diff --git a/config.json b/config.json
deleted file mode 100644
index b4a8f0d278960843c5b09435d38aefeaceb8bdbb..0000000000000000000000000000000000000000
--- a/config.json
+++ /dev/null
@@ -1,40 +0,0 @@
-{
-  "apply_residual_connection_post_layernorm": false,
-  "architectures": [
-    "TelechatForCausalLM"
-  ],
-  "auto_map": {
-	"AutoConfig": "configuration_telechat.TelechatConfig",
-	"AutoModelForCausalLM": "modeling_telechat.TelechatForCausalLM"
-  },
-  "attention_dropout": 0.0,
-  "attention_softmax_in_fp32": true,
-  "bias_dropout_fusion": true,
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "hidden_dropout": 0.0,
-  "hidden_size": 8192,
-  "initializer_range": 0.02,
-  "layer_norm_epsilon": 1e-05,
-  "masked_softmax_fusion": true,
-  "model_type": "telechat",
-  "n_head": 64,
-  "n_inner": null,
-  "num_key_value_heads": 8,
-  "n_layer": 96,
-  "pad_token_id": 3,
-  "pretraining_tp": 2,
-  "skip_bias_add": false,
-  "skip_bias_add_qkv": false,
-  "slow_but_exact": false,
-  "unk_token_id": 0,
-  "use_cache": true,
-  "vocab_size": 131072,
-  "ffn_hidden_size": 40960,
-  "flash_attn":true,
-  "tie_word_embeddings":false,
-  "training_seqlen":8192,
-  "base_seqlen":8192,
-  "seq_length": 8192
-}
-
diff --git a/configuration.json b/configuration.json
deleted file mode 100644
index 9fbcf957f0338085ab0d7245f17f9fd1a1b64a65..0000000000000000000000000000000000000000
--- a/configuration.json
+++ /dev/null
@@ -1 +0,0 @@
-{"task":"text-generation"}
\ No newline at end of file
diff --git a/configuration_telechat.py b/configuration_telechat.py
deleted file mode 100644
index 6c6169db242f100ed18215302d25dc375e7e5033..0000000000000000000000000000000000000000
--- a/configuration_telechat.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# coding=utf-8
-# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team.  All rights reserved.
-#
-# 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.
-
-""" Telechat configuration"""
-
-from packaging import version
-from collections import OrderedDict
-from transformers.utils import is_torch_available, logging
-from transformers.configuration_utils import PretrainedConfig
-from typing import TYPE_CHECKING, Any, List, Mapping, Optional
-
-logger = logging.get_logger(__name__)
-
-class TelechatConfig(PretrainedConfig):
-    """
-    Args:
-        vocab_size (`int`, *optional*, defaults to 160256): Vocabulary size of the Telechat model.
-        hidden_size (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states.
-        ffn_hidden_size (`int`, *optional*, defaults to 12288): Dimensionality of the feed-forward hidden states.
-        n_layer (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer
-        n_head (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer.
-        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in the layer normalization layers.
-        initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`): If enabled, use the layer norm of the hidden states as the residual in the transformer blocks
-        hidden_dropout (`float`, *optional*, defaults to 0.0): Dropout rate of the dropout function on the bias dropout.
-        attention_dropout (`float`, *optional*, defaults to 0.0): Dropout rate applied to the attention probs
-        use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions.
-        training_seqlen (`int`, *optional*, defaults to 8192): Sequence length during last finetuning.
-        logn (`bool`, *optional*, defaults to `True`): Whether or not to use logN during extrapolation.
-        embed_layernorm (`bool`, *optional*, defaults to `True`): Whether or not to use embedding layernorm.
-
-    """
-
-    model_type = "telechat"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {
-        "num_hidden_layers": "n_layer",
-        "num_attention_heads": "n_head",
-    }
-
-    def __init__(
-        self,
-        vocab_size=160256,
-        hidden_size=4096,
-        n_layer=30,
-        n_head=32,
-        layer_norm_epsilon=1e-5,
-        initializer_range=0.02,
-        use_cache=True,
-        bos_token_id=1,
-        eos_token_id=2,
-        apply_residual_connection_post_layernorm=False,
-        hidden_dropout=0.0,
-        attention_dropout=0.0,
-        ffn_hidden_size=12288,
-        training_seqlen = 8192,
-        logn = True,
-        embed_layernorm = False,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        n_embed = kwargs.pop("n_embed", None)
-        self.hidden_size = hidden_size if n_embed is None else n_embed
-        self.n_layer = n_layer
-        self.n_head = n_head
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-        self.use_cache = use_cache
-        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
-        self.hidden_dropout = hidden_dropout
-        self.attention_dropout = attention_dropout
-        self.bos_token_id = bos_token_id
-        self.eos_token_id = eos_token_id
-        self.logn = logn
-        self.ffn_hidden_size = ffn_hidden_size
-        self.training_seqlen = training_seqlen
-        self.embed_layernorm = embed_layernorm
-        self.num_key_value_heads= kwargs.pop("num_key_value_heads", None)
-
-
-        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
-
diff --git a/generation_config.json b/generation_config.json
deleted file mode 100644
index 67fd08a69f94fd250bd5c50c8905f64e9297ecb8..0000000000000000000000000000000000000000
--- a/generation_config.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
-  "max_length": 8192,
-  "do_sample": false,
-  "use_cache": true,
-  "temperature": 0.3,
-  "top_k": 5,
-  "top_p": 0.85,
-  "repetition_penalty": 1.03,
-  "pad_token_id": 3,
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "user_token_id": 4,
-  "bot_token_id": 5
-}
diff --git a/generation_utils.py b/generation_utils.py
deleted file mode 100644
