[Intel GPU] Docs of XPUInductorQuantizer #3293

ZhiweiYan-96 · 2025-03-18T05:45:07Z

Description

Add tutorials for XPUInductorQuantzer, which serves as the INT8 quantization backend for Intel GPU inside PT2E.

pytorch-bot · 2025-03-18T05:45:11Z

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prototype_source/pt2e_quant_xpu_inductor.rst

CuiYifeng · 2025-03-18T08:12:37Z

prototype_source/pt2e_quant_xpu_inductor.rst

+::
+    quantizer = XPUInductorQuantizer()
+    quantizer.set_global(get_xpu_inductor_symm_quantization_config())
+


The code format has not taken effect.

thanks for reminding, added the fix.

riverliuintel · 2025-03-23T23:13:18Z

prototype_source/prototype_index.rst

@@ -96,6 +96,13 @@ Prototype features are not available as part of binary distributions like PyPI o
   :link: ../prototype/pt2e_quant_x86_inductor.html
   :tags: Quantization

+.. customcarditem::
+   :header: PyTorch 2 Export Quantization with Intel GPU Backend through Inductor


At previous stage when we upload RFCs, we recommend using GPU instead of XPU for readability for users. Do we have some changes on this description desicsion?

riverliuintel · 2025-03-23T23:13:46Z

prototype_source/pt2e_quant_xpu_inductor.rst

@@ -0,0 +1,234 @@
+PyTorch 2 Export Quantization with Intel GPU Backend through Inductor


Suggested change

PyTorch 2 Export Quantization with Intel GPU Backend through Inductor

Export Quantization with Intel GPU Backend through Inductor

riverliuintel · 2025-03-23T23:16:09Z

prototype_source/pt2e_quant_xpu_inductor.rst

+utilizes PyTorch 2 Export Quantization flow and lowers the quantized model into the inductor.
+
+The pytorch 2 export quantization flow uses the torch.export to capture the model into a graph and perform quantization transformations on top of the ATen graph.
+This approach is expected to have significantly higher model coverage, better programmability, and a simplified UX.


This approach is expected to have significantly higher model coverage with better programmability and a simplified user experience.

Thanks for suggestions, modified.

riverliuintel · 2025-03-23T23:18:04Z

prototype_source/pt2e_quant_xpu_inductor.rst

+The quantization flow mainly includes three steps:
+
+- Step 1: Capture the FX Graph from the eager Model based on the `torch export mechanism <https://pytorch.org/docs/main/export.html>`_.
+- Step 2: Apply the Quantization flow based on the captured FX Graph, including defining the backend-specific quantizer, generating the prepared model with observers,


Apply the quantization flow based on the captured FX Graph, including defining the backend-specific quantizer, generating the prepared model with observers,

Thanks for suggestions, has changed the description here.

riverliuintel · 2025-03-23T23:23:01Z

prototype_source/pt2e_quant_xpu_inductor.rst

+  performing the prepared model's calibration, and converting the prepared model into the quantized model.
+- Step 3: Lower the quantized model into inductor with the API ``torch.compile``. 
+
+During Step 3, the inductor would decide which kernels are dispatched into. There are two kinds of kernels the Intel GPU would obtain benefits, oneDNN kernels and triton kernels. `Intel oneAPI Deep Neural Network Library (oneDNN) <https://github.com/uxlfoundation/oneDNN>`_ contains 


If a end-user documentation, I think we could focus on PyTorch itself, and remove this section explanation.

Thanks for suggestion, I removed the prolonged description over oneDNN and triton. Instead, I add a simple mention at Step 3 above.

riverliuintel · 2025-03-23T23:24:14Z

prototype_source/pt2e_quant_xpu_inductor.rst

+Post Training Quantization
+----------------------------
+
+Static quantization is the only method we support currently. QAT and dynamic quantization will be available in later versions.


remove the further ready context from current introduction - "QAT and dynamic quantization will be available in later versions."

