[Eager] Add warpctc yaml

paddle/phi/api/yaml/legacy_api.yaml

@@ -2345,6 +2345,18 @@
     func : viterbi_decode
     data_type : input
 
+- api : 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
+
 - api : where
   args : (Tensor condition, Tensor x, Tensor y)
   output : Tensor

paddle/phi/api/yaml/legacy_backward.yaml

@@ -2326,6 +2326,18 @@
   inplace : (out_grad -> x_grad)
   backward : unsqueeze_double_grad
 
+- backward_api : 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_api : where_grad
   forward : where (Tensor condition, Tensor x, Tensor y) -> Tensor(out)
   args : (Tensor condition, Tensor x, Tensor y, Tensor out_grad)

paddle/phi/infermeta/multiary.cc

@@ -2049,7 +2049,7 @@ void WarpctcInferMeta(const MetaTensor& logits,
                       const MetaTensor& labels_length,
                       int blank,
                       bool norm_by_times,
-                      MetaTensor* warpctc_grad,
+                      MetaTensor* warpctcgrad,
                       MetaTensor* loss) {
   auto logits_dims = logits.dims();
   int sequence_width = 0;

paddle/phi/infermeta/multiary.h

@@ -358,7 +358,7 @@ void WarpctcInferMeta(const MetaTensor& logits,
                       const MetaTensor& labels_length,
                       int blank,
                       bool norm_by_times,
-                      MetaTensor* warpctc_grad,
+                      MetaTensor* warpctcgrad,
                       MetaTensor* loss);
 
 void WhereInferMeta(const MetaTensor& condition,

paddle/phi/kernels/impl/warpctc_grad_kernel_impl.h

@@ -29,33 +29,33 @@ namespace phi {
 
 template <typename T, typename Context>
 void WarpctcGradKernel(const Context& dev_ctx,
-                       const DenseTensor& warpctc_grad,
                        const DenseTensor& logits,
-                       const DenseTensor& loss_grad,
                        const paddle::optional<DenseTensor>& logits_length,
+                       const DenseTensor& warpctcgrad,
+                       const DenseTensor& loss_grad,
                        int blank,
                        bool norm_by_times,
                        DenseTensor* logits_grad) {
   dev_ctx.template Alloc<T>(logits_grad);
 
   if (logits_length.is_initialized()) {
-    int max_seq_length = warpctc_grad.dims()[0];  // Tmax
-    int num_sequences = warpctc_grad.dims()[1];   // B
-    int seq_width = warpctc_grad.dims()[2];       // D
+    int max_seq_length = warpctcgrad.dims()[0];  // Tmax
+    int num_sequences = warpctcgrad.dims()[1];   // B
+    int seq_width = warpctcgrad.dims()[2];       // D
 
     // B
     auto logits_len_e = EigenTensor<int64_t, 1>::From(*logits_length);
     // (B, 1)
     auto loss_grad_e = EigenTensor<T, 2>::From(loss_grad);
     // (T, B, D)
-    auto warpctc_grad_e = EigenTensor<T, 3>::From(warpctc_grad);
+    auto warpctcgrad_e = EigenTensor<T, 3>::From(warpctcgrad);
 
     auto logits_grad_e = EigenTensor<T, 3>::From(*logits_grad);
 
     Eigen::DSizes<int, 3> grad_shape(1, num_sequences, 1);
     Eigen::DSizes<int, 3> bcast(max_seq_length, 1, seq_width);
-    auto logits_g = warpctc_grad_e *
-                    loss_grad_e.reshape(grad_shape).broadcast(bcast).eval();
+    auto logits_g =
+        warpctcgrad_e * loss_grad_e.reshape(grad_shape).broadcast(bcast).eval();
 
     auto* place = dev_ctx.eigen_device();
     if (norm_by_times) {
@@ -71,7 +71,7 @@ void WarpctcGradKernel(const Context& dev_ctx,
   } else {
     paddle::operators::math::UnpaddingLoDTensorFunctor<Context, T>()(
         dev_ctx,
-        warpctc_grad,
+        warpctcgrad,
         logits_grad,
         -1,
         0,

paddle/phi/kernels/impl/warpctc_kernel_impl.h

@@ -233,8 +233,8 @@ void WarpctcKernel(const Context& dev_ctx,
                    const paddle::optional<DenseTensor>& labels_length,
                    int blank,
                    bool norm_by_times,
-                   DenseTensor* warpctc_grad,
-                   DenseTensor* loss) {
+                   DenseTensor* loss,
+                   DenseTensor* warpctcgrad) {
   size_t num_sequences, sequence_width, max_sequence_length;
   paddle::framework::Vector<size_t> logits_lod;
   paddle::framework::Vector<size_t> label_lod;
@@ -383,11 +383,11 @@ void WarpctcKernel(const Context& dev_ctx,
 
