[IR] IR attribute printer and support mutable attribute

[kangguangli]

PR types

New features

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Description

Current printer result is like:

(%902) = "pd.scale" (%900, %901){bias_after_scale:1,bias:0} : (tensor<f32>, tensor<1xf32>) -> tensor<f32>
(%903) = "pd.full" (){place:Place(cpu),dtype:float32,value:-1,shape:IntArray<1 >} : () -> tensor<1xf32>
(%904) = "pd.scale" (%902, %903){bias_after_scale:1,bias:0} : (tensor<f32>, tensor<1xf32>) -> tensor<f32>
(%905) = "pd.full" (){place:Place(cpu),dtype:int16,value:1,shape:IntArray<>} : () -> tensor<f32>
(%906) = "pd.mean_grad" (%879, %905){reduce_all:1,keepdim:0,axis:IntArray<>} : (tensor<-1x1xf32>, tensor<f32>) -> tensor<-1x1xf32>
(%907) = "pd.cross_entropy_with_softmax_grad" (%876, %878, %906){axis:-1,ignore_index:-100,numeric_stable_mode:1,use_softmax:1,soft_label:0} : (tensor<-1x1xi64>, tensor<-1x1000xf32>, tensor<-1x1xf32>) -> tensor<-1x1000xf32>
(%908, %909) = "pd.add_grad" (%873, %425, %907){axis:-1} : (tensor<-1x1000xf32>, tensor<1000xf32>, tensor<-1x1000xf32>) -> tensor<-1x1000xf32>, tensor<1000xf32>
(%910, %911) = "pd.matmul_grad" (%871, %427, %908){transpose_y:0,transpose_x:0} : (tensor<-1x2048xf32>, tensor<2048x1000xf32>, tensor<-1x1000xf32>) -> tensor<-1x2048xf32>, tensor<2048x1000xf32>
(%912) = "pd.flatten_grad" (%872, %910){} : (tensor<0x-1x2048x1x1xf32>, tensor<-1x2048xf32>) -> tensor<-1x2048x1x1xf32>
(%913) = "pd.pool2d_grad" (%868, %870, %912){padding_algorithm:EXPLICIT,adaptive:1,global_pooling:0,data_format:NCHW,exclusive:1,ceil_mode:0,paddings:array<0, 0>,pooling_type:avg,strides:array<1, 1>,kernel_size:IntArray<1 1 >} : (tensor<-1x2048x7x7xf32>, tensor<-1x2048x1x1xf32>, tensor<-1x2048x1x1xf32>) -> tensor<-1x2048x7x7xf32>
(%914) = "pd.relu_grad" (%868, %913){} : (tensor<-1x2048x7x7xf32>, tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32>

Others

Pcard-67164

[paddle-bot]

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[winter-wang]

同在ir命名空间下，就没有显示描述ir::了吧

[kangguangli]

好的，这里我改一下

[winter-wang]

类似，同命名空间没必要写ir::