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Deprecate unused OpenLlama architecture (#24922)
* Resolve typo in check_repo.py * Specify encoding when opening modeling files * Deprecate the OpenLlama architecture * Add disclaimer pointing to Llama I'm open to different wordings here * Match the capitalisation of LLaMA
This commit is contained in:
Tom Aarsen
GitHub
parent
8fd8c8e49e
commit
79444f370f
@ -16,6 +16,21 @@ rendered properly in your Markdown viewer.
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# Open-Llama
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<Tip warning={true}>
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This model is in maintenance mode only, so we won't accept any new PRs changing its code.
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If you run into any issues running this model, please reinstall the last version that supported this model: v4.31.0.
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You can do so by running the following command: `pip install -U transformers==4.31.0`.
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</Tip>
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<Tip warning={true}>
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This model differs from the [OpenLLaMA models](https://huggingface.co/models?search=openllama) on the Hugging Face Hub, which primarily use the [LLaMA](llama) architecture.
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</Tip>
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## Overview
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The Open-Llama model was proposed in [Open-Llama project](https://github.com/s-JoL/Open-Llama) by community developer s-JoL.
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@ -278,6 +278,7 @@ _import_structure = {
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"MCTCTProcessor",
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],
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"models.deprecated.mmbt": ["MMBTConfig"],
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"models.deprecated.open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
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"models.deprecated.retribert": [
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"RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
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"RetriBertConfig",
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@ -445,7 +446,6 @@ _import_structure = {
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"NystromformerConfig",
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],
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"models.oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig", "OneFormerProcessor"],
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"models.open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
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"models.openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig", "OpenAIGPTTokenizer"],
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"models.opt": ["OPTConfig"],
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"models.owlvit": [
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@ -1536,6 +1536,9 @@ else:
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]
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)
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_import_structure["models.deprecated.mmbt"].extend(["MMBTForClassification", "MMBTModel", "ModalEmbeddings"])
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_import_structure["models.deprecated.open_llama"].extend(
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["OpenLlamaForCausalLM", "OpenLlamaForSequenceClassification", "OpenLlamaModel", "OpenLlamaPreTrainedModel"]
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)
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_import_structure["models.deprecated.retribert"].extend(
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["RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RetriBertModel", "RetriBertPreTrainedModel"]
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)
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@ -2300,9 +2303,6 @@ else:
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"OneFormerPreTrainedModel",
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]
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)
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_import_structure["models.open_llama"].extend(
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["OpenLlamaForCausalLM", "OpenLlamaForSequenceClassification", "OpenLlamaModel", "OpenLlamaPreTrainedModel"]
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)
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_import_structure["models.openai"].extend(
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[
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"OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
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@ -4239,6 +4239,7 @@ if TYPE_CHECKING:
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MCTCTProcessor,
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)
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from .models.deprecated.mmbt import MMBTConfig
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from .models.deprecated.open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
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from .models.deprecated.retribert import (
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RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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RetriBertConfig,
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@ -4390,7 +4391,6 @@ if TYPE_CHECKING:
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from .models.nllb_moe import NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP, NllbMoeConfig
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from .models.nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
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from .models.oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig, OneFormerProcessor
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from .models.open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
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from .models.openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig, OpenAIGPTTokenizer
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from .models.opt import OPTConfig
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from .models.owlvit import (
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@ -5334,6 +5334,12 @@ if TYPE_CHECKING:
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MCTCTPreTrainedModel,
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)
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from .models.deprecated.mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
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from .models.deprecated.open_llama import (
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OpenLlamaForCausalLM,
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OpenLlamaForSequenceClassification,
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OpenLlamaModel,
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OpenLlamaPreTrainedModel,
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)
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from .models.deprecated.retribert import (
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RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
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RetriBertModel,
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@ -5954,12 +5960,6 @@ if TYPE_CHECKING:
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OneFormerModel,
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OneFormerPreTrainedModel,
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)
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from .models.open_llama import (
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OpenLlamaForCausalLM,
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OpenLlamaForSequenceClassification,
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OpenLlamaModel,
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OpenLlamaPreTrainedModel,
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)
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from .models.openai import (
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OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
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OpenAIGPTDoubleHeadsModel,
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@ -145,7 +145,6 @@ from . import (
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nllb_moe,
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nystromformer,
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oneformer,
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open_llama,
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openai,
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opt,
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owlvit,
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@ -652,6 +652,7 @@ DEPRECATED_MODELS = [
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"bort",
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"mctct",
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"mmbt",
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"open_llama",
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"retribert",
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"tapex",
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"trajectory_transformer",
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@ -13,7 +13,7 @@
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# limitations under the License.
