mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
# Copyright 2024 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import TYPE_CHECKING, Sequence
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import torch
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from transformers.integrations import is_deepspeed_zero3_enabled
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from transformers.utils.versions import require_version
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if TYPE_CHECKING:
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from transformers import PretrainedConfig, PreTrainedModel
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from ...hparams import ModelArguments
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def _set_z3_leaf_modules(model: "PreTrainedModel", leaf_modules: Sequence["torch.nn.Module"]) -> None:
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require_version("deepspeed>=0.13.0", "To fix: pip install deepspeed>=0.13.0")
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from deepspeed.utils import set_z3_leaf_modules # type: ignore
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set_z3_leaf_modules(model, leaf_modules)
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def add_z3_leaf_module(model: "PreTrainedModel") -> None:
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r"""
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Sets module as a leaf module to skip partitioning in deepspeed zero3.
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"""
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if not is_deepspeed_zero3_enabled():
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return
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model_type = getattr(model.config, "model_type", None)
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if model_type == "dbrx":
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from transformers.models.dbrx.modeling_dbrx import DbrxFFN
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_set_z3_leaf_modules(model, [DbrxFFN])
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if model_type == "jamba":
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from transformers.models.jamba.modeling_jamba import JambaSparseMoeBlock
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_set_z3_leaf_modules(model, [JambaSparseMoeBlock])
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if model_type == "jetmoe":
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from transformers.models.jetmoe.modeling_jetmoe import JetMoeMoA, JetMoeMoE
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_set_z3_leaf_modules(model, [JetMoeMoA, JetMoeMoE])
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if model_type == "mixtral":
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from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
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_set_z3_leaf_modules(model, [MixtralSparseMoeBlock])
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if model_type == "qwen2moe":
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from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
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_set_z3_leaf_modules(model, [Qwen2MoeSparseMoeBlock])
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def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
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model_type = getattr(config, "model_type", None)
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if model_args.moe_aux_loss_coef is not None:
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if model_type in ["jamba", "mixtral", "qwen2_moe"]:
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setattr(config, "router_aux_loss_coef", model_args.moe_aux_loss_coef)
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elif model_type == "deepseek":
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setattr(config, "aux_loss_alpha", model_args.moe_aux_loss_coef)
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elif model_type == "jetmoe":
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setattr(config, "aux_loss_coef", model_args.moe_aux_loss_coef)
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if model_type in ["dbrx", "jamba", "jetmoe", "mixtral", "qwen2_moe"]:
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setattr(config, "output_router_logits", is_trainable)
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