update MIN_SUPPORTED to 2.13-pytorch-AtomGit

Mci/docker/README.md+4-4

	@@ -33,15 +33,15 @@ docker run -it -v /{code_path}/pytorch:/home/pytorch manylinux-builder:v1 bash
33		33
34	Compile torch_npu	34	Compile torch_npu
35		35
36	-Take Python 3.9 as an example	36	+Take Python 3.10 as an example
37		37
38	```Shell	38	```Shell
39	cd /home/pytorch	39	cd /home/pytorch
40	-bash ci/build.sh --python=3.9	40	+bash ci/build.sh --python=3.10
41	```	41	```
42		42
43	-Use `--torch=<version>` to target a specific PyTorch version (supported: 2.10.0, 2.11.0, 2.12.0, 2.13.0). The installed PyTorch must match.	43	+Use `--torch=<version>` to target a specific PyTorch version (supported: 2.13.0, 2.14.0). The installed PyTorch must match.
44		44
45	```Shell	45	```Shell
46	-bash ci/build.sh --python=3.9 --torch=2.11.0	46	+bash ci/build.sh --python=3.10 --torch=2.13.0
47	```	47	```

Mtorch_npu/_compat/distributed.py+2-8

	@@ -2,17 +2,11 @@ from torch_npu._compat.version import CURRENT_VERSION
2		2
3	__all__ = [	3	__all__ = [
4	"register_op_strategy",	4	"register_op_strategy",
5	- "register_prop_rule",
6	"_mm_like_strategy",	5	"_mm_like_strategy",
7	]	6	]
8		7
9	-# COMPAT(>= 2.11): register_op_strategy / register_prop_rule moved from	8	+# register_op_strategy moved from _ops.registration to _ops.utils in PyTorch 2.11.
10	-# _ops.registration to _ops.utils in PyTorch 2.11.	9	+from torch.distributed.tensor._ops.utils import register_op_strategy
11	-# CAN REMOVE else branch when MIN_SUPPORTED >= (2, 11)
12	-if CURRENT_VERSION >= (2, 11):
13	- from torch.distributed.tensor._ops.utils import register_op_strategy, register_prop_rule
14	-else:
15	- from torch.distributed.tensor._ops.registration import register_op_strategy, register_prop_rule
16		10
17		11
18	# COMPAT(>= 2.14): upstream pytorch#186667 removed the helper	12	# COMPAT(>= 2.14): upstream pytorch#186667 removed the helper

Dtorch_npu/_compat/inductor.py+0-18

	@@ -1,18 +0,0 @@
1	-from torch_npu._compat.version import CURRENT_VERSION
2	-
3	-# COMPAT(>= 2.11): sizevars.var_to_val renamed to sizevars.backed_var_to_val.
4	-# CAN REMOVE when MIN_SUPPORTED >= (2, 11): use sizevars.backed_var_to_val directly
5	-def get_sizevars_backed_var_to_val(sizevars):
6	- if hasattr(sizevars, "backed_var_to_val"):
7	- return sizevars.backed_var_to_val
8	- return sizevars.var_to_val
9	-
10	-
11	-# COMPAT(>= 2.11): gen_common_triton_imports changed from module-level function
12	-# to instance method on the kernel object.
13	-# CAN REMOVE when MIN_SUPPORTED >= (2, 11): call kernel.gen_common_triton_imports() directly
14	-def gen_common_triton_imports(kernel):
15	- if CURRENT_VERSION >= (2, 11):
16	- return kernel.gen_common_triton_imports()
17	- from torch._inductor.codegen.triton import gen_common_triton_imports as _fn
18	- return _fn()

Dtorch_npu/_compat/utils.py+0-11

	@@ -1,11 +0,0 @@
1	-from torch_npu._compat.version import CURRENT_VERSION
2	-
3	-
4	-# COMPAT(>= 2.12): _ConfigEntry.__init__ gained a required `name` parameter.
5	-# Always pass `name=` at call sites; this wrapper drops it for older versions.
6	-# CAN REMOVE when MIN_SUPPORTED >= (2, 12): construct _ConfigEntry directly.
7	-def make_config_entry(config, *, name: str):
8	- from torch.utils._config_module import _ConfigEntry
9	- if CURRENT_VERSION >= (2, 12):
10	- return _ConfigEntry(config, name=name)
11	- return _ConfigEntry(config) # type: ignore[call-arg] - may missing `name` args for 2.13+ versions

