finish bitnet i2 e2e

convert-hf-to-gguf.py

@@ -1431,7 +1431,7 @@ class BitnetModel(Model):
             if x[i] != 0:
                 scale = x[i]
                 break
-        x = np.divide(x, scale)
+        x = np.where(x * scale > 0, 1, np.where(x * scale < 0, -1, x))
         x = x.astype(np.uint8)
         x = np.reshape(x, [x.shape[0] // 4, 4])
         keep_bit = {0:192, 1:48, 2:12, 3:3}

ggml-quants.c

@@ -3741,7 +3741,7 @@ void ggml_vec_dot_i2_q8_0(int n, float * restrict s, size_t bs, const void * res
         sumi += (int)y[i*4+2] * weight[2];
         sumi += (int)y[i*4+3] * weight[3];
     }
-    *s = (float)(sumi);
+    *s = (float)sumi;
 
 }
 

ggml.c

@@ -2630,7 +2630,7 @@ inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) {
     *s = idx;
 }
 
-inline static void ggml_vec_absmaxclamp_f32(const int n, float * s, const float * x, float min) {
+inline static void ggml_vec_absmaxclamp_f32(const int n, float * s, float * x, float min) {
     float max = min;
     for (int i = 0; i < n; ++i) {
         max = MAX(max, fabs(x[i]));
@@ -2646,12 +2646,12 @@ inline static void ggml_vec_scaleroundclamp_f32(const int n, float * s, const fl
     }
 }
 inline static void ggml_vec_scaleroundclamp_f32_v2(const int n, float * s, int8_t* inp, float scale, float min, float max) {
-
+    float temp;
     for (int i = 0; i < n; ++i) {
-        s[i] = round(s[i] * scale);
+        temp = round(s[i] * scale);
-        if (s[i] > max) s[i] = max;
+        if (temp > max) temp = max;
-        if (s[i] < min) s[i] = min;
+        if (temp < min) temp = min;
-        inp[i] = (int8_t)(s[i]);
+        inp[i] = (int8_t)(temp);
     }
 
 }
@@ -2745,10 +2745,9 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "CROSS_ENTROPY_LOSS",
     "CROSS_ENTROPY_LOSS_BACK",
 
-    "BITLINEAR_QUANT"
 };
 
-static_assert(GGML_OP_COUNT == 75, "GGML_OP_COUNT != 75");
+static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -2835,10 +2834,9 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "cross_entropy_loss(x,y)",
     "cross_entropy_loss_back(x,y)",
 
-    "bitlinear(x)",
 };
 
-static_assert(GGML_OP_COUNT == 75, "GGML_OP_COUNT != 75");
+static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -4873,28 +4871,6 @@ struct ggml_tensor * ggml_mean(
     return result;
 }
 
-// ggml_bitlinear_quant for bitnet
-
-struct ggml_tensor * ggml_bitlinear_quant(
-        struct ggml_context * ctx,
-        struct ggml_tensor * a) {
-    bool is_node = false;
-
-    if (a->grad) {
-        GGML_ASSERT(false); // TODO: implement
-        is_node = true;
-    }
-
-    int64_t ne[GGML_MAX_DIMS] = { a->ne[0], a->ne[1], a->ne[2], a->ne[3] };
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, ggml_n_dims(a), ne);
-
-    result->op   = GGML_OP_BITLINEAR_QUANT;
-    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
-    result->src[0] = a;
-
-    return result;
-}
-
 // ggml_argmax
 
 struct ggml_tensor * ggml_argmax(
@@ -10805,62 +10781,6 @@ static void ggml_compute_forward_mean(
     }
 }
 
-static void ggml_compute_forward_bitlinear_quant_f32(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        struct ggml_tensor * dst) {
-    assert(params->ith == 0);
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
-    assert(src0->nb[0] == sizeof(float));
-
-    GGML_TENSOR_UNARY_OP_LOCALS
-
-    assert(ne0 == ne00);
-    assert(ne1 == ne01);
-    assert(ne2 == ne02);
-    assert(ne3 == ne03);
-
-    UNUSED(ne0);
-    UNUSED(ne1);
-    UNUSED(ne2);
-    UNUSED(ne3);
-
-    for (int64_t i03 = 0; i03 < ne03; i03++) {
-        for (int64_t i02 = 0; i02 < ne02; i02++) {
-            for (int64_t i01 = 0; i01 < ne01; i01++) {
-                float rowmax = 0.00001;
-                ggml_vec_absmaxclamp_f32(ne00, &rowmax, (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), 0.00001);
-                float s = 127 / rowmax;
-
-                ggml_vec_scaleroundclamp_f32(ne00,
-                        (float *) ((char *)  dst->data + i01*nb1  + i02*nb2  + i03*nb3),
-                        (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
-                        s, -128, 127);
-            }
-        }
-    }
-}
-
-static void ggml_compute_forward_bitlinear_quant(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        struct ggml_tensor * dst) {
-    switch (src0->type) {
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_bitlinear_quant_f32(params, src0, dst);
-            } break;
-        default:
-            {
-                GGML_ASSERT(false);
-            } break;
-    }
-}
-
 // ggml_compute_forward_argmax
 
