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zqa)(~d*%O1{2nk8?F;|)r~`c^11Ft@C6xBX{}?ai^WJ`i!sTy|*WYYpa|8TSmRFan IkTDPaKVbvAHvj+t literal 0 HcmV?d00001 diff --git a/docs/guides/06_distributed_training/index_cn.rst b/docs/guides/06_distributed_training/index_cn.rst index 1d8fb54ac55..0ff1984a6eb 100644 --- a/docs/guides/06_distributed_training/index_cn.rst +++ b/docs/guides/06_distributed_training/index_cn.rst @@ -12,6 +12,7 @@ - `张量模型并行 <./model_parallel_cn.html>`_ : 使用飞桨框架完成张量模型并行训练。 - `流水线并行 <./pipeline_parallel_cn.html>`_ : 使用飞桨框架完成流水线并行训练。 - `分组切分并行 <./group_sharded_parallel_cn.html>`_ : 使用飞桨框架完成分组切分并行训练。 +- `MoE <./moe_cn.html>`_ : 使用飞桨框架完成MoE模型训练。 .. toctree:: :hidden: @@ -24,3 +25,4 @@ model_parallel_cn.rst pipeline_parallel_cn.rst group_sharded_parallel_cn.rst + moe_cn.rst diff --git a/docs/guides/06_distributed_training/moe_cn.rst b/docs/guides/06_distributed_training/moe_cn.rst new file mode 100644 index 00000000000..5c92ef27e5d --- /dev/null +++ b/docs/guides/06_distributed_training/moe_cn.rst @@ -0,0 +1,131 @@ +.. _moe: + +MoE +======================= + +通常来讲,模型规模的扩展会导致训练成本显著增加,计算资源的限制成为了大规模密集模型训练的瓶颈。为了解决这个问题,`《Outrageously large neural networks: The sparsely-gated mixture-of-experts layer》 ` 提出了 +一种基于稀疏MoE层的深度学习模型架构,即将大模型拆分成多个小模型并引入稀疏门(gate)机制,根据样本来决定激活一部分小模型用于机器,达到了节省计算资源的效果;门机制需要确保稀疏性,以保证计算能力的优化。 + +一、原理介绍 +------------------- + +.. image:: ./images/moe_layer.png + :width: 400 + :alt: moe_layer + :align: center + +与密集模型不同,MoE将模型的某一层扩展为多个具有相同结构的expert,并由门(gate)网络决定激活哪些expert用于计算,从而实现超大规模稀疏模型的训练。以上图为例,示例模型包含3个模型层;若将中间层扩展为具有 ``n`` 个expert的MoE结构,并引入 ``Gating network`` 和 ``Top_k`` 机制, +门网络的输出看作每个expert的权重,每个输入 ``x`` 需要分配 ``k`` 个权重最大的expert进行计算,然后只对选中的 ``k`` 个专家网络的输出进行加权求和,最后得到整个MoE Layer的计算结果。 + + +二、功能效果 +------------------------- + +使用MoE结构,可以在计算成本次线性增加的同时实现超大规模模型训练,为恒定的计算资源预算带来巨大增益。 + + +三、动态图使用方法 +------------------------ + +下面我们将分别介绍如何在动态图模式下使用飞桨框架进行MoE架构的适配和训练。以下代码(train_moe.py)在Paddle2.3以上可以运行,建议将Paddle版本升级到最新版. + +首先导入需要的包 + +.. code-block:: python + + import paddle + from paddle.nn import Layer, LayerList, Linear, Dropout + from paddle.incubate.distributed.models.moe import MoELayer + from paddle.distributed.collective import Group + from paddle.distributed import fleet + import numpy as np + +构建一个可以正常训练的模型 + +.. code-block:: python + + num_experts = 8 + d_model = 512 + d_hidden = 2048 + + class ExpertLayer(Layer): + def __init__(self, d_model, d_hidden, name=None): + super(ExpertLayer, self).