[Feat]: Add support for kleidiai quantization schemes #1447

ng-05 · 2024-12-19T10:44:27Z

Description:
Allow int8_dynamic_activation_intx_weight to work with aten _dyn_quant_matmul_4bit op

Needs : pytorch/pytorch#134124 or Pytorch > 2.6.0

pytorch-bot · 2024-12-19T10:44:31Z

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torchao/quantization/GPTQ.py

ng-05 · 2025-01-08T12:14:06Z

Hello @jerryzh168 ,
We want to support two diff type of int4 schemes.

symmetric_groupwise -> groupsize [ 32, 64, 128 etc ]
symmetric_channelwise -> groupsize is equal to channelsize of the matmul weights

How should we take this input from user regarding quantization schemes. Groupsize parameter can not server the purpose as channelsize will change for diff matmuls in a model?

Currently I am using "scheme" parameter to differentiate between the two.
aarch64_cpu_channelwise.json
aarch64_cpu_groupwise.json

jerryzh168 · 2025-01-08T17:44:42Z

How should we take this input from user regarding quantization schemes. Groupsize parameter can not server the purpose as channelsize will change for diff matmuls in a model?

yeah, you can use https://github.com/pytorch/ao/blob/main/torchao/quantization/granularity.py: PerGroup and PerAxis(axis=0) (assuming channel dimension is 0), examples:

ao/torchao/quantization/quant_api.py

Line 1069 in 070345d

granularity: Optional[

,

ao/tutorials/calibration_flow/static_quant.py

Line 168 in 070345d

    
           weight_obs = AffineQuantizedMinMaxObserver(mapping_type, target_dtype, granularity_type=PerAxis(axis=0), eps=torch.finfo(torch.float32).eps, scale_dtype=torch.float32, zero_point_dtype=torch.float32)

ng-05 · 2025-01-09T02:00:37Z

How should we take this input from user regarding quantization schemes. Groupsize parameter can not server the purpose as channelsize will change for diff matmuls in a model?

yeah, you can use https://github.com/pytorch/ao/blob/main/torchao/quantization/granularity.py: PerGroup and PerAxis(axis=0) (assuming channel dimension is 0), examples:

ao/torchao/quantization/quant_api.py

Line 1069 in 070345d

granularity: Optional[

,

ao/tutorials/calibration_flow/static_quant.py

Line 168 in 070345d

weight_obs = AffineQuantizedMinMaxObserver(mapping_type, target_dtype, granularity_type=PerAxis(axis=0), eps=torch.finfo(torch.float32).eps, scale_dtype=torch.float32, zero_point_dtype=torch.float32)

Thanks for the inputs @jerryzh168.

I have initial change ready which extends int4_weight_only quantizer.

The 4 bit KleidiAI kernels quantizes the weight in torchao and input to 8 bit within the kernel itself instead of quantizing the input in the torchao the way int8_dynamic_activation_int4_weight does.
For this reason I am extending the int4_weight_only api. I am slightly confused if the intention of this api is to convey NO input quantisation to user?

Currently neither int4_weight_only nor int8_dynamic_activation_int4_weight fully aligns with the way kelidiai 4 bit kernels are working.

I feel int4_weight_only is closest to what we want to do, what are your thoughts on this?

jerryzh168 · 2025-01-09T02:33:56Z

I feel int4_weight_only is closest to what we want to do, what are your thoughts on this?

yeah int4_weight_only means no input quantization, I think it aligns better with int8_dynamic_activation_int4_weight, you can use a different layout and customize the logic for input quantization.

we also have

ao/torchao/experimental/quant_api.py

Line 485 in 4738377

def int8_dynamic_activation_intx_weight(

that is the same as your use case. there is some ongoing refactors/updates there as well right now

You can also check out: #995

ng-05 · 2025-01-11T01:43:08Z

Hello @jerryzh168 , I am planning to migrate int8_dynamic_activation_intx_weight api to int8_dynamic_activation_intx_weight_v2.
For now I have kept the API separate for review and testing.

Can you please review this change, specially the change the in _get_linear_subclass_inserter which allow bias propagation. The bias needed by torch.ops.aten._dyn_quant_pack_4bit_weight.

