BVhTxdZddlZddlmZGddeZej dZdZ d dZ d Z d Z d Z y) zGradient record utilities.N) pywrap_tfec.eZdZdZdgZdZdZdZdZy)VariableWatchera/A scope that tracks all trainable variable accesses within it. This explicitly ignores variables that are not marked as trainable. Sample usage: var = tf.Variable(0.0) with VariableWatcher() as variable_watcher: var.assign_add(1.0) assert variable_watcher.watched_variables == [var] _variable_watchercd|_yN)rselfs N/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/eager/record.py__init__zVariableWatcher.__init__&s !Dc8tj|_|Sr)rTFE_Py_VariableWatcherNewrr s r __enter__zVariableWatcher.__enter__)s'AACD Kr cBtj|jyr)rTFE_Py_VariableWatcherRemover)r typvalue tracebacks r __exit__zVariableWatcher.__exit__-s++D,B,BCr c@tj|jS)z7Returns a tuple of variables accessed under this scope.)r&TFE_Py_VariableWatcherWatchedVariablesrr s r watched_variablesz!VariableWatcher.watched_variables0s  < <   r N) __name__ __module__ __qualname____doc__ __slots__r rrrr r rrs& ##)"D r rc#Ktj} |stjd|stjyy#|stjwwxYww)z7Stop all gradient recording (backprop and forwardprop).N)rTFE_Py_TapeSetIsStoppedTFE_Py_TapeSetStopOnThreadTFE_Py_TapeSetRestartOnThread) is_stoppeds r stop_recordingr%6sU113*1 ++- ..0 :..0 sA&A A& A##A&c,tj|S)aReturns true if any tape in the stack watches any of these tensors. Only takes GradientTapes into account, not forward accumulators. Args: tensors: Tensors to check, typically inputs to an operation. Returns: Boolean, whether any tape watches any of `tensors`. )r"TFE_Py_TapeSetShouldRecordBackprop)tensorss r should_record_backpropr)Cs  6 6w ??r c6tj|||||y)z0Records the operation on all tapes in the stack.N)rTFE_Py_TapeSetRecordOperation)op_typeoutput_tensors input_tensorsbackward_functionforward_functions r record_operationr1Qs **7N+8:K+;=r c4tj||||y)z9Records the operation on all backward tapes in the stack.N)r%TFE_Py_TapeSetRecordOperationBackprop)r,r-r.r/s r record_operation_backprop_onlyr4Ys 227N3@3DFr c6tj|||||y)aRecords the operation on all forward accumulators in the stack. Args: op_type: a string for the operation type, used in the backprop code output_tensors: a list of Python Tensor objects output by the operation input_tensors: a list of input Tensors to the recorded operation backward_function: the function to be called to, given the gradients of the output tensors, produce the gradients of the input tensors. This function is automatically transposed to produce output gradients given input gradients. forwardprop_output_indices: indicates any output_tensors which contain JVPs. Typically these will have come from TFE_Py_PackForwardGradients. May be None or an empty sequence if there are no JVP outputs from the operation. N)r(TFE_Py_TapeSetRecordOperationForwardprop)r,r-r.r/forwardprop_output_indicess r !record_operation_forwardprop_onlyr8as" 55 ~}.? "r c,tj S)z#Returns True if any tape is active.)rTFE_Py_TapeSetIsEmptyrr r could_possibly_recordr;ws  - - / //r r) r contextlibtensorflow.pythonrobjectrcontextmanagerr%r)r1r4r8r;rr r r@sW!( f @  1 1 @'+=F",0r