__init__() + self.htoh4 = Linear(d_model, d_hidden) + self.h4toh = Linear(d_hidden, d_model) + + def forward(self, x): + x = self.htoh4(x) + x = self.h4toh(x) + return x + +然后初始化分布式环境,并构建expert通信组moe_group + +.. code-block:: python + + fleet.init(is_collective=True) + moe_group = paddle.distributed.new_group(list(range(fleet.worker_num()))) + +设置门网络的gate策略和top_k机制,并将模型单层扩展为 ``num_expert`` 个相同结构的专家网络 + +.. code-block:: python + + gate_config = { + "type": "gshard", + "top_k": 2, + } + + experts_list = LayerList() + for expi in range(num_experts): + exp_layer = ExpertLayer(d_model, d_hidden) + experts_list.append(exp_layer) + +接着调用 ``MoELayer`` API 封装并创建出MoE模型 + +.. code-block:: python + + class Model(Layer): + def __init__(self, d_model, d_hidden, name=None): + super(Model, self).__init__() + self.linear1 = Linear(d_model, d_model) + self.moe_layer = MoELayer(d_model = d_model, + experts=experts_list, + gate=gate_config, + moe_group=moe_group, + recompute_interval=0) + + self.linear2 = Linear(d_model, d_model) + self.dropout = Dropout(p=0.1) + + def forward(self, x): + x = self.linear1(x) + x = self.moe_layer(x) + x = self.linear2(x) + x = self.dropout(x) + return x + + model = Model(d_model, d_hidden) + optim = paddle.optimizer.SGD(parameters=model.parameters()) + +最后创建数据集,开始训练 + +.. code-block:: python + + for step in range(1, 100): + x = paddle.rand([4, 256, d_model]) + + y = model(x) + loss = y.mean() + loss.backward() + optim.step() + + optim.clear_grad() + + print("=== step : {}, loss : {}".format(step, loss.numpy())) + +运行方式: + +.. code-block:: bash + + export CUDA_VISIBLE_DEVICES=0 + python train_moe.py From d0f382013eaa9683200c3b5ce4cc89aff46670cb Mon Sep 17 00:00:00 2001 From: haohongxiang Date: Fri, 10 Jun 2022 09:17:30 +0000 Subject: [PATCH 2/4] update --- .../group_sharded_parallel_cn.rst | 45 ++++++++++--------- .../guides/06_distributed_training/moe_cn.rst | 7 +-- 2 files changed, 28 insertions(+), 24 deletions(-) diff --git a/docs/guides/06_distributed_training/group_sharded_parallel_cn.rst b/docs/guides/06_distributed_training/group_sharded_parallel_cn.rst index 66733a5bd98..60716fa4d57 100644 --- a/docs/guides/06_distributed_training/group_sharded_parallel_cn.rst +++ b/docs/guides/06_distributed_training/group_sharded_parallel_cn.rst @@ -88,20 +88,22 @@ GroupSharded 结合 amp (O2) + recompute,可以在 8 张 40GB A100 并行的 .. code-block:: import paddle - from paddle.fluid.dygraph.nn import Linear + from paddle.vision.models import ResNet + from paddle.vision.models.resnet import BasicBlock from paddle.distributed import fleet - from