def _WhileGrad(op, *grads): # pylint: disable=invalid-name """The gradient of a While op produced by while_loop.""" body_graph = _get_body_graph(op) # Replace None gradients with zeros. This is needed because `grads` could have # None incoming gradients for the TensorLists. If we pass None's through, the # custom gradient of TensorListPopBack will create an EmptyTensorList inside # the FuncGraph which is undesirable. # TODO(b/80444525): There might be an issue with treating no gradient as zero # gradient in certain cases. Consider replacing None gradients with Zeros # for accumulators only. grads = [ g if g is not None else array_ops.zeros_like(output) for g, output in zip(grads, op.outputs) ] body_grad_graph, args = _create_grad_func( body_graph, grads, _get_unique_name("%s_grad" % body_graph.name), op) intermediate_tensors = _get_intermediates(body_grad_graph) for intermediate_tensor in intermediate_tensors: tensor_list = list_ops.empty_tensor_list( element_dtype=intermediate_tensor.dtype, element_shape=_get_tensor_convertible_shape(intermediate_tensor.shape)) with body_grad_graph.as_default(): tensor_list_ph = body_grad_graph.capture(tensor_list, whitelisted=True) # Push the intermediate tensor to the tensor list. appended_tensor_list = list_ops.tensor_list_push_back(tensor_list_ph, intermediate_tensor) # Add this modified tensor list to the list of outputs. body_grad_graph.outputs.append(appended_tensor_list) def grad_cond(counter, max_iters, *unused_args): return counter < max_iters loop_vars = args + body_grad_graph.external_captures grad_cond_name = _get_unique_name("%s_grad_cond" % op.name) cond_grad_graph = function.func_graph_from_py_func( grad_cond_name, grad_cond, loop_vars, {}, func_graph=util.WhileCondFuncGraph(grad_cond_name)) assert len(loop_vars) == len(body_grad_graph.inputs) assert len(loop_vars) == len(body_grad_graph.outputs) assert len(loop_vars) == len(cond_grad_graph.inputs) outputs = gen_functional_ops._while( loop_vars, util.create_new_tf_function(cond_grad_graph), util.create_new_tf_function(body_grad_graph), output_shapes=[t.shape for t in body_grad_graph.outputs], name=_get_unique_name("%s_grad" % op.name)) _copy_handle_data(body_grad_graph.outputs, outputs) _maybe_set_lowering_attr(outputs[0].op) # outputs[0] is the loop counter. # outputs[1] is the total number of loop iterations. return outputs[2:2 + len(op.inputs)]
def while_loop(cond, body, loop_vars, shape_invariants=None, parallel_iterations=10, maximum_iterations=None, name=None, return_same_structure=True, back_prop=True): """Like tf.while_loop, except emits a single While op.""" # Keep the original loop_vars around to know which args were TensorArrays. orig_loop_vars = loop_vars # Cache its length since we use it at multiple places below. len_orig_loop_vars = len(orig_loop_vars) # Convert TensorArrays to their flow variables. These get converted back to # TensorArrays before calling `cond` and `body`. See `wrapped_cond` and # `wrapped_body` below. loop_vars = list(_tensor_array_to_flow(orig_loop_vars)) loop_vars = nest.map_structure( ops.internal_convert_to_tensor_or_indexed_slices, loop_vars, expand_composites=True) if shape_invariants is not None: nest.assert_same_structure(orig_loop_vars, shape_invariants, expand_composites=False) signature = nest.map_structure( control_flow_ops._shape_invariant_to_type_spec, loop_vars, list(shape_invariants), expand_composites=False) shape_invariants = nest.map_structure( control_flow_ops._get_shape_invariant, loop_vars, list(shape_invariants), expand_composites=False) else: signature = nest.map_structure( type_spec.type_spec_from_value, loop_vars, expand_composites=False) shape_invariants = nest.map_structure( control_flow_ops._get_shape_invariant, loop_vars, expand_composites=False) if not name: name = "while" with ops.name_scope(name) as scope: with ops.name_scope(None): cond_name = util.unique_fn_name(scope, "cond") body_name = util.unique_fn_name(scope, "body") maximum_iterations_loop_var = _build_maximum_iterations_loop_var( maximum_iterations) loop_counter = constant_op.constant( 0, dtype=maximum_iterations_loop_var.dtype if maximum_iterations is not None else None, name="loop_counter") # Add loop counter needed for computing gradients. loop_vars = [loop_counter, maximum_iterations_loop_var] + loop_vars shape_invariants = [tensor_shape.TensorShape([])] * 2 + shape_invariants signature = ( [tensor_spec.TensorSpec.from_tensor(loop_counter), tensor_spec.TensorSpec.from_tensor(maximum_iterations_loop_var)] + signature) # Automatic control dependencies are added in defuns, but not in v1 # graphs. Propagate that behavior here. add_control_dependencies = ops.get_default_graph()._add_control_dependencies def wrapped_cond(loop_counter, maximum_iterations_arg, *args): """Extra `cond` wrapper that can handle the extra counter loop_var.""" # Convert the flow variables in `args` to TensorArrays. `args` should # already have the same structure as `orig_loop_vars` but currently there # is no nest.zip so we call `_pack_sequence_as` which flattens both # `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays # and packs it into the structure of `orig_loop_vars`. pred = cond(*_pack_sequence_as(orig_loop_vars, args)) if (tensor_util.is_tensor(pred) and (pred.shape.dims is None or pred.shape.dims)): pred = array_ops.squeeze_v2(pred) if maximum_iterations is None: return pred else: return math_ops.logical_and( loop_counter < maximum_iterations_arg, pred) # NOTE(skyewm): we set collections to the outer graph's collections for # compatibility with TPUEstimator. cond_graph = func_graph_module.func_graph_from_py_func( cond_name, wrapped_cond, [], # We provide signature instead of args. {}, signature=signature, func_graph=util.WhileCondFuncGraph( cond_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access add_control_dependencies=add_control_dependencies) def wrapped_body(loop_counter, maximum_iterations_arg, *args): """Loop body augmented with counter update. Args: loop_counter: Loop counter which needs to be incremented in the body. maximum_iterations_arg: Maximum iterations of the loop. *args: List of args Returns: A list of tensors the same length as args. """ # Capture the tensors already captured in cond_graph so that they appear # in the same order in body_graph.external_captures. for t in cond_graph.external_captures: ops.get_default_graph().capture(t) # Convert the flow variables in `args` to TensorArrays. `args` should # already have the same structure