Thanks for suggestion, removed.

riverliuintel · 2025-03-23T23:26:12Z

prototype_source/pt2e_quant_xpu_inductor.rst

+
+::
+
+    pip install torchvision pytorch-triton-xpu --index-url https://download.pytorch.org/whl/nightly/xpu


Let's use standard "pip install torch torchvision torchaudio", not separate internal commands to highlight the internal dependencies command.

We may need keep using our own channels, since torchvision is customized on XPU, we need let user could run example in this doc successfully. Standard channel would have runtime error. Synced with @jingxu10 I changed to use pip3 install torch torchvision torchaudio pytorch-triton-xpu --index-url https://download.pytorch.org/whl/xpu, instead of nightly wheel.

CuiYifeng · 2025-03-24T07:14:40Z

prototype_source/pt2e_quant_xpu_inductor.rst

+
+The high-level architecture of this flow could look like this:
+
+.. image:: ../_static/img/pt2e_quant_xpu_inductor.png


Please note that Float Model, Example Input and XPUInductorQuantizer is invisible in dark mode.

thanks for reminding, the pictures is moidified

CuiYifeng · 2025-03-24T07:17:05Z

prototype_source/pt2e_quant_xpu_inductor.rst

+PyTorch 2 Export Quantization with Intel GPU Backend through Inductor
+==================================================================
+
+**Author**: `Yan Zhiwei <https://github.com/ZhiweiYan-96>`_, `Wang Eikan <https://github.com/EikanWang>`_, `Zhang, Liangang <https://github.com/liangan1>`_, `Liu River <https://github.com/riverliuintel>`_, `Cui Yifeng <https://github.com/CuiYifeng>`_


Please unify the style of names.

thanks, modified

CuiYifeng · 2025-03-24T07:37:58Z

prototype_source/pt2e_quant_xpu_inductor.rst

+            quant_min=-128,
+            quant_max=127,
+            qscheme=torch.per_tensor_symmetric,


Please consider whether we need more detailed annotations here to explain the meaning of these key parameters to users.

thanks, explanation is added.

CuiYifeng · 2025-03-24T07:38:55Z

prototype_source/pt2e_quant_xpu_inductor.rst

+            dtype=torch.int8,
+            quant_min=-128,
+            quant_max=127,
+            qscheme=torch.per_channel_symmetric,


thanks, explanation is added.

prototype_source/pt2e_quant_xpu_inductor.rst

alexsin368 · 2025-03-28T23:14:51Z

prototype_source/pt2e_quant_xpu_inductor.rst

+--------------
+
+This tutorial introduces XPUInductorQuantizer aiming for serving the quantized model inference on Intel GPUs. The tutorial will cover how it 
+utilizes PyTorch 2 Export Quantization flow and lowers the quantized model into the inductor.


What are you trying to say in this phrase: "lowers the quantized model into the inductor"?

It's the terminology in torch.compile

prototype_source/pt2e_quant_xpu_inductor.rst

alexsin368 · 2025-03-28T23:34:55Z

prototype_source/pt2e_quant_xpu_inductor.rst

+            optimized_model(*example_inputs)
+
+
+Put all these codes together, we will have the toy example code.


No need to have this sentence, just delete it.

Thanks for advice, removed.

alexsin368 · 2025-03-28T23:35:44Z

prototype_source/pt2e_quant_xpu_inductor.rst

+
+
+Put all these codes together, we will have the toy example code.
+Please note that since the Inductor ``freeze`` feature does not turn on by default yet, run your example code with ``TORCHINDUCTOR_FREEZING=1``.


