Silence warnings about expanding indexed slices into dense tensors where it is intended.

PiperOrigin-RevId: 240179107

tensorflow/python/ops/array_grad.py

@@ -18,6 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import warnings
+
 from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
@@ -406,6 +408,10 @@ def _GatherGrad(op, grad):
   indices = op.inputs[1]
   size = array_ops.expand_dims(array_ops.size(indices), 0)
   values_shape = array_ops.concat([size, params_shape[1:]], 0)
+  with warnings.catch_warnings():
+    warnings.filterwarnings(
+        "ignore",
+        message="Converting sparse IndexedSlices to a dense Tensor.*")
     values = array_ops.reshape(grad, values_shape)
   indices = array_ops.reshape(indices, size)
   return [ops.IndexedSlices(values, indices, params_shape), None]
@@ -437,6 +443,10 @@ def _GatherV2Grad(op, grad):
     else:
       params_tail_shape = params_shape[1:]
     values_shape = array_ops.concat([indices_size, params_tail_shape], 0)
+    with warnings.catch_warnings():
+      warnings.filterwarnings(
+          "ignore",
+          message="Converting sparse IndexedSlices to a dense Tensor.*")
       values = array_ops.reshape(grad, values_shape)
     indices = array_ops.reshape(indices, indices_size)
     return [ops.IndexedSlices(values, indices, params_shape), None, None]
@@ -451,6 +461,10 @@ def _GatherV2Grad(op, grad):
                                       outer_dims + 1 + inner_dims)
 
   values_shape = array_ops.concat([outer_shape, indices_size, inner_shape], 0)
+  with warnings.catch_warnings():
+    warnings.filterwarnings(
+        "ignore",
+        message="Converting sparse IndexedSlices to a dense Tensor.*")
     values = array_ops.reshape(grad, values_shape)
   indices = array_ops.reshape(indices, indices_size)
 
@@ -519,6 +533,10 @@ ops.NotDifferentiable("StopGradient")
 
 @ops.RegisterGradient("Reshape")
 def _ReshapeGrad(op, grad):
+  with warnings.catch_warnings():
+    warnings.filterwarnings(
+        "ignore",
+        message="Converting sparse IndexedSlices to a dense Tensor.*")
     return [array_ops.reshape(grad, array_ops.shape(op.inputs[0])), None]
 
 
@@ -527,6 +545,10 @@ ops.NotDifferentiable("InvertPermutation")
 
 def _ReshapeToInput(op, grad):
   """Reshapes the gradient to the shape of the original input."""
+  with warnings.catch_warnings():
+    warnings.filterwarnings(
+        "ignore",
+        message="Converting sparse IndexedSlices to a dense Tensor.*")
     return array_ops.reshape(grad, array_ops.shape(op.inputs[0]))
 
 
@@ -783,6 +805,10 @@ def _ExtractImagePatchesGrad(op, grad):
                                    (1, 0),
                                    (input_indices_num - 1, output_indices_num))
 
+  with warnings.catch_warnings():
+    warnings.filterwarnings(
+        "ignore",
+        message="Converting sparse IndexedSlices to a dense Tensor.*")
     grad_expanded = array_ops.transpose(
         array_ops.reshape(
             grad, (batch_size, rows_out, cols_out, ksize_r, ksize_c, channels)),
@@ -844,6 +870,10 @@ def _ExtractVolumePatchesGrad(op, grad):
   sp_mat = sparse_ops.sparse_slice(sp_mat_full, (1, 0),
                                    (input_indices_num - 1, output_indices_num))
 
+  with warnings.catch_warnings():
+    warnings.filterwarnings(
+        "ignore",
+        message="Converting sparse IndexedSlices to a dense Tensor.*")
     grad_expanded = array_ops.transpose(
         array_ops.reshape(grad, (batch_size, planes_out, rows_out, cols_out,
                                  ksize_p, ksize_r, ksize_c, channels)),