mirror of
https://github.com/gentoo-mirror/gentoo.git
synced 2026-11-07 05:57:07 -08:00
sci-ml/pytorch: add 2.14.0
Signed-off-by: Alfredo Tupone <tupone@gentoo.org>
This commit is contained in:
Alfredo Tupone
@@ -2,3 +2,4 @@ DIST composable_kernel-f1746955.tar.gz 7687217 BLAKE2B 973bf5b8eece2944e053c17eb
|
||||
DIST flash-attention-2.7.4.gh.tar.gz 5841323 BLAKE2B 432999d763f2b3d732580ddfea5d3e01370351db0656546259a5e500a07516dd03c98828bfb55855dabe4adc651033b5d97ea4725ca46158b9970f0fbc662710 SHA512 05a4afb09e666f7404d6a3f8b5256e7bed6eba60a6f1bde2b7dbb96d318975f0b458c2521c7a38d88e97b6e4c27f29077cf787849daf82586e33f43a3d9a84b3
|
||||
DIST pytorch-2.12.0.tar.gz 64740318 BLAKE2B e5e46a93422454da420563d0a8057c87d1ed4b27e0bd9b3b30e46b5e40cfbb489072144ac00267af2f085432d55e12adf750daf15ea165a1252449b00dca1ff9 SHA512 f5ab0f6933d88271f772b416f8c9b3b0d3e1ffaf8d00838b455206266b40d7c805e34003d84b01cddc7f1ad917dd72d6f79a05063b0962cbf223d9042cff3206
|
||||
DIST pytorch-2.13.0.tar.gz 66369404 BLAKE2B 373df49b4f2d504aafedd948e34562696335d316ebcd497dbd86a54a3a3c39310d1595050cc2bcae858d0085e6a0c59ed5299962d6a2706441ab28c1699c4821 SHA512 0f7465835ae9206746d507e1b927afb94f8860248473f2d1c5d71ccd40defbd23d0e3e27a1b09deb907e753a7c9008352830170b53441701ade0916d68a42562
|
||||
DIST pytorch-2.14.0.tar.gz 69721997 BLAKE2B 8f6ba5cca094ca3dc4d7b3cf2bd9972196b71b84506c54ecf40f9fa80f0f87b469f3bd04b1200766c19aa4faf94b5bf4f1c6477bb98db1a0b26217fa4b2de10d SHA512 403f160b73800739772c54208b0edb2e4a462a45d282cf03b38e8b1e3f3e45053974bfdba57c4249a1d0cc47f7807fd1ff11c22a321306bf74e758f2ccb4cb18
|
||||
|
||||
@@ -0,0 +1,10 @@
|
||||
--- a/pyproject.toml 2026-09-03 16:46:07.080977356 +0200
|
||||
+++ b/pyproject.toml 2026-09-03 18:11:07.550648831 +0200
|
||||
@@ -354,7 +354,6 @@
|
||||
"LICENSE",
|
||||
"third_party/**/LICENSE",
|
||||
"third_party/**/LICENSE.txt",
|
||||
- "third_party/**/LICENSE.rst",
|
||||
]
|
||||
authors = [{ name = "PyTorch Team", email = "packages@pytorch.org" }]
|
||||
keywords = ["pytorch", "machine learning"]
|
||||
@@ -0,0 +1,288 @@
|
||||
From 37e33881b9e995f8328da0a149d71f4314bd7aa1 Mon Sep 17 00:00:00 2001
|
||||
From: Samaresh Kumar Singh <ssam3003@gmail.com>
|
||||
Date: Tue, 31 Mar 2026 21:39:08 -0500
|
||||
Subject: [PATCH 1/3] profiler: fix USE_KINETO=OFF build failure due to
|
||||
unconditional ActivityType.h include
|
||||
|
||||
kineto_shim.h was unconditionally including <ActivityType.h>, which is a Kineto header, causing a fatal compile error when building with USE_KINETO=OFF and Kineto is not installed (e.g. on Gentoo with system libraries).