index 82410f2eeb3e8ef64f995d7786f2da4419c0f0e7..0000000000000000000000000000000000000000
--- a/generation_utils.py
+++ /dev/null
@@ -1,162 +0,0 @@
-from typing import Optional
-from collections import deque
-from queue import Queue
-import copy
-
-
-class History:
-
-    def __init__(self, tokenizer, history):
-        '''
-        init from a list of dict
-        '''
-        # use deque to meet some special situation
-        self.input_history = deque()
-        self.tokenizer = tokenizer
-        if history:
-            self._transfer_from_list(history)
-
-    def _transfer_from_list(self, history):
-        for message in history:
-            content = message.get("content")
-            # the token result may not be equal to the result model gen
-            message.update(self.tokenizer(content))
-            self.input_history.append(message)
-
-    def append(self, message):
-        content = message.get("content")
-        if "input_ids" not in message or "attention_mask" not in message:
-            message.update(self.tokenizer(content))
-        self.input_history.append(message)
-
-    def append_left(self, message):
-        content = message.get("content")
-        if "input_ids" not in message or "attention_mask" not in message:
-            message.update(self.tokenizer(content))
-        self.input_history.appendleft(message)
-
-    def pop(self):
-        x = self.input_history.pop()
-        return x
-
-    def pop_left(self):
-        x = self.input_history.pop_left()
-        return x
-
-    def update(self, message):
-        self.input_history.pop()
-        self.append(message)
-
-    def __len__(self):
-        return self.input_history.__len__()
-
-    def __str__(self):
-        return self.input_history.__str__()
-
-    def __copy__(self):
-        new_instance = type(self)(self.tokenizer, [])
-        new_instance.input_history = copy.copy(self.input_history)
-        return new_instance
-
-    def __deepcopy__(self, memodict={}):
-        new_instance = type(self)(self.tokenizer, [])
-        new_instance.input_history = copy.deepcopy(self.input_history)
-        return new_instance
-
-
-class TelechatIterTextStreamer:
-    """
-    With reference to the TextIterStreamers in transformers, we have rewritten this class
-    """
-
-    def __init__(
-            self, tokenizer, history: History = None, skip_prompt: bool = False, timeout: Optional[float] = None,
-            **decode_kwargs
-    ):
-
-        self.tokenizer = tokenizer
-        self.history = history
-        self.skip_prompt = skip_prompt
-        self.timeout = timeout
-        self.decode_kwargs = decode_kwargs
-
-        self.text_queue = Queue()
-        self.cache_time = 0
-        self.text_until = ""
-        self.token_until = []
-        self.stop_signal = None
-        self.next_tokens_are_prompt = True
-
-        self.history.append({"role": "bot", "content": self.text_until})
-
-    def put(self, value):
-        """
-        put printable text into queue
-        """
-        if len(value.shape) > 1 and value.shape[0] > 1:
-            raise ValueError("TextStreamer only supports batch size 1")
-        elif len(value.shape) > 1:
-            value = value[0]
-
-        if self.skip_prompt and self.next_tokens_are_prompt:
-            self.next_tokens_are_prompt = False
-            return
-
-        if value[-1] == self.tokenizer.eos_token_id:
-            return
-
-        # there may be some smart way to decode.
-        self.token_until.extend(value.tolist())
-        text = self.tokenizer.decode(self.token_until, **self.decode_kwargs)
-
-
-        if self._is_printable(text) or self.cache_time >= 6:
-            output_text = text[len(self.text_until):]
-            self.text_until = text
-
-        else:
-            self.cache_time+=1
-            return
-
-        self.on_finalized_text(output_text)
-
-    def end(self):
-        """Flushes any remaining cache and prints a newline to stdout."""
-        # Flush the cache, if it exists
-        text = self.tokenizer.decode(self.token_until, **self.decode_kwargs)
-        output_text = text[len(self.text_until):]
-        self.text_until = text
-        self.on_finalized_text(output_text, stream_end=True)
-        self.clear_cache()
-
-    def clear_cache(self):
-        self.cache_time = 0
-        self.token_until = []
-        self.text_until = ""
-        self.history = None
-        self.next_tokens_are_prompt = True
-
-    def on_finalized_text(self, text: str, stream_end: bool = False):
-        """Put the text tuple in the queue."""
-        self.history.update({"role": "bot", "content": self.text_until, "input_ids": self.token_until,
-                             "attention_mask": [1] * len(self.token_until)})
-        self.text_queue.put((text, self.history), timeout=self.timeout)
-        if stream_end:
-            self.text_queue.put((self.stop_signal, self.history), timeout=self.timeout)
-
-    @staticmethod
-    def _is_printable(cp):
-        """Checks whether tokens can be decoded or not"""
-        if "�" in cp:
-            return False
-        return True
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        value_now, history_until = self.text_queue.get(timeout=self.timeout)
-        if value_now == self.stop_signal:
-            raise StopIteration()
-        else:
-            return value_now, history_until
\ No newline at end of file
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\ No newline at end of file
diff --git a/modeling_telechat.py b/modeling_telechat.py
deleted file mode 100644
index a7e1c8890288de2ea68b5fb6d3f32248d34a39aa..0000000000000000000000000000000000000000
--- a/modeling_telechat.py
+++ /dev/null
@@ -1,939 +0,0 @@
-# coding=utf-8
-# Copyright 2022 HuggingFace Inc. team and BigScience workshop.