   // warpctc computes loss and gradient in one call, gradient data also stored
   // in batch format
-  warpctc_grad->Resize(warpctc_logits.dims());
-  T* warpctc_grad_data = dev_ctx.template Alloc<T>(warpctc_grad);
+  warpctcgrad->Resize(warpctc_logits.dims());
+  T* warpctcgrad_data = dev_ctx.template Alloc<T>(warpctcgrad);
 
   phi::funcs::SetConstant<Context, T>()(
-      dev_ctx, warpctc_grad, static_cast<T>(0));
+      dev_ctx, warpctcgrad, static_cast<T>(0));
 
   // warpctc accesses labels in CPU memory
   DenseTensor warpctc_label;
@@ -439,7 +439,7 @@ void WarpctcKernel(const Context& dev_ctx,
   T* warpctc_loss_data = dev_ctx.template HostAlloc<T>(&warpctc_loss);
   WarpCTCFunctor<Context, T>()(dev_ctx,
                                warpctc_logits_data,
-                               warpctc_grad_data,
+                               warpctcgrad_data,
                                warpctc_label_data,
                                warpctc_label_lengths.data(),
                                warpctc_logits_lengths.data(),

paddle/phi/kernels/warpctc_grad_kernel.h

@@ -21,10 +21,10 @@ namespace phi {
 
 template <typename T, typename Context>
 void WarpctcGradKernel(const Context& dev_ctx,
-                       const DenseTensor& warpctc_grad,
                        const DenseTensor& logits,
-                       const DenseTensor& loss_grad,
                        const paddle::optional<DenseTensor>& logits_length,
+                       const DenseTensor& warpctcgrad,
+                       const DenseTensor& loss_grad,
                        int blank,
                        bool norm_by_times,
                        DenseTensor* logits_grad);

paddle/phi/kernels/warpctc_kernel.h

@@ -27,7 +27,7 @@ void WarpctcKernel(const Context& dev_ctx,
                    const paddle::optional<DenseTensor>& labels_length,
                    int blank,
                    bool norm_by_times,
-                   DenseTensor* warpctc_grad,
-                   DenseTensor* loss);
+                   DenseTensor* loss,
+                   DenseTensor* warpctcgrad);
 
 }  // namespace phi

paddle/phi/ops/compat/warpctc_sig.cc

@@ -20,13 +20,13 @@ KernelSignature WarpctcOpArgumentMapping(const ArgumentMappingContext& ctx) {
   return KernelSignature("warpctc",
                          {"Logits", "Label", "LogitsLength", "LabelLength"},
                          {"blank", "norm_by_times"},
-                         {"WarpCTCGrad", "Loss"});
+                         {"Loss", "WarpCTCGrad"});
 }
 
 KernelSignature WarpctcGradOpArgumentMapping(
     const ArgumentMappingContext& ctx) {
   return KernelSignature("warpctc_grad",
-                         {"WarpCTCGrad", "Logits", "Loss@GRAD", "LogitsLength"},
+                         {"Logits", "LogitsLength", "WarpCTCGrad", "Loss@GRAD"},
                          {"blank", "norm_by_times"},
                          {"Logits@GRAD"});
 }

python/paddle/fluid/layers/loss.py

@@ -546,6 +546,15 @@ def warpctc(input,
                                   fetch_list=[cost.name])
             print(output)
     """
+    if in_dygraph_mode():
+        if input_length is None or label_length is None:
+            raise ValueError(
+                "input_length and label_length must not be None in dygraph mode!"
+            )
+        loss_out = _C_ops.final_state_warpctc(input, label, input_length,
+                                              label_length, blank,
+                                              norm_by_times)
+        return loss_out
     if _non_static_mode():
         if input_length is None or label_length is None:
             raise ValueError(

python/paddle/fluid/tests/unittests/test_warpctc_op.py

@@ -191,6 +191,16 @@ def forward(self):
         return self.loss
 
 
+def python_api(logits,
+               label,
+               logits_length=None,
+               labels_length=None,
+               blank=0,
+               norm_by_times=False):
+    return paddle.fluid.layers.warpctc(logits, label, blank, norm_by_times,
+                                       logits_length, labels_length)
+
+
 class TestWarpCTCOp(OpTest):
 
     def config(self):
@@ -280,6 +290,8 @@ def config(self):
 
     def setUp(self):
         self.op_type = "warpctc"
+        self.python_api = python_api
+        self.python_out_sig = ["Loss"]
         self.config()
 
         logits = np.random.uniform(
@@ -344,7 +356,7 @@ def setUp(self):
         }
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_check_grad(self):
         self.outputs['WarpCTCGrad'] = self.gradient
@@ -387,6 +399,8 @@ def config(self):
 
     def setUp(self):
         self.op_type = "warpctc"
+        self.python_api = python_api
+        self.python_out_sig = ["Loss"]
         self.config()
 
         logits = np.random.uniform(
@@ -451,11 +465,11 @@ def setUp(self):
         }
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_check_grad(self):
         self.outputs['WarpCTCGrad'] = self.gradient
-        self.check_grad(["Logits"], "Loss")
+        self.check_grad(["Logits"], "Loss", check_eager=True)
 
 
 class TestWarpCTCOpError(unittest.TestCase):