Mtorch_npu/_compat/version.py+1-1

	@@ -9,4 +9,4 @@ def _parse(version_str: str) -> tuple:
9	CURRENT_VERSION: tuple = _parse(torch.__version__)	9	CURRENT_VERSION: tuple = _parse(torch.__version__)
10		10
11	# Bump this when dropping old version support; run tools/check_compat.py to find stale COMPAT blocks.	11	# Bump this when dropping old version support; run tools/check_compat.py to find stale COMPAT blocks.
12	-MIN_SUPPORTED_VERSION: tuple = (2, 10)	12	+MIN_SUPPORTED_VERSION: tuple = (2, 13)

Mtorch_npu/_inductor/codegen/catlass/catlass_utils.py+1-2

	@@ -119,14 +119,13 @@ def _normalize_npu_arch_to_atlas(arch: str) -> str:
119	else:	119	else:
120	raise NotImplementedError(f"Unsupported npu arch: {arch}")	120	raise NotImplementedError(f"Unsupported npu arch: {arch}")
121		121
122	-from torch_npu._compat.inductor import get_sizevars_backed_var_to_val
123	def _trans_sympy_to_int(input_tuple, default_shape=8192):	122	def _trans_sympy_to_int(input_tuple, default_shape=8192):
124	output = []	123	output = []
125	for x in input_tuple:	124	for x in input_tuple:
126	if isinstance(x, (int, sympy.Integer)):	125	if isinstance(x, (int, sympy.Integer)):
127	output.append(int(x))	126	output.append(int(x))
128	elif isinstance(x, (sympy.Symbol, sympy.Expr)):	127	elif isinstance(x, (sympy.Symbol, sympy.Expr)):
129	- x = x.subs(get_sizevars_backed_var_to_val(V.graph.sizevars))	128	+ x = x.subs(V.graph.sizevars.backed_var_to_val)
130	try:	129	try:
131	output.append(int(x))	130	output.append(int(x))
132	except Exception:	131	except Exception:

Mtorch_npu/_inductor/codegen/catlass/gemm_template.py+1-2

	@@ -28,7 +28,6 @@ from . import catlass_utils
28	from .catlass_python_evg import CatlassEVGCodegen	28	from .catlass_python_evg import CatlassEVGCodegen
29	from .catlass_kernel import CATLASSTemplateBuffer, CATLASSTemplateKernel	29	from .catlass_kernel import CATLASSTemplateBuffer, CATLASSTemplateKernel
30	from .catlass_template import CATLASSTemplate	30	from .catlass_template import CATLASSTemplate
31	-from torch_npu._compat.inductor import get_sizevars_backed_var_to_val
32		31
33	log = logging.getLogger("torch._inductor")	32	log = logging.getLogger("torch._inductor")
34		33
	@@ -173,7 +172,7 @@ class CATLASSGemmTemplate(CATLASSTemplate, ABC):
173	if isinstance(x, (int, sympy.Integer)):	172	if isinstance(x, (int, sympy.Integer)):
174	shape_desc[i] = int(x)	173	shape_desc[i] = int(x)
175	elif isinstance(x, (sympy.Symbol, sympy.Expr)):	174	elif isinstance(x, (sympy.Symbol, sympy.Expr)):
176	- x = x.subs(get_sizevars_backed_var_to_val(V.graph.sizevars))	175	+ x = x.subs(V.graph.sizevars.backed_var_to_val)
177	try:	176	try:
178	shape_desc[i] = int(x)	177	shape_desc[i] = int(x)
179	except Exception:	178	except Exception:

Mtorch_npu/_inductor/codegen/kernel_analysis.py+1-2

	@@ -111,7 +111,6 @@ def _kernel_axis_vars(kernel):
111	return axis_vars	111	return axis_vars
112		112
113		113
114	-from torch_npu._compat.inductor import get_sizevars_backed_var_to_val
115	class IndexAnalysis:	114	class IndexAnalysis:
116	def __init__(self, kernel, raw_index, is_store_index=False, is_index_expr=False):	115	def __init__(self, kernel, raw_index, is_store_index=False, is_index_expr=False):
117	self.kernel = kernel	116	self.kernel = kernel
	@@ -121,7 +120,7 @@ class IndexAnalysis:
121	# only contains tiling axis vars	120	# only contains tiling axis vars
122	self.var_list = tuple(x[0] for x in self.var_stride if x[0] in self.tiling_axis)	121	self.var_list = tuple(x[0] for x in self.var_stride if x[0] in self.tiling_axis)
123	self.stride_list = tuple(x[1] for x in self.var_stride if x[0] in self.tiling_axis)	122	self.stride_list = tuple(x[1] for x in self.var_stride if x[0] in self.tiling_axis)
124	- self.index = raw_index.subs(get_sizevars_backed_var_to_val(V.graph.sizevars))	123	+ self.index = raw_index.subs(V.graph.sizevars.backed_var_to_val)
125	all_var_stride = [	124	all_var_stride = [
126	(key, coeff)	125	(key, coeff)
127	for key, coeff in self.index.as_coefficients_dict().items()	126	for key, coeff in self.index.as_coefficients_dict().items()

Mtorch_npu/_inductor/codegen/split_tiling.py+3-4

	@@ -12,7 +12,6 @@ from .triton_utils import get_byte_per_numel
12	from .. import config as npu_config	12	from .. import config as npu_config
13	from ..config import num_vector_core, log	13	from ..config import num_vector_core, log
14	from ..runtime.symbolic_grouping import GroupFeatureSpec, GroupedKernelMeta	14	from ..runtime.symbolic_grouping import GroupFeatureSpec, GroupedKernelMeta
15	-from torch_npu._compat.inductor import get_sizevars_backed_var_to_val
16		15
17		16
18	_ELEMENTWISE_UNSUPPORTED_OPS = ("masked", "scan", "sort", "rand", "randn", "load_seed")	17	_ELEMENTWISE_UNSUPPORTED_OPS = ("masked", "scan", "sort", "rand", "randn", "load_seed")
	@@ -67,7 +66,7 @@ class SplitTiling:
67	def get_length_val(x):	66	def get_length_val(x):
68	length_expr = x.length	67	length_expr = x.length
69	if not isinstance(length_expr, sympy.Integer):	68	if not isinstance(length_expr, sympy.Integer):
70	- return length_expr.subs(get_sizevars_backed_var_to_val(V.graph.sizevars))	69	+ return length_expr.subs(V.graph.sizevars.backed_var_to_val)
71	else:	70	else:
72	return length_expr	71	return length_expr
73		72
	@@ -809,10 +808,10 @@ class SplitTiling:
809	xnumel = x	808	xnumel = x
810	ynumel = y	809	ynumel = y
811	if isinstance(xnumel, (sympy.Symbol, sympy.Expr)) and not isinstance(xnumel, sympy.Integer):	810	if isinstance(xnumel, (sympy.Symbol, sympy.Expr)) and not isinstance(xnumel, sympy.Integer):
812	- xnumel = xnumel.subs(get_sizevars_backed_var_to_val(V.graph.sizevars))	811	+ xnumel = xnumel.subs(V.graph.sizevars.backed_var_to_val)
813		812
814	if isinstance(ynumel, (sympy.Symbol, sympy.Expr)) and not isinstance(ynumel, sympy.Integer):	813	if isinstance(ynumel, (sympy.Symbol, sympy.Expr)) and not isinstance(ynumel, sympy.Integer):
815	- ynumel = ynumel.subs(get_sizevars_backed_var_to_val(V.graph.sizevars))	814	+ ynumel = ynumel.subs(V.graph.sizevars.backed_var_to_val)
816		815
817	if isinstance(xnumel, sympy.Integer) and isinstance(ynumel, int):	816	if isinstance(xnumel, sympy.Integer) and isinstance(ynumel, int):
818	ynumel = sympy.Integer(ynumel)	817	ynumel = sympy.Integer(ynumel)