|
||||
|
||||
Guard the include with #ifdef USE_KINETO and provide a minimal stub (enum class ActivityType : uint8_t { NONE = 0 }) so the data structures and function signatures that reference libkineto::ActivityType still compile. Guard the corresponding function bodies in kineto_shim.cpp (addCPUActivity,
|
||||
deviceTypeFromActivity) and collection.cpp (scopeToType, kinetoType) that use concrete enum values, adding no-op stubs for the non-Kineto paths.Fixes the bug #178939
|
||||
---
|
||||
torch/csrc/profiler/collection.cpp | 8 ++++++++
|
||||
torch/csrc/profiler/kineto_shim.cpp | 16 ++++++++++++++--
|
||||
torch/csrc/profiler/kineto_shim.h | 7 +++++++
|
||||
3 files changed, 29 insertions(+), 2 deletions(-)
|
||||
|
||||
diff --git a/torch/csrc/profiler/collection.cpp b/torch/csrc/profiler/collection.cpp
|
||||
index 1be2d80310910..e8054d4f44df7 100644
|
||||
--- a/torch/csrc/profiler/collection.cpp
|
||||
+++ b/torch/csrc/profiler/collection.cpp
|
||||
@@ -646,11 +646,13 @@ std::string toString(const ExtraFields<EventType::PyCall>& e) {
|
||||
e.callsite_.funcname_.str());
|
||||
}
|
||||
|
||||
+#ifdef USE_KINETO
|
||||
auto scopeToType(at::RecordScope scope) {
|
||||
return scope == at::RecordScope::USER_SCOPE
|
||||
? libkineto::ActivityType::USER_ANNOTATION
|
||||
: libkineto::ActivityType::CPU_OP;
|
||||
}
|
||||
+#endif
|
||||
|
||||
int64_t torchOpEndNS(
|
||||
const ExtraFields<EventType::TorchOp>& e,
|
||||
@@ -699,6 +701,7 @@ std::string Result::overload_name() const {
|
||||
[](const auto& e) -> std::string { return ""; }));
|
||||
}
|
||||
|
||||
+#ifdef USE_KINETO
|
||||
libkineto::ActivityType Result::kinetoType() const {
|
||||
return visit(c10::overloaded(
|
||||
ATTRIBUTE(TorchOp, scopeToType(e.scope_)),
|
||||
@@ -711,6 +714,11 @@ libkineto::ActivityType Result::kinetoType() const {
|
||||
ATTRIBUTE(PythonGC, libkineto::ActivityType::PYTHON_FUNCTION),
|
||||
ATTRIBUTE(Kineto, e.activity_type_)));
|
||||
}
|
||||
+#else
|
||||
+libkineto::ActivityType Result::kinetoType() const {
|
||||
+ return libkineto::ActivityType::NONE;
|
||||
+}
|
||||
+#endif
|
||||
|
||||
uint64_t Result::correlationID() const {
|
||||
return visit(c10::overloaded(
|
||||
diff --git a/torch/csrc/profiler/kineto_shim.cpp b/torch/csrc/profiler/kineto_shim.cpp
|
||||
index fa232e1a01016..0ddf62997a07e 100644
|
||||
--- a/torch/csrc/profiler/kineto_shim.cpp
|
||||
+++ b/torch/csrc/profiler/kineto_shim.cpp
|
||||
@@ -196,6 +196,7 @@ TraceWrapper::TraceWrapper(const int64_t start_time, const std::string& name)
|
||||
}
|
||||
#endif // USE_KINETO
|
||||
|
||||
+#ifdef USE_KINETO
|
||||
activity_t* TraceWrapper::addCPUActivity(
|
||||
const std::string& name,
|
||||
const libkineto::ActivityType type,
|
||||
@@ -203,7 +204,6 @@ activity_t* TraceWrapper::addCPUActivity(
|
||||
const uint64_t correlation_id,
|
||||
const int64_t start_time,
|
||||
const int64_t end_time) {
|
||||
-#ifdef USE_KINETO
|
||||
TORCH_CHECK((bool)(*this), "Cannot add event to non-existent trace.");
|
||||
cpu_trace_->emplace_activity(cpu_trace_->span, type, name);
|
||||
auto& act = libkineto::CpuTraceBuffer::toRef(cpu_trace_->activities.back());
|
||||
@@ -215,10 +215,18 @@ activity_t* TraceWrapper::addCPUActivity(
|
||||
act.endTime = end_time;
|
||||
}
|
||||
return cpu_trace_->activities.back().get();
|
||||
+}
|
||||
#else
|
||||
+activity_t* TraceWrapper::addCPUActivity(
|
||||
+ const std::string& name,
|
||||
+ const libkineto::ActivityType type,
|
||||
+ const DeviceAndResource device_and_resource,
|
||||
+ const uint64_t correlation_id,
|
||||
+ const int64_t start_time,
|
||||
+ const int64_t end_time) {
|
||||
return nullptr;
|
||||
-#endif // USE_KINETO
|
||||
}
|
||||
+#endif // USE_KINETO
|
||||
|
||||
void TraceWrapper::transferCpuTrace(int64_t end_time) {
|
||||
#ifdef USE_KINETO
|
||||
@@ -498,6 +506,7 @@ void logInvariantViolation(
|
||||
|
||||
namespace autograd::profiler {
|
||||
c10::DeviceType deviceTypeFromActivity(libkineto::ActivityType activity_type) {
|
||||
+#ifdef USE_KINETO
|
||||
// PrivateUse1 kineto backend reuse some ActivityTypes,
|
||||
// If PrivateUse1 backend is enabled, this should return
|
||||
// c10::DeviceType::PrivateUse1.
|
||||
@@ -551,6 +560,9 @@ c10::DeviceType deviceTypeFromActivity(libkineto::ActivityType activity_type) {
|
||||
return c10::DeviceType::CPU;
|
||||
}
|
||||
}
|
||||
+#else
|
||||
+ return c10::DeviceType::CPU;
|
||||
+#endif // USE_KINETO
|
||||
}
|
||||
|
||||
void addMetadataJson(const std::string& key, const std::string& value) {
|
||||
diff --git a/torch/csrc/profiler/kineto_shim.h b/torch/csrc/profiler/kineto_shim.h
|
||||
index 4f9bdc6770507..44bcb0e18a6e0 100644
|
||||
--- a/torch/csrc/profiler/kineto_shim.h
|
||||
+++ b/torch/csrc/profiler/kineto_shim.h
|
||||
@@ -12,7 +12,14 @@
|
||||
#undef USE_KINETO
|
||||
#endif
|
||||
|
||||
+#ifdef USE_KINETO
|
||||
#include <ActivityType.h>
|
||||
+#else
|
||||
+// Minimal stub so non-Kineto builds can compile types that hold ActivityType.
