity index 100% rename from doc/design/mkldnn/image/gradients.png rename to doc/design/mkl/image/gradients.png diff --git a/doc/design/mkldnn/image/layers.png b/doc/design/mkl/image/layers.png similarity index 100% rename from doc/design/mkldnn/image/layers.png rename to doc/design/mkl/image/layers.png diff --git a/doc/design/mkldnn/image/matrix.png b/doc/design/mkl/image/matrix.png similarity index 100% rename from doc/design/mkldnn/image/matrix.png rename to doc/design/mkl/image/matrix.png diff --git a/doc/design/mkldnn/image/overview.png b/doc/design/mkl/image/overview.png similarity index 100% rename from doc/design/mkldnn/image/overview.png rename to doc/design/mkl/image/overview.png diff --git a/doc/design/mkldnn/README.MD b/doc/design/mkl/mkldnn.md similarity index 99% rename from doc/design/mkldnn/README.MD rename to doc/design/mkl/mkldnn.md index 61d453de24..e2fe1e6b26 100644 --- a/doc/design/mkldnn/README.MD +++ b/doc/design/mkl/mkldnn.md @@ -208,4 +208,3 @@ if use_mkldnn 但是在PaddlePaddle中，无论是重构前的layer还是重构后的op，都不会想要知道next layer/op的信息。 4. MKL-DNN的高性能格式与PaddlePaddle原有的`NCHW`不同(PaddlePaddle中的cuDNN部分使用的也是`NCHW`，所以不存在这个问题)。 所以需要引入一个转换方法，并且只需要在必要的时候转换这种格式，才能更好的发挥MKL-DNN的性能。 - From 1670ec081bd35c36e63298de2b31f0b88007888b Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Thu, 14 Dec 2017 21:03:13 +0800 Subject: [PATCH 40/54] add mkl packed desgin doc --- doc/design/mkl/mkl_packed.md | 91 ++++++++++++++++++++++++++++++++++++ 1 file changed, 91 insertions(+) create mode 100644 doc/design/mkl/mkl_packed.md diff --git a/doc/design/mkl/mkl_packed.md b/doc/design/mkl/mkl_packed.md new file mode 100644 index 0000000000..a9fb56d709 --- /dev/null +++ b/doc/design/mkl/mkl_packed.md @@ -0,0 +1,91 @@ +# Intel® MKL Packed Optimization on PaddlePaddle: Design Doc + + +## Contents + +- [Overview](#overview) +- [Key Points](#key-points) + - [Background](#background) + - [Solution](#solution) +- [Actions](#actions) + - [CMake](#cmake) + - [Layers](#layers) + - [Unit Tests](#unit-tests) + - [Python API](#python-api) + - [Benchmarking](#benchmarking) + + +## Overview +我们计划将 Intel® MKL 中引入的 GEMM Packed APIs\[[1](#references)\] 集成到 PaddlePaddle 中，充分发挥英特尔平台的优势，有效提升PaddlePaddle在英特尔架构上的性能。 +现阶段的优化主要针对 Recurrent Neural Network(以下简称RNN)相关层（包括`RecurrentLayer`, `GatedRecurrentLayer`和`LstmLayer`）， 以及 PaddlePaddle V1 API。 + +## Key Points + +### Background +为了达到最佳性能， Intel® MKL 中的 cblas_?gemm 会在计算前将原数据转换为更适合英特尔平台的Packed格式， 这一数据格式的转换操作 (Packing)，在问题本身的计算量比较小的时候显得相对来说较为耗时。 +在现有的某些情况下（例如RNN），多次调用 cblas_?gemm 时会使用相同的原数据，每次调用时对原数据的重复Packing便成为了冗余。 + +为了最大程度减少多次调用 cblas_?gemm 在Packing上的耗时，Intel® MKL 引入了以下四个API: + * cblas_?gemm_alloc + * cblas_?gemm_pack + * cblas_?gemm_compute + * cblas_?gemm_free + +通过使用这些API，我们可以先完成对原数据的Packing操作，再把已转换为Packed格式的数据传递给那些复用同一数据的gemm_compute函数，从而避免了Packing冗余。 + +### Solution +在RNN的case下，同一次 forward/backward 过程中所有time state共享同一个weight矩阵。当只做 inference 时，各次 forward 之间也都使用相同的weight矩阵，没有必要在每次forward中每个time state的计算时对weight进行重复的packing操作。 + +我们通过使用新引入的GEMM Packed APIs，在layer init时先完成对weight的packing操作，然后在 forward/backward 时复用已pack过后的weight，并在每次weight更新后重新Packing。 + +* 优化前，对于sequence length = `T` 的model, `N` 次iteration执行的Packing次数为： + - `inference`: `N * T` + - `training`: `2 * N * T` +* 优化后，对于sequence length = `T` 的model, `N` 次iteration执行的Packing次数减少至： + - `inference`: `1` + - `training`: `2 * N` + +## Actions + +添加的相关文件和目录结构如下： + +```txt +PaddlePaddle/Paddle +├── ... +└── paddle/ + ├── ... + └── gserver/ + ├── ... + ├── layers/ + │ ├── ... + │ ├── MKLPackedRecurrentLayer.* + | ├── MKLPackedGatedRecurrentLayer.* + | ├── MKLPackedLstmLayer.* + | └── MKLPackedGemm.h + └── tests/ + ├── ... + └── test_MKLPacked.cpp +``` + +### CMake +在对应的`CMakeLists.txt`中根据`WITH_MKL`是否打开，来决定是否开启MKL Packed相关功能。 + +### Layers +所有的`MKLPacked*Layer`都继承于PaddlePaddle的基类`Layer`, 并添加头文件 `MKLPackedGemm.h`，该文件中实现的对相关GEMM Packed APIs做了封装。 + +### Unit Tests +我们会添加`test_MKLPacked.cpp`用于MKL Packed优化后layer的测试。 +对于每一个新加的RNN layer，我们会对比如下2个方面： +1. 对比优化后layer自身，sequence mode（`rnn_use_batch=false`）与batch mode(`rnn_use_batch=true`)的结果。 +2. 对比优化后layer与相对应的PaddlePaddle原有layer, 在batch mode下的结果。 + +### Python API +TBD + +### Benchmarking +会添加相应的脚本用于测试和对比在使用MKL Packed recurrent layers 前后的网络性能。 + +## References +1. [Introducing the new Packed APIs for GEMM](https://software.intel.com/en-us/articles/introducing-the-new-packed-apis-for-gemm) + + From 3f2fa0a1f65f6ca7d6881a4bac93d190b2032b78 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Sat, 16 Dec 2017 13:50:37 +0800 Subject: [PATCH 41/54] follow comments and refine doc --- doc/design/mkl/mkl_packed.md | 32 ++++++++++++++++++-------------- 1 file changed, 18 insertions(+), 14 deletions(-) diff --git a/doc/design/mkl/mkl_packed.md b/doc/design/mkl/mkl_packed.md index a9fb56d709..55d13d777c 100644 --- a/doc/design/mkl/mkl_packed.md +++ b/doc/design/mkl/mkl_packed.md @@ -1,4 +1,4 @@ -# Intel® MKL Packed Optimization on PaddlePaddle: Design Doc +# Intel® MKL Packed on PaddlePaddle: Design Doc ## Contents @@ -17,13 +17,17 @@ ## Overview 我们计划将 Intel® MKL 中引入的 GEMM Packed APIs\[[1](#references)\] 集成到 PaddlePaddle 中，充分发挥英特尔平台的优势，有效提升PaddlePaddle在英特尔架构上的性能。 -现阶段的优化主要针对 Recurrent Neural Network(以下简称RNN)相关层（包括`RecurrentLayer`, `GatedRecurrentLayer`和`LstmLayer`）， 以及 PaddlePaddle V1 API。 +现阶段的优化主要针对 Recurrent Neural Network（以下简称RNN）相关层（包括`RecurrentLayer`, `GatedRecurrentLayer`和`LstmLayer`）， 以及 PaddlePaddle V1 API。 ## Key Points ### Background -为了达到最佳性能， Intel® MKL 中的 cblas_?gemm 会在计算前将原数据转换为更适合英特尔平台的Packed格式， 这一数据格式的转换操作 (Packing)，在问题本身的计算量比较小的时候显得相对来说较为耗时。 -在现有的某些情况下（例如RNN），多次调用 cblas_?gemm 时会使用相同的原数据，每次调用时对原数据的重复Packing便成为了冗余。 +目前PaddlePaddle采用了 Intel® MKL库的cblas_?gemm函数，这个函数本身会在计算前将原数据转换为更适合英特尔平台的内部格式。 + +1. 转换耗时 \ +这一数据格式的转换操作（Packing），在问题本身的计算量比较小的时候，显得相对来说较为耗时。