add autogen code support for warpctc op

paddle/fluid/operators/CMakeLists.txt

@@ -96,7 +96,7 @@ endif()
 
 set(OP_HEADER_DEPS ${OP_HEADER_DEPS} phi phi_utils backward_infermeta sparse_backward_infermeta static_prim_api get_expected_kernel_func)
 
-register_operators(EXCLUDES py_func_op warpctc_op dgc_op generated_op1 generated_op2 generated_op3 generated_op4 load_combine_op lstm_op run_program_op quantize_linear_op
+register_operators(EXCLUDES py_func_op dgc_op generated_op1 generated_op2 generated_op3 generated_op4 load_combine_op lstm_op run_program_op quantize_linear_op
         recurrent_op save_combine_op sparse_attention_op sync_batch_norm_op activation_op ${OP_MKL_DEPS} DEPS ${OP_HEADER_DEPS})
 
 op_library(generated_op UNITY SRCS generated_op1.cc generated_op2.cc generated_op3.cc generated_op4.cc DEPS ${OP_HEADER_DEPS})
@@ -115,20 +115,10 @@ else()
 endif()
 
 if (WITH_GPU OR WITH_ROCM)
-    if(WITH_ROCM)
-        op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale SRCS warpctc_op.cc)
-    # warpctc_op needs cudnn 7 above
-    elseif(${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
-        op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale SRCS warpctc_op.cc)
-    else()
-        op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
-    endif()
     op_library(sync_batch_norm_op)
     if ((NOT WIN32) AND (NOT WITH_ROCM) AND (NOT PADDLE_WITH_ARM) AND (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_LESS 11.3) )
         op_library(sparse_attention_op)
     endif()
-else()
-    op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
 endif()
 
 if (WITH_ASCEND_CL)

paddle/phi/api/yaml/backward.yaml

@@ -1869,6 +1869,19 @@
   kernel :
     func : unstack_grad
 
+- backward_op : warpctc_grad
+  forward : warpctc (Tensor logits, Tensor label, Tensor logits_length, Tensor labels_length, int blank = 0, bool norm_by_times = false) -> Tensor(loss), Tensor(warpctcgrad)
+  args : (Tensor logits, Tensor logits_length, Tensor warpctcgrad, Tensor loss_grad, int blank, bool norm_by_times)
+  output : Tensor(logits_grad)
+  infer_meta :
+    func : UnchangedInferMeta
+    param : [logits]
+  kernel :
+    func : warpctc_grad
+    data_type : loss_grad
+  optional : logits_length
+  no_need_buffer : logits
+
 - backward_op : warprnnt_grad
   forward : warprnnt (Tensor input, Tensor label, Tensor input_lengths, Tensor label_lengths, int blank = 0, float fastemit_lambda = 0.0) -> Tensor(loss), Tensor(warprnntgrad)
   args : (Tensor input, Tensor input_lengths, Tensor warprnntgrad, Tensor loss_grad, int blank = 0, float fastemit_lambda = 0.0)

paddle/phi/api/yaml/legacy_backward.yaml

@@ -1224,18 +1224,6 @@
     func : uniform_inplace_grad
   inplace : (out_grad -> x_grad)
 
-- backward_op : warpctc_grad
-  forward : warpctc (Tensor logits, Tensor label, Tensor logits_length, Tensor labels_length, int blank, bool norm_by_times) -> Tensor(loss), Tensor(warpctcgrad)
-  args : (Tensor logits, Tensor logits_length, Tensor warpctcgrad, Tensor loss_grad, int blank, bool norm_by_times)
-  output : Tensor(logits_grad)
-  infer_meta :
-    func : UnchangedInferMeta
-    param : [logits]
-  kernel :
-    func : warpctc_grad
-  optional : logits_length
-  no_need_buffer : logits
-
 - backward_op : yolo_loss_grad
   forward : yolo_loss(Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, int[] anchors, int[] anchor_mask, int class_num, float ignore_thresh, int downsample_ratio, bool use_label_smooth=true, float scale_x_y=1.0) -> Tensor(loss), Tensor(objectness_mask), Tensor(gt_match_mask)
   args : (Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, Tensor objectness_mask, Tensor gt_match_mask, Tensor loss_grad, int[] anchors, int[] anchor_mask, int class_num, float ignore_thresh, int downsample_ratio, bool use_label_smooth=true, float scale_x_y=1.0)

paddle/phi/api/yaml/legacy_ops.yaml

@@ -1563,18 +1563,6 @@
     data_type: x
   backward: unpool3d_grad
 
-- op : warpctc
-  args : (Tensor logits, Tensor label, Tensor logits_length, Tensor labels_length, int blank, bool norm_by_times)
-  output :  Tensor(loss), Tensor(warpctcgrad)
-  infer_meta :
-    func : WarpctcInferMeta
-  kernel :
-    func : warpctc
-    data_type: logits
-  optional: logits_length, labels_length
-  intermediate: warpctcgrad
-  backward : warpctc_grad
-
 - op : yolo_box
   args : (Tensor x, Tensor img_size, int[] anchors, int class_num, float conf_thresh, int downsample_ratio, bool clip_bbox, float scale_x_y=1.0, bool iou_aware=false, float iou_aware_factor=0.5)
   output : Tensor(boxes), Tensor(scores)

paddle/phi/api/yaml/op_compat.yaml

@@ -2173,6 +2173,13 @@
   outputs :
     {scores : Scores, path : Path}
 
+- op : warpctc
+  backward : warpctc_grad
+  inputs :
+    {logits : Logits, label : Label, logits_length : LogitsLength, labels_length : LabelLength}
+  outputs :
+    {warpctcgrad : WarpCTCGrad, loss : Loss}
+
 - op : where
   backward : where_grad
   inputs :

paddle/phi/api/yaml/ops.yaml

@@ -1809,6 +1809,18 @@
     func : viterbi_decode
     data_type : potentials
 
+- op : warpctc
+  args : (Tensor logits, Tensor label, Tensor logits_length, Tensor labels_length, int blank = 0, bool norm_by_times = false)
+  output :  Tensor(loss), Tensor(warpctcgrad)
+  infer_meta :
+    func : WarpctcInferMeta
+  kernel :
+    func : warpctc
+    data_type: logits
+  optional: logits_length, labels_length
+  intermediate: warpctcgrad
+  backward : warpctc_grad
+
 - op : warprnnt
   args : (Tensor input, Tensor label, Tensor input_lengths, Tensor label_lengths, int blank = 0, float fastemit_lambda = 0.0)
   output :  Tensor(loss), Tensor(warprnntgrad)