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tensorflow/c/experimental/next_pluggable_device/tensor_pjrt_buffer_util_test.cc
tensorflow::Tensor tensor(allocator.get(), DT_FLOAT, {1}); EXPECT_THAT( GetPjRtCBufferFromTensor(&tensor), StatusIs(error::INTERNAL, HasSubstr(absl::StrCat( "Input tensor does not have PjRtBuffer")))); } TEST(TensorPjRtBufferUtilTest, GetPjRtCBufferFromTensorIncoorectType) { auto allocator = std::make_unique<AsyncValueAllocator>();
C++ - Registered: Tue Feb 27 12:39:08 GMT 2024 - Last Modified: Mon Oct 30 19:20:20 GMT 2023 - 7.2K bytes - Viewed (0) -
tensorflow/c/c_api_experimental_test.cc
const std::vector<TF_Tensor*>& input_tensors, const absl::optional<std::vector<int64_t>>& expected_shape) { // Create input_shapes. TF_ShapeAndTypeList* input_shapes = TF_NewShapeAndTypeList(input_shapes_vec.size()); for (size_t i = 0; i < input_shapes_vec.size(); ++i) { const auto& input_shape = input_shapes_vec[i];
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Jan 17 22:27:52 GMT 2023 - 13.1K bytes - Viewed (1) -
tensorflow/c/experimental/gradients/math_grad.cc
namespace gradients { namespace { static Status SafeConj(AbstractContext* ctx, AbstractTensorHandle* const input, AbstractTensorHandle** output, const char* name) { auto dtype = input->DataType(); if (DataTypeIsFloating(BaseType(dtype)) || DataTypeIsInteger(BaseType(dtype))) { return tensorflow::ops::Identity(ctx, input, output, name); } else if (!DataTypeIsComplex(BaseType(dtype)) &&
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 15.2K bytes - Viewed (0) -
tensorflow/c/eager/c_api_remote_test_util.cc
absl::StrCat(" signature {" " name: 'MatMulFunction'" " input_arg {" " name: 'a'" " type: DT_FLOAT" " }" " input_arg {" " name: 'b'" " type: DT_FLOAT" " }"
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Dec 11 22:56:03 GMT 2020 - 9.1K bytes - Viewed (0) -
tensorflow/c/eager/unified_api_testutil.cc
std::vector<AbstractTensorHandle*>* params) { tracing::TracingTensorHandle* handle = nullptr; for (auto input : inputs) { PartialTensorShape shape; TF_RETURN_IF_ERROR(input->Shape(&shape)); TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(ctx)->AddParameter( input->DataType(), shape, &handle)); params->emplace_back(handle); } return absl::OkStatus(); }
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Feb 27 13:57:45 GMT 2024 - 5.7K bytes - Viewed (0) -
tensorflow/c/eager/c_api_unified_experimental_graph.cc
device_name_ = name; return absl::OkStatus(); } Status AddInput(AbstractTensorHandle* input) override { GraphTensor* t = dyn_cast<GraphTensor>(input); if (!t) { return tensorflow::errors::InvalidArgument( "Unable to cast input to GraphTensor"); } TF_AddInput(op_.get(), t->output_); return absl::OkStatus(); }
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Mar 12 20:00:09 GMT 2024 - 15.4K bytes - Viewed (1) -
tensorflow/c/c_api_function_test.cc
} // Specification for an expected edge. // src is either: // - input name (as it appears in FunctionDef) // - name of output tensor (in nested "add:z:0" format) // dst is either: // - output name (as it appears in FunctionDef) // - <name_of_node>:<index_of_this_input_into_node> (this looks the same as // output tensor naming, but it the index is actually an input index) struct EdgeSpec : public std::pair<string, string> {
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Jul 20 22:08:54 GMT 2023 - 63.6K bytes - Viewed (6) -
tensorflow/c/eager/gradient_checker_test.cc
Model model, AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs, int input_index, float* expected_grad, int num_grad, bool use_function, double abs_error = 1e-2) { Status s; AbstractTensorHandlePtr numerical_grad; { AbstractTensorHandle* numerical_grad_raw; s = CalcNumericalGrad(ctx, model, inputs, input_index, use_function, &numerical_grad_raw);
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Apr 14 10:03:59 GMT 2023 - 6.5K bytes - Viewed (0) -
tensorflow/c/eager/c_api_unified_experimental_test.cc
// Build an abstract input tensor. TFE_Context* eager_ctx = TF_ExecutionContextGetTFEContext(eager_execution_ctx, status.get()); ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); TFE_TensorHandle* input_eager = TestScalarTensorHandle(eager_ctx, 2.0f); TF_AbstractTensor* input_t = TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get());
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri May 19 21:44:52 GMT 2023 - 39.1K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/tape/tape_operation.cc
} Status TapeOperation::AddInput(AbstractTensorHandle* input) { TF_RETURN_IF_ERROR(parent_op_->AddInput(input)); forward_op_.inputs.push_back(input); return OkStatus(); } Status TapeOperation::AddInputList( absl::Span<AbstractTensorHandle* const> inputs) { TF_RETURN_IF_ERROR(parent_op_->AddInputList(inputs)); for (auto input : inputs) { forward_op_.inputs.push_back(input); } return OkStatus(); }
C++ - Registered: Tue Feb 27 12:39:08 GMT 2024 - Last Modified: Tue Jun 07 01:53:35 GMT 2022 - 9K bytes - Viewed (1)