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tensorflow/cc/experimental/libtf/runtime/runtime.h
"Shape must be fully-defined, got: shape[", dim, "] = ", shape[dim])); } num_elements *= shape[dim]; } if (data.size() != num_elements) { return tensorflow::errors::InvalidArgument(absl::StrCat( "Mismatched shape and data size: \n", "Shape num_elements: ", num_elements, "\n", "Data size: ", data.size(), "\n")); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Sep 01 11:18:25 UTC 2022 - 4.4K 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/c/eager/dlpack_test.cc
dltensor_in->device = {kDLCPU, 0}; dltensor_in->ndim = static_cast<int32_t>(shape.size()); dltensor_in->dtype = {kDLFloat, 32, 1}; dltensor_in->shape = shape.data(); dltensor_in->strides = strides.data(); TFE_TensorHandle* handle = TFE_HandleFromDLPack(&dlm_in, status, ctx); ASSERT_NE(handle, nullptr) << TF_Message(status) << " (shape=[" << absl::StrJoin(shape, ",") << "], strides=[" << absl::StrJoin(strides, ",") << "])";
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jun 30 03:04:46 UTC 2023 - 4.4K 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) -
pkg/scheduler/framework/plugins/helper/shape_score.go
// Shape[i].Score represents function values at meeting points. // // function f(p) is defined as: // // shape[0].Score for p < shape[0].Utilization // shape[n-1].Score for p > shape[n-1].Utilization // // and linear between points (p < shape[i].Utilization) func BuildBrokenLinearFunction(shape FunctionShape) func(int64) int64 { return func(p int64) int64 { for i := 0; i < len(shape); i++ { if p <= int64(shape[i].Utilization) {
Registered: Sat Jun 15 01:39:40 UTC 2024 - Last Modified: Tue Jul 26 17:14:05 UTC 2022 - 1.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/structured_output.py
@tf.function(input_signature=[]) def f0001_multiple_results_no_punctuation(self): return tf.constant(1.0, shape=[1]), tf.constant(1.0, shape=[2]) # Check index paths for results written explicitly with parentheses. # This is semantically equivalent to the earlier test without parentheses,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 5.4K 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)