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[TF Hub][TF FE] Support TensorListLength and TensorListResize operati…
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…ons (#19390)

* [TF Hub][TF FE] Support TensorListLength and TensorListResize operations

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add test with empty tensor list

* remove assert

---------

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
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@rkazants
rkazants authored Aug 24, 2023
1 parent ba6cca8 commit 8df85ba
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Showing 5 changed files with 249 additions and 46 deletions.
2 changes: 2 additions & 0 deletions src/frontends/tensorflow/src/op_table.cpp
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Expand Up @@ -263,10 +263,12 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
{"Switch", CreatorFunction(translate_switch_op)},
{"TensorListFromTensor", CreatorFunction(translate_tensor_list_from_tensor_op)},
{"TensorListGetItem", CreatorFunction(translate_tensor_list_get_item_op)},
{"TensorListLength", CreatorFunction(translate_tensor_list_length_op)},
{"TensorListPushBack", CreatorFunction(translate_tensor_list_push_back_op)},
{"TensorListSetItem", CreatorFunction(translate_tensor_list_set_item_op)},
{"TensorListStack", CreatorFunction(translate_tensor_list_stack_op)},
{"TensorListReserve", CreatorFunction(translate_tensor_list_reserve_op)},
{"TensorListResize", CreatorFunction(translate_tensor_list_resize_op)},
{"Tile", CreatorFunction(translate_tile_op)},
{"TopK", CreatorFunction(translate_top_k_op)},
{"TopKV2", CreatorFunction(translate_top_k_v2_op)},
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2 changes: 2 additions & 0 deletions src/frontends/tensorflow_common/include/common_op_table.hpp
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Expand Up @@ -132,10 +132,12 @@ OP_CONVERTER(translate_strided_slice_op);
OP_CONVERTER(translate_sqrt_op);
OP_CONVERTER(translate_tensor_list_from_tensor_op);
OP_CONVERTER(translate_tensor_list_get_item_op);
OP_CONVERTER(translate_tensor_list_length_op);
OP_CONVERTER(translate_tensor_list_push_back_op);
OP_CONVERTER(translate_tensor_list_reserve_op);
OP_CONVERTER(translate_tensor_list_set_item_op);
OP_CONVERTER(translate_tensor_list_stack_op);
OP_CONVERTER(translate_tensor_list_resize_op);
OP_CONVERTER(translate_tile_op);
OP_CONVERTER_NAMED(translate_top_k_op);
OP_CONVERTER_NAMED(translate_top_k_v2_op);
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159 changes: 113 additions & 46 deletions src/frontends/tensorflow_common/src/op/tensor_list_operations.cpp
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Expand Up @@ -3,12 +3,26 @@
//

#include "common_op_table.hpp"
#include "openvino/opsets/opset10.hpp"
#include "openvino/op/add.hpp"
#include "openvino/op/broadcast.hpp"
#include "openvino/op/concat.hpp"
#include "openvino/op/constant.hpp"
#include "openvino/op/convert.hpp"
#include "openvino/op/convert_like.hpp"
#include "openvino/op/gather.hpp"
#include "openvino/op/maximum.hpp"
#include "openvino/op/reshape.hpp"
#include "openvino/op/scatter_update.hpp"
#include "openvino/op/shape_of.hpp"
#include "openvino/op/slice.hpp"
#include "openvino/op/squeeze.hpp"
#include "openvino/op/subtract.hpp"
#include "openvino/op/unsqueeze.hpp"
#include "utils.hpp"

using namespace std;
using namespace ov;
using namespace opset10;
using namespace ov::op;

namespace ov {
namespace frontend {
Expand All @@ -22,7 +36,7 @@ OutputVector translate_tensor_list_reserve_op(const NodeContext& node) {
// all tensor elements will be saved in the flatten form in the list
// because we want to cover a case of dynamic rank tensor list
// the real shape of the tensor elements will be restored by TensorListStack operations
auto empty_constant = make_shared<Constant>(element_dtype, Shape{0, 0});
auto empty_constant = make_shared<v0::Constant>(element_dtype, Shape{0, 0});
set_node_name(node.get_name(), empty_constant);
return {empty_constant};
}
Expand All @@ -41,14 +55,14 @@ OutputVector translate_tensor_list_stack_op(const NodeContext& node) {
auto element_shape = node.get_input(1);

// compute number of tensor elements in the list
Output<Node> num_elements = make_shared<ShapeOf>(input_handle, element::i32);
auto zero_const = make_shared<Constant>(element::i32, Shape{1}, 0);
auto one_const = make_shared<Constant>(element::i32, Shape{1}, 1);
num_elements = make_shared<Slice>(num_elements, zero_const, one_const, one_const);
Output<Node> num_elements = make_shared<v3::ShapeOf>(input_handle, element::i32);
auto zero_const = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
auto one_const = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
num_elements = make_shared<v8::Slice>(num_elements, zero_const, one_const, one_const);