 static void ggml_compute_forward_argmax_f32(
@@ -12453,17 +12373,7 @@ static void ggml_compute_forward_mul_mat_one_chunk(
     float tmp[32];
     uint8_t *i_weight = (uint8_t*) (src0->data);
     float * scale = (float * )((i_weight) + (ne00 * ne01 / 4));
-    float * act_scales = (float*) ((char *) wdata + ((ne11*nb11) / 4));
+    float * act_scales = (float*) ((char *) wdata + (ne11 * ne10));
-        // printf("src0->name:%s\n", src0->name);
-        // printf("src1->name:%s\n", src1->name);
-        // printf("ne03:%ld\n", ne03);
-        // printf("ne02:%ld\n", ne02);
-        // printf("ne01:%ld\n", ne01);
-        // printf("ne00:%ld\n", ne00);
-        // printf("ne13:%ld\n", ne13);
-        // printf("ne12:%ld\n", ne12);
-        // printf("ne11:%ld\n", ne11);
-        // printf("ne10:%ld\n", ne10);
 
     for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) {
         for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) {
@@ -12481,9 +12391,7 @@ static void ggml_compute_forward_mul_mat_one_chunk(
                 const int64_t i3 = i13;
 
                 const char * src0_row = (const char*)src0->data + (0 + i02 * nb02 + i03 * nb03);
-                // if (src0->type == 31) {
+
-                // printf("src0->%ld\n", (0 + i02 * nb02 + i03 * nb03));
-                // }
                 // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
                 //       if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
                 //       the original src1 data pointer, so we should index using the indices directly
@@ -12492,17 +12400,13 @@ static void ggml_compute_forward_mul_mat_one_chunk(
                     (src1_cont || src1->type != vec_dot_type
                         ? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size
                         : (i11 * nb11 + i12 * nb12 + i13 * nb13));
-                // if (src0->type == 31) {
+
-                // printf("src1->%ld\n", (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size);
-                // }
                 float * dst_col = (float*)((char*)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3));
 
                 //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) {
                 //    vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
                 //}
-                // if (src0->type == 31) {
+
-                // printf("dst->%ld\n", (i1 * nb1 + i2 * nb2 + i3 * nb3));
-                // }
                 for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) {
                 if (src0->type == 31) {
                     // printf("row->%ld\n", (ir0 * nb01 / 4));
@@ -12513,8 +12417,6 @@ static void ggml_compute_forward_mul_mat_one_chunk(
                 }
 
                 }
-                // printf("num_rows_per_vec_dot->%ld\n", num_rows_per_vec_dot);
-                // printf("iir0->%ld\n", iir0);
                 for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) {
                     memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float));
                 }
@@ -12572,7 +12474,7 @@ static void ggml_compute_forward_bitnet_mul_mat(
         }
         atomic_store(&state->shared->current_chunk, nth);
         char * wdata = params->wdata;
-        float* act_scales = (float*) ((char *) wdata + ((ne11*nb11) / 4));
+        float* act_scales = (float*) ((char *) wdata + (ne11 * ne10));
         for (int64_t i13 = 0; i13 < ne13; i13++) {
             for (int64_t i12 = 0; i12 < ne12; i12++) {
                 for (int64_t i11 = 0; i11 < ne11; i11++) {
@@ -17634,10 +17536,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_mean(params, tensor);
             } break;
-        case GGML_OP_BITLINEAR_QUANT:
-            {
-                ggml_compute_forward_bitlinear_quant(params, tensor->src[0], tensor);
-            } break;
         case GGML_OP_ARGMAX:
             {
                 ggml_compute_forward_argmax(params, tensor);
@@ -18804,10 +18702,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
-        case GGML_OP_BITLINEAR_QUANT:
-            {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
         case GGML_OP_ARGSORT:
             {
                 GGML_ASSERT(false); // TODO: not implemented
@@ -19573,7 +19467,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
         case GGML_OP_GET_REL_POS:
         case GGML_OP_MAP_UNARY:
         case GGML_OP_MAP_BINARY:
-        case GGML_OP_BITLINEAR_QUANT:
         case GGML_OP_MAP_CUSTOM1_F32:
         case GGML_OP_MAP_CUSTOM2_F32:
         case GGML_OP_MAP_CUSTOM3_F32:

ggml.h

@@ -507,8 +507,6 @@ extern "C" {
         GGML_OP_CROSS_ENTROPY_LOSS,
         GGML_OP_CROSS_ENTROPY_LOSS_BACK,
 
-        GGML_OP_BITLINEAR_QUANT,
-
         GGML_OP_COUNT,
     };
 
