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tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.h
bool CanBeRefined(Type type); // Returns a new arg type based on the shape and element type. If there are // dynamic bounds attribute to the arg, update the bounds based on the shape // as well. Type GetNewArgType(Type old_arg_type, ArrayRef<int64_t> shape, Type element_type, mlir::MLIRContext* context); // Refines all the shapes in a module, skipping the inference for all ops // whose type is in ops_to_skip.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 12:49:45 UTC 2024 - 3.5K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_host_send_device_context.h
se::Stream* stream, se::DeviceMemoryBase* device_memory_base, const xla::Shape& shape, tsl::AsyncValueRef<std::unique_ptr<se::Event>>& done_event) : stream_(stream), device_memory_base_(device_memory_base), shape_(shape), done_event_(done_event) {} // Copies 'cpu_tensor' to `device_memory_base_` with `shape_`. // `device_tensor` is unused.
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/jit/test_util.cc
TF_RET_CHECK(sit != shape_info.end()) << "Missing shape information for node " << node->name(); std::vector<PartialTensorShape> shapes; for (const auto& output : sit->second) shapes.push_back(output.shape); auto it = expected_shapes.find(node->name()); if (it != expected_shapes.end()) { if (!PartialTensorShapeUtils::AreIdentical(shapes, it->second)) { return errors::InvalidArgument(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Feb 09 11:36:41 UTC 2024 - 3.7K bytes - Viewed (0) -
tensorflow/compiler/jit/shape_inference.h
namespace tensorflow { struct InferredShape { // Shape of the argument tensor. PartialTensorShape shape; // If the argument is a resource variable, the type and shape of the // variable's value. DataType handle_type = DT_INVALID; PartialTensorShape handle_shape; }; typedef std::unordered_map<string, std::vector<InferredShape>> GraphShapeInfo; // Infer shapes for all Tensors in a graph, and save them in a map. The vector
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 31 00:41:19 UTC 2024 - 2.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/convert_tensor.h
// Converts a shape from MLIR to a TensorFlow tensor shape proto. void ConvertToTensorShapeProto(llvm::ArrayRef<int64_t> shape, TensorShapeProto* output_shape); // Converts an MLIR type to a TensorFlow tensor shape. PartialTensorShape ConvertTypeToTensorShape(const mlir::Type& type); // Converts an MLIR shaped type to a TensorFlow shape attribute.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Apr 26 09:37:10 UTC 2024 - 2.9K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_host_recv_device_context.h
public: XlaHostRecvDeviceContext( se::Stream* stream, const se::DeviceMemoryBase& device_memory_base, const xla::Shape& shape, tsl::AsyncValueRef<std::unique_ptr<se::Event>>& done_event) : stream_(stream), device_memory_base_(device_memory_base), shape_(shape), done_event_(done_event) {} void CopyCPUTensorToDevice(const Tensor* cpu_tensor, Device* device,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 22:46:36 UTC 2024 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_device_compiler_client.cc
#include "xla/client/local_client.h" namespace tensorflow { namespace { std::vector<const xla::Shape*> GetShapePointers( absl::Span<const xla::Shape> shapes) { std::vector<const xla::Shape*> shape_ptrs; shape_ptrs.reserve(shapes.size()); for (const auto& shape : shapes) { shape_ptrs.push_back(&shape); } return shape_ptrs; } } // namespace absl::StatusOr<std::unique_ptr<xla::LocalExecutable>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 09:53:30 UTC 2024 - 4.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/shape_inference_with_shape_specialization.mlir
// RUN: tf-opt %s -tf-shape-inference=input-arg-shapes=1 -verify-diagnostics -split-input-file | FileCheck %s // RUN: not tf-opt %s -tf-shape-inference=input-arg-shapes=* 2>&1 | FileCheck --check-prefix=INPUT_ARG_SHAPES_ERROR %s // INPUT_ARG_SHAPES_ERROR: Missing input argument shapes module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} { // CHECK-LABEL: func.func @main
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 12:49:45 UTC 2024 - 2.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul_disabled.pbtxt
node { name: "Placeholder" op: "Placeholder" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "shape" value { shape { dim { size: 2 } dim {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/prepare_tpu_computation_for_tf_export.mlir
// CHECK-SAME: key = "" // CHECK-SAME: recv_key = "host_compute_channel_recv" // CHECK-SAME: send_key = "host_compute_channel_send" // CHECK-SAME: shape_inference_graph = @host_func // CHECK-SAME: shapes = [#tf_type.shape<*>, #tf_type.shape<3x?>] // CHECK-SAME: tpu_core = 0 : i64 // CHECK: func @host_func // CHECK: %[[RECV_OUTPUT:[0-9]*]]:2 = "tf._XlaRecvAtHost" // CHECK-SAME: key = "host_compute_channel_send"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 18:46:36 UTC 2024 - 9.2K bytes - Viewed (0)