Αiw%SSKJr SSKrSSKJrJrJr SSKJr SSK r SSK J r SSK J r SSKJr SS KJrJrJrJr \(aSS K JrJr SS KJr \\\\\4rS \S 'SSKJr /SQr\"SSSSS9S0S1Sjj5r \"SSSSS9S0S2Sjj5r!S3Sjr"S4Sjr#S5Sjr$S0S6Sjjr%S0S6Sjjr&S0S2Sjjr'S0S2Sjjr(S0S6Sjjr)S0S6S jjr*S7S!jr+\"SS"SS#S9\ S8S$j55r,S0S9S%jjr-S0S:S&jjr.S0S;S'jjr/S0SS*jr2S0S6S+jjr3S0S2S,jjr4S?S@S-jjr5S?S@S.jjr6\ SAS/j5r7g)B) annotationsN) TYPE_CHECKINGNoReturnUnion) TypeAlias)core)signature_safe_contextmanager) deprecated)EventStream create_event create_stream) CUDAPlace CustomPlace)_gpuDevicePropertiesr_CudaPlaceLike)MemoryAnalysisTool)r r current_stream synchronize device_count empty_cachemax_memory_allocatedmax_memory_reservedmemory_allocatedmemory_reserved stream_guardget_device_propertiesget_device_nameget_device_capabilityreset_max_memory_allocatedreset_max_memory_reservedmemory_summary vmm_compactz2.5.0zpaddle.device.current_streamz>current_stream in paddle.device.cuda will be removed in future)since update_tolevelreasonchSnUb[U[5(aUnO[U[R5(aUR 5nOP[U[ 5(a0[ RRU5nUR 5nO [S5e[R"U5$)a Return the current CUDA stream by the device. Args: device(paddle.CUDAPlace()|int|None, optional): The device or the ID of the device which want to get stream from. If device is None, the device is the current device. Default: None. Returns: CUDAStream: the stream to the device. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> s1 = paddle.device.cuda.current_stream() >>> s2 = paddle.device.cuda.current_stream(0) >>> s3 = paddle.device.cuda.current_stream(paddle.CUDAPlace(0)) z+device type must be int or paddle.CUDAPlace) isinstanceintrr get_device_idstrpaddledevice_convert_to_place ValueError_get_current_streamr0 device_idplaces [/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/device/cuda/__init__.pyrr?s@I  fc " "I  / /,,.I  $ $MM33F;E++-IJK K  # #I ..zpaddle.device.synchronizez;synchronize in paddle.device.cuda will be removed in futurecRUb[U[5(aUnO[U[R5(aUR 5nO[U[ 5(a=UR S5(a[USS5nOUS:XaSnO[SUS35e[S5e[RR5n[R"5(a0[U[R5(aUR 5nOS n[R"U5$) a Wait for the compute on the given CUDA device to finish. Args: device(paddle.CUDAPlace()|int|None, optional): The device or the ID of the device. If device is None, the device is the current device. Default: None. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.cuda.synchronize() >>> paddle.device.cuda.synchronize(0) >>> paddle.device.cuda.synchronize(paddle.CUDAPlace(0)) Ngpu:gpurThe current string z is not expected. Because paddle.device.cuda.synchronize only support string which is like 'gpu:x' or 'gpu'. Please input appropriate string again!z0device type must be int, str or paddle.CUDAPlacer*) r+r,rrr-r. startswithr2r/ framework_current_expected_placeis_compiled_with_cuda_device_synchronizer4s r7rros2 fc " "I  / /,,.I  $ $  ((qr O 5  )&2== OP P  88:  ' ' ) )j 6##/ / ++-II  # #I ..r8cb[[S5(a[R"5nU$SnU$)z Return the number of GPUs available. Returns: int: the number of GPUs available. Examples: .. code-block:: python >>> import paddle >>> paddle.device.cuda.device_count() get_cuda_device_countr)hasattrrrD)num_gpuss r7rrs=$ 40 1 1 ""$ O Or8cd[R"5(a[R"5 gg)a~ Releases idle cached memory held by the allocator so that those can be used in other GPU application and visible in `nvidia-smi`. In most cases you don't need to use this function, Paddle does not release the memory back to the OS when you remove Tensors on the GPU, Because it keeps gpu memory in a pool so that next allocations can be done much faster. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> tensor = paddle.randn([512, 512, 512], "float64") >>> del tensor >>> paddle.device.cuda.empty_cache() N)rrAcuda_empty_cacher8r7rrs$& !!