as `orig_loop_vars` but currently there # is no nest.zip so we call `_pack_sequence_as` which flattens both # `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays # and packs it into the structure of `orig_loop_vars`. outputs = body(*_pack_sequence_as(orig_loop_vars, args)) if not nest.is_sequence_or_composite(outputs): outputs = [outputs] # Compare the structure of input and output of body converting the # top-level tuples to list to be compatible with legacy while_loop. nest.assert_same_structure(list(outputs), list(orig_loop_vars), expand_composites=True) outputs = _tensor_array_to_flow(outputs) # TODO(srbs): Update lowering code to create _Enter nodes with # is_constant=True for inputs that are directly passed to outputs. return [loop_counter + 1, maximum_iterations_arg] + list(outputs) body_graph = func_graph_module.func_graph_from_py_func( body_name, wrapped_body, [], # We provide signature instead of args. {}, signature=signature, func_graph=util.WhileBodyFuncGraph( body_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access add_control_dependencies=add_control_dependencies) # Add external captures of body to the list of loop vars. # Note that external tensors will be treated as loop invariants, i.e., # the value of that tensor in each iteration is the same as it was at the # beginning of the loop execution. loop_vars = loop_vars + body_graph.external_captures # TODO(srbs): Update lowering code to create _Enter nodes with # is_constant=True for inputs that are directly passed to outputs. body_graph.outputs.extend(body_graph.internal_captures) # Capture the extra `external_captures` of `body_graph` in `cond_graph` so # that it expects to receive those as arguments. with cond_graph.as_default(): num_cond_captures = len(cond_graph.external_captures) assert (cond_graph.external_captures == body_graph.external_captures[:num_cond_captures]) cond_graph_captures = object_identity.ObjectIdentitySet( cond_graph.external_captures) for body_capture in body_graph.external_captures[num_cond_captures:]: assert body_capture not in cond_graph_captures cond_graph.capture(body_capture) # Make sure that the shapes of the loop outputs are compatible with the # shape invariants, or the shapes of the loop vars if the invariants are not # specified. num_flattened_outputs = len(nest.flatten(orig_loop_vars, expand_composites=True)) # First var is loop counter and second var is maximum_iterations. first_loop_var_index = 2 _check_shapes_compat( body_graph.outputs[first_loop_var_index:first_loop_var_index + num_flattened_outputs], nest.flatten( shape_invariants[first_loop_var_index:first_loop_var_index + len_orig_loop_vars], expand_composites=True), nest.flatten(loop_vars[first_loop_var_index:first_loop_var_index + len_orig_loop_vars], expand_composites=True)) num_original_outputs = len(body_graph.outputs) if back_prop and util.output_all_intermediates(): # Export all tensors in the loop body that may be needed for gradient # computation. We do this by accumulating the intermediate values in # TensorLists. intermediate_tensors = _get_intermediates(body_graph) for intermediate_tensor in intermediate_tensors: tensor_list = list_ops.empty_tensor_list( element_dtype=intermediate_tensor.dtype, element_shape=intermediate_tensor.shape, max_num_elements=maximum_iterations) loop_vars.append(tensor_list) with cond_graph.as_default(): # Add a placeholder to cond_graph's inputs corresponding to the # tensor_list. cond_graph.capture(tensor_list) with body_graph.as_default(): # Push the intermediate tensor to the tensor list. This captures the # `tensor_list` as well. appended_tensor_list = list_ops.tensor_list_push_back( tensor_list, intermediate_tensor) # Add this modified tensor list to the list of outputs. body_graph.outputs.append(appended_tensor_list) flattened_loop_vars = nest.flatten(loop_vars, expand_composites=True) _check_num_inputs_outputs(cond_graph, body_graph, len(flattened_loop_vars)) _check_inputs_outputs_types_match(body_graph, flattened_loop_vars) with ops.control_dependencies( list(cond_graph.control_captures) + list(body_graph.control_captures)): output_shapes = [t.shape for t in body_graph.outputs] orig_loop_vars_range = slice(first_loop_var_index, first_loop_var_index + num_flattened_outputs) output_shapes[orig_loop_vars_range] = nest.flatten( shape_invariants, expand_composites=True)[orig_loop_vars_range] cond_stateful_ops = [ op for op in cond_graph.get_operations() if op._is_stateful ] body_stateful_ops = [ op for op in body_graph.get_operations() if op._is_stateful ] if (cond_stateful_ops or body_stateful_ops): op_fn = gen_functional_ops._while else: op_fn = gen_functional_ops.stateless_while outputs = op_fn( flattened_loop_vars, util.create_new_tf_function(cond_graph), util.create_new_tf_function(body_graph), output_shapes=output_shapes, parallel_iterations=parallel_iterations, name=scope) # This is needed so we do not compute derivative wrt these extra outputs. outputs[0].op._set_attr("_num_original_outputs", attr_value_pb2.AttrValue(i=num_original_outputs)) _copy_handle_data(body_graph.outputs, outputs) util.maybe_set_lowering_attr(outputs[0].op) util.maybe_propagate_compile_time_consts_in_xla(outputs[0].op) # Return identities for each output of the While op, rather than the output # of the While op directly. This makes pruning work if the output of # while_loop() is fetched: the lowering pass converts the While outputs into # IdentityN outputs, which if fetched will cause all ops in the body to be # run (since it takes all exit ops as input). After lowering, each output # identity op will end up with only the appropriate exit op as input. outputs = tuple(array_ops.identity(t) for t in outputs) outputs = _pack_sequence_as( orig_loop_vars, outputs[first_loop_var_index:first_loop_var_index + num_flattened_outputs]) if return_same_structure: return outputs flattened_outputs = nest.flatten(outputs, expand_composites=True) if len(flattened_outputs) == 1: return flattened_outputs[0] else: return outputs
