Enable fast qlinear_dynamic path for AArch64 through ACL directly #145942

fadara01 · 2025-01-29T17:19:32Z

This enables a fast path for eager mode dynamic quantization for AArch64 through Arm Compute Library (ACL) directly.

Context: PR #126687 enabled an optimized implementation for qlinear_dynamic for AArch64 through ideep → oneDNN → ACL which improved performance by ~10x compared to the previous implementation.

However, the current qlinear_dynamic path (ideep → oneDNN → ACL) suffers from high overhead due to the API friction between the stateless oneDNN API and the stateful ACL low-precision GEMM (lowp_gemm) API - for example, ACL's lowp_gemm objects cache information like weights reduction or weights in optimized memory format which oneDNN does not allow due to its stateless nature. Hence, ACL currently runs a (redundant) sum of columns and pre-transposition (to the gemm kernel's optimal format) for each GEMM operation.

This PR addresses the sub-optimalities above by integrating ACL directly with qlinear_dynamic. This approach yields an average speedup (averaged over context_lengths of 2^3 up to 2^9) of ~ 50% for bert-base-uncased, bert-large-uncased, roberta-base, distilbert-base-uncased with 16 threads on a Neoverse-V1 (with transformers==4.48) - See benchmark code below. To achieve this, we:

Use ACL which is already built with PyTorch as a shared library when USE_MKLDNN_ACL is set.
Add ACL to ATen's CPU include and dependency libs
Introduce PackedLinearWeightsACL (as a subclasses of PackedLinearWeightsOnednn) with an implementation of qlinear_dynamic that uses ACL directly, while qlinear still follows the oneDNN path.
A future PR will introduce a direct ACL implementation qlinear and will allow us to remove the dependence on PackedLinearWeightsOnednn

The following code was used to benchmark qlinear_dynamic performance:

# SPDX-FileCopyrightText: Copyright 2025 Arm Limited and/or its affiliate <[email protected]>
# SPDX-License-Identifier: BSD-3-Clause
import torch
from transformers import AutoModel, AutoConfig
import time
import numpy as np
from argparse import ArgumentParser

class ModelArgumentParser(ArgumentParser):
    def __init__(self) -> None:
        super().__init__(description="huggingface model")
        self.add_argument("--context_length",
                            help="context length - number of input tokens",
                            type=int,
                            default=64
        )
        self.add_argument("--model",
                            help="model checkpoint - i.e. 'bert-base-uncased'",
                            type=str,
                            default=None)
        self.add_argument("--iters",
                          help="benchmark iterations",
                          default=500)

if __name__ == "__main__":
    parser = ModelArgumentParser()
    args = parser.parse_args()
    model_name = args.model
    config = AutoConfig.from_pretrained(model_name)
    batch_size = 1
    model = AutoModel.from_pretrained(model_name)
    model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
    model.eval()
    inputs = torch.randint(config.vocab_size, (batch_size, args.context_length), dtype=torch.long, device="cpu")
    times = []
    with torch.no_grad():
        # warmup
        for _ in range(10):
            model(inputs)
        # benchmark
        for _ in range(args.iters):
            s = time.time_ns()
            model(inputs)
            times.append((time.time_ns() - s) / 1e6)

    print("Model = ", model_name)         
    print("Context Length = ", args.context_length)
    print("Min (ms) = ", min(times))
    print("Mean (ms) = ", np.mean(times))

Fixes #ISSUE_NUMBER

cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @malfet @snadampal @milpuz01

pytorch-bot · 2025-01-29T17:19:36Z

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fadara01 · 2025-01-29T17:20:24Z