I am also not sure if int8_dynamic_activation_intx_weight* quantizer can be accessed by torchchat currently? Do you have an example how torchchat can pass args like granularity, mapping_type from torchchat cli to torchao ?

jerryzh168 · 2025-01-11T02:31:43Z

torchao/experimental/_linear_8bit_act_xbit_weight_layout.py

    target: Target

+    # Allow bias access via layout
+    bias: Optional[torch.Tensor] = None


layout is more of a "type" actually, why is bias Tensor passed here?

the corresponding "storage" is TensorImpl

I want to access the bias to be packed with my weights and scales. I can not find any other existing way to pass bias to from_plain() api via

ao/torchao/dtypes/affine_quantized_tensor.py

Line 281 in ad61822

tensor_impl = tensor_impl_ctr(data, scale, zero_point, _layout)

How do you think I should access bias in the packing function here.

ao/torchao/experimental/_linear_8bit_act_xbit_weight_layout.py

Line 77 in ad61822

def _pack_weights_native(

@ng-05 - bias is not required to differentiate this layout i.e. you can dispatch to this layout with and without bias.

That said, @jerryzh168 - we do need to figure out how to get the bias to the from_plain method. I know it doesn't play nice with the tensor representation abstraction for AQT, do you have any other suggestions?

Perhaps until then can we just do a add op followed by gemm, and put a TODO on fixing APIs?

If it does not fit into AQT, I think it's fine to create a new tensor subclass, but putting bias Tensor in the layout is bit conflicting the design (has_bias boolean is fine) since it's a "type", should not store data there

layout is following the design of https://pytorch.org/docs/stable/tensor_attributes.html#torch.layout

torchao/quantization/quant_api.py

jerryzh168

looks good to me overall, can you add some tests?

jerryzh168 · 2025-01-11T02:37:36Z

I am also not sure if int8_dynamic_activation_intx_weight* quantizer can be accessed by torchchat currently? Do you have an example how torchchat can pass args like granularity, mapping_type from torchchat cli to torchao ?

I don't think we need to expose these fine grained args to torchchat cli, we just need these high level args like: https://github.com/pytorch/torchchat/blob/main/torchchat/quant_config/mobile.json

we are also working on migrating torchchat to use torchao quant api btw

torchao/experimental/_linear_8bit_act_xbit_weight_layout.py

torchao/experimental/quant_api.py

torchao/experimental/_linear_8bit_act_xbit_weight_layout.py

torchao/experimental/quant_api.py

metascroy · 2025-01-11T04:24:33Z

Hello @jerryzh168 , I am planning to migrate int8_dynamic_activation_intx_weight api to int8_dynamic_activation_intx_weight_v2. For now I have kept the API separate for review and testing.

Can you please review this change, specially the change the in _get_linear_subclass_inserter which allow bias propagation. The bias needed by torch.ops.aten._dyn_quant_pack_4bit_weight.

I am also not sure if int8_dynamic_activation_intx_weight* quantizer can be accessed by torchchat currently? Do you have an example how torchchat can pass args like granularity, mapping_type from torchchat cli to torchao ?

torchchat does not currently use int8_dynamic_activation_intx_weight, but instead a submodule swap API here: https://github.com/pytorch/ao/blob/main/torchao/experimental/quant_api.py#L438

We will be switching torchchat to use int8_dynamic_activation_intx_weight instead, but I first need to land some changes for perf/clarity: #1553

kimishpatel

I understand that this quant API now connects kernels we landed in aten with quant API. If the kernels you guys landed in aten are actually new ops, unlike int4pack_mm and friends, then why did we land them there in the first place. In order to reach those kernels you need ao dep anyway? (@digantdesai I know you tagged me on that PR but i never really deep dived into that so maybe you have context here)

Besides taht i have a couple of questions.