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from typing import TYPE_CHECKING
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from ...utils import (
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from ....utils import (
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OptionalDependencyNotAvailable,
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_LazyModule,
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is_sentencepiece_available,
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@ -19,8 +19,8 @@
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# limitations under the License.
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""" Open-Llama model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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from ....configuration_utils import PretrainedConfig
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from ....utils import logging
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logger = logging.get_logger(__name__)
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@ -26,10 +26,10 @@ import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
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from ...modeling_utils import PreTrainedModel
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from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
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from ....activations import ACT2FN
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from ....modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
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from ....modeling_utils import PreTrainedModel
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from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
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from .configuration_open_llama import OpenLlamaConfig
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@ -2396,6 +2396,34 @@ class ModalEmbeddings(metaclass=DummyObject):
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requires_backends(self, ["torch"])
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class OpenLlamaForCausalLM(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class OpenLlamaForSequenceClassification(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class OpenLlamaModel(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class OpenLlamaPreTrainedModel(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
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@ -5461,34 +5489,6 @@ class OneFormerPreTrainedModel(metaclass=DummyObject):
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requires_backends(self, ["torch"])
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class OpenLlamaForCausalLM(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class OpenLlamaForSequenceClassification(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class OpenLlamaModel(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class OpenLlamaPreTrainedModel(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = None
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@ -1,370 +0,0 @@
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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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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""" Testing suite for the PyTorch Open-Llama model. """
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import unittest
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from parameterized import parameterized
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from transformers import OpenLlamaConfig, is_torch_available, set_seed
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from transformers.testing_utils import require_torch, torch_device
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
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class OpenLlamaModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=False,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return OpenLlamaConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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is_decoder=False,
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initializer_range=self.initializer_range,
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use_stable_embedding=False,
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)
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def create_and_check_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = OpenLlamaModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_model_as_decoder(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = OpenLlamaModel(config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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)
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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)
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_for_causal_lm(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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model = OpenLlamaForCausalLM(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_decoder_model_past_large_inputs(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.is_decoder = True
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config.add_cross_attention = True
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model = OpenLlamaForCausalLM(config=config)
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model.to(torch_device)
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model.eval()
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# first forward pass
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outputs = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=True,
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)
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past_key_values = outputs.past_key_values
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
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output_from_no_past = model(