例如在DeepSpeech2 \[[2](#references)\] 的Vanilla RNN部分中，矩阵大小是`batch_size * 2048`。 +2. 转换冗余 \ +由于在现有的某些情况下（例如RNN），多次调用 cblas_?gemm 会使用相同的原数据，因此，每次调用时对原数据的重复Packing便成为了冗余。 为了最大程度减少多次调用 cblas_?gemm 在Packing上的耗时，Intel® MKL 引入了以下四个API: * cblas_?gemm_alloc @@ -34,16 +38,16 @@ 通过使用这些API，我们可以先完成对原数据的Packing操作，再把已转换为Packed格式的数据传递给那些复用同一数据的gemm_compute函数，从而避免了Packing冗余。 ### Solution -在RNN的case下，同一次 forward/backward 过程中所有time state共享同一个weight矩阵。当只做 inference 时，各次 forward 之间也都使用相同的weight矩阵，没有必要在每次forward中每个time state的计算时对weight进行重复的packing操作。 +在RNN的情况下，同一次**前向/后向**（forward/backward）过程中所有**时间步**（time step）共享同一个**权重**（weight）。当只做**预测**（inference）时，各次**前向**之间也都使用了相同的**权重**，没有必要在每次**前向**中每个**时间步**的计算时对**权重**进行重复的Packing操作。 -我们通过使用新引入的GEMM Packed APIs，在layer init时先完成对weight的packing操作，然后在 forward/backward 时复用已pack过后的weight，并在每次weight更新后重新Packing。 +我们通过使用新引入的GEMM Packed APIs，在层**初始化**的时时候，先完成对**权重**的Packing操作，然后在**前向/后向**时复用已经转换过的**权重**，并在每次**权重**更新后，对新的**权重**进行转换用于下次迭代。 -* 优化前，对于sequence length = `T` 的model, `N` 次iteration执行的Packing次数为： - - `inference`: `N * T` - - `training`: `2 * N * T` -* 优化后，对于sequence length = `T` 的model, `N` 次iteration执行的Packing次数减少至： - - `inference`: `1` - - `training`: `2 * N` +* 优化前，对于序列长度（sequence length）为`T`的网络模型（model）, `N`次迭代执行的转换次数为： + - `inference`： `N * T` + - `training`： `2 * N * T` +* 优化后，对于同样设置的网络模型，其转换次数减少至： + - `inference`： `1` + - `training`： `2 * N` ## Actions @@ -71,7 +75,7 @@ PaddlePaddle/Paddle 在对应的`CMakeLists.txt`中根据`WITH_MKL`是否打开，来决定是否开启MKL Packed相关功能。 ### Layers -所有的`MKLPacked*Layer`都继承于PaddlePaddle的基类`Layer`, 并添加头文件 `MKLPackedGemm.h`，该文件中实现的对相关GEMM Packed APIs做了封装。 +所有的`MKLPacked*Layer`都继承于PaddlePaddle的基类`Layer`, 并添加头文件 `MKLPackedGemm.h`，该文件对相关GEMM Packed APIs做了封装。 ### Unit Tests 我们会添加`test_MKLPacked.cpp`用于MKL Packed优化后layer的测试。 @@ -87,5 +91,5 @@ TBD ## References 1. [Introducing the new Packed APIs for GEMM](https://software.intel.com/en-us/articles/introducing-the-new-packed-apis-for-gemm) - +2. [DeepSpeech2 on PaddlePaddle](https://github.com/PaddlePaddle/DeepSpeech#deepspeech2-on-paddlepaddle) From c1a6870706bf593e6f8e394d5cbcbf20f9fd1f23 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Sat, 16 Dec 2017 15:06:17 +0800 Subject: [PATCH 42/54] follow comments and refine doc --- doc/design/mkl/mkl_packed.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/design/mkl/mkl_packed.md b/doc/design/mkl/mkl_packed.md index 55d13d777c..c07f7d0cbe 100644 --- a/doc/design/mkl/mkl_packed.md +++ b/doc/design/mkl/mkl_packed.md @@ -22,7 +22,7 @@ ## Key Points ### Background -目前PaddlePaddle采用了 Intel® MKL库的cblas_?gemm函数，这个函数本身会在计算前将原数据转换为更适合英特尔平台的内部格式。 +目前PaddlePaddle采用了 Intel® MKL库的[cblas_?gemm](https://software.intel.com/en-us/mkl-developer-reference-c-cblas-gemm)函数，这个函数本身会在计算前将原数据转换为更适合英特尔平台的内部格式。 1. 转换耗时 \ 这一数据格式的转换操作（Packing），在问题本身的计算量比较小的时候，显得相对来说较为耗时。例如在DeepSpeech2 \[[2](#references)\] 的Vanilla RNN部分中，矩阵大小是`batch_size * 2048`。 @@ -38,9 +38,9 @@ 通过使用这些API，我们可以先完成对原数据的Packing操作，再把已转换为Packed格式的数据传递给那些复用同一数据的gemm_compute函数，从而避免了Packing冗余。 ### Solution -在RNN的情况下，同一次**前向/后向**（forward/backward）过程中所有**时间步**（time step）共享同一个**权重**（weight）。当只做**预测**（inference）时，各次**前向**之间也都使用了相同的**权重**，没有必要在每次**前向**中每个**时间步**的计算时对**权重**进行重复的Packing操作。 +在RNN的情况下，同一次前向、后向（forward/backward）过程中所有时间步（time step）共享同一个权重（weight）。当只做推断（inference）时，各次前向之间也都使用了相同的权重，没有必要在每次前向中每个时间步的计算时对权重进行重复的Packing操作。 -我们通过使用新引入的GEMM Packed APIs，在层**初始化**的时时候，先完成对**权重**的Packing操作，然后在**前向/后向**时复用已经转换过的**权重**，并在每次**权重**更新后，对新的**权重**进行转换用于下次迭代。 +我们通过使用新引入的GEMM Packed APIs，在层初始化的时候，先完成对权重的Packing操作，然后在前向，后向时复用已经转换过的权重，并在每次权重更新后，对新的权重进行转换用于下次迭代。 * 优化前，对于序列长度（sequence length）为`T`的网络模型（model）, `N`次迭代执行的转换次数为： - `inference`： `N * T` From 0fce0fe6983f4f167b873465fc90cff08fc31bd9 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Fri, 15 Dec 2017 15:48:58 +0800 Subject: [PATCH 43/54] Reduce memory usage in conv layer and RoI layer for mobile inference. --- paddle/function/GemmConvOp.cpp | 5 +++++ paddle/gserver/layers/ROIPoolLayer.cpp | 27 +++++++++++++++++--------- 2 files changed, 23 insertions(+), 9 deletions(-) diff --git a/paddle/function/GemmConvOp.cpp b/paddle/function/GemmConvOp.cpp index 8d34eee886..ffbf366fa9 100644 --- a/paddle/function/GemmConvOp.cpp +++ b/paddle/function/GemmConvOp.cpp @@ -233,6 +233,11 @@ public: inputGrad += inputChannels * inputHeight * inputWidth; outputGrad += outputChannels * outputHeight * outputWidth; } +#ifdef PADDLE_MOBILE_INFERENCE + if (Device == DEVICE_TYPE_CPU) { + delete memory_; + } +#endif } }; diff --git a/paddle/gserver/layers/ROIPoolLayer.cpp b/paddle/gserver/layers/ROIPoolLayer.cpp index 2c8256b91c..7d7c30b4d8 100644 --- a/paddle/gserver/layers/ROIPoolLayer.cpp +++ b/paddle/gserver/layers/ROIPoolLayer.cpp @@ -84,12 +84,15 @@ void ROIPoolLayer::forward(PassType passType) { size_t poolChannelOffset = pooledHeight_ * pooledWidth_; real* outputData = outputValue->getData(); - Matrix::resizeOrCreate(maxIdxs_, - numROIs, - channels_ * pooledHeight_ * pooledWidth_, - false, - false); - real* argmaxData = maxIdxs_->getData(); + real* argmaxData = nullptr; + if (passType != PASS_TEST) { + Matrix::resizeOrCreate(maxIdxs_, + numROIs, + channels_ * pooledHeight_ * pooledWidth_, + false, + false); + argmaxData = maxIdxs_->getData(); + } for (size_t n = 0; n < numROIs; ++n) { // the first five elememts of each RoI should be: @@ -128,14 +131,18 @@ void ROIPoolLayer::forward(PassType passType) { bool isEmpty = (hend <= hstart) || (wend <= wstart); size_t poolIndex = ph * pooledWidth_ + pw; outputData[poolIndex] = isEmpty ? 