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tensorflow/c/experimental/next_pluggable_device/tensor_pjrt_buffer_util_test.cc
auto c_api_client = down_cast<xla::PjRtCApiClient*>(pjrt_client->get()); std::vector<int32_t> data(1, 0); xla::Shape shape = xla::ShapeUtil::MakeShape(xla::S32, {1}); auto buffer = c_api_client->pjrt_c_client()->client->BufferFromHostBuffer( data.data(), shape.element_type(), shape.dimensions(), /*byte_strides=*/std::nullopt, xla::PjRtClient::HostBufferSemantics::kImmutableOnlyDuringCall, nullptr,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 01 16:29:40 UTC 2024 - 7.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/lstm.mlir
// CHECK-NEXT: subgraphs: [ { // CHECK-NEXT: tensors: [ { // CHECK-NEXT: shape: [ 1, 4 ], // CHECK-NEXT: buffer: 1, // CHECK-NEXT: name: "arg0", // CHECK-NEXT: quantization: { // CHECK-EMPTY: // CHECK-NEXT: }, // CHECK-NEXT: has_rank: true // CHECK-NEXT: }, { // CHECK-NEXT: shape: [ 4, 4 ], // CHECK-NEXT: buffer: 2, // CHECK-NEXT: name: "arg1",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 06 18:55:51 UTC 2023 - 10.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/lstm_asym_attr.mlir
// CHECK-NEXT: subgraphs: [ { // CHECK-NEXT: tensors: [ { // CHECK-NEXT: shape: [ 1, 4 ], // CHECK-NEXT: buffer: 1, // CHECK-NEXT: name: "arg0", // CHECK-NEXT: quantization: { // CHECK-EMPTY: // CHECK-NEXT: }, // CHECK-NEXT: has_rank: true // CHECK-NEXT: }, { // CHECK-NEXT: shape: [ 4, 4 ], // CHECK-NEXT: buffer: 2, // CHECK-NEXT: name: "arg1",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 06 18:55:51 UTC 2023 - 10.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/utils.h
return transposed_type; } // Returns shape of a ranked tensor. // Precondition: output_val's is ranked tensor. // Returns a truncated shape when `truncate` is set to true. inline DenseElementsAttr GetShape(Value output_val, bool truncate = false) { auto output_shape = output_val.getType().dyn_cast<ShapedType>().getShape(); SmallVector<int32_t> shape; shape.reserve(output_shape.size());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v1/compile_mlir_util.cc
// Extracts shape from XlaArgument as TensorShape. If shape is a xla::Shape, // that is converted to a TensorShape. absl::StatusOr<TensorShape> GetTensorShapeFromXlaArgument( const XlaArgument& arg) { if (absl::holds_alternative<xla::Shape>(arg.shape)) { TensorShape arg_shape; TF_RETURN_IF_ERROR( XLAShapeToTensorShape(std::get<xla::Shape>(arg.shape), &arg_shape)); return arg_shape;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 17:24:39 UTC 2024 - 45.3K bytes - Viewed (0) -
tensorflow/c/kernels/ops/bitcast.cc
if (input_type_size < output_type_size) { TF_ShapeInferenceContextWithRankAtLeast(ctx, shape, 1, shape, status); if (TF_GetCode(status) == TF_OK) { TF_DimensionHandle* last_dim = TF_NewDimensionHandle(); size_t divisor_val = output_type_size / input_type_size; TF_ShapeInferenceContextDim(ctx, shape, -1, last_dim); if (!TF_DimensionHandleValueKnown(last_dim) ||
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 07:51:50 UTC 2024 - 5.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/lstm.json
{ "tensors": [ { "shape": [1, 5, 2], "name": "input0" }, { "shape": [2, 5], "buffer": 1, "name": "input2input_weights1" }, { "shape": [2, 5], "buffer": 2, "name": "input2forget_weights2" }, { "shape": [2, 5], "buffer": 3,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 06:25:50 UTC 2024 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/jit/shape_inference.cc
// Merge node causes a loop so we remove NextIteration->Merge edge before // performing shape inference. But removing those edges also prevents us // from inferring output shape for Merge node (we need shapes for all its // inputs). // For loop invariant resource input's Merge node, we set output resource // shape as Enter node's resource shape. // TODO(b/129367850): clean this up.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 31 00:41:19 UTC 2024 - 13K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_host_send_device_context.h
// se::DeviceMemoryBase gpu_dst{device_tensor.data(), 4 * sizeof(float)}; // xla::Shape shape(xla::F32, {2, 2}, {}, {}) // tsl::AsyncValueRef<std::unique_ptr<se::Event>> done_event = // tsl::MakeConstructedAsyncValueRef<std::unique_ptr<se::Event>>(stream.parent()); // done_event->Init(); // // XlaHostSendDeviceContext device_context(&stream, &gpu_dst, // shape, done_event); // device_context.CopyCPUTensorToDeviceSync(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 22:46:36 UTC 2024 - 3.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/tests/end2end.mlir
// CHECK-NEXT: %[[SHAPE:.*]] = "tf.RiscShape"(%arg0) {T = i32} : (tensor<2x3xf32>) -> tensor<*xi32> // CHECK-NEXT: %[[ALPHA1:.*]] = "tf.RiscBroadcast"(%[[ALPHA]], %[[SHAPE]]) : (tensor<f32>, tensor<*xi32>) -> tensor<*xf32> // CHECK-NEXT: %[[MAX:.*]] = "tf.RiscMaximum"(%arg0, %[[ALPHA1]]) : (tensor<2x3xf32>, tensor<*xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 13.4K bytes - Viewed (0)