【编译系列】Torch.compile()训练编译—

1. 背景：

torch.compile()中，Dynamo作为前端负责计算图的捕获，后端有inductor、tvm等进行编译优化。

Dynamo：在Python字节码层面注入pass，实现bytecode-to-bytecode的优化，通过对bytecode逐行进行解析构建FX Graph
Inductor：负责对FX Graph进行AOTAutograd生成joint-graph、decompose成PrimTorch基础op、基于硬件生成对应的kernel代码

本次主要分享以下几个方面：

TorchInductor中的算子融合逻辑
如何在torch.compile中自定义融合算子

2. TorchInductor算子融合逻辑：

在这里插入图片描述
在TorchInductor中负责算子融合的主要有三类：

FX Graph上进行的算子融合：FX IR的target可能还是torch.ops级别，属于比较粗粒度的融合，通常在推理场景下生效，如推理场景下会对Conv+BN进行算子融合，而训练场景下因为权重更新问题不会生效。
GraphLowering过程中的inline：GraphLowering主要负责将FX Graph转为Inductor IR，在转换成Inductor IR的过程中对那些纯计算的中间结果进行inline实现融合效果。
Inductor IR上的算子融合：Scheduler对GraphLowering后所有内存分配的Inductor IR(Inductor里面称为buffer)中有共享内存访问的算子进行融合。

2.1. FX Graph上的算子融合：

此阶段存在尚未decompose的op，且还需要进行AOTAutograd构建反传计算图，因此训练场景下进行算子融合的话会比较受限(通常需要提供算子的反传函数)，但相对而言能在更顶层上进行融合收益也会更明显。以Conv+BN代码为例，

# Conv+BN
# code
import torch
from typing import List
import torch._dynamo as dynamo
import torch
import torch.nn as nnclass ConvNet(nn.Module):def __init__(self, num_classes=10):super(ConvNet, self).__init__()self.conv_1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=2) # assuming input is 3-channel imageself.bn_1 = nn.BatchNorm2d(16)self.relu_1 = nn.ReLU()def forward(self, x):out = self.conv_1(x)out = self.bn_1(out)out = self.relu_1(out)return outdef test():data = torch.randn((2,3,128,128),requires_grad=True,device="cuda")model = ConvNet().to("cuda")model.eval()model = dynamo.optimize("inductor")(model)output = model(data)test()

