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# 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 transformers.configuration_utils import PretrainedConfig
class Telechat3Config(PretrainedConfig):
model_type = "telechat3"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
attention_bias=False,
attention_dropout=0.0,
bos_token_id=1,
eos_token_id=2,
head_dim=128,
hidden_act="silu",
hidden_size=6144,
initializer_range=0.0048,
intermediate_size=24576,
max_position_embeddings=2048,
mlp_bias=False,
model_type="telechat3",
num_attention_heads=48,
num_hidden_layers=64,
num_key_value_heads=None,
original_max_position_embeddings=8192,
pad_token_id=None,
pretraining_tp=1,
rms_norm_eps=1e-5,
rope_scaling=None,
rope_theta=1000000.0,
tie_word_embeddings=False,
use_cache=True,
vocab_size=131072,
**kwargs,
):
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.hidden_size = hidden_size
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.mlp_bias = mlp_bias
self.max_position_embeddings = max_position_embeddings
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.initializer_range = initializer_range
self.pretraining_tp = pretraining_tp
self.rms_norm_eps = rms_norm_eps
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.use_cache = use_cache
self.vocab_size = vocab_size
if head_dim is not None and head_dim != self.hidden_size // self.num_attention_heads:
raise ValueError("head_dim != hidden_size//num_attention_head.Please check the config.")
self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, copy it it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
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