Merged commit includes the following changes:

723477680  by A. Unique TensorFlower<[email protected]>:

    [XLA] Tag timeout tests as `not_run:arm`

    Similarly to cl/722883015 tagging also:

    * //third_party/tensorflow/compiler/xla/python/transfer:socket_bulk_transport_test
    * //third_party/tensorflow/compiler/xla/python/transfer:socket-server_test
    * //third_party/tensorflow/compiler/xla/python/transfer:event_loop_test

--
723476384  by A. Unique TensorFlower<[email protected]>:

    Parse XLA_FLAGS environment variable every time, conditionally on xla_flags_reset flag.

--
723471749  by A. Unique TensorFlower<[email protected]>:

    [XLA:GPU] Rename `IsSyncCollective` and move to a GPU specific file.

    The implementation is specific to the GPU backend.

--
723470593  by A. Unique TensorFlower<[email protected]>:

    [XLA:GPU] move DotDecompose out of simplification pipeline

    That seems to be a better approach then moving TransposeFold to simplification-2 in 961e5c25fbd4082a1ac4f2e0865ad28163d12f7d:

    1. There is a report that previous change has resulted in perf degradation openxla/xla#22081

    2. I have found another case when DotDecompose is competing with algsimp. Added a test for that.

    Overall, having an pass that expands operation together with passes that are trying to do the simplification asks for such infinite loops.

    ---

    For archeologists:

    passes DotDimensionSorter and DotDecomposer were added along with GpuAlgebraicSimplifier as it previously could have added multiple contracting dimensions to dot. But cudnn does not support dots with 2+ dimensions, forcing us to use a less efficient loop emitter. - That what "// AlgebraicSimplifier may add contracting dimensions to a dot." comment was about.

    After a while simplifier started to use supports_non_canonical_dots to guard against this case. So it should be safe to remove dot decomposer and friends.

--
723469960  by A. Unique TensorFlower<[email protected]>:

    PR tensorflow#22334: [ROCm] Fix flaky gpu compiler test when building with rocm

    Imported from GitHub PR openxla/xla#22334

    This change fixes the flaky gpu compiler test used to run on rocm CI pipeline gate.
    Triton pipeline was wrongly using the TritonGPUAccelerateMatmul pass which supports cuda only.
    In rocm there is a different pass which is now used in the rocm pipeline.

    https://github.com/triton-lang/triton/blob/main/third_party/amd/lib/TritonAMDGPUTransforms/AccelerateAMDMatmul.cpp
    Copybara import of the project:

    --
    c5f600f03aa87d155bb624bedb0584e635af190e by Alexandros Theodoridis <[email protected]>:

    Fix flaky gpu compiler test when building with rocm

    Merging this change closes tensorflow#22334

--
723453199  by A. Unique TensorFlower<[email protected]>:

    Automated Code Change

--
723445422  by A. Unique TensorFlower<[email protected]>:

    Automated Code Change

--
723443292  by A. Unique TensorFlower<[email protected]>:

    [pjrt] Removed deprecated `PjRtBuffer::CopyToDevice`

--
723434255  by A. Unique TensorFlower<[email protected]>:

    Automated Code Change

--
723430683  by A. Unique TensorFlower<[email protected]>:

    Automated Code Change

--
723426786  by A. Unique TensorFlower<[email protected]>:

    PR tensorflow#22258: [GPU][NFC] Avoid always printing complete PGLE profiles.

    Imported from GitHub PR openxla/xla#22258

    Copybara import of the project:

    --
    025352635a155e447559d83c471369559aad5981 by Ilia Sergachev <[email protected]>:

    [GPU][NFC] Avoid always printing complete PGLE profiles.

    Merging this change closes tensorflow#22258

--
723426773  by A. Unique TensorFlower<[email protected]>:

    PR tensorflow#21375: [ds-fusion] Get While loop analysis with copy fusion

    Imported from GitHub PR openxla/xla#21375

    In later stages of optimization, there are instances of copy fusion on the parameter of the while body. With this, we need to allow inlining of fusions while getting the induction variable index, otherwise we cannot deduce the tuple index.
    Copybara import of the project:

    --
    3147ec926aa1c6fdfa2f4376668434c9a2fbeb87 by Shraiysh Vaishay <[email protected]>:

    [ds-fusion] Get While loop analysis with copy fusion

    In later stages of optimization, there are instances of copy fusion on
    the parameter of the while body. With this, we need to allow inlining of
    fusions while getting the induction variable index, otherwise we cannot
    deduce the tuple index.

