如何使用TVM Pass Relay
随着Relay / tir中优化遍数的增加，执行并手动维护其依赖关系变得很棘手。引入了一个基础结构来管理优化过程，将其应用于TVM堆栈中IR的不同层。
Relay / tir程序的优化可以以各种粒度应用，分别使用tvm.relay.transform.FunctionPass/ tvm.tir.transform.PrimFuncPass和的功能级别和模块级别tvm.transform.ModulePass 。用户可以依靠在tvm.transform.Sequential relay/ tir程序上应用一系列Pass，其中Pass之间的依赖性可以passPass下文解决。
本文主要说明开发人员如何使用pass infra进行特定的优化，创建用于Relay程序的优化管道。同样的方法也可以用于tir。
import numpy as np
import tvm
from tvm import te
import tvm.relay as relay
创建一个示例 relay程序
创建一个简单的Relay程序。该程序将用于本文中示例的各种优化。用户可以编写一个tir基本函数并应用tirPass。
def example():
shape = (1, 64, 54, 54)
c_data = np.empty(shape).astype(“float32”)
c = relay.const(c_data)
weight = relay.var(“weight”, shape=(64, 64, 3, 3))
x = relay.var(“x”, relay.TensorType((1, 64, 56, 56), “float32”))
conv = relay.nn.conv2d(x, weight)
y = relay.add(c, c)
y = relay.multiply(y, relay.const(2, “float32”))
y = relay.add(conv, y)
z = relay.add(y, c)
z1 = relay.add(y, c)
z2 = relay.add(z, z1)
return relay.Function([x, weight], z2)
为conv2d op注册布局更改，在示例中应用布局更改通道。alter layout pass如何工作不在本文的讨论范围之内。
@relay.op.register_alter_op_layout(“nn.conv2d”, level=101)
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs[“data_layout”] = “NCHW16c”
return relay.nn.conv2d(data, weight, **new_attrs)
优化程序
现在要优化程序。 relay具有许多优化功能。将选择其中一些以应用于此示例程序。
有多种优化 relay程序的方法。下面将为每个示例提供示例。
手动应用优化Pass
Let’s first create a relay Module which contains one or multiple Relay
functions for optimization.
f = example()
mod = tvm.IRModule.from_expr(f)
Now we can apply constant folding on the module.
fold_const here is a callback that doesn’t take any parameters.
fold_const = relay.transform.FoldConstant()
Then, we can invoke the pass on the given module. Note that the constant
folding pass works at the function-level. That being said, each function in
the module will be applied with the optimization. Users don’t need to iterate
through individual functions manually to apply this pass.
mod = fold_const(mod)
We can see from the updated program that the constants are folded.
print(mod)
输出：
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%3 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %3) /ty=Tensor[(1, 64, 54, 54), float32]/
}
以类似方式应用更多优化。例如，消除z和z1使用的通用表达式。
mod = relay.transform.EliminateCommonSubexpr()(mod)
print(mod)
输出：
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %2) /ty=Tensor[(1, 64, 54, 54), float32] */
}
一些优化（例如融合）也是参数化的。例如，选择级别0不允许将算子融合在一起。用户可以传递 fuse_opt_level来启用此功能。
mod = relay.transform.FuseOps(fuse_opt_level=0)(mod)
We can observe that the optimized module contains functions that only have
a signle primitive op.
print(mod)
输出：
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/
};
%1 = %0(%x, %weight) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = fn (%p01: Tensor[(1, 64, 54, 54), float32], %p11: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
add(%p01, %p11) /ty=Tensor[(1, 64, 54, 54), float32]/
};
%3 = %2(%1, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%4 = fn (%p02: Tensor[(1, 64, 54, 54), float32], %p12: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
add(%p02, %p12) /ty=Tensor[(1, 64, 54, 54), float32]/
};
%5 = %4(%3, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%6 = fn (%p03: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
add(%p03, %p03) /ty=Tensor[(1, 64, 54, 54), float32]/
};
%6(%5) /ty=Tensor[(1, 64, 54, 54), float32] */
}
使用顺序来应用Pass序列
应用Pass实际上是乏味的，需要用户更好地了解之间的依赖性。例如，融合目前不适用于let绑定。如果relay.transform.ToANormalForm()在融合之前应用算子，无法融合在一起，此过程为每个表达式生成let绑定，以规范化Relay程序。
Relaytvm.transform.Sequentialpass指定每个遍历，将打包为整体来减轻开发人员显式处理这些问题的负担。例如，现在可以使用以下顺序样式应用。tvm.transform.Sequential与torch.nn.sequential 和mxnet.gluon.block类似。例如，torch.nn.sequential用于包含一系列PyTorch模块，这些模块将被添加，以构建网络，着重于网络层。取而代之的是tvm.transform.Sequential，下面的过程中的基础工作于优化过程。
Now let’s execute some passes through :py:class:tvm.transform.Sequential
f = example()
mod = tvm.IRModule.from_expr(f)
Glob the interested passes.
