AutoAndroidController/py/venv/Lib/site-packages/modelscope/models/nlp/gpt_moe/configuration.py

# Copyright 2021-2022 The Alibaba PAI Team Authors.
# Copyright (c) 2019, 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.

import torch
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger()


class GPTMoEConfig(PretrainedConfig):

    model_type = 'gpt-moe'

    def __init__(
            self,
            vocab_size=25600,
            hidden_size=768,
            ffn_hidden_size=None,
            num_hidden_layers=12,
            num_attention_heads=12,
            intermediate_size=3072,
            hidden_act='gelu',
            hidden_dropout_prob=0.1,
            attention_probs_dropout_prob=0.1,
            max_position_embeddings=2048,
            type_vocab_size=2,
            layernorm_epsilon=1e-12,
            bias_gelu_fusion=True,
            fp32_residual_connection=False,
            sequence_parallel=False,
            fp16=False,
            bf16=False,
            apply_query_key_layer_scaling=True,
            attention_softmax_in_fp32=False,
            kv_channels=None,
            masked_softmax_fusion=True,
            attention_dropout=0.1,
            bias_dropout_fusion=True,
            apply_residual_connection_post_layernorm=False,
            hidden_dropout=0.1,
            init_method_std=0.02,
            # generate
            eod_id=7,
            tokens_to_generate=100,
            top_k=0,
            top_p=0.9,
            num_experts=[0],
            use_tutel=False,
            top_k_linear_strategy='standard',
            use_expert_residual_network=False,
            load_ds_ckpts=False,
            model_dir=None,
            **kwargs):
        super().__init__(layer_norm_eps=layernorm_epsilon, **kwargs)

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.ffn_hidden_size = 4 * hidden_size \
            if ffn_hidden_size is None else ffn_hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.layernorm_epsilon = layernorm_epsilon
        self.bias_gelu_fusion = bias_gelu_fusion
        self.fp32_residual_connection = fp32_residual_connection
        self.sequence_parallel = sequence_parallel
        self.fp16 = fp16
        self.bf16 = bf16
        assert not (fp16 and bf16)
        self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
        if kv_channels is None:
            assert hidden_size % num_attention_heads == 0
            self.kv_channels = hidden_size // num_attention_heads
        self.masked_softmax_fusion = masked_softmax_fusion
        self.attention_dropout = attention_dropout
        self.bias_dropout_fusion = bias_dropout_fusion
        self.apply_residual_connection_post_layernorm = \
            apply_residual_connection_post_layernorm
        self.hidden_dropout = hidden_dropout
        self.init_method_std = init_method_std
        self.eod_id = eod_id
        self.tokens_to_generate = tokens_to_generate
        self.top_k = top_k
        self.top_p = top_p
        self.num_experts = num_experts
        self.use_tutel = use_tutel
        self.top_k_linear_strategy = top_k_linear_strategy
        self.use_expert_residual_network = use_expert_residual_network
        self.load_ds_ckpts = load_ds_ckpts
        self.model_dir = model_dir

        if self.num_experts[0] > torch.cuda.device_count():
            self.moe_expert_parallel_size = torch.cuda.device_count()
        else:
            self.moe_expert_parallel_size = self.num_experts[0]

        TORCH_MAJOR = int(torch.__version__.split('.')[0])
        TORCH_MINOR = int(torch.__version__.split('.')[1])
        self.no_persist_layer_norm = \
            TORCH_MAJOR < 1 or (TORCH_MAJOR == 1 and TORCH_MINOR < 11)

    @property
    def params_dtype(self):
        if self.fp16:
            return torch.half
        elif self.bf16:
            return torch.bfloat16
        else:
            return torch.float