[LLM] Add Aquila2 model

paddlenlp/transformers/aquila2/__init__.py

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+# Copyright (c) 2024 PaddlePaddle Authors. 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.
+
+# 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.
+
+from .configuration import *
+from .modeling import *
+from .tokenizer import *

paddlenlp/transformers/aquila2/configuration.py

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+# Copyright © 2024 BAAI. All rights reserved.
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2022 EleutherAI and the 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.
+""" Aquila model configuration"""
+
+# modified from https://github.com/PaddlePaddle/PaddleNLP/blob/7947bca07f0dfb37172a4c0040defd0cdbbc10a0/paddlenlp/transformers/llama/configuration.py
+
+from ..configuration_utils import PretrainedConfig
+
+__all__ = [
+    "AquilaConfig",
+    "AQUILA_PRETRAINED_INIT_CONFIGURATION",
+    "AQUILA_PRETRAINED_RESOURCE_FILES_MAP",
+]
+
+AQUILA_PRETRAINED_INIT_CONFIGURATION = {}
+
+AQUILA_PRETRAINED_RESOURCE_FILES_MAP = {
+    "model_state": {},
+}
+
+
+class AquilaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`~AquilaModel`]. It is used to instantiate an Aquila
+    model according to the specified arguments, defining the model architecture.
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Aquila model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`~AquilaModel`].
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+            Enable rope fusion or not.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+    ```"""
+    model_type = "aquila"
+    attribute_map = {
+        "n_positions": "max_position_embeddings",
+        "n_embd": "hidden_size",
+        "n_layer": "num_hidden_layers",
+        "n_head": "num_attention_heads",
+        "n_inner": "intermediate_size",
+        "activation_function": "hidden_act",
+    }
+    pretrained_init_configuration = AQUILA_PRETRAINED_INIT_CONFIGURATION
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        max_position_embeddings=2048,
+        seq_length=2048,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        rope_theta=10000.0,
+        use_cache=True,
+        fuse_attention_qkv=False,
+        fuse_attention_ffn=False,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        alibi=False,
+        rope_scaling_factor=1.0,
+        rope_scaling_type=None,
+        long_sequence_strategy_type=None,
+        long_sequence_strategy_name=None,
+        long_sequence_init_args=None,
+        use_long_sequence_strategies=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.max_position_embeddings = max_position_embeddings
+        self.seq_length = seq_length
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        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.rms_norm_eps = rms_norm_eps
+        self.rope_theta = rope_theta
+
+        self.use_cache = use_cache
+        self.fuse_attention_qkv = fuse_attention_qkv
+        self.fuse_attention_ffn = fuse_attention_ffn
+
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.alibi = alibi
+
+        self.rope_scaling_factor = rope_scaling_factor
+        self.rope_scaling_type = rope_scaling_type
+
+        self.long_sequence_strategy_type = long_sequence_strategy_type
+        self.long_sequence_strategy_name = long_sequence_strategy_name
+        self.long_sequence_init_args = {} if long_sequence_init_args is None else long_sequence_init_args
+        self.use_long_sequence_strategies = use_long_sequence_strategies
+
+        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,
+        )
+
+    @property
+    def rope(self):
+        return not self.alibi