* paddle::framework::vectorize() templatization

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* update pybind/imperative.cc

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* revert update on unsqueeze_op.cc and warpctc_cudnn_op.cu.cc

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* remove assert.h

* change PADDLE_ASSERT_MSG to PADDLE_ENFORCE

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* fix tensorrt paddle_enforce

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* fix mask rcnn bug:
1. affine channel fuse (diff)
2. condition block op (memory leak)
3. merge lod tensor op (diff)
4. memroy optim (diff)
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* fix ci aboud PADDLE_ENFOCE
fix merge lod infer op ut
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* add tensorrt support for windows

* Fix Mask rcnn predictor
    1. refine memory optim algorithm to support the model with the block op.
    2. output diff : modify the affine channel fuse
    3. add condition_block_infer op
add interface for setting trt calib table dir
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* add the missing files.
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* 1 add trt fp16 support
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* fix trt fp16 ce error
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* add an vlog if the user use trt4 and specify fp16.
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* Fix Mask rcnn predictor
    1. refine memory optim algorithm to support the model with the block op.
    2. output diff : modify the affine channel fuse
    3. add condition_block_infer op
add interface for setting trt calib table dir
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* add the missing files.
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* 1 add trt fp16 support
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Also fix a dependency error which may cause compile error

* update paddle-trt for:
    1. fix bug: when batch > 2, core in split plugin.
    2. add leaky_relu trt5.0 support (yolov3 from 65ms to 42ms.)
    3. add new attr to dropout.
    4. shuffle channel, swish, relu6 support
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* 1. fix ci
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* fluid int8 train and trt int8 predict align.
trt int8 predict init
op converter

* 2. align fluid int8 train and trt int8 inference.
enhance quant dequant fuse pass
enhance op converter, trt engine, trt engine op, trt subgraph pass.

* 3. add delete_quant_dequant_pass for trt

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* 4. add the missing file
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* 5. i modify the c++ interface, but forget to modify the pybind code
fix the IS_TRT_VERSION_GE bug, and fix elementwise op converter
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ban all trt ut. will fix it later.

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1). add static trt load model
2). fix bug: when device_id is not 0, the trt will have a bug
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add simple static mode loading
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1. bind trt input and output to fluid tensors

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1). add static trt load model
2). fix bug: when device_id is not 0, the trt will have a bug
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add simple static mode loading
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1. bind trt input and output to fluid tensors

BUG: After continuing to input different data, the output cannot be aligned
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fix comments

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