    --
    a435fbd2eadc17269d7bccbe141dcf7a21cc20e8 by Shraiysh Vaishay <[email protected]>:

    Relay control dependencies while converting fusion to call (extractor)

    Merging this change closes tensorflow#21375

--
723425710  by A. Unique TensorFlower<[email protected]>:

    [XLA] Add const reference versions of `ForEachInstructionWithPred` and `ForEachInstructionWithOpcode`.

    These are more permissive and semantically equivalent.

--
723425622  by A. Unique TensorFlower<[email protected]>:

    Remove dead code (NFC)

    We compute the total number of tiles in a variable `num_tiles` but then never
    use it. So remove it.

--
723419822  by A. Unique TensorFlower<[email protected]>:

    Automated Code Change

--
723402058  by A. Unique TensorFlower<[email protected]>:

    compat: Update forward compatibility horizon to 2025-02-05

--
723401869  by A. Unique TensorFlower<[email protected]>:

    Update GraphDef version to 2129.

--
723396271  by A. Unique TensorFlower<[email protected]>:

    [XLA] Support different operand and result types in AlgebraicSimplifierVisitor::HandlePad.

    I checked that none of the other cases in HandlePad require any adjustments.

--
723389764  by A. Unique TensorFlower<[email protected]>:

    Automated Code Change

--
723370461  by A. Unique TensorFlower<[email protected]>:

    Use matchers_oss in vendor code

--
723367856  by A. Unique TensorFlower<[email protected]>:

    Update users of TSL headers and targets to new location in XLA

    Updating:
     - `env.h`
     - `env_time.h`
     - `errors.h`
     - `file_statistics.h`
     - `file_system.h`
     - `file_system_helper.h`
     - `logging.h`
     - `macros.h`
     - `status.h`
     - `status_matchers.h`
     - `status_to_from_proto.h`
     - `statusor.h`
     - `test.h`
     - `test_benchmark.h`
     - `threadpool.h`
     - `threadpool_async_executor.h`
     - `threadpool_interface.h`
     - `threadpool_options.h`
     - `types.h`

    and associated targets.

--
723349025  by A. Unique TensorFlower<[email protected]>:

    Fix inference request analysis aggregated on batch size, by aggregating only the requests included in a single batch, as large request split into multiple batches will introduce confusing results (eg. the device time will be the sum of the 2 batch processing).

--
723344172  by A. Unique TensorFlower<[email protected]>:

    Automated Code Change

--
723340771  by A. Unique TensorFlower<[email protected]>:

    Automated Code Change

--
723337100  by A. Unique TensorFlower<[email protected]>:

    Automated Code Change

--
723321370  by A. Unique TensorFlower<[email protected]>:

    Stop modifying the TraceEventsContainer in DoStoreAsLevelDbTable. This behavior
    is not intuitive (modifying a const value that was passed in) and unnecessary.

--
723307829  by A. Unique TensorFlower<[email protected]>:
    Automated rollback of changelist 723246423.

723278167  by A. Unique TensorFlower<[email protected]>:

    Update users of TSL headers and targets to new location in XLA

    Updating:
     - `env.h`
     - `env_time.h`
     - `errors.h`
     - `file_statistics.h`
     - `file_system.h`
     - `file_system_helper.h`
     - `logging.h`
     - `macros.h`
     - `status.h`
     - `status_matchers.h`
     - `status_to_from_proto.h`
     - `statusor.h`
     - `test.h`
     - `test_benchmark.h`
     - `threadpool.h`
     - `threadpool_async_executor.h`
     - `threadpool_interface.h`
     - `threadpool_options.h`
     - `types.h`

    and associated targets.

--
723265881  by A. Unique TensorFlower<[email protected]>:

    Add the list of Qualcomm SoCs supporting NPU.

--
723248792  by A. Unique TensorFlower<[email protected]>:

    Add Q/DQ annotation lowering support.

    LowerQuantAnnotationsPass now supports quant.quantize and quant.dequantize composite lowering. These patterns make adjustments to the function signatures if necessary.

