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# 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.
"""PyTorch INFLM model."""
import torch
from torch import nn
from transformers.models.llama.modeling_llama import (
LlamaDecoderLayer,
LlamaModel,
LlamaForCausalLM
)
from .configuration_inflm import INFLMConfig
_CONFIG_FOR_DOC = "INFLMConfig"
class INFLMDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: INFLMConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
class INFLMModel(LlamaModel):
config_class = INFLMConfig
_no_split_modules = ["INFLMDecoderLayer"]
def __init__(self, config: INFLMConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList([INFLMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
class INFLMForCausalLM(LlamaForCausalLM):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: INFLMConfig):
super().__init__(config)
self.model = INFLMModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
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