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
@@ -406,7 +408,11 @@ 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)
-  values = array_ops.reshape(grad, values_shape)
+  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]
 
@@ -437,7 +443,11 @@ def _GatherV2Grad(op, grad):
     else:
       params_tail_shape = params_shape[1:]
     values_shape = array_ops.concat([indices_size, params_tail_shape], 0)
-    values = array_ops.reshape(grad, values_shape)
+    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]
 
@@ -451,7 +461,11 @@ def _GatherV2Grad(op, grad):
                                       outer_dims + 1 + inner_dims)
 
   values_shape = array_ops.concat([outer_shape, indices_size, inner_shape], 0)
-  values = array_ops.reshape(grad, values_shape)
+  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)
 
   # We need to sum up every slice `values[..., i, ....]` corresponding to
@@ -519,7 +533,11 @@ ops.NotDifferentiable("StopGradient")
 
 @ops.RegisterGradient("Reshape")
 def _ReshapeGrad(op, grad):
-  return [array_ops.reshape(grad, array_ops.shape(op.inputs[0])), None]
+  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]
 
 
 ops.NotDifferentiable("InvertPermutation")
@@ -527,7 +545,11 @@ ops.NotDifferentiable("InvertPermutation")
 
 def _ReshapeToInput(op, grad):
   """Reshapes the gradient to the shape of the original input."""
-  return array_ops.reshape(grad, array_ops.shape(op.inputs[0]))
+  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]))
 
 
 @ops.RegisterGradient("ExpandDims")
@@ -783,10 +805,14 @@ def _ExtractImagePatchesGrad(op, grad):
                                    (1, 0),
                                    (input_indices_num - 1, output_indices_num))
 
-  grad_expanded = array_ops.transpose(
-      array_ops.reshape(
-          grad, (batch_size, rows_out, cols_out, ksize_r, ksize_c, channels)),
-      (1, 2, 3, 4, 0, 5))
+  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)),
+        (1, 2, 3, 4, 0, 5))
   grad_flat = array_ops.reshape(grad_expanded, (-1, batch_size * channels))
 
   jac = sparse_ops.sparse_tensor_dense_matmul(sp_mat, grad_flat)
@@ -844,10 +870,14 @@ def _ExtractVolumePatchesGrad(op, grad):
   sp_mat = sparse_ops.sparse_slice(sp_mat_full, (1, 0),
                                    (input_indices_num - 1, output_indices_num))
 
-  grad_expanded = array_ops.transpose(
-      array_ops.reshape(grad, (batch_size, planes_out, rows_out, cols_out,
-                               ksize_p, ksize_r, ksize_c, channels)),
-      (1, 2, 3, 4, 5, 6, 0, 7))
+  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)),
+        (1, 2, 3, 4, 5, 6, 0, 7))
   grad_flat = array_ops.reshape(grad_expanded, (-1, batch_size * channels))
 
   jac = sparse_ops.sparse_tensor_dense_matmul(sp_mat, grad_flat)