PyTorch 2.2 中文官方教程（二十）（3）https://developer.aliyun.com/article/1482632
构建我们的嵌入模型
在这里，我们使用 TorchRec 提供的EmbeddingBagCollection来构建我们的嵌入包模型与嵌入表。
在这里，我们创建了一个包含四个嵌入包的 EmbeddingBagCollection（EBC）。我们有两种类型的表：大表和小表，通过它们的行大小差异区分：4096 vs 1024。每个表仍然由 64 维嵌入表示。
我们为表配置ParameterConstraints数据结构，为模型并行 API 提供提示，以帮助决定表的分片和放置策略。在 TorchRec 中，我们支持* table-wise：将整个表放在一个设备上；* row-wise：按行维度均匀分片表，并将一个分片放在通信世界的每个设备上；* column-wise：按嵌入维度均匀分片表，并将一个分片放在通信世界的每个设备上；* table-row-wise：针对可用的快速主机内部通信进行优化的特殊分片，例如 NVLink；* data_parallel：为每个设备复制表；
请注意我们最初在设备“meta”上分配 EBC。这将告诉 EBC 暂时不分配内存。
from torchrec.distributed.planner.types import ParameterConstraints from torchrec.distributed.embedding_types import EmbeddingComputeKernel from torchrec.distributed.types import ShardingType from typing import Dict large_table_cnt = 2 small_table_cnt = 2 large_tables=[ torchrec.EmbeddingBagConfig( name="large_table_" + str(i), embedding_dim=64, num_embeddings=4096, feature_names=["large_table_feature_" + str(i)], pooling=torchrec.PoolingType.SUM, ) for i in range(large_table_cnt) ] small_tables=[ torchrec.EmbeddingBagConfig( name="small_table_" + str(i), embedding_dim=64, num_embeddings=1024, feature_names=["small_table_feature_" + str(i)], pooling=torchrec.PoolingType.SUM, ) for i in range(small_table_cnt) ] def gen_constraints(sharding_type: ShardingType = ShardingType.TABLE_WISE) -> Dict[str, ParameterConstraints]: large_table_constraints = { "large_table_" + str(i): ParameterConstraints( sharding_types=[sharding_type.value], ) for i in range(large_table_cnt) } small_table_constraints = { "small_table_" + str(i): ParameterConstraints( sharding_types=[sharding_type.value], ) for i in range(small_table_cnt) } constraints = {**large_table_constraints, **small_table_constraints} return constraints
ebc = torchrec.EmbeddingBagCollection( device="cuda", tables=large_tables + small_tables )
多进程中的分布式模型并行
现在，我们有一个单进程执行函数，用于模拟SPMD执行期间一个等级的工作。
此代码将与其他进程一起分片模型并相应地分配内存。它首先设置进程组，并使用规划器进行嵌入表放置，并使用DistributedModelParallel生成分片模型。
def single_rank_execution( rank: int, world_size: int, constraints: Dict[str, ParameterConstraints], module: torch.nn.Module, backend: str, ) -> None: import os import torch import torch.distributed as dist from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder from torchrec.distributed.model_parallel import DistributedModelParallel from torchrec.distributed.planner import EmbeddingShardingPlanner, Topology from torchrec.distributed.types import ModuleSharder, ShardingEnv from typing import cast def init_distributed_single_host( rank: int, world_size: int, backend: str, # pyre-fixme[11]: Annotation `ProcessGroup` is not defined as a type. ) -> dist.ProcessGroup: os.environ["RANK"] = f"{rank}" os.environ["WORLD_SIZE"] = f"{world_size}" dist.init_process_group(rank=rank, world_size=world_size, backend=backend) return dist.group.WORLD if backend == "nccl": device = torch.device(f"cuda:{rank}") torch.cuda.set_device(device) else: device = torch.device("cpu") topology = Topology(world_size=world_size, compute_device="cuda") pg = init_distributed_single_host(rank, world_size, backend) planner = EmbeddingShardingPlanner( topology=topology, constraints=constraints, ) sharders = [cast(ModuleSharder[torch.nn.Module], EmbeddingBagCollectionSharder())] plan: ShardingPlan = planner.collective_plan(module, sharders, pg) sharded_model = DistributedModelParallel( module, env=ShardingEnv.from_process_group(pg), plan=plan, sharders=sharders, device=device, ) print(f"rank:{rank},sharding plan: {plan}") return sharded_model
多进程执行
现在让我们在多个 GPU 等级中执行代码。
import multiprocess def spmd_sharing_simulation( sharding_type: ShardingType = ShardingType.TABLE_WISE, world_size = 2, ): ctx = multiprocess.get_context("spawn") processes = [] for rank in range(world_size): p = ctx.Process( target=single_rank_execution, args=( rank, world_size, gen_constraints(sharding_type), ebc, "nccl" ), ) p.start() processes.append(p) for p in processes: p.join() assert 0 == p.exitcode
表分片
现在让我们在两个进程中为 2 个 GPU 执行代码。我们可以在计划打印中看到我们的表如何跨 GPU 分片。每个节点将有一个大表和一个小表，显示我们的规划器尝试为嵌入表实现负载平衡。对于许多中小型表的负载平衡，表方式是默认的分片方案。
