Python gradients_impl.gradients函数代码示例

  def testNanFromGradsDontPropagate(self):
    """Test that update with NaN gradients does not cause NaN in results."""
    def _nan_log_prob_with_nan_gradient(x):
      return np.nan * math_ops.reduce_sum(x)

    with self.test_session() as sess:
      initial_x = math_ops.linspace(0.01, 5, 10)
      updated_x, acceptance_probs, new_log_prob, new_grad = hmc.kernel(
          2., 5, initial_x, _nan_log_prob_with_nan_gradient, [0])
      initial_x_val, updated_x_val, acceptance_probs_val = sess.run(
          [initial_x, updated_x, acceptance_probs])

      logging.vlog(1, 'initial_x = {}'.format(initial_x_val))
      logging.vlog(1, 'updated_x = {}'.format(updated_x_val))
      logging.vlog(1, 'acceptance_probs = {}'.format(acceptance_probs_val))

      self.assertAllEqual(initial_x_val, updated_x_val)
      self.assertEqual(acceptance_probs_val, 0.)

      self.assertAllFinite(
          gradients_impl.gradients(updated_x, initial_x)[0].eval())
      self.assertTrue(
          gradients_impl.gradients(new_grad, initial_x)[0] is None)

      # Gradients of the acceptance probs and new log prob are not finite.
      _ = new_log_prob  # Prevent unused arg error.

  def testReduction(self):
    g = ops.Graph()

    # BN0 is computing batch normed matrix along rows.
    def BN0(x):
      mean = math_ops.reduce_mean(x, [0])
      var = math_ops.reduce_mean(math_ops.square(x - mean))  # biased var
      rstd = math_ops.rsqrt(var + 1e-8)
      return (x - mean) * rstd

    # Wraps BatchNorm in a tf function.
    @function.Defun(dtypes.float32)
    def BN1(x):
      return BN0(x)

    with g.as_default():
      x = array_ops.placeholder(dtypes.float32)
      y0 = BN0(x)  # A plain graph
      y1 = BN1(x)  # A tf function
      dx0, = gradients_impl.gradients([y0], [x])
      dx1, = gradients_impl.gradients([y1], [x])

    # Both should produce the same result and gradient.
    with self.test_session(graph=g) as sess:
      vals = sess.run([y0, y1, dx0, dx1], {x: np.random.uniform(size=(3, 7))})
      self.assertAllClose(vals[0], vals[1])
      self.assertAllClose(vals[2], vals[3])

    def body(it, cost):
      embedding = embedding_ops.embedding_lookup(embedding_matrix, [0])
      cost = control_flow_ops.cond(
          math_ops.equal(it, 3), lambda: math_ops.square(cost),
          (lambda: cost + math_ops.reduce_sum(embedding)))
      return it + 1, cost

      _, cost = control_flow_ops.while_loop(
          cond, body, [constant_op.constant(0),
                       constant_op.constant(0.0)])

      dynamic_grads = gradients_impl.gradients(cost, [embedding_matrix])[0]
      dynamic_grads = math_ops.segment_sum(dynamic_grads.values,
                                           dynamic_grads.indices)

      embedding = embedding_ops.embedding_lookup(embedding_matrix, [0])
      static = math_ops.square(
          math_ops.reduce_sum(embedding) + math_ops.reduce_sum(embedding) +
          math_ops.reduce_sum(embedding)) + math_ops.reduce_sum(embedding)
      static_grads = gradients_impl.gradients(static, [embedding_matrix])[0]
      static_grads = math_ops.segment_sum(static_grads.values,
                                          static_grads.indices)

      with self.cached_session():
        self.evaluate(variables.global_variables_initializer())
        self.assertAllEqual(*self.evaluate([static_grads, dynamic_grads]))

  def testShapePassedToGradient(self):
    with ops.Graph().as_default():
      @custom_gradient.custom_gradient
      def differentiable_scatter_update(handle, indices, values):
        with ops.control_dependencies([
            resource_variable_ops.resource_scatter_update(
                handle, indices, values)]):
          new_handle = array_ops.identity(handle)