-#
-# 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.
-
-# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
-
-# Copyright (c) 2021 EleutherAI
-# This file is based on code by the authors denoted below and has been modified from its original version.
-#
-# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
-#
-# 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.
-
-
-"""PyTorch TELECHAT model."""
-
-import warnings
-from typing import Optional, Tuple, Union, List, Dict
-from threading import Thread
-
-import torch
-import math
-import copy
-from torch import nn
-import torch.utils.checkpoint
-from torch.nn import functional as F
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPastAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import logging
-from transformers import GenerationConfig
-
-from .configuration_telechat import TelechatConfig
-from .generation_utils import History, TelechatIterTextStreamer
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "telechat"
-_CONFIG_FOR_DOC = "TelechatConfig"
-
-TELECHAT_PRETRAINED_MODEL_ARCHIVE_LIST = []
-
-try:
-    from einops import rearrange
-except ImportError:
-    rearrange = None
-
-use_flash_attn = True
-try:
-    from flash_attn.flash_attn_interface import flash_attn_unpadded_func
-except ImportError:
-    try:
-        from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_unpadded_func
-    except ImportError:
-        flash_attn_unpadded_func = None
-
-
-class RotaryEmbedding(torch.nn.Module):
-    # Extracted from: https://github.com/EleutherAI/gpt-neox
-    def __init__(self, dim, config, base=10000, precision=torch.half):
-        super().__init__()
-        self.config = config
-        self.dim = dim
-        self.base = base
-        self.inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float().half() / dim)).cuda()
-        self.max_seq_len_cached = None
-        self.cos_cached = None
-        self.sin_cached = None
-        self.precision = precision
-
-    def get_mscale(self, scale=1):
-        if scale <= 1:
-            return 1.0
-        return 0.1 * math.log(scale) + 1.0
-
-    def get_ntk_alpha(self, true_seq_len):
-        context_value = math.log(true_seq_len / self.config.base_seqlen, 2) + 1
-        # ntk_alpha = 2 ** context_value - 1
-        ntk_alpha = 2 ** math.ceil(context_value) - 1
-        ntk_alpha = max(ntk_alpha, 1)
-        return ntk_alpha
-
-    def forward(self, x, seq_dim=0, seq_len=None):
-        if seq_len is None:
-            seq_len = x.shape[seq_dim]
-        seq_len = max(seq_len, self.config.training_seqlen)
-        ntk_alpha = self.get_ntk_alpha(seq_len)
-        self.mscale = float(self.get_mscale(seq_len / self.config.training_seqlen))
-        if True:
-            base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
-            self.inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, device=x.device).float() / self.dim))
-            self.max_seq_len_cached = seq_len
-            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
-            freqs = torch.einsum('i,j->ij', t, self.inv_freq)
-            # Different from paper, but it uses a different permutation in order to obtain the same calculation
-            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
-            if self.precision == torch.bfloat16:
-                emb = emb.float()
-            # [sx, 1 (b * np), hn]
-            self.cos_cached = self.mscale * emb.cos()[:, None, :].half()
-            self.sin_cached = self.mscale * emb.sin()[:, None, :].half()
-            if self.precision == torch.bfloat16:
-                self.cos_cached = self.cos_cached.bfloat16()
-                self.sin_cached = self.sin_cached.bfloat16()
-        return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...]
-
-
-# rotary pos emb helpers:
-def rotate_half(x):
-    x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
-    return torch.cat((-x2, x1), dim=x1.ndim - 1)  # dim=-1 triggers a bug in earlier torch versions
-
-
-def apply_rotary_pos_emb_torch(q, k, cos, sin, offset: int = 0):  # jitting fails with bf16
-    cos, sin = cos[offset:q.shape[0] + offset, ...], sin[offset:q.shape[0] + offset, ...]
-    return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
-
-
-class MixedFusedRMSNorm(nn.Module):
-    # Extracted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
-    def __init__(self, hidden_size, eps=1e-6):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-
-
-class FlashSelfAttention(torch.nn.Module):
-    # Extracted from https://github.com/microsoft/Megatron-DeepSpeed/blob/main/megatron/model/transformer.py
-    """Implement the scaled dot product attention with softmax.
-    Arguments
-    ---------
-        softmax_scale: The temperature to use for the softmax attention.
-                      (default: 1/sqrt(d_keys) where d_keys is computed at
-                      runtime)
-        attention_dropout: The dropout rate to apply to the attention
-                           (default: 0.0)
-    """
-
-    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0,
-                 device=None, dtype=None):
-        super().__init__()
-        assert flash_attn_unpadded_func is not None, ('Please install FlashAttention first, '
-                                                      'e.g., with pip install flash-attn')
-        assert rearrange is not None, 'Please install einops first, e.g., with pip install einops'
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-        self.dropout_p = attention_dropout
-
-    def forward(self, q, k, v):
-        """Implements the multihead softmax attention.