spmd_sharing_simulation(ShardingType.TABLE_WISE)
rank:1,sharding plan: {'': {'large_table_0': ParameterSharding(sharding_type='table_wise', compute_kernel='batched_fused', ranks=[0], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[4096, 64], placement=rank:0/cuda:0)])), 'large_table_1': ParameterSharding(sharding_type='table_wise', compute_kernel='batched_fused', ranks=[1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[4096, 64], placement=rank:1/cuda:1)])), 'small_table_0': ParameterSharding(sharding_type='table_wise', compute_kernel='batched_fused', ranks=[0], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1024, 64], placement=rank:0/cuda:0)])), 'small_table_1': ParameterSharding(sharding_type='table_wise', compute_kernel='batched_fused', ranks=[1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1024, 64], placement=rank:1/cuda:1)]))}} rank:0,sharding plan: {'': {'large_table_0': ParameterSharding(sharding_type='table_wise', compute_kernel='batched_fused', ranks=[0], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[4096, 64], placement=rank:0/cuda:0)])), 'large_table_1': ParameterSharding(sharding_type='table_wise', compute_kernel='batched_fused', ranks=[1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[4096, 64], placement=rank:1/cuda:1)])), 'small_table_0': ParameterSharding(sharding_type='table_wise', compute_kernel='batched_fused', ranks=[0], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1024, 64], placement=rank:0/cuda:0)])), 'small_table_1': ParameterSharding(sharding_type='table_wise', compute_kernel='batched_fused', ranks=[1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1024, 64], placement=rank:1/cuda:1)]))}}
探索其他分片模式
我们最初探讨了表格分片的外观以及它如何平衡表格的放置。现在我们将更加专注于负载平衡的分片模式：按行分片。按行分片专门解决了由于大嵌入行数导致内存增加而单个设备无法容纳的大表格。它可以解决模型中超大表格的放置问题。用户可以在打印计划日志中的shard_sizes部分看到，表格按行维度减半，分布到两个 GPU 上。
spmd_sharing_simulation(ShardingType.ROW_WISE)
rank:1,sharding plan: {'': {'large_table_0': ParameterSharding(sharding_type='row_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[2048, 64], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[2048, 0], shard_sizes=[2048, 64], placement=rank:1/cuda:1)])), 'large_table_1': ParameterSharding(sharding_type='row_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[2048, 64], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[2048, 0], shard_sizes=[2048, 64], placement=rank:1/cuda:1)])), 'small_table_0': ParameterSharding(sharding_type='row_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[512, 64], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[512, 0], shard_sizes=[512, 64], placement=rank:1/cuda:1)])), 'small_table_1': ParameterSharding(sharding_type='row_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[512, 64], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[512, 0], shard_sizes=[512, 64], placement=rank:1/cuda:1)]))}} rank:0,sharding plan: {'': {'large_table_0': ParameterSharding(sharding_type='row_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[2048, 64], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[2048, 0], shard_sizes=[2048, 64], placement=rank:1/cuda:1)])), 'large_table_1': ParameterSharding(sharding_type='row_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[2048, 64], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[2048, 0], shard_sizes=[2048, 64], placement=rank:1/cuda:1)])), 'small_table_0': ParameterSharding(sharding_type='row_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[512, 64], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[512, 0], shard_sizes=[512, 64], placement=rank:1/cuda:1)])), 'small_table_1': ParameterSharding(sharding_type='row_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[512, 64], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[512, 0], shard_sizes=[512, 64], placement=rank:1/cuda:1)]))}}
列式分割另一方面，解决了具有大嵌入维度的表格的负载不平衡问题。我们将表格垂直分割。用户可以在打印计划日志中的shard_sizes部分看到，表格按嵌入维度减半，分布到两个 GPU 上。
spmd_sharing_simulation(ShardingType.COLUMN_WISE)