// restore the real shape of tensor elements
auto new_shape = make_shared<Concat>(OutputVector{num_elements, element_shape}, 0);
auto reshape = make_shared<Reshape>(input_handle, new_shape, false);
auto new_shape = make_shared<v0::Concat>(OutputVector{num_elements, element_shape}, 0);
auto reshape = make_shared<v1::Reshape>(input_handle, new_shape, false);

set_node_name(node.get_name(), reshape);
return {reshape};
Expand All @@ -62,12 +76,12 @@ OutputVector translate_tensor_list_get_item_op(const NodeContext& node) {
auto element_dtype = node.get_attribute<element::Type>("element_dtype");

// squeeze index tensor to have a scalar
index = make_shared<Squeeze>(index);
index = make_shared<v0::Squeeze>(index);

// gather tensor element by the required position
auto gather_axis = make_shared<Constant>(element::i32, Shape{1}, 0);
Output<Node> tensor_element = make_shared<Gather>(input_handle, index, gather_axis);
tensor_element = make_shared<Convert>(tensor_element, element_dtype);
auto gather_axis = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
Output<Node> tensor_element = make_shared<v8::Gather>(input_handle, index, gather_axis);
tensor_element = make_shared<v0::Convert>(tensor_element, element_dtype);

set_node_name(node.get_name(), tensor_element.get_node_shared_ptr());
return {tensor_element};
Expand All @@ -80,44 +94,44 @@ OutputVector translate_tensor_list_set_item_op(const NodeContext& node) {
auto item = node.get_input(2);

// squeeze index tensor to have a scalar
index = make_shared<Squeeze>(index);
index = make_shared<v0::Squeeze>(index);

// flatten item to be inserted since
// the tensor list saves elements in the flatten form
auto new_item_shape = make_shared<Constant>(element::i32, Shape{1}, -1);
item = make_shared<Reshape>(item, new_item_shape, false);
auto item_shape = make_shared<ShapeOf>(item, element::i32);
auto new_item_shape = make_shared<v0::Constant>(element::i32, Shape{1}, -1);
item = make_shared<v1::Reshape>(item, new_item_shape, false);
auto item_shape = make_shared<v3::ShapeOf>(item, element::i32);

// reshape the tensor list to the shape [num_elements, -1]
// that is because in the first iteration we have empty constant of a shape [0,0]
auto minus_one = make_shared<Constant>(element::i32, Shape{1}, -1);
auto new_input_handle_shape = make_shared<Concat>(OutputVector{minus_one, item_shape}, 0);
input_handle = make_shared<Reshape>(input_handle, new_input_handle_shape, false);
input_handle = make_shared<ConvertLike>(input_handle, item);
auto minus_one = make_shared<v0::Constant>(element::i32, Shape{1}, -1);
auto new_input_handle_shape = make_shared<v0::Concat>(OutputVector{minus_one, item_shape}, 0);
input_handle = make_shared<v1::Reshape>(input_handle, new_input_handle_shape, false);
input_handle = make_shared<v1::ConvertLike>(input_handle, item);

// compute the current length of the list
Output<Node> list_length = make_shared<ShapeOf>(input_handle, element::i32);
auto zero_const = make_shared<Constant>(element::i32, Shape{1}, 0);
auto one_const = make_shared<Constant>(element::i32, Shape{1}, 1);
list_length = make_shared<Slice>(list_length, zero_const, one_const, one_const);
Output<Node> list_length = make_shared<v3::ShapeOf>(input_handle, element::i32);
auto zero_const = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
auto one_const = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
list_length = make_shared<v8::Slice>(list_length, zero_const, one_const, one_const);

// compute a size of the dummy tensor that serves to fill holes in the list
// if no tensor is inserted at this position
auto one_const_scalar = make_shared<Constant>(element::i32, Shape{1}, 1);
auto index_plus_one = make_shared<Add>(index, one_const_scalar);
Output<Node> max_length = make_shared<Maximum>(list_length, index_plus_one);
Output<Node> dummy_tensor_size = make_shared<Subtract>(max_length, list_length);
auto one_const_scalar = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
auto index_plus_one = make_shared<v1::Add>(index, one_const_scalar);
Output<Node> max_length = make_shared<v1::Maximum>(list_length, index_plus_one);
Output<Node> dummy_tensor_size = make_shared<v1::Subtract>(max_length, list_length);