-        Arguments
-        ---------
-            q, k, v: The tensor containing the query, key, and value. (B, S, H, D)
-        """
-        assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q, k, v)))
-        assert all((i.is_cuda for i in (q, k, v)))
-
-        batch_size, seqlen_q = q.shape[0], q.shape[1]
-        seqlen_k = k.shape[1]
-
-        q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [q, k, v]]
-        cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32,
-                                    device=q.device)
-        # self.training = False
-        if self.training:
-            # during training q,k,v always have same seqlen
-            assert seqlen_k == seqlen_q
-
-            is_causal = self.causal
-            cu_seqlens_k = cu_seqlens_q
-            dropout_p = self.dropout_p
-        else:
-            # turn off FA causal mask after first inference autoregressive iteration
-            # only on first autoregressive step q,k,v have same seqlen
-            is_causal = seqlen_q == seqlen_k
-            cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
-                                        device=q.device)
-            dropout_p = 0
-
-        output = flash_attn_unpadded_func(
-            q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
-            dropout_p=dropout_p,
-            softmax_scale=self.softmax_scale, causal=is_causal
-        )
-
-        output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
-        return output
-
-
-def _make_causal_mask(
-        input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
-) -> torch.BoolTensor:
-    """
-    Make causal mask used for self-attention.
-    """
-    batch_size, target_length = input_ids_shape
-    mask = torch.empty((target_length, target_length + past_key_values_length), dtype=torch.bool, device=device)
-    # ONNX doesn't support `torch.Tensor.triu` properly, thus we use this workaround
-    seq_ids = torch.arange(target_length, device=device)
-    mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :]
-
-    if past_key_values_length > 0:
-        mask[:, :past_key_values_length] = False
-
-    expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
-    return expanded_mask
-
-
-def _expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor:
-    """
-    Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`.
-    """
-    batch_size, src_length = mask.shape
-    tgt_length = tgt_length if tgt_length is not None else src_length
-
-    expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
-    return expanded_mask.expand(batch_size, 1, tgt_length, src_length)
-
-
-def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
-    """
-    Dropout add function
-
-    Args:
-        x (`torch.tensor`, *required*):
-            input tensor
-        residual (`torch.tensor`, *required*):
-            residual tensor
-        prob (`float`, *required*):
-            dropout probability
-        training (`bool`, *required*):
-            training mode
-    """
-    out = F.dropout(x, p=prob, training=training)
-    out = residual + out
-    return out
-
-
-def telechat_gelu_forward(x: torch.Tensor) -> torch.Tensor:
-    """
-    Custom bias GELU function. Adapted from Megatron-DeepSpeed code. Here we use a simple implementation (inference) to
-    make the model jitable.
-
-    Args:
-        x (`torch.tensor`, *required*):
-            input hidden states
-    """
-    return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)))
-
-
-def telechat_gelu_back(g: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
-    """
-    gradient of tanh approximation of gelu gradient of actual gelu is: 0.5 * (1. + torch.erf(x * 0.70710678)) +
-    0.3989423 * x * torch.exp(-0.5 * x * x)
-
-    Args:
-        g (`torch.tensor`, *required*):
-            gradient output tensor
-        x (`torch.tensor`, *required*):
-            input tensor
-    """
-    x = x[0]  # x is a tuple of 1 element, needs to unpack it first
-    tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))
-    # sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243
-    ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (1 + tanh_out)
-    return ff * g
-
-
-class GeLUFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, input: torch.Tensor) -> torch.Tensor:
-        ctx.save_for_backward(input)
-        return telechat_gelu_forward(input)
-
-    @staticmethod
-    def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:
-        input = ctx.saved_tensors
-        tmp = telechat_gelu_back(grad_output, input)
-        return tmp
-
-
-class TelechatGelu(nn.Module):
-    """
-    TelechatBiasGelu wrapper function that make use of the simple function on inference mode to make the model
-    torchscriptable and use the autograd function in training mode to get the accurate results of the gradients Partly
-    copied from Megatron-DeepSpeed code and adapted for our needs
-
-    See here why autograd functions are not torchscriptable: https://github.com/pytorch/pytorch/issues/22329
-    """
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if self.training:
-            return GeLUFunction.apply(x)
-        else:
-            return telechat_gelu_forward(x)
-
-
-class TelechatAttention(nn.Module):
-    def __init__(self, config: TelechatConfig, layer_idx):
-        super().__init__()
-        self.kv_cache = None
-        self.layer_idx = layer_idx
-
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.n_head
-        self.head_dim = self.hidden_size // self.num_heads
-        self.split_size = self.hidden_size
-        self.hidden_dropout = config.hidden_dropout
-        self.config = config
-
-        if self.head_dim * self.num_heads != self.hidden_size:
-            raise ValueError(
-                f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
-                f" {self.num_heads})."