|
||||
+namespace libkineto {
|
||||
+enum class ActivityType : uint8_t { NONE = 0 };
|
||||
+} // namespace libkineto
|
||||
+#endif
|
||||
|
||||
#include <torch/csrc/Export.h>
|
||||
#include <torch/csrc/profiler/api.h>
|
||||
|
||||
From f5e3a9d9aa12c6e2006a9243c97ef7703fcbbe2f Mon Sep 17 00:00:00 2001
|
||||
From: Samaresh Kumar Singh <ssam3003@gmail.com>
|
||||
Date: Wed, 1 Apr 2026 10:34:24 -0500
|
||||
Subject: [PATCH 2/3] Fixed the USER_ANNOTATION/GPU_USER_ANNOTATION build
|
||||
errors with external kineto
|
||||
|
||||
When building against a system-installed kineto (e.g. Gentoo's sci-ml/kineto) that lacks USER_ANNOTATION and GPU_USER_ANNOTATION in its ActivityType enum,
|
||||
init.cpp failed to compile because it referenced those enum member names directly.
|
||||
---
|
||||
torch/csrc/autograd/init.cpp | 7 +------
|
||||
torch/csrc/autograd/profiler_kineto.cpp | 7 +++++++
|
||||
torch/csrc/autograd/profiler_kineto.h | 1 +
|
||||
torch/csrc/profiler/kineto_shim.h | 11 +++++++++--
|
||||
4 files changed, 18 insertions(+), 8 deletions(-)
|
||||
|
||||
diff --git a/torch/csrc/autograd/init.cpp b/torch/csrc/autograd/init.cpp
|
||||
index 1e2442a5d99db..f8e8795c5805e 100644
|
||||
--- a/torch/csrc/autograd/init.cpp
|
||||
+++ b/torch/csrc/autograd/init.cpp
|
||||
@@ -310,12 +310,7 @@ PyObject* THPAutograd_initExtension(PyObject* _unused, PyObject* unused) {
|
||||
.def("privateuse1_elapsed_us", &KinetoEvent::privateuse1ElapsedUs)
|
||||
.def(
|
||||
"is_user_annotation",
|
||||
- [](const KinetoEvent& e) {
|
||||
- return e.activityType() ==
|
||||
- (uint8_t)libkineto::ActivityType::USER_ANNOTATION ||
|
||||
- e.activityType() ==
|
||||
- (uint8_t)libkineto::ActivityType::GPU_USER_ANNOTATION;
|
||||
- })
|
||||
+ [](const KinetoEvent& e) { return e.isUserAnnotation(); })
|
||||
.def(
|
||||
"is_python_function",
|
||||
[](const KinetoEvent& e) { return e.isPythonFunction(); })
|
||||
diff --git a/torch/csrc/autograd/profiler_kineto.cpp b/torch/csrc/autograd/profiler_kineto.cpp
|
||||
index 9a076f58d7143..6b5165f0872fb 100644
|
||||
--- a/torch/csrc/autograd/profiler_kineto.cpp
|
||||
+++ b/torch/csrc/autograd/profiler_kineto.cpp
|
||||
@@ -1165,6 +1165,13 @@
|
||||
&privateuse1_event_start, &privateuse1_event_end);
|
||||
}
|
||||
|
||||
+bool KinetoEvent::isUserAnnotation() const {
|
||||
+ constexpr uint8_t kUserAnnotation = 1;
|
||||
+ constexpr uint8_t kGpuUserAnnotation = 2;
|
||||
+ const auto type = activityType();
|
||||
+ return type == kUserAnnotation || type == kGpuUserAnnotation;
|
||||
+}
|
||||
+
|
||||
void KinetoEvent::getPerfEventCounters(std::vector<uint64_t>& in) const {
|
||||
return result_->visit(c10::overloaded(
|
||||
[&in](const ExtraFields<EventType::TorchOp>& e) -> void {
|
||||
diff --git a/torch/csrc/autograd/profiler_kineto.h b/torch/csrc/autograd/profiler_kineto.h
|
||||
index 777b8a5851ed5..c3135c61245d4 100644
|
||||
--- a/torch/csrc/autograd/profiler_kineto.h
|
||||
+++ b/torch/csrc/autograd/profiler_kineto.h
|
||||
@@ -72,6 +72,7 @@ struct TORCH_API KinetoEvent {
|
||||
bool isPythonFunction() const;
|
||||
int64_t cudaElapsedUs() const;
|
||||
int64_t privateuse1ElapsedUs() const;
|
||||
+ bool isUserAnnotation() const;
|
||||
void getPerfEventCounters(torch::profiler::perf_counters_t& /*in*/) const;
|
||||
extra_meta_t extraMeta() const;
|
||||
const typed_metadata_t& typedMetadata() const;
|
||||
diff --git a/torch/csrc/profiler/kineto_shim.h b/torch/csrc/profiler/kineto_shim.h
|
||||
index 44bcb0e18a6e0..ddc00c03fa9cc 100644
|
||||
--- a/torch/csrc/profiler/kineto_shim.h
|
||||
+++ b/torch/csrc/profiler/kineto_shim.h
|
||||
@@ -15,9 +15,16 @@
|
||||
#ifdef USE_KINETO
|
||||
#include <ActivityType.h>
|
||||
#else
|
||||
-// Minimal stub so non-Kineto builds can compile types that hold ActivityType.