// create dummy tensor and concatenate it
auto zero_element = create_same_type_const_scalar<int32_t>(item, 0);
auto dummy_tensor_shape = make_shared<Concat>(OutputVector{dummy_tensor_size, item_shape}, 0);
auto dummy_tensor = make_shared<Broadcast>(zero_element, dummy_tensor_shape);
input_handle = make_shared<Concat>(OutputVector{input_handle, dummy_tensor}, 0);
auto dummy_tensor_shape = make_shared<v0::Concat>(OutputVector{dummy_tensor_size, item_shape}, 0);
auto dummy_tensor = make_shared<v3::Broadcast>(zero_element, dummy_tensor_shape);
input_handle = make_shared<v0::Concat>(OutputVector{input_handle, dummy_tensor}, 0);

// update the resulted tensor using ScatterUpdate
index = make_shared<Unsqueeze>(index, zero_const);
item = make_shared<Unsqueeze>(item, zero_const);
auto scatter_update = make_shared<ScatterUpdate>(input_handle, index, item, zero_const);
index = make_shared<v0::Unsqueeze>(index, zero_const);
item = make_shared<v0::Unsqueeze>(item, zero_const);
auto scatter_update = make_shared<v3::ScatterUpdate>(input_handle, index, item, zero_const);

set_node_name(node.get_name(), scatter_update);
return {scatter_update};
Expand All @@ -132,29 +146,82 @@ OutputVector translate_tensor_list_push_back_op(const NodeContext& node) {
// the tensor list saves elements in the flatten form
// because we want to cover a case of dynamic rank tensor list
// the real shape of the tensor elements will be restored by TensorListStack operations
auto new_tensor_shape = make_shared<Constant>(element::i32, Shape{1}, -1);
tensor = make_shared<Reshape>(tensor, new_tensor_shape, false);
auto tensor_shape = make_shared<ShapeOf>(tensor, element::i32);
auto new_tensor_shape = make_shared<v0::Constant>(element::i32, Shape{1}, -1);
tensor = make_shared<v1::Reshape>(tensor, new_tensor_shape, false);
auto tensor_shape = make_shared<v3::ShapeOf>(tensor, element::i32);

// reshape the tensor list to the shape [num_elements, -1]
// that is because in the first iteration we have empty constant of a shape [0,0]
Output<Node> num_elements = make_shared<ShapeOf>(input_handle, element::i32);
auto zero_const = make_shared<Constant>(element::i32, Shape{1}, 0);
auto one_const = make_shared<Constant>(element::i32, Shape{1}, 1);
num_elements = make_shared<Slice>(num_elements, zero_const, one_const, one_const);
auto new_input_handle_shape = make_shared<Concat>(OutputVector{num_elements, tensor_shape}, 0);
input_handle = make_shared<Reshape>(input_handle, new_input_handle_shape, false);
Output<Node> num_elements = make_shared<v3::ShapeOf>(input_handle, element::i32);
auto zero_const = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
auto one_const = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
num_elements = make_shared<v8::Slice>(num_elements, zero_const, one_const, one_const);
auto new_input_handle_shape = make_shared<v0::Concat>(OutputVector{num_elements, tensor_shape}, 0);
input_handle = make_shared<v1::Reshape>(input_handle, new_input_handle_shape, false);

// unsqueeze tensor to be inserted into the list
tensor = make_shared<Unsqueeze>(tensor, zero_const);
tensor = make_shared<v0::Unsqueeze>(tensor, zero_const);

// insert the tensor into the end
auto updated_list = make_shared<Concat>(OutputVector{input_handle, tensor}, 0);
auto updated_list = make_shared<v0::Concat>(OutputVector{input_handle, tensor}, 0);

set_node_name(node.get_name(), updated_list);
return {updated_list};
}

OutputVector translate_tensor_list_resize_op(const NodeContext& node) {
default_op_checks(node, 2, {"TensorListResize"});
auto input_handle = node.get_input(0);
auto size = node.get_input(1);

// create auxiliary constants
auto zero_const = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
auto one_const = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
auto max_const = make_shared<v0::Constant>(element::i32, Shape{1}, numeric_limits<int32_t>::max());

// compute the current length of the list and item shape
auto tensor_list_shape = make_shared<v3::ShapeOf>(input_handle, element::i32);
auto list_length = make_shared<v8::Slice>(tensor_list_shape, zero_const, one_const, one_const);
auto item_shape = make_shared<v8::Slice>(tensor_list_shape, one_const, max_const, one_const);

// compute a size of the dummy tensor to resize
// and clip it by zero if it is negative
Output<Node> dummy_tensor_size = make_shared<v1::Subtract>(size, list_length);
dummy_tensor_size = make_shared<v1::Maximum>(dummy_tensor_size, zero_const);