seq = tvm.transform.Sequential(
[
relay.transform.FoldConstant(),
relay.transform.EliminateCommonSubexpr(),
relay.transform.FuseOps(fuse_opt_level=2),
]
)
mod1 = seq(mod)
print(mod1)
输出：
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%4 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, %p2) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, %p3) /ty=Tensor[(1, 64, 54, 54), float32]/;
%3 = add(%1, %p3) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %3) /ty=Tensor[(1, 64, 54, 54), float32]/
};
%4(%x, %weight, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/
}
从转换后的Relay程序中，可以看到仍然有两个相同的加法运算。这是EliminateCommonSubexpr 未实际执行。只有优化级别小于或等于2的过程才被执行 tvm.transform.Sequential。下面的pass提供了一个配置界面，供用户自定义要执行的优化级别。
with tvm.transform.PassContext(opt_level=3):
mod2 = seq(mod)
print(mod2)
输出：
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%3 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]) /ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, %p2) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, %p3) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %2) /ty=Tensor[(1, 64, 54, 54), float32]/
};
%3(%x, %weight, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/
}
可以看到仅保留了两个相同的加法之一。
用户可以使用disabled_pass配置有选择地禁用某些pass，这类似于通用编译器（例如Clang和GCC）使用的-fno-xxx选项。例如，可以禁用EliminateCommonSubexpr，如下所示。打印的模块将再次显示两个相同的加法运算。
with tvm.transform.PassContext(opt_level=3, disabled_pass=[“EliminateCommonSubexpr”]):
mod3 = seq(mod)
print(mod3)
输出：
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%4 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]) /ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, %p2) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, %p3) /ty=Tensor[(1, 64, 54, 54), float32]/;
%3 = add(%1, %p3) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %3) /ty=Tensor[(1, 64, 54, 54), float32]/
};
%4(%x, %weight, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32] */
}
应用的Pass与目标无关。下文的Pass还提供了具有目标意识的方法。例如，布局变更阶段属于这种类别。
with tvm.transform.PassContext(opt_level=3):
mod4 = seq(mod)
print(mod4)
seq1 = tvm.transform.Sequential([relay.transform.AlterOpLayout()])
with tvm.transform.PassContext(opt_level=3):
with tvm.target.Target(“llvm”):
mod5 = seq1(mod)
print(mod5)
输出：
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%3 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, %p2) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, %p3) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %2) /ty=Tensor[(1, 64, 54, 54), float32]/
};
%3(%x, %weight, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/
}
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = layout_transform(%x, src_layout=“NCHW”, dst_layout=“NCHW16c”) /ty=Tensor[(1, 4, 56, 56, 16), float32]/;
%1 = nn.conv2d(%0, %weight, padding=[0, 0, 0, 0], data_layout=“NCHW16c”) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
%2 = add(meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%3 = multiply(%2, 2f /ty=float32/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%4 = layout_transform(%3, src_layout=“NCHW”, dst_layout=“NCHW16c”) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
%5 = add(%1, %4) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
%6 = layout_transform(meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/, src_layout=“NCHW”, dst_layout=“NCHW16c”) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
%7 = add(%5, %6) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
%8 = add(%5, %6) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
%9 = add(%7, %8) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
layout_transform(%9, src_layout=“NCHW16c”, dst_layout=“NCHW”) /ty=Tensor[(1, 64, 54, 54), float32] */
}
使用Python Decorator实施Pass
下一个示例说明了如何使用Python装饰器pass传递基础流程来编排定制的优化管道。极大地简化了Pass的实施。例如，用户可以简单地定义一个修饰的类，进行功能级别的优化，如以下示例所示。transform_function包装一个类，以用c的倍数替换所有常量。调用自定义过程时，将访问给定模块中的每个函数，并且将替换函数中的每个常量。
@relay.transform.function_pass(opt_level=1)
class CustomPipeline:
“”“Simple test function to replace one argument to another.”""