|
||||
namespace libkineto {
|
||||
-enum class ActivityType : uint8_t { NONE = 0 };
|
||||
+enum class ActivityType : uint8_t {
|
||||
+ CPU_OP = 0,
|
||||
+ USER_ANNOTATION,
|
||||
+ GPU_USER_ANNOTATION,
|
||||
+ NONE = CPU_OP,
|
||||
+};
|
||||
+inline const char* toString(ActivityType) {
|
||||
+ return "CPU_OP";
|
||||
+}
|
||||
} // namespace libkineto
|
||||
#endif
|
||||
|
||||
|
||||
From 717526b7ce90d242cb917db90f4cb13c85c93661 Mon Sep 17 00:00:00 2001
|
||||
From: Samaresh Kumar Singh <ssam3003@gmail.com>
|
||||
Date: Fri, 3 Apr 2026 13:01:01 -0500
|
||||
Subject: [PATCH 3/3] profiler: guard GPU ActivityType checks with KINETO GPU
|
||||
backend macros
|
||||
|
||||
The externalId() function referenced GPU-specific libkineto::ActivityType
|
||||
values (GPU_MEMCPY, GPU_MEMSET, CONCURRENT_KERNEL, CUDA_RUNTIME,
|
||||
CUDA_DRIVER, PRIVATEUSE1_RUNTIME, PRIVATEUSE1_DRIVER) that are only
|
||||
present in kineto builds with GPU backend support.
|
||||
|
||||
System-installed kineto packages built without CUDA/ROCm support
|
||||
(LIBKINETO_NOCUPTI + LIBKINETO_NOROCTRACER) omit these enum values,
|
||||
causing compile errors. Guard the check with the existing GPU backend
|
||||
macros. Also restructures the condition to an early-return to avoid
|
||||
duplicating the correlation ID lookup.
|
||||
---
|
||||
torch/csrc/autograd/profiler_kineto.cpp | 38 ++++++++++++++-----------
|
||||
1 file changed, 22 insertions(+), 16 deletions(-)
|
||||
|
||||
diff --git a/torch/csrc/autograd/profiler_kineto.cpp b/torch/csrc/autograd/profiler_kineto.cpp
|
||||
index 6b5165f0872fb..466e8d66ab518 100644
|
||||
--- a/torch/csrc/autograd/profiler_kineto.cpp
|
||||
+++ b/torch/csrc/autograd/profiler_kineto.cpp
|
||||
@@ -1209,18 +1209,25 @@
|
||||
return static_cast<int64_t>(linked);
|
||||
}
|
||||
|
||||
+#if defined(USE_KINETO) && \
|
||||
+ (!defined(LIBKINETO_NOCUPTI) || !defined(LIBKINETO_NOROCTRACER))
|
||||
// Orphaned GPU activities (no linked CPU op) in these types should not get
|
||||
// an External id, to avoid incorrect cross-linking in trace viewers.
|
||||
+ // These GPU-specific ActivityType values are only present when kineto is
|
||||
+ // built with GPU backend support (CUPTI or ROCtracer). CPU-only kineto
|
||||
+ // builds (e.g. system packages without GPU support) omit them.
|
||||
auto type = static_cast<libkineto::ActivityType>(activityType());
|
||||
- if (type != libkineto::ActivityType::GPU_MEMCPY &&
|
||||
- type != libkineto::ActivityType::GPU_MEMSET &&
|
||||
- type != libkineto::ActivityType::CONCURRENT_KERNEL &&
|
||||
- type != libkineto::ActivityType::CUDA_RUNTIME &&
|
||||
- type != libkineto::ActivityType::CUDA_DRIVER &&
|
||||
- type != libkineto::ActivityType::XPU_RUNTIME &&
|
||||
- type != libkineto::ActivityType::XPU_DRIVER &&
|
||||
- type != libkineto::ActivityType::PRIVATEUSE1_RUNTIME &&
|
||||
- type != libkineto::ActivityType::PRIVATEUSE1_DRIVER) {
|
||||
+ if (type == libkineto::ActivityType::GPU_MEMCPY ||
|
||||
+ type == libkineto::ActivityType::GPU_MEMSET ||
|
||||
+ type == libkineto::ActivityType::CONCURRENT_KERNEL ||
|
||||
+ type == libkineto::ActivityType::CUDA_RUNTIME ||
|
||||
+ type == libkineto::ActivityType::CUDA_DRIVER ||
|
||||
+ type == libkineto::ActivityType::PRIVATEUSE1_RUNTIME ||
|
||||
+ type == libkineto::ActivityType::PRIVATEUSE1_DRIVER) {
|
||||
+ return 0;
|
||||
+ }
|
||||
+#endif
|
||||
+
|
||||
return static_cast<int64_t>(result_->visit(c10::overloaded(
|
||||
[](const ExtraFields<EventType::TorchOp>& e) -> uint64_t {
|
||||
return e.correlation_id_;
|
||||
@@ -1229,9 +1236,6 @@
|
||||
return e.correlation_id_;
