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src/internal/types/testdata/check/decls2/decls2a.go
// it's double-declared (it would cost extra in the common case to verify // this). But the MethodSet computation will not find it due to the name // collision caused by the double-declaration, leading to an internal // inconsistency while we are verifying one computation against the other. // var _ = T1c{}.Pointer // T2's method declared before the type. func (*T2) f /* ERROR "field and method" */ () {}
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu May 30 19:19:55 UTC 2024 - 3.3K bytes - Viewed (0) -
tensorflow/compiler/aot/codegen.cc
{{DECLS_FROM_OBJ_FILE}} {{NS_START}} // {{CLASS}} represents a computation previously specified in a // TensorFlow graph, now compiled into executable code. This extends the generic // XlaCompiledCpuFunction class with statically type-safe arg and result // methods. Usage example: // // {{CLASS}} computation; // // ...set args using computation.argN methods // CHECK(computation.Run());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 01:20:01 UTC 2024 - 36.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v1/compile_mlir_util.h
// use_tuple_args: when this is true, always create a tuple argument for the // entry computation. // enable_op_fallback: when this is true, prefer tf2xla fallback kernels over // MLIR // native kernels for legalization to HLO. // return_tuple: when this is true, always create a tuple result for the // entry computation. // shape_determination_fns: Contains layout preference fn and shape
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 17:24:39 UTC 2024 - 10.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_patterns.td
// supports float types. tf.round with integer input type will become an // identity op, so we will never face an mhlo.floor with an integer input type. // The pattern matched executes the following computation: // frac = x - floor(x) // to_even = (floor(x) - 2 * floor(0.5 * x)) == 1 // if frac > 0.5 || (frac == 0.5 && to_even) // return floor(x) + 1 // else // return floor(x) def : Pat<(MHLO_SelectOp
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Feb 03 08:58:22 UTC 2024 - 34K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_launch_util.h
// For case 3, we need to create a PjRtBuffer from the raw device mem pointer, // and we need to ensure the PjRtBuffer persists till XLA computation is // complete. Therefore we put the newly created PjRtBuffer into `owned_args`. // Caller is responsible to ensure `owned_args` lives till the end of XLA // computation. Status PreparePjRtExecutableArguments( int num_missing_prefix_ctx_inputs, const std::vector<int>& input_mapping,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 09:53:30 UTC 2024 - 11.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/internal/passes/extract_outside_compilation.cc
} } } // Since we have the outputs from host and device computation after moving // outside compiled ops, we can create the actual parallel_execute regions. // Still, one region is for the host computation for outside compilation and // the other one is for the original Device cluster computation. mlir::tf_device::ParallelExecuteOp CreateFinalParallelExecuteOp(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 21:25:12 UTC 2024 - 68.3K bytes - Viewed (0) -
RELEASE.md
* Introducing `tf.types.experimental.AtomicFunction` as the fastest way to perform TF computations in Python. * Can be accessed through `inference_fn` property of `ConcreteFunction`s * Does not support gradients. * See `tf.types.experimental.AtomicFunction` documentation for how to call and use it.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 730.3K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_compile_on_demand_op.cc
XlaCompiler::CompileOptions GetCompileOptions(bool for_pjrt = false) { XlaCompiler::CompileOptions compile_options; compile_options.is_entry_computation = true; // Optimization: where possible, have the computation return a naked array // rather than a one-element tuple. compile_options.always_return_tuple = false; if (for_pjrt) { compile_options.use_tuple_arg = false; compile_options.always_return_tuple = true; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 29 08:39:39 UTC 2024 - 13.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/internal/legalize_tf_to_hlo.h
namespace tf2xla { namespace internal { // Legalize the given MLIR module to XLA HLO using a combination of the MLIR // Bridge and XlaBuilder absl::StatusOr<XlaCompilationResult> LegalizeTfToHlo( const tpu::MlirToHloArgs& computation, const tpu::TPUCompileMetadataProto& metadata, bool use_tuple_args, llvm::StringRef device_type, XlaShapeLayoutHelpers::ShapeDeterminationFns shape_determination_fns,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sun Apr 14 20:29:34 UTC 2024 - 2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/ir/mlrt/tf_ops.td
$mlir_module is a serialized MLIR module with a `main` function that contains target computation. $metadata is a serialized TPUCompileMetadataProto describing the shapes and types of the inputs to the computation, as well as a mapping onto the TPU pod topology.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 22 21:35:32 UTC 2024 - 6.7K bytes - Viewed (0)