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Fixed some issues around compiling on Windows. #15444

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Fixed some issues around compiling on Windows. #15444

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Fixed some issues around compiling on Windows.
eaplatanios Jul 29, 2024
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24 changes: 12 additions & 12 deletions xla/backends/profiler/gpu/cupti_buffer_events.cc
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Expand Up @@ -186,18 +186,18 @@ void AddGraphTraceActivityEvent(CuptiEventCollectorDelegate &collector,
AnnotationMap::AnnotationInfo info = collector.annotation_map.LookUp(
graph_trace->deviceId, graph_trace->correlationId);
collector.receive(CuptiTracerEvent{
.type = CuptiTracerEventType::CudaGraph,
.source = CuptiTracerEventSource::Activity,
.name = absl::StrCat("CudaGraphExec:", graph_trace->graphId),
.annotation = info.annotation,
.nvtx_range = info.nvtx_range,
.start_time_ns = graph_trace->start,
.end_time_ns = graph_trace->end,
.device_id = graph_trace->deviceId,
.correlation_id = graph_trace->correlationId,
.context_id = graph_trace->contextId,
.stream_id = graph_trace->streamId,
.graph_id = graph_trace->graphId,
/* .type = */ CuptiTracerEventType::CudaGraph,
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XLA is configured to build using C++ 17. However, this is a C++ 20 feature, resulting in the following error when trying to compile on Windows:

error C7555: use of designated initializers requires at least '/std:c++20'

/* .source = */ CuptiTracerEventSource::Activity,
/* .name = */ absl::StrCat("CudaGraphExec:", graph_trace->graphId),
/* .annotation = */ info.annotation,
/* .nvtx_range = */ info.nvtx_range,
/* .start_time_ns = */ graph_trace->start,
/* .end_time_ns = */ graph_trace->end,
/* .device_id = */ graph_trace->deviceId,
/* .correlation_id = */ graph_trace->correlationId,
/* .context_id = */ graph_trace->contextId,
/* .stream_id = */ graph_trace->streamId,
/* .graph_id = */ graph_trace->graphId,
});
}

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2 changes: 1 addition & 1 deletion xla/backends/profiler/gpu/cupti_buffer_events.h
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Expand Up @@ -56,7 +56,7 @@ struct MemcpyDetails {
int8_t dst_mem_kind;

// ID of the hardware channel on which this operation ran.
uint32_t channel_id = -1;
uint32_t channel_id = static_cast<uint32_t>(-1);
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This resulted in an implicit type narrowing error (I believe it was C2397). The explicit static cast fixes it.

// CUpti_ChannelType of the channel above.
int8_t channel_type = 0; // CUPTI_CHANNEL_TYPE_INVALID
};
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2 changes: 2 additions & 0 deletions xla/hlo/evaluator/hlo_evaluator.cc
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Expand Up @@ -535,7 +535,9 @@ std::optional<DynamicOrStaticInteger> EvaluateWhileLoopParamInitValue(

namespace internal {

#if !defined(_MSC_VER)
constexpr absl::string_view kEvalErrorDetailUrl = "EvalErrorDetailUrl";
#endif

std::optional<EvalErrorDetail> ParseEvalErrorDetail(const absl::Status& error) {
auto error_detail = error.GetPayload(kEvalErrorDetailUrl);
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4 changes: 4 additions & 0 deletions xla/hlo/evaluator/hlo_evaluator.h
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Expand Up @@ -530,7 +530,11 @@ enum class EvalErrorDetail : uint32_t {
kDynamicValueDependence = 0,
};

#if defined(_MSC_VER)
extern const absl::string_view kEvalErrorDetailUrl = "EvalErrorDetailUrl";
#else
extern const absl::string_view kEvalErrorDetailUrl;
#endif

std::optional<EvalErrorDetail> ParseEvalErrorDetail(const absl::Status& error);

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2 changes: 1 addition & 1 deletion xla/pjrt/c/pjrt_c_api_wrapper_impl.cc
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Expand Up @@ -2129,7 +2129,7 @@ PJRT_Error* PJRT_Layouts_MemoryLayout_Serialize(
PJRT_Layouts_MemoryLayout_Serialize_Args_STRUCT_SIZE, args->struct_size));

PJRT_Layouts_SerializedLayout* s_layout = new PJRT_Layouts_SerializedLayout{
.serialized = args->layout->layout->Serialize()};
/* .serialized = */ args->layout->layout->Serialize()};
args->serialized_layout = s_layout;
args->serialized_bytes = s_layout->serialized.data();
args->serialized_bytes_size = s_layout->serialized.size();
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14 changes: 8 additions & 6 deletions xla/pjrt/gpu/se_gpu_pjrt_compiler.cc
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Expand Up @@ -199,13 +199,15 @@ StreamExecutorGpuCompiler::Compile(CompileOptions options,
#endif
}

STREAM_EXECUTOR_REGISTER_MODULE_INITIALIZER(pjrt_register_se_gpu_compiler, {
PjRtRegisterCompiler(
#if TENSORFLOW_USE_ROCM
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The nested macro seems to not be supported by MSVC. Pushing the inner ifdef outside the other macro seems to work and doesn't change the behavior/functionality of the code here.