@@ -996,11 +994,6 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
-    // for bitnet
-    GGML_API struct ggml_tensor * ggml_bitlinear_quant(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a);
-
     // argmax along rows
     GGML_API struct ggml_tensor * ggml_argmax(
             struct ggml_context * ctx,

llama.cpp

@@ -6827,13 +6827,6 @@ static struct ggml_tensor * llm_build_norm(
     return cur;
 }
 
-static struct ggml_tensor * llm_build_qbitlinear(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * cur)
-    {
-        return ggml_bitlinear_quant(ctx, cur);
-    }
-
 static struct ggml_tensor * llm_build_ffn(
         struct ggml_context * ctx,
          struct ggml_tensor * cur,
@@ -7138,8 +7131,6 @@ static struct ggml_tensor * llm_build_kqv(
                         LLM_NORM_RMS, cb, il);
         cb(cur, "attn_sub_norm", il);
         
-        // B2 for wo
-        // cur = llm_build_qbitlinear(ctx, cur);
     }
 
     ggml_build_forward_expand(graph, cur);
@@ -11561,8 +11552,6 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                // B1.Q
-                // cur = llm_build_qbitlinear(ctx0, cur);
                 struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
@@ -11625,17 +11614,6 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                // cur = llm_build_ffn(ctx0, cur,
-                //         model.layers[il].ffn_up,   NULL,
-                //         model.layers[il].ffn_gate, NULL,
-                //         model.layers[il].ffn_down, NULL,
-                //         NULL,
-                //         LLM_FFN_SILU, LLM_FFN_PAR, cb, il, hparams, model.layers[il].ffn_sub_norm, isbitnet);
-                // cb(cur, "ffn_out", il);
-    
-    
-                // cur = llm_build_qbitlinear(ctx0, cur);
-
                 struct ggml_tensor *tmp = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur);
     
                 cb(tmp, "ffn_up", il);
@@ -11656,9 +11634,6 @@ struct llm_build_context {
                                 LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_sub_norm", il);
 
-                // B4 for w2
-                // cur = llm_build_qbitlinear(ctx0, cur);
-
                 cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down, cur);
                 cb(cur, "ffn_down", il);
     