Perhaps it might be better to put this near the top so developers are aware they need to run with this env variable set

Thanks for suggestions, I moved to the top of example code section.

alexsin368

Reviewed for grammar, sentence flow. General feedback

Co-authored-by: alexsin368 <[email protected]>

CuiYifeng · 2025-04-01T07:43:39Z

prototype_source/pt2e_quant_xpu_inductor.rst

+        optimized_model(*example_inputs)
+
+In a more advanced scenario, int8-mixed-bf16 quantization comes into play. In this instance,
+a convolution or GEMM operator produces the output in BFloat16 instead of Float32 in the absence


Suggested change

a convolution or GEMM operator produces the output in BFloat16 instead of Float32 in the absence

a Convolution or GEMM operator produces the output in BFloat16 instead of Float32 in the absence

or

Suggested change

a convolution or GEMM operator produces the output in BFloat16 instead of Float32 in the absence

a Conv or GEMM operator produces the output in BFloat16 instead of Float32 in the absence

Thanks for suggestion. We may keep this as here is a vanilla noun.

CuiYifeng · 2025-04-01T07:49:41Z

prototype_source/pt2e_quant_xpu_inductor.rst

+--------------
+
+This tutorial introduces XPUInductorQuantizer, which aims to serve quantized models for inference on Intel GPUs.
+It utilizes the PyTorch 2 Export Quantization flow and lowers the quantized model into the inductor.


Can we standardize capitalization of Inductor?

thanks for reminding, has align the style now

ZhiweiYan-96 · 2025-04-02T05:05:56Z

hi, @svekars @AlannaBurke could you please help review our documentation? The PR serves as a tutorial for PT2E int8 on Intel GPU backend. Appreciation for your feedback and suggestions.

svekars

A few editorial suggestions.

svekars · 2025-04-04T21:30:04Z

prototype_source/pt2e_quant_xpu_inductor.rst

+---------------
+
+-  `PyTorch 2 Export Post Training Quantization <https://pytorch.org/tutorials/prototype/pt2e_quant_ptq.html>`_
+-  `TorchInductor and torch.compile concepts in PyTorch <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_


Suggested change

- `TorchInductor and torch.compile concepts in PyTorch <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_

- `TorchInductor and torch.compile concepts in PyTorch <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_

- PyTorch 2.7 or later

svekars · 2025-04-04T21:30:20Z

prototype_source/pt2e_quant_xpu_inductor.rst

@@ -0,0 +1,234 @@
+PyTorch 2 Export Quantization with Intel GPU Backend through Inductor


Suggested change

PyTorch 2 Export Quantization with Intel GPU Backend through Inductor

Export Quantization with Intel GPU Backend through Inductor

svekars · 2025-04-04T21:31:01Z

prototype_source/pt2e_quant_xpu_inductor.rst

+Introduction
+--------------
+
+This tutorial introduces XPUInductorQuantizer, which aims to serve quantized models for inference on Intel GPUs.


Suggested change

This tutorial introduces XPUInductorQuantizer, which aims to serve quantized models for inference on Intel GPUs.

This tutorial introduces ``XPUInductorQuantizer``, which aims to serve quantized models for inference on Intel GPUs.

svekars · 2025-04-04T21:31:31Z

prototype_source/pt2e_quant_xpu_inductor.rst

+--------------
+
+This tutorial introduces XPUInductorQuantizer, which aims to serve quantized models for inference on Intel GPUs.
+It utilizes the PyTorch 2 Export Quantization flow and lowers the quantized model into the inductor.


Suggested change

It utilizes the PyTorch 2 Export Quantization flow and lowers the quantized model into the inductor.

``XPUInductorQuantizer`` use the PyTorch Export Quantization flow and lowers the quantized model into the inductor.

svekars · 2025-04-04T21:32:37Z

prototype_source/pt2e_quant_xpu_inductor.rst

+This tutorial introduces XPUInductorQuantizer, which aims to serve quantized models for inference on Intel GPUs.
+It utilizes the PyTorch 2 Export Quantization flow and lowers the quantized model into the inductor.
+
+The Pytorch 2 Export Quantization flow uses `torch.export` to capture the model into a graph and perform quantization transformations on top of the ATen graph.


