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Results 91 - 100 of 104 for HLO (0.03 sec)
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tensorflow/compiler/mlir/tf2xla/transforms/legalization_op_config.cc
TypeID::get<TF::ConstOp>(), // AssertOp with string types are not supported by the fallback. TypeID::get<TF::AssertOp>(), // TF2XLA fallback pattern doesn't support these op as MLIR hlo builder // doesn't override the necessary builder methods. These ops have simple // lowering pattern so this should be safe. TypeID::get<TF::CrossReplicaSumOp>(), TypeID::get<TF::InfeedDequeueTupleOp>(),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 04:08:35 UTC 2024 - 21.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/internal/passes/mark_ops_for_outside_compilation.cc
AddCanonicalizationPatterns(module.getContext(), &patterns); // `supported_ops` contains the name of all of the ops that can potentially be // lowered into HLO on the device. This doesn't always mean that the op can // be lowered in the future passes but if the op is not in this set, it can't // be lowered in a subsequent pass. llvm::DenseSet<OperationName> supported_ops;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 21.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/tfl_legalize_hlo.mlir
// COM: This file is there to check that the `tfl-legalize-hlo` pass exists in `odml-to-stablehlo-opt`. // RUN: odml-to-stablehlo-opt %s -tfl-legalize-hlo -split-input-file | FileCheck %s --dump-input=fail func.func @main(%arg0: tensor<5x7xf32>) -> tensor<5x7xf32> { func.return %arg0: tensor<5x7xf32> // CHECK-LABEL: main // CHECK: return %arg0 : tensor<5x7xf32> } // - transpose //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 40.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo.cc
See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ // This file implements logic for legalizing HLO to TensorFlow. #include <cassert> #include <cstddef> #include <cstdint> #include <cstdlib> #include <functional> #include <memory> #include <numeric> #include <optional> #include <string>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 154.9K bytes - Viewed (0) -
tensorflow/compiler/jit/kernels/xla_ops.cc
input_output_alias); OP_REQUIRES_OK(ctx, execution_inputs.status()); } xla::ExecutableRunOptions run_options; // Host callbacks used for HLO send/recv. xla::SendDeviceMemoryFunction send_function = GetSendDeviceMemoryFunction(ctx, key); run_options.set_send_device_memory_function(&send_function); xla::RecvDeviceMemoryFunction recv_function =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 22:46:36 UTC 2024 - 41.4K bytes - Viewed (0) -
tensorflow/compiler/aot/codegen.cc
: ""; const string include_hlo_profile_printer_data_proto = opts.gen_hlo_profile_printer_data ? R"(#include "xla/service/hlo_profile_printer_data.pb.h")" : ""; // When HLO profiling is disabled we only forward declare the // HloProfilePrinter protobuf. So we can only conditionally emit this code // calling HloProfilePrinter::profile_counters_size. const string assign_profile_counters_size =
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/jit/BUILD
"@com_google_absl//absl/status", "@com_google_absl//absl/types:span", "@local_tsl//tsl/framework:device_id_utils", "@local_xla//xla:executable_run_options", "@local_xla//xla/hlo/ir:hlo", "@local_xla//xla/pjrt:pjrt_client", "@local_xla//xla/pjrt:tf_pjrt_client", "@local_xla//xla/service:compiler", "@local_xla//xla/service:executable",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 31 00:41:19 UTC 2024 - 61.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
Type new_arg_type = tensorflow::GetTypeFromTFTensorShape(shape, element_type); if (auto input_ty = mlir::dyn_cast<RankedTensorType>(old_arg_type)) { ArrayRef<int64_t> bounds = hlo::encodingToBounds(input_ty.getEncoding()); // The input type has bounded dynamic dimension. if (!bounds.empty()) { SmallVector<int64_t> new_bounds(bounds.begin(), bounds.end());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 08 07:28:49 UTC 2024 - 134.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.td
let description = [{ This pass looks for the usage of the result of TPUCopyWithDynamicShapeOp and sets the shape of these inputs to be dynamic shaped. This will ensure that the generated HLO program is correctly reflecting the dynamic shape. }]; // Required for mhlo bounded shape extension. let dependentDialects = ["mhlo::MhloDialect"];
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:18:05 UTC 2024 - 99.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/g3doc/_includes/tf_passes.md
This pass looks for the usage of the result of TPUCopyWithDynamicShapeOp and sets the shape of these inputs to be dynamic shaped. This will ensure that the generated HLO program is correctly reflecting the dynamic shape. ### `-tf-tpu-cleanup-cluster-attributes` _Eliminate _replication_info and other attributes from ops in a cluster_
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 02 02:26:39 UTC 2023 - 96.4K bytes - Viewed (0)