In the current form it is only making aten op you guys added available via tensor subclass api, so what happens to say torch.compile (maybe this works?) or AOTI usecase?
I would also like to see if we can leverage this op in executorch, for which integration into AO would have been a better choice compared to this being aten op
If kleidi's op performs better than whats in this repo (and note that @digantdesai has actually integrated some of the kleidi ops that I guess you guys are aware of), then can we just use that op directly or have a path to kleidi's impl for the cpu ops that exist under experimental/ops?

kimishpatel · 2025-01-11T04:28:57Z

Hello @jerryzh168 , I am planning to migrate int8_dynamic_activation_intx_weight api to int8_dynamic_activation_intx_weight_v2. For now I have kept the API separate for review and testing.
Can you please review this change, specially the change the in _get_linear_subclass_inserter which allow bias propagation. The bias needed by torch.ops.aten._dyn_quant_pack_4bit_weight.
I am also not sure if int8_dynamic_activation_intx_weight* quantizer can be accessed by torchchat currently? Do you have an example how torchchat can pass args like granularity, mapping_type from torchchat cli to torchao ?

torchchat does not currently use int8_dynamic_activation_intx_weight, but instead a submodule swap API here: main/torchao/experimental/quant_api.py#L438

We will be switching torchchat to use int8_dynamic_activation_intx_weight instead, but I first need to land some changes for perf/clarity: #1553

Any specific reason why use subclass API instead of module swap?

ng-05 · 2025-01-13T12:41:49Z

I understand that this quant API now connects kernels we landed in aten with quant API. If the kernels you guys landed in aten are actually new ops, unlike int4pack_mm and friends, then why did we land them there in the first place. In order to reach those kernels you need ao dep anyway? (@digantdesai I know you tagged me on that PR but i never really deep dived into that so maybe you have context here)

Besides taht i have a couple of questions.

In the current form it is only making aten op you guys added available via tensor subclass api, so what happens to say torch.compile (maybe this works?) or AOTI usecase?

I would also like to see if we can leverage this op in executorch, for which integration into AO would have been a better choice compared to this being aten op

If kleidi's op performs better than whats in this repo (and note that @digantdesai has actually integrated some of the kleidi ops that I guess you guys are aware of), then can we just use that op directly or have a path to kleidi's impl for the cpu ops that exist under experimental/ops?

I am unaware of executorch status and what performance you get with klediai kernels over there. I tested this change with torch.compile() and it seems to be working fine.

ng-05 · 2025-01-13T13:01:32Z

@jerryzh168 @kimishpatel are we testing the 4 bit symmetric quantization anywhere without adding a dequant layer on the result? In my testing I am seeing very poor accuracy with symmetric 4 bit quant scheme with this PR.
For comparison, the mean relative error jumps from 0.0006 (with llama.cpp algo ) to 0.0044 (torchao algo)with kleidiai kernels.
This is the reference scheme that I am using for the 4 bit symmetric quant. ggerganov/llama.cpp#729

torchao/experimental/_linear_8bit_act_xbit_weight_layout.py

digantdesai · 2025-01-13T16:41:41Z

torchao/experimental/_linear_8bit_act_xbit_weight_layout.py

    target: Target

+    # Allow bias access via layout
+    bias: Optional[torch.Tensor] = None


@ng-05 - bias is not required to differentiate this layout i.e. you can dispatch to this layout with and without bias.

That said, @jerryzh168 - we do need to figure out how to get the bias to the from_plain method. I know it doesn't play nice with the tensor representation abstraction for AQT, do you have any other suggestions?

Perhaps until then can we just do a add op followed by gemm, and put a TODO on fixing APIs?

torchao/experimental/_linear_8bit_act_xbit_weight_layout.py

torchao/experimental/quant_api.py

metascroy · 2025-01-13T18:56:28Z

Hello @jerryzh168 , I am planning to migrate int8_dynamic_activation_intx_weight api to int8_dynamic_activation_intx_weight_v2. For now I have kept the API separate for review and testing.
Can you please review this change, specially the change the in _get_linear_subclass_inserter which allow bias propagation. The bias needed by torch.ops.aten._dyn_quant_pack_4bit_weight.
I am also not sure if int8_dynamic_activation_intx_weight* quantizer can be accessed by torchchat currently? Do you have an example how torchchat can pass args like granularity, mapping_type from torchchat cli to torchao ?

torchchat does not currently use int8_dynamic_activation_intx_weight, but instead a submodule swap API here: main/torchao/experimental/quant_api.py#L438
We will be switching torchchat to use int8_dynamic_activation_intx_weight instead, but I first need to land some changes for perf/clarity: #1553

Any specific reason why use subclass API instead of module swap?

