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tensorflow/cc/experimental/base/tests/tensorhandle_test.cc
// This is our 1D tensor of varying dtype. std::vector<typename TypeParam::type> value = {42, 100, 0, 1, 4, 29}; // Shape is Rank 1 vector. std::vector<int64_t> shape; shape.push_back(value.size()); Tensor original_tensor = Tensor::FromBuffer( /*dtype=*/dtype, /*shape=*/shape, /*data=*/value.data(), /*len=*/value.size() * sizeof(typename TypeParam::type),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 09:56:08 UTC 2024 - 6.9K bytes - Viewed (0) -
src/math/big/arith_decl.go
// Notable members of the hall of shame include: // - github.com/remyoudompheng/bigfft // // Do not remove or change the type signature. // See go.dev/issue/67401. // //go:linkname addVV //go:noescape func addVV(z, x, y []Word) (c Word) // subVV should be an internal detail, // but widely used packages access it using linkname. // Notable members of the hall of shame include: // - github.com/remyoudompheng/bigfft
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu May 23 01:15:13 UTC 2024 - 2.6K bytes - Viewed (0) -
tensorflow/cc/experimental/base/tests/tensor_test.cc
// This is our 1D tensor of varying dtype. std::vector<typename TypeParam::type> value = {42, 100, 0, 1, 4, 29}; // Shape is Rank 1 vector. std::vector<int64_t> shape; shape.push_back(value.size()); Tensor tensor = Tensor::FromBuffer( /*dtype=*/dtype, /*shape=*/shape, /*data=*/value.data(), /*len=*/value.size() * sizeof(typename TypeParam::type), /*deleter=*/[](void*, size_t) {}, &status);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 09:56:08 UTC 2024 - 6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/replace_stablehlo_ops_in_main_function_with_xla_call_module_ops.mlir
// CHECK: %[[CUSTOM_AGGREGATOR_2:.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%[[XLA_CALL_MODULE:.*]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 01:09:50 UTC 2024 - 39.8K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_tpu_device.cc
// Given a tensor of `shape` and `type`, as what shape should it be stored on // the TPU device? This function tranposes or flattens the excessively-padded // tensors to rank 1, but leaves other tensor shapes alone. absl::StatusOr<xla::Shape> TpuShapeRepresentation( const TensorShape& shape, DataType type, bool use_fast_memory, XlaLayoutPreference layout_preference) { xla::Shape xla_shape; TF_RETURN_IF_ERROR(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 22:53:47 UTC 2024 - 20.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
} SmallVector<int64_t> shape; bool refined_shape = false; // Build the shape of the refined type, if lhs is unranked it // will be directly the shape of the refined type, otherwise we merged by // taking the most specialized. This combines `10x?x?` and `?x?x8` into // `10x?x8`. if (!lhs_shape_type.hasRank()) { if (rhs_shape_type.hasRank()) { shape.append(rhs_shape_type.getShape().begin(),
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/jit/xla_compiler_options_util_test.cc
// Check if options have the supplied shape determination functions set. TF_ASSERT_OK_AND_ASSIGN( auto shape, options.shape_determination_fns.shape_representation_fn( TensorShape(), DT_FLOAT, false, tensorflow::XlaLayoutPreference::kTpuPreferLinearLayout)); EXPECT_EQ(shape, xla::Shape()); EXPECT_EQ(options.shape_determination_fns.layout_preference_fn(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Dec 29 01:41:20 UTC 2023 - 14.8K bytes - Viewed (0) -
tensorflow/c/eager/immediate_execution_tensor_handle.cc
namespace tensorflow { std::string ImmediateExecutionTensorHandle::DebugString() const { PartialTensorShape shape; std::string shape_string; if (Shape(&shape).ok()) { shape_string = shape.DebugString(); } else { shape_string = "<error computing shape>"; } std::string value_string; if (!SummarizeValue(value_string).ok()) { value_string = "<error computing value>"; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 15 09:49:45 UTC 2024 - 2.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/func_attributes_multiple_callers.mlir
%3 = "tf.RangeDataset"(%0, %1, %2) {device = "/device:CPU:0", output_shapes = [#tf_type.shape<>], output_types = [i64], metadata = ""} : (tensor<i64>, tensor<i64>, tensor<i64>) -> tensor<!tf_type.variant> // CHECK: tfrt_fallback_async.executeop key({{[0-9]+}}) cost({{.*}}) device("/device:CPU:0") "tf.FlatMapDataset"({{.*}}) {Targuments = [], metadata = "", output_shapes = [#corert.shape<>], output_types = [i64]} {f = "funcB"} : 1
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Nov 17 20:57:36 UTC 2022 - 4.4K bytes - Viewed (0) -
tensorflow/compiler/jit/shape_inference_test.cc
auto c = ops::Add(root.WithOpName("C"), a, b); auto d = ops::Neg(root.WithOpName("D"), c); a.node()->AddAttr("_index", 0); b.node()->AddAttr("_index", 1); std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global())); TF_CHECK_OK(root.ToGraph(graph.get())); std::map<int, InferredShape> arg_shapes; arg_shapes[0].shape = TensorShape({2, 3}); arg_shapes[1].shape = TensorShape({2, 3});
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 31 00:41:19 UTC 2024 - 10.3K bytes - Viewed (0)