## $r8c Uc[R"5$[U[5(aUn[R"5(aSnGOSn[R "5n[ U5S:Xa!US:XaUSnOD[SUSUSS35eUH)nURS5upg[U5U:XdM'UnM+ Uc[SUS U35eO[U[R5(aSnUR5nO[U[R5(a!UR5nUR5nO[U[5(a]URS 5(a[US S5nOJSU;a URSS5up8[U5nO$[S US US35e[SUS US35eUS:d SUS35e[R"5(a&U[5:dSUS[535eU$U[R "W5:d$SUSUS[R "U535eU$)a~ Return the id of the given device. It is just a utility that will not be exposed to users. Args: device(paddle.CUDAPlace|paddle.CustomPlace|int|str): The device, the id of the device or the string name of device like 'gpu:x' or 'custom_device:x'. Default: None. Return: int: The id of the given device. If device is None, return the id of current device. Nr<r rz Device id z) not found in available_custom_devices: [z:0]:z( not found in available_custom_devices: r:r;r=z is not expected. Because zi only support string which is like 'gpu:x' or ':x'. Please input appropriate string again!The device type z only support int, str (format 'gpu:x' or ':x'), paddle.CUDAPlace or paddle.CustomPlace. Please input appropriate device again!z4The device id must be not less than 0, but got id = .zThe device id z exceeds gpu card number z exceeds z device card number )rget_cuda_current_device_idr+r,rAget_available_custom_devicelenr2splitrr-rget_device_typer.r>rget_custom_device_count) r0op_namer5 device_typeavailable_custom_devicesddev_typedev_id device_id_strs r7extract_cuda_device_idr[s~..00&#  % % ' 'KK'+'G'G'I $+,1Q;":1"=K$$VH,UVnopVqUrruv2A'(wws|$H6{f,&. 2"  (PQiPjk# FDNN + + ((* FD,, - -,,. ((* FC   V $ $F12JI 6M)/c1)= &KM*I%fX-GyQ99  vh&@ J5 5  > >ykK> !!##<>) YK'@@P Q ) 477 DD YKy =QRVRnRnozR{Q| } D r8cSn[RR5n[R"5(d4U(a[R "US5(d[ SUS35e[XS9n[R"SU5$)as Return the peak size of memory that is allocated to tensor of the given device. Note: The size of memory allocated to tensor is 256-byte aligned in Paddle, which may larger than the memory size that tensor actually need. For instance, a float32 0-D Tensor with shape [] will take up 256 bytes memory, even though storing a float32 data requires only 4 bytes. Args: device(paddle.CUDAPlace|int|str|None, optional): The device, the id of the device or the string name of device like 'gpu:x'. If device is None, the device is the current device. Default: None. Return: int: The peak size of memory that is allocated to tensor of the given device, in bytes. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') # or '' >>> max_memory_allocated_size = paddle.device.cuda.max_memory_allocated(paddle.CUDAPlace(0)) >>> max_memory_allocated_size = paddle.device.cuda.max_memory_allocated(0) >>> max_memory_allocated_size = paddle.device.cuda.max_memory_allocated("gpu:0") z'paddle.device.cuda.max_memory_allocatedrThe API } is not supported in CPU-only PaddlePaddle. Please reinstall PaddlePaddle with GPU or custom device support to call this API.rT Allocated r/r0get_all_custom_device_typerrAis_compiled_with_custom_devicer2r[device_memory_stat_peak_valuer0namecustom_devicesr5s r7rr#s6 5D]]==?N ""$$ 33N14EFFtfZ [  'v>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') # or '' >>> max_memory_reserved_size = paddle.device.cuda.max_memory_reserved(paddle.CUDAPlace(0)) >>> max_memory_reserved_size = paddle.device.cuda.max_memory_reserved(0) >>> max_memory_reserved_size = paddle.device.cuda.max_memory_reserved("gpu:0") z&paddle.device.cuda.max_memory_reservedrr]r^r_Reservedrares r7rrNs. 4D]]==?N ""$$ 33N14EFFtfZ [  'v>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') # or '' >>> paddle.device.cuda.reset_max_memory_allocated(paddle.CUDAPlace(0)) >>> paddle.device.cuda.reset_max_memory_allocated(0) >>> paddle.device.cuda.reset_max_memory_allocated("gpu:0") z-paddle.device.cuda.reset_max_memory_allocatedrr]r^r_r`N