My understanding from @jerryzh168 is that long-term, torchao plans to support pt2e and subclass/quantize_ based quantization long-term. I believe torchchat is working on (and has already partially completed) moving module-swap based quantization over to use quantize_ (cc @Jack-Khuu to keep me honest there).

digantdesai

I think with extended AQT it is close. Left some comments.

digantdesai · 2025-01-23T00:13:58Z

torchao/experimental/packed_linear_int8_dynamic_activation_intx_weight_layout.py

    ):
        assert isinstance(layout, PackedLinearInt8DynamicActivationIntxWeightLayout)
        assert layout.has_params_set(), "PackedLinearInt8DynamicActivationIntxWeightLayout params must be set before calling from_plain"
+        assert layout.target in {Target.NATIVE, Target.ATEN}, f"Unexpected target: {layout.target}"


Do we want to assert bias is None when layout.target != ATEN?

if bias is none and target layout.target != ATEN then execution will never reach this point. It will be executed via the "native" target which has bias assert. Please check here: https://github.com/pytorch/ao/pull/1447/files#diff-3e4ffa192fe9921999dd6a798fc3fa620377896ef9ba65245b1e5ab8c0d7d344R593

digantdesai · 2025-01-23T00:26:35Z

torchao/experimental/packed_linear_int8_dynamic_activation_intx_weight_layout.py

+    """Enum that indicates the backend target"""
+
+    NATIVE = auto()
+    ATEN = auto()


What makes it ATEN specific? Should it be Kleidi? I am thinking from longer term perspective where we will use this layout arg to differentiate how to pack weights.

I decided to stick to aten so that more ops from aten can be added in future if needed. This enables this layout to work with torchao ops and aten ops.
dyn_quant_matmul_4bit is not supposed to be KleidiAI specific only

But the aten implementation and the packing routine it selects is Kleidi specific.

digantdesai · 2025-01-23T03:45:03Z

torchao/experimental/packed_linear_int8_dynamic_activation_intx_weight_layout.py

 register_aqt_quantized_linear_dispatch(
    _linear_check,
    _linear_impl,
 )
+
+
+class PackedLinearInt8DynamicActivationIntxWeightAtenTensor(AffineQuantizedTensor):


Curious, (1) why adding bias as optional to_affine_quantized_intx isn't an option? or alternatively, (2) for native kernels we will add bias support sooner rather than later, so should we use this for both native and aten?

ng-05 · 2025-01-23T10:44:39Z

I can not run this test as the op build is broken for aarch64 linux.

Can you remind me of this issue? how can we resolve this?

We need to check for aarch64 in cmake processor along with arm64. Even after that I am seeing few build issues. I did not have a deeper look at them.

ng-05 · 2025-01-23T11:17:17Z

@digantdesai
for this comment :
Curious, (1) why adding bias as optional to_affine_quantized_intx isn't an option? or alternatively, (2) for native kernels we will add bias support sooner rather than later, so should we use this for both native and aten?

Please check
#1447 (comment) and #1447 (comment)

This change was suggested by @jerryzh168

ng-05 · 2025-01-24T16:49:07Z

@pytorchbot label "arm priority"

pytorch-bot · 2025-01-24T16:49:10Z

Didn't find following labels among repository labels: arm priority

digantdesai · 2025-01-27T20:12:45Z

@digantdesai for this comment : Curious, (1) why adding bias as optional to_affine_quantized_intx isn't an option? or alternatively, (2) for native kernels we will add bias support sooner rather than later, so should we use this for both native and aten?

Please check #1447 (comment) and #1447 (comment)

This change was suggested by @jerryzh168

What about (2)? This is where I am trying to figure out how to make both native and aten have bias, and set it up for the future where we will, potentially delegate aten impl.

digantdesai · 2025-01-27T20:14:59Z

I am ok with merging this for now, and since we will have to figure out details between native and aten for intx, we can resolve this later.

Stamping to unblock you.

ng-05 · 2025-01-28T10:50:45Z

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metascroy · 2025-01-28T21:45:10Z

Overall I think it's close. Can you be sure to run torchao/experimental/tests/test_packed_linear_int8_dynamic_activation_intx_weight_layout.py to make sure the tests pass. It is not currently enabled in CI.
You should also add some test cases for your new target to that file to check accuracy/exportability.
I'll be out for the week of January 20, but @digantdesai should be able to help with review during that time.

