Pin update March 2024

.bazelversion

@@ -1 +1 @@
-5.3.0
+6.5.0

.circleci/common.sh

@@ -144,10 +144,13 @@ function run_torch_xla_python_tests() {
         PJRT_DEVICE=CUDA torchrun --nnodes=1 --node_rank=0 --nproc_per_node=$num_devices test/test_train_mp_imagenet.py --fake_data --pjrt_distributed --batch_size=16 --num_epochs=1  --num_steps=25 --model=resnet18
 
         # single-host-SPMD
-        XLA_USE_SPMD=1 PJRT_DEVICE=CUDA torchrun --nnodes=1 --node_rank=0 --nproc_per_node=1 test/spmd/test_train_spmd_imagenet.py --fake_data --batch_size 16 --model=resnet50 --sharding=batch --num_epochs=1  --num_steps=25 --model=resnet18
+        # TODO: Reduce BS due to GPU test OOM in CI after pin update to 03/05/2024 (#6677)
+        XLA_USE_SPMD=1 PJRT_DEVICE=CUDA torchrun --nnodes=1 --node_rank=0 --nproc_per_node=1 test/spmd/test_train_spmd_imagenet.py --fake_data --batch_size 8 --model=resnet50 --sharding=batch --num_epochs=1  --num_steps=25 --model=resnet18
 
-        PJRT_DEVICE=CUDA python test/test_train_mp_imagenet_fsdp.py --fake_data --use_nested_fsdp --use_small_fake_sample --num_epochs=1
-        PJRT_DEVICE=CUDA python test/test_train_mp_imagenet_fsdp.py --fake_data --auto_wrap_policy type_based --use_small_fake_sample --num_epochs=1
+        # TODO: Reduce BS due to GPU test OOM in CI after pin update to 03/05/2024 (#6677)
+        PJRT_DEVICE=CUDA python test/test_train_mp_imagenet_fsdp.py --fake_data --use_nested_fsdp --use_small_fake_sample --num_epochs=1 --batch_size 32 --test_set_batch_size 32
+        # TODO: Reduce BS due to GPU test OOM in CI after pin update to 03/05/2024 (#6677)
+        PJRT_DEVICE=CUDA python test/test_train_mp_imagenet_fsdp.py --fake_data --auto_wrap_policy type_based --use_small_fake_sample --num_epochs=1 --batch_size 32 --test_set_batch_size 32
         XLA_DISABLE_FUNCTIONALIZATION=1 PJRT_DEVICE=CUDA python test/test_train_mp_imagenet_fsdp.py --fake_data --use_nested_fsdp --use_small_fake_sample --num_epochs=1
         # Syncfree SGD optimizer tests
         if [ -d ./torch_xla/amp/syncfree ]; then

WORKSPACE

@@ -51,9 +51,9 @@ http_archive(
         "//openxla_patches:f16_abi_clang.diff",
         "//openxla_patches:quant_dequant_converter.diff",
     ],
-    strip_prefix = "xla-419a3d736bdfe6891bc40e9ab5c78b466c8e0dc6",
+    strip_prefix = "xla-18cbd2019898d3a7b563aeb73683f0c5a6ce14fd",
     urls = [
-        "https://github.com/openxla/xla/archive/419a3d736bdfe6891bc40e9ab5c78b466c8e0dc6.tar.gz",
+        "https://github.com/openxla/xla/archive/18cbd2019898d3a7b563aeb73683f0c5a6ce14fd.tar.gz",
     ],
 )
 