def _WhileGrad(op, *grads): # pylint: disable=invalid-name """The gradient of a While op produced by while_loop.""" # Note that op is not always the same as while_op because the gradient tape, # for eager mode compatibility, forgets information about the proper op. Since # the loop cannot run in eager mode, however, we can safely introspect into # the graph here. while_op = op.outputs[0].op cond_graph = _get_graph(while_op, "cond") body_graph = _get_graph(while_op, "body") orig_num_params = len(body_graph.outputs) maximum_iterations = op.inputs[1] parallel_iterations = op.get_attr("parallel_iterations") try: num_original_outputs = while_op.get_attr("_num_original_outputs") except: # pylint: disable=bare-except num_original_outputs = len(while_op.outputs) num_intermediates = len(while_op.outputs) - num_original_outputs grads = [ _preprocess_grad(grad, body_out, while_out) # pylint: disable=g-complex-comprehension for grad, body_out, while_out in zip( grads[:num_original_outputs], body_graph.outputs[:num_original_outputs], while_op.outputs[:num_original_outputs]) ] + [None] * num_intermediates # We compute the gradient for the sub-graph between trainable ys and xs # with non-None incoming gradients. We later pad the None's to the list of # outputs. ys, xs, non_none_grads = zip(*[(y, x, grad) for (y, x, grad) in zip( body_graph.outputs, body_graph.inputs, grads) if grad is not None]) body_grad_graph, args = _create_grad_func( ys, xs, non_none_grads, cond_graph, body_graph, util.unique_grad_fn_name(body_graph.name), op, maximum_iterations) if body_grad_graph.while_op_needs_rewrite: # Modify 'op' to output the intermediate accumulators needed by the grad # function. # NOTE(skyewm): if there are any active sessions, this modification to `op` # may make them unrunnable! cond_graph.name += "_rewritten" body_graph.name += "_rewritten" new_inputs = body_grad_graph.empty_tensor_lists new_outputs = body_graph.outputs[orig_num_params:] while_op._set_func_attr("cond", util.create_new_tf_function(cond_graph)) while_op._set_func_attr("body", util.create_new_tf_function(body_graph)) while_op._set_type_list_attr("T", body_graph.output_types) while_op._set_shape_list_attr("output_shapes", body_graph.output_shapes) while_op._add_while_inputs(new_inputs) while_op._add_outputs([t.dtype for t in new_outputs], [t.shape for t in new_outputs]) _copy_handle_data(new_outputs, op.outputs[orig_num_params:]) # Do not ingore grads wrt extra outputs when computing higher order # derivatives. while_op._set_attr("_num_original_outputs", attr_value_pb2.AttrValue(i=len(while_op.outputs))) captured_inputs = _resolve_grad_captures(body_graph, body_grad_graph, while_op) loop_vars = args + captured_inputs # This modifies body_grad_graph. loop_vars = while_v2_indexed_slices_rewriter.rewrite_grad_indexed_slices( grads, body_grad_graph, loop_vars, while_op.inputs) def grad_cond(counter, unused_maximum_iterations_arg, forward_loop_iters, *unused_args): return counter < forward_loop_iters grad_cond_name = util.unique_grad_fn_name(op.get_attr("cond").name) cond_grad_graph = func_graph_module.func_graph_from_py_func( grad_cond_name, grad_cond, loop_vars, {}, func_graph=util.WhileCondFuncGraph(grad_cond_name)) _check_num_inputs_outputs(cond_grad_graph, body_grad_graph, len(loop_vars)) outputs = gen_functional_ops._while( loop_vars, util.create_new_tf_function(cond_grad_graph), util.create_new_tf_function(body_grad_graph), output_shapes=[t.shape for t in body_grad_graph.outputs], parallel_iterations=parallel_iterations, name="%s_grad" % while_op.name) grad_op = outputs[0].op _copy_handle_data(body_grad_graph.outputs, outputs) util.maybe_set_lowering_attr(grad_op) util.maybe_propagate_compile_time_consts_in_xla(grad_op) # See comment in while_loop. outputs = [array_ops.identity(t) for t in outputs] return _get_structured_grad_output(outputs, grads, body_grad_graph)
def while_loop(cond, body, loop_vars, shape_invariants=None, maximum_iterations=None, name=None, return_same_structure=True): """Like tf.while_loop, except emits a single While op.""" maximum_iterations = _validate_and_convert_to_tensor(maximum_iterations) # Keep the original loop_vars around to know which args were TensorArrays. orig_loop_vars = loop_vars # Cache its length since we use it at multiple places below. len_orig_loop_vars = len(orig_loop_vars) # Convert TensorArrays to their flow variables. These get converted back to # TensorArrays before calling `cond` and `body`. See `wrapped_cond` and # `wrapped_body` below. loop_vars = list(_tensor_array_to_flow(orig_loop_vars)) loop_vars = nest.map_structure( ops.internal_convert_to_tensor_or_indexed_slices, loop_vars) if shape_invariants is not None: nest.assert_same_structure(orig_loop_vars, shape_invariants) else: shape_invariants = nest.map_structure(lambda t: t.shape, loop_vars) if not name: name = "while" with ops.name_scope(name) as scope: with ops.name_scope(None): cond_name = util.unique_fn_name(scope, "cond") body_name = util.unique_fn_name(scope, "body") loop_counter = constant_op.constant( 0, dtype=maximum_iterations.dtype if maximum_iterations is not None else None, name="loop_counter") # Add loop counter needed for computing gradients. loop_vars = [loop_counter] + loop_vars shape_invariants = type(shape_invariants)([tensor_shape.scalar() ]) + shape_invariants # Automatic control dependencies are added in defuns, but not in v1 # graphs. Propagate that behavior here. add_control_dependencies = ops.get_default_graph( )._add_control_dependencies # Build a `cond` wrapper that can handle the extra counter loop_var. def wrapped_cond(loop_counter, *args): # Convert the flow variables in `args` to TensorArrays. `args` should # already have the same structure as `orig_loop_vars` but currently there # is no nest.zip so we call `_pack_sequence_as` which flattens both # `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays # and packs it into the structure of `orig_loop_vars`. if maximum_iterations is None: return cond(*_pack_sequence_as(orig_loop_vars, args)) else: return math_ops.logical_and( loop_counter < maximum_iterations, cond(*_pack_sequence_as(orig_loop_vars, args))) cond_graph = func_graph_module.func_graph_from_py_func( cond_name, wrapped_cond, loop_vars, {}, signature=_build_signature(loop_vars, shape_invariants), func_graph=util.WhileCondFuncGraph(cond_name), add_control_dependencies=add_control_dependencies) # Add external_captures of cond to the list of loop vars. # Note that external tensors will be treated as loop invariants, i.e., # the value of that tensor in each iteration is the same as it was at the # beginning of the loop execution. loop_vars = loop_vars + cond_graph.external_captures shape_invariants = shape_invariants + type(shape_invariants)( [t.shape for t in cond_graph.external_captures]) def wrapped_body(loop_counter, *args): """Loop body augmented with counter update. Args: loop_counter: Loop counter which needs to be incremented in the