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next_input_ids,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
output_hidden_states=True,
|
||||
)["hidden_states"][0]
|
||||
output_from_past = model(
|
||||
next_tokens,
|
||||
attention_mask=next_attention_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
output_hidden_states=True,
|
||||
)["hidden_states"][0]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
||||
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
|
||||
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
|
||||
|
||||
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
|
||||
|
||||
# test that outputs are equal for slice
|
||||
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = config_and_inputs
|
||||
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
class OpenLlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (
|
||||
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
|
||||
)
|
||||
all_generative_model_classes = (OpenLlamaForCausalLM,) if is_torch_available() else ()
|
||||
pipeline_model_mapping = (
|
||||
{
|
||||
"feature-extraction": OpenLlamaModel,
|
||||
"text-classification": OpenLlamaForSequenceClassification,
|
||||
"text-generation": OpenLlamaForCausalLM,
|
||||
"zero-shot": OpenLlamaForSequenceClassification,
|
||||
}
|
||||
if is_torch_available()
|
||||
else {}
|
||||
)
|
||||
test_headmasking = False
|
||||
test_pruning = False
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = OpenLlamaModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=OpenLlamaConfig, hidden_size=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_model_various_embeddings(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
for type in ["absolute", "relative_key", "relative_key_query"]:
|
||||
config_and_inputs[0].position_embedding_type = type
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_open_llama_sequence_classification_model(self):
|
||||
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.num_labels = 3
|
||||
input_ids = input_dict["input_ids"]
|
||||
attention_mask = input_ids.ne(1).to(torch_device)
|
||||
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
|
||||
model = OpenLlamaForSequenceClassification(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
|
||||
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
|
||||
|
||||
def test_open_llama_sequence_classification_model_for_single_label(self):
|
||||
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.num_labels = 3
|
||||
config.problem_type = "single_label_classification"
|
||||
input_ids = input_dict["input_ids"]
|
||||
attention_mask = input_ids.ne(1).to(torch_device)
|
||||
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
|
||||
model = OpenLlamaForSequenceClassification(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
|
||||
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
|
||||
|
||||
def test_open_llama_sequence_classification_model_for_multi_label(self):
|
||||
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.num_labels = 3
|
||||
config.problem_type = "multi_label_classification"
|
||||
input_ids = input_dict["input_ids"]
|
||||
attention_mask = input_ids.ne(1).to(torch_device)
|
||||
sequence_labels = ids_tensor(
|
||||
[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
|
||||
).to(torch.float)
|
||||
model = OpenLlamaForSequenceClassification(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
|
||||
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
|
||||
|
||||
@unittest.skip("Open-Llama buffers include complex numbers, which breaks this test")
|
||||
def test_save_load_fast_init_from_base(self):
|
||||
pass
|
||||
|
||||
@parameterized.expand([("linear",), ("dynamic",)])
|
||||
def test_model_rope_scaling(self, scaling_type):
|
||||
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
short_input = ids_tensor([1, 10], config.vocab_size)
|
||||
long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
|
||||
|
||||
set_seed(42) # Fixed seed at init time so the two models get the same random weights
|
||||
original_model = OpenLlamaModel(config)
|
||||
original_model.to(torch_device)
|
||||
original_model.eval()
|
||||
original_short_output = original_model(short_input).last_hidden_state
|
||||
original_long_output = original_model(long_input).last_hidden_state
|
||||
|
||||
set_seed(42) # Fixed seed at init time so the two models get the same random weights
|
||||
config.rope_scaling = {"type": scaling_type, "factor": 10.0}
|
||||
scaled_model = OpenLlamaModel(config)
|
||||
scaled_model.to(torch_device)
|
||||
scaled_model.eval()
|
||||
scaled_short_output = scaled_model(short_input).last_hidden_state
|
||||
scaled_long_output = scaled_model(long_input).last_hidden_state
|
||||
|
||||
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
|
||||
# maximum sequence length, so the outputs for the short input should match.
|
||||
if scaling_type == "dynamic":
|
||||
self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
|
||||
else:
|
||||
self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
|
||||
|
||||
# The output should be different for long inputs
|
||||
self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
|
||||
@ -238,7 +238,7 @@ def check_config_attributes_being_used(config_class):
|
||||
modeling_sources = []
|
||||
for path in modeling_paths:
|
||||
if os.path.isfile(path):
|
||||
with open(path) as fp:
|
||||
with open(path, encoding="utf8") as fp:
|
||||
modeling_sources.append(fp.read())
|
||||
|
||||
unused_attributes = []
|
||||
|
||||
@ -1093,7 +1093,7 @@ def check_deprecated_constant_is_up_to_date():
|
||||
if len(missing_models) != 0:
|
||||
missing_models = ", ".join(missing_models)
|
||||
message.append(
|
||||
"The following models are in the deprecated folder, make sur to add them to `DEPRECATED_MODELS` in "
|
||||
"The following models are in the deprecated folder, make sure to add them to `DEPRECATED_MODELS` in "
|
||||
f"`models/auto/configuration_auto.py`: {missing_models}."
|
||||
)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user