-            )
-
-        # Layer-wise attention scaling
-        self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
-        self.beta = 1.0
-
-        self.num_key_value_heads = config.num_key_value_heads if config.num_key_value_heads else self.num_heads
-        self.kv_projection_size = self.head_dim * self.num_key_value_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        self.query = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
-        self.key_value = nn.Linear(self.hidden_size, self.kv_projection_size * 2, bias=False)
-        self.dense = nn.Linear(self.hidden_size, self.hidden_size)
-        self.attention_dropout = nn.Dropout(config.attention_dropout)
-        self.rotary_emb = RotaryEmbedding(self.head_dim, config=config)
-
-        self.core_attention_flash = FlashSelfAttention(
-            causal=True, attention_dropout=config.attention_dropout
-        )
-
-        self.last_key_layer = None
-        # logn_list = [math.log(i, 4096) if i > 4096 else 1 for i in range(1, 32768)]
-        # self.logn_tensor = torch.tensor(logn_list)[None, :, None, None].half().cuda()
-
-    def repeat_kv(self, hidden_states, n_rep):
-        slen, batch, num_key_value_heads_per_partition, head_dim = hidden_states.shape
-        if n_rep == 1:
-            return hidden_states
-        hidden_states = hidden_states[:, :, :, None, :].expand(slen, batch, num_key_value_heads_per_partition, n_rep,
-                                                               head_dim)
-        return hidden_states.reshape(slen, batch, num_key_value_heads_per_partition * n_rep, head_dim)
-
-    def split_tensor_along_last_dim(self,
-                                    tensor: torch.Tensor,
-                                    num_partitions: int,
-                                    contiguous_split_chunks: bool = False,
-                                    ):
-
-        # Get the size and dimension.
-        last_dim = tensor.dim() - 1
-        last_dim_size = tensor.size()[last_dim] // num_partitions
-        # Split.
-        tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
-        # Note: torch.split does not create contiguous tensors by default.
-        if contiguous_split_chunks:
-            return tuple(chunk.contiguous() for chunk in tensor_list)
-
-        return tensor_list
-
-    def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
-        batch_size_and_num_heads, seq_length, _ = x.shape
-        batch_size = batch_size_and_num_heads // self.num_heads
-        x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
-        x = x.permute(0, 2, 1, 3)
-        return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
-
-    def forward(
-            self,
-            hidden_states: torch.Tensor,
-            residual: torch.Tensor,
-            attention_mask: torch.Tensor,
-            layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-            use_cache: bool = False,
-            output_attentions: bool = False,
-    ):
-        hidden_states = hidden_states.transpose(1, 0)
-        query_layer = self.query(hidden_states)
-        new_tensor_shape = query_layer.size()[:-1] + \
-                           (self.num_heads,
-                            self.head_dim)
-        query_layer = query_layer.view(*new_tensor_shape)
-
-        mixed_kv_layer = self.key_value(hidden_states)
-        new_tensor_shape = mixed_kv_layer.size()[:-1] + \
-                           (self.num_key_value_heads,
-                            2 * self.head_dim)
-        mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape)
-        (key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_kv_layer, 2)
-
-        output_size = (query_layer.size(1),
-                       query_layer.size(2),
-                       query_layer.size(0),
-                       key_layer.size(0),
-                       key_layer.size(2)
-                       )
-
-        query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
-        key_layer = key_layer.view(output_size[3], output_size[0] * output_size[4], -1)
-
-        apply_rotary_fn = apply_rotary_pos_emb_torch
-
-        seq_len = key_layer.shape[0]
-        offset = 0
-
-        if use_cache and layer_past != None:
-            past_key, past_value = layer_past
-            offset = past_key.shape[0]
-            seq_len += offset
-
-        cos, sin = self.rotary_emb(value_layer, seq_len=seq_len)
-
-        query_layer, key_layer = apply_rotary_fn(query_layer, key_layer, cos, sin, offset=offset)
-        if use_cache:
-            if layer_past != None:
-                past_key, past_value = layer_past
-                key_layer = torch.cat((past_key, key_layer[-1, ...].unsqueeze(0)), dim=0)
-                value_layer = torch.cat((past_value, value_layer[-1, ...].unsqueeze(0)), dim=0)
-            layer_past = key_layer, value_layer
-
-        s_value, bz, kv_head, dim = value_layer.shape
-        s_key = key_layer.shape[0]
-        s_query = query_layer.shape[0]
-        q_head = output_size[1]
-
-        query_layer = query_layer.reshape((s_query, bz, q_head, dim))
-        key_layer = key_layer.reshape((s_key, bz, kv_head, dim))
-