@pytorchbot label "module: arm"

fadara01 · 2025-01-30T11:56:43Z

@pytorchbot label "ciflow/linux-aarch64"

fadara01 · 2025-01-31T11:41:45Z

@pytorchbot rebase

pytorchmergebot · 2025-01-31T11:43:17Z

@pytorchbot started a rebase job onto refs/remotes/origin/viable/strict. Check the current status here

pytorchmergebot · 2025-01-31T11:43:20Z

Successfully rebased acl_qlinear_dynamic onto refs/remotes/origin/viable/strict, please pull locally before adding more changes (for example, via git checkout acl_qlinear_dynamic && git pull --rebase)

malfet

Thank you for the PR, mostly looks good, though if possible, please submit a separate PR that updates ACL version.

malfet · 2025-02-05T02:57:08Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

+#pragma once
+
+#include <ATen/Config.h>
+#if defined(__aarch64__) && AT_MKLDNN_ACL_ENABLED()


Why do you need an arch guard there?

Suggested change

#if defined(__aarch64__) && AT_MKLDNN_ACL_ENABLED()

#if AT_MKLDNN_ACL_ENABLED()

We do not, I removed it. AT_MKLDNN_ACL_ENABLED is enough

malfet · 2025-02-05T02:58:21Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

+    int64_t // NUM_THREADS
+    >;
+
+enum ACLDynamicQuantMatmulCacheKeyIndex {


Suggested change

enum ACLDynamicQuantMatmulCacheKeyIndex {

enum class ACLDynamicQuantMatmulCacheKeyIndex {

malfet · 2025-02-05T02:59:01Z

aten/src/ATen/native/quantized/cpu/qlinear_dynamic.cpp

  return dim == 2 ? output : output.reshape(output_size);
 }
+
+#if defined(__aarch64__) && AT_MKLDNN_ACL_ENABLED()


Why this needs arch check?

Suggested change

#if defined(__aarch64__) && AT_MKLDNN_ACL_ENABLED()

#if AT_MKLDNN_ACL_ENABLED()

We do not need it, I removed it. AT_MKLDNN_ACL_ENABLED is enough

aten/src/ATen/native/quantized/cpu/ACLUtils.h

malfet · 2025-02-05T03:06:06Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

+    if (with_bias) {
+      bia_tensor.allocator()->free();
+    }


Wouldn't it be better to express something like that by defining bias tensor as std::optional<arm_compute::Tensor> bias_tensor;?

Done, I now use std::optional for bia_tensor and bia_tensor_info

…tly. This enables a fast path for eager mode static quantization for AArch64 through Arm Compute Library (ACL) directly. PR pytorch#145942 addressed the high overhead in qlinear_dynamic on AArch64 (due to redundant weight pretranspositions and reductions) by enabling a path that calls ACL directly. This does the same thing but for (static) qlinear.

ACL is already built with PyTorch as a shared library when USE_MKLDNN_ACL is set. Currently, it is only used indirectly in ATen via oneDNN for AArch64 targets. However there are cases where it makes sense to utilize ACL directly without oneDNN as an intermediary - e.g. quantization. See pytorch#145942, pytorch#147337, pytorch#146620. This patch enables such use cases by exposing ACL to ATen

This enables a fast path for eager mode dynamic quantization for AArch64 through Arm Compute Library (ACL) directly. Context: PR pytorch#126687 enabled an optimized implementation for qlinear_dynamic for aarch64 through ideep → oneDNN → ACL which improved performance by ~10x compared to the previous implementation. However, the current qlinear_dynamic path (ideep → oneDNN → ACL) suffers from high overhead due to the API friction between the stateless oneDNN API and the stateful ACL low-precision GEMM (lowp_gemm) API - for example, ACL's lowp_gemm objects cache information like weights reduction or weights in optimized memory format which oneDNN does not allow due to its stateless nature. Hence, ACL currently runs a (redundant) sum of columns and pre-transposition (to the gemm kerne's optimal format) for each GEMM operation. This PR addresses the sub-optimalities above by integrating ACL directly with qlinear_dynamic. This approach yields an average speedup (averaged over context_lengths of 2^3 up to 2^9) of ~ 50% for bert-base-uncased, bert-large-uncased, roberta-base, distilbert-base-uncased with 16 threads on a Neoverse-V1 (with transformers==4.48). To achieve this we introduce PackedLinearWeightsACL (as a subclasses of PackedLinearWeightsOnednn ) with an implementation of qlinear_dynamic that uses ACL directly, while qlinear still follows the oneDNN path.