body. *args: List of args args[:len_orig_loop_vars] - Args for the original loop body. args[len_orig_loop_vars:] - External captures of cond. These get passed through as is. Returns: A list of tensors the same length as args. """ # Convert the flow variables in `args` to TensorArrays. `args` should # already have the same structure as `orig_loop_vars` but currently there # is no nest.zip so we call `_pack_sequence_as` which flattens both # `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays # and packs it into the structure of `orig_loop_vars`. outputs = body( *_pack_sequence_as(orig_loop_vars, args[:len_orig_loop_vars])) if not nest.is_sequence(outputs): outputs = [outputs] # Compare the structure of input and output of body converting the # top-level tuples to list to be compatible with legacy while_loop. nest.assert_same_structure(list(outputs), list(orig_loop_vars)) outputs = _tensor_array_to_flow(outputs) # Return the external_captures of cond_graph as is, i.e., treat them as # loop invariants. # TODO(srbs): Update lowering code to create _Enter nodes with # is_constant=True for inputs that are directly passed to outputs. return [loop_counter + 1] + list(outputs) + list( args[len_orig_loop_vars:]) body_graph = func_graph_module.func_graph_from_py_func( body_name, wrapped_body, loop_vars, {}, signature=_build_signature(loop_vars, shape_invariants), func_graph=util.WhileBodyFuncGraph(body_name), add_control_dependencies=add_control_dependencies) # Add external captures of body to the list of loop vars. # Note that external tensors will be treated as loop invariants, i.e., # the value of that tensor in each iteration is the same as it was at the # beginning of the loop execution. loop_vars = loop_vars + body_graph.external_captures # TODO(srbs): Update lowering code to create _Enter nodes with # is_constant=True for inputs that are directly passed to outputs. body_graph.outputs.extend(body_graph.internal_captures) # Capture `external_captures` of `body_graph` in `cond_graph` so that it # expects to receive those as arguments. # TODO(b/118457764): Dedup tensors that are captured in both the cond and # body. This logic already exists in cond_v2. with cond_graph.as_default(): for external_capture in body_graph.external_captures: assert external_capture not in cond_graph.captures, ( "Looks like both cond and body are capturing the same tensor %s. " "This is not supported yet. For now consider passing," " this as a loop variable." % str(external_capture)) cond_graph.capture(external_capture) # Make sure that the shapes of the loop outputs are compatible with the # shape invariants, or the shapes of the loop vars if the invariants are not # specified. num_flattened_outputs = len(nest.flatten(orig_loop_vars)) _check_shapes_compat( body_graph.outputs[1:1 + num_flattened_outputs], nest.flatten(shape_invariants[1:1 + len_orig_loop_vars]), nest.flatten(loop_vars[1:1 + len_orig_loop_vars])) flattened_loop_vars = nest.flatten(loop_vars) _check_num_inputs_outputs(cond_graph, body_graph, len(flattened_loop_vars)) outputs = gen_functional_ops._while( flattened_loop_vars, util.create_new_tf_function(cond_graph), util.create_new_tf_function(body_graph), output_shapes=[t.shape for t in body_graph.outputs], name=scope) _copy_handle_data(body_graph.outputs, outputs) util.maybe_set_lowering_attr(outputs[0].op) _maybe_set_maximum_iterations_attr(outputs[0].op, maximum_iterations) # Return identities for each output of the While op, rather than the output # of the While op directly. This makes pruning work if the output of # while_loop() is fetched: the lowering pass converts the While outputs into # IdentityN outputs, which if fetched will cause all ops in the body to be # run (since it takes all exit ops as input). After lowering, each output # identity op will end up with only the appropriate exit op as input. outputs = tuple(array_ops.identity(t) for t in outputs) # First var is loop counter. outputs = _pack_sequence_as(orig_loop_vars, outputs[1:1 + num_flattened_outputs]) if return_same_structure: return outputs flattened_outputs = nest.flatten(outputs) if len(flattened_outputs) == 1: return flattened_outputs[0] else: return outputs
def _WhileGrad(op, *grads): # pylint: disable=invalid-name """The gradient of a While op produced by while_loop.""" # Note that op is not always the same as while_op because the gradient tape, # for eager mode compatibility, forgets information about the proper op. Since # the loop cannot run in eager mode, however, we can safely introspect into # the graph here. while_op = op.outputs[0].op cond_graph = _get_graph(while_op, "cond") body_graph = _get_graph(while_op, "body") orig_num_params = len(body_graph.outputs) maximum_iterations = op.get_attr( "_maximum_iterations") if _is_in_xla_context() else None assert not _is_in_xla_context() or maximum_iterations is not None maximum_iterations = _validate_and_convert_to_tensor(maximum_iterations) # Set the incoming gradient of non-trainable inputs to None. It is possible # that we receive non-None gradients for non-trainable types in nested while # loops because we accumulate outputs of the inner while as variant tensors # which are trainable and hence receive zeros_like tensors in the gradient # pass. The non-trainable tensors then receive the popped zeros tensor from # this zeros variant. The gradient for the loop vars corresponding to these # tensors is None or zeros (this happens only if the loop var is accumulated # as well) in _grad_fn so we reset these. # TODO(b/118712257): Remove the IsTrainable filter once we can handle None # output grads in _grad_fn. grads = [ None if not _is_trainable(output) else grad for grad, output in zip(grads, body_graph.outputs) ] # We compute the gradient for the sub-graph between trainable ys and xs # with non-None incoming gradients. We later pad the None's to the list of # outputs. ys, xs, non_none_grads = zip( *[(y, x, grad) for (y, x, grad) in zip(body_graph.outputs, body_graph.inputs, grads) if grad is not None]) body_grad_graph, args = _create_grad_func( ys, xs, non_none_grads, cond_graph, body_graph, util.unique_grad_fn_name(body_graph.name), op, maximum_iterations) if body_grad_graph.while_op_needs_rewrite: # Modify 'op' to output the intermediate accumulators needed by the grad # function. # NOTE(skyewm): if there are any active sessions, this modification to `op` # may make them unrunnable! cond_graph.name += "_rewritten" body_graph.name += "_rewritten" new_inputs = body_grad_graph.empty_tensor_lists new_outputs = body_graph.outputs[orig_num_params:] while_op._set_func_attr("cond", util.create_new_tf_function(cond_graph)) while_op._set_func_attr("body", util.create_new_tf_function(body_graph)) while_op._set_type_list_attr("T", body_graph.output_types) while_op._set_shape_list_attr("output_shapes", body_graph.output_shapes) while_op._add_while_inputs(new_inputs) while_op._add_outputs([t.dtype for t in new_outputs], [t.shape for t in new_outputs]) _copy_handle_data(new_outputs, op.outputs[orig_num_params:]) captured_inputs = _resolve_grad_captures(body_graph, body_grad_graph, while_op) loop_vars = args + captured_inputs def grad_cond(counter, max_iters, *unused_args): return counter < max_iters grad_cond_name = util.unique_grad_fn_name(op.get_attr("cond").name) cond_grad_graph = func_graph_module.func_graph_from_py_func( grad_cond_name, grad_cond, loop_vars, {}, func_graph=util.WhileCondFuncGraph(grad_cond_name)) _check_num_inputs_outputs(cond_grad_graph, body_grad_graph, len(loop_vars)) outputs = gen_functional_ops._while( loop_vars, util.create_new_tf_function(cond_grad_graph), util.create_new_tf_function(body_grad_graph), output_shapes=[t.shape for t in body_grad_graph.outputs], name="%s_grad" % while_op.name) _copy_handle_data(body_grad_graph.outputs, outputs) util.maybe_set_lowering_attr(outputs[0].op) _maybe_set_maximum_iterations_attr(outputs[0].op, maximum_iterations) # See comment in while_loop. outputs = [array_ops.identity(t) for t in outputs] # Set None as the output gradient for tensors with None input gradient. # outputs[0] is the loop counter. # outputs[1] is the total number of loop iterations. index = 2 none_padded_outputs = [] for g in grads: if g is None: none_padded_outputs.append(None) else: none_padded_outputs.append(outputs[index]) index += 1 return none_padded_outputs
def _WhileGrad(op, *grads): # pylint: disable=invalid-name """The gradient of a While op produced by while_loop.""" body_graph = _get_body_graph(op) # Set the incoming gradient of TensorArray handles to None. The gradient # implementation currently assumes all resource tensors correspond to float32 # ResourceVariables, which can lead to runtime shape errors when used with a # TensorArray. This is a workaround until TensorArrays are reimplemented with # TensorLists instead of resources. # Also set the incoming gradient of non-trainable inputs to None. It is # possible that we receive non-None gradients for non-trainable types in # nested while loops because we accumulate outputs of the inner while as # variant tensors which are trainable and hence receive zeros_like tensors in # the gradient pass. The non-trainable tensors then receive the popped zeros # tensor from this zeros variant. The gradient for the loop vars corresponding # to these tensors is None or zeros (this happens only if the loop var is # accumulated as well) in _grad_fn so we reset these. # TODO(b/118712257): Remove the IsTrainable filter once we can handle None # output grads in _grad_fn. grads = [ None if _is_tensor_array_handle(output) or not gradients_impl.IsTrainable(output) else grad for grad, output in zip(grads, op.outputs) ] # Ensure that all non-resource trainable outputs have incoming gradients. assert all(g is not None or o.dtype == dtypes.resource or not gradients_impl.IsTrainable(o) for o, g in zip(op.outputs, grads) ), "All trainable loop vars must receive incoming gradients." # We compute the gradient for the sub-graph between trainable ys and xs # with non-None incoming gradients. We later pad the None's to the list of # outputs. ys, xs, non_none_grads = zip( *[(y, x, grad) for (y, x, grad) in zip(body_graph.outputs, body_graph.inputs, grads) if grad is not None]) body_grad_graph, args = _create_grad_func( ys, xs, non_none_grads, body_graph, util.unique_grad_fn_name(body_graph.name), op) intermediate_tensors = _get_intermediates(body_grad_graph) maximum_iterations = op.get_attr( "_maximum_iterations") if _is_in_xla_context() else None assert not _is_in_xla_context() or maximum_iterations is not None for intermediate_tensor in intermediate_tensors: tensor_list = list_ops.empty_tensor_list( element_dtype=intermediate_tensor.dtype, element_shape=intermediate_tensor.shape, max_num_elements=maximum_iterations) with body_grad_graph.as_default(): tensor_list_ph = body_grad_graph.capture(tensor_list, whitelisted=True) # Push the intermediate tensor to the tensor list. appended_tensor_list = list_ops.tensor_list_push_back( tensor_list_ph, intermediate_tensor) # Add this modified tensor list to the list of outputs. body_grad_graph.outputs.append(appended_tensor_list) def grad_cond(counter, max_iters, *unused_args): return counter < max_iters loop_vars = args + body_grad_graph.external_captures grad_cond_name = util.unique_grad_fn_name(op.get_attr("cond").name) cond_grad_graph = func_graph_module.func_graph_from_py_func( grad_cond_name, grad_cond, loop_vars, {}, func_graph=util.WhileCondFuncGraph(grad_cond_name)) _check_num_inputs_outputs(cond_grad_graph, body_grad_graph, len(loop_vars)) outputs = gen_functional_ops._while( loop_vars, util.create_new_tf_function(cond_grad_graph), util.create_new_tf_function(body_grad_graph), output_shapes=[t.shape for t in body_grad_graph.outputs], name="%s_grad" % op.name) _copy_handle_data(body_grad_graph.outputs, outputs) util.maybe_set_lowering_attr(outputs[0].op) _maybe_set_maximum_iterations_attr(outputs[0].op, maximum_iterations) # See comment in while_loop. outputs = [array_ops.identity(t) for t in outputs] # Set None as the output gradient for tensors with None input gradient # e.g. TensorArray handles. # outputs[0] is the loop counter. # outputs[1] is the total number of loop iterations. index = 2 none_padded_outputs = [] for g in grads: if g is None: none_padded_outputs.append(None) else: none_padded_outputs.append(outputs[index]) index += 1 return none_padded_outputs
