FLUID models roadmap

[mrysztow]

Do you have a roadmap of introducing models to FLUID? We would like to align our plan of providing MKL-DNN OP kernels with model roadmap.

[luotao1]

In #7561, we have discussed PaddlePaddle's 10 aspects in 2018. For models of FLUID, there are NLP,Speech and Image support:

• NLP support: Enhance the CPU power of some NLP ops (mainly RNN/LSTM/GRU) based on specific workload.
• Speech support: Enhance the CPU power of some Speech ops (mainly RNN/CNN) based on specific workload.
• Image support: Enhance the CPU power of some Image ops (mainly CNN and Detection) based on specific workload.

[lcy-seso]

I give some brief information on NLP support. We have a plan to first focus on some state-of-art models in neural machine translation task in NLP field.

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• Generally, for most many NLP tasks, RNN modules (including but not limited to) LSTM (LSTM operator , LSTM unit operator), GRU (GRU operator，GRU unit operator), and Simple RNN, are among the most important compuatation units. These operators are expected to be highly optimized.

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• For NMT task, three models are expected to be highly optimized:

  • RNN search (RNN encoder-decoder with attention): Fluid's implementation
  • ConvS2S: Fluid's implementation is WIP.
  • Transformer: Fluid's implementation

• About the time schedule:

  • The first milestone for the NMT task is at the end of March, at then, the implementation of the three models are finished, their learning performances are verified and a basic profiling report is given. You can check this project: https://github.com/PaddlePaddle/Paddle/projects/37

• Important operators expected to be highly optimized:

  • matmul operator
  • layer normalization

[mrysztow]

Thank you for pointing particular NMT models.
What topologies are the most important for image recognition and speech? Are they Resnet-50 and DS2?

[luotao1]

@qingqing01 @kuke Can you help to answer it? Thanks very much!

[qingqing01]

About computer vision, what we are doing now are as follows:

• Image Classification , now mainly SE-ResNeXt model. The mainly related operators are:

  • Conv2D, Pool2D, GlobalPooling, BatchNorm, Fc, ElementwiseMul( support broadcast), SoftMax, CrossEntropy, Relu

• Object Detection, now mainly MobileNet-SSD model. The mainly related operators are:

  • Conv2D, DepthwiseConv, Pool2D, BatchNorm, PriorBox, BipartiteMatch, TargetAssign, IouSimilarity, BoxCoder, DetectionMap, MineHardExamples.

• OCR Recognition: now mainly CRNN CTC model, The mainly related operators are:

  • Conv2D, Pool2D, BatchNorm, GRU, Fc, WarpCTC, CTC greedy decoder, EditDistance

[mrysztow]

@qingqing01 thank you for the list
Does SE-ResNeXt is going to replace classic Resnet50, already implemented for Fluid (https://github.com/dzhwinter/benchmark/blob/master/fluid/resnet50.py) ?

[qingqing01]

@mrysztow Both two networks are classic. This two are all needed. They have the same basic operators.

[kuke]

For the application in speech, we are now developing a recognition system DeepASR. The two important operators used are Conv1D and LSTMP.

In Q2, we plan to implement a wake-up system, the main structure is also CNNs+RNNs.

[mrysztow]

@luotao1 is https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/fluid/machine_translation.py implementation of Conv seq2seq, mentioned earlier in this thread (#8450 (comment))? Or it is another seq2seq model?

[luotao1]

@mrysztow https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/fluid/machine_translation.py is seq2seq model. And conv seq2seq is in PaddlePaddle/models#686.

[mrysztow]

Thank you

[luotao1]

Tier 1	Tier 2	Tier 3
ResNet50	MobileNet-SSD	Conv seq2seq(PR)
Transformer	RNN Search(PR)	CRNN CTC
SE-ResNeXt
DeepASR

[luotao1]

Feeds support :

• text_classification
• Transformer
• language_model
• chinese_ner

The mainly related operators are:

• FC, GRU, LSTM, Self-Attention

[mrysztow]

@luotao1
I would like to confirm, that the current priority list is, considering current CPU deployments and feeds model, would be the following:

Tier 1	Tier 2	Tier 3	Tier 4
ResNet50	text_classification	MobileNet-SSD	Conv seq2seq(PR)
CRNN-CTC	transformer	SE-ResNeXt
	language_model	DeepASR
	chinese_ner	RNN Search

[luotao1]

@mrysztow The priority list is OK now.