tokenization_bitnet.py

@@ -1,482 +0,0 @@
-# coding=utf-8
-# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Tokenization classes for LLaMA."""
-import os
-from shutil import copyfile
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
-
-import sentencepiece as spm
-
-from transformers.convert_slow_tokenizer import import_protobuf
-from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
-from transformers.utils import logging
-
-
-if TYPE_CHECKING:
-    from transformers.tokenization_utils_base import TextInput
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
-    },
-    "tokenizer_file": {
-        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
-    },
-}
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "hf-internal-testing/llama-tokenizer": 2048,
-}
-SPIECE_UNDERLINE = "▁"
-
-B_INST, E_INST = "[INST]", "[/INST]"
-B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
-
-# fmt: off
-DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
-answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
- that your responses are socially unbiased and positive in nature.
-
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
-correct. If you don't know the answer to a question, please don't share false information."""
-# fmt: on
-
-
-class BitnetTokenizer(PreTrainedTokenizer):
-    """
-    Construct a Bitnet tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
-    no padding token in the original model.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-        pad_token (`str` or `tokenizers.AddedToken`, *optional*):
-            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
-            attention mechanisms or loss computation.
-        sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
-            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
-            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
-            to set:
-
-            - `enable_sampling`: Enable subword regularization.
-            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
-
-              - `nbest_size = {0,1}`: No sampling is performed.
-              - `nbest_size > 1`: samples from the nbest_size results.
-              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
-                using forward-filtering-and-backward-sampling algorithm.
-
-            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
-              BPE-dropout.
-
-        add_bos_token (`bool`, *optional*, defaults to `True`):
-            Whether or not to add an `bos_token` at the start of sequences.
-        add_eos_token (`bool`, *optional*, defaults to `False`):
-            Whether or not to add an `eos_token` at the end of sequences.
-        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
-            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
-            extra spaces.
-        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
-            Whether or not the default system prompt for Bitnet should be used.
-        spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
-            Whether or not to add spaces between special tokens.
-        legacy (`bool`, *optional*):
-            Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
-            and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple
-            example:
-
-            - `legacy=True`:
-            ```python
-            >>> from transformers import T5Tokenizer
-
-            >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=True)
-            >>> tokenizer.encode("Hello <extra_id_0>.")
-            [8774, 32099, 3, 5, 1]
-            ```
-            - `legacy=False`:
-            ```python
-            >>> from transformers import T5Tokenizer
-
-            >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=False)
-            >>> tokenizer.encode("Hello <extra_id_0>.")  # the extra space `[3]` is no longer here
-            [8774, 32099, 5, 1]
-            ```
-            Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
-        add_prefix_space (`bool`, *optional*, defaults to `True`):
-            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
-            other word.
-
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file,
-        unk_token="<unk>",
-        bos_token="<s>",
-        eos_token="</s>",
-        pad_token=None,
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        add_bos_token=True,
-        add_eos_token=False,
-        clean_up_tokenization_spaces=False,
-        use_default_system_prompt=False,
-        spaces_between_special_tokens=False,
-        legacy=None,
-        add_prefix_space=True,
-        **kwargs,
-    ):
-        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-        bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
-        eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
-        unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
-        pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
-
-        if legacy is None:
-            logger.warning_once(
-                f"You are using the default legacy behaviour of the {self.__class__}. This is"
-                " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
-                " If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
-                " means, and thoroughly read the reason why this was added as explained in"
-                " https://github.com/huggingface/transformers/pull/24565"
-            )
-            legacy = True
-
-        self.legacy = legacy
-        self.vocab_file = vocab_file
-        self.add_bos_token = add_bos_token
-        self.add_eos_token = add_eos_token
-        self.use_default_system_prompt = use_default_system_prompt
-        self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
-        self.add_prefix_space = add_prefix_space
-
-        super().__init__(
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            add_bos_token=add_bos_token,
-            add_eos_token=add_eos_token,
-            sp_model_kwargs=self.sp_model_kwargs,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            use_default_system_prompt=use_default_system_prompt,
-            spaces_between_special_tokens=spaces_between_special_tokens,
-            legacy=legacy,
-            add_prefix_space=add_prefix_space,
-            **kwargs,
-        )
-
-    @property
-    def unk_token_length(self):
-        return len(self.sp_model.encode(str(self.unk_token)))
-
-    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
-    def get_spm_processor(self, from_slow=False):
-        tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        if self.legacy or from_slow:  # no dependency on protobuf
-            tokenizer.Load(self.vocab_file)
-            return tokenizer
-
-        with open(self.vocab_file, "rb") as f:
-            sp_model = f.read()
-            model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
-            model = model_pb2.ModelProto.FromString(sp_model)
-            normalizer_spec = model_pb2.NormalizerSpec()
-            normalizer_spec.add_dummy_prefix = False
-            model.normalizer_spec.MergeFrom(normalizer_spec)
-            sp_model = model.SerializeToString()
-            tokenizer.LoadFromSerializedProto(sp_model)
-        return tokenizer
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["sp_model"] = None
-        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
-        return state
-
-    def __setstate__(self, d):
-        self.__dict__ = d
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
-
-    @property
-    def vocab_size(self):
-        """Returns vocab size"""
-        return self.sp_model.get_piece_size()
-
-    def get_vocab(self):
-        """Returns vocab as a dict"""
-        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
-    def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
-        """
-        Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
-        first token is special.
-        """
-        if self.legacy or len(text) == 0:
-            return super().tokenize(text, **kwargs)
-
-        text = text.replace(SPIECE_UNDERLINE, " ")