-        key_layer = self.repeat_kv(key_layer, self.num_key_value_groups)
-        value_layer = self.repeat_kv(value_layer, self.num_key_value_groups)
-
-        if self.config.flash_attn:
-            q, k, v = [rearrange(x, 's b ... -> b s ...').contiguous() for x in
-                       (query_layer, key_layer, value_layer)]
-            context_layer = self.core_attention_flash(q, k, v)
-            context_layer = rearrange(context_layer, 'b s h d -> b s (h d)').contiguous()
-        else:
-            ##[sq, b, np, hn] -> [sq, b * np, hn]
-            query_layer = query_layer.reshape(s_query, bz * self.num_heads, dim)
-            # [sk, b, np, hn] -> [sk, b * np, hn]
-            key_layer = key_layer.reshape(s_key, bz * self.num_heads, dim)
-            matmul_result = self.inv_norm_factor * torch.einsum('bik,bkj->bij', query_layer.transpose(0, 1),
-                                                                key_layer.transpose(0, 1).transpose(1, 2))
-
-            attention_scores = matmul_result.view(bz, self.num_heads, s_query, s_key)
-
-            input_dtype = attention_scores.dtype
-            if input_dtype == torch.float16:
-                attention_scores = attention_scores.to(torch.float)
-            attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
-            attention_probs = F.softmax(attn_weights, dim=-1).to(input_dtype)  ##dtype = torch.float32
-            attention_probs = self.attention_dropout(attention_probs)
-            attention_probs_reshaped = attention_probs.view(bz * self.num_heads, s_query, s_key)
-
-            value_layer = value_layer.reshape(s_key, bz * self.num_heads, dim)
-            context_layer = torch.bmm(attention_probs_reshaped, value_layer.transpose(0, 1))
-            context_layer = self._merge_heads(context_layer)
-        output_tensor = self.dense(context_layer)
-
-        output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
-        present = None
-        outputs = (output_tensor, present)
-        if output_attentions:
-            outputs += (attention_probs,)
-
-        return output_tensor, layer_past
-
-
-class TelechatMLP(nn.Module):
-    def __init__(self, config: TelechatConfig):
-        super().__init__()
-        hidden_size = config.hidden_size
-        self.gate_proj = nn.Linear(hidden_size, config.ffn_hidden_size, bias=False)
-        self.up_proj = nn.Linear(hidden_size, config.ffn_hidden_size, bias=False)
-        self.down_proj = nn.Linear(config.ffn_hidden_size, hidden_size, bias=True)
-        self.hidden_dropout = config.hidden_dropout
-
-    def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor:
-        intermediate_output = self.down_proj(F.silu(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
-        output = dropout_add(intermediate_output, residual, self.hidden_dropout, self.training)
-        return output
-
-
-class TelechatBlock(nn.Module):
-    def __init__(self, config: TelechatConfig, layer_idx):
-        super().__init__()
-        hidden_size = config.hidden_size
-
-        self.input_layernorm = MixedFusedRMSNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.num_heads = config.n_head
-        self.layer_idx = layer_idx
-        self.self_attention = TelechatAttention(config, layer_idx)
-        self.post_attention_layernorm = MixedFusedRMSNorm(hidden_size, eps=config.layer_norm_epsilon)
-
-        self.mlp = TelechatMLP(config)
-
-        self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
-        self.hidden_dropout = config.hidden_dropout
-
-    def forward(
-            self,
-            hidden_states: torch.Tensor,
-            attention_mask: torch.Tensor,
-            layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-            use_cache: bool = False,
-            output_attentions: bool = False,
-    ):
-        layernorm_output = self.input_layernorm(hidden_states)
-        if self.apply_residual_connection_post_layernorm:
-            residual = layernorm_output
-        else:
-            residual = hidden_states
-
-        attn_outputs = self.self_attention(
-            layernorm_output,
-            residual,
-            layer_past=layer_past,
-            attention_mask=attention_mask,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-        )
-
-        attention_output = attn_outputs[0]
-        outputs = attn_outputs[1:]
-        layernorm_output = self.post_attention_layernorm(attention_output)
-
-        if self.apply_residual_connection_post_layernorm:
-            residual = layernorm_output
-        else:
-            residual = attention_output
-        output = self.mlp(layernorm_output, residual)
-
-        if use_cache:
-            outputs = (output,) + outputs
-        else:
-            outputs = (output,) + outputs[1:]
-
-        return outputs
-
-
-class TelechatPreTrainedModel(PreTrainedModel):
-    config_class = TelechatConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["TelechatBlock"]
-    _skip_keys_device_placement = "past_key_values"
-
-    def __init__(self, *inputs, **kwargs):
-        super().__init__(*inputs, **kwargs)
-
-    def _init_weights(self, module: nn.Module):
-        """Initialize the weights."""