Do we need to call it "PyTorch 2 Export Quantization flow" or can it be just "Export Quantization flow"?

Suggested change

The Pytorch 2 Export Quantization flow uses `torch.export` to capture the model into a graph and perform quantization transformations on top of the ATen graph.

The PyTorch 2 Export Quantization flow uses ``torch.export`` to capture the model into a graph and perform quantization transformations on top of the ATen graph.

svekars · 2025-04-04T21:33:43Z

prototype_source/pt2e_quant_xpu_inductor.rst

+
+The Pytorch 2 Export Quantization flow uses `torch.export` to capture the model into a graph and perform quantization transformations on top of the ATen graph.
+This approach is expected to have significantly higher model coverage with better programmability and a simplified user experience.
+TorchInductor is the compiler backend that compiles the FX Graphs generated by TorchDynamo into optimized C++/Triton kernels.


Suggested change

TorchInductor is the compiler backend that compiles the FX Graphs generated by TorchDynamo into optimized C++/Triton kernels.

TorchInductor is a compiler backend that transforms FX Graphs generated by ``TorchDynamo`` into optimized C++/Triton kernels.

svekars · 2025-04-04T21:34:47Z

prototype_source/pt2e_quant_xpu_inductor.rst

+- Step 1: Capture the FX Graph from the eager model based on the `torch export mechanism <https://pytorch.org/docs/main/export.html>`_.
+- Step 2: Apply the quantization flow based on the captured FX Graph, including defining the backend-specific quantizer, generating the prepared model with observers,
+  performing the prepared model's calibration, and converting the prepared model into the quantized model.
+- Step 3: Lower the quantized model into inductor with the API ``torch.compile``, which would call triton kernels or oneDNN GEMM/Convolution kernels.


Suggested change

- Step 3: Lower the quantized model into inductor with the API ``torch.compile``, which would call triton kernels or oneDNN GEMM/Convolution kernels.

- Step 3: Lower the quantized model into inductor with the API ``torch.compile``, which would call Triton kernels or oneDNN GEMM/Convolution kernels.

svekars · 2025-04-04T21:35:23Z

prototype_source/pt2e_quant_xpu_inductor.rst

+    pip3 install torch torchvision torchaudio pytorch-triton-xpu --index-url https://download.pytorch.org/whl/xpu
+
+
+Please note that since the inductor ``freeze`` feature does not turn on by default yet, run your example code with ``TORCHINDUCTOR_FREEZING=1``.


Suggested change

Please note that since the inductor ``freeze`` feature does not turn on by default yet, run your example code with ``TORCHINDUCTOR_FREEZING=1``.

Please note that since the inductor ``freeze`` feature does not turn on by default yet, you must run your example code with ``TORCHINDUCTOR_FREEZING=1``.

svekars · 2025-04-04T21:36:40Z

prototype_source/pt2e_quant_xpu_inductor.rst

+    quantizer.set_global(get_xpu_inductor_symm_quantization_config())
+
+After the backend-specific quantizer is imported, prepare the model for post-training quantization.
+``prepare_pt2e`` folds BatchNorm operators into preceding Conv2d operators, and inserts observers into appropriate places in the model.


Suggested change

``prepare_pt2e`` folds BatchNorm operators into preceding Conv2d operators, and inserts observers into appropriate places in the model.