I can not run this test as the op build is broken for aarch64 linux.

I added CI to run torchao/experimental/tests/test_packed_linear_int8_dynamic_activation_intx_weight_layout.py so you don't have to do it locally (.github/workflows/torchao_experimental_test.yml). Just make sure the "Run TorchAO Experimental Tests" job finishes successfully before merging.

I don't see any tests for your new quantization code added. For now, can you add a new test file (e.g., torchao/experimental/tests/test_packed_linear_int8_dynamic_activation_intx_weight_layout_target_aten.py) that tests your quantizer/kernel code. Later I can merge the existing test_packed_linear_int8_dynamic_activation_intx_weight_layout.py and the tests for aten target in the new file you create into one test.

You can your test to CI by adding a line here: https://github.com/pytorch/ao/blob/main/.github/workflows/torchao_experimental_test.yml#L42

digantdesai · 2025-01-28T22:16:24Z

Good point about tests, @ng-05 can we please add them? Also in the CI now?

Description: Allow int8_dynamic_activation_intx_weight to work with aten _dyn_quant_matmul_4bit op Needs : pytorch/pytorch#134124 or Pytorch > 2.6.0 Signed-off-by: Nikhil Gupta <[email protected]>

Signed-off-by: Nikhil Gupta <[email protected]>

ng-05 · 2025-01-30T16:29:14Z

Good point about tests, @ng-05 can we please add them? Also in the CI now?

Done.

ng-05 · 2025-01-30T16:29:43Z

Overall I think it's close. Can you be sure to run torchao/experimental/tests/test_packed_linear_int8_dynamic_activation_intx_weight_layout.py to make sure the tests pass. It is not currently enabled in CI.
You should also add some test cases for your new target to that file to check accuracy/exportability.
I'll be out for the week of January 20, but @digantdesai should be able to help with review during that time.

I can not run this test as the op build is broken for aarch64 linux.

I added CI to run torchao/experimental/tests/test_packed_linear_int8_dynamic_activation_intx_weight_layout.py so you don't have to do it locally (.github/workflows/torchao_experimental_test.yml). Just make sure the "Run TorchAO Experimental Tests" job finishes successfully before merging.

I don't see any tests for your new quantization code added. For now, can you add a new test file (e.g., torchao/experimental/tests/test_packed_linear_int8_dynamic_activation_intx_weight_layout_target_aten.py) that tests your quantizer/kernel code. Later I can merge the existing test_packed_linear_int8_dynamic_activation_intx_weight_layout.py and the tests for aten target in the new file you create into one test.

You can your test to CI by adding a line here: https://github.com/pytorch/ao/blob/main/.github/workflows/torchao_experimental_test.yml#L42

Thank you very much. I have done the requested changes around tests. Please review

ng-05 marked this pull request as draft December 19, 2024 10:44

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ng-05 added 3 commits January 30, 2025 14:08

[Feat]: Add support for kleidiai quantization schemes

de51c5f

Description: Allow int8_dynamic_activation_intx_weight to work with aten _dyn_quant_matmul_4bit op Needs : pytorch/pytorch#134124 or Pytorch > 2.6.0 Signed-off-by: Nikhil Gupta <[email protected]>

[Fix]: Use Custom Tensor for dyn_quant_matmul_4bit aten op

8fdf6a9

Signed-off-by: Nikhil Gupta <[email protected]>

[Refactor]: Move target attribute to Layout Class & fix target checks

ca8a5f1

Signed-off-by: Nikhil Gupta <[email protected]>

ng-05 force-pushed the kai_integration branch from 0f49f3d to 952ab42 Compare January 30, 2025 16:19

[Fix]: Enable SYMMETRIC_NO_CLIPPING_ERR Mapping type and tests

eb61a24

Signed-off-by: Nikhil Gupta <[email protected]>

ng-05 force-pushed the kai_integration branch from 952ab42 to eb61a24 Compare January 30, 2025 16:28

digantdesai merged commit 7815262 into pytorch:main Jan 30, 2025
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[Feat]: Add support for kleidiai quantization schemes #1447

[Feat]: Add support for kleidiai quantization schemes #1447

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