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tensorflow/c/eager/c_api.cc
tensorflow::CustomDevice* device, tensorflow::DataType dtype, void* data, TFE_CustomDeviceTensorHandleMethods methods) : tensorflow::CustomDeviceTensorHandle(context, device, dtype), data_(data), methods_(methods) {} ~CAPICustomDeviceTensorHandle() override { methods_.deallocator(data_); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 08:11:23 UTC 2024 - 44K bytes - Viewed (0) -
tensorflow/c/eager/c_api_distributed_test.cc
" attr {" " key: 'dtype'" " value {" " type: DT_FLOAT" " }" " }" " }" " node_def {" " name: 'read1'" " op: 'ReadVariableOp'" " input: 'var'" " device: '/job:localhost/replica:0/task:1/device:CPU:0'" " attr {" " key: 'dtype'" " value {"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 15 09:49:45 UTC 2024 - 23.5K bytes - Viewed (0) -
tensorflow/cc/experimental/libtf/value.h
/// @return The unwrapped value. If this `TaggedValue` type does not currently /// contain a value of type `T`, the program terminates via a call to /// `assert`. template <typename T> T& get() { assert(type_ == EnumValueOf<T>::value); return UnionAccess<T>::unsafe_reference(*this); } /// @brief Get the underlying value based on type. /// /// @tparam T The desired return type.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 05:23:45 UTC 2024 - 20.4K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_cluster_util_test.cc
FunctionDef make_ref_float = FunctionDefHelper::Define( "RefFloatFn", {}, {"r:float"}, {}, {{{"var"}, "VariableV2", {}, {{"dtype", DT_FLOAT}, {"shape", TensorShape({})}}}, {{"r"}, "Identity", {"var"}, {{"T", DT_FLOAT}}}}); *fdef_lib->add_function() = make_ref_float; } void AddRegularFunctionFunctionDef(FunctionDefLibrary* fdef_lib) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 09:53:30 UTC 2024 - 10.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v1/compile_mlir_util.cc
size_t idx = type_and_idx.index(); auto result_ty = mlir::cast<mlir::RankedTensorType>(type_and_idx.value()); // If the result type isn't static, then the owner of the result may be a // cast op from a more specific bounded type to an unbounded dynamic type. // Use the bounded type to get the buffer size. mlir::RankedTensorType buffer_ty = result_ty; if (!buffer_ty.hasStaticShape()) {
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/eager/c_api_test_util.cc
TF_Status* status = TF_NewStatus(); // Create the variable handle. TFE_Op* op = TFE_NewOp(ctx, "VarHandleOp", status); if (TF_GetCode(status) != TF_OK) return nullptr; TFE_OpSetAttrType(op, "dtype", TF_FLOAT); TFE_OpSetAttrShape(op, "shape", {}, 0, status); TFE_OpSetAttrString(op, "container", "localhost", 0); TFE_OpSetAttrString(op, "shared_name", "", 0); if (!device_name.empty()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 22:37:46 UTC 2024 - 23.5K bytes - Viewed (0) -
tensorflow/c/experimental/grappler/grappler_test.cc
tensorflow::OpInfo::TensorProperties in_props; Status s = tensorflow::BufferToMessage(in_props_buf[0], &in_props); TF_ASSERT_OK(s); EXPECT_EQ(DT_FLOAT, in_props.dtype()); EXPECT_FALSE(in_props.shape().unknown_rank()); EXPECT_EQ(2, in_props.shape().dim_size()); EXPECT_EQ(10, in_props.shape().dim(0).size()); EXPECT_EQ(1, in_props.shape().dim(1).size());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 13 22:30:58 UTC 2023 - 11.6K bytes - Viewed (0) -
tensorflow/cc/framework/cc_op_gen_util.cc
} strings::StrAppend(&ret, "}"); return ret; } string PrintTensor(const TensorProto& tensor_proto) { Tensor t(tensor_proto.dtype()); CHECK(t.FromProto(tensor_proto)); const int64_t num_elts = t.NumElements(); switch (t.dtype()) { case DT_FLOAT: return PrintArray(num_elts, t.flat<float>().data()); case DT_DOUBLE: return PrintArray(num_elts, t.flat<double>().data());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Feb 26 00:57:05 UTC 2024 - 25K bytes - Viewed (0) -
tensorflow/c/experimental/next_pluggable_device/c_api.cc
const tensorflow::Tensor& arg_tensor = cc_ctx->input(index); absl::Status cc_status; if (arg_tensor.dtype() != tensorflow::DT_RESOURCE) { cc_status = absl::InvalidArgumentError( absl::StrCat("Trying to obtain resource handle from Input[", index, "], which is not type DT_RESOURCE.")); status->status = cc_status; return nullptr; } const tensorflow::ResourceHandle& handle =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 05:48:24 UTC 2024 - 13.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/passes/raise_to_tf.cc
} // Derive the output types. The result type is derived by using the // attributes attched to the result type of the signature. The attribute // value should be either in the attribute argument list or the derived // attribute from the input tensors. All the result type // are unranked, and shape inference should be applied afterwards. SmallVector<Type, 4> output_types;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 21.8K bytes - Viewed (0)