0 : -FLT_MAX; - argmaxData[poolIndex] = -1; + if (argmaxData) { + argmaxData[poolIndex] = -1; + } for (size_t h = hstart; h < hend; ++h) { for (size_t w = wstart; w < wend; ++w) { size_t index = h * width_ + w; if (batchData[index] > outputData[poolIndex]) { outputData[poolIndex] = batchData[index]; - argmaxData[poolIndex] = index; + if (argmaxData) { + argmaxData[poolIndex] = index; + } } } } @@ -143,7 +150,9 @@ void ROIPoolLayer::forward(PassType passType) { } batchData += channelOffset; outputData += poolChannelOffset; - argmaxData += poolChannelOffset; + if (argmaxData) { + argmaxData += poolChannelOffset; + } } bottomROIs += roiOffset; } From 349609207e0d6b6a2ff1548b8d0773c7857989f0 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Fri, 15 Dec 2017 16:22:19 +0800 Subject: [PATCH 44/54] Fix the error function/GemmConvOp. --- paddle/function/GemmConvOp.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/paddle/function/GemmConvOp.cpp b/paddle/function/GemmConvOp.cpp index ffbf366fa9..3387cae396 100644 --- a/paddle/function/GemmConvOp.cpp +++ b/paddle/function/GemmConvOp.cpp @@ -126,6 +126,11 @@ public: inputData += inputChannels * inputHeight * inputWidth; outputData += outputChannels * outputHeight * outputWidth; } +#ifdef PADDLE_MOBILE_INFERENCE + if (Device == DEVICE_TYPE_CPU) { + delete memory_; + } +#endif } }; @@ -233,11 +238,6 @@ public: inputGrad += inputChannels * inputHeight * inputWidth; outputGrad += outputChannels * outputHeight * outputWidth; } -#ifdef PADDLE_MOBILE_INFERENCE - if (Device == DEVICE_TYPE_CPU) { - delete memory_; - } -#endif } }; From 1b0c7d7c7afd4c5f09eb43358dd4851ba1735c3f Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 15 Dec 2017 16:24:27 +0800 Subject: [PATCH 45/54] Simplize system_allocator and fix GPU_INFO (#6653) --- paddle/memory/detail/system_allocator.cc | 50 ++++++------------------ paddle/platform/gpu_info.cc | 19 ++++----- 2 files changed, 23 insertions(+), 46 deletions(-) diff --git a/paddle/memory/detail/system_allocator.cc b/paddle/memory/detail/system_allocator.cc index 6a815a1b57..509250debc 100644 --- a/paddle/memory/detail/system_allocator.cc +++ b/paddle/memory/detail/system_allocator.cc @@ -19,6 +19,7 @@ limitations under the License. */ #include // for malloc and free #include // for mlock and munlock +#include // for std::max #include "gflags/gflags.h" @@ -28,7 +29,7 @@ limitations under the License. */ // of memory available to the system for paging. So, by default, we // should set false to use_pinned_memory. DEFINE_bool(use_pinned_memory, true, "If set, allocate cpu pinned memory."); - +DECLARE_double(fraction_of_gpu_memory_to_use); namespace paddle { namespace memory { namespace detail { @@ -77,45 +78,20 @@ void* GPUAllocator::Alloc(size_t& index, size_t size) { // CUDA documentation doesn't explain if cudaMalloc returns nullptr // if size is 0. We just make sure it does. if (size <= 0) return nullptr; - - size_t available = 0; - size_t capacity = 0; - paddle::platform::GpuMemoryUsage(available, capacity); - - // Reserve memory for page tables, etc. - size_t reserving = 0.05 * capacity + paddle::platform::GpuMinChunkSize(); - size_t usable = available > reserving ? available - reserving : 0; - - // If remaining size no less than expected size, using general - // cudaMalloc to allocate GPU memory. - void* p = 0; - if (size <= usable) { - cudaError_t result = cudaMalloc(&p, size); - if (result == cudaSuccess) { - index = 0; - gpu_alloc_size_ += size; - return p; - } - } - - // If remaining size less than expected size or cudaMalloc failed, - // cudaMallocHost will be considered as a fallback allocator. - // - // NOTE: here, we use GpuMaxAllocSize() as the maximum memory size - // of host fallback allocation. Allocates too much would reduce - // the amount of memory available to the underlying system for paging. - usable = paddle::platform::GpuMaxAllocSize() - fallback_alloc_size_; - - if (size > usable) return nullptr; - - cudaError_t result = cudaMallocHost(&p, size); + void* p; + cudaError_t result = cudaMalloc(&p, size); if (result == cudaSuccess) { - index = 1; - fallback_alloc_size_ += size; + index = 0; + gpu_alloc_size_ += size; return p; + } else { + LOG(WARNING) + << "Cannot malloc " << size / 1024.0 / 1024.0 + << " MB GPU memory. Please shrink FLAGS_fraction_of_gpu_memory_to_use " + "environment variable to a lower value. Current value is " + << FLAGS_fraction_of_gpu_memory_to_use; + return nullptr; } - - return nullptr; } void GPUAllocator::Free(void* p, size_t size, size_t index) { diff --git a/paddle/platform/gpu_info.cc b/paddle/platform/gpu_info.cc index 4fa2eaed31..541eca5f39 100644 --- a/paddle/platform/gpu_info.cc +++ b/paddle/platform/gpu_info.cc @@ -73,19 +73,20 @@ size_t GpuMaxChunkSize() { size_t available = 0; GpuMemoryUsage(available, total); - - // Reserving the rest memory for page tables, etc. - size_t reserving = 0.05 * total; - + VLOG(10) << "GPU Usage " << available / 1024 / 1024 << "M/" + << total / 1024 / 1024 << "M"; + size_t reserving = static_cast(0.05 * total); // If available less than minimum chunk size, no usable memory exists. available = - std::max(std::max(available, GpuMinChunkSize()) - GpuMinChunkSize(), - reserving) - - reserving; + std::min(std::max(available, GpuMinChunkSize()) - GpuMinChunkSize(), + total - reserving); + + // Reserving the rest memory for page tables, etc. - size_t allocating = FLAGS_fraction_of_gpu_memory_to_use * total; + size_t allocating = static_cast(FLAGS_fraction_of_gpu_memory_to_use * + (total - reserving)); - PADDLE_ENFORCE_LT(allocating, available); + PADDLE_ENFORCE_LE(allocating, available); return allocating; } From e1049c478697329b504ac682125210112fc13295 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Fri, 15 Dec 2017 17:18:28 +0800 Subject: [PATCH 46/54] add fluid python api in chinese website --- doc/api/index_cn.