[PIR] Reify InferSymbolicShapeInterface

[zhangbopd]

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Pcard-67164

Reify InferSymbolicShapeInterface

IS_DEBUG_DY_SHAPE=1 FLAGS_pir_apply_shape_optimization_pass=true GLOG_v=3 ctest -R test_cinn_sub_graph_symbolic -VV > log.log  2>&1 

{
 (%0) = "pd_op.data" () {dtype:(pd_op.DataType)float32,name:"x",place:(pd_op.Place)Place(undefined:0),shape:(pd_op.IntArray)[-1,256],stop_gradient:[false],symbolic_shape:(shape_data)[S0,256]_[nullopt]} : () -> pd_op.tensor<-1x256xf32>
 (%1) = "pd_op.exp" (%0) {stop_gradient:[false],symbolic_shape:(shape_data)[S0,256]_[nullopt]} : (pd_op.tensor<-1x256xf32>) -> pd_op.tensor<-1x256xf32>
 (%2) = "pd_op.subtract" (%1, %0) {stop_gradient:[false],symbolic_shape:(shape_data)[S0,256]_[nullopt]} : (pd_op.tensor<-1x256xf32>, pd_op.tensor<-1x256xf32>) -> pd_op.tensor<-1x256xf32>
 (%3) = "pd_op.shape" (%0) {stop_gradient:[true],symbolic_shape:(shape_data)[2]_[S0,256]} : (pd_op.tensor<-1x256xf32>) -> pd_op.tensor<2xi32>
 (%4) = "pd_op.full_int_array" () {dtype:(pd_op.DataType)int64,place:(pd_op.Place)Place(cpu),stop_gradient:[true],symbolic_shape:(shape_data)[1]_[0],value:[(Int64)0]} : () -> pd_op.tensor<1xi64>
 (%5) = "pd_op.full_int_array" () {dtype:(pd_op.DataType)int64,place:(pd_op.Place)Place(cpu),stop_gradient:[true],symbolic_shape:(shape_data)[1]_[1],value:[(Int64)1]} : () -> pd_op.tensor<1xi64>
 (%6) = "pd_op.slice" (%3, %4, %5) {axes:[(Int64)0],decrease_axis:[(Int64)0],infer_flags:[(Int64)1],stop_gradient:[true]} : (pd_op.tensor<2xi32>, pd_op.tensor<1xi64>, pd_op.tensor<1xi64>) -> pd_op.tensor<i32>
 (%7) = "pd_op.shape" (%1) {stop_gradient:[true],symbolic_shape:(shape_data)[2]_[S0,256]} : (pd_op.tensor<-1x256xf32>) -> pd_op.tensor<2xi32>
 (%8) = "pd_op.full_int_array" () {dtype:(pd_op.DataType)int64,place:(pd_op.Place)Place(cpu),stop_gradient:[true],symbolic_shape:(shape_data)[1]_[1],value:[(Int64)1]} : () -> pd_op.tensor<1xi64>
 (%9) = "pd_op.full_int_array" () {dtype:(pd_op.DataType)int64,place:(pd_op.Place)Place(cpu),stop_gradient:[true],symbolic_shape:(shape_data)[1]_[2],value:[(Int64)2]} : () -> pd_op.tensor<1xi64>
 (%10) = "pd_op.slice" (%7, %8, %9) {axes:[(Int64)0],decrease_axis:[(Int64)0],infer_flags:[(Int64)1],stop_gradient:[true]} : (pd_op.tensor<2xi32>, pd_op.tensor<1xi64>, pd_op.tensor<1xi64>) -> pd_op.tensor<i32>
 (%11) = "pd_op.cast" (%6) {dtype:(pd_op.DataType)int64,stop_gradient:[true],symbolic_shape:(shape_data)[]_[nullopt]} : (pd_op.tensor<i32>) -> pd_op.tensor<i64>
 (%12) = "pd_op.cast" (%10) {dtype:(pd_op.DataType)int64,stop_gradient:[true],symbolic_shape:(shape_data)[]_[nullopt]} : (pd_op.tensor<i32>) -> pd_op.tensor<i64>
 (%13) = "builtin.combine" (%11, %12) {stop_gradient:[true],symbolic_shape:(shape_data)[]_[nullopt]} : (pd_op.tensor<i64>, pd_op.tensor<i64>) -> vec[pd_op.tensor<i64>,pd_op.tensor<i64>]
 (%14) = "pd_op.stack" (%13) {axis:(Int32)0,stop_gradient:[true],symbolic_shape:(shape_data)[]_[nullopt]} : (vec[pd_op.tensor<i64>,pd_op.tensor<i64>]) -> pd_op.tensor<2xi64>
 (%15, %16) = "pd_op.reshape" (%2, %14) {stop_gradient:[false,false],symbolic_shape:(shape_data)[]_[nullopt]} : (pd_op.tensor<-1x256xf32>, pd_op.tensor<2xi64>) -> pd_op.tensor<-1x-1xf32>, pd_op.tensor<0x-1x256xi64>
 () = "builtin.shadow_output" (%15) {output_name:"output_0"} : (pd_op.tensor<-1x-1xf32>) -> 
}

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[zyfncg]

将shape_data绑定在op attribute上是不是不太合适？

[zhangbopd]

这个新增的attribute和 pir 那边同学以及宏宇讨论过，attribute存放 动态shape信息是合理的，当时是想借助 pir 组件完成 shape 推导的，但是因为 isShapeEqual 等接口的使用，所以还是保留了 shapeAnlysis，在 PR 描述里加了program的打印日志，这样也方便调试的时候对 shape 推导进行验证和debug

[zyfncg]

SymbolAttribute为什么要成为一个Attribute类型？什么场景下会被使用？

[zhangbopd]

同上

[lanxianghit]

PADDLE_ENFORCE规范问题请立即提PR修复

[zhangbopd]

PADDLE_ENFORCE规范问题请立即提PR修复

OK, done in #60644

[zyfncg]

ShapeData是否要作为attribute后面再讨论下