…tly. This enables a fast path for eager mode static quantization for AArch64 through Arm Compute Library (ACL) directly. PR pytorch#145942 addressed the high overhead in qlinear_dynamic on AArch64 (due to redundant weight pretranspositions and reductions) by enabling a path that calls ACL directly. This does the same thing but for (static) qlinear.

fadara01 · 2025-03-05T11:47:00Z

I created a standalone PR #148542 for all cmake related changes and removed them from here to ease the review process.

malfet

I don't know much about this particular codepath, but requesting changes solely for the integration strategy. (speaking of strategy, it would be good to have an RFC issue outlining ACL/oneDNN integration - i.e. what is the end goal: fully decouple ACL from oneDNN or keep some direct usage until oneDNN integration is done, or it it about something else)

So back to integration:

Please move logic that searches fro ACL into a separate PR (you have write permissions, so you can you ghstack, can't you) and use modern cmake (that defines target rather than global variables) to introduce new dependency
Avoid explicit memory management (i.e. if something needs to free the memory, wrap it into a simple unique_ptr)
Avoid implementing methods in headers unless those are inline methods or templates
Also, as much as possible please use Torch memory allocator, rather than mix ACL and Torch ones, as it will make memory tracking/reporting easier

Last but not least: you've added the script that benchmarks the perf, but did not share the numbers before and after, that would help one understand the benefits this PR brings

malfet · 2025-02-26T19:57:06Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

+
+  int64_t k_;
+  int64_t n_;
+  int64_t wei_zero_point_;


Is there a document outlining the naming convention? Underscore usually means private variable, but they are public, as this is a struct.

Good catch, I meant to actually make them private.
I addressed this in the new ghstack PR, please see this line

malfet · 2025-03-05T15:19:53Z

cmake/Modules/FindACL.cmake

+# FindACL
+# ----------
+#
+# Finds the Arm Compute Library


Does this file exists somewhere else? If so, please reference where it was copied from
If this is create exclusively for PyTorch, please use modern CMAKE, i.e. instead of (or in addition to) defining global variables add libraries/targets, something like

add_library(ArmComputeLib INTERFACE) target_link_libraries(ArmComputeLib INTERFACE ${ACL_LIBRARIES}) target_include_directories(ArmComputeLib INTERFACE ${ACL_INCLUDE_DIRS})

This file was not exclusively create for PyTorch, it was copied from oneDNN: https://github.com/oneapi-src/oneDNN/blob/main/cmake/FindACL.cmake

I referenced that in the new ghstack PR - see this line

malfet · 2025-03-05T15:25:34Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

+  ~ACLDynamicQuantMatmul() {
+    // this will free memory allocated for the quantized src tensor since the
+    // allocation happened through ACL: src_s8_tensor.allocator()->allocate()
+    src_s8_tensor.allocator()->free();


This is a very unsafe programming model: there are no default constructor, so this structure could be allocated uninitialized, and then freed. I have not tried it, but it's very likely if someone writes something like

{ ACLDynamicQuantMatmul v; }

it will crash in the destructor, as wei_tensor has not be allocated, but it's allocator()->free() methods is called unconditionally

Actually, sorry for the confusion, my comment above is not right.
tensor.allocator()->free() never frees any memory (whether that memory was allocated by ACL or not). It just tells ACL that we're no longer using the pointer - See here - this can't lead to crashes.

The memory allocated by ACL is freed automatically.

