HuggingFace.Quantization_in_Depth/Transcript/10_Replace PyTorch layers with Quantized Layers_HFQD.txt

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We have now all

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our building blocks
to build our quantizer.

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So the quantizer is going to be
a quantization pipeline that will,

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iterate over
all linear modules of your original model

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and replace them with our new W8A16,

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linear layer module and call quantize
on using the original weights.

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Yeah. So let's do that step by step.

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So let's first build,
a method called replace linear with target

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that is going to loop over the model,
identify

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the modules that are an instance of torch

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that is not linear
and replace it with the new module.

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Yeah.

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So this is going to be
the signature of our method.

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So it's going to take a module
or also model.

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But since the method
is going to be recursive

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I decided to call it module so that yeah

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it's clear that you can pass a model,
but you can also pass a module.

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Target class is yeah, the target
class, of the new class that you're going

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to set, in replacement to the linear
layer and module name to exclude

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is the name of the module that we're going
to exclude in this replacement logic.

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So we're going to see later
for language models that usually it's

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better to keep the last module
unquantized for better results.

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So this is going to be useful for
you know this specific use cases.

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So we're going to simply loop over

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the modules named children.

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And if the sub module is an instance of
an nn linear

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and you don't have any name
that matches the names

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that are inside
the module name to exclude,

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then we're going to move forward
with the module replacement.

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So we're going to get the bias
of the sub module here,

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because we're going to use it
to create our new target class.

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And then we can create our new module.

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Which is going to be target class of.

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So in features out features should be
the same as the linear layers.

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The original layers one bias.

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We're just simply going to check
if old bias is not "none".

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Then we're going to use the same data
type as the submodules weight.

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And we're going to call set
attributes to the parent module.

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We're going to replace
the current attribute of the module

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by calling set attribute module name.

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Because name gives you
then the name of the current attribute

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we're going to modify and then new module.

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So this is simply going to replace
the parent modules attributes

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that has the name "name",
with the new module.

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And if the old module has a bias

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we're going to explicitly set the bias
of the new module to old bias.

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And yeah and as I said previously, we're
going to call that method recursively.

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So if we're not in this case
we're going to call that method again.

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But this time on the child module
using the same arguments.

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Okay.
So let's let's try this this method out.

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So we're going to create a
dummy module for testing purposes.

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Yeah with two

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linear layers, one language model head,

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which is usually the last module
in a transformer model.

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Since the method modifies
the model in place,

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we're going to create new two models.

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So one where we're going to test out
the module name to exclude feature,

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and the other one

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which is just going to replace all linear
layer instances with the new one.

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So let's try out the first case.

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So yeah we just have to call replace

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with target model one our target class.

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So this time we don't want to replace
the LM head with the new class.

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So perfect. It worked.

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And we were able to replace
all linear layers with new ones.

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Except for the Lm head.

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And let's see what happens
if we pass an empty list.

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Yeah.

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So as expected,

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for the second case, we replaced

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all instances of linear layers
within with the target class.

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Yeah. So now let's just tweak a bit.

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this method, in addition to replacing,

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all linear layers with target

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class, we're
also going to quantize the new module

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once we have replaced the old module
with the new one.

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So just going to copy this method

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and slightly replace it

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in order to quantize
the new module as well.

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So here
we can also retrieve the old weight.

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Perfect.

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So I think the quantization should happen
here.

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Once we have replaced the module
with the new module

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we can get that module again
with get attribute module name.

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And at this point
this should return the new module

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and call quantize and pass the old weight.

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Let's also

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update the recursive function call.

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So let's try out again
just to see if it works.

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Using a new dummy model.

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Perfect.

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So yeah, it seems that it worked.

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Great.