// create dummy tensor and concatenate it
auto zero_const_same_type = create_same_type_const<float>(input_handle, vector<float>{0.0f}, Shape{});
auto dummy_tensor_shape = make_shared<v0::Concat>(OutputVector{dummy_tensor_size, item_shape}, 0);
auto dummy_tensor = make_shared<v3::Broadcast>(zero_const_same_type, dummy_tensor_shape);
input_handle = make_shared<v0::Concat>(OutputVector{input_handle, dummy_tensor}, 0);

// reshape size to have 1D tensor with one element
auto new_size_shape = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
size = make_shared<v1::Reshape>(size, new_size_shape, false);

// resize can also shrink the input tensor list
input_handle = make_shared<v8::Slice>(input_handle, zero_const, size, one_const);

set_node_name(node.get_name(), input_handle.get_node_shared_ptr());
return {input_handle};
}

OutputVector translate_tensor_list_length_op(const NodeContext& node) {
default_op_checks(node, 1, {"TensorListLength"});
auto input_handle = node.get_input(0);

// create auxiliary constants
auto zero_const = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
auto one_const = make_shared<v0::Constant>(element::i32, Shape{1}, 1);

// compute the current length of the list
auto tensor_list_shape = make_shared<v3::ShapeOf>(input_handle, element::i32);
auto list_length = make_shared<v8::Slice>(tensor_list_shape, zero_const, one_const, one_const);

set_node_name(node.get_name(), list_length);
return {list_length};
}

} // namespace op
} // namespace tensorflow
} // namespace frontend
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83 changes: 83 additions & 0 deletions tests/layer_tests/tensorflow_tests/test_tf_TensorListLength.py
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@@ -0,0 +1,83 @@
# Copyright (C) 2018-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import numpy as np
import pytest
import tensorflow as tf
from common.tf_layer_test_class import CommonTFLayerTest


class TestTensorListLength(CommonTFLayerTest):
def _prepare_input(self, inputs_info):
assert 'x' in inputs_info
x_shape = inputs_info['x']
inputs_data = {}
inputs_data['x'] = np.random.randint(-10, 10, x_shape).astype(self.input_type)
return inputs_data

def create_tensor_list_length(self, input_shape, input_type):
self.input_type = input_type
tf.compat.v1.reset_default_graph()
# Create the graph and model
with tf.compat.v1.Session() as sess:
x = tf.compat.v1.placeholder(input_type, input_shape, 'x')
tensor_list = tf.raw_ops.TensorListFromTensor(tensor=x,
element_shape=tf.constant(input_shape[1:], dtype=tf.int32))
tf.raw_ops.TensorListLength(input_handle=tensor_list)
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def

return tf_net, None

test_data_basic = [
dict(input_shape=[7], input_type=np.float32),
dict(input_shape=[10, 20], input_type=np.float32),
dict(input_shape=[2, 3, 4], input_type=np.int32),
]

@pytest.mark.parametrize("params", test_data_basic)
@pytest.mark.precommit_tf_fe
@pytest.mark.nightly
def test_tensor_list_length_basic(self, params, ie_device, precision, ir_version, temp_dir,
use_new_frontend, use_old_api):
self._test(*self.create_tensor_list_length(**params),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_new_frontend=use_new_frontend, use_old_api=use_old_api)


class TestTensorListLengthEmptyList(CommonTFLayerTest):
def _prepare_input(self, inputs_info):
inputs_data = {}
inputs_data['tensor_list_size'] = np.array([self.tensor_list_size], dtype=np.int32)
return inputs_data

def create_tensor_list_length_empty_list(self, tensor_list_size, element_shape):
self.tensor_list_size = tensor_list_size
tf.compat.v1.reset_default_graph()
# Create the graph and model
with tf.compat.v1.Session() as sess:
tensor_list_size = tf.compat.v1.placeholder(tf.int32, [1], 'tensor_list_size')
tf_element_shape = tf.constant(element_shape, dtype=tf.int32)
tensor_shape = tf.concat([tensor_list_size, tf_element_shape], 0)
tensor = tf.broadcast_to(tf.constant(0.0, dtype=tf.float32), tensor_shape)
tensor_list = tf.raw_ops.TensorListFromTensor(tensor=tensor,
element_shape=tf_element_shape)
tf.raw_ops.TensorListLength(input_handle=tensor_list)
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def

return tf_net, None

test_data_tensor_list_length_empty_list = [
dict(tensor_list_size=0, element_shape=[]),
dict(tensor_list_size=0, element_shape=[2, 3]),
]

@pytest.mark.parametrize("params", test_data_tensor_list_length_empty_list)
@pytest.mark.precommit_tf_fe
@pytest.mark.nightly
def test_tensor_list_length_empty_list(self, params, ie_device, precision, ir_version, temp_dir,
use_new_frontend, use_old_api):
self._test(*self.create_tensor_list_length_empty_list(**params),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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