I agree the structure here is not nice.
I added constructors and made sure all memory allocations happen through PyTorch - See here

malfet · 2025-03-05T15:27:11Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

@@ -0,0 +1,257 @@
+#pragma once


Would be good to add some sort of a comment explaining what ACL is (in computing, this acronym is most commonly associated with access control lists, see https://en.wikipedia.org/wiki/ACL ) and what classes/functions defined in this header are supposed to do

Good idea, done in the ghstack PR here

malfet · 2025-03-05T15:28:00Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

+  at::Tensor apply_dynamic_relu(at::Tensor input, bool reduce_range = false)
+      override;
+
+  std::shared_ptr<ACLDynamicQuantMatmul> get_acl_dynamic_quant_matmul(


Just curious what is the though process here of having implementation in the header vs respective CPP file?

My initial PoC was simple enough that it did not require a cpp file for implementation.
I agree that the implementation has gotten complex enough that it needs a cpp file.
I addressed this in the new ghstack PR, please see here

malfet · 2025-03-05T15:36:48Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

+
+  std::shared_ptr<ACLDynamicQuantMatmul> get_acl_dynamic_quant_matmul(
+      const ACLDynamicQuantMatmulCacheKey& key) {
+    // We're only maintaining a 2 element LRU cache


Sorry for nitpicks. Few thoughts:

LRU cache idea does not seem to be unique to ACL, so implementation should exist someplace else. If not, please add the implementation to say c10/utils/lru_cache.h and then use it here

Can variable name be shorter here? (just cache or quant_cache?

Again, naming convention, as variable is private, shouldn't its name end with _

LRU cache is not unique to ACL indeed. I could not find an implementation to use and given that our LRU cache impl just keeps track of two elements only, I don't see the point of making it global to PyTorch outside ACLUtils.h
If we end up implementing a (real) more complex LRU cache in the future, we'll add it to c10/utils/lru_cache.h and use it from there.

I agree with your comments about the name, it is now cache_ in the new ghstack PR, please see this line

malfet · 2025-03-05T15:38:34Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

+      std::rotate(
+          acl_dynamic_quant_cache.begin(),
+          acl_dynamic_quant_cache.begin() + 1,
+          acl_dynamic_quant_cache.end());


If your cache size is two, wouldn't std::swap(cache[0], cache[1]) would be an equivalent?

Good idea, thank you!
I addressed this in the new ghstack PR here

malfet · 2025-03-05T15:39:16Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

+              &acl_gemm->dst_tensor_info,
+              &acl_gemm->dst_tensor_info,
+              acl_gemm->acl_relu_info);
+      if (relu_status.error_code() != arm_compute::ErrorCode::OK) {


Don't you want to add TORCH_WARN or something, so that users know something went wrong?

Great Idea, done here

malfet · 2025-03-05T15:39:27Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

+
+    // validate that ACL can handle the given problem and inputs.
+    if (fuse_relu) {
+      arm_compute::Status relu_status =


Why not use auto there?

Suggested change

arm_compute::Status relu_status =

auto relu_status =

malfet · 2025-03-05T15:41:20Z

aten/src/ATen/native/quantized/cpu/ACLUtils.h

+
+    // allocate memory only for the quantized tensor, the rest will use memory
+    // already avaliable from PyTorch
+    acl_gemm->src_s8_tensor.allocator()->allocate();


Just curious, why use ACL allocator instead of an existing PyTorch one? So that memory tracking story is cleaner, i.e. all memory is allocated/tracked and freed by PyTorch caching allocator?

I agree with you.
I now use PyTorch for all allocations - ACL just import pointers but does not explicitly allocate/deallocate any memory.
See here

fadara01 · 2025-03-05T17:12:24Z

Last but not least: you've added the script that benchmarks the perf, but did not share the numbers before and after, that would help one understand the benefits this PR brings

Please check the PR description above, where I say:

This approach yields an average speedup (averaged over context_lengths of 2^3 up to 2^9) of ~ 50% for bert-base-uncased, bert-large-uncased, roberta-base, distilbert-base-uncased with 16 threads on a Neoverse-V1 (with transformers==4.48) - See benchmark code below.