-        if self.add_prefix_space:
-            text = SPIECE_UNDERLINE + text
-
-        tokens = super().tokenize(text, **kwargs)
-
-        if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
-            tokens = tokens[1:]
-        return tokens
-
-    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
-    def _tokenize(self, text, **kwargs):
-        """
-        Returns a tokenized string.
-
-        We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
-        SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
-        `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
-        `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
-        `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
-        """
-        tokens = self.sp_model.encode(text, out_type=str)
-        if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
-            return tokens
-
-        # 1. Encode string + prefix ex: "<unk> Hey"
-        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
-        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
-        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
-
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.sp_model.piece_to_id(token)
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        token = self.sp_model.IdToPiece(index)
-        return token
-
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        # since we manually add the prefix space, we have to remove it when decoding
-        if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
-            tokens[0] = tokens[0][1:]
-
-        current_sub_tokens = []
-        out_string = ""
-        prev_is_special = False
-        for i, token in enumerate(tokens):
-            # make sure that special tokens are not decoded using sentencepiece model
-            if token in self.all_special_tokens:
-                if not prev_is_special and i != 0 and self.legacy:
-                    out_string += " "
-                out_string += self.sp_model.decode(current_sub_tokens) + token
-                prev_is_special = True
-                current_sub_tokens = []
-            else:
-                current_sub_tokens.append(token)
-                prev_is_special = False
-        out_string += self.sp_model.decode(current_sub_tokens)
-        return out_string
-
-    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        """
-        Save the vocabulary and special tokens file to a directory.
-
-        Args:
-            save_directory (`str`):
-                The directory in which to save the vocabulary.
-
-        Returns:
-            `Tuple(str)`: Paths to the files saved.
-        """
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        out_vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-        elif not os.path.isfile(self.vocab_file):
-            with open(out_vocab_file, "wb") as fi:
-                content_spiece_model = self.sp_model.serialized_model_proto()
-                fi.write(content_spiece_model)
-
-        return (out_vocab_file,)
-
-    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
-        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
-        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
-
-        output = bos_token_id + token_ids_0 + eos_token_id
-
-        if token_ids_1 is not None:
-            output = output + bos_token_id + token_ids_1 + eos_token_id
-
-        return output
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` method.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        bos_token_id = [1] if self.add_bos_token else []
-        eos_token_id = [1] if self.add_eos_token else []
-
-        if token_ids_1 is None:
-            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
-        return (
-            bos_token_id
-            + ([0] * len(token_ids_0))
-            + eos_token_id
-            + bos_token_id
-            + ([0] * len(token_ids_1))
-            + eos_token_id
-        )
-
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
-        sequence pair mask has the following format:
-
-        ```
-        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
-        | first sequence    | second sequence |
-        ```
-
-        if token_ids_1 is None, only returns the first portion of the mask (0s).
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of ids.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
-        """
-        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
-        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
-
-        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
-
-        if token_ids_1 is not None:
-            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
-
-        return output
-
-    @property
-    def default_chat_template(self):
-        """
-        LLaMA uses [INST] and [/INST] to indicate user messages, and <<SYS>> and <</SYS>> to indicate system messages.
-        Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict
-        user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering
-        rather than needing special tokens. The system message is partly 'embedded' in the first user message, which
-        results in an unusual token ordering when it is present. This template should definitely be changed if you wish
-        to fine-tune a model with more flexible role ordering!
-
-        The output should look something like:
-
-        <bos>[INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer <eos><bos>[INST] Prompt [/INST] Answer <eos>
-        <bos>[INST] Prompt [/INST]
-
-        The reference for this chat template is [this code
-        snippet](https://github.com/facebookresearch/llama/blob/556949fdfb72da27c2f4a40b7f0e4cf0b8153a28/llama/generation.py#L320-L362)
-        in the original repository.
-        """
-        logger.warning_once(
-            "\nNo chat template is defined for this tokenizer - using the default template "
-            f"for the {self.__class__.__name__} class. If the default is not appropriate for "
-            "your model, please set `tokenizer.chat_template` to an appropriate template. "
-            "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
-        )
-        template = (
-            "{% if messages[0]['role'] == 'system' %}"
-            "{% set loop_messages = messages[1:] %}"  # Extract system message if it's present
-            "{% set system_message = messages[0]['content'] %}"
-            "{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}"
-            "{% set loop_messages = messages %}"  # Or use the default system message if the flag is set
-            "{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
-            "{% else %}"
-            "{% set loop_messages = messages %}"
-            "{% set system_message = false %}"
-            "{% endif %}"
-            "{% for message in loop_messages %}"  # Loop over all non-system messages
-            "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}"
-            "{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}"
-            "{% endif %}"
-            "{% if loop.index0 == 0 and system_message != false %}"  # Embed system message in first message
-            "{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}"
-            "{% else %}"
-            "{% set content = message['content'] %}"
-            "{% endif %}"
-            "{% if message['role'] == 'user' %}"  # After all of that, handle messages/roles in a fairly normal way
-            "{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}"
-            "{% elif message['role'] == 'system' %}"
-            "{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}"
-            "{% elif message['role'] == 'assistant' %}"
-            "{{ ' '  + content.strip() + ' ' + eos_token }}"
-            "{% endif %}"
-            "{% endfor %}"
-        )
-        template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false")
-        default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'")
-        template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
-
-        return template