Optimize gradient calculation for +, *, and / in graph mode.

This change adds a graph-level cache for the results of `broadcast_gradient_args()`
when the inputs have statically known shapes. Using this cache, it can avoid
generating unnecessary ops, which shrinks the graph and improves startup time.

PiperOrigin-RevId: 260239456

tensorflow/python/framework/ops.py

@@ -2839,6 +2839,10 @@ class Graph(object):
     self._add_control_dependencies = False
     # Cache for OpDef protobufs retrieved via the C API.
     self._op_def_cache = {}
+    # Cache for constant results of `broadcast_gradient_args()`. The keys are
+    # tuples of fully-defined shapes: (x_shape_tuple, y_shape_tuple), and the
+    # values are tuples of reduction indices: (rx, ry).
+    self._bcast_grad_args_cache = {}
 
     # TODO(skyewm): fold as much of the above as possible into the C
     # implementation

tensorflow/python/ops/math_grad.py

@@ -19,6 +19,7 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python import pywrap_tensorflow as c_api
 from tensorflow.python.compat import compat
 from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
@@ -52,6 +53,79 @@ def _ArgMinGrad(op, grad):
 ops.NotDifferentiable("EuclideanNorm")
 
 
+def SmartBroadcastGradientArgs(x, y, grad):
+  """Optimized version of `broadcast_gradient_args` that caches results.
+
+  This implementation avoids creating `broadcast_gradient_args` ops in the case
+  that the input shapes are fully defined, and provides hints to the calling
+  code that can be used to avoid creating reduction and reshaping ops.
+
+  Args:
+    x: The left input tensor to a broadcasting binary op.
+    y: The right input tensor to a broadcasting binary op.
+    grad: The incoming gradient tensor for a broadcasting binary op.
+
+  Returns:
+    A pair of tuples, containing:
+      * A 3-tuple of broadcast information for x, containing:
+        * The shape of x (as a tuple or Tensor).
+        * The reduction indices for x (as a tuple or Tensor).
+        * A boolean, which if True, indicates that x's shape differs from grad's
+          shape (and so x's gradient must be reduced and/or reshaped).
+      * A 3-tuple of broadcast information for y, containing the respective
+        details for y.
+  """
+  # NOTE: It may be productive to apply these optimizations in the eager case
+  # as well.
+  if context.executing_eagerly() or not (
+      isinstance(x, ops.Tensor) and isinstance(y, ops.Tensor)
+      and isinstance(grad, ops.Tensor)):
+    sx = array_ops.shape(x)
+    sy = array_ops.shape(y)
+    rx, ry = gen_array_ops.broadcast_gradient_args(sx, sy)
+    return (sx, rx, True), (sy, ry, True)
+
+  # pylint: disable=protected-access
+  x_shape_tuple = x._shape_tuple()
+  y_shape_tuple = y._shape_tuple()
+  grad_shape_tuple = grad._shape_tuple()
+  # pylint: enable=protected-access
+
+  if (x_shape_tuple is None or None in x_shape_tuple or
+      y_shape_tuple is None or None in y_shape_tuple):
+    sx = array_ops.shape_internal(x, optimize=False)
+    sy = array_ops.shape_internal(y, optimize=False)
+    rx, ry = gen_array_ops.broadcast_gradient_args(sx, sy)
+    return (sx, rx, True), (sy, ry, True)
+
+  x_needs_reduction = x_shape_tuple != grad_shape_tuple
+  y_needs_reduction = y_shape_tuple != grad_shape_tuple
+
+  # Get the default graph rather than relying on `x.graph`, `y.graph`, or
+  # `grad.graph`, because these may be eager tensors.
+  g = ops.get_default_graph()
+
+  try:
+    rx, ry = g._bcast_grad_args_cache[(x_shape_tuple, y_shape_tuple)]  # pylint: disable=protected-access
+    return (x_shape_tuple, rx, x_needs_reduction), (
+        y_shape_tuple, ry, y_needs_reduction)
+  except KeyError:
+    rx, ry = array_ops.broadcast_gradient_args(x_shape_tuple, y_shape_tuple)
+    # TODO(mrry): If this becomes a bottleneck, add a multi-output version of
+    # `TF_TryEvaluateConstant()`.
+    rx_value = tuple(c_api.TF_TryEvaluateConstant_wrapper(
+        rx.graph._c_graph, rx._as_tf_output()))  # pylint: disable=protected-access
+    assert rx_value is not None
+    ry_value = tuple(c_api.TF_TryEvaluateConstant_wrapper(
+        ry.graph._c_graph, ry._as_tf_output()))  # pylint: disable=protected-access
+    assert ry_value is not None
+    g._bcast_grad_args_cache[(x_shape_tuple, y_shape_tuple)] = (  # pylint: disable=protected-access
+        rx_value, ry_value)
+
+    return (x_shape_tuple, rx_value, x_needs_reduction), (
+        y_shape_tuple, ry_value, y_needs_reduction)
+
+
 _empty_tuple = ()
 