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-        elif isinstance(module, LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
-        if isinstance(module, TelechatModel):
-            module.gradient_checkpointing = value
-
-
-class TelechatModel(TelechatPreTrainedModel):
-    def __init__(self, config: TelechatConfig):
-        super().__init__(config)
-
-        self.embed_dim = config.hidden_size
-        self.num_heads = config.n_head
-        self.config = config
-        self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
-        if self.config.embed_layernorm:
-            self.word_embeddings_layernorm = MixedFusedRMSNorm(self.embed_dim, eps=config.layer_norm_epsilon)
-
-        self.h = nn.ModuleList([TelechatBlock(config, _) for _ in range(config.num_hidden_layers)])
-        self.ln_f = MixedFusedRMSNorm(self.embed_dim, eps=config.layer_norm_epsilon)
-        self.gradient_checkpointing = False
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.word_embeddings
-
-    def _prepare_attn_mask(
-            self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
-    ) -> torch.BoolTensor:
-        combined_attention_mask = None
-        device = attention_mask.device
-        _, src_length = input_shape
-
-        if src_length > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape, device=device, past_key_values_length=past_key_values_length
-            )
-        expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
-        combined_attention_mask = (
-            expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
-        )
-
-        return combined_attention_mask
-
-    def set_input_embeddings(self, new_embeddings: torch.Tensor):
-        self.word_embeddings = new_embeddings
-
-    def forward(
-            self,
-            input_ids: Optional[torch.LongTensor] = None,
-            past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            inputs_embeds: Optional[torch.LongTensor] = None,
-            use_cache: Optional[bool] = None,
-            output_attentions: Optional[bool] = None,
-            output_hidden_states: Optional[bool] = None,
-            return_dict: Optional[bool] = None,
-            **deprecated_arguments,
-    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is not None:
-            batch_size, seq_length = input_ids.shape
-        elif inputs_embeds is not None:
-            batch_size, seq_length, _ = inputs_embeds.shape
-
-        if past_key_values is None:
-            past_key_values = tuple([None] * len(self.h))
-        # input_ids = torch.load("Megatron-LM-0624-3B/tensors/input_ids.pt").to(input_ids.device)
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-        hidden_states = inputs_embeds
-        # print(f"[INFO_Telechat]: inputs_embeds={inputs_embeds}")
-        if self.config.embed_layernorm:
-            hidden_states = self.word_embeddings_layernorm(inputs_embeds)
-
-        presents = () if use_cache else None
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                use_cache = False
-
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-        if past_key_values[0] is not None:
-            past_key_values_length = past_key_values[0][0].shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
-        if attention_mask is None:
-            attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
-        else:
-            attention_mask = attention_mask.to(hidden_states.device)
-        causal_mask = self._prepare_attn_mask(
-            attention_mask,
-            input_shape=(batch_size, seq_length),
-            past_key_values_length=past_key_values_length,
-        )
-
-        # print(f"[INFO_Telechat]: word_embeddings_layernorm={hidden_states}")
-        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
-
-                    return custom_forward
-
-                outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    causal_mask,
-                    layer_past,
-                )
-            else:
-                outputs = block(
-                    hidden_states,
-                    layer_past=layer_past,
-                    attention_mask=causal_mask,
-                    use_cache=use_cache,
-                    output_attentions=output_attentions,
-                )
-
-            # print(f"[INFO_Telechat]: outputs{i}={outputs}")
-            hidden_states = outputs[0]
-            if use_cache is True:
-                presents = presents + (outputs[1],)
-
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
-        hidden_states = self.ln_f(hidden_states)
-        # print(f"[INFO_Telechat]: hidden_states={hidden_states}")
-        # ref = torch.load("Megatron-LM-0624-3B/tensors/final_layernorm.pt")
-        # print(hidden_states.squeeze()[2048:])
-        # print(ref.squeeze())
-        # print(torch.max(hidden_states.squeeze()[2048:] - ref.squeeze().to(hidden_states.device)))
-        # exit()
-        # print(ref.shape,hidden_states.shape)
-        # print(hidden_states)
-        # exit()
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-        if not return_dict:
-            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=presents,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-        )
-
-
-class TelechatForCausalLM(TelechatPreTrainedModel):
-    # _tied_weights_keys = ["lm_head.weight"]
-    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
-
-    def __init__(self, config: TelechatConfig):
-        super().__init__(config)
-        self.transformer = TelechatModel(config)
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.post_init()
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings: torch.Tensor):
-        self.lm_head = new_embeddings
-
-    def prepare_inputs_for_generation(
-            self,
-            input_ids: torch.LongTensor,
-            past_key_values: Optional[torch.Tensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            inputs_embeds: Optional[torch.Tensor] = None,
-            **kwargs,
-    ) -> dict:
-        if past_key_values:
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-        if inputs_embeds is not None and past_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        model_inputs.update(
-            {
-                "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
-                "attention_mask": attention_mask,
-            }
-        )
-        return model_inputs
-
-    def forward(
-            self,
-            input_ids: Optional[torch.LongTensor] = None,
-            past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            inputs_embeds: Optional[torch.Tensor] = None,
-            labels: Optional[torch.Tensor] = None,
-            use_cache: Optional[bool] = None,
-            output_attentions: Optional[bool] = None,
-            output_hidden_states: Optional[bool] = None,
-            return_dict: Optional[bool] = None,
-            **deprecated_arguments,
-    ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.transformer(
-            input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-        lm_logits = self.lm_head(hidden_states)
-
-        loss = None
-        if labels is not None:
-            labels = labels.to(lm_logits.device)
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            batch_size, seq_length, vocab_size = shift_logits.shape
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(
-                shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
-            )
-
-        if not return_dict:
-            output = (lm_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return CausalLMOutputWithCrossAttentions(
-            loss=loss,
-            logits=lm_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-    def chat(self, tokenizer, question: str = '', history: Union[List[Dict], History] = None, stream: bool = False,
-             generation_config: Optional[GenerationConfig] = None, **kwargs):
-        """
-        Args:
-            tokenizer:  the tokenizer of  telechat
-            question: question which the model reply in this turn
-            history: history which will format the input for telechat
-            stream: if return the full text at last or yield the text in token
-            generation_config:  configuration for generation
-            **kwargs: args which will update the generation config or pass to model forward
-        """
-        generation_config = generation_config or self.generation_config
-        if not generation_config:
-            logger.error("generation_config is None")
-            raise ValueError("generation_config must not be None")
-        if not question:
-            logger.error("question is empty")
-            raise ValueError("question must not be empty")
-        if history is None:
-            history = []
-
-        # we update and check generate_config here for building inputs.
