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Results 1 - 6 of 6 for TPUCopyWithDynamicShapeOp (0.43 sec)
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tensorflow/compiler/mlir/tensorflow/transforms/extract_tpu_copy_with_dynamic_shape_op.cc
return value.getDefiningOp(); } // Check if the TPUCopyWithDynamicShapeOp is valid. // 1. The op should be wrapped inside a launch op. // 2. The wrapped launch op should be placed on CPU. LogicalResult CheckOpIsValid(Operation* op) { auto launch_op = llvm::dyn_cast<tf_device::LaunchOp>(op->getParentOp()); if (!launch_op) { op->emitError() << "TPUCopyWithDynamicShapeOp is not in a launch"; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/colocate_tpu_copy_with_dynamic_shape.cc
solver.load<dataflow::SparseConstantPropagation>(); solver.load<DeviceDataflowAnalysis>(symbolTables); if (failed(solver.initializeAndRun(module))) return signalPassFailure(); module->walk([&](TF::TPUCopyWithDynamicShapeOp op) { const Device *state; for (auto result : op->getResults()) { state = solver.lookupState<Device>(result); if (state) break; } if (!state || !state->hasDevice()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 23 00:30:27 UTC 2023 - 5.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/passes.h
// partitioned resource variables. std::unique_ptr<OperationPass<func::FuncOp>> CreateTPUResourceReadsWritesPartitioningPass(); // Creates a pass that looks for usage of the result of // TPUCopyWithDynamicShapeOp and annotate these values to be dynamic shape. This // ensures that the generated tpu program has the correct inputs annotation. std::unique_ptr<OperationPass<ModuleOp>> CreateTPUAnnotateDynamicShapeInputsPass();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:18:05 UTC 2024 - 31.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.td
def ExtractTPUCopyWithDynamicShapeOpPass : Pass<"tf-extract-tpu-copy-with-dynamic-shape-op", "mlir::func::FuncOp"> { let summary = "Extract the TPUCopyWithDynamicShapeOp out of the host launch and place it on device launch"; let description = [{ This pass looks for TPUCopyWithDynamicShapeOp which wraps in a `tf_device.launch` with host device attribute. It extracts the ops and wrap
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:18:05 UTC 2024 - 99.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/g3doc/_includes/tf_passes.md
}) : () -> tensor<f32> return %0 : tensor<f32> } ``` ### `-tf-extract-tpu-copy-with-dynamic-shape-op` _Extract the TPUCopyWithDynamicShapeOp out of the host launch and place it on device launch_ This pass looks for TPUCopyWithDynamicShapeOp which wraps in a `tf_device.launch` with host device attribute. It extracts the ops and wrap them in `tf_device.launch` with tpu device attribute so that ops can be
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 02 02:26:39 UTC 2023 - 96.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td
} def TF_TPUAnnotateTensorsWithDynamicShapeOp : TF_Op<"TPUAnnotateTensorsWithDynamicShape", [Pure]> { let summary = [{ Placeholder op which takes the output of TPUCopyWithDynamicShapeOp and pass them to the following tpu ops. }]; let description = [{ This op serves as an annotation for the dynamic shaped tensor and will be removed during the bridge rewrite. }];
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 04:08:35 UTC 2024 - 90.5K bytes - Viewed (0)