RocmName(),
STREAM_EXECUTOR_REGISTER_MODULE_INITIALIZER(pjrt_register_se_gpu_compiler, {
PjRtRegisterCompiler(RocmName(),
std::make_unique<StreamExecutorGpuCompiler>());
});
#else
CudaName(),
#endif
std::make_unique<StreamExecutorGpuCompiler>());
STREAM_EXECUTOR_REGISTER_MODULE_INITIALIZER(pjrt_register_se_gpu_compiler, {
PjRtRegisterCompiler(CudaName(),
std::make_unique<StreamExecutorGpuCompiler>());
});
#endif
} // namespace xla
8 changes: 4 additions & 4 deletions xla/service/cpu/runtime/conv_impl.h
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Expand Up @@ -41,7 +41,7 @@ void EigenConv2DImpl(
Eigen::Index padding_y_after, Eigen::Index lhs_x_dilation,
Eigen::Index lhs_y_dilation, Eigen::Index rhs_x_dilation,
Eigen::Index rhs_y_dilation, Eigen::Index feature_group_count,
std::optional<std::function<void()>> done_callback = std::nullopt) {
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This resulted in this error:

error C2765: 'tensorflow::xla::EigenConv2DImpl': an explicit specialization or instantiation of a function template cannot have any default arguments

I just removed a couple default arguments that were causing this error and propagated them at call sites where they were missing.

std::optional<std::function<void()>> done_callback) {
const Eigen::TensorMap<Eigen::Tensor<const ScalarType, 4, Eigen::RowMajor>,
Eigen::Aligned>
input(lhs, input_batch, input_x, input_y, input_channels);
Expand Down Expand Up @@ -129,7 +129,7 @@ void EigenConv3DImpl(
Eigen::Index lhs_z_dilation, Eigen::Index rhs_x_dilation,
Eigen::Index rhs_y_dilation, Eigen::Index rhs_z_dilation,
Eigen::Index feature_group_count,
std::optional<std::function<void()>> done_callback = std::nullopt) {
std::optional<std::function<void()>> done_callback) {
using ConstTType =
Eigen::TensorMap<Eigen::Tensor<const ScalarType, 5, Eigen::RowMajor>,
Eigen::Aligned>;
Expand Down Expand Up @@ -223,7 +223,7 @@ void EigenConv3DImpl(
Eigen::Index padding_y_after, Eigen::Index lhs_x_dilation, \
Eigen::Index lhs_y_dilation, Eigen::Index rhs_x_dilation, \
Eigen::Index rhs_y_dilation, Eigen::Index feature_group_count, \
std::optional<std::function<void()>> done_callback = std::nullopt)
std::optional<std::function<void()>> done_callback)

CONV2D_EXTERN_TEMPLATE(Eigen::DefaultDevice, Eigen::half);
CONV2D_EXTERN_TEMPLATE(Eigen::DefaultDevice, float);
Expand All @@ -249,7 +249,7 @@ CONV2D_EXTERN_TEMPLATE(Eigen::ThreadPoolDevice, float);
Eigen::Index lhs_z_dilation, Eigen::Index rhs_x_dilation, \
Eigen::Index rhs_y_dilation, Eigen::Index rhs_z_dilation, \
Eigen::Index feature_group_count, \
std::optional<std::function<void()>> done_callback = std::nullopt)
std::optional<std::function<void()>> done_callback)

CONV3D_EXTERN_TEMPLATE(Eigen::DefaultDevice, Eigen::half);
CONV3D_EXTERN_TEMPLATE(Eigen::DefaultDevice, float);
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6 changes: 4 additions & 2 deletions xla/service/cpu/runtime_conv2d.cc
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Expand Up @@ -15,6 +15,8 @@ limitations under the License.