|
||||
},
|
||||
[](const auto&) -> uint64_t { return 0; })));
|
||||
- }
|
||||
-
|
||||
- return 0;
|
||||
}
|
||||
|
||||
#define FORWARD_FROM_RESULT(method_name, result_expr) \
|
||||
@@ -0,0 +1,18 @@
|
||||
--- a/cmake/Dependencies.cmake
|
||||
+++ b/cmake/Dependencies.cmake
|
||||
@@ -276,15 +276,8 @@
|
||||
# --- [ PocketFFT
|
||||
set(AT_POCKETFFT_ENABLED 0)
|
||||
if(NOT AT_MKL_ENABLED)
|
||||
- set(POCKETFFT_INCLUDE_DIR "${CMAKE_SOURCE_DIR}/third_party/pocketfft/")
|
||||
- if(NOT EXISTS "${POCKETFFT_INCLUDE_DIR}")
|
||||
- message(FATAL_ERROR "pocketfft directory not found, expected ${POCKETFFT_INCLUDE_DIR}")
|
||||
- elseif(NOT EXISTS "${POCKETFFT_INCLUDE_DIR}/pocketfft_hdronly.h")
|
||||
- message(FATAL_ERROR "pocketfft headers not found in ${POCKETFFT_INCLUDE_DIR}")
|
||||
- endif()
|
||||
|
||||
set(AT_POCKETFFT_ENABLED 1)
|
||||
- message(STATUS "Using pocketfft in directory: ${POCKETFFT_INCLUDE_DIR}")
|
||||
endif()
|
||||
|
||||
# ---[ Dependencies
|
||||
@@ -0,0 +1,365 @@
|
||||
# Copyright 2022-2026 Gentoo Authors
|
||||
# Distributed under the terms of the GNU General Public License v2
|
||||
|
||||
EAPI=8
|
||||
|
||||
DISTUTILS_USE_PEP517=scikit-build-core
|
||||
PYTHON_COMPAT=( python3_{12..14} )
|
||||
DISTUTILS_SINGLE_IMPL=1
|
||||
DISTUTILS_EXT=1
|
||||
|
||||
ROCM_VERSION=6.1
|
||||
inherit distutils-r1 prefix cuda flag-o-matic rocm multiprocessing
|
||||
|
||||
# pytorch-2.13.0 depends on specific commit of composable kernel
|
||||
# TODO: replace it with DEPEND in the future
|
||||
CK_COMMIT=f1746955fdaf80a3414de814bf32437686dac347
|
||||
CK_P=composable_kernel-${CK_COMMIT:0:8}
|
||||
|
||||
# Starting from 2.7.0 pytorch moved flash attention out-of-tree,
|
||||
# but hardcoded it as third_party subproject
|
||||
# TODO: unbundle
|
||||
FLASH_PV=2.7.4
|
||||
FLASH_PN=flash-attention
|
||||
FLASH_P=${FLASH_PN}-${FLASH_PV}
|
||||
FLASH_ATT_URI="https://github.com/Dao-AILab/${FLASH_PN}/archive/refs/tags/v${FLASH_PV}.tar.gz -> ${FLASH_P}.gh.tar.gz"
|
||||
|
||||
DESCRIPTION="Tensors and Dynamic neural networks in Python with strong GPU acceleration"
|
||||
HOMEPAGE="https://pytorch.org/"
|
||||
SRC_URI="https://github.com/pytorch/${PN}/archive/refs/tags/v${PV}.tar.gz
|
||||
-> ${P}.tar.gz
|
||||
rocm? (
|
||||
https://github.com/ROCm/composable_kernel/archive/${CK_COMMIT}.tar.gz
|
||||
-> ${CK_P}.tar.gz
|
||||
)
|
||||
cuda? (
|
||||
flash? ( ${FLASH_ATT_URI} )
|
||||
memefficient? ( ${FLASH_ATT_URI} )
|
||||
)
|
||||
"
|
||||
|
||||
LICENSE="BSD"
|
||||
SLOT="0"
|
||||
KEYWORDS="~amd64 ~arm64"
|
||||
IUSE="cuda cusparselt distributed fbgemm flash gloo kineto memefficient
|
||||
mimalloc mkl mpi nccl nnpack +numpy onednn openblas opencl openmp qnnpack
|
||||
rocm xnnpack"
|
||||
RESTRICT="test"
|
||||
|
||||
REQUIRED_USE="
|
||||
${PYTHON_REQUIRED_USE}
|
||||
mpi? ( distributed )
|
||||
gloo? ( distributed )
|
||||
?? ( cuda rocm )
|
||||
rocm? (
|
||||
|| ( ${ROCM_REQUIRED_USE} )
|
||||
memefficient? ( flash )
|
||||
)
|
||||
cusparselt? ( || ( cuda rocm ) )
|
||||
flash? ( || ( cuda rocm ) )
|
||||
memefficient? ( || ( cuda rocm ) )
|
||||
nccl? ( rocm )
|
||||
"
|
||||
|
||||
RDEPEND="
|
||||
${PYTHON_DEPS}
|
||||
!sci-ml/caffe2
|
||||
dev-cpp/abseil-cpp:=
|
||||
dev-cpp/gflags:=
|
||||
>=dev-cpp/glog-0.6.0:=
|
||||
>=dev-libs/cpuinfo-2025.11.14
|
||||
dev-libs/libfmt:=
|
||||
dev-libs/protobuf:=
|
||||
dev-libs/sleef
|
||||
sci-ml/onnx
|
||||
$(python_gen_cond_dep '
|
||||
dev-python/sympy[${PYTHON_USEDEP}]
|
||||
dev-python/typing-extensions[${PYTHON_USEDEP}]
|
||||
')
|
||||
!mkl? ( !openblas? ( virtual/blas ) )
|
||||
cuda? (
|
||||