openxla_patches/quant_dequant_converter.diff

@@ -1,31 +1,36 @@
-// TODO(lsy323): This is a patch on the HLO->StableHLO converter, this allows the custom call to
-// stablehlo.uniform_quantize/dequantize to be converted to stablehlo.uniform_quantize/dequantize.
-// The patch can be removed after quantize/dequantize, quantized dtype support is added to HLO.
 diff --git a/xla/translate/hlo_to_mhlo/BUILD b/xla/translate/hlo_to_mhlo/BUILD
-index cd6e427c3..9deecdcb2 100644
+index a75baa9dad..a09fca4898 100644
 --- a/xla/translate/hlo_to_mhlo/BUILD
 +++ b/xla/translate/hlo_to_mhlo/BUILD
-@@ -68,6 +68,7 @@ cc_library(
-         "@llvm-project//mlir:ArithDialect",
+@@ -40,6 +40,7 @@ cc_library(
+         "@llvm-project//llvm:Support",
          "@llvm-project//mlir:AsmParser",
-         "@llvm-project//mlir:FuncDialect",
-+        "@llvm-project//mlir:QuantOps",
          "@llvm-project//mlir:IR",
-         "@llvm-project//mlir:SparseTensorDialect",
-         "@tsl//tsl/platform:statusor",
-diff --git a/xla/translate/hlo_to_mhlo/hlo_function_importer.cc b/xla/translate/hlo_to_mhlo/hlo_function_importer.cc
-index 5e5e32652..d246383bd 100644
---- a/xla/translate/hlo_to_mhlo/hlo_function_importer.cc
-+++ b/xla/translate/hlo_to_mhlo/hlo_function_importer.cc
-@@ -669,6 +669,71 @@ StatusOr<mlir::Operation*> HloFunctionImporter::ImportInstruction(
-   return importer.ImportInstructionWithLayout(instr, operands, builder, mode);
- }
-
-+Type getQuantizedType(mlir::DictionaryAttr& backend_config) {
++        "@llvm-project//mlir:QuantOps",
+         "@llvm-project//mlir:Support",
+     ],
+ )
+diff --git a/xla/translate/hlo_to_mhlo/custom_call_importer.cc b/xla/translate/hlo_to_mhlo/custom_call_importer.cc
+index 9250747210..9cba4c992a 100644
+--- a/xla/translate/hlo_to_mhlo/custom_call_importer.cc
++++ b/xla/translate/hlo_to_mhlo/custom_call_importer.cc
+@@ -21,6 +21,7 @@ limitations under the License.
+ #include "absl/strings/match.h"
+ #include "llvm/ADT/STLExtras.h"
+ #include "mlir/AsmParser/AsmParser.h"  // from @llvm-project
++#include "mlir/Dialect/Quant/QuantTypes.h" // from @llvm-project
+ #include "mlir/IR/Builders.h"  // from @llvm-project
+ #include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project
+ #include "mlir/IR/Location.h"  // from @llvm-project
+@@ -93,6 +94,66 @@ absl::StatusOr<mlir::Operation*> ImportRealDynamicSliceOp(
+
+ }  // namespace
+
++mlir::Type getQuantizedType(mlir::DictionaryAttr& backend_config) {
 +  std::vector<double> scales;
 +  std::vector<int64_t> zero_points;
 +  int64_t quantization_dimension = -1, storage_max = 0, storage_min = 0;
-+  Type storage_type, expressed_type;
++  mlir::Type storage_type, expressed_type;
 +
 +  auto scales_attr = backend_config.get("scale");
 +  if (scales_attr) {
@@ -61,15 +66,11 @@ index 5e5e32652..d246383bd 100644
 +  auto storage_type_attr = backend_config.get("storage_type");
 +  if (storage_type_attr) {
 +    storage_type = storage_type_attr.cast<mlir::TypeAttr>().getValue();
-+    //.cast<mlir::ShapedType>()
-+    //.getElementType();
 +  }
 +
 +  auto expressed_type_attr = backend_config.get("expressed_type");
 +  if (expressed_type_attr) {
 +    expressed_type = expressed_type_attr.cast<mlir::TypeAttr>().getValue();
-+    //.cast<mlir::ShapedType>()
-+    //.getElementType();
 +  }
 +
 +  auto is_signed = storage_type.cast<mlir::IntegerType>().isSignless();
@@ -85,54 +86,37 @@ index 5e5e32652..d246383bd 100644
 +  }
 +}
 +
+ absl::StatusOr<mlir::Operation*> ImportCustomCallAsOp(
+     const HloCustomCallInstruction* instruction, mlir::Location loc,
+     mlir::Type result_type, mlir::ValueRange operands,
+@@ -112,6 +173,30 @@ absl::StatusOr<mlir::Operation*> ImportCustomCallAsOp(
+     return ImportRealDynamicSliceOp(backend_config_str, loc, result_type,
+                                     operands, builder);
+   }
 +
- StatusOr<mlir::Operation*> HloFunctionImporter::ImportCustomCallAsOp(
-     const HloInstruction* instruction, mlir::Location loc,
-     const Type result_type, mlir::ValueRange operands,
-@@ -992,6 +1057,25 @@ StatusOr<mlir::Operation*> HloFunctionImporter::ImportInstructionImpl(
-                 "Couldn't parse backend config into a dictionary attribute");
-
-           attributes.push_back(builder_->getNamedAttr("backend_config", attr));
-+          auto backend_config = attr.cast<mlir::DictionaryAttr>();
-+          if (custom_call->custom_call_target() ==
-+              "stablehlo.uniform_quantize") {
-+            return func_builder
-+                ->create<mlir::mhlo::UniformQuantizeOp>(
-+                    loc,
-+                    mlir::RankedTensorType::get(
-+                        result_type.cast<RankedTensorType>().getShape(),
-+                        getQuantizedType(backend_config)),
-+                    operands)
-+                .getOperation();
-+          }
++  auto attr = mlir::parseAttribute(backend_config_str, builder->getContext())
++                  .dyn_cast<mlir::DictionaryAttr>();
++  if (!attr) {
++    return Internal(
++        "Couldn't parse backend config into a dictionary attribute");
++  }
++  auto backend_config = attr.cast<mlir::DictionaryAttr>();
++  if (custom_call_target == "mhlo.uniform_quantize") {
++    return builder
++        ->create<mlir::mhlo::UniformQuantizeOp>(
++            loc,
++            mlir::RankedTensorType::get(
++                result_type.cast<mlir::RankedTensorType>().getShape(),
++                getQuantizedType(backend_config)),
++            operands)
++        .getOperation();
++  }
 +
-+          if (custom_call->custom_call_target() ==
-+              "stablehlo.uniform_dequantize") {
-+            return func_builder
-+                ->create<mlir::mhlo::UniformDequantizeOp>(
-+                    loc, result_type, operands) .getOperation();
-+          }
-         }
-       } else {
-         attributes.push_back(builder_->getNamedAttr(
-diff --git a/xla/translate/hlo_to_mhlo/hlo_module_importer.cc b/xla/translate/hlo_to_mhlo/hlo_module_importer.cc
-index f9edd1272..23a747fb1 100644
---- a/xla/translate/hlo_to_mhlo/hlo_module_importer.cc
-+++ b/xla/translate/hlo_to_mhlo/hlo_module_importer.cc
-@@ -19,6 +19,8 @@ limitations under the License.
- #include <memory>
- #include <vector>
-
-+#include "mlir/Dialect/Quant/QuantOps.h"
-+#include "mlir/Dialect/Quant/QuantTypes.h"
- #include "mlir/IR/Attributes.h"  // from @llvm-project
- #include "xla/hlo/ir/hlo_computation.h"
- #include "xla/hlo/ir/hlo_instruction.h"
-@@ -41,6 +43,7 @@ HloModuleImporter::HloModuleImporter(mlir::ModuleOp module,
-   module.getContext()->loadDialect<mlir::arith::ArithDialect>();
-   module.getContext()->loadDialect<mlir::func::FuncDialect>();
-   module.getContext()->loadDialect<mlir::mhlo::MhloDialect>();
-+  module.getContext()->loadDialect<mlir::quant::QuantizationDialect>();
++  if (custom_call_target == "mhlo.uniform_dequantize") {
++    return builder
++        ->create<mlir::mhlo::UniformDequantizeOp>(loc, result_type, operands)
++        .getOperation();
++  }
+   return InvalidArgument("Unsupported MHLO op custom_call %s",
+                          custom_call_target);
  }
-
- namespace {