Mtorch_npu/_inductor/codegen/triton.py+2-3

	@@ -92,7 +92,6 @@ from .split_tiling import SplitTiling
92	from .triton_utils import NPUKernelType	92	from .triton_utils import NPUKernelType
93	from torch._inductor.shape_propagation import BlockShapeType	93	from torch._inductor.shape_propagation import BlockShapeType
94	from enum import auto, Enum	94	from enum import auto, Enum
95	-from torch_npu._compat.inductor import get_sizevars_backed_var_to_val
96		95
97	class NPUSymT(Enum):	96	class NPUSymT(Enum):
98	SIZE = auto()	97	SIZE = auto()
	@@ -3115,7 +3114,7 @@ class NPUIndexTritonKernel(TritonKernel):
3115	def load_store_index_in_all_tiling_list(self):	3114	def load_store_index_in_all_tiling_list(self):
3116	res = False	3115	res = False
3117	for index in self.load_store_indexing:	3116	for index in self.load_store_indexing:
3118	- index = index.subs(get_sizevars_backed_var_to_val(V.graph.sizevars))	3117	+ index = index.subs(V.graph.sizevars.backed_var_to_val)
3119	analyze = IndexAnalysis(self, index)	3118	analyze = IndexAnalysis(self, index)
3120	res = res or self.all_tiling_in_var_list(analyze.var_list)	3119	res = res or self.all_tiling_in_var_list(analyze.var_list)
3121	return res	3120	return res
	@@ -3613,7 +3612,7 @@ class NPUIndexTritonKernel(TritonKernel):
3613	longest = None	3612	longest = None
3614	maximum_length = 0	3613	maximum_length = 0
3615	for index in self.load_store_indexing:	3614	for index in self.load_store_indexing:
3616	- index = index.subs(get_sizevars_backed_var_to_val(V.graph.sizevars))	3615	+ index = index.subs(V.graph.sizevars.backed_var_to_val)
3617	analyze = IndexAnalysis(self, index)	3616	analyze = IndexAnalysis(self, index)
3618	if len(analyze.var_list) > maximum_length and self.all_tiling_in_var_list(	3617	if len(analyze.var_list) > maximum_length and self.all_tiling_in_var_list(
3619	analyze.var_list	3618	analyze.var_list

Dtorch_npu/csrc/_compat/autograd.h+0-50

	@@ -1,50 +0,0 @@
1	-#pragma once
2	-
3	-#include <c10/util/intrusive_ptr.h>
4	-#include <torch/csrc/autograd/function.h>
5	-
6	-#include <torch_npu/csrc/_compat/version.h>
7	-
8	-#include <memory>
9	-#include <utility>
10	-
11	-// Compatibility layer for autograd Node smart-pointer changes introduced in
12	-// PyTorch 2.13 (#181782): grad_fn allocation was migrated from
13	-// std::shared_ptr<Op>(new Op(...), torch::autograd::deleteNode)
14	-// to
15	-// c10::make_intrusive<Op>(...)
16	-// and SavedVariable::unpack() takes c10::intrusive_ptr<Node> instead of
17	-// std::shared_ptr<Node>. deleteNode was removed entirely.
18	-//
19	-// CAN REMOVE the version branches below when MIN_SUPPORTED >= (2, 13).
20	-
21	-namespace torch_npu {
22	-namespace compat {
23	-
24	-#if TORCH_NPU_VERSION_GE(2, 13)
25	-
26	-template <typename T>
27	-using GradFnPtr = c10::intrusive_ptr<T>;
28	-
29	-template <typename Op, typename... Args>
30	-inline GradFnPtr<Op> make_grad_fn(Args&&... args) {
L L li_jing_hw5 天前已过期这个是不是直接在引用源修改就行，这里可以删了 H huangyunlong 5 天前评论： 5 天前评论：
31	- return c10::make_intrusive<Op>(std::forward<Args>(args)...);
32	-}
33	-
34	-#else
35	-
36	-template <typename T>
37	-using GradFnPtr = std::shared_ptr<T>;
38	-
39	-template <typename Op, typename... Args>
40	-inline GradFnPtr<Op> make_grad_fn(Args&&... args) {
41	- return std::shared_ptr<Op>(new Op(std::forward<Args>(args)...), torch::autograd::deleteNode);
42	-}
43	-
44	-#endif
45	-
46	-// Type used by SavedVariable::unpack() — see comment at top of file.
47	-using SavedForPtr = GradFnPtr<torch::autograd::Node>;
48	-
49	-} // namespace compat
50	-} // namespace torch_npu