def __init__(self, multiplier):self.multiplier = multiplier# This function can define a pass.def transform_function(self, func, mod, ctx):obj = selfclass ReplaceConstant(tvm.relay.ExprMutator):def visit_constant(self, c):return relay.multiply(obj.multiplier, c)return ReplaceConstant().visit(func)
f = example()
mod = tvm.IRModule.from_expr(f)
custom_pass = CustomPipeline(multiplier=relay.const(3, “float32”))
assert custom_pass.info.name == “CustomPipeline”
mod3 = custom_pass(mod)
print(mod3)
输出：
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = multiply(3f /ty=float32/, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, %1) /ty=Tensor[(1, 64, 54, 54), float32]/;
%3 = multiply(3f /ty=float32/, 2f /ty=float32/) /ty=float32/;
%4 = multiply(%2, %3) /ty=Tensor[(1, 64, 54, 54), float32]/;
%5 = add(%0, %4) /ty=Tensor[(1, 64, 54, 54), float32]/;
%6 = add(%5, %1) /ty=Tensor[(1, 64, 54, 54), float32]/;
%7 = add(%5, %1) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%6, %7) /ty=Tensor[(1, 64, 54, 54), float32] */
}
调试Pass
TVM为用户提供了一个插用式的调试通道，在pass特殊通道（PrintIR）来转储整个模块的IR之后，将IR打印出来。顺序传递示例的略微修改版本，类似于以下内容，以启用IR转储以进行FoldConstant优化。
f = example()
mod = tvm.IRModule.from_expr(f)
seq = tvm.transform.Sequential(
[
relay.transform.FoldConstant(),
tvm.transform.PrintIR(),
relay.transform.EliminateCommonSubexpr(),
relay.transform.FuseOps(),
relay.transform.AlterOpLayout(),
]
)
By inserting thePrintIRpass afterFoldConstant, the pass infra will
dump out the module IR whenFoldConstantis done. Users can plug in this
pass after any pass they want to debug for viewing the optimization effect.
There is a more flexible debugging mechanism also exposed by the build configuration
object. One can pass a tracing function which can be used to execute arbitrary code
before and/or after each pass. A tracing function will receive a :py::class:tvm.IRModule,
a :py:class:tvm.transform.PassInfoobject,
and a boolean indicating whether you are executing before, or after a pass.
An example is below.
def print_ir(mod, info, is_before):
“”“Print the name of the pass, the IR, only before passes execute.”""
if is_before:
print(“Running pass: {}”, info)
print(mod)
with tvm.transform.PassContext(opt_level=3, trace=print_ir):
with tvm.target.Target(“llvm”):
# Perform the optimizations.
mod = seq(mod)
print(mod)
print(“done”)
输出：
Running pass: {} The meta data of the pass: pass name: FoldConstantopt_level: 2required passes: [
]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) {
%0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]);
%1 = add(meta[relay.Constant][0], meta[relay.Constant][0]);
%2 = multiply(%1, 2f);
%3 = add(%0, %2);
%4 = add(%3, meta[relay.Constant][0]);
%5 = add(%3, meta[relay.Constant][0]);
add(%4, %5)
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main() {
add(meta[relay.Constant][0], meta[relay.Constant][0])
}
Running pass: {} The meta data of the pass: pass name: FuseOpsopt_level: 1required passes: [
InferType, ]
def @main() -> Tensor[(1, 64, 54, 54), float32] {
add(meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32]/, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32] */
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main() -> Tensor[(1, 64, 54, 54), float32] {
%0 = fn (%p0: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
add(%p0, %p0)
};
%0(meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */)
}
Running pass: {} The meta data of the pass: pass name: ToANormalFormopt_level: 1required passes: [
]
def @main() -> Tensor[(1, 64, 54, 54), float32] {
%0 = fn (%p0: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
add(%p0, %p0) /* ty=Tensor[(1, 64, 54, 54), float32]/
};
%0(meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32] */
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main() -> Tensor[(1, 64, 54, 54), float32] {
let %x = meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32]/;
let %x1 = fn (%p0: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
add(%p0, %p0) /ty=Tensor[(1, 64, 54, 54), float32] */
};
let %x2 = %x1(%x);
%x2
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main() {
multiply(meta[relay.Constant][0], 2f)
}
Running pass: {} The meta data of the pass: pass name: FuseOpsopt_level: 1required passes: [
InferType, ]
def @main() -> Tensor[(1, 64, 54, 54), float32] {
multiply(meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32]/, 2f /ty=float32/) /ty=Tensor[(1, 64, 54, 54), float32] */
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main() -> Tensor[(1, 64, 54, 54), float32] {
%0 = fn (%p0: Tensor[(1, 64, 54, 54), float32], %p1: float32, Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
multiply(%p0, %p1)
};
%0(meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32]/, 2f /ty=float32 */)
}
Running pass: {} The meta data of the pass: pass name: ToANormalFormopt_level: 1required passes: [
]
def @main() -> Tensor[(1, 64, 54, 54), float32] {
%0 = fn (%p0: Tensor[(1, 64, 54, 54), float32], %p1: float32, Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
