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Results 51 - 60 of 142 for varhandle_op (0.23 sec)
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tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/side_effects.mlir
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain) -> !tfrt.chain func.func @assign_variable() { // CHECK: [[ch1:%.*]], %results = tfrt_fallback_async.executeop.seq([[in_chain]]) key(0) cost({{.*}}) device("/device:CPU:0") "tf.VarHandleOp" // CHECK-NEXT: [[ch2:%.*]] = tfrt_fallback_async.executeop.seq([[in_chain]]) key(1) cost({{.*}}) device("/device:CPU:0") "tf.AssignVariableOp" // CHECK-NEXT: [[out_ch:%.*]] = tfrt.merge.chains [[ch1]], [[ch2]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 28 12:06:33 UTC 2022 - 1008 bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/integration/node_expansion_test.py
# Regression test for an issue where VarHandleOp wasn't being properly # imported into MLIR for "no-op" node expansion. def testVarHandleOp(self): x = constant_op.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) # Note: we purposely make multiple calls to VarHandleOp to exercise the # cached kernal lookup path that was exhibiting the VarHandleOp import # issue.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/analyze-variables.mlir
// RUN: tf-opt %s -split-input-file -tfl-analyze-variables-pass --cse | FileCheck %s // CHECK: module attributes {tfl._legalize_tfl_variables = true} module { func.func @f() -> tensor<*xi32> { %0 = "tf.VarHandleOp"() {container = "c", shared_name = "v"} : () -> tensor<*x!tf_type.resource<tensor<*xi32>>> %2 = "tf.ReadVariableOp"(%0) {dtype = i32} : (tensor<*x!tf_type.resource<tensor<*xi32>>>) -> tensor<*xi32> func.return %2 : tensor<*xi32> } }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 09 11:49:28 UTC 2022 - 5.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/hoist_loop_invariant.mlir
// readonly. // CHECK-LABEL: readvariableop_is_hoisted_if_readonly2 // CHECK: [[CST_0:%.*]] = "tf.Const" // CHECK: [[VAR_1:%.*]] = "tf.VarHandleOp" // CHECK-SAME: "shared_name_var1" // CHECK: [[VAR_2:%.*]] = "tf.VarHandleOp" // CHECK-SAME: "shared_name_var2" // CHECK: [[CST_1:%.*]] = "tf.Const" // CHECK: [[VAR_VAL:%.*]] = "tf.ReadVariableOp"([[VAR_1]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Aug 22 17:12:02 UTC 2023 - 14.2K bytes - Viewed (0) -
tensorflow/cc/tools/freeze_saved_model.cc
} // Returns the name of the VarHandleOp that provides input (possibly indirectly) // to node with node_name. A typical indirect chain of nodes (that can occur due // to graph inlining) is the following: VarHandleOp -> Identity -> Identity -> // ReadVariableOp. Calling the function on any of these nodes would return the // name of the VarHandleOp. StatusOr<string> GetVarHandleName(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 11 08:05:36 UTC 2023 - 11.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/localize_var_handles.cc
} else if (auto next = llvm::dyn_cast<TF::IteratorGetNextOp>(op)) { resource = next.getIterator(); } if (llvm::dyn_cast_or_null<TF::VarHandleOp>(resource.getDefiningOp())) { return; // We're already directly after a VarHandleOp. } const TF::ResourceDataflowState* state = solver.lookupState<TF::ResourceDataflowState>(resource); if (!state) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Mar 06 23:53:00 UTC 2024 - 5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/convert_ref_variables.mlir
// Test the basic cases where all uses of a ref variable can be converted. // CHECK-LABEL: @init func.func @init() { // CHECK-NOT: tf.VariableV2 // CHECK-NOT: tf.Assign // CHECK: [[handle:%.*]] = "tf.VarHandleOp" // CHECK-SAME: shared_name = "x" // CHECK: "tf.AssignVariableOp"([[handle]], {{%.*}}) %0 = "tf.VariableV2"() {container = "", shape = #tf_type.shape<>, shared_name = "x"} : () -> tensor<!tf_type.int32ref>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 28 12:06:33 UTC 2022 - 4.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/fuse_tpu_compile_and_execute_ops.mlir
func.return %3 : tensor<*xi32> } } // ----- module attributes {tf_saved_model.semantics} { // CHECK-LABEL: func private @reorder_execute_arg_defining_ops // CHECK: tf.VarHandleOp // CHECK-NEXT: tf.ReadVariableOp // CHECK-NEXT: tf.TPUCompileMlirAndExecute func.func private @reorder_execute_arg_defining_ops(%arg0: tensor<1x3xf32> {tf.device = "/CPU:0"}) -> (tensor<1x1xf32> {tf.device = "/TPU:0"}) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 13.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/mlrt/rewrite_ifrt_load_variable.mlir
// Variable is used by both CPU and TPU // // CHECK-LABEL: func @serving_default(%arg0: tensor<1x3xf32>) -> tensor<1x1xf32> // CHECK-NEXT: [[HANDLE:%.*]] = "tf.VarHandleOp"() // CHECK-NEXT: [[ARRAYKEY:%.*]], [[FURTURE:%.*]] = "tf_mlrt.tf_ifrt_load_variable"([[HANDLE]]) // CHECK-SAME: <{used_by_host = true}> : (tensor<!tf_type.resource<tensor<3x1xf32>>>) -> (tensor<!tf_type.string>, !mlrt.future)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 22 21:35:32 UTC 2024 - 1.7K bytes - Viewed (0) -
tensorflow/compiler/jit/shape_inference_test.cc
TEST(ShapeInferenceTest, WhileLoopWithResource) { // Graph: // x = resource_variable_ops.var_handle_op(dtype=dtypes.float32, shape=[2, 3]) // y = control_flow_ops.while_loop(lambda _: true, lambda x: x, [x]) Graph graph(OpRegistry::Global()); { Scope scope = Scope::NewRootScope().ExitOnError(); auto x = ops::VarHandleOp(scope.WithOpName("x"), DT_FLOAT, TensorShape({2, 3})); auto enter =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 31 00:41:19 UTC 2024 - 10.3K bytes - Viewed (0)