def while_loop(cond, body, loop_vars, shape_invariants=None, name=None): """Like tf.while_loop, except emits a single While op.""" flattened_loop_vars = nest.flatten(loop_vars) if shape_invariants is not None: nest.assert_same_structure(loop_vars, shape_invariants) flattened_shapes = nest.flatten(shape_invariants) else: flattened_shapes = [t.shape for t in flattened_loop_vars] del shape_invariants if not name: name = "while" with ops.name_scope(name) as scope: with ops.name_scope(None): cond_name = util.unique_fn_name(scope, "cond") body_name = util.unique_fn_name(scope, "body") num_outputs = len(flattened_loop_vars) # Add loop counter needed for computing gradients. flattened_loop_vars = [constant_op.constant(0., name="loop_counter") ] + flattened_loop_vars flattened_shapes = [tensor_shape.scalar()] + flattened_shapes # Build a `cond` wrapper that can handle the extra counter loop_var. def wrapped_cond(unused_loop_counter, *loop_vars): return cond(*loop_vars) signature = [ tensor_spec.TensorSpec(shape, t.dtype) for shape, t in zip(flattened_shapes, flattened_loop_vars) ] cond_graph = function.func_graph_from_py_func( cond_name, wrapped_cond, flattened_loop_vars, {}, signature=signature, func_graph=util.WhileCondFuncGraph(cond_name)) # Add external_captures of cond to the list of loop vars. # Note that external tensors will be treated as loop invariants, i.e., # the value of that tensor in each iteration is the same as it was at the # beginning of the loop execution. flattened_loop_vars = flattened_loop_vars + cond_graph.external_captures flattened_shapes = flattened_shapes + [ t.shape for t in cond_graph.external_captures ] def wrapped_body(loop_counter, *args): """Loop body augmented with counter update. Args: loop_counter: Loop counter which needs to be incremented in the body. *args: List of args args[:num_outputs] - Args for the original loop body. args[num_outputs:] - External captures of cond. These get passed through as is. Returns: A list of tensors the same length as args. """ outputs = body(*args[:num_outputs]) if not isinstance(outputs, collections.Sequence): outputs = [outputs] # Return the external_captures of cond_graph as is, i.e., treat them as # loop invariants. # TODO(srbs): Update lowering code to create _Enter nodes with # is_constant=True for inputs that are directly passed to outputs. return [loop_counter + 1] + list(outputs) + list( args[num_outputs:]) signature = [ tensor_spec.TensorSpec(shape, t.dtype) for shape, t in zip(flattened_shapes, flattened_loop_vars) ] body_graph = function.func_graph_from_py_func( body_name, wrapped_body, flattened_loop_vars, {}, signature=signature, func_graph=util.WhileBodyFuncGraph(body_name)) # Add external captures of body to the list of loop vars. # Note that external tensors will be treated as loop invariants, i.e., # the value of that tensor in each iteration is the same as it was at the # beginning of the loop execution. flattened_loop_vars = flattened_loop_vars + body_graph.external_captures # TODO(srbs): Update lowering code to create _Enter nodes with # is_constant=True for inputs that are directly passed to outputs. body_graph.outputs.extend(body_graph.internal_captures) # Capture `external_captures` of `body_graph` in `cond_graph` so that it # expects to receive those as arguments. # TODO(srbs): Dedup tensors that are captured in both the cond and body. # This logic already exists in cond_v2. with cond_graph.as_default(): for external_capture in body_graph.external_captures: cond_graph.capture(external_capture) # Export all tensors in the loop body that may be needed for gradient # computation. We do this by accumulating the intermediate values in # TensorLists. intermediate_tensors = _get_intermediates(body_graph) for intermediate_tensor in intermediate_tensors: # TODO(srbs): Cache and re-use empty tensor lists. tensor_list = list_ops.empty_tensor_list( element_dtype=intermediate_tensor.dtype, element_shape=_get_tensor_convertible_shape( intermediate_tensor.shape)) flattened_loop_vars.append(tensor_list) with cond_graph.as_default(): # Add a placeholder to cond_graph's inputs corresponding to the # tensor_list. cond_graph.capture(tensor_list) with body_graph.as_default(): # Push the intermediate tensor to the tensor list. This captures the # `tensor_list` as well. appended_tensor_list = list_ops.tensor_list_push_back( tensor_list, intermediate_tensor) # Add this modified tensor list to the list of outputs. body_graph.outputs.append(appended_tensor_list) # Make sure that the shapes of the loop outputs are compatible with the # shape invariants, or the shapes of the loop vars if the invariants are not # specified. _check_shapes_compat(body_graph.outputs[1:1 + num_outputs], flattened_shapes[1:1 + num_outputs], flattened_loop_vars[1:1 + num_outputs]) outputs = gen_functional_ops._while( flattened_loop_vars, util.create_new_tf_function(cond_graph), util.create_new_tf_function(body_graph), output_shapes=[t.shape for t in body_graph.outputs], name=scope) _copy_handle_data(body_graph.outputs, outputs) _maybe_set_lowering_attr(outputs[0].op) # First var is loop counter. if num_outputs == 1: return outputs[1] else: return nest.pack_sequence_as(loop_vars, outputs[1:1 + num_outputs])
def while_loop(cond, body, loop_vars, shape_invariants=None, parallel_iterations=10, maximum_iterations=None, name=None, return_same_structure=True): """Like tf.while_loop, except emits a single While op.""" # Keep the original loop_vars around to know which args were TensorArrays. orig_loop_vars = loop_vars # Cache its length since we use it at multiple places below. len_orig_loop_vars = len(orig_loop_vars) # Convert TensorArrays to their flow variables. These get converted back to # TensorArrays before calling `cond` and `body`. See `wrapped_cond` and # `wrapped_body` below. loop_vars = list(_tensor_array_to_flow(orig_loop_vars)) loop_vars = nest.map_structure( ops.internal_convert_to_tensor_or_indexed_slices, loop_vars) if shape_invariants is not None: nest.assert_same_structure(orig_loop_vars, shape_invariants) else: shape_invariants = nest.map_structure(lambda t: t.shape, loop_vars) if not name: name = "while" with ops.name_scope(name) as scope: with ops.name_scope(None): cond_name = util.unique_fn_name(scope, "cond") body_name = util.unique_fn_name(scope, "body") maximum_iterations_loop_var = _build_maximum_iterations_loop_var( maximum_iterations) loop_counter = constant_op.constant( 0, dtype=maximum_iterations_loop_var.dtype if maximum_iterations is not None else None, name="loop_counter") # Add loop counter needed for computing gradients. loop_vars = [loop_counter, maximum_iterations_loop_var] + loop_vars shape_invariants = type(shape_invariants)( [tensor_shape.scalar(), tensor_shape.scalar()]) + shape_invariants # Automatic control dependencies are added in defuns, but not in v1 # graphs. Propagate that behavior here. add_control_dependencies = ops.get_default_graph( )._add_control_dependencies # Build a `cond` wrapper that can handle the extra counter loop_var. def