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/api/index_cn.rst b/doc/api/index_cn.rst index 9be0b370ee..84f9097a6c 100644 --- a/doc/api/index_cn.rst +++ b/doc/api/index_cn.rst @@ -7,3 +7,4 @@ API 模型配置 数据访问 训练与应用 + v2/fluid.rst From 7968b66d72d44edb1648b76acf90df37f0bd73c2 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Fri, 15 Dec 2017 19:17:48 +0800 Subject: [PATCH 47/54] Fix error in function/GemmConvOp.cpp. --- paddle/function/GemmConvOp.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/function/GemmConvOp.cpp b/paddle/function/GemmConvOp.cpp index 3387cae396..de7b70e271 100644 --- a/paddle/function/GemmConvOp.cpp +++ b/paddle/function/GemmConvOp.cpp @@ -128,7 +128,7 @@ public: } #ifdef PADDLE_MOBILE_INFERENCE if (Device == DEVICE_TYPE_CPU) { - delete memory_; + memory_.reset(); } #endif } From 84cb542c13905f5113a6e91d86129ec7a1a90e06 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Fri, 15 Dec 2017 17:23:03 +0800 Subject: [PATCH 48/54] use intel openmp to speedup seq2batch when WITH_MKL --- paddle/gserver/layers/SequenceToBatch.cpp | 25 +++++++++++++++++------ 1 file changed, 19 insertions(+), 6 deletions(-) diff --git a/paddle/gserver/layers/SequenceToBatch.cpp b/paddle/gserver/layers/SequenceToBatch.cpp index 5fa7b6f488..7892d475a6 100644 --- a/paddle/gserver/layers/SequenceToBatch.cpp +++ b/paddle/gserver/layers/SequenceToBatch.cpp @@ -171,12 +171,25 @@ void SequenceToBatch::sequence2BatchCopy(Matrix &batch, hl_sequence2batch_copy( batchData, seqData, idxData, seqWidth, batchCount, seq2batch); } else { - for (int i = 0; i < batchCount; ++i) { - if (seq2batch) { - memcpy(batch.rowBuf(i), - sequence.rowBuf(idxData[i]), - seqWidth * sizeof(real)); - } else { + if (seq2batch) { + const int blockMemSize = 8 * 1024; + const int blockSize = blockMemSize / sizeof(real); +#ifdef PADDLE_USE_MKLML +#pragma omp parallel for collapse(2) +#endif + for (int i = 0; i < batchCount; ++i) { + for (int j = 0; j < seqWidth; j += blockSize) { + memcpy(batch.rowBuf(i) + j, + sequence.rowBuf(idxData[i]) + j, + (j + blockSize > seqWidth) ? (seqWidth - j) * sizeof(real) + : blockMemSize); + } + } + } else { +#ifdef PADDLE_USE_MKLML +#pragma omp parallel for +#endif + for (int i = 0; i < batchCount; ++i) { memcpy(sequence.rowBuf(idxData[i]), batch.rowBuf(i), seqWidth * sizeof(real)); From 9c27c13e469f4c2f11f00c6e2570927e1d640e3c Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Sat, 16 Dec 2017 21:16:01 +0800 Subject: [PATCH 49/54] follow comments using macro to separate the original implements --- paddle/gserver/layers/SequenceToBatch.cpp | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/paddle/gserver/layers/SequenceToBatch.cpp b/paddle/gserver/layers/SequenceToBatch.cpp index 7892d475a6..6b769378d2 100644 --- a/paddle/gserver/layers/SequenceToBatch.cpp +++ b/paddle/gserver/layers/SequenceToBatch.cpp @@ -172,11 +172,10 @@ void SequenceToBatch::sequence2BatchCopy(Matrix &batch, batchData, seqData, idxData, seqWidth, batchCount, seq2batch); } else { if (seq2batch) { +#ifdef PADDLE_USE_MKLML const int blockMemSize = 8 * 1024; const int blockSize = blockMemSize / sizeof(real); -#ifdef PADDLE_USE_MKLML #pragma omp parallel for collapse(2) -#endif for (int i = 0; i < batchCount; ++i) { for (int j = 0; j < seqWidth; j += blockSize) { memcpy(batch.rowBuf(i) + j, @@ -185,6 +184,13 @@ void SequenceToBatch::sequence2BatchCopy(Matrix &batch, : blockMemSize); } } +#else + for (int i = 0; i < batchCount; ++i) { + memcpy(batch.rowBuf(i), + sequence.rowBuf(idxData[i]), + seqWidth * sizeof(real)); + } +#endif } else { #ifdef PADDLE_USE_MKLML #pragma omp parallel for From 77cf7d4f9e42d4c153aad648bbe5d7049ab2ff29 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sat, 16 Dec 2017 18:49:50 +0800 Subject: [PATCH 50/54] Fix read_source.md (#6673) Optimizer is not a part of fluid. The `paddle::platform` was missing. --- doc/howto/read_source.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/howto/read_source.md b/doc/howto/read_source.md index 383acb0c82..e4211abb3b 100644 --- a/doc/howto/read_source.md +++ b/doc/howto/read_source.md @@ -6,10 +6,10 @@ Core: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework Operator: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators -Optimizer: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/optimizer - Memory: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/memory +Platform: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/platform + # Compile Time The following **defines** the NN. The definition goes into this [protocol buffer](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto). From 1eac27630047e59cb461846022bada5bf0f490c1 Mon Sep 17 00:00:00 2001 From: sweetsky0901 Date: Sun, 17 Dec 2017 22:09:29 +0800 Subject: [PATCH 51/54] add spp avg --- paddle/operators/spp_op.cc | 5 +++ paddle/operators/spp_op.h | 34 ++++++++++++++++----- python/paddle/v2/fluid/tests/test_spp_op.py | 23 +++++++++++--- 3 files changed, 51 insertions(+), 11 deletions(-) diff --git a/paddle/operators/spp_op.cc b/paddle/operators/spp_op.cc index c4bd4f5ab3..b1807b6261 100644 --- a/paddle/operators/spp_op.cc +++ b/paddle/operators/spp_op.cc @@ -30,6 +30,11 @@ class SppOpMaker : public framework::OpProtoAndCheckerMaker { "N * M." "M = C * H * W"); AddAttr("pyramid_height", "(int), multi level pooling"); + AddAttr( + "pooling_type", + "(string), pooling type, can be \"max\" for max-pooling " + "and \"avg\" for average-pooling.") + .InEnum({"max", "avg"}); AddComment(R"DOC( "With spatial pyramid pooling, the input image can be of any sizes. This not only allows arbitrary aspect diff --git a/paddle/operators/spp_op.h b/paddle/operators/spp_op.h index 16510cb826..f35b305d02 100644 --- a/paddle/operators/spp_op.h +++ b/paddle/operators/spp_op.h @@ -27,6 +27,8 @@ class SppKernel : public framework::OpKernel { const framework::Tensor* in_x = context.Input("X"); auto* out = context.Output("Out"); int pyramid_height = context.template Attr("pyramid_height"); + std::string pooling_type = + context.template Attr("pooling_type"); out->mutable_data(context.GetPlace()); auto out_stride = framework::stride(out->dims()); int input_h = in_x->dims()[2]; @@ -48,10 +50,17 @@ class SppKernel : public framework::OpKernel { framework::DDim output_shape(framework::make_ddim(output_shape_vec)); out_level.mutable_data(output_shape, context.GetPlace()); // pooling - math::Pool2dFunctor, T> pool_forward; - math::MaxPool max_process; - pool_forward(context.template device_context(), *in_x, - kernel_size, strides, paddings, max_process, &out_level); + if (pooling_type == "max") { + math::Pool2dFunctor, T> pool_forward; + math::MaxPool max_process; + pool_forward(context.template device_context(), *in_x, + kernel_size, strides, paddings, max_process, &out_level); + } else if (pooling_type == "avg") { + math::Pool2dFunctor, T> pool_forward; + math::AvgPool avg_process; + pool_forward(context.template device_context(), *in_x, + kernel_size, strides, paddings, avg_process, &out_level); + } // flatten pooling output shape int output_flatten_w = in_x->dims()[1] * bins * bins; std::vector output_flatten_shape_vec( @@ -79,6 +88,8 @@ class SppGradKernel : public framework::OpKernel { framework::Tensor* in_x_grad = context.Output(framework::GradVarName("X")); int pyramid_height = context.template Attr("pyramid_height"); + std::string pooling_type = + context.template Attr("pooling_type"); auto& device_ctx = context.template device_context(); math::SetConstant zero; in_x_grad->mutable_data(context.GetPlace()); @@ -130,10 +141,19 @@ class SppGradKernel : public framework::OpKernel { outgrad_level.ShareDataWith(outgrad_level); outgrad_level.Resize(out_shape); // pooling backward - math::MaxPool2dGradFunctor pool2d_backward; - pool2d_backward(context.template device_context(), *in_x, + if (pooling_type == "max") { + math::MaxPool2dGradFunctor pool2d_backward; + pool2d_backward(context.template device_context(), *in_x, + *&out_level, *&outgrad_level, kernel_size, strides, + paddings, in_x_grad); + } else if (pooling_type == "avg") { + math::Pool2dGradFunctor, T> + pool_backward; + math::AvgPoolGrad avg_process; + pool_backward(context.template device_context(), *in_x, *&out_level, *&outgrad_level, kernel_size, strides, - paddings, in_x_grad); + paddings, avg_process, in_x_grad); + } } } }; diff --git a/python/paddle/v2/fluid/tests/test_spp_op.py b/python/paddle/v2/fluid/tests/test_spp_op.py index b57f4a795d..007723f0e3 100644 --- a/python/paddle/v2/fluid/tests/test_spp_op.py +++ b/python/paddle/v2/fluid/tests/test_spp_op.py @@ -2,6 +2,7 @@ import unittest import numpy as np from op_test import OpTest from test_pool2d_op import max_pool2D_forward_naive +from test_pool2d_op import avg_pool2D_forward_naive class TestSppOp(OpTest): @@ -24,8 +25,8 @@ class TestSppOp(OpTest): bins.astype("double")).astype("int32") padding[1] = ( (kernel_size[1] * bins - wsize + 1) / 2).astype("int32") - out_level = max_pool2D_forward_naive(input, kernel_size, - kernel_size, padding) + out_level = self.pool2D_forward_naive(input, kernel_size, + kernel_size, padding) out_level_flatten.append( out_level.reshape(nsize, bins * bins * csize)) if i == 0: @@ -34,7 +35,10 @@ class TestSppOp(OpTest): output = np.concatenate((output, out_level_flatten[i]), 1) # output = np.concatenate(out_level_flatten.tolist(), 0); self.inputs = {'X': input.astype('float32'), } - self.attrs = {'pyramid_height': self.pyramid_height} + self.attrs = { + 'pyramid_height': self.pyramid_height, + 'pooling_type': self.pool_type + } self.outputs = {'Out': output.astype('float32')} @@ -42,11 +46,22 @@ class TestSppOp(OpTest): self.check_output() def test_check_grad(self): - self.check_grad(['X'], 'Out', max_relative_error=0.05) + if self.pool_type != "avg": + self.check_grad(['X'], 'Out', max_relative_error=0.05) def init_test_case(self): self.shape = [3, 2, 4, 4] self.pyramid_height = 3 + self.pool2D_forward_naive = max_pool2D_forward_naive + self.pool_type = "max" + + +class TestCase2(TestSppOp): + def init_test_case(self): + self.shape = [3, 2, 4, 4] + self.pyramid_height = 3 + self.pool2D_forward_naive = avg_pool2D_forward_naive + self.pool_type = "avg" if __name__ == '__main__': From 93a2d9c59d46589d3fefaaee09a3003f45fda9e5 Mon Sep 17 00:00:00 2001 From: QI JUN Date: Mon, 18 Dec 2017 11:53:22 +0800 Subject: [PATCH 52/54] add more place test and rename Cudnn to CUDNN (#6621) * add more place_test and rename Cudnn to CUDNN * fix ci --- paddle/operators/math/math_function.cu | 2 +- paddle/platform/device_context.cc | 8 ++++---- paddle/platform/device_context.h | 8 ++++---- paddle/platform/device_context_test.cc | 10 +++++----- paddle/platform/place.h | 8 ++++---- paddle/platform/place_test.cc | 6 ++++++ 6 files changed, 24 insertions(+), 18 deletions(-) diff --git a/paddle/operators/math/math_function.cu b/paddle/operators/math/math_function.cu index e33070c40f..7852bb53a9 100644 --- a/paddle/operators/math/math_function.cu +++ b/paddle/operators/math/math_function.cu @@ -274,7 +274,7 @@ void set_constant_with_place( } template <> -void set_constant_with_place( +void set_constant_with_place( const platform::DeviceContext& context, framework::Tensor* tensor, float value) { set_constant_with_place(context, tensor, value); diff --git a/paddle/platform/device_context.cc b/paddle/platform/device_context.cc index 1c72b50559..8cdc5f4340 100644 --- a/paddle/platform/device_context.cc +++ b/paddle/platform/device_context.cc @@ -125,21 +125,21 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() const { return cudnn_handle_; } cudaStream_t