ACL is already built with PyTorch as a shared library when USE_MKLDNN_ACL is set. Currently, it is only used indirectly in ATen via oneDNN for AArch64 targets. However there are cases where it makes sense to utilize ACL directly without oneDNN as an intermediary - e.g. quantization. See #145942, #147337, #146620. This patch enables such use cases by exposing ACL to ATen ghstack-source-id: 266c621 Pull Request resolved: #148581

…tly. This enables a fast path for eager mode static quantization for AArch64 through Arm Compute Library (ACL) directly. PR #145942 addressed the high overhead in qlinear_dynamic on AArch64 (due to redundant weight pretranspositions and reductions) by enabling a path that calls ACL directly. This does the same thing but for (static) qlinear. ghstack-source-id: 05435a0 Pull Request resolved: #148586

This enables qint8 and quint8 add for AArch64 through Arm Compute Library (ACL) directly. It's based on changes in PR pytorch#145942 which enables the use of ACL directly in ATen. Relative performance improvement using OMP_NUM_THREADS=1 is ~15x, using OMP_NUM_THREADS=32 it’s ~5.4x.

fadara01 · 2025-03-06T23:57:29Z

Please move logic that searches fro ACL into a separate PR (you have write permissions, so you can you ghstack, can't you) and use modern cmake (that defines target rather than global variables) to introduce new dependency

I created ghtack PRs for this:

Enable Direct Use of Arm Compute Library (ACL) in ATen #148584: for all cmake/build related changes
Enable fast qlinear static/dynamic path for AArch64 through ACL directly #148585: for this PR and Enable a fast path for (static) qlinear for AArch64 through ACL directly. #147337 - I thought it'd be easier for review and for addressing comments to have both in the same PR given they're very similar.

I addressed reviews on this PR in #148584 - it's equivalent ghstack one.

Unfortunately, I couldn't convert these PRs to be ghtack PRs because they're on feature branches belonging to my PyTorch fork, hence the new PRs.

fadara01 · 2025-03-06T23:58:53Z

what is the end goal: fully decouple ACL from oneDNN or keep some direct usage until oneDNN integration is done, or it it about something else)

The plan for now is to enable this fast path until direct fast path to ACL from oneDNN is implemented

ACL is already built with PyTorch as a shared library when USE_MKLDNN_ACL is set. Currently, it is only used indirectly in ATen via oneDNN for AArch64 targets. However there are cases where it makes sense to utilize ACL directly without oneDNN as an intermediary - e.g. quantization. See #145942, #147337, #146620. This patch enables such use cases by exposing ACL to ATen Pull Request resolved: #148584 Approved by: https://github.com/malfet

fadara01 · 2025-03-10T19:37:17Z

Closing in favor of ghstack PR #148585 which has all comments addressed

fadara01 requested review from a team, digantdesai, jeffdaily, jerryzh168, jianyuh, kimishpatel and salilsdesai as code owners January 29, 2025 17:19

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fadara01 force-pushed the acl_qlinear_dynamic branch from 7b44387 to 802e2a6 Compare January 30, 2025 11:35

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Enable qlinear_dynamic path for AArch64 through Arm Compute Library directly ARM-software/Tool-Solutions#281

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Enable qint8 and quint8 add for AArch64 using ACL directly #146620

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Enable Direct Use of Arm Compute Library (ACL) in ATen #148542

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Enable Direct Use of Arm Compute Library (ACL) in ATen #148581

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Enable a fast path for (static) qlinear for AArch64 through ACL directly. #148586

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Enable fast qlinear static/dynamic path for AArch64 through ACL directly #148585

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	#if defined(__aarch64__) && AT_MKLDNN_ACL_ENABLED()
	#if AT_MKLDNN_ACL_ENABLED()

	enum ACLDynamicQuantMatmulCacheKeyIndex {
	enum class ACLDynamicQuantMatmulCacheKeyIndex {

Enable fast qlinear_dynamic path for AArch64 through ACL directly #145942

Enable fast qlinear_dynamic path for AArch64 through ACL directly #145942

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