Enable AWQ on Intel GPU. #2248

xiaowangintel · 2025-05-23T03:35:07Z

Following pytorch/pytorch#153019 requests, we enable awq-uint4 for Intel GPU in pytorch/ao after RTN ready.

How to run awq quantization model:

cd torchao/prototype/awq

python example.py --device xpu  huggingface-model(such as meta-llama/Llama-3.1-8B-Instruct) awq-uint4-128

#Results of meta-llama/Llama-3.1-8B-Instruct on Intel GPU:
{'perplexity': {'perplexity': 10.099576950073242, 'prediction_time': 0.20489671968780787}}

#Results of meta-llama/Llama-3.1-8B-Instruct on NVIDIA-A100 GPU:
Results: {'perplexity': {'perplexity': 10.160041809082031, 'prediction_time': 0.4466673863672577}}

pytorch-bot · 2025-05-23T03:35:10Z

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xiaowangintel · 2025-05-23T06:17:57Z

@liangan1 Can you help to review this PR?

liangan1 · 2025-05-23T06:27:34Z

how about perplexity on cuda?

liangan1

LGTM

liangan1 · 2025-05-23T07:32:24Z

@EikanWang

jerryzh168 · 2025-05-29T00:18:30Z

torchao/dtypes/uintx/int4_xpu_layout.py

@@ -429,15 +428,14 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
            # TODO: move this to `unpack_tinygemm_scales_and_zeros`?
            scale = scale.reshape(scale.shape[:-1]).contiguous()
            zero = zero.reshape(zero.shape[:-1]).contiguous()
-            int_data = quantize_affine(
+            int_data = quantize_affine_float_zero_point(


this is actually specific to fbgemm I think, we'd need to rename in a future PR cc @jainapurva

jerryzh168 · 2025-05-29T00:19:02Z

torchao/prototype/awq/api.py

+        if "xpu" in device.type:
+            _layout = Int4XPULayout()
+        else:
+            _layout = TensorCoreTiledLayout(inner_k_tiles=8)


can layout be explicitly passed in instead of inferred from device?

I think it should be OK. We should follow the Int4WeightOnlyConfig to let user to specify the layout information.

Yes, modified done.

jerryzh168 · 2025-05-29T00:19:18Z

torchao/prototype/awq/api.py

@@ -114,6 +116,7 @@ class AWQUIntXConfig(AOBaseConfig):
    group_size: int = 64
    use_hqq: bool = False
    set_inductor_config: bool = True
+    zero_point_domain: Optional[ZeroPointDomain] = ZeroPointDomain.FLOAT


can this be removed if we have layout?

Yes. I agree with you. Following the logic of #2149, preserve_zero and zero_point_domain is too complex to be used in the user UX. It is better way to use layout to decide the zero_point_domain information.

Yes, modified done.

jerryzh168 · 2025-05-29T00:22:15Z

torchao/prototype/awq/example.py

+from torchao.dtypes import Int4XPULayout
+
+
+zero_point_domain_dict = {"float":ZeroPointDomain.FLOAT, "int":ZeroPointDomain.INT, "none":ZeroPointDomain.NONE}


FYI, we used to use this for distinguish between different types of kernels, but now we are keeping the default path of integer zero point and preserve zero for the common path, and split out the other q/dq ops for specific kernels like tinygemm: #2149

I think it's just different ways to implement things and we not necessarily need to have these categorizations like zero_point_domain and preserve_zero since it might complicate the UX.

Yes, modified done.

jerryzh168

I feel we can use layout as a user facing interface

jerryzh168 · 2025-05-29T00:23:52Z

torchao/quantization/utils.py

@@ -473,6 +459,8 @@ def groupwise_affine_quantize_tensor_from_qparams(
            not (check_xpu_version(int_data.device))
        ):
            int_data = (int_data[::, ::2] << 4 | int_data[::, 1::2]).to(torch.uint8)
+        if check_xpu_version(int_data.device):


should probably encapsulate these better when we have a better design for layout conversions: #2249

liangan1 · 2025-05-29T08:03:49Z

torchao/prototype/awq/api.py

@@ -5,6 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 import types
 from dataclasses import dataclass
+from typing import Any, Callable, Dict, Optional, Tuple, Union


Suggested change

from typing import Any, Callable, Dict, Optional, Tuple, Union

from typing import Optional

liangan1 · 2025-05-29T22:05:14Z

@pytorchbot label topic: new feature

pytorch-bot · 2025-05-29T22:05:16Z

Didn't find following labels among repository labels: topic:,new,feature

liangan1 · 2025-05-30T07:01:33Z

@pytorchbot merge

pytorchmergebot · 2025-05-30T07:02:15Z

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liangan1 approved these changes May 23, 2025

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xiaowangintel changed the title ~~[WIP]Enable AWQ on Intel GPU.~~ Enable AWQ on Intel GPU. May 23, 2025

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xiaowangintel requested a review from jerryzh168 May 29, 2025 09:22

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jerryzh168 added the topic: new feature Use this tag if this PR adds a new feature label May 29, 2025

xiaowangintel added 8 commits May 29, 2025 19:01

Enable AWQ on Intel GPU.

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xiaowangintel force-pushed the xw/int4-awq branch from 2617ab1 to 4c8f036 Compare May 30, 2025 02:06

Enable AWQ on Intel GPU.

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xiaowangintel force-pushed the xw/int4-awq branch from 4c8f036 to 4f11061 Compare May 30, 2025 02:22

liangan1 mentioned this pull request May 30, 2025

[RFC][API-Unstable]Enable A16W4 on XPU Device pytorch/pytorch#153019

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Enable AWQ on Intel GPU.

f60041c

pytorchmergebot added the merging label May 30, 2025

pytorchmergebot closed this in abb309a May 30, 2025

pytorchmergebot added Merged and removed merging labels May 30, 2025

		from torchao.dtypes import Int4XPULayout


		zero_point_domain_dict = {"float":ZeroPointDomain.FLOAT, "int":ZeroPointDomain.INT, "none":ZeroPointDomain.NONE}

	from typing import Any, Callable, Dict, Optional, Tuple, Union
	from typing import Optional

Enable AWQ on Intel GPU. #2248

Enable AWQ on Intel GPU. #2248

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/2248

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