dev-libs/cudnn
|
||||
>=sci-ml/cudnn-frontend-1.12.0:=
|
||||
>=dev-util/nvidia-cuda-toolkit-12.9:=[profiler]
|
||||
cusparselt? ( dev-libs/cusparselt )
|
||||
)
|
||||
distributed? (
|
||||
!rocm? ( sci-ml/tensorpipe[cuda?] )
|
||||
dev-cpp/cpp-httplib:=
|
||||
)
|
||||
fbgemm? ( >=sci-ml/FBGEMM-1.4 )
|
||||
gloo? ( >=sci-ml/gloo-2025.06.04[cuda?,rocm?] )
|
||||
kineto? ( ~sci-ml/kineto-0.4.0_p20260603 )
|
||||
mimalloc? ( dev-libs/mimalloc )
|
||||
mkl? ( sci-libs/mkl )
|
||||
mpi? ( virtual/mpi )
|
||||
nnpack? (
|
||||
sci-ml/NNPACK
|
||||
dev-libs/pthreadpool
|
||||
)
|
||||
numpy? ( $(python_gen_cond_dep '
|
||||
dev-python/numpy[${PYTHON_USEDEP}]
|
||||
') )
|
||||
onednn? ( sci-ml/oneDNN )
|
||||
openblas? ( sci-libs/openblas )
|
||||
opencl? ( virtual/opencl )
|
||||
qnnpack? (
|
||||
!sci-libs/QNNPACK
|
||||
sci-ml/gemmlowp
|
||||
dev-libs/pthreadpool
|
||||
)
|
||||
rocm? (
|
||||
nccl? ( >=dev-libs/rccl-6.3:= <dev-libs/rccl-7.3:= )
|
||||
>=dev-util/hip-6.3:= <dev-util/hip-7.3:=
|
||||
>=dev-util/roctracer-6.3:= <dev-util/roctracer-7.3:=
|
||||
>=sci-libs/hipBLAS-6.3:= <sci-libs/hipBLAS-7.3:=[rocsolver(+)]
|
||||
>=sci-libs/hipBLASLt-6.3:= <sci-libs/hipBLASLt-7.3:=
|
||||
>=sci-libs/hipFFT-6.3:= <sci-libs/hipFFT-7.3:=
|
||||
>=sci-libs/hipRAND-6.3:= <sci-libs/hipRAND-7.3:=
|
||||
>=sci-libs/hipSOLVER-6.3:= <sci-libs/hipSOLVER-7.3:=
|
||||
>=sci-libs/hipSPARSE-6.3:= <sci-libs/hipSPARSE-7.3:=
|
||||
>=sci-libs/miopen-6.3:= <sci-libs/miopen-7.3:=
|
||||
>=sci-libs/rocBLAS-6.3:= <sci-libs/rocBLAS-7.3:=
|
||||
>=sci-libs/rocRAND-6.3:= <sci-libs/rocRAND-7.3:=
|
||||
>=sci-libs/rocSOLVER-6.3:= <sci-libs/rocSOLVER-7.3:=
|
||||
memefficient? ( =sci-libs/aotriton-bin-0.13*:= )
|
||||
distributed? (
|
||||
>=dev-util/rocm-smi-6.3:= <dev-util/rocm-smi-7.3:=
|
||||
>=dev-util/amdsmi-6.3:= <dev-util/amdsmi-7.3:=
|
||||
)
|
||||
cusparselt? ( >=sci-libs/hipsparselt-6.3:= <sci-libs/hipsparselt-7.3:= )
|
||||
)
|
||||
xnnpack? (
|
||||
>=sci-ml/XNNPACK-2024.11
|
||||
dev-libs/pthreadpool
|
||||
)
|
||||
"
|
||||
|
||||
DEPEND="
|
||||
${RDEPEND}
|
||||
dev-cpp/nlohmann_json
|
||||
dev-libs/flatbuffers
|
||||
dev-libs/FXdiv
|
||||
dev-libs/pocketfft
|
||||
dev-libs/psimd
|
||||
sci-ml/FP16
|
||||
$(python_gen_cond_dep '
|
||||
<dev-python/pybind11-3.0.5[${PYTHON_USEDEP}]
|
||||
dev-python/pyyaml[${PYTHON_USEDEP}]
|
||||
dev-python/typing-extensions[${PYTHON_USEDEP}]
|
||||
')
|
||||
cuda? ( ~dev-libs/cutlass-4.4.2[tools(+)] )
|
||||
onednn? ( sci-ml/ideep )
|
||||
rocm? (
|
||||
>=sci-libs/hipCUB-6.3:= <sci-libs/hipCUB-7.3:=
|
||||
>=sci-libs/rocPRIM-6.3:= <sci-libs/rocPRIM-7.3:=
|
||||
>=sci-libs/rocThrust-6.3:= <sci-libs/rocThrust-7.3:=
|
||||
)
|
||||
qnnpack? ( dev-libs/clog )
|
||||
"
|
||||
|
||||
BDEPEND="dev-build/cmake"
|
||||
|
||||
PATCHES=(
|
||||
"${FILESDIR}"/${PN}-2.13.0-aotriton-fixes.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-glog.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-mimalloc.patch
|
||||
"${FILESDIR}"/${P}-removekineto-pr178960.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-rocm-assert-fix.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-unbundle_fbgemm.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-unbundle_flatbuffers.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-unbundle_fmt.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-unbundle_fxdiv.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-unbundle_httplib.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-unbundle_kineto.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-unbundle_mkldnn.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-unbundle_nnpack.patch