paddle.distributed.GroupSharded import group_sharded_parallel, save_group_sharded_model + from paddle.distributed.sharding import group_sharded_parallel, save_group_sharded_model fleet.init(is_collective=True) group = paddle.distributed.new_group([0, 1]) - model = Linear(1000, 1000) + use_pure_fp16 = True clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0) + model = ResNet(BasicBlock, 18) optimizer = paddle.optimizer.AdamW(learning_rate=0.001, parameters=model.parameters(), weight_decay=0.00001, grad_clip=clip) scaler = None if use_pure_fp16: - scaler = paddle.amp.GradScaler(init_loss_scaling=scale_loss) + scaler = paddle.amp.GradScaler(init_loss_scaling=32768) # level O2 means converting the network to FP16 model = paddle.amp.decorate( models=model, @@ -109,23 +111,24 @@ GroupSharded 结合 amp (O2) + recompute,可以在 8 张 40GB A100 并行的 save_dtype='float32') # wrap GroupSharded model, optimizer and scaler - model, optimizer, scaler = group_sharded_parallel(model, optimizer, "p_g", scaler=scaler) - - img, label = data - label.stop_gradient = True - img.stop_gradient = True - - out = model(img) - loss = paddle.nn.functional.cross_entropy(input=out, label=label) - - if use_pure_fp16: - scaler.scale(loss).backward() - scaler.step(optimizer) - scaler.update() - else: - loss.backward() - optimizer.step() - optimizer.clear_grad() + model, optimizer, scaler = group_sharded_parallel(model, optimizer, "os_g", scaler=scaler) + + for step_id in range(1, 100): + x = paddle.rand([1, 3, 224, 224]) + with paddle.amp.auto_cast(use_pure_fp16): + out = model(x) + loss = out.mean() + + if use_pure_fp16: + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + else: + loss.backward() + optimizer.step() + optimizer.clear_grad() + + print("=== step_id : {} loss : {}".format(step_id, loss.numpy())) # save model and optimizer state_dict save_group_sharded_model(model, optimizer, output=output_dir) diff --git a/docs/guides/06_distributed_training/moe_cn.rst b/docs/guides/06_distributed_training/moe_cn.rst index 5c92ef27e5d..71ed5b97e51 100644 --- a/docs/guides/06_distributed_training/moe_cn.rst +++ b/docs/guides/06_distributed_training/moe_cn.rst @@ -3,14 +3,15 @@ MoE ======================= -通常来讲,模型规模的扩展会导致训练成本显著增加,计算资源的限制成为了大规模密集模型训练的瓶颈。为了解决这个问题,`《Outrageously large neural networks: The sparsely-gated mixture-of-experts layer》 ` 提出了 -一种基于稀疏MoE层的深度学习模型架构,即将大模型拆分成多个小模型并引入稀疏门(gate)机制,根据样本来决定激活一部分小模型用于机器,达到了节省计算资源的效果;门机制需要确保稀疏性,以保证计算能力的优化。 +通常来讲,模型规模的扩展会导致训练成本显著增加,计算资源的限制成为了大规模密集模型训练的瓶颈。