        def grad(dresult):
          self.assertIsNotNone(
              tensor_util.constant_value(dresult.dense_shape))
          return [dresult, None, None]

        return new_handle, grad

      var = variable_scope.get_variable(
          "foo", shape=[20], initializer=init_ops.zeros_initializer,
          dtype=dtypes.float64, use_resource=True)

      indices = math_ops.range(10)
      updates = math_ops.range(9, -1, -1, dtype=dtypes.float64)
      new_handle = differentiable_scatter_update(var.handle, indices, updates)
      gathered = resource_variable_ops.resource_gather(
          new_handle, indices, dtype=var.dtype)
      gradients_impl.gradients([gathered], [updates])

  def testNanFromGradsDontPropagate(self):
    """Test that update with NaN gradients does not cause NaN in results."""
    def _nan_log_prob_with_nan_gradient(x):
      return np.nan * math_ops.reduce_sum(x)

    with self.test_session() as sess:
      initial_x = math_ops.linspace(0.01, 5, 10)
      updated_x, kernel_results = hmc.kernel(
          target_log_prob_fn=_nan_log_prob_with_nan_gradient,
          current_state=initial_x,
          step_size=2.,
          num_leapfrog_steps=5,
          seed=47)
      initial_x_, updated_x_, acceptance_probs_ = sess.run(
          [initial_x, updated_x, kernel_results.acceptance_probs])

      logging_ops.vlog(1, "initial_x = {}".format(initial_x_))
      logging_ops.vlog(1, "updated_x = {}".format(updated_x_))
      logging_ops.vlog(1, "acceptance_probs = {}".format(acceptance_probs_))

      self.assertAllEqual(initial_x_, updated_x_)
      self.assertEqual(acceptance_probs_, 0.)

      self.assertAllFinite(
          gradients_ops.gradients(updated_x, initial_x)[0].eval())
      self.assertAllEqual([True], [g is None for g in gradients_ops.gradients(
          kernel_results.proposed_grads_target_log_prob, initial_x)])
      self.assertAllEqual([False], [g is None for g in gradients_ops.gradients(
          kernel_results.proposed_grads_target_log_prob,
          kernel_results.proposed_state)])

  def testWithIsRecomputeKwarg(self):

    kwarg_values = []

    @rev_block_lib.recompute_grad
    def layer_with_recompute(inputs, is_recomputing=False):
      kwarg_values.append(is_recomputing)
      out = core_layers.dense(inputs, 2)
      out = normalization_layers.batch_normalization(out, training=True)
      if is_recomputing:
        # Ensure that the updates are not duplicated by popping off the latest
        # 2 additions.
        update_ops = ops.get_collection_ref(ops.GraphKeys.UPDATE_OPS)
        update_ops.pop()
        update_ops.pop()
      return out

    x = array_ops.ones((2, 4), dtypes.float32)
    with variable_scope.variable_scope("layer1", use_resource=True):
      y = layer_with_recompute(x)
    loss = math_ops.reduce_sum(y)
    tvars = variables.trainable_variables()
    gradients_impl.gradients(loss, [x] + tvars)

    update_ops = ops.get_collection(ops.GraphKeys.UPDATE_OPS)
    self.assertEqual(2, len(update_ops))
    self.assertEqual([False, True], kwarg_values)

  def __getitem__(self, spec):
    slice_var = self.var[spec]
    slice_val = self.val[spec]

    # compute analytic 2nd derivative
    analytic_grad2 = 2 * slice_val

    dy = variables.Variable(
        array_ops.ones(
            shape=slice_var.get_shape(), dtype=dtypes.int32))
    assign = dy.assign(slice_var)
    slice_val_grad, = gradients_impl.gradients(slice_val, self.var, grad_ys=dy)
    slice_val_grad2, = gradients_impl.gradients(
        slice_val_grad, dy, grad_ys=self.var)
    self.sess.run(assign)
    slice_val_grad_evaled, slice_val_grad2_evaled = (
        self.sess.run([slice_val_grad, slice_val_grad2]))
    analytic_grad2_evaled = analytic_grad2.eval()
    self.test.assertAllEqual(slice_val_grad2_evaled, analytic_grad2_evaled)