Mtorch_npu/csrc/_compat/version.h+1-1

	@@ -15,4 +15,4 @@
15		15
16	// Keep in sync with MIN_SUPPORTED_VERSION in torch_npu/_compat/version.py.	16	// Keep in sync with MIN_SUPPORTED_VERSION in torch_npu/_compat/version.py.
17	#define TORCH_NPU_MIN_SUPPORTED_MAJOR 2	17	#define TORCH_NPU_MIN_SUPPORTED_MAJOR 2
18	-#define TORCH_NPU_MIN_SUPPORTED_MINOR 10	18	+#define TORCH_NPU_MIN_SUPPORTED_MINOR 13

Mtorch_npu/csrc/aten/VariableFallbackKernel.cpp+2-3

	@@ -9,7 +9,6 @@
9	#include <ATen/core/ivalue.h>	9	#include <ATen/core/ivalue.h>
10		10
11	#include <torch_npu/csrc/core/npu/NPUException.h>	11	#include <torch_npu/csrc/core/npu/NPUException.h>
12	-#include <torch_npu/csrc/_compat/autograd.h>
13	#include <torch_npu/csrc/framework/utils/CpuFallbackUtils.h>	12	#include <torch_npu/csrc/framework/utils/CpuFallbackUtils.h>
14		13
15	/*	14	/*
	@@ -120,7 +119,7 @@ static void npuBasicAutogradNotImplementedFallbackImpl(
120	// by putting it after the requires_grad checks.	119	// by putting it after the requires_grad checks.
121	any_input_requires_grad = any_input_requires_grad && at::GradMode::is_enabled();	120	any_input_requires_grad = any_input_requires_grad && at::GradMode::is_enabled();
122		121
123	- torch_npu::compat::GradFnPtr<WarnNotImplemented> grad_fn;	122	+ c10::intrusive_ptr<WarnNotImplemented> grad_fn;
124	if (any_input_requires_grad) {	123	if (any_input_requires_grad) {
125	// NB: It is standard to collect edges from all tensors	124	// NB: It is standard to collect edges from all tensors
126	// (see generated/VariableTypeEverything.cpp for examples)	125	// (see generated/VariableTypeEverything.cpp for examples)
	@@ -130,7 +129,7 @@ static void npuBasicAutogradNotImplementedFallbackImpl(
130	stack,	129	stack,
131	stack_start,	130	stack_start,
132	num_arguments);	131	num_arguments);
133	- grad_fn = torch_npu::compat::make_grad_fn<WarnNotImplemented>(op_name, all_tensors_on_stack.size());	132	+ grad_fn = c10::make_intrusive<WarnNotImplemented>(op_name, all_tensors_on_stack.size());
134	grad_fn->set_next_edges(torch::autograd::collect_next_edges(all_tensors_on_stack));	133	grad_fn->set_next_edges(torch::autograd::collect_next_edges(all_tensors_on_stack));
135	}	134	}
136		135