rank:0,sharding plan: {'': {'large_table_0': ParameterSharding(sharding_type='column_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[4096, 32], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[0, 32], shard_sizes=[4096, 32], placement=rank:1/cuda:1)])), 'large_table_1': ParameterSharding(sharding_type='column_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[4096, 32], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[0, 32], shard_sizes=[4096, 32], placement=rank:1/cuda:1)])), 'small_table_0': ParameterSharding(sharding_type='column_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1024, 32], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[0, 32], shard_sizes=[1024, 32], placement=rank:1/cuda:1)])), 'small_table_1': ParameterSharding(sharding_type='column_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1024, 32], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[0, 32], shard_sizes=[1024, 32], placement=rank:1/cuda:1)]))}} rank:1,sharding plan: {'': {'large_table_0': ParameterSharding(sharding_type='column_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[4096, 32], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[0, 32], shard_sizes=[4096, 32], placement=rank:1/cuda:1)])), 'large_table_1': ParameterSharding(sharding_type='column_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[4096, 32], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[0, 32], shard_sizes=[4096, 32], placement=rank:1/cuda:1)])), 'small_table_0': ParameterSharding(sharding_type='column_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1024, 32], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[0, 32], shard_sizes=[1024, 32], placement=rank:1/cuda:1)])), 'small_table_1': ParameterSharding(sharding_type='column_wise', compute_kernel='batched_fused', ranks=[0, 1], sharding_spec=EnumerableShardingSpec(shards=[ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1024, 32], placement=rank:0/cuda:0), ShardMetadata(shard_offsets=[0, 32], shard_sizes=[1024, 32], placement=rank:1/cuda:1)]))}}
对于table-row-wise，不幸的是，由于其在多主机设置下运行的特性，我们无法模拟它。我们将在未来提供一个 Python SPMD示例，以使用table-row-wise训练模型。
使用数据并行，我们将为所有设备重复表格。
spmd_sharing_simulation(ShardingType.DATA_PARALLEL)
rank:0,sharding plan: {'': {'large_table_0': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'large_table_1': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'small_table_0': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'small_table_1': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None)}} rank:1,sharding plan: {'': {'large_table_0': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'large_table_1': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'small_table_0': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'small_table_1': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None)}}
多模态
TorchMultimodal 教程：微调 FLAVA
原文：
pytorch.org/tutorials/beginner/flava_finetuning_tutorial.html译者：飞龙
注意
点击这里下载完整示例代码
多模态人工智能最近变得非常流行，因为它的普遍性，从图像字幕和视觉搜索等用例到最近的应用，如根据文本生成图像。TorchMultimodal 是一个由 Pytorch 提供支持的库，包含构建模块和端到端示例，旨在促进和加速多模态研究。
在本教程中，我们将演示如何使用 TorchMultimodal 库中的 预训练 SoTA 模型 FLAVA 进行多模态任务微调，即视觉问答（VQA）。该模型由两个基于 transformer 的文本和图像单模态编码器以及一个多模态编码器组成，用于组合这两个嵌入。它使用对比、图像文本匹配以及文本、图像和多模态掩码损失进行预训练。
安装
我们将在本教程中使用 TextVQA 数据集和 Hugging Face 的 bert tokenizer。因此，除了 TorchMultimodal，您还需要安装 datasets 和 transformers。
注意
在 Google Colab 中运行本教程时，请通过创建一个新单元格并运行以下命令来安装所需的包：
!pip install torchmultimodal-nightly !pip install datasets !pip install transformers
步骤
- 通过运行以下命令将 Hugging Face 数据集下载到计算机上的一个目录中：
wget http://dl.fbaipublicfiles.com/pythia/data/vocab.tar.gz tar xf vocab.tar.gz
- 注意
如果您在 Google Colab 中运行本教程，请在新单元格中运行这些命令，并在这些命令前加上感叹号（!）。 - 在本教程中，我们将将 VQA 视为一个分类任务，其中输入是图像和问题（文本），输出是一个答案类别。因此，我们需要下载包含答案类别的词汇文件，并创建答案到标签的映射。
我们还从 Hugging Face 加载包含 34602 个训练样本（图像、问题和答案）的 textvqa 数据集。
我们看到有 3997 个答案类别，包括一个代表未知答案的类别。
with open("data/vocabs/answers_textvqa_more_than_1.txt") as f: vocab = f.readlines() answer_to_idx = {} for idx, entry in enumerate(vocab): answer_to_idx[entry.strip("\n")] = idx print(len(vocab)) print(vocab[:5]) from datasets import load_dataset dataset = load_dataset("textvqa")