    # compute analytic gradient for slice
    np_val_grad = (2 * self.varnp * self.varnp)
    np_sliceval_grad = np.zeros(self.var.get_shape())
    np_sliceval_grad[spec] = np_val_grad[spec]
    # verify gradient
    self.test.assertAllEqual(slice_val_grad_evaled, np_sliceval_grad)

def _create_multi_lstm_cell_ops(batch_size, num_units, input_depth,
                                num_layers, max_time, compiled):
  with variable_scope.variable_scope(
      "root",
      initializer=init_ops.random_uniform_initializer(-0.1, 0.1, seed=2)):
    inputs = variable_scope.get_variable(
        "inputs", initializer=random_ops.random_uniform(
            (max_time, batch_size, input_depth), seed=1))
    maybe_xla = lambda c: rnn_cell.CompiledWrapper(c) if compiled else c
    cell = core_rnn_cell_impl.MultiRNNCell(
        [maybe_xla(core_rnn_cell_impl.LSTMCell(num_units))
         for _ in range(num_layers)])
    initial_state = cell.zero_state(
        batch_size=batch_size, dtype=dtypes.float32)
    outputs, final_state = rnn.dynamic_rnn(
        cell=cell, inputs=inputs, initial_state=initial_state,
        time_major=True)
    flat_final_state = nest.flatten(final_state)
    trainable_variables = variables.trainable_variables()
    outputs_grad = gradients_impl.gradients(
        [outputs],
        trainable_variables + [inputs] + nest.flatten(initial_state))
    final_state_grad = gradients_impl.gradients(
        flat_final_state,
        trainable_variables + [inputs] + nest.flatten(initial_state))

    return {"outputs": outputs,
            "final_state": flat_final_state,
            "outputs_grad": outputs_grad,
            "final_state_grad": final_state_grad}

def fwd_gradients(ys, xs, grad_xs=None, stop_gradients=None):
  """Compute forward-mode gradients."""
  # See b/37888268.

  # This version of forward-mode autodiff is based on code by Tim Cooijmans
  # and handles list arguments and certain special cases such as when the
  # ys doesn't depend on one or more of the xs, and when ops.IndexedSlices are
  # generated by the first gradients_impl.gradients call.

  us = [array_ops.zeros_like(y) + float("nan") for y in ys]
  dydxs = gradients_impl.gradients(
      ys, xs, grad_ys=us, stop_gradients=stop_gradients)

  # Deal with strange types that gradients_impl.gradients returns but can't
  # deal with.
  dydxs = [
      ops.convert_to_tensor(dydx)
      if isinstance(dydx, ops.IndexedSlices) else dydx for dydx in dydxs
  ]
  dydxs = [
      array_ops.zeros_like(x) if dydx is None else dydx
      for x, dydx in zip(xs, dydxs)
  ]

  dysdx = gradients_impl.gradients(dydxs, us, grad_ys=grad_xs)

  return dysdx

  def doTestIndexedSlicesGradientInCondInWhileLoop(self, use_resource=False):
    with ops.Graph().as_default():
      embedding_matrix = variable_scope.get_variable(
          "embedding_matrix", [5, 5],
          initializer=init_ops.random_normal_initializer(),
          use_resource=use_resource)

      def Cond(it, _):
        return it < 5

      def Body(it, cost):
        embedding = embedding_ops.embedding_lookup(embedding_matrix, [0])
        cost = control_flow_ops.cond(
            math_ops.equal(it, 3), lambda: math_ops.square(cost),
            lambda: cost + math_ops.reduce_sum(embedding))
        return it + 1, cost