setup.py

@@ -64,7 +64,7 @@
 
 base_dir = os.path.dirname(os.path.abspath(__file__))
 
-_date = '20240213'
+_date = '20240305'
 _libtpu_version = f'0.1.dev{_date}'
 _libtpu_storage_path = f'https://storage.googleapis.com/cloud-tpu-tpuvm-artifacts/wheels/libtpu-nightly/libtpu_nightly-{_libtpu_version}-py3-none-any.whl'
 _jax_version = f'0.4.25.dev{_date}'

torch_xla/csrc/ops/dequant_tensor.cpp

@@ -39,7 +39,7 @@ XlaOpVector DequantizeTensor::Lower(LoweringContext* loctx) const {
   xla::Shape output_shape = xla::ShapeUtil::MakeShape(xla::PrimitiveType::F32,
                                                       input_shape.dimensions());
   // TODO(lsy323): Lower to HLO directly once qdtype is added to HLO.
-  static const std::string opname = "stablehlo.uniform_dequantize";
+  static const std::string opname = "mhlo.uniform_dequantize";
   auto qparams = QuantParams(scale_, zero_point_, quant_min_, quant_max_, axis_,
                              dtype_, xla::PrimitiveType::F32);
 

torch_xla/csrc/ops/quant_tensor.cpp

@@ -40,7 +40,7 @@ XlaOpVector QuantizeTensor::Lower(LoweringContext* loctx) const {
       GetTorchIntDtypeToHloDtype(dtype_), input_shape.dimensions());
 
   // TODO(lsy323): Lower to HLO directly once qdtype is added to HLO.
-  static const std::string opname = "stablehlo.uniform_quantize";
+  static const std::string opname = "mhlo.uniform_quantize";
   auto qparams = QuantParams(scale_, zero_point_, quant_min_, quant_max_, axis_,
                              dtype_, input_shape.element_type());
 

torch_xla/csrc/runtime/pjrt_computation_client.cc

@@ -56,7 +56,9 @@ std::unordered_map<int, int> build_index_map(
 xla::Shape host_output_shape(xla::PjRtBuffer* buffer) {
   xla::Shape shape = xla::ShapeUtil::MakeShape(
       buffer->element_type(), buffer->logical_dimensions().value());
-  *shape.mutable_layout() = buffer->layout();
+  *shape.mutable_layout() =
+      dynamic_cast<const xla::PjRtXlaLayout*>(buffer->layout().get())
+          ->xla_layout();
 
   return xla::ShapeUtil::DeviceShapeToHostShape(shape);
 }

torch_xla/csrc/runtime/profiler.cc

@@ -17,10 +17,10 @@ namespace profiler {
 namespace {
 
 const PLUGIN_Profiler_Api* FindProfilerApi(const PJRT_Api* pjrt_api) {
-  const PJRT_Structure_Base* next =
-      reinterpret_cast<const PJRT_Structure_Base*>(pjrt_api->extension_start);
+  const PJRT_Extension_Base* next =
+      reinterpret_cast<const PJRT_Extension_Base*>(pjrt_api->extension_start);
   while (next != nullptr &&
-         next->type != PJRT_Structure_Type::PJRT_Structure_Type_Profiler) {
+         next->type != PJRT_Extension_Type::PJRT_Extension_Type_Profiler) {
     next = next->next;
   }
   if (next == nullptr) {