CUDADeviceContext::stream() const { return stream_; } -CudnnDeviceContext::CudnnDeviceContext(CudnnPlace place) +CUDNNDeviceContext::CUDNNDeviceContext(CUDNNPlace place) : CUDADeviceContext(place), place_(place) { PADDLE_ENFORCE(dynload::cudnnCreate(&cudnn_handle_)); PADDLE_ENFORCE(dynload::cudnnSetStream(cudnn_handle_, stream())); } -CudnnDeviceContext::~CudnnDeviceContext() { +CUDNNDeviceContext::~CUDNNDeviceContext() { SetDeviceId(place_.device); Wait(); PADDLE_ENFORCE(dynload::cudnnDestroy(cudnn_handle_)); } -Place CudnnDeviceContext::GetPlace() const { return CudnnPlace(); } +Place CUDNNDeviceContext::GetPlace() const { return CUDNNPlace(); } -cudnnHandle_t CudnnDeviceContext::cudnn_handle() const { return cudnn_handle_; } +cudnnHandle_t CUDNNDeviceContext::cudnn_handle() const { return cudnn_handle_; } #endif diff --git a/paddle/platform/device_context.h b/paddle/platform/device_context.h index f67194993d..56813a1d5b 100644 --- a/paddle/platform/device_context.h +++ b/paddle/platform/device_context.h @@ -86,10 +86,10 @@ class CUDADeviceContext : public DeviceContext { cublasHandle_t cublas_handle_; }; -class CudnnDeviceContext : public CUDADeviceContext { +class CUDNNDeviceContext : public CUDADeviceContext { public: - explicit CudnnDeviceContext(CudnnPlace place); - virtual ~CudnnDeviceContext(); + explicit CUDNNDeviceContext(CUDNNPlace place); + virtual ~CUDNNDeviceContext(); /*! \brief Return place in the device context. */ Place GetPlace() const final; @@ -99,7 +99,7 @@ class CudnnDeviceContext : public CUDADeviceContext { private: cudnnHandle_t cudnn_handle_; - CudnnPlace place_; + CUDNNPlace place_; }; #endif diff --git a/paddle/platform/device_context_test.cc b/paddle/platform/device_context_test.cc index be3b2af5af..109c13a881 100644 --- a/paddle/platform/device_context_test.cc +++ b/paddle/platform/device_context_test.cc @@ -47,14 +47,14 @@ TEST(Device, CUDADeviceContext) { } } -TEST(Device, CudnnDeviceContext) { - using paddle::platform::CudnnDeviceContext; - using paddle::platform::CudnnPlace; +TEST(Device, CUDNNDeviceContext) { + using paddle::platform::CUDNNDeviceContext; + using paddle::platform::CUDNNPlace; if (paddle::platform::dynload::HasCUDNN()) { int count = paddle::platform::GetCUDADeviceCount(); for (int i = 0; i < count; ++i) { - CudnnDeviceContext* device_context = - new CudnnDeviceContext(CudnnPlace(i)); + CUDNNDeviceContext* device_context = + new CUDNNDeviceContext(CUDNNPlace(i)); cudnnHandle_t cudnn_handle = device_context->cudnn_handle(); ASSERT_NE(nullptr, cudnn_handle); ASSERT_NE(nullptr, device_context->stream()); diff --git a/paddle/platform/place.h b/paddle/platform/place.h index 4526945792..ca98920d41 100644 --- a/paddle/platform/place.h +++ b/paddle/platform/place.h @@ -51,9 +51,9 @@ struct GPUPlace { int device; }; -struct CudnnPlace : public GPUPlace { - CudnnPlace() : GPUPlace() {} - explicit CudnnPlace(int d) : GPUPlace(d) {} +struct CUDNNPlace : public GPUPlace { + CUDNNPlace() : GPUPlace() {} + explicit CUDNNPlace(int d) : GPUPlace(d) {} }; struct IsGPUPlace : public boost::static_visitor { @@ -72,7 +72,7 @@ struct IsMKLDNNPlace : public boost::static_visitor { // should be less equal than 2^(NUM_PLACE_TYPE_LIMIT_IN_BIT) #define NUM_PLACE_TYPE_LIMIT_IN_BIT 4 -typedef boost::variant Place; +typedef boost::variant Place; // static check number of place types is less equal than // 2^(NUM_PLACE_TYPE_LIMIT_IN_BIT) diff --git a/paddle/platform/place_test.cc b/paddle/platform/place_test.cc index 184af12c23..c536b59ed8 100644 --- a/paddle/platform/place_test.cc +++ b/paddle/platform/place_test.cc @@ -5,16 +5,22 @@ TEST(Place, Equality) { paddle::platform::CPUPlace cpu; paddle::platform::GPUPlace g0(0), g1(1), gg0(0); + paddle::platform::CUDNNPlace d0(0), d1(1), dd0(0); EXPECT_EQ(cpu, cpu); EXPECT_EQ(g0, g0); EXPECT_EQ(g1, g1); EXPECT_EQ(g0, gg0); + EXPECT_EQ(d0, dd0); EXPECT_NE(g0, g1); + EXPECT_NE(d0, d1); EXPECT_TRUE(paddle::platform::places_are_same_class(g0, gg0)); EXPECT_FALSE(paddle::platform::places_are_same_class(g0, cpu)); + + EXPECT_TRUE(paddle::platform::is_gpu_place(d0)); + EXPECT_FALSE(paddle::platform::places_are_same_class(g0, d0)); } TEST(Place, Default) { From 964f01e3e880d4c835489ce5fad8215a964c188d Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 18 Dec 2017 11:57:20 +0800 Subject: [PATCH 53/54] fix simple_gru2 doc --- python/paddle/trainer_config_helpers/networks.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/python/paddle/trainer_config_helpers/networks.py b/python/paddle/trainer_config_helpers/networks.py index 9776ae1805..8bfe56d795 100644 --- a/python/paddle/trainer_config_helpers/networks.py +++ b/python/paddle/trainer_config_helpers/networks.py @@ -1119,8 +1119,9 @@ def simple_gru2(input, :param gru_bias_attr: bias parameter attribute of gru layer, False means no bias, None means default bias. :type gru_bias_attr: ParameterAttribute|False|None - :param gru_layer_attr: Extra attribute of the gru layer. - :type gru_layer_attr: ExtraLayerAttribute + :param gru_param_attr: param parameter attribute of gru layer, + None means default param. + :type gru_param_attr: ParameterAttribute|None :return: the gru group. :rtype: LayerOutput """ From 24fda392207e1fd608e66ea376a79f9f9716c3ca Mon Sep 17 00:00:00 2001 From: dzhwinter Date: Mon, 18 Dec 2017 13:01:41 +0800 Subject: [PATCH 54/54] Feature/global context (#6537) * "add DeviceContextPool" * "add devicecontextpool in pybind" * "add comments in python side " * "fix static link error" * "fix CI error" * "add executor.py" * "fix CI error" * "add with gpu macro" * "remove comment out codes" * "add TODO items" * "update init devices" --- paddle/framework/CMakeLists.txt | 3 ++ paddle/framework/ddim_test.cc | 13 +++++ paddle/framework/executor.cc | 33 ++---------- paddle/framework/executor.h | 84 ++++++++++++++++++++++++++++-- paddle/framework/init.cc | 80 ++++++++++++++++++++++++++++ paddle/framework/init.h | 28 ++++++++++ paddle/framework/init_test.cc | 27 ++++++++++ paddle/pybind/CMakeLists.txt | 2 +- paddle/pybind/pybind.cc | 23 ++------ python/paddle/v2/fluid/executor.py | 7 +++ 10 files changed, 248 insertions(+), 52 deletions(-) create mode 100644 paddle/framework/init.cc create mode 100644 paddle/framework/init.h create mode 100644 paddle/framework/init_test.cc diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index 4b0eff3adb..206e298eb2 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -58,3 +58,6 @@ cc_test(var_type_inference_test SRCS var_type_inference_test.cc DEPS op_registry proto_desc) cc_library(selected_rows SRCS selected_rows.cc DEPS tensor) cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows) + +cc_library(init SRCS init.cc DEPS gflags executor place stringpiece) +cc_test(init_test SRCS init_test.cc DEPS init) diff --git a/paddle/framework/ddim_test.cc b/paddle/framework/ddim_test.cc index 756232b1b5..bd5ea09d7d 100644 --- a/paddle/framework/ddim_test.cc +++ b/paddle/framework/ddim_test.cc @@ -1,3 +1,16 @@ +/* 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 #include diff --git a/paddle/framework/executor.cc b/paddle/framework/executor.cc index 83aa927c29..a8b8a6f8e8 100644 --- a/paddle/framework/executor.cc +++ b/paddle/framework/executor.cc @@ -33,32 +33,12 @@ namespace framework { const std::string kFeedOpType = "feed"; const std::string kFetchOpType = "fetch"; -Executor::Executor(const std::vector& places) : own_(true) { - PADDLE_ENFORCE_GT(places.size(), 0); - device_contexts_.resize(places.size()); - for (size_t i = 0; i < places.size(); i++) { - if (platform::is_cpu_place(places[i])) { - device_contexts_[i] = new platform::CPUDeviceContext( - boost::get(places[i])); - } else if (platform::is_gpu_place(places[i])) { -#ifdef PADDLE_WITH_CUDA - device_contexts_[i] = new platform::CUDADeviceContext( - boost::get(places[i])); -#else - PADDLE_THROW( - "'GPUPlace' is not supported, Please re-compile with WITH_GPU " - "option"); -#endif - } - } -} +DeviceContextPool* DeviceContextPool::pool = nullptr; -Executor::~Executor() { - if (own_) { - for (auto& device_context : device_contexts_) { - delete device_context; - } - } +Executor::Executor(const std::vector& places) { + DeviceContextPool& pool = DeviceContextPool::Get(); + auto borrowed_contexts = pool.Borrow(places); + device_contexts_.swap(borrowed_contexts); } static void CreateTensor(Variable* var, VarDesc::VarType var_type) { @@ -132,8 +112,5 @@ void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id, } } -Executor::Executor(const platform::DeviceContext& device) - : device_contexts_({&device}), own_(false) {} - } // namespace framework } // namespace paddle diff --git a/paddle/framework/executor.h b/paddle/framework/executor.h index b745f4f647..073e04729b 100644 --- a/paddle/framework/executor.h +++ b/paddle/framework/executor.h @@ -14,19 +14,98 @@ limitations under the License. */ #pragma once +#include +#include + #include "paddle/framework/op_info.h" #include "paddle/framework/program_desc.h" #include "paddle/framework/scope.h" #include "paddle/framework/tensor.h" +#include "paddle/platform/device_context.h" namespace paddle { namespace framework { +class DeviceContextPool { + public: + static DeviceContextPool& Get() { + PADDLE_ENFORCE_NOT_NULL(pool, "Need to Create DeviceContextPool first!"); + return *pool; + } + + static DeviceContextPool& Create(const std::vector& places) { + if (pool == nullptr) { + pool = new DeviceContextPool(places); + } + return *pool; + } + + std::vector Borrow( + const std::vector& places) { + PADDLE_ENFORCE_GT(places.size(), 0); + PADDLE_ENFORCE_LE(places.size(), device_contexts_.size()); + std::vector borrowed_contexts; + for (auto& place : places) { + auto range = device_contexts_.equal_range(place); + if (range.first == range.second) { + PADDLE_THROW( + "'Place' is not supported, Please re-compile with WITH_GPU " + "option"); + } + // TODO(dzhwinter) : assign the first found device. Will enhanced later. + // device load balancer maybe useful here. + borrowed_contexts.emplace_back(range.first->second); + } + return borrowed_contexts; + } + + explicit DeviceContextPool(const std::vector& places) { + PADDLE_ENFORCE_GT(places.size(), 0); + for (size_t i = 0; i < places.size(); i++) { + if (platform::is_cpu_place(places[i])) { + device_contexts_.emplace( + places[i], new platform::CPUDeviceContext( + boost::get(places[i]))); + } else if (platform::is_gpu_place(places[i])) { +#ifdef PADDLE_WITH_CUDA + device_contexts_.emplace( + places[i], new platform::CUDADeviceContext( + boost::get(places[i]))); +#else + PADDLE_THROW( + "'GPUPlace' is not supported, Please re-compile with WITH_GPU " + "option"); +#endif + } + } + } + + ~DeviceContextPool() {} + + private: + static DeviceContextPool* pool; + struct Hash { + std::hash hash_; + size_t operator()(const platform::Place& place) const { + return hash_(place.which()); + } + }; + std::unordered_multimap + device_contexts_; + DISABLE_COPY_AND_ASSIGN(DeviceContextPool); +}; + class Executor { public: + // TODO(dzhwinter) : Do not rely on this function, it will be removed + explicit Executor(const platform::DeviceContext& device) + : Executor(std::vector({device.GetPlace()})) {} + + explicit Executor(const platform::Place& place) + : Executor(std::vector({place})) {} + explicit Executor(const std::vector& places); - explicit Executor(const platform::DeviceContext& devices); - ~Executor(); /* @Brief * Runtime evaluation of the given ProgramDesc under certain Scope @@ -39,7 +118,6 @@ class Executor { private: std::vector device_contexts_; - bool own_; }; } // namespace framework diff --git a/paddle/framework/init.cc b/paddle/framework/init.cc new file mode 100644 index 0000000000..1c4476f4b3 --- /dev/null +++ b/paddle/framework/init.cc @@ -0,0 +1,80 @@ +/* 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 +#include + +#include "paddle/framework/executor.h" +#include "paddle/framework/init.h" +#include "paddle/platform/place.h" +#include "paddle/string/piece.h" + +namespace paddle { +namespace framework { + +std::once_flag gflags_init_flag; + +// TODO(qijun) move init gflags to init.cc +void InitGflags(std::vector &argv) { + std::call_once(gflags_init_flag, [&]() { + int argc = argv.size(); + char **arr = new char *[argv.size()]; + std::string line; + for (size_t i = 0; i < argv.size(); i++) { + arr[i] = &argv[i][0]; + line += argv[i]; + line += ' '; + } + google::ParseCommandLineFlags(&argc, &arr, true); + VLOG(1) << "Init commandline: " << line; + }); +} + +bool InitDevices(const std::vector &devices) { + // device format + // CPU + // GPU:1 + // TODO(dzhwinter) : add device format annotation for users. + std::vector places; + for (auto &device : devices) { + auto p = string::Piece(device); + if (string::Find(p, ':', 0) == string::Piece::npos) { + places.emplace_back(platform::CPUPlace()); + } else if (string::HasPrefix(p, "GPU")) { +#ifdef PADDLE_WITH_CUDA + auto pos = string::RFind(p, ':', string::Piece::npos); + auto number = device.substr(pos + 1); + places.emplace_back(platform::GPUPlace(std::stoi(number))); +#else + LOG(WARNING) + << "'GPU' is not supported, Please re-compile with WITH_GPU option"; +#endif + } else { + return false; + } + } + + if (std::find_if(places.begin(), places.end(), + [&](const platform::Place &place) { + return platform::is_cpu_place(place); + }) == places.end()) { + places.emplace_back(platform::CPUPlace()); + LOG(WARNING) << "Not specified any device, use CPU by Default."; + } + DeviceContextPool::Create(places); + return true; + return true; +} + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/init.h b/paddle/framework/init.h new file mode 100644 index 0000000000..1715cd81e6 --- /dev/null +++ b/paddle/framework/init.h @@ -0,0 +1,28 @@ +/* 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 +#include + +#include "gflags/gflags.h" +#include "glog/logging.h" + +namespace paddle { +namespace framework { + +void InitGflags(std::vector &argv); + +bool InitDevices(const std::vector &devices); + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/init_test.cc b/paddle/framework/init_test.cc new file mode 100644 index 0000000000..f65e881a76 --- /dev/null +++ b/paddle/framework/init_test.cc @@ -0,0 +1,27 @@ +/* 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 "gtest/gtest.h" + +#include "paddle/framework/init.h" + +TEST(Init, InitDevices) { + using paddle::framework::InitDevices; + std::vector ds1 = {"CPU"}; + ASSERT_EQ(InitDevices(ds1), true); + +#ifdef PADDLE_WITH_CUDA + std::vector ds2 = {"CPU", "GPU:0", "GPU:1"}; + ASSERT_EQ(InitDevices(ds2), true); +#endif +} diff --git a/paddle/pybind/CMakeLists.txt b/paddle/pybind/CMakeLists.txt index fd55f410d3..1fb69de90d 100644 --- a/paddle/pybind/CMakeLists.txt +++ b/paddle/pybind/CMakeLists.txt @@ -1,7 +1,7 @@ if(WITH_PYTHON) cc_library(paddle_pybind SHARED SRCS pybind.cc exception.cc protobuf.cc - DEPS pybind python backward proto_desc paddle_memory executor prune + DEPS pybind python backward proto_desc paddle_memory executor prune init ${GLOB_OP_LIB}) endif(WITH_PYTHON) diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc index 1faf24bcb8..4248db34c6 100644 --- a/paddle/pybind/pybind.cc +++ b/paddle/pybind/pybind.cc @@ -16,11 +16,11 @@ limitations under the License. */ #include // for call_once #include -#include "gflags/gflags.h" #include "paddle/framework/backward.h" #include "paddle/framework/executor.h" #include "paddle/framework/feed_fetch_method.h" #include "paddle/framework/framework.pb.h" +#include "paddle/framework/init.h" #include "paddle/framework/lod_rank_table.h" #include "paddle/framework/lod_tensor.h" #include "paddle/framework/lod_tensor_array.h" @@ -51,24 +51,6 @@ static size_t UniqueIntegerGenerator(const std::string &prefix) { return generators[prefix].fetch_add(1); } -std::once_flag gflags_init_flag; - -// TODO(qijun) move init gflags to init.cc -void InitGflags(std::vector &argv) { - std::call_once(gflags_init_flag, [&]() { - int argc = argv.size(); - char **arr = new char *[argv.size()]; - std::string line; - for (size_t i = 0; i < argv.size(); i++) { - arr[i] = &argv[i][0]; - line += argv[i]; - line += ' '; - } - google::ParseCommandLineFlags(&argc, &arr, true); - VLOG(1) << "Init commandline: " << line; - }); -} - bool IsCompileGPU() { #ifndef PADDLE_WITH_CUDA return false; @@ -438,7 +420,8 @@ All parameter, weight, gradient are variables in Paddle. .def("run", &Executor::Run); m.def("unique_integer", UniqueIntegerGenerator); - m.def("init_gflags", InitGflags); + m.def("init_gflags", framework::InitGflags); + m.def("init_devices", &framework::InitDevices); m.def("is_compile_gpu", IsCompileGPU); m.def("set_feed_variable", framework::SetFeedVariable); diff --git a/python/paddle/v2/fluid/executor.py b/python/paddle/v2/fluid/executor.py index bdc82eede9..9a99b045dc 100644 --- a/python/paddle/v2/fluid/executor.py +++ b/python/paddle/v2/fluid/executor.py @@ -46,6 +46,13 @@ class Executor(object): p.set_place(each) act_places.append(p) + # TODO(dzhwinter) : consider that our fluid tests all written in + # GPUPlace(gpu_id), this will be changed in next PR. + if core.is_compile_gpu(): + core.init_devices(["CPU", "GPU:0"]) + else: + core.init_devices(["CPU"]) + self.executor = core.Executor(act_places) self.places = places