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Results 31 - 40 of 191 for output_types (0.57 sec)
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tensorflow/compiler/mlir/lite/tests/analyze-variables.mlir
output_shapes = [#tf_type.shape<?>], output_types = [!tf_type.string]} : (tensor<*x!tf_type.variant>, tensor<i64>) -> tensor<!tf_type.variant> %1 = "tf.ReduceDataset"(%0, %cst_1, %arg0) { Targuments = [!tf_type.resource], Tstate = [i32], device = "", f = @__reduce_func, f._tf_data_function = true, output_shapes = [#tf_type.shape<>],
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/transforms/host_runtime/tpu_rewrite_pass.cc
llvm::SmallVector<Type, 4> output_types; auto result = tensorflow::GetOutputTypesForLogicalDeviceComputation( core, output_sharding_config, cluster_func, &output_types, &(*cluster_to_core_index)[core]); if (failed(result)) return failure(); for (Type t : output_types) concatenated_output_types.emplace_back(t); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 21:25:12 UTC 2024 - 29.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h
} // Collect all the quantized outputs and replace them by the results of // the new quantized op. llvm::SmallDenseMap<Value, int> outputs_replaced; SmallVector<Type, 4> output_types; output_types.reserve(quantizing_op->getNumResults()); for (const auto& enumerated_result : llvm::enumerate(quantizing_op->getResults())) { Value result = enumerated_result.value();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 20:30:06 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_cluster_util.cc
return std::find(node.input_types().begin(), node.input_types().end(), DT_RESOURCE) != node.input_types().end() || std::find(node.output_types().begin(), node.output_types().end(), DT_RESOURCE) != node.output_types().end(); } void RemoveFromXlaCluster(NodeDef* node_def) { node_def->mutable_attr()->erase(kXlaClusterAttr); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 29 08:39:39 UTC 2024 - 21.3K bytes - Viewed (0) -
tensorflow/compiler/jit/build_xla_ops_pass.cc
MemoryTypeVector input_mtypes, output_mtypes; DeviceType device_type(""); TF_RETURN_IF_ERROR( DeviceNameToDeviceType(n->assigned_device_name(), &device_type)); TF_RETURN_IF_ERROR(MemoryTypesForNode(root.graph()->op_registry(), device_type, n->def(), &input_mtypes, &output_mtypes)); return output_mtypes; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 12 06:33:33 UTC 2024 - 24.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/unroll_batch_matmul.cc
SmallVector<int64_t, 3> slice_size = {1, num_rows, num_cols}; Type slice_result_type = RankedTensorType::get(slice_size, element_type); llvm::SmallVector<Type, 4> output_types(batch_size, slice_result_type); auto split_op = rewriter.create<TF::SplitOp>(loc, output_types, split_dimension_op.getOutput(), reshape_op.getOutput());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/insert_custom_aggregation_ops.cc
"max_percentile", rewriter.getF32FloatAttr( calib_opts_.calibration_parameters().max_percentile())), }; SmallVector<Type, 4> output_types{ value.getType(), RankedTensorType::get({}, rewriter.getF32Type()), RankedTensorType::get({}, rewriter.getF32Type()),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 14.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/decompose_optionals.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:18:05 UTC 2024 - 4.5K bytes - Viewed (0) -
tensorflow/cc/framework/cc_op_gen_util.h
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Feb 26 00:57:05 UTC 2024 - 4.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/functional-control-flow-to-regions.mlir
finalize_func = @finalize, init_func = @init, next_func = @next, operandSegmentSizes = array<i32: 1, 2, 1>, output_shapes = [#tf_type.shape<>], output_types = [!tf_type.string], metadata = ""} : ( tensor<4xf32>, tensor<3xf32>, tensor<!tf_type.resource>, tensor<2xf32>) -> tensor<!tf_type.variant> return
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Nov 06 21:59:28 UTC 2023 - 11.9K bytes - Viewed (0)