AVh@dZddlZddlmZddlmZddlmZddlmZddlm Z ejZ da dZ ejd d Zd Zd Zd ZdZejddZdZejddZdZdZdZddZy)z@Ops for GPU collective operations implemented using NVIDIA nccl.N)context) def_functiondevice)ops) gen_nccl_opsctd|S)aReturns a list of tensors with the all-reduce sum across `tensors`. The computation is done with an all-reduce operation, so if only some of the returned tensors are evaluated then the computation will hang. Args: tensors: The input tensors across which to sum; must be assigned to GPU devices. Returns: List of tensors, each with the sum of the input tensors, where tensor i has the same device as `tensors[i]`. sum_apply_all_reducetensorss N/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/nccl_ops.pyall_sumr 5' ** NcclAllReducecT|jddk7r tdt||j|jd}|jddz}t j|j5t j |d|| cd d d S#1swYy xYw) a)The gradients for `all_sum`. Args: op: The `all_sum` `Operation` that we are differentiating. grad: Gradient with respect to the output of the `all_sum` op. Returns: The gradient with respect to the output of `all_sum`. Raises: LookupError: If `reduction` is not `sum`. reductionsumANo gradient defined for NcclAllReduce except for reduction="sum".expected num_devices shared_names_gradr inputrrrN)get_attr LookupError _check_devicerrrnccl_all_reduce)opgradrrs r _all_sum_gradr$.s[[' ) **ryy) M*+ M*X5+ zz"))!  ' '  !!!!s ;BB'ctd|S)aReturns a list of tensors with the all-reduce product across `tensors`. The computation is done with an all-reduce operation, so if only some of the returned tensors are evaluated then the computation will hang. Args: tensors: The input tensors across which to multiply; must be assigned to GPU devices. Returns: List of tensors, each with the product of the input tensors, where tensor i has the same device as `tensors[i]`. prodr r s rall_prodr'Ls 67 ++rctd|S)aReturns a list of tensors with the all-reduce min across `tensors`. The computation is done with an all-reduce operation, so if only some of the returned tensors are evaluated then the computation will hang. Args: tensors: The input tensors across which to reduce; must be assigned to GPU devices. Returns: List of tensors, each with the minimum of the input tensors, where tensor i has the same device as `tensors[i]`. minr r s rall_minr*]rrctd|S)aReturns a list of tensors with the all-reduce max across `tensors`. The computation is done with an all-reduce operation, so if only some of the returned tensors are evaluated then the computation will hang. Args: tensors: The input tensors across which to reduce; must be assigned to GPU devices. Returns: List of tensors, each with the maximum of the input tensors, where tensor i has the same device as `tensors[i]`. maxr r s rall_maxr-nrrctd|S)aReturns a tensor with the reduce sum across `tensors`. The computation is done with a reduce operation, so only one tensor is returned. Args: tensors: The input tensors across which to sum; must be assigned to GPU devices. Returns: A tensor containing the sum of the input tensors. Raises: LookupError: If context is not currently using a GPU device. r ) _apply_reducer s r reduce_sumr0s ug &&r NcclReducecJ|jddk7r tdt||jt j|j5t j ||j}dddgt|jzS#1swY"xYw)a\The gradients for input `Operation` of `reduce_sum`. Args: op: The `sum send` `Operation` that we are differentiating. grad: Gradient with respect to the output of the `reduce_sum` op. Returns: The gradient with respect to the input of `reduce_sum` op. Raises: LookupError: If the reduction attribute of op is not `sum`. rrrrrshapeN) rrr rrrnccl_broadcastr4leninputs)r"r#results r_reduce_sum_gradr9s[[' ) **ryy) zz"))G  ( (t4:: FFG C N ""GGs "BB"ct|tj|j5tj||j cdddS#1swYyxYw)a Returns a tensor that can be efficiently transferred to other devices. Args: tensor: The tensor to send; must be assigned to a GPU device. Returns: A tensor with the value of `src_tensor`, which can be used as input to ops on other GPU devices. r3N)r rrrr5r4)tensors r broadcastr<sI zz&-- I  & &V6<< HIIIs !AA NcclBroadcastc|jjDcgc]}|}}|D] }t|tj|j5t j |dcdddScc}w#1swYyxYw)a-The gradients for input `Operation` of `broadcast`. Args: op: The `broadcast send` `Operation` that we are differentiating. accumulated_grad: Accumulated gradients with respect to the output of the `broadcast` op. Returns: Gradients with respect to the input of `broadcast`. r rrN)r"r7r rrr nccl_reduce)r"accumulated_gradtgradss r_broadcast_gradrDs'))00 11 1% 1 a! zz"))B  # #%5 ABB 2BBs A6A;;Bcs tdtfd}tjrt j |S|S)z$Helper function for all_* functions.z-Must pass >0 tensors to all reduce operationsc g}D]f}t|tj|j5|jt j |t dddh|S#1swYtxYw)zCall nccl allreduce.rN)r rrappendrr!r6)resrBrrrs r _all_reducez&_apply_all_reduce.._all_reducesz C *A ::ahh *  ( (#L'  ) **** J**s 2A11A: ) ValueError_get_shared_namerexecuting_eagerlyrfunction)rrrIrs`` @rr r sM  D EE "+    .<  - // =rc|s td|D] }t|tj|| t fd|DS#t $r tdwxYw)z'Helper function for reduce_* functions.z)Must pass >0 tensors to reduce operationsr?c3VK|] }|jjk(s|"ywNr).0rBr8s r z _apply_reduce..s 9qqxx6==89s))z3One input tensor must be assigned to current device)rJr rr@next StopIteration)rrrBr8s @rr/r/su  @ AA a!  # #'Y G&L9G99 - L J KKLs AA$cht5t}tdz adddd|zS#1swYdzSxYw)Nzc%s) _module_lock_shared_name_counter)vals rrKrKs? CA  s$1c tj|jstd|d|r/||jk7rtd|d|jd|dyy)NzDevice assignment for tensor=z! required for nccl collective opszExpected device z, got z for tensor=.)rcanonical_namerJ)r;rs rr r sw   v}} - 4VH=&& '' (fmm+ 'z G%ha) **,XrrP)__doc__ threadingtensorflow.python.eagerrrtensorflow.python.frameworkrrtensorflow.python.opsrLockrWrXrRegisterGradientr$r'r*r-r0r9r<rDr r/rKr rrresG+0.+.y~~ +"o&!'!:,"+"+"'&l##$#0 I o&B'B(: *r