--
723246423  by A. Unique TensorFlower<[email protected]>:

    PR tensorflow#85476: Support Qnn Wrappers for LiteRt

    Imported from GitHub PR tensorflow#85476

    # WHAT
    - Basic wrapper for QNN types, handle dynamic resources along with wrapper instances.
    - Make these wrappers independent to LiteRT/tflite
    - Only depend on QNN and STL

    ### `ScalarParamWrapper`
    - Wrap `Qnn_Param_t` with `QNN_PARAMTYPE_SCALAR` for `paramType`
    - Choose correct `QNN_DATATYPE` based on the data type

    ### `TensorParamWrapper`
    - Wrap `Qnn_Param_t` with `QNN_PARAMTYPE_TENSOR` for `paramType`

    ### `UndefinedQuantizeParamsWrapper`
    - Wrap `Qnn_QuantizeParams_t`
    - Default for quantization parameter

    ### `ScaleOffsetQuantizeParamsWrapper`
    - Wrap `Qnn_QuantizeParams_t` for per-tensor quantization

    ### `AxisScaleOffsetQuantizeParamsWrapper`
    - Wrap `Qnn_QuantizeParams_t`  for per-axis quantization

    ### `TensorWrapper`
    - Wrap `Qnn_TensorType_t`
    - Handle dynamic resource, e.g. name, dimensions, weight data.

    ### `OpWrapper`
    - Wrap `Qnn_OpConfig_t`
    - Handle dynamic resource, e.g. name, input output tensors, params
    Copybara import of the project:

    --
    4833a20 by weilhuan-quic <[email protected]>:

    LiteRt Qualcomm wrappers

    --
    725f571 by weilhuan-quic <[email protected]>:

    TensorWrapper GetDataTypeSize() return bytes instead of bits

    --
    dd3f251 by weilhuan-quic <[email protected]>:

    comment qnn_lib_headers

    --
    06e0616 by weilhuan-quic <[email protected]>:

    Change license

    Merging this change closes tensorflow#85476

--

PiperOrigin-RevId: 723477680

Author: tensorflower-gardener

tensorflow/compiler/jit/pjrt_device_context.cc

@@ -271,7 +271,8 @@ void PjRtDeviceToDeviceCopy(DeviceContext* send_dev_context,
           .value();
 
   absl::StatusOr<std::unique_ptr<xla::PjRtBuffer>> buffer_or =
-      src_device_buffer->CopyToDevice(pjrt_dst_device);
+      src_device_buffer->CopyToMemorySpace(
+          *pjrt_dst_device->default_memory_space());
   if (!buffer_or.ok()) {
     done(buffer_or.status());
     return;

tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.h

@@ -20,6 +20,7 @@ limitations under the License.
 #include <unordered_set>
 #include <vector>
 
+#include "absl/status/status.h"
 #include "mlir/IR/BuiltinOps.h"  // from @llvm-project
 #include "mlir/IR/MLIRContext.h"  // from @llvm-project
 #include "mlir/IR/OwningOpRef.h"  // from @llvm-project

tensorflow/compiler/mlir/lite/transforms/lower_quant_annotations_helper.cc

@@ -37,7 +37,8 @@ namespace mlir::TFL {
 LogicalResult FillCompositeParams(stablehlo::CompositeOp op,
                                   SmallVector<double, 4>& scales,
                                   SmallVector<int64_t, 4>& zero_points,
-                                  int& num_bits, bool& is_signed) {
+                                  int& num_bits, bool& is_signed,
+                                  bool& is_narrow_range) {
   auto scale_attr = llvm::dyn_cast_or_null<DenseFPElementsAttr>(
       op.getCompositeAttributes().get("scale"));
   if (scale_attr == nullptr) {
@@ -68,10 +69,19 @@ LogicalResult FillCompositeParams(stablehlo::CompositeOp op,
   if (dtype == "i8") {
     num_bits = 8;
     is_signed = true;
+  } else if (dtype == "i4") {
+    num_bits = 4;
+    is_signed = true;
   } else {
-    // TODO(majiddadashi) currently only tested with i8.
     return failure();
   }
+  auto narrow_range_attr = llvm::dyn_cast_or_null<BoolAttr>(
+      op.getCompositeAttributes().get("narrow_range"));
+  if (narrow_range_attr == nullptr) {
+    return failure();
+  }
+  is_narrow_range = narrow_range_attr.getValue();
+
   return success();
 }
 

tensorflow/compiler/mlir/lite/transforms/lower_quant_annotations_helper.h

@@ -31,7 +31,8 @@ namespace mlir::TFL {
 LogicalResult FillCompositeParams(stablehlo::CompositeOp op,
                                   SmallVector<double, 4>& scales,
                                   SmallVector<int64_t, 4>& zero_points,
-                                  int& num_bits, bool& is_signed);
+                                  int& num_bits, bool& is_signed,
+                                  bool& is_narrow_range);
 
 LogicalResult GetStorageParams(unsigned num_bits, bool narrow_range,
                                bool is_signed, MLIRContext* ctx,