 
@@ -1000,55 +1074,96 @@ def _AddGrad(op, grad):
   if (isinstance(grad, ops.Tensor) and
       _ShapesFullySpecifiedAndEqual(x, y, grad)):
     return grad, grad
-  sx = array_ops.shape(x)
-  sy = array_ops.shape(y)
-  rx, ry = gen_array_ops.broadcast_gradient_args(sx, sy)
+  (sx, rx, must_reduce_x), (sy, ry, must_reduce_y) = (
+      SmartBroadcastGradientArgs(x, y, grad))
   if skip_input_indices is not None and 0 in skip_input_indices:
     gx = None
+  elif not must_reduce_x:
+    gx = grad
   else:
     gx = array_ops.reshape(math_ops.reduce_sum(grad, rx), sx)
   if skip_input_indices is not None and 1 in skip_input_indices:
     gy = None
+  elif not must_reduce_y:
+    gy = grad
   else:
     gy = array_ops.reshape(math_ops.reduce_sum(grad, ry), sy)
   return (gx, gy)
 
 
-
 @ops.RegisterGradient("Sub")
 def _SubGrad(op, grad):
   """Gradient for Sub."""
-  x = op.inputs[0]
   y = op.inputs[1]
+  skip_input_indices = None
+  try:
+    skip_input_indices = op.skip_input_indices
+    if skip_input_indices is not None and 1 in skip_input_indices and _IsScalar(
+        y):
+      return grad, None
+  except AttributeError:
+    # No gradient skipping, so do the full gradient computation
+    pass
+  x = op.inputs[0]
   if (isinstance(grad, ops.Tensor) and
       _ShapesFullySpecifiedAndEqual(x, y, grad)):
     return grad, -grad
-  sx = array_ops.shape(x)
-  sy = array_ops.shape(y)
-  rx, ry = gen_array_ops.broadcast_gradient_args(sx, sy)
-  return (array_ops.reshape(math_ops.reduce_sum(grad, rx), sx),
-          array_ops.reshape(-math_ops.reduce_sum(grad, ry), sy))
+  (sx, rx, must_reduce_x), (sy, ry, must_reduce_y) = (
+      SmartBroadcastGradientArgs(x, y, grad))
+  if skip_input_indices is not None and 0 in skip_input_indices:
+    gx = None
+  elif not must_reduce_x:
+    gx = grad
+  else:
+    gx = array_ops.reshape(math_ops.reduce_sum(grad, rx), sx)
+  if skip_input_indices is not None and 1 in skip_input_indices:
+    gy = None
+  elif not must_reduce_y:
+    gy = -grad
+  else:
+    gy = array_ops.reshape(math_ops.reduce_sum(-grad, ry), sy)
+  return (gx, gy)
 
 
 @ops.RegisterGradient("Mul")
 def _MulGrad(op, grad):
   """The gradient of scalar multiplication."""
-  x = op.inputs[0]
   y = op.inputs[1]
+  skip_input_indices = None
+  try:
+    skip_input_indices = op.skip_input_indices
+    if skip_input_indices is not None and 1 in skip_input_indices and _IsScalar(
+        y):
+      return gen_math_ops.mul(grad, math_ops.conj(y)), None
+  except AttributeError:
+    # No gradient skipping, so do the full gradient computation
+    pass
+  x = op.inputs[0]
   if (isinstance(grad, ops.Tensor) and
       _ShapesFullySpecifiedAndEqual(x, y, grad) and
       grad.dtype in (dtypes.int32, dtypes.float32)):
     return gen_math_ops.mul(grad, y), gen_math_ops.mul(grad, x)
   assert x.dtype.base_dtype == y.dtype.base_dtype, (x.dtype, " vs. ", y.dtype)
-  sx = array_ops.shape(x)
-  sy = array_ops.shape(y)
-  rx, ry = gen_array_ops.broadcast_gradient_args(sx, sy)
+
+  (sx, rx, must_reduce_x), (sy, ry, must_reduce_y) = (
+      SmartBroadcastGradientArgs(x, y, grad))
   x = math_ops.conj(x)
   y = math_ops.conj(y)
-  return (array_ops.reshape(
-      math_ops.reduce_sum(gen_math_ops.mul(grad, y), rx), sx),
-          array_ops.reshape(
-              math_ops.reduce_sum(gen_math_ops.mul(x, grad), ry), sy))
+  if skip_input_indices is not None and 0 in skip_input_indices:
+    gx = None
+  elif not must_reduce_x:
+    gx = gen_math_ops.mul(grad, y)
+  else:
+    gx = array_ops.reshape(
+        math_ops.reduce_sum(gen_math_ops.mul(grad, y), rx), sx)
+  if skip_input_indices is not None and 1 in skip_input_indices:
+    gy = None
+  elif not must_reduce_y:
+    gy = gen_math_ops.mul(x, grad)
+  else:
+    gy = array_ops.reshape(
+        math_ops.reduce_sum(gen_math_ops.mul(x, grad), ry), sy)
+  return (gx, gy)
 
 
 @ops.RegisterGradient("MulNoNan")