[Tokenizers] String type support in Tokenizers

modules/custom_operations/user_ie_extensions/tokenizer/python/README.md

@@ -31,7 +31,7 @@ pip install -e .[all]
 ```python
 from transformers import AutoTokenizer
 from openvino import compile_model
-from openvino_tokenizers import convert_tokenizer, pack_strings
+from openvino_tokenizers import convert_tokenizer
 
 hf_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 ov_tokenizer = convert_tokenizer(hf_tokenizer)
@@ -40,7 +40,7 @@ compiled_tokenzier = compile_model(ov_tokenizer)
 text_input = "Test string"
 
 hf_output = hf_tokenizer([text_input], return_tensors="np")
-ov_output = compiled_tokenzier(pack_strings([text_input]))
+ov_output = compiled_tokenzier([[text_input]])  # TODO: Remove the second pair of square brackets when Python API is ready
 
 for output_name in hf_output:
     print(f"OpenVINO {output_name} = {ov_output[output_name]}")
@@ -58,7 +58,7 @@ for output_name in hf_output:
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from openvino import compile_model, convert_model
-from openvino_tokenizers import convert_tokenizer, pack_strings, connect_models
+from openvino_tokenizers import convert_tokenizer, connect_models
 
 checkpoint = "mrm8488/bert-tiny-finetuned-sms-spam-detection"
 hf_tokenizer = AutoTokenizer.from_pretrained(checkpoint)
@@ -73,7 +73,7 @@ ov_model = convert_model(hf_model, example_input=hf_input.data)
 combined_model = connect_models(ov_tokenizer, ov_model)
 compiled_combined_model = compile_model(combined_model)
 
-openvino_output = compiled_combined_model(pack_strings(text_input))
+openvino_output = compiled_combined_model([text_input])  # TODO: Remove the second pair of square brackets when Python API is ready
 
 print(f"OpenVINO logits: {openvino_output['logits']}")
 # OpenVINO logits: [[ 1.2007061 -1.4698029]]
@@ -83,12 +83,11 @@ print(f"HuggingFace logits {hf_output.logits}")
 
 ### Use Extension With Converted (De)Tokenizer or Model With (De)Tokenizer
 
-To work with converted tokenizer you need `pack_strings`/`unpack_strings` functions.
+To work with converted tokenizer and detokenizer, numpy string tensors are used.
 
 ```python
 import numpy as np
 from openvino import Core
-from openvino_tokenizers import unpack_strings
 
 core = Core()
 
@@ -98,7 +97,7 @@ compiled_detokenizer = core.compile_model("detokenizer.xml")
 token_ids = np.random.randint(100, 1000, size=(3, 5))
 openvino_output = compiled_detokenizer(token_ids)
 