Mtorch_npu/csrc/distributed/reducer.hpp+2-7

	@@ -25,8 +25,6 @@
25	#include <c10d/logger.hpp>	25	#include <c10d/logger.hpp>
26	#include <c10d/debug.h>	26	#include <c10d/debug.h>
27		27
28	-#include <torch_npu/csrc/_compat/autograd.h>
29	-
30	namespace c10d_npu {	28	namespace c10d_npu {
31		29
32	constexpr int kDefaultFirstBucketBytes = int(1024 * 1024);	30	constexpr int kDefaultFirstBucketBytes = int(1024 * 1024);
	@@ -242,14 +240,11 @@ class Reducer {
242	// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)	240	// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
243	std::vector<bool> expect_sparse_gradients_;	241	std::vector<bool> expect_sparse_gradients_;
244		242
245	- // COMPAT(>= 2.13): grad_accumulators_ and hooks_ element type changed from	243	+ std::vector<c10::intrusive_ptr<torch::autograd::Node>>
246	- // std::shared_ptr<Node> to c10::intrusive_ptr<Node> (#181782).
247	- // CAN REMOVE the alias indirection when MIN_SUPPORTED >= (2, 13).
248	- std::vector<torch_npu::compat::GradFnPtr<torch::autograd::Node>>
249	grad_accumulators_; // NOLINT(cppcoreguidelines-non-private-member-variables-in-classes)	244	grad_accumulators_; // NOLINT(cppcoreguidelines-non-private-member-variables-in-classes)
250	// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)	245	// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
251	std::unordered_map<torch::autograd::Node*, size_t> gradAccToVariableMap_;	246	std::unordered_map<torch::autograd::Node*, size_t> gradAccToVariableMap_;
252	- std::vector<std::pair<uintptr_t, torch_npu::compat::GradFnPtr<torch::autograd::Node>>>	247	+ std::vector<std::pair<uintptr_t, c10::intrusive_ptr<torch::autograd::Node>>>
253	hooks_; // NOLINT(cppcoreguidelines-non-private-member-variables-in-classes)	248	hooks_; // NOLINT(cppcoreguidelines-non-private-member-variables-in-classes)
254		249
255	// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)	250	// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)

Mtorch_npu/csrc/framework/autograd/VariableTypeManual.cpp+2-3

	@@ -11,7 +11,6 @@
11		11
12	#include <torch_npu/csrc/framework/autograd/FunctionsManual.h>	12	#include <torch_npu/csrc/framework/autograd/FunctionsManual.h>
13	#include <torch_npu/csrc/core/npu/NPUException.h>	13	#include <torch_npu/csrc/core/npu/NPUException.h>
14	-#include <torch_npu/csrc/_compat/autograd.h>
15		14
16	using namespace at;	15	using namespace at;
17	using namespace at_npu::autograd::generated;	16	using namespace at_npu::autograd::generated;
	@@ -100,9 +99,9 @@ namespace {
100	// Taken from codegened version	99	// Taken from codegened version
101	Tensor _fw_primal(c10::DispatchKeySet ks, const Tensor& self, int64_t level) {	100	Tensor _fw_primal(c10::DispatchKeySet ks, const Tensor& self, int64_t level) {
102	auto& self_ = unpack(self, "self", 0);	101	auto& self_ = unpack(self, "self", 0);
103	- torch_npu::compat::GradFnPtr<Identity> grad_fn;	102	+ c10::intrusive_ptr<Identity> grad_fn;
104	if (compute_requires_grad(self)) {	103	if (compute_requires_grad(self)) {
105	- grad_fn = torch_npu::compat::make_grad_fn<Identity>();	104	+ grad_fn = c10::make_intrusive<Identity>();
106	grad_fn->set_next_edges(collect_next_edges(self));	105	grad_fn->set_next_edges(collect_next_edges(self));
107	}	106	}
108		107

