add config.json

config.json

@@ -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
-}
-

configuration.json

@@ -1 +0,0 @@
-{"task":"text-generation"}

configuration_telechat.py

@@ -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)
-

generation_config.json

@@ -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
-}

generation_utils.py

@@ -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

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