wrapped_cond(loop_counter, maximum_iterations_arg, *args): # Convert the flow variables in `args` to TensorArrays. `args` should # already have the same structure as `orig_loop_vars` but currently there # is no nest.zip so we call `_pack_sequence_as` which flattens both # `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays # and packs it into the structure of `orig_loop_vars`. if maximum_iterations is None: return cond(*_pack_sequence_as(orig_loop_vars, args)) else: return math_ops.logical_and( loop_counter < maximum_iterations_arg, cond(*_pack_sequence_as(orig_loop_vars, args))) # NOTE(skyewm): we set collections to the outer graph's collections for # compatibility with TPUEstimator. cond_graph = func_graph_module.func_graph_from_py_func( cond_name, wrapped_cond, [], # We provide signature instead of args. {}, signature=_build_signature(loop_vars, shape_invariants), func_graph=util.WhileCondFuncGraph( cond_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access add_control_dependencies=add_control_dependencies) def wrapped_body(loop_counter, maximum_iterations_arg, *args): """Loop body augmented with counter update. Args: loop_counter: Loop counter which needs to be incremented in the body. maximum_iterations_arg: Maximum iterations of the loop. *args: List of args Returns: A list of tensors the same length as args. """ # Capture the tensors already captured in cond_graph so that they appear # in the same order in body_graph.external_captures. for t in cond_graph.external_captures: ops.get_default_graph().capture(t) # Convert the flow variables in `args` to TensorArrays. `args` should # already have the same structure as `orig_loop_vars` but currently there # is no nest.zip so we call `_pack_sequence_as` which flattens both # `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays # and packs it into the structure of `orig_loop_vars`. outputs = body(*_pack_sequence_as(orig_loop_vars, args)) if not nest.is_sequence(outputs): outputs = [outputs] # Compare the structure of input and output of body converting the # top-level tuples to list to be compatible with legacy while_loop. nest.assert_same_structure(list(outputs), list(orig_loop_vars)) outputs = _tensor_array_to_flow(outputs) # TODO(srbs): Update lowering code to create _Enter nodes with # is_constant=True for inputs that are directly passed to outputs. return [loop_counter + 1, maximum_iterations_arg] + list(outputs) body_graph = func_graph_module.func_graph_from_py_func( body_name, wrapped_body, [], # We provide signature instead of args. {}, signature=_build_signature(loop_vars, shape_invariants), func_graph=util.WhileBodyFuncGraph( body_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access add_control_dependencies=add_control_dependencies) # Add external captures of body to the list of loop vars. # Note that external tensors will be treated as loop invariants, i.e., # the value of that tensor in each iteration is the same as it was at the # beginning of the loop execution. loop_vars = loop_vars + body_graph.external_captures # TODO(srbs): Update lowering code to create _Enter nodes with # is_constant=True for inputs that are directly passed to outputs. body_graph.outputs.extend(body_graph.internal_captures) # Capture the extra `external_captures` of `body_graph` in `cond_graph` so # that it expects to receive those as arguments. with cond_graph.as_default(): num_cond_captures = len(cond_graph.external_captures) assert (cond_graph.external_captures == body_graph.external_captures[:num_cond_captures]) for body_capture in body_graph.external_captures[ num_cond_captures:]: assert body_capture not in cond_graph.captures cond_graph.capture(body_capture) # Make sure that the shapes of the loop outputs are compatible with the # shape invariants, or the shapes of the loop vars if the invariants are not # specified. num_flattened_outputs = len(nest.flatten(orig_loop_vars)) # First var is loop counter and second var is maximum_iterations. first_loop_var_index = 2 _check_shapes_compat( body_graph.outputs[first_loop_var_index:first_loop_var_index + num_flattened_outputs], nest.flatten( shape_invariants[first_loop_var_index:first_loop_var_index + len_orig_loop_vars]), nest.flatten(loop_vars[first_loop_var_index:first_loop_var_index + len_orig_loop_vars])) flattened_loop_vars = nest.flatten(loop_vars) _check_num_inputs_outputs(cond_graph, body_graph, len(flattened_loop_vars)) with ops.control_dependencies( list(cond_graph.control_captures) + list(body_graph.control_captures)): outputs = gen_functional_ops._while( flattened_loop_vars, util.create_new_tf_function(cond_graph), util.create_new_tf_function(body_graph), output_shapes=[t.shape for t in body_graph.outputs], parallel_iterations=parallel_iterations, name=scope) _copy_handle_data(body_graph.outputs, outputs) util.maybe_set_lowering_attr(outputs[0].op) util.maybe_propagate_compile_time_consts_in_xla(outputs[0].op) # Return identities for each output of the While op, rather than the output # of the While op directly. This makes pruning work if the output of # while_loop() is fetched: the lowering pass converts the While outputs into # IdentityN outputs, which if fetched will cause all ops in the body to be # run (since it takes all exit ops as input). After lowering, each output # identity op will end up with only the appropriate exit op as input. outputs = tuple(array_ops.identity(t) for t in outputs) outputs = _pack_sequence_as( orig_loop_vars, outputs[first_loop_var_index:first_loop_var_index + num_flattened_outputs]) if return_same_structure: return outputs flattened_outputs = nest.flatten(outputs) if len(flattened_outputs) == 1: return flattened_outputs[0] else: return outputs