Mtorch_npu/utils/_dynamo.py+5-7

Mtorchnpugen/autograd/templates/Functions.h+2-3

	@@ -12,7 +12,6 @@
12	#include <torch/csrc/Export.h>	12	#include <torch/csrc/Export.h>
13		13
14	#include <c10/core/SymIntArrayRef.h>	14	#include <c10/core/SymIntArrayRef.h>
15	-#include <torch_npu/csrc/_compat/autograd.h>
16		15
17	using namespace torch::autograd;	16	using namespace torch::autograd;
18		17
	@@ -28,7 +27,7 @@ using at::ScalarType;
28	using c10::optional;	27	using c10::optional;
29	using c10::fmap;	28	using c10::fmap;
30		29
31	-inline std::vector<Tensor> unpack_list(at::ArrayRef<SavedVariable> xs, torch_npu::compat::SavedForPtr saved_for = nullptr)	30	+inline std::vector<Tensor> unpack_list(at::ArrayRef<SavedVariable> xs, c10::intrusive_ptr<Node> saved_for = nullptr)
32	{	31	{
33	// NB: we must explicitly do the conversion in the lambda, otherwise template	32	// NB: we must explicitly do the conversion in the lambda, otherwise template
34	// deduction will give a Tensor of Variable which is not convertible	33	// deduction will give a Tensor of Variable which is not convertible
	@@ -37,7 +36,7 @@ inline std::vector<Tensor> unpack_list(at::ArrayRef<SavedVariable> xs, torch_npu
37	});	36	});
38	}	37	}
39		38
40	-inline c10::List<c10::optional<Tensor>> unpack_opt_list(at::ArrayRef<SavedVariable> xs, torch_npu::compat::SavedForPtr saved_for = nullptr)	39	+inline c10::List<c10::optional<Tensor>> unpack_opt_list(at::ArrayRef<SavedVariable> xs, c10::intrusive_ptr<Node> saved_for = nullptr)
41	{	40	{
42	torch::List<c10::optional<Tensor>> result;	41	torch::List<c10::optional<Tensor>> result;
43	result.reserve(xs.size());	42	result.reserve(xs.size());

	@@ -163,9 +163,7 @@ def patch_inductor_wrapper():
163	from typing import Any, Optional	163	from typing import Any, Optional
164		164
165	from torch import _TorchCompileInductorWrapper	165	from torch import _TorchCompileInductorWrapper
166	- from torch.utils._config_module import Config, ConfigModule	166	+ from torch.utils._config_module import Config, ConfigModule, _ConfigEntry
167	-
168	- from torch_npu._compat.utils import make_config_entry
169		167
170	src_apply_options = _TorchCompileInductorWrapper.apply_options	168	src_apply_options = _TorchCompileInductorWrapper.apply_options
171	src_init = _TorchCompileInductorWrapper.__init__	169	src_init = _TorchCompileInductorWrapper.__init__
	@@ -190,28 +188,28 @@ def patch_inductor_wrapper():
190	return ori_dict	188	return ori_dict
191	if "npu_backend" not in ori_dict:	189	if "npu_backend" not in ori_dict:
192	ori_dict["npu_backend"] = "default"	190	ori_dict["npu_backend"] = "default"
193	- self._config["npu_backend"] = make_config_entry(	191	+ self._config["npu_backend"] = _ConfigEntry(
194	Config(default="default", value_type=str),	192	Config(default="default", value_type=str),
195	name="npu_backend",	193	name="npu_backend",
196	)	194	)
197		195
198	if "enable_shape_handling" not in ori_dict:	196	if "enable_shape_handling" not in ori_dict:
199	ori_dict["enable_shape_handling"] = False	197	ori_dict["enable_shape_handling"] = False
200	- self._config["enable_shape_handling"] = make_config_entry(	198	+ self._config["enable_shape_handling"] = _ConfigEntry(
201	Config(default=False, value_type=bool),	199	Config(default=False, value_type=bool),
202	name="enable_shape_handling",	200	name="enable_shape_handling",
203	)	201	)
204		202
205	if "shape_handling_configs" not in ori_dict:	203	if "shape_handling_configs" not in ori_dict:
206	ori_dict["shape_handling_configs"] = []	204	ori_dict["shape_handling_configs"] = []
207	- self._config["shape_handling_configs"] = make_config_entry(	205	+ self._config["shape_handling_configs"] = _ConfigEntry(
208	Config(default=[], value_type=list),	206	Config(default=[], value_type=list),
209	name="shape_handling_configs",	207	name="shape_handling_configs",
210	)	208	)
211		209
212	if "shape_handling_dict" not in ori_dict:	210	if "shape_handling_dict" not in ori_dict:
213	ori_dict["shape_handling_dict"] = None	211	ori_dict["shape_handling_dict"] = None
214	- self._config["shape_handling_dict"] = make_config_entry(	212	+ self._config["shape_handling_dict"] = _ConfigEntry(
215	Config(default=None, value_type=dict),	213	Config(default=None, value_type=dict),
216	name="shape_handling_dict",	214	name="shape_handling_dict",
217	)	215	)