[Cherry-pick] Two detail fix prs (#41728)

* [Eager] Remove elementwise add in conv (#41515)

* remove elementwise add in conv

* use reshape

* fix warpctc grad kernel dep eror (#41598)

paddle/phi/kernels/CMakeLists.txt

@@ -61,7 +61,7 @@ kernel_library(triangular_solve_grad_kernel DEPS ${COMMON_KERNEL_DEPS} matrix_re
 kernel_library(rnn_kernel DEPS ${COMMON_KERNEL_DEPS} concat_and_split_functor lstm_compute gru_compute)
 kernel_library(rnn_grad_kernel DEPS ${COMMON_KERNEL_DEPS} concat_and_split_functor lstm_compute gru_compute)
 kernel_library(warpctc_kernel DEPS ${COMMON_KERNEL_DEPS} phi_dynload_warpctc sequence_padding sequence_scale)
-kernel_library(warpctc_grad_kernel DEPS ${COMMON_KERNEL_DEPS} sequence_padding sequence_scale)
+kernel_library(warpctc_grad_kernel DEPS ${COMMON_KERNEL_DEPS} phi_dynload_warpctc sequence_padding sequence_scale)
 
 # 4. auto parse and build kernel targets by cmake
 register_kernels(EXCLUDES ${COMMON_BAISC_KERNELS} ${MANUAL_BUILD_KERNELS} DEPS ${COMMON_KERNEL_DEPS} ${COMMON_BAISC_KERNELS} )

python/paddle/nn/functional/conv.py

@@ -127,8 +127,12 @@ def _conv_nd(x,
             x, weight, stride, padding, padding_algorithm, groups, dilation,
             data_format, False, -1, False)
         if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
-            return out
+            channel_dim = channel_dim + len(
+                x.shape) if channel_dim < 0 else channel_dim
+            tmp_bias = _C_ops.final_state_reshape(
+                bias, bias.shape +
+                [1 for i in range(len(x.shape) - channel_dim - 1)])
+            return _C_ops.final_state_add(pre_bias, tmp_bias)
         else:
             return pre_bias
     if in_dynamic_mode():