-
-        generation_config = copy.deepcopy(generation_config)
-        user_id = generation_config.user_token_id
-        bot_id = generation_config.bot_token_id
-        model_kwargs = generation_config.update(**kwargs)
-        generation_config.validate()
-
-        # transfer to History
-        if not isinstance(history, History):
-            history = History(tokenizer, history)
-
-        inputs = self.build_inputs_for_chat(tokenizer, question, history, generation_config, user_id, bot_id)
-        history.append({"role": "user", "content": question})
-        if stream:
-            streamer = TelechatIterTextStreamer(tokenizer, history, skip_prompt=True)
-            Thread(target=self.generate, kwargs=dict(
-                inputs=inputs.to(self.device), streamer=streamer,
-                generation_config=generation_config, **model_kwargs
-            )).start()
-            return streamer
-        else:
-            outputs = self.generate(inputs.to(self.device), generation_config=generation_config, **model_kwargs)
-            response = tokenizer.decode(outputs[0][len(inputs[0]):-1])
-            history.append({"role": "bot", "content": response})
-            return response, history
-
-    def build_inputs_for_chat(self, tokenizer, question, history, generation_config, usr_id, bot_id):
-        """
-        check history and  build inputs here
-        """
-        # first tokenize question
-        q_token = tokenizer(question)
-        qa_history = copy.deepcopy(history)
-
-        # get the max length we should build our inputs in
-        model_max_length = self.config.seq_length
-        build_max_length = max(0, model_max_length - generation_config.max_new_tokens - 1) \
-            if generation_config.max_new_tokens else max(0, generation_config.max_length)
-        if build_max_length < 3:
-            logger.warning("the model can not meet the  requirements of input length,Please check config")
-            raise ValueError("")
-
-        # trunc left
-        input_tokens = [usr_id] + q_token["input_ids"][-build_max_length + 1:] + [bot_id]
-        length = len(input_tokens)
-
-        while len(qa_history) != 0:
-            message = qa_history.pop()
-            if message["role"] == "user":
-                tokens = [usr_id] + message["input_ids"]
-            elif message["role"] == "bot":
-                tokens = [bot_id] + message["input_ids"] + [generation_config.eos_token_id]
-            else:
-                tokens = []
-            if len(tokens) + length >= build_max_length:
-                break
-            else:
-                input_tokens = tokens + input_tokens
-
-        input_tokens = [generation_config.bos_token_id] + input_tokens
-
-        return torch.tensor([input_tokens], dtype=torch.int64)
diff --git a/tokenization_telechat.py b/tokenization_telechat.py
deleted file mode 100644
index 6ac4fb87adaa33ad7850e7964157b9f5b335b435..0000000000000000000000000000000000000000
--- a/tokenization_telechat.py
+++ /dev/null
@@ -1,220 +0,0 @@
-import os
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple
-import sentencepiece as spm
-from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
-
-# TODO: when we get download url from huggingface, refresh the map
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {},
-    "tokenizer_file": {},
-}
-
-
-class TelechatTokenizer(PreTrainedTokenizer):
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file,
-        unk_token="<unk>",
-        bos_token="<_start>",
-        eos_token="<_end>",
-        pad_token="<_pad>",
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        add_bos_token=True,
-        add_eos_token=False,
-        clean_up_tokenization_spaces=False,
-        **kwargs,
-    ):
-        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
-        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
-        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
-        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(vocab_file)
-        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,
-            **kwargs,
-        )
-        self.vocab_file = vocab_file
-        self.add_bos_token = add_bos_token
-        self.add_eos_token = add_eos_token
-
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["sp_model"] = None
-        return state
-
-    def __setstate__(self, d):
-        self.__dict__ = d
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(self.vocab_file)
-
-    @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
-
-    def _tokenize(self, text):
-        """Returns a tokenized string."""
-        return self.sp_model.encode(text, out_type=str)
-
-    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."""
-        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:
-                    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
diff --git a/tokenizer.model b/tokenizer.model
deleted file mode 100644
index cd47d1356749ba43803322ca2ca295c2c776b036..0000000000000000000000000000000000000000
--- a/tokenizer.model
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:db947024849f75ec4bd9af5d4c84fa71e96a26971eb353a70acd66194fc7a69b
-size 2197489
diff --git a/tokenizer_config.json b/tokenizer_config.json
deleted file mode 100644
index 8e8c261678ad76df6865abca3d3250e2ba3c04a7..0000000000000000000000000000000000000000
--- a/tokenizer_config.json
+++ /dev/null
@@ -1,39 +0,0 @@
-{
-  "tokenizer_class": "TelechatTokenizer",
-  "auto_map": {
-    "AutoTokenizer": [
-      "tokenization_telechat.TelechatTokenizer",
-      null
-    ]
-  },
-  "add_bos_token": false,
-  "add_eos_token": false,
-  "use_fast": false,
-  "clean_up_tokenization_spaces": false,
-  "eos_token": {
-    "__type": "AddedToken",
-    "content": "<_end>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": true
-  },
-  "model_max_length": 100000000,
-  "sp_model_kwargs": {},
-  "pad_token": {
-    "__type": "AddedToken",
-    "content": "<_pad>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": true
-  },
-  "unk_token": {
-    "__type": "AddedToken",
-    "content": "<_end>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": true
-  }
-}