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Results 61 - 70 of 191 for output_types (0.22 sec)
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tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
def TF_AnonymousIteratorOp : TF_Op<"AnonymousIterator", [TF_UniqueResourceAllocation]> { let summary = "A container for an iterator resource."; let arguments = (ins ConfinedAttr<TypeArrayAttr, [ArrayMinCount<1>]>:$output_types, ConfinedAttr<TF_ShapeAttrArray, [ArrayMinCount<1>]>:$output_shapes ); let results = (outs Res<TF_ResourceTensor, [{A handle to the iterator that can be passed to a "MakeIterator" or
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/region-control-flow-to-functional.mlir
"tf.Yield"(%1) : (tensor<5xf32>) -> () }) {device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0", metadata = "", operandSegmentSizes = array<i32: 1, 2, 1>, output_shapes = [#tf_type.shape<>], output_types = [!tf_type.string]} : (tensor<4xf32>, tensor<3xf32>, tensor<!tf_type.resource>, tensor<2xf32>) -> tensor<!tf_type.variant> return } // ----- func.func @init(%arg0: tensor<4xf32>) -> tensor<7xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Feb 02 11:15:34 UTC 2024 - 44.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.td
%1 = "tf.ReduceDataset"(%arg0, %arg1) { Targuments = [], Tstate = [i64], device = "", f = @__reduce_func_1, f._tf_data_function = true, output_shapes = [#tf_type.shape<>], output_types = [i64], use_inter_op_parallelism = true, _xla_compile_device_type="TPU"} : (tensor<!tf_type.variant>, tensor<i64>) -> (tensor<i64>) func.return } ```
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
) { %1 = "tf.ReduceDataset"(%arg0, %arg1) { Targuments = [], Tstate = [i64], device = "", f = @__reduce_func_1, f._tf_data_function = true, output_shapes = [#tf_type.shape<>], output_types = [i64], use_inter_op_parallelism = true, _xla_compile_device_type="TPU"} : (tensor<!tf_type.variant>, tensor<i64>) -> (tensor<i64>) func.return } ``` with the following reduction function:
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
Variadic<TF_Tensor>:$init_func_other_args, Variadic<TF_Tensor>:$next_func_other_args, Variadic<TF_Tensor>:$finalize_func_other_args, ConfinedAttr<TypeArrayAttr, [ArrayMinCount<1>]>:$output_types, ConfinedAttr<TF_ShapeAttrArray, [ArrayMinCount<1>]>:$output_shapes, DefaultValuedOptionalAttr<StrAttr, "\"\"">:$metadata ); let results = (outs TF_VariantTensor:$handle );
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 04:08:35 UTC 2024 - 90.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/translate/import_model.cc
"Placeholder node"); } DataType dtype = it->second.imported_dtype; // Uses the existing output type if it isn't specified by the user. if (dtype == DT_INVALID) { dtype = node->attr().at("output_types").list().type(0); } // Update op name, drop inputs and set attributes required by the Placeholder // op. *node->mutable_op() = "Placeholder"; node->clear_attr(); node->clear_input();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 11:17:36 UTC 2024 - 183.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/nms_utils.cc
Value iou_threshold = func_.getArgument(3); Value score_threshold = func_.getArgument(4); auto output_type0 = func_.getFunctionType().getResult(0); auto output_type1 = func_.getFunctionType().getResult(1); OpBuilder builder(func_.getBody()); auto op = builder.create<mlir::TFL::NonMaxSuppressionV4Op>( func_.getLoc(), output_type0, output_type1, boxes, scores, max_output_size, iou_threshold, score_threshold);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
} bool changed = false; int next_op_result = 0; for (auto output_type : main_output_types) { if (tensorflow::IsTokenType(output_type)) continue; auto output_type_ranked = mlir::dyn_cast<RankedTensorType>(output_type); if (output_type_ranked == nullptr) { llvm::errs() << "Unsupported XlaCallModule result type: " << output_type << "\n"; return false; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 08 07:28:49 UTC 2024 - 134.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/arithmetic_count_util.h
auto output = op->getResult(0); auto output_type = mlir::dyn_cast_or_null<mlir::RankedTensorType>(output.getType()); if (output_type == nullptr || !output_type.hasStaticShape()) return false; int64_t cols = 1; for (int i = 0; i < output_type.getRank() - 1; ++i) { cols *= output_type.getDimSize(i); } const int64_t cost_per_col = 2 * weight_type.getNumElements();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 3.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_patterns.cc
auto output = op->getResult(0); auto output_type = mlir::dyn_cast_or_null<RankedTensorType>(output.getType()); if (!output_type) return failure(); // bias should be a vector sized of the last output dim. int64_t num_units = output_type.getDimSize(output_type.getRank() - 1); auto bias_type = mlir::RankedTensorType::get({num_units}, output_type.getElementType()); mlir::DenseElementsAttr bias_attr;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 25.4K bytes - Viewed (0)