      _, cost = control_flow_ops.while_loop(
          Cond, Body, [constant_op.constant(0), constant_op.constant(0.0)])

      dynamic_grads = gradients_impl.gradients(cost, [embedding_matrix])[0]
      dynamic_grads = math_ops.segment_sum(dynamic_grads.values,
                                           dynamic_grads.indices)

      embedding = embedding_ops.embedding_lookup(embedding_matrix, [0])
      static = math_ops.square(
          math_ops.reduce_sum(embedding) + math_ops.reduce_sum(embedding) +
          math_ops.reduce_sum(embedding)) + math_ops.reduce_sum(embedding)
      static_grads = gradients_impl.gradients(static, [embedding_matrix])[0]
      static_grads = math_ops.segment_sum(static_grads.values,
                                          static_grads.indices)

      with self.test_session() as sess:
        sess.run(variables.global_variables_initializer())
        self.assertAllEqual(*sess.run([static_grads, dynamic_grads]))

  def _RunAndVerifyBackprop(self, input_sizes, filter_sizes, output_sizes,
                            strides, dilations, padding, data_format, use_gpu,
                            err, mode):
    total_input_size = 1
    total_filter_size = 1
    for s in input_sizes:
      total_input_size *= s
    for s in filter_sizes:
      total_filter_size *= s
    # Initializes the input tensor with array containing incrementing
    # numbers from 1.
    x1 = [f * 1.0 for f in range(1, total_input_size + 1)]
    x2 = [f * 1.0 for f in range(1, total_filter_size + 1)]
    default_dilations = (
        dilations[0] == 1 and dilations[1] == 1 and dilations[2] == 1)

    # If any dilation rate is larger than 1, only do test on the GPU
    # because we currently do not have a CPU implementation for arbitrary
    # dilation rates.
    if default_dilations or use_gpu:
      with self.cached_session(use_gpu=use_gpu) as sess:
        if data_format == "NCDHW":
          input_sizes = test_util.NHWCToNCHW(input_sizes)
        t1 = constant_op.constant(x1, shape=input_sizes)
        t2 = constant_op.constant(x2, shape=filter_sizes)
        full_strides = [1] + strides + [1]
        full_dilations = [1] + dilations + [1]
        if data_format == "NCDHW":
          full_strides = test_util.NHWCToNCHW(full_strides)
          full_dilations = test_util.NHWCToNCHW(full_dilations)
        actual = nn_ops.conv3d(
            t1,
            t2,
            strides=full_strides,
            dilations=full_dilations,
            padding=padding,
            data_format=data_format)
        expected = nn_ops.convolution(
            t1,
            t2,
            padding=padding,
            strides=strides,
            dilation_rate=dilations,
            data_format=data_format)
        if data_format == "NCDHW":
          actual = test_util.NCHWToNHWC(actual)
          expected = test_util.NCHWToNHWC(expected)
        actual_grad = gradients_impl.gradients(actual, t1
                                               if mode == "input" else t2)[0]
        expected_grad = gradients_impl.gradients(expected, t1
                                                 if mode == "input" else t2)[0]
        # "values" consists of two tensors for two backprops
        actual_value = self.evaluate(actual_grad)
        expected_value = self.evaluate(expected_grad)
        self.assertShapeEqual(actual_value, actual_grad)
        self.assertShapeEqual(expected_value, expected_grad)
      print("expected = ", expected_value)
      print("actual = ", actual_value)
      self.assertArrayNear(expected_value.flatten(), actual_value.flatten(),
                           err)

  def testGradientFloat16(self):
    with self.test_session(use_gpu=True) as sess:
      # Randomly construct a 1D shape from [1, 40)
      shape = random_ops.random_uniform(
          [1], minval=1, maxval=40, dtype=dtypes.int32)

      # Construct the fp32 graph and its gradient.
      x = random_ops.random_uniform(shape, minval=-1, maxval=1, name="x")
      y1 = nn_ops.relu(x, name="relu_fp32")
      l1 = nn_ops.l2_loss(y1)
      dx_f32 = gradients_impl.gradients(l1, x)

      # Construct the fp16 graph and its gradient.
      # It starts with the same x, in fp32. But before it reaches Relu, it is
      # cast into fp16. So during backprop, the gradient computation is in fp16.
      x2 = math_ops.cast(x, dtype=dtypes.float16, name="cast")
      y2 = nn_ops.relu(x2, name="relu_fp16")
      l2 = nn_ops.l2_loss(y2)
      dx_f16 = gradients_impl.gradients(l2, x)

