标签：amp attrs 调试过程 for 一起 bit inf 加法 http
如何使用TVM Pass红外线
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_pa??ss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 = self
class 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(),
relay.transform.EliminateCommonSubexpr(),
relay.transform.AlterOpLayout(),
]
)
# By inserting the ``PrintIR`` pass after ``FoldConstant``, the pass infra will
# dump out the module IR when ``FoldConstant`` is 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.PassInfo` object,
# 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。
标签：amp attrs 调试过程 for 一起 bit inf 加法 http
原文地址：https://www.cnblogs.com/wujianming-110117/p/14532537.html