r/r0rbrrArcr2r[#device_memory_stat_reset_peak_valueres r7r!r!us* ;D]]==?N ""$$ 33N14EFFtfZ [  'v>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') # or '' >>> paddle.device.cuda.reset_max_memory_reserved(paddle.CUDAPlace(0)) >>> paddle.device.cuda.reset_max_memory_reserved(0) >>> paddle.device.cuda.reset_max_memory_reserved("gpu:0") z,paddle.device.cuda.reset_max_memory_reservedrr]r^r_riNrkres r7r"r"s* :D]]==?N ""$$ 33N14EFFtfZ [  'v>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') # or '' >>> memory_allocated_size = paddle.device.cuda.memory_allocated(paddle.CUDAPlace(0)) >>> memory_allocated_size = paddle.device.cuda.memory_allocated(0) >>> memory_allocated_size = paddle.device.cuda.memory_allocated("gpu:0") z#paddle.device.cuda.memory_allocatedrr]r^r_r` r/r0rbrrArcr2r[ device_memory_stat_current_valueres r7rrs6 1D]]==?N ""$$ 33N14EFFtfZ [  'v>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') # or '' >>> memory_reserved_size = paddle.device.cuda.memory_reserved(paddle.CUDAPlace(0)) >>> memory_reserved_size = paddle.device.cuda.memory_reserved(0) >>> memory_reserved_size = paddle.device.cuda.memory_reserved("gpu:0") z"paddle.device.cuda.memory_reservedrr]r^r_rirores r7rrs. 0D]]==?N ""$$ 33N14EFFtfZ [  'v[U[RRR5(d [ S5e[ 5nUb[U5[U5:XaSv g[U5nSv [U5ng![U5nf=f7f)as Notes: This API only supports dynamic graph mode currently. A context manager that specifies the current stream context by the given stream. Parameters: stream(paddle.device.cuda.Stream): the selected stream. If stream is None, just yield. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> s = paddle.device.cuda.Stream() >>> data1 = paddle.ones(shape=[20]) >>> data2 = paddle.ones(shape=[20]) >>> with paddle.device.cuda.stream_guard(s): ... data3 = data1 + data2 Nrs) r+r/r0rtr rurrvrw)rxry pre_streams r7rr&s|@*VV]]5G5G5N5N"O"OIJJ!J ~Fr*~5 (0  5 (4F(4FsA7B:B > B BBc[R"5(d [S5eUb[U[5(aUnO[U[R 5(aUR 5nOc[U[5(a=URS5(a[ USS5nO)US:XaSnO [SUS35e[S US 35eS n[R"U5$) a Return the properties of given device. Args: device(paddle.CUDAPlace|int|str|None, optional): The device, the id of the device or the string name of device like 'gpu:x' which to get the properties of the device from. If device is None, the device is the current device. Default: None. Returns: _gpuDeviceProperties: The properties of the device which include ASCII string identifying device, major compute capability, minor compute capability, global memory available and the number of multiprocessors on the device. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> paddle.device.cuda.get_device_properties() >>> # _gpuDeviceProperties(name='A100-SXM4-40GB', major=8, minor=0, total_memory=40536MB, multi_processor_count=108) >>> paddle.device.cuda.get_device_properties(0) >>> # _gpuDeviceProperties(name='A100-SXM4-40GB', major=8, minor=0, total_memory=40536MB, multi_processor_count=108) >>> paddle.device.cuda.get_device_properties('gpu:0') >>> # _gpuDeviceProperties(name='A100-SXM4-40GB', major=8, minor=0, total_memory=40536MB, multi_processor_count=108) >>> paddle.device.cuda.get_device_properties(paddle.CUDAPlace(0)) >>> # _gpuDeviceProperties(name='A100-SXM4-40GB', major=8, minor=0, total_memory=40536MB, multi_processor_count=108) zThe API paddle.device.cuda.get_device_properties is not supported in CPU-only PaddlePaddle. Please reinstall PaddlePaddle with GPU support to call this API.Nr:r;r<rr=z is not expected. Because paddle.device.cuda.get_device_properties only support string which is like 'gpu:x' or 'gpu'. Please input appropriate string again!rLz is not expected. Because paddle.device.cuda.get_device_properties only support int, str or paddle.CUDAPlace. Please input appropriate device again!r*) rrAr2r+r,rr-r.r>rr0r5s r7rrTsN  % % ' '    fc " "I  / /,,.I  $ $  ((qr O 5  )&2== "6(+99    % %i 00r8c,[U5R$)a Return the name of the device which is got from CUDA function `cudaDeviceProp `_. Parameters: device(paddle.CUDAPlace|int|None, optional): The device or the ID of the device. If device is None (default), the device is the current device. Returns: str: The name of the device. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> paddle.device.cuda.get_device_name() >>> paddle.device.cuda.get_device_name(0) >>> paddle.device.cuda.get_device_name(paddle.CUDAPlace(0)) )rrf)r0s r7rrs4 ! ( - --r8cH[U5nURUR4$)aq Return the major and minor revision numbers defining the device's compute capability which are got from CUDA function `cudaDeviceProp `_. Parameters: device(paddle.CUDAPlace|int|None, optional): The device or the ID of the device. If device is None (default), the device is the current device. Returns: tuple(int,int): the major and minor revision numbers defining the device's compute capability. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> paddle.device.cuda.get_device_capability() >>> paddle.device.cuda.get_device_capability(0) >>> paddle.device.cuda.get_device_capability(paddle.CUDAPlace(0)) )rmajorminor)r0props r7r r s!6 ! (D ::tzz !!r8c([RRU5n[U[R 5(a#[R "5R5$[R"UR55R5$)z Get the random state for the default generator. Returns: Tensor: The random state tensor. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.get_rng_state() ) r/r0device_to_placer+rCPUPlacedefault_cpu_generator get_statedefault_cuda_generatorr-)r0r6s r7 get_rng_statersd MM ) )& 1E%''))+5577  & &u':':'< = G G IIr8c0[RRU5n[U[R 5(a%[R "5RU5 g[R"UR55RU5 g)aP Set the random number generator state of the specified device. Args: new_state (core.GeneratorState): The desired RNG state to set. This should be a state object previously obtained from ``get_rng_state()``. device (DeviceLike, optional): The device to set the RNG state for. If not specified, uses the current default device (as returned by ``paddle.framework._current_expected_place_()``). Can be a device object, integer device ID, or device string. Returns: None Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> # Save RNG state >>> state = paddle.device.get_rng_state() >>> # Do some random operations >>> x = paddle.randn([2, 3]) >>> # Restore RNG state >>> paddle.device.set_rng_state(state) N) r/r0rr+rrr set_staterr-) new_stater0r6s r7 set_rng_statersb8 MM ) )& 1E%'' ""$..y9 ##E$7$7$9:DDYOr8cD[U5n[RR5n[ U[ R 5(a%[ R"5RU5 g[ R"UR55RU5 g)aSet the seed for generating random numbers for the current Device. .. warning:: If you are working with a multi-Device model, this function is insufficient to get determinism. To seed all Devices, use :func:`manual_seed_all`. If current Device is CPU, this function will set the seed of the default CPU generator. Sets the seed for global default generator, which manages the random number generation. Args: seed(int): The random seed to set. Returns: None Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:CUSTOM_DEVICE) >>> import paddle >>> paddle.device.manual_seed(102) >>> # paddle.cuda.manual_seed(102) is equivalent to paddle.device.manual_seed(102) >>> paddle.cuda.manual_seed(102) N) r,r/r?_current_expected_place_r+rrr manual_seedrr-)seedr6s r7rrsk2 t9D    5 5 7E%'' ""$006 ##E$7$7$9:FFtLr8cSn[R"5(d[SUS35e[XS9n[R"U5$)a Defragment the free memory blocks managed by the Virtual Memory Management (VMM) allocator of the given device. Args: device(paddle.CUDAPlace|int|str|None, optional): The device, the id of the device or the string name of device like 'gpu:x'. If device is None, the device is the current device. Default: None. Returns: int: The amount of memory (in bytes) that was moved during the compaction. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') # or '' >>> moved_bytes = paddle.device.cuda.vmm_compact(0) >>> print(f"Bytes moved during compaction: {moved_bytes}") zpaddle.device.cuda.vmm_compactr]zl is not supported in CPU-only PaddlePaddle. Please reinstall PaddlePaddle with GPU support to call this API.r_)rrAr2r[r$)r0rfr5s r7r$r$4sU. ,D  & & ( (tfI J  'v>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') # or '' >>> paddle.device.cuda.memory_summary(0) r#r_N)r[rr#r}s r7r#r#Ts8'v7GHI%%i0r8c[USS9n[RRU5nUnUbUS:Xa8[R R [R"5SUS35nO[R RU5n[R RU5n[R RU5upxUSUU3n [R R XY5n[R RU5nU(a:[R RU5(d[R"U5 [R"X45 g)a{ Retrieve recorded Allocate events on the specified device and prints the events directly to the terminal; these events are only counted when FLAGS_record_alloc_event is set to true. Args: device(paddle.CUDAPlace|int|str|None, optional): The device, the id of the device or the string name of device like 'gpu:x'. If device is None, the device is the current device. Default: None. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') # or '' >>> paddle.device.cuda.allocate_record_table(0) allocate_record_tabler_Nmemory_analysis_idz.txt_id)r[r/rget_allocate_recordospathjoingetcwddirnamebasenamesplitextexistsmakedirsrr r0 save_pathr5dataupdated_save_pathdir_name base_namefile_name_without_extext new_file_names r7rrts *'v7NOI ;; * *9 5D!IOGGLL IIK-i[= 77??9-GG$$Y/ %'WW%5%5i%@"01YKuE GGLLAww01Hx00 H,,TEr8c[USS9n[RRU5nUnUbUS:Xa8[R R [R"5SUS35nO[R RU5n[R RU5n[R RU5upxUSUU3n [R R XY5n[R RU5nU(a:[R RU5(d[R"U5 [R"X45 g)a Retrieve recorded Allocate events on the specified device and plot the events, default name is 'memory_analysis.png', saved at current working directory; these events are only counted when FLAGS_record_alloc_event is enabled. Args: device(paddle.CUDAPlace|int|str|None, optional): The device, the id of the device or the string name of device like 'gpu:x'. If device is None, the device is the current device. Default: None. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') # or '' >>> paddle.device.cuda.allocate_record_plot(0) allocate_record_plotr_Nrrz.pngr)r[r/rrrrrrrrrrrrrrs r7rrs *'v7MNI ;; * *9 5D!IOGGLL IIK-i[= 77??9-GG$$Y/ %'WW%5%5i%@"01YKuE GGLLAww01Hx00 H++DDr8c#L# [RRS5n[R"S5SnU(a,S[RS'[R "SS05 O+S[RS'[R "SS05 Sv Uc[RS OU[RS'[R "SU05 g!Uc[RS OU[RS'[R "SU05 f=f7f)a Notes: This API only supports dynamic graph mode currently. A context manager that enables/disables allocate record guard. Parameters: flag(bool): whether to record allocate events. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> data1 = paddle.ones(shape=[20]) >>> data2 = paddle.ones(shape=[20]) >>> with paddle.device.cuda.allocate_record_guard(True): ... data3 = data1 + data2 FLAGS_record_alloc_eventTrueTFalseFN)renvirongetr/ get_flags set_flags)flagtmp_envtmp_cpps r7allocate_record_guardrs0jjnn78G9:"G @ 5;BJJ1 2   8$? @5? ? 565?s9D$A"CAD$AD!!D$)N)r0_CudaPlaceLike | Nonereturncore.CUDAStream)r0rrNone)rr,)rr)r0rrTr.rr,)r0rrr,)rxr rr)rxr rr)r0rrr)r0rrr.)r0rrztuple[int, int])r0rrcore.GeneratorState)rrr0rrr)rr,rr)NN)r0rrz str | Nonerr)rboolrr)8 __future__rrtypingrrrtyping_extensionsrr/ paddle.baserpaddle.base.wrapped_decoratorr paddle.utilsr streamsr r rrrrpaddle.base.libpaddlerr.r,r__annotations__memory_analyzerr__all__rrrrr[rrr!r"rrrwrrrr rrrr$r#rrrrIr8r7rs# 11' G#??-: %   !NI0 , , K  '/  '/T ) H  -/  -/`2 .K\(FV$EN"EJ"DJ(IV$HN,* * I  $5 $5P%)G1 !G1G1T.<%)" !"">J.EI P" P,A P  PFMB'@1BCG&F !&F5?&F &FTCG&E !&E5?&E &ER(@(@r8