|
||||
"${FILESDIR}"/${P}-unbundle_pocketfft.patch
|
||||
"${FILESDIR}"/${PN}-2.13.0-xnnpack.patch
|
||||
"${FILESDIR}"/${P}-license.patch
|
||||
)
|
||||
|
||||
src_prepare() {
|
||||
if use cuda && ( use flash || use memefficient ); then
|
||||
mv "${WORKDIR}"/${FLASH_P}/* third_party/${FLASH_PN}/ || die
|
||||
fi
|
||||
filter-lto #bug 862672
|
||||
|
||||
eapply "${FILESDIR}/${PN}-2.10.0-cpp-extension-multilib.patch"
|
||||
# Replace placeholders added by cpp-extension.patch
|
||||
sed -e "s|%LIB_DIR%|$(get_libdir)|g" \
|
||||
-i torch/utils/cpp_extension.py || die
|
||||
|
||||
# Unbundle fmt
|
||||
sed -i \
|
||||
-e 's|::fmt-header-only||' \
|
||||
c10/CMakeLists.txt \
|
||||
cmake/Dependencies.cmake \
|
||||
torch/CMakeLists.txt \
|
||||
|| die
|
||||
|
||||
# tensorpipe is in system, not a build target of pytorch
|
||||
sed -e '/target_compile_options_if_supported(tensorpipe/d' -i cmake/Dependencies.cmake || die
|
||||
|
||||
# Drop third_party from CMake tree
|
||||
sed -i \
|
||||
-e '/add_subdirectory.*third_party/d' \
|
||||
CMakeLists.txt \
|
||||
cmake/Dependencies.cmake \
|
||||
cmake/ProtoBuf.cmake \
|
||||
aten/src/ATen/CMakeLists.txt \
|
||||
|| die
|
||||
|
||||
# Add needed file for cutlass as symbolic link
|
||||
ln -sf /usr/share/cutlass/examples third_party/cutlass/examples || die
|
||||
|
||||
# cudnn_frontend is unbundled, but some targets still look for its
|
||||
# headers under third_party/cudnn_frontend/include.
|
||||
if use cuda; then
|
||||
mkdir -p third_party/cudnn_frontend || die
|
||||
ln -sf /usr/include third_party/cudnn_frontend/include || die
|
||||
fi
|
||||
|
||||
distutils-r1_src_prepare
|
||||
|
||||
# Noisy warnings from Logging.h
|
||||
sed -i 's/-Wextra-semi//' cmake/public/utils.cmake || die
|
||||
|
||||
pushd torch/csrc/jit/serialization > /dev/null || die
|
||||
flatc --cpp --gen-mutable --scoped-enums mobile_bytecode.fbs || die
|
||||
popd > /dev/null || die
|
||||
|
||||
# prefixify the hardcoded paths, after all patches are applied
|
||||
hprefixify \
|
||||
aten/CMakeLists.txt \
|
||||
caffe2/CMakeLists.txt \
|
||||
cmake/Metal.cmake \
|
||||
cmake/Modules/*.cmake \
|
||||
cmake/Modules_CUDA_fix/FindCUDNN.cmake \
|
||||
cmake/Modules_CUDA_fix/upstream/FindCUDA/make2cmake.cmake \
|
||||
cmake/Modules_CUDA_fix/upstream/FindPackageHandleStandardArgs.cmake \
|
||||
cmake/public/LoadHIP.cmake \
|
||||
cmake/public/cuda.cmake \
|
||||
cmake/Dependencies.cmake \
|
||||
tools/setup_helpers/env.py \
|
||||
torch/CMakeLists.txt \
|
||||
CMakeLists.txt
|
||||
|
||||
if use rocm; then
|
||||
# TODO: delete, when caffe2 depends on systemwide composable_kernel
|
||||
sed -e "s:third_party/composable_kernel:../composable_kernel-${CK_COMMIT}:g" \
|
||||
-i aten/src/ATen/CMakeLists.txt || die
|
||||
|
||||
# Workaround for libc++ issue https://github.com/llvm/llvm-project/issues/100802
|
||||
sed -e 's/std::memcpy/memcpy/g' \
|
||||
-i torch/headeronly/util/Half.h \
|
||||
-i aten/src/ATen/native/cuda/int4mm.cu || die
|
||||
|
||||
ebegin "HIPifying cuda sources"
|
||||
FBCODE_BUILD_TOOL="buck" ${EPYTHON} tools/amd_build/build_amd.py || die
|
||||
eend $?
|
||||
fi
|
||||
}
|
||||
|
||||
src_configure() {
|
||||
if use cuda && [[ -z ${TORCH_CUDA_ARCH_LIST} ]]; then
|
||||
ewarn "WARNING: pytorch is being built with its default CUDA compute capabilities: 3.5 and 7.0."
|
||||
ewarn "These may not be optimal for your GPU."
|
||||
ewarn ""
|
||||
ewarn "To configure pytorch with the CUDA compute capability that is optimal for your GPU,"
|
||||
ewarn "set TORCH_CUDA_ARCH_LIST in your make.conf, and re-emerge pytorch."