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让我们展示数据集中的一个样本条目：
import matplotlib.pyplot as plt import numpy as np idx = 5 print("Question: ", dataset["train"][idx]["question"]) print("Answers: " ,dataset["train"][idx]["answers"]) im = np.asarray(dataset["train"][idx]["image"].resize((500,500))) plt.imshow(im) plt.show()
Question: what year is shown in the photo? Answers: ['2011', '2011', '2011', '2011', '2011', '2011', '2011', '2011', '2011', '2011']
3. 接下来，我们编写转换函数，将图像和文本转换为模型可消耗的张量 - 对于图像，我们使用 torchvision 中的转换将其转换为张量并调整为统一大小 - 对于文本，我们使用 Hugging Face 的 BertTokenizer 对其进行标记化（和填充） - 对于答案（即标签），我们将最常出现的答案作为训练标签：
import torch from torchvision import transforms from collections import defaultdict from transformers import BertTokenizer from functools import partial def transform(tokenizer, input): batch = {} image_transform = transforms.Compose([transforms.ToTensor(), transforms.Resize([224,224])]) image = image_transform(input["image"][0].convert("RGB")) batch["image"] = [image] tokenized=tokenizer(input["question"],return_tensors='pt',padding="max_length",max_length=512) batch.update(tokenized) ans_to_count = defaultdict(int) for ans in input["answers"][0]: ans_to_count[ans] += 1 max_value = max(ans_to_count, key=ans_to_count.get) ans_idx = answer_to_idx.get(max_value,0) batch["answers"] = torch.as_tensor([ans_idx]) return batch tokenizer=BertTokenizer.from_pretrained("bert-base-uncased",padding="max_length",max_length=512) transform=partial(transform,tokenizer) dataset.set_transform(transform)
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4. 最后，我们从 torchmultimodal 中导入 flava_model_for_classification。它默认加载预训练的 FLAVA 检查点，并包含一个分类头。
模型的前向函数将图像通过视觉编码器，问题通过文本编码器。然后将图像和问题嵌入传递给多模态编码器。对应于 CLS 令牌的最终嵌入通过 MLP 头传递，最终给出每个可能答案的概率分布。
from torchmultimodal.models.flava.model import flava_model_for_classification model = flava_model_for_classification(num_classes=len(vocab))
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flava_for_pretraining_unified_text_encoder.pt: 80%|#######9 | 1.14G/1.43G [00:04<00:01, 287MB/s] flava_for_pretraining_unified_text_encoder.pt: 81%|########1 | 1.17G/1.43G [00:04<00:00, 267MB/s] flava_for_pretraining_unified_text_encoder.pt: 83%|########2 | 1.19G/1.43G [00:04<00:00, 252MB/s] flava_for_pretraining_unified_text_encoder.pt: 85%|########4 | 1.22G/1.43G [00:04<00:00, 261MB/s] flava_for_pretraining_unified_text_encoder.pt: 87%|########6 | 1.24G/1.43G [00:04<00:00, 268MB/s] flava_for_pretraining_unified_text_encoder.pt: 89%|########8 | 1.27G/1.43G [00:04<00:00, 273MB/s] flava_for_pretraining_unified_text_encoder.pt: 91%|######### | 1.30G/1.43G [00:04<00:00, 277MB/s] flava_for_pretraining_unified_text_encoder.pt: 93%|#########2| 1.33G/1.43G [00:05<00:00, 269MB/s] flava_for_pretraining_unified_text_encoder.pt: 95%|#########4| 1.35G/1.43G [00:05<00:00, 269MB/s] flava_for_pretraining_unified_text_encoder.pt: 97%|#########6| 1.38G/1.43G [00:05<00:00, 274MB/s] flava_for_pretraining_unified_text_encoder.pt: 99%|#########8| 1.41G/1.43G [00:05<00:00, 276MB/s] flava_for_pretraining_unified_text_encoder.pt: 1.43GB [00:05, 266MB/s]
5. 我们将数据集和模型放在一个玩具训练循环中，以演示如何训练模型进行 3 次迭代：
from torch import nn BATCH_SIZE = 2 MAX_STEPS = 3 from torch.utils.data import DataLoader train_dataloader = DataLoader(dataset["train"], batch_size= BATCH_SIZE) optimizer = torch.optim.AdamW(model.parameters()) epochs = 1 for _ in range(epochs): for idx, batch in enumerate(train_dataloader): optimizer.zero_grad() out = model(text = batch["input_ids"], image = batch["image"], labels = batch["answers"]) loss = out.loss loss.backward() optimizer.step() print(f"Loss at step {idx} = {loss}") if idx >= MAX_STEPS-1: break
Loss at step 0 = 8.290360450744629 Loss at step 1 = 8.358966827392578 Loss at step 2 = 8.274675369262695
结论
本教程介绍了如何使用 TorchMultimodal 中的 FLAVA 在多模态任务上进行微调的基础知识。请还查看库中的其他示例，如 MDETR，这是一个用于目标检测的多模态模型，以及 Omnivore，这是一个跨图像、视频和 3D 分类的多任务模型。
脚本的总运行时间：（2 分 31.510 秒）
下载 Python 源代码：flava_finetuning_tutorial.py
下载 Jupyter 笔记本：flava_finetuning_tutorial.ipynb