在训练场景下，可以发现并没有在FX IR上进行任何算子融合的操作，就是先计算conv—》计算均值方差—》计算BN。

# 训练场景下的Conv+BN的FXGraph
def forward(self, primals, tangents):primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, tangents_1, = fx_pytree.tree_flatten_spec([primals, tangents], self._in_spec)convolution = torch.ops.aten.convolution.default(primals_8, primals_1, primals_2, [1, 1], [2, 2], [1, 1], False, [0, 0], 1);  primals_2 = Noneconvert_element_type = torch.ops.prims.convert_element_type.default(primals_5, torch.float32)convert_element_type_1 = torch.ops.prims.convert_element_type.default(primals_6, torch.float32)add = torch.ops.aten.add.Tensor(convert_element_type_1, 1e-05);  convert_element_type_1 = Nonesqrt = torch.ops.aten.sqrt.default(add);  add = Nonereciprocal = torch.ops.aten.reciprocal.default(sqrt);  sqrt = Nonemul = torch.ops.aten.mul.Tensor(reciprocal, 1);  reciprocal = Noneunsqueeze = torch.ops.aten.unsqueeze.default(convert_element_type, -1);  convert_element_type = Noneunsqueeze_1 = torch.ops.aten.unsqueeze.default(unsqueeze, -1);  unsqueeze = Noneunsqueeze_2 = torch.ops.aten.unsqueeze.default(mul, -1);  mul = Noneunsqueeze_3 = torch.ops.aten.unsqueeze.default(unsqueeze_2, -1);  unsqueeze_2 = Nonesub = torch.ops.aten.sub.Tensor(convolution, unsqueeze_1);  unsqueeze_1 = Nonemul_1 = torch.ops.aten.mul.Tensor(sub, unsqueeze_3);  sub = unsqueeze_3 = Noneunsqueeze_4 = torch.ops.aten.unsqueeze.default(primals_3, -1)unsqueeze_5 = torch.ops.aten.unsqueeze.default(unsqueeze_4, -1);  unsqueeze_4 = Nonemul_2 = torch.ops.aten.mul.Tensor(mul_1, unsqueeze_5);  mul_1 = unsqueeze_5 = Noneunsqueeze_6 = torch.ops.aten.unsqueeze.default(primals_4, -1);  primals_4 = Noneunsqueeze_7 = torch.ops.aten.unsqueeze.default(unsqueeze_6, -1);  unsqueeze_6 = Noneadd_1 = torch.ops.aten.add.Tensor(mul_2, unsqueeze_7);  mul_2 = unsqueeze_7 = Nonerelu = torch.ops.aten.relu.default(add_1);  add_1 = Nonealias = torch.ops.aten.alias.default(relu)alias_1 = torch.ops.aten.alias.default(alias);  alias = Nonealias_2 = torch.ops.aten.alias.default(alias_1);  alias_1 = Nonealias_3 = torch.ops.aten.alias.default(alias_2);  alias_2 = Nonele = torch.ops.aten.le.Scalar(alias_3, 0);  alias_3 = Nonescalar_tensor = torch.ops.aten.scalar_tensor.default(0, dtype = torch.float32, layout = torch.strided, device = device(type='cuda', index=0))where = torch.ops.aten.where.self(le, scalar_tensor, tangents_1);  le = scalar_tensor = tangents_1 = Noneadd_2 = torch.ops.aten.add.Tensor(primals_6, 1e-05);  primals_6 = Nonersqrt = torch.ops.aten.rsqrt.default(add_2);  add_2 = Noneunsqueeze_8 = torch.ops.aten.unsqueeze.default(primals_5, 0);  primals_5 = Noneunsqueeze_9 = torch.ops.aten.unsqueeze.default(unsqueeze_8, 2);  unsqueeze_8 = Noneunsqueeze_10 = torch.ops.aten.unsqueeze.default(unsqueeze_9, 3);  unsqueeze_9 = Nonesum_1 = torch.ops.aten.sum.dim_IntList(where, [0, 2, 3])sub_1 = torch.ops.aten.sub.Tensor(convolution, unsqueeze_10);  convolution = unsqueeze_10 = Nonemul_3 = torch.ops.aten.mul.Tensor(where, sub_1);  sub_1 = Nonesum_2 = torch.ops.aten.sum.dim_IntList(mul_3, [0, 2, 3]);  mul_3 = Nonemul_8 = torch.ops.aten.mul.Tensor(rsqrt, primals_3);  primals_3 = Noneunsqueeze_17 = torch.ops.aten.unsqueeze.default(mul_8, 0);  mul_8 = Noneunsqueeze_18 = torch.ops.aten.unsqueeze.default(unsqueeze_17, 2);  unsqueeze_17 = Noneunsqueeze_19 = torch.ops.aten.unsqueeze.default(unsqueeze_18, 3);  unsqueeze_18 = Nonemul_9 = torch.ops.aten.mul.Tensor(where, unsqueeze_19);  where = unsqueeze_19 = Nonemul_10 = torch.ops.aten.mul.Tensor(sum_2, rsqrt);  sum_2 = rsqrt = Nonesum_3 = torch.ops.aten.sum.dim_IntList(mul_9, [0, 2, 3])convolution_backward = torch.ops.aten.convolution_backward.default(mul_9, primals_8, primals_1, [16], [1, 1], [2, 2], [1, 1], False, [0, 0], 1, [True, True, False]);  mul_9 = primals_8 = primals_1 = Nonegetitem = convolution_backward[0]getitem_1 = convolution_backward[1];  convolution_backward = Nonereturn pytree.tree_unflatten([relu, getitem_1, sum_3, mul_10, sum_1, None, None, None, getitem], self._out_spec)

在推理场景下，可以发现此时的FX Graph发生了变化，会先计算均值方差----》和Conv的weight和bias进行加法----》计算conv，即对应将BN的权重融合到Conv中进行算子融合的操作。

# 推理场景下的Conv+BN的FXGraph
def forward(self, primals, tangents):primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, tangents_1, = fx_pytree.tree_flatten_spec([primals, tangents], self._in_spec)add = torch.ops.aten.add.Tensor(primals_6, 1e-05);  primals_6 = Nonersqrt = torch.ops.aten.rsqrt.default(add);  add = Noneview = torch.ops.aten.view.default(rsqrt, [-1, 1, 1, 1]);  rsqrt = Noneview_1 = torch.ops.aten.view.default(primals_3, [16, 1, 1, 1]);  primals_3 = Nonemul = torch.ops.aten.mul.Tensor(view_1, view);  view_1 = Nonemul_1 = torch.ops.aten.mul.Tensor(primals_1, mul)view_2 = torch.ops.aten.view.default(mul, [16])sub = torch.ops.aten.sub.Tensor(primals_2, primals_5);  primals_2 = primals_5 = Nonemul_2 = torch.ops.aten.mul.Tensor(view_2, sub)add_1 = torch.ops.aten.add.Tensor(primals_4, mul_2);  primals_4 = mul_2 = Noneconvolution = torch.ops.aten.convolution.default(primals_8, mul_1, add_1, [1, 1], [2, 2], [1, 1], False, [0, 0], 1);  add_1 = Nonerelu = torch.ops.aten.relu.default(convolution);  convolution = Nonealias = torch.ops.aten.alias.default(relu)alias_1 = torch.ops.aten.alias.default(alias);  alias