|
||||
ewarn "For example, to use CUDA capability 7.5 & 3.5, add: TORCH_CUDA_ARCH_LIST=7.5 3.5"
|
||||
ewarn "For a Maxwell model GPU, an example value would be: TORCH_CUDA_ARCH_LIST=Maxwell"
|
||||
ewarn ""
|
||||
ewarn "You can look up your GPU's CUDA compute capability at https://developer.nvidia.com/cuda-gpus"
|
||||
ewarn "or by running /opt/cuda/extras/demo_suite/deviceQuery | grep 'CUDA Capability'"
|
||||
fi
|
||||
}
|
||||
|
||||
python_compile() {
|
||||
local -x BUILD_TEST=OFF
|
||||
local -x CMAKE_BUILD_DIR="${BUILD_DIR}"
|
||||
local -x MAX_JOBS=$(makeopts_jobs)
|
||||
local -x PYTORCH_BUILD_VERSION=${PV}
|
||||
local -x PYTORCH_BUILD_NUMBER=0
|
||||
local -x USE_CCACHE=OFF
|
||||
local -x USE_CUDA=$(usex cuda)
|
||||
local -x USE_DISTRIBUTED=$(usex distributed)
|
||||
local -x USE_FBGEMM=$(usex fbgemm)
|
||||
local -x USE_FLASH_ATTENTION=$(usex flash)
|
||||
local -x USE_GFLAGS=ON
|
||||
local -x USE_GLOG=ON
|
||||
local -x USE_GLOO=$(usex gloo)
|
||||
local -x USE_ITT=OFF
|
||||
local -x USE_KINETO=$(usex kineto)
|
||||
local -x USE_KLEIDIAI=OFF # TODO
|
||||
local -x USE_MAGMA=OFF # TODO: In GURU as sci-libs/magma
|
||||
local -x USE_MEM_EFF_ATTENTION=$(usex memefficient)
|
||||
local -x USE_MIMALLOC=$(usex mimalloc)
|
||||
local -x USE_MKLDNN=$(usex onednn)
|
||||
local -x USE_MPI=$(usex mpi)
|
||||
local -x USE_NNPACK=$(usex nnpack)
|
||||
local -x USE_NUMA=OFF
|
||||
local -x USE_NUMPY=$(usex numpy)
|
||||
local -x USE_OPENCL=$(usex opencl)
|
||||
local -x USE_OPENMP=$(usex openmp)
|
||||
local -x USE_PYTORCH_QNNPACK=$(usex qnnpack)
|
||||
local -x USE_PYTORCH_METAL=OFF
|
||||
local -x USE_ROCM=$(usex rocm)
|
||||
local -x USE_SYSTEM_LIBS=ON
|
||||
local -x USE_SYSTEM_XNNPACK=$(usex xnnpack)
|
||||
local -x USE_TENSORPIPE=$(usex distributed $(usex !rocm))
|
||||
local -x DUSE_UCC=OFF
|
||||
local -x USE_VALGRIND=OFF
|
||||
local -x USE_XNNPACK=$(usex xnnpack)
|
||||
local -x USE_XPU=OFF
|
||||
|
||||
if use mkl; then
|
||||
local -x USE_BLAS=ON
|
||||
local -x BLAS=MKL
|
||||
elif use openblas; then
|
||||
local -x USE_BLAS=ON
|
||||
local -x BLAS=OpenBLAS
|
||||
else
|
||||
# Note: USE_BLAS=OFF does not properly work, instead pytorch still searches for libraries.
|
||||
# Even though it is easy to patch pytorch to disable BLAS usage completely,
|
||||
# it is practically not useful, as it would render the library barely usable.
|
||||
local -x USE_BLAS=ON
|
||||
local -x BLAS=Generic
|
||||
local -x GENERIC_BLAS_LIBRARIES=cblas
|
||||
fi
|
||||
|
||||
if use cuda; then
|
||||
# bug 867706 926116
|
||||
cuda_add_sandbox
|
||||
addpredict "/dev/char/"
|
||||
|
||||
local -x CMAKE_CUDA_FLAGS="$(cuda_gccdir -f | tr -d \")"
|
||||
local -x TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST:-7.0}"
|
||||
local -x USE_CUDNN=ON
|
||||
local -x USE_FLASH_ATTENTION=OFF
|
||||
local -x USE_MEM_EFF_ATTENTION=OFF
|
||||
local -x USE_NCCL=OFF # TODO: NVIDIA Collective Communication Library
|
||||
|
||||
elif use rocm; then
|
||||
export PYTORCH_ROCM_ARCH="$(get_amdgpu_flags)"
|
||||
|
||||
if use memefficient; then
|
||||
export AOTRITON_INSTALLED_PREFIX="${ESYSROOT}/usr"
|
||||
fi
|
||||
|
||||
local -x CMAKE_REQUIRE_FIND_PACKAGE_HIP=ON
|
||||
local -x USE_NCCL=$(usex nccl)
|
||||
local -x CMAKE_DISABLE_FIND_PACKAGE_hipsparselt=$(usex !cusparselt) # disable automagic
|
||||
local -x USE_ROCM_CK_SDPA=OFF # requires flash + aiter, works only on gfx90a/gfx942/gfx950
|
||||
local -x ROCM_PATH=/usr
|
||||
local -x HIP_CLANG_PATH=$(hipconfig --hipclangpath)
|
||||
|
||||
# ROCm libraries produce too much warnings
|
||||
append-cxxflags -Wno-deprecated-declarations -Wno-unused-result -Wno-unused-value
|
||||
else
|
||||
local -x USE_NCCL=OFF
|
||||
fi
|
||||
|
||||
distutils-r1_python_compile develop sdist
|
||||
}
|
||||
Reference in New Issue
Block a user