-print(unpack_strings(openvino_output["string_output"]))
+print(openvino_output["string_output"])
 # ['sc�ouition�', 'intvenord hasient', 'g shouldwer M more']
 ```
 
@@ -108,12 +107,7 @@ print(unpack_strings(openvino_output["string_output"]))
 import numpy as np
 from openvino import compile_model, convert_model
 from transformers import AutoModelForCausalLM, AutoTokenizer
-from openvino_tokenizers import (
-    add_greedy_decoding,
-    convert_tokenizer,
-    pack_strings,
-    unpack_strings,
-)
+from openvino_tokenizers import add_greedy_decoding, convert_tokenizer
 
 # Use different repo for the tokenizer because the original repo doesn't have .model file
 # Sentencepiece(Unigram) tokenizer supported only with .model file
@@ -128,7 +122,7 @@ ov_tokenizer, ov_detokenizer = convert_tokenizer(hf_tokenizer, with_detokenizer=
 compiled_tokenizer = compile_model(ov_tokenizer)
 
 # transform input text into tokens
-ov_input = compiled_tokenizer(pack_strings(text_input))
+ov_input = compiled_tokenizer([text_input])  # TODO: Remove the second pair of square brackets when Python API is ready
 hf_input = hf_tokenizer(text_input, return_tensors="pt")
 
 # convert Pytorch model to OpenVINO IR and add greedy decoding pipeline to it
@@ -158,7 +152,7 @@ hf_token_ids = hf_model.generate(
 
 # decode model output
 compiled_detokenizer = compile_model(ov_detokenizer)
-ov_output = unpack_strings(compiled_detokenizer(ov_token_ids)["string_output"])
+ov_output = compiled_detokenizer(ov_token_ids)["string_output"]
 hf_output = hf_tokenizer.batch_decode(hf_token_ids, skip_special_tokens=True)
 print(f"OpenVINO output string: `{ov_output}`")
 # OpenVINO output string: `['Quick brown fox was walking through the forest. He was looking for something']`

modules/custom_operations/user_ie_extensions/tokenizer/python/openvino_tokenizers/hf_parser.py

@@ -353,7 +353,7 @@ def convert_sentencepiece_model_tokenizer(
             hf_slow_tokenizer = hf_tokenizer.slow_tokenizer_class.from_pretrained(tmp)
             fairseq_offset = getattr(hf_slow_tokenizer, "fairseq_offset", None)
 
-    input_node = op.Parameter(Type.u8, PartialShape(["?"]))
+    input_node = op.Parameter(Type.string, PartialShape(["?"]))
     input_node.set_friendly_name("string_input")
 
     if is_chatglm:

modules/custom_operations/user_ie_extensions/tokenizer/python/openvino_tokenizers/tokenizer_pipeline.py

@@ -729,7 +729,7 @@ def __getitem__(self, item: int) -> BasePipelineStep:
         return self.steps[item]
 
     def get_tokenizer_ov_subgraph(self) -> Model:
-        string_inputs = [op.Parameter(Type.u8, PartialShape(["?"])) for _ in range(self.number_of_inputs)]
+        string_inputs = [op.Parameter(Type.string, PartialShape(["?"])) for _ in range(self.number_of_inputs)]
 
         processing_outputs = []
         for input_node in string_inputs:

modules/custom_operations/user_ie_extensions/tokenizer/python/tests/tokenizers_test.py

@@ -4,15 +4,20 @@
 
 import numpy as np
 import pytest
-from openvino import Core
-from openvino_tokenizers import (
-    convert_tokenizer,
-    pack_strings,
-    unpack_strings,
-)
+from openvino import Core, Tensor
+from openvino_tokenizers import convert_tokenizer
 from transformers import AutoTokenizer
 
 
+# Left these two methods for convenient transition from legay u8 representation to native string tensors
+# TODO: Remove the methods when transition is over
+def pack_strings(strings):
+    return [strings]
+
+def unpack_strings(strings):
+    return list(strings)
+
+
 core = Core()
 
 eng_test_strings = [

modules/custom_operations/user_ie_extensions/tokenizer/sentence_piece.cpp

@@ -105,28 +105,9 @@ void SentencepieceTokenizer::validate_and_infer_types() {
     FRONT_END_GENERAL_CHECK(get_input_size() == 2, "SentencepieceTokenizer expects two inputs: sp model and input sentences");
     FRONT_END_GENERAL_CHECK(get_input_element_type(0) == element::u8, "SentencepieceTokenizer accepts sp model as the first input and it should be of type u8 tensor");
 
-    #if USE_STRING_TENSORS
-
-        #if OPENVINO_USE_INPUT_OUTPUT_STRING_TENSOR_HACK
-        FRONT_END_GENERAL_CHECK(
-            get_input_element_type(1) == element::string || get_input_element_type(1) == element::u8,
-            "SentencepieceTokenizer accepts sentences as the second input and it should be of type u8 or string depending on the current stage of model preparation");
-        #else
-        FRONT_END_GENERAL_CHECK(
-            get_input_element_type(1) == element::string,
-            "SentencepieceTokenizer accepts sentences as the second input and it should be of type string tensor");
-        #endif
-
-    #else
-
-#if 0   // change to 0 when compiled with master and the bug with data propagation from within inline context is not solved
     FRONT_END_GENERAL_CHECK(
-        get_input_element_type(1) == element::u8,
-        "SentencepieceTokenizer accepts sentences as the second input and it should be of type u8 tensor, but got " +
-            get_input_element_type(1).get_type_name());
-#endif
-
-    #endif
+        get_input_element_type(1) == element::string || get_input_element_type(1) == element::u8,
+        "SentencepieceTokenizer accepts sentences as the second input and it should be of type string tensor");
 
     #endif
 
