pyrox.dev
python3Packages.torch: fix on aarch64-linux (#439489)
+5
pkgs/development/python-modules/torch/source/default.nix
+5
pkgs/development/python-modules/torch/source/default.nix
···
306
url = "https://github.com/pytorch/pytorch/commit/231c72240d80091f099c95e326d3600cba866eee.patch";
307
hash = "sha256-BBCjxzz2TUkx4nXRyRILA82kMwyb/4+C3eOtYqf5dhk=";
308
})
309
+
310
+
# Fixes GCC-14 compatibility on ARM
311
+
# Adapted from https://github.com/pytorch/pytorch/pull/157867
312
+
# TODO: remove at the next release
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+
./gcc-14-arm-compat.path
314
]
315
++ lib.optionals cudaSupport [
316
./fix-cmake-cuda-toolkit.patch
+49
pkgs/development/python-modules/torch/source/gcc-14-arm-compat.path
+49
pkgs/development/python-modules/torch/source/gcc-14-arm-compat.path
···
···
1
+
diff --git a/aten/src/ATen/cpu/vec/sve/vec_bfloat16.h b/aten/src/ATen/cpu/vec/sve/vec_bfloat16.h
2
+
index 7f05c2ad166..1632b595c4c 100644
3
+
--- a/aten/src/ATen/cpu/vec/sve/vec_bfloat16.h
4
+
+++ b/aten/src/ATen/cpu/vec/sve/vec_bfloat16.h
5
+
@@ -220,8 +220,12 @@ class Vectorized<BFloat16> {
6
+
Vectorized<BFloat16> le(const Vectorized<BFloat16>& other) const;
7
+
};
8
+
9
+
-inline std::tuple<Vectorized<float>, Vectorized<float>> convert_bfloat16_float(
10
+
- const Vectorized<c10::BFloat16>& a) {
11
+
+#if defined(__GNUC__) && __GNUC__ == 14
12
+
+// Workaround for gcc-14.2.0 ICE during RTL pass: vregs when compiling for SVE
13
+
+__attribute__((optimize("no-tree-vectorize")))
14
+
+#endif
15
+
+inline std::tuple<Vectorized<float>, Vectorized<float>>
16
+
+convert_bfloat16_float(const Vectorized<c10::BFloat16>& a) {
17
+
static_assert(
18
+
Vectorized<c10::BFloat16>::size() == 2 * Vectorized<float>::size());
19
+
auto zero = svreinterpret_bf16_f32(svdup_n_f32(0.0f));
20
+
diff --git a/aten/src/ATen/native/cpu/Activation.cpp b/aten/src/ATen/native/cpu/Activation.cpp
21
+
index 52d5383e60f..00c9f4eb253 100644
22
+
--- a/aten/src/ATen/native/cpu/Activation.cpp
23
+
+++ b/aten/src/ATen/native/cpu/Activation.cpp
24
+
@@ -26,6 +26,10 @@ namespace at::native {
25
+
26
+
namespace {
27
+
28
+
+#if defined(__GNUC__) && __GNUC__ == 14 && defined(__aarch64__) && !defined(__ARM_FEATURE_SVE)
29
+
+// Workaround for gcc-14.2.0 ICE during RTL pass: expand when compiling for NEON
30
+
+__attribute__((optimize("no-tree-vectorize")))
31
+
+#endif
32
+
static void log_sigmoid_cpu_kernel(TensorBase &output, TensorBase &buffer, const TensorBase &input) {
33
+
if (at::isReducedFloatingType(input.scalar_type())) {
34
+
AT_DISPATCH_REDUCED_FLOATING_TYPES(input.scalar_type(), "log_sigmoid_cpu", [&]() {
35
+
diff --git a/aten/src/ATen/native/cpu/Unfold2d.cpp b/aten/src/ATen/native/cpu/Unfold2d.cpp
36
+
index 8ef0741e77a..8c94decfff0 100644
37
+
--- a/aten/src/ATen/native/cpu/Unfold2d.cpp
38
+
+++ b/aten/src/ATen/native/cpu/Unfold2d.cpp
39
+
@@ -169,6 +169,10 @@ static void unfolded2d_acc_channels_last(
40
+
41
+
/* note: due to write issues, this one cannot be parallelized as well as
42
+
* unfolded2d_copy */
43
+
+#if defined(__GNUC__) && __GNUC__ == 14 && defined(__ARM_FEATURE_SVE) && !defined(__ARM_FEATURE_BF16)
44
+
+// Workaround for gcc-14.2.0 ICE during RTL pass: vregs when compiling for SVE without BF16
45
+
+__attribute__((optimize("no-tree-vectorize")))
46
+
+#endif
47
+
void unfolded2d_acc_kernel(
48
+
ScalarType dtype,
49
+
void *finput_data,