为了解决这个问题, +`《Outrageously large neural networks: The sparsely-gated mixture-of-experts layer》 `__ +提出了一种基于稀疏MoE层的深度学习模型架构,即将大模型拆分成多个小模型并引入稀疏门(gate)机制,根据样本来决定激活一部分小模型用于机器,达到了节省计算资源的效果;门机制需要确保稀疏性,以保证计算能力的优化。 一、原理介绍 ------------------- .. image:: ./images/moe_layer.png - :width: 400 + :width: 700 :alt: moe_layer :align: center From f806634eece4b41d487c6a46827373f1675b445d Mon Sep 17 00:00:00 2001 From: haohongxiang Date: Mon, 13 Jun 2022 18:20:09 +0000 Subject: [PATCH 3/4] update --- docs/guides/06_distributed_training/moe_cn.rst | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/docs/guides/06_distributed_training/moe_cn.rst b/docs/guides/06_distributed_training/moe_cn.rst index 71ed5b97e51..028e43c5985 100644 --- a/docs/guides/06_distributed_training/moe_cn.rst +++ b/docs/guides/06_distributed_training/moe_cn.rst @@ -5,7 +5,8 @@ MoE 通常来讲,模型规模的扩展会导致训练成本显著增加,计算资源的限制成为了大规模密集模型训练的瓶颈。为了解决这个问题, `《Outrageously large neural networks: The sparsely-gated mixture-of-experts layer》 `__ -提出了一种基于稀疏MoE层的深度学习模型架构,即将大模型拆分成多个小模型并引入稀疏门(gate)机制,根据样本来决定激活一部分小模型用于机器,达到了节省计算资源的效果;门机制需要确保稀疏性,以保证计算能力的优化。 +提出了一种基于稀疏MoE层的深度学习模型架构,即将大模型拆分成多个小模型(专家, expert), 每轮迭代根据样本决定激活一部分专家用于计算,达到了节省计算资源的效果; +并引入可训练并确保稀疏性的门(gate)机制,以保证计算能力的优化。 一、原理介绍 ------------------- @@ -15,8 +16,17 @@ MoE :alt: moe_layer :align: center -与密集模型不同,MoE将模型的某一层扩展为多个具有相同结构的expert,并由门(gate)网络决定激活哪些expert用于计算,从而实现超大规模稀疏模型的训练。以上图为例,示例模型包含3个模型层;若将中间层扩展为具有 ``n`` 个expert的MoE结构,并引入 ``Gating network`` 和 ``Top_k`` 机制, -门网络的输出看作每个expert的权重,每个输入 ``x`` 需要分配 ``k`` 个权重最大的expert进行计算,然后只对选中的 ``k`` 个专家网络的输出进行加权求和,最后得到整个MoE Layer的计算结果。 +与密集模型不同,MoE将模型的某一层扩展为多个具有相同结构的专家网络(expert),并由门(gate)网络决定激活哪些 ``expert`` 用于计算,从而实现超大规模稀疏模型的训练。 +以上图为例,示例模型包含3个模型层;若将中间层扩展为具有 ``n`` 个 ``expert`` 的MoE结构,并引入 ``Gating network`` 和 ``Top_k`` 机制,门网络的输出看作每个 ``expert`` 的 +权重,每个输入 ``x`` 需要分配 ``k`` 个权重最大的 ``expert`` 进行计算,然后只对选中的 ``k`` 个专家网络的输出进行加权求和,最后得到整个 ``MoE Layer`` 的计算结果。公示表示如下: + +.. math:: + MoE\left ( {x} \right )=\sum ^{n}_{i=1} \left ( {{G\left ( {x} \right )}_{i}{E}_{i}\left ( {x} \right )} \right ) +.. math:: + G\left ( {x} \right )=TopK\left ( {softmax\left ( {{W}_{g}\left ( {x} \right )+ϵ} \right )} \right ) + +上述第1个公式表示了包含 ``n`` 个专家的MoE层的计算过程。具体来讲,首先对样本 ``x`` 进行门控计算, ``W`` 表示权重矩阵;然后由 ``Softmax`` 处理后获得样本 ``x`` 被分配到各个专家的权重; +然后取前 ``k`` (通常取 1 或者 2)个权重,最终MoE层的输出就是前 ``k`` 个专家的加权和。 