      # Repeat the experiment for 100 times. All tensor shapes and its tensor
      # values are randomly generated for each run.
      for _ in xrange(100):
        dx_f32_v, dx_f16_v = sess.run([dx_f32, dx_f16])
        self.assertAllClose(dx_f32_v, dx_f16_v, atol=3e-4)

  def testSumOfTwoReadVariablesWithoutRepeatGrad(self):
    with self.test_session(use_gpu=True) as session:
      a = array_ops.identity(
          np.arange(
              3 * 5, dtype=np.float32).reshape(3, 5) + 1)
      b = array_ops.identity(
          np.arange(
              3 * 5, dtype=np.float32).reshape(3, 5) + 1 + 3 * 5)
      ta = tensor_array_ops.TensorArray(dtype=dtypes.float32, size=2)
      ta = ta.write(0, a, name="write_a")
      ta = ta.write(1, b, name="write_b")
      c = (
          ta.read(
              0, name="read_a_0") +  # a + b
          ta.read(
              1, name="read_b_0"))
      g0 = -(np.arange(3 * 5, dtype=np.float32).reshape(3, 5) + 1)
      grad_a = gradients_impl.gradients([c], [a], [g0])[0]  # d(a+b)/da = 1
      grad_b = gradients_impl.gradients([c], [b], [g0])[0]  # d(a+b)/db = 1

      # Test gradients calculated individually
      grad_a_t, = session.run([grad_a])
      self.assertAllEqual(grad_a_t, g0)

      grad_b_t, = session.run([grad_b])
      self.assertAllEqual(grad_b_t, g0)

      # Test gradients calculated jointly
      joint_grad_a_t, joint_grad_b_t = session.run([grad_a, grad_b])
      self.assertAllEqual(joint_grad_a_t, g0)
      self.assertAllEqual(joint_grad_b_t, g0)

  def testEntropyGradient(self):
    with self.cached_session() as sess:
      logits = constant_op.constant([[1., 2., 3.], [2., 5., 1.]])

      probabilities = nn_ops.softmax(logits)
      log_probabilities = nn_ops.log_softmax(logits)
      true_entropy = - math_ops.reduce_sum(
          probabilities * log_probabilities, axis=-1)

      categorical_distribution = categorical.Categorical(probs=probabilities)
      categorical_entropy = categorical_distribution.entropy()

      # works
      true_entropy_g = gradients_impl.gradients(true_entropy, [logits])
      categorical_entropy_g = gradients_impl.gradients(
          categorical_entropy, [logits])

      res = sess.run({"true_entropy": true_entropy,
                      "categorical_entropy": categorical_entropy,
                      "true_entropy_g": true_entropy_g,
                      "categorical_entropy_g": categorical_entropy_g})
      self.assertAllClose(res["true_entropy"],
                          res["categorical_entropy"])
      self.assertAllClose(res["true_entropy_g"],
                          res["categorical_entropy_g"])

      def fn():
        ta = tensor_array_ops.TensorArray(
            dtype=dtypes.as_dtype(dtype),
            tensor_array_name="foo",
            size=3,
            infer_shape=False)

        value_0 = constant_op.constant(c([[4.0, 5.0]]))
        value_1 = constant_op.constant(c([[3.0, 3.5]]))

        w0 = ta.write(0, value_0)
        w1 = w0.write(1, value_1)
        r0 = w1.read(0)
        r1 = w1.read(1)
        r0_2 = w1.read(0)

        # Test individual components' gradients
        grad_just_r0 = gradients_impl.gradients(
            ys=[r0], xs=[value_0], grad_ys=[c([[2.0, 3.0]])])
        grad_r0_r0_2 = gradients_impl.gradients(
            ys=[r0, r0_2],
            xs=[value_0],
            grad_ys=[c([[2.0, 3.0]]), c([[1.0, -1.0]])])
        grad_just_r1 = gradients_impl.gradients(
            ys=[r1], xs=[value_1], grad_ys=[c([[-2.0, -4.0]])])
        # Test combined gradients
        grad = gradients_impl.gradients(
            ys=[r0, r0_2, r1],
            xs=[value_0, value_1],
            grad_ys=[c([[2.0, 3.0]]),
                     c([[1.0, -1.0]]),
                     c([[-2.0, -10.0]])])

        return [grad_just_r0, grad_r0_r0_2, grad_just_r1, grad]