def _WhileGrad(op, *grads): # pylint: disable=invalid-name """The gradient of a While op produced by while_loop.""" body_graph = _get_body_graph(op) # Set the incoming gradient of TensorArray handle to None. # TODO(b/118164915): We need a way of distinguising b/w TensorArray resource # handles and ResourceVariables and set the default gradient of only the # TensorArray handle to None. grads = [ None if output.dtype == dtypes.resource else g for g, output in zip(grads, op.outputs) ] # Ensure that all non-resource trainable outputs have incoming gradients. assert all(g is not None or o.dtype == dtypes.resource or not gradients_impl.IsTrainable(o) for o, g in zip(op.outputs, grads) ), "All trainable loop vars must receive incoming gradients." # We compute the gradient for the sub-graph between trainable ys and xs # with non-None incoming gradients. We later pad the None's to the list of # outputs. ys, xs, non_none_grads = zip( *[(y, x, grad) for (y, x, grad) in zip(body_graph.outputs, body_graph.inputs, grads) if grad is not None]) body_grad_graph, args = _create_grad_func( ys, xs, non_none_grads, body_graph, util.unique_grad_fn_name(body_graph.name), op) intermediate_tensors = _get_intermediates(body_grad_graph) for intermediate_tensor in intermediate_tensors: tensor_list = list_ops.empty_tensor_list( element_dtype=intermediate_tensor.dtype, element_shape=_get_tensor_convertible_shape( intermediate_tensor.shape)) with body_grad_graph.as_default(): tensor_list_ph = body_grad_graph.capture(tensor_list, whitelisted=True) # Push the intermediate tensor to the tensor list. appended_tensor_list = list_ops.tensor_list_push_back( tensor_list_ph, intermediate_tensor) # Add this modified tensor list to the list of outputs. body_grad_graph.outputs.append(appended_tensor_list) def grad_cond(counter, max_iters, *unused_args): return counter < max_iters loop_vars = args + body_grad_graph.external_captures grad_cond_name = util.unique_grad_fn_name(op.get_attr("cond").name) cond_grad_graph = func_graph_module.func_graph_from_py_func( grad_cond_name, grad_cond, loop_vars, {}, func_graph=util.WhileCondFuncGraph(grad_cond_name)) _check_num_inputs_outputs(cond_grad_graph, body_grad_graph, len(loop_vars)) outputs = gen_functional_ops._while( loop_vars, util.create_new_tf_function(cond_grad_graph), util.create_new_tf_function(body_grad_graph), output_shapes=[t.shape for t in body_grad_graph.outputs], name="%s_grad" % op.name) _copy_handle_data(body_grad_graph.outputs, outputs) _maybe_set_lowering_attr(outputs[0].op) # Set None as the output gradient for tensors with None input gradient # e.g. TensorArray handles. # outputs[0] is the loop counter. # outputs[1] is the total number of loop iterations. index = 2 none_padded_outputs = [] for g in grads: if g is None: none_padded_outputs.append(None) else: none_padded_outputs.append(outputs[index]) index += 1 return none_padded_outputs
def _WhileGrad(op, *grads): # pylint: disable=invalid-name """The gradient of a While op produced by while_loop.""" cond_graph = _get_graph(op, "cond") body_graph = _get_graph(op, "body") orig_num_params = len(body_graph.outputs) maximum_iterations = op.get_attr( "_maximum_iterations") if _is_in_xla_context() else None assert not _is_in_xla_context() or maximum_iterations is not None # Set the incoming gradient of TensorArray handles to None. The gradient # implementation currently assumes all resource tensors correspond to float32 # ResourceVariables, which can lead to runtime shape errors when used with a # TensorArray. This is a workaround until TensorArrays are reimplemented with # TensorLists instead of resources. # Also set the incoming gradient of non-trainable inputs to None. It is # possible that we receive non-None gradients for non-trainable types in # nested while loops because we accumulate outputs of the inner while as # variant tensors which are trainable and hence receive zeros_like tensors in # the gradient pass. The non-trainable tensors then receive the popped zeros # tensor from this zeros variant. The gradient for the loop vars corresponding # to these tensors is None or zeros (this happens only if the loop var is # accumulated as well) in _grad_fn so we reset these. # TODO(b/118712257): Remove the IsTrainable filter once we can handle None # output grads in _grad_fn. grads = [ None if _is_tensor_array_handle(output) or not _is_trainable(output) else grad for grad, output in zip(grads, body_graph.outputs) ] # Ensure that all non-resource trainable outputs have incoming gradients. assert all( g is not None or o.dtype == dtypes.resource or not _is_trainable(o) for o, g in zip(body_graph.outputs, grads) ), "All trainable loop vars must receive incoming gradients." # We compute the gradient for the sub-graph between trainable ys and xs # with non-None incoming gradients. We later pad the None's to the list of # outputs. ys, xs, non_none_grads = zip( *[(y, x, grad) for (y, x, grad) in zip(body_graph.outputs, body_graph.inputs, grads) if grad is not None]) body_grad_graph, args = _create_grad_func( ys, xs, non_none_grads, cond_graph, body_graph, util.unique_grad_fn_name(body_graph.name), op, maximum_iterations) if body_grad_graph.while_op_needs_rewrite: # Modify 'op' to output the intermediate accumulators needed by the grad # function. # NOTE(skyewm): if there are any active sessions, this modification to `op` # may make them unrunnable! cond_graph.name += "_rewritten" body_graph.name += "_rewritten" new_inputs = body_grad_graph.empty_tensor_lists new_outputs = body_graph.outputs[orig_num_params:] op._set_func_attr("cond", util.create_new_tf_function(cond_graph)) op._set_func_attr("body", util.create_new_tf_function(body_graph)) op._set_type_list_attr("T", body_graph.output_types) op._set_shape_list_attr("output_shapes", body_graph.output_shapes) op._add_while_inputs(new_inputs) op._add_outputs([t.dtype for t in new_outputs], [t.shape for t in new_outputs]) _copy_handle_data(new_outputs, op.outputs[orig_num_params:]) captured_inputs = _resolve_grad_captures(body_graph, body_grad_graph, op) loop_vars = args + captured_inputs def grad_cond(counter, max_iters, *unused_args): return counter < max_iters grad_cond_name = util.unique_grad_fn_name(op.get_attr("cond").name) cond_grad_graph = func_graph_module.func_graph_from_py_func( grad_cond_name, grad_cond, loop_vars, {}, func_graph=util.WhileCondFuncGraph(grad_cond_name)) _check_num_inputs_outputs(cond_grad_graph, body_grad_graph, len(loop_vars)) outputs = gen_functional_ops._while( loop_vars, util.create_new_tf_function(cond_grad_graph), util.create_new_tf_function(body_grad_graph), output_shapes=[t.shape for t in body_grad_graph.outputs], name="%s_grad" % op.name) _copy_handle_data(body_grad_graph.outputs, outputs) util.maybe_set_lowering_attr(outputs[0].op) _maybe_set_maximum_iterations_attr(outputs[0].op, maximum_iterations) # See comment in while_loop. outputs = [array_ops.identity(t) for t in outputs] # Set None as the output gradient for tensors with None input gradient # e.g. TensorArray handles. # outputs[0] is the loop counter. # outputs[1] is the total number of loop iterations. index = 2 none_padded_outputs = [] for g in grads: if g is None: none_padded_outputs.append(None) else: none_padded_outputs.append(outputs[index]) index += 1 return none_padded_outputs