``prepare_pt2e`` folds ``BatchNorm`` operators into preceding Conv2d operators, and inserts observers into appropriate places in the model.

svekars · 2025-04-04T21:40:12Z

prototype_source/pt2e_quant_xpu_inductor.rst

+            optimized_model = torch.compile(converted_model)
+
+            # Running some benchmark
+            optimized_model(*example_inputs)


Please write a conclusion for this tutorial. For example:

Suggested change

optimized_model(*example_inputs)

optimized_model(*example_inputs)

Conclusion

-----------

In this tutorial, we have learned how to utilize the ``XPUInductorQuantizer`` to perform post-training quantization on models for inference on Intel GPUs, leveraging PyTorch 2's Export Quantization flow. We covered the step-by-step process of capturing an FX Graph, applying quantization, and lowering the quantized model into the inductor backend using ``torch.compile``. Additionally, we explored the benefits of using int8-mixed-bf16 quantization for improved memory efficiency and potential performance gains, especially when using ``BFloat16`` autocast.

[Intel GPU] Docs of XPUInductorQuantizer

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		@@ -0,0 +1,234 @@
		PyTorch 2 Export Quantization with Intel GPU Backend through Inductor

	PyTorch 2 Export Quantization with Intel GPU Backend through Inductor
	Export Quantization with Intel GPU Backend through Inductor


		::

		pip install torchvision pytorch-triton-xpu --index-url https://download.pytorch.org/whl/nightly/xpu


		The high-level architecture of this flow could look like this:

		.. image:: ../_static/img/pt2e_quant_xpu_inductor.png

		optimized_model(*example_inputs)


		Put all these codes together, we will have the toy example code.



		Put all these codes together, we will have the toy example code.
		Please note that since the Inductor ``freeze`` feature does not turn on by default yet, run your example code with ``TORCHINDUCTOR_FREEZING=1``.

	a convolution or GEMM operator produces the output in BFloat16 instead of Float32 in the absence
	a Convolution or GEMM operator produces the output in BFloat16 instead of Float32 in the absence

	a convolution or GEMM operator produces the output in BFloat16 instead of Float32 in the absence
	a Conv or GEMM operator produces the output in BFloat16 instead of Float32 in the absence

	- `TorchInductor and torch.compile concepts in PyTorch <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_
	- `TorchInductor and torch.compile concepts in PyTorch <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_
	- PyTorch 2.7 or later

	This tutorial introduces XPUInductorQuantizer, which aims to serve quantized models for inference on Intel GPUs.
	This tutorial introduces ``XPUInductorQuantizer``, which aims to serve quantized models for inference on Intel GPUs.

	It utilizes the PyTorch 2 Export Quantization flow and lowers the quantized model into the inductor.
	``XPUInductorQuantizer`` use the PyTorch Export Quantization flow and lowers the quantized model into the inductor.

	The Pytorch 2 Export Quantization flow uses `torch.export` to capture the model into a graph and perform quantization transformations on top of the ATen graph.
	The PyTorch 2 Export Quantization flow uses ``torch.export`` to capture the model into a graph and perform quantization transformations on top of the ATen graph.

	TorchInductor is the compiler backend that compiles the FX Graphs generated by TorchDynamo into optimized C++/Triton kernels.
	TorchInductor is a compiler backend that transforms FX Graphs generated by ``TorchDynamo`` into optimized C++/Triton kernels.

	- Step 3: Lower the quantized model into inductor with the API ``torch.compile``, which would call triton kernels or oneDNN GEMM/Convolution kernels.
	- Step 3: Lower the quantized model into inductor with the API ``torch.compile``, which would call Triton kernels or oneDNN GEMM/Convolution kernels.

		pip3 install torch torchvision torchaudio pytorch-triton-xpu --index-url https://download.pytorch.org/whl/xpu


		Please note that since the inductor ``freeze`` feature does not turn on by default yet, run your example code with ``TORCHINDUCTOR_FREEZING=1``.

	``prepare_pt2e`` folds BatchNorm operators into preceding Conv2d operators, and inserts observers into appropriate places in the model.
	``prepare_pt2e`` folds ``BatchNorm`` operators into preceding Conv2d operators, and inserts observers into appropriate places in the model.

[Intel GPU] Docs of XPUInductorQuantizer #3293

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[Intel GPU] Docs of XPUInductorQuantizer #3293

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