@@ -161,37 +142,37 @@ bool SentencepieceTokenizer::evaluate(TensorVector& outputs, const TensorVector&
 
 #else
 
-#if USE_STRING_TENSORS
-
-    #if OPENVINO_USE_INPUT_OUTPUT_STRING_TENSOR_HACK
-    const ov::Tensor& strings_tensor = **reinterpret_cast<ov::Tensor**>(inputs[1].data<uint8_t>());
-    #else
-    const ov::Tensor& strings_tensor = inputs[1];
-    #endif
-
-    const std::string* strings = strings_tensor.data<std::string>();
-    size_t batch_size = ov::shape_size(strings_tensor.get_shape());
+    auto input_element_type = get_input_element_type(1);
+    int32_t batch_size;
 
-#else
+    // used in case of string tensors
+    const std::string* strings;
 
-    int32_t batch_size;
+    // used in case of u8 packed representation
     const int32_t* begin_ids;
     const int32_t* end_ids;
     const uint8_t* data;
-    parse_packed_strings(inputs[1], batch_size, begin_ids, end_ids, data);
 
-#endif
+    if(input_element_type == ov::element::string) {
+        strings = inputs[1].data<const std::string>();
+        batch_size = static_cast<int32_t>(ov::shape_size(inputs[1].get_shape()));
+    } else if(input_element_type == ov::element::u8) {
+        parse_packed_strings(inputs[1], batch_size, begin_ids, end_ids, data);
+    }
 
 #endif
+
     size_t max_token_id = 0;
     for (size_t batch_ind = 0; batch_ind < batch_size; ++batch_ind) {
-#if USE_STRING_TENSORS && !SENTENCE_PIECE_EXTENSION_DECOMPOSED_STRINGS
-        const std::string& sentence = strings[batch_ind];
-#else
-        auto begin_ind = begin_ids[batch_ind];
-        auto end_ind = end_ids[batch_ind];
-        absl::string_view sentence((const char*)data + begin_ind, end_ind - begin_ind);
-#endif
+        absl::string_view sentence;
+        if(input_element_type == ov::element::string) {
+            sentence = strings[batch_ind];
+        } else if(input_element_type == ov::element::u8) {
+            auto begin_ind = begin_ids[batch_ind];
+            auto end_ind = end_ids[batch_ind];
+            sentence = absl::string_view((const char*)data + begin_ind, end_ind - begin_ind);
+        }
+
         std::vector<int32_t> ids;
         CHECK_OK(m_sp->SampleEncode(sentence, m_nbest_size, m_alpha, &ids));
         // put into resulted vectors

modules/custom_operations/user_ie_extensions/tokenizer/string_tensor_pack.cpp

@@ -11,46 +11,23 @@ using namespace ov;
 void StringTensorPack::validate_and_infer_types() {
     OPENVINO_ASSERT(m_mode == "begins_ends", "StringTensorPack supports only 'begins_ends' mode, but get " + m_mode);
     check_string_input(this, 0);
-    #if USE_STRING_TENSORS
     set_output_type(0, element::string, get_input_partial_shape(0));
-    #else
-    set_output_type(0, element::u8, PartialShape{Dimension()});
-    #endif
 }
 
 bool StringTensorPack::evaluate(ov::TensorVector& outputs, const ov::TensorVector& inputs) const {
-#if USE_STRING_TENSORS
-    // TODO
-    return false;
-#else
-    auto rank = inputs[0].get_shape().size();
-    if (rank != 1) {
-        std::cerr << "[ WARNING ] StringTensorPack ignores the rank " << rank << " of input tensor and set rank=1 in the output\n";
-    }
-
-    auto num_elements = shape_size(inputs[0].get_shape());
-    auto num_chars = shape_size(inputs[2].get_shape());
-    auto num_output_elements = 4*(1 + 1 + num_elements) + num_chars;
-    outputs[0].set_shape(Shape{num_output_elements});
+    auto num_strings = outputs[0].get_size();
+    OPENVINO_ASSERT(inputs[0].get_size() == num_strings);
+    OPENVINO_ASSERT(inputs[1].get_size() == num_strings);
 
-    // FIXME: Do the repacking, otherwise cannot handle string tensors with gaps between strings
-    //auto begins = inputs[0].data<const int32_t>();    // this is not needed as no repacking happens in this version of code
+    auto begins = inputs[0].data<const int32_t>();
     auto ends   = inputs[1].data<const int32_t>();
-    auto chars  = inputs[2].data<const uint8_t>();
-
-    auto output = outputs[0].data<uint8_t>();
-    auto output_int32 = reinterpret_cast<int32_t*>(output);
+    auto chars  = reinterpret_cast<const char*>(inputs[2].data<const uint8_t>());
 
-    *output_int32++ = num_elements;
-    *output_int32++ = 0;
-    output_int32 = std::copy(ends, ends + num_elements, output_int32);
-    output = reinterpret_cast<uint8_t*>(output_int32);
-    output = std::copy(chars, chars + num_chars, output);
+    auto strings = outputs[0].data<std::string>();
 
-    OPENVINO_ASSERT(num_output_elements == output - outputs[0].data<uint8_t>(), "[ INTERNAL ERROR ] StringTensorPack output tensor is corrupted");
-
-    // WARNING! Chars are not repacked. If there are gaps between strings, they will remain.
+    for(size_t i = 0; i < num_strings; ++i) {
+        strings[i].assign(chars + begins[i], chars + ends[i]);
+    }
 
     return true;
-#endif
 }

modules/custom_operations/user_ie_extensions/tokenizer/string_tensor_pack.hpp

@@ -6,8 +6,7 @@
 
 #include <openvino/op/op.hpp>
 
-// Having a decomposed representation for a tensor, converts it to a single string tensor
-// (packed u8 or natively supported element::string depending on whether or not USE_STRING_TENSORS defined).
+// Having a decomposed representation for a tensor, converts it to a single string tensor with element::string element type.
 class StringTensorPack : public ov::op::Op {
 public:
     OPENVINO_OP("StringTensorPack");