FTVMQnnCanonicalize#
源码：tvm/include/tvm/relay/qnn/transform.h
/*!
* \brief Legalizes a QNN expr. Contains specifically two types of Legalizations. First,
* converts/Lowers an expression containing QNN ops to an expression containing only core Relay ops.
* Each QNN op is lowered to a sequence of exisiting Relay ops. This is a target-independent pass.
* One can register the lowering/transformation function for this op using FTVMQnnCanonicalize
* attr_name for FTVMLegalize op attribute. Second, as opposed to Relay Legalize, this one legalizes
* only QNN ops. One can register a transformation/legalization function for an op by using the
* FTVMQnnLegalize attr_name for FTVMLegalize op attribute. The isolation of QNN and Relay Legalize
* gives us separation of concerns, leading to a better software practice. The legalization can be
* configured to happen per target.
*
* \return The pass.
*/
TVM_DLL Pass Legalize();
这段代码是一个名为 Legalize 的函数，它的作用是将 QNN 表达式合法化。具体来说，它包含两种类型的合法化：
将包含 QNN 算子的表达式转换为仅包含核心 Relay 算子的表达式。每个 QNN 算子都会被转换为一系列现有的 Relay 算子。这是一个与目标 target 无关的传递。可以使用
FTVMQnnCanonicalize属性名称为FTVMLegalize算子属性注册 transformation/legalization 函数。与 Relay Legalize 不同，这个函数只对 QNN 算子进行合法化。可以通过使用
FTVMQnnLegalize属性名称为FTVMLegalize算子属性注册一个算子的 transformation/legalization 函数。QNN 和 Relay Legalize 的隔离使我们能够更好地分离关注点，从而得到更好的软件实践。合法化可以针对每个目标（target）进行配置。
函数返回 Pass 对象。
CanonicalizeOps()#
源码：tvm/python/tvm/relay/qnn/transform.py
import testing
from tvm.relay.qnn.transform import CanonicalizeOps
CanonicalizeOps?
Signature: CanonicalizeOps()
Docstring:
Converts/Lowers an expression containing QNN ops to an expression containing only core
(non-Dialect) Relay ops. Each QNN op is lowered to a sequence of existing Relay ops. This is a
target-independent pass. One can register the lowering/transformation function for this op using
FTVMQnnCanonicalize attr_name for FTVMLegalize op attribute. An example of this transformation
is below
Examples
________
.. code-block:: python
# Original expression
qnn_expr = relay.qnn.op.requantize(y,
input_scale=1,
input_zero_point=0,
output_scale=1,
output_zero_point=0,
out_dtype='int8')
# We want to utilize all the existing Relay infrastructure. So, instead of supporting this
# QNN requantize op, we convert it into a sequence of existing Relay operators.
mod = tvm.IRModule.from_expr(qnn_expr)
mod = relay.qnn.transform.CanonicalizeOps()(mod)
relay_expr = mod['main']
print(relay_expr)
def @main(%quantized_data: Tensor[(200), int32]) -> Tensor[(200), int8] {
%0 = cast(%quantized_data, dtype="int64") /* ty=Tensor[(200), int64] */;
%1 = multiply(%0, 2 /* ty=int64 */) /* ty=Tensor[(200), int64] */;
%2 = multiply(%1, 1073741824 /* ty=int64 */) /* ty=Tensor[(200), int64] */;
%3 = add(%2, 1073741824 /* ty=int64 */) /* ty=Tensor[(200), int64] */;
%4 = right_shift(%3, 31 /* ty=int64 */) /* ty=Tensor[(200), int64] */;
%5 = add(0 /* ty=int64 */, %4) /* ty=Tensor[(200), int64] */;
%6 = clip(%5, a_min=-128f, a_max=127f) /* ty=Tensor[(200), int64] */;
cast(%6, dtype="int8") /* ty=Tensor[(200), int8] */
}
Returns
-------
ret : tvm.transform.Pass
The registered pass that canonicalizes QNN ops to Relay ops.
File: /media/pc/data/lxw/ai/tvm/python/tvm/relay/qnn/transform.py
Type: function
这段代码定义了一个名为 CanonicalizeOps 的函数，它的作用是将包含 QNN 算子的表达式转换为仅包含核心（非 Dialect）Relay 算子的表达式。每个 QNN 算子都会被转换为一系列现有的 Relay 算子。这是一个与目标无关的传递。
函数返回 tvm.transform.Pass 对象，该对象将 QNN 算子规范化为 Relay 算子。
可以使用 FTVMQnnCanonicalize 属性名称为 FTVMLegalize 算子属性注册 lowering/transformation 函数。
from tvm.relay.qnn.op import register_qnn_canonicalize
register_qnn_canonicalize?
Signature: register_qnn_canonicalize(op_name, legal_op=None, level=10)
Docstring:
Register canonicalization function for a QNN op.
This transforms QNN ops to mainline Relay components.
Parameters
----------
op_name : str
The name of the operator
legal_op: function (Attrs, List[Expr], List[relay.Type]) -> Expr
The function for transforming an expr to another expr.
level : int
The priority level
File: /media/pc/data/lxw/ai/tvm/python/tvm/relay/qnn/op/op.py
Type: function