二、功能效果 From d003773415e1181a5a6e79d889e84b4a8e63461b Mon Sep 17 00:00:00 2001 From: haohongxiang Date: Tue, 14 Jun 2022 03:09:37 +0000 Subject: [PATCH 4/4] update --- .../guides/06_distributed_training/moe_cn.rst | 20 +++++++++---------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/docs/guides/06_distributed_training/moe_cn.rst b/docs/guides/06_distributed_training/moe_cn.rst index 028e43c5985..0fa8cc1f3b1 100644 --- a/docs/guides/06_distributed_training/moe_cn.rst +++ b/docs/guides/06_distributed_training/moe_cn.rst @@ -5,8 +5,8 @@ MoE 通常来讲,模型规模的扩展会导致训练成本显著增加,计算资源的限制成为了大规模密集模型训练的瓶颈。为了解决这个问题, `《Outrageously large neural networks: The sparsely-gated mixture-of-experts layer》 `__ -提出了一种基于稀疏MoE层的深度学习模型架构,即将大模型拆分成多个小模型(专家, expert), 每轮迭代根据样本决定激活一部分专家用于计算,达到了节省计算资源的效果; -并引入可训练并确保稀疏性的门(gate)机制,以保证计算能力的优化。 +提出了一种基于稀疏MoE层的深度学习模型架构,即将大模型拆分成多个小模型(专家, ``expert`` ), 每轮迭代根据样本决定激活一部分专家用于计算,达到了节省计算资源的效果; +并引入可训练并确保稀疏性的门( ``gate`` )机制,以保证计算能力的优化。 一、原理介绍 ------------------- @@ -16,17 +16,16 @@ MoE :alt: moe_layer :align: center -与密集模型不同,MoE将模型的某一层扩展为多个具有相同结构的专家网络(expert),并由门(gate)网络决定激活哪些 ``expert`` 用于计算,从而实现超大规模稀疏模型的训练。 -以上图为例,示例模型包含3个模型层;若将中间层扩展为具有 ``n`` 个 ``expert`` 的MoE结构,并引入 ``Gating network`` 和 ``Top_k`` 机制,门网络的输出看作每个 ``expert`` 的 -权重,每个输入 ``x`` 需要分配 ``k`` 个权重最大的 ``expert`` 进行计算,然后只对选中的 ``k`` 个专家网络的输出进行加权求和,最后得到整个 ``MoE Layer`` 的计算结果。公示表示如下: +与密集模型不同,MoE将模型的某一层扩展为多个具有相同结构的专家网络( ``expert`` ),并由门( ``gate`` )网络决定激活哪些 ``expert`` 用于计算,从而实现超大规模稀疏模型的训练。 +以上图为例,示例模型包含3个模型层;如(a)到(b),将中间层扩展为具有 ``n`` 个 ``expert`` 的MoE结构,并引入 ``Gating network`` 和 ``Top_k`` 机制,MoE细节见图(c),计算过程如下述公式。 .. math:: MoE\left ( {x} \right )=\sum ^{n}_{i=1} \left ( {{G\left ( {x} \right )}_{i}{E}_{i}\left ( {x} \right )} \right ) .. math:: G\left ( {x} \right )=TopK\left ( {softmax\left ( {{W}_{g}\left ( {x} \right )+ϵ} \right )} \right ) -上述第1个公式表示了包含 ``n`` 个专家的MoE层的计算过程。具体来讲,首先对样本 ``x`` 进行门控计算, ``W`` 表示权重矩阵;然后由 ``Softmax`` 处理后获得样本 ``x`` 被分配到各个专家的权重; -然后取前 ``k`` (通常取 1 或者 2)个权重,最终MoE层的输出就是前 ``k`` 个专家的加权和。 +上述第1个公式表示了包含 ``n`` 个专家的MoE层的计算过程。具体来讲,首先对样本 ``x`` 进行门控计算, ``W`` 表示权重矩阵;然后由 ``Softmax`` 处理后获得样本 ``x`` 被分配到各个 ``expert`` 的权重; +然后只取前 ``k`` (通常取 1 或者 2)个最大权重,最终整个 ``MoE Layer`` 的计算结果就是选中的 ``k`` 个专家网络输出的加权和。 二、功能效果 @@ -77,7 +76,7 @@ MoE fleet.init(is_collective=True) moe_group = paddle.distributed.new_group(list(range(fleet.worker_num()))) -设置门网络的gate策略和top_k机制,并将模型单层扩展为 ``num_expert`` 个相同结构的专家网络 +设置门网络的 ``gate`` 策略和 ``top_k`` 机制,并将模型单层扩展为 ``num_expert`` 个相同结构的专家网络 .. code-block:: python @@ -137,6 +136,5 @@ MoE 运行方式: .. code-block:: bash - - export CUDA_VISIBLE_DEVICES=0 - python train_moe.py + + python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 --log_dir logs train_moe.py

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