multiply(%p0, %p1) /* ty=Tensor[(1, 64, 54, 54), float32]/
};
%0(meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/, 2f /ty=float32/) /ty=Tensor[(1, 64, 54, 54), float32] */
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main() -> Tensor[(1, 64, 54, 54), float32] {
let %x = meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32]/;
let %x1 = 2f /ty=float32/;
let %x2 = fn (%p0: Tensor[(1, 64, 54, 54), float32], %p1: float32, Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
multiply(%p0, %p1) /ty=Tensor[(1, 64, 54, 54), float32] */
};
let %x3 = %x2(%x, %x1);
%x3
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) {
%0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]);
%1 = add(%0, meta[relay.Constant][0]);
%2 = add(%1, meta[relay.Constant][1]);
%3 = add(%1, meta[relay.Constant][1]);
add(%2, %3)
}
Running pass: {} The meta data of the pass: pass name: PrintIRopt_level: 0required passes: [
]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%3 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %3) /ty=Tensor[(1, 64, 54, 54), float32] */
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%3 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %3) /ty=Tensor[(1, 64, 54, 54), float32] */
}
Running pass: {} The meta data of the pass: pass name: EliminateCommonSubexpropt_level: 3required passes: [
InferType, ]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%3 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %3) /ty=Tensor[(1, 64, 54, 54), float32] */
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32] */;
add(%2, %2)
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %2) /ty=Tensor[(1, 64, 54, 54), float32] */
}
Running pass: {} The meta data of the pass: pass name: FuseOpsopt_level: 1required passes: [
InferType, ]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %2) /ty=Tensor[(1, 64, 54, 54), float32] */
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%3 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]);
%1 = add(%0, %p2);
%2 = add(%1, %p3);
add(%2, %2)
};
%3(%x, %weight, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32]/, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32] */)
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%3 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, %p2) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, %p3) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %2) /ty=Tensor[(1, 64, 54, 54), float32]/
};
%3(%x, %weight, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32] */
}
Running pass: {} The meta data of the pass: pass name: AlterOpLayoutopt_level: 3required passes: [
InferType, ]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%3 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
%0 = nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32]/;
%1 = add(%0, %p2) /ty=Tensor[(1, 64, 54, 54), float32]/;
%2 = add(%1, %p3) /ty=Tensor[(1, 64, 54, 54), float32]/;
add(%2, %2) /ty=Tensor[(1, 64, 54, 54), float32]/
};
%3(%x, %weight, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32] */
}
Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
]
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%7 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
%0 = layout_transform(%p0, src_layout=“NCHW”, dst_layout=“NCHW16c”);
%1 = nn.conv2d(%0, %p1, padding=[0, 0, 0, 0], data_layout=“NCHW16c”);
%2 = layout_transform(%p2, src_layout=“NCHW”, dst_layout=“NCHW16c”);
%3 = add(%1, %2);
%4 = layout_transform(%p3, src_layout=“NCHW”, dst_layout=“NCHW16c”);
%5 = add(%3, %4);
%6 = add(%5, %5);
layout_transform(%6, src_layout=“NCHW16c”, dst_layout=“NCHW”)
};
%7(%x, %weight, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32]/, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32] */)
}
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%7 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
%0 = layout_transform(%p0, src_layout=“NCHW”, dst_layout=“NCHW16c”) /* ty=Tensor[(1, 4, 56, 56, 16), float32]/;
%1 = nn.conv2d(%0, %p1, padding=[0, 0, 0, 0], data_layout=“NCHW16c”) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
%2 = layout_transform(%p2, src_layout=“NCHW”, dst_layout=“NCHW16c”) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
%3 = add(%1, %2) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
%4 = layout_transform(%p3, src_layout=“NCHW”, dst_layout=“NCHW16c”) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
%5 = add(%3, %4) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
%6 = add(%5, %5) /ty=Tensor[(1, 4, 54, 54, 16), float32]/;
layout_transform(%6, src_layout=“NCHW16c”, dst_layout=“NCHW”) /ty=Tensor[(1, 64, 54, 54), float32]/
};
%7(%x, %weight, meta[relay.Constant][0] /ty=Tensor[(1, 64, 54, 54), float32]/, meta[relay.Constant][1] /ty=Tensor[(1, 64, 54, 54), float32]/) /ty=Tensor[(1, 64, 54, 54), float32] */
}
done
概括
本文介绍了如何使用Pass基础更加方便地在TVM中编写和调用Pass。讨论了调用Pass的不同方法。使用tvm.transform.Sequential可以极大地帮助用户简化处理多个优化过程及其依赖项的工作。提供了一个示例来说明如何使用PrintIR和跟踪调试过程。