#include "xla/service/cpu/runtime_conv2d.h"

#include <optional>

#define EIGEN_USE_THREADS

#include "absl/base/dynamic_annotations.h"
Expand All @@ -41,7 +43,7 @@ ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenConv2DF32(
kernel_channels, kernel_filters, output_rows, output_cols, row_stride,
col_stride, padding_top, padding_bottom, padding_left, padding_right,
lhs_row_dilation, lhs_col_dilation, rhs_row_dilation, rhs_col_dilation,
feature_group_count);
feature_group_count, std::nullopt);
}

ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenConv2DF16(
Expand All @@ -63,5 +65,5 @@ ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenConv2DF16(
kernel_channels, kernel_filters, output_rows, output_cols, row_stride,
col_stride, padding_top, padding_bottom, padding_left, padding_right,
lhs_row_dilation, lhs_col_dilation, rhs_row_dilation, rhs_col_dilation,
feature_group_count);
feature_group_count, std::nullopt);
}
6 changes: 4 additions & 2 deletions xla/service/cpu/runtime_conv3d.cc
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Expand Up @@ -15,6 +15,8 @@ limitations under the License.

#include "xla/service/cpu/runtime_conv3d.h"

#include <optional>

#define EIGEN_USE_THREADS

#include "absl/base/dynamic_annotations.h"
Expand Down Expand Up @@ -44,7 +46,7 @@ ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenConv3DF32(
y_stride, z_stride, padding_x_before, padding_x_after, padding_y_before,
padding_y_after, padding_z_before, padding_z_after, lhs_x_dilation,
lhs_y_dilation, lhs_z_dilation, rhs_x_dilation, rhs_y_dilation,
rhs_z_dilation, feature_group_count);
rhs_z_dilation, feature_group_count, std::nullopt);
}

ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenConv3DF16(
Expand All @@ -69,5 +71,5 @@ ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenConv3DF16(
y_stride, z_stride, padding_x_before, padding_x_after, padding_y_before,
padding_y_after, padding_z_before, padding_z_after, lhs_x_dilation,
lhs_y_dilation, lhs_z_dilation, rhs_x_dilation, rhs_y_dilation,
rhs_z_dilation, feature_group_count);
rhs_z_dilation, feature_group_count, std::nullopt);
}
6 changes: 4 additions & 2 deletions xla/service/cpu/runtime_single_threaded_conv2d.cc
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Expand Up @@ -15,6 +15,8 @@ limitations under the License.

#include "xla/service/cpu/runtime_single_threaded_conv2d.h"

#include <optional>

#include "absl/base/dynamic_annotations.h"
#include "xla/service/cpu/runtime/conv_impl.h"

Expand All @@ -35,7 +37,7 @@ __xla_cpu_runtime_EigenSingleThreadedConv2DF16(
kernel_filters, output_rows, output_cols, row_stride, col_stride,
padding_top, padding_bottom, padding_left, padding_right,
lhs_row_dilation, lhs_col_dilation, rhs_row_dilation, rhs_col_dilation,
feature_group_count);
feature_group_count, std::nullopt);
}

ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY void
Expand All @@ -55,5 +57,5 @@ __xla_cpu_runtime_EigenSingleThreadedConv2DF32(
kernel_filters, output_rows, output_cols, row_stride, col_stride,
padding_top, padding_bottom, padding_left, padding_right,
lhs_row_dilation, lhs_col_dilation, rhs_row_dilation, rhs_col_dilation,
feature_group_count);
feature_group_count, std::nullopt);
}
6 changes: 4 additions & 2 deletions xla/service/cpu/runtime_single_threaded_conv3d.cc
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Expand Up @@ -15,6 +15,8 @@ limitations under the License.

#include "xla/service/cpu/runtime_single_threaded_conv3d.h"

#include <optional>

#include "absl/base/dynamic_annotations.h"
#include "xla/service/cpu/runtime/conv_impl.h"

Expand All @@ -38,7 +40,7 @@ __xla_cpu_runtime_EigenSingleThreadedConv3DF32(
z_stride, padding_x_before, padding_x_after, padding_y_before,
padding_y_after, padding_z_before, padding_z_after, lhs_x_dilation,
lhs_y_dilation, lhs_z_dilation, rhs_x_dilation, rhs_y_dilation,
rhs_z_dilation, feature_group_count);
rhs_z_dilation, feature_group_count, std::nullopt);
}

ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY void
Expand All @@ -61,5 +63,5 @@ __xla_cpu_runtime_EigenSingleThreadedConv3DF16(
z_stride, padding_x_before, padding_x_after, padding_y_before,
padding_y_after, padding_z_before, padding_z_after, lhs_x_dilation,
lhs_y_dilation, lhs_z_dilation, rhs_x_dilation, rhs_y_dilation,
rhs_z_dilation, feature_group_count);
rhs_z_dilation, feature_group_count, std::nullopt);
}
12 changes: 6 additions & 6 deletions xla/service/gpu/fusions/mlir/computation_partitioner.cc
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Expand Up @@ -300,12 +300,12 @@ PartitionedComputation::PartitionedComputation(
absl::StrJoin(roots, "_", [](std::string* out, const auto* root) {
absl::StrAppend(out, root->name());
})));
subgraphs_.push_back(
Subgraph{.name = std::move(name),
.instructions = {instructions.begin(), instructions.end()},
.roots = std::move(roots),
.index_ranges = std::move(ranges),
.root_indexing = std::move(root_indexing)});
subgraphs_.push_back(Subgraph{
/* .name = */ std::move(name),
/* .instructions = */ {instructions.begin(), instructions.end()},
/* .roots = */ std::move(roots),
/* .index_ranges = */ std::move(ranges),
/* .root_indexing = */ std::move(root_indexing)});
}

for (const auto& subgraph : subgraphs_) {
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3 changes: 1 addition & 2 deletions xla/service/gpu/fusions/mlir/erase_dead_functions.cc
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Expand Up @@ -77,8 +77,7 @@ class EraseDeadFunctionsPass

} // namespace

std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>>
CreateEraseDeadFunctionsPass() {
std::unique_ptr<mlir::Pass> CreateEraseDeadFunctionsPass() {
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I pushed this after the approval to resolve a linking error. The header uses mlir::Pass and MSVC appears to be sensitive to that resulting in mismatching symbol names that fail to link.

return std::make_unique<EraseDeadFunctionsPass>();
}

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3 changes: 1 addition & 2 deletions xla/service/gpu/fusions/mlir/merge_pointers_to_same_slice.cc
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Expand Up @@ -108,8 +108,7 @@ void MergePointersToSameSlicePass::runOnOperation() {

} // namespace

std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>>
CreateMergePointersToSameSlicePass() {
std::unique_ptr<mlir::Pass> CreateMergePointersToSameSlicePass() {
return std::make_unique<MergePointersToSameSlicePass>();
}

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3 changes: 1 addition & 2 deletions xla/service/gpu/fusions/mlir/optimize_loops.cc
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Expand Up @@ -306,8 +306,7 @@ class OptimizeLoopsPass

} // namespace

std::unique_ptr<mlir::OperationPass<mlir::func::FuncOp>>
CreateOptimizeLoopsPass() {
std::unique_ptr<mlir::Pass> CreateOptimizeLoopsPass() {
return std::make_unique<OptimizeLoopsPass>();
}

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3 changes: 1 addition & 2 deletions xla/service/gpu/fusions/mlir/unswitch_loops.cc
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Expand Up @@ -97,8 +97,7 @@ void UnswitchLoopsPass::runOnOperation() {

} // namespace

std::unique_ptr<mlir::OperationPass<mlir::func::FuncOp>>
CreateUnswitchLoopsPass() {
std::unique_ptr<mlir::Pass> CreateUnswitchLoopsPass() {
return std::make_unique<UnswitchLoopsPass>();
}

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3 changes: 1 addition & 2 deletions xla/service/gpu/fusions/mlir/vectorize_loads_stores.cc
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Expand Up @@ -350,8 +350,7 @@ class VectorizeLoadsAndStoresPass

} // namespace

std::unique_ptr<mlir::OperationPass<mlir::func::FuncOp>>
CreateVectorizeLoadsAndStoresPass() {
std::unique_ptr<mlir::Pass> CreateVectorizeLoadsAndStoresPass() {
return std::make_unique<VectorizeLoadsAndStoresPass>();
}

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25 changes: 20 additions & 5 deletions xla/service/gpu/kernels/BUILD
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Expand Up @@ -325,7 +325,10 @@ cc_library(
cuda_library(
name = "cutlass_gemm_adaptor",
hdrs = if_cuda_is_configured(["cutlass_gemm_adaptor.cu.h"]),
copts = ["-Wno-unknown-attributes"], # __grid_constant__ is not supported by clang
copts = select({
"@xla//xla/tsl:windows": [],
"//conditions:default": ["-Wno-unknown-attributes"], # __grid_constant__ is not supported by clang
}),
deps = if_cuda_is_configured([
":cutlass_gemm",
"@cutlass_archive//:cutlass",
Expand Down Expand Up @@ -367,7 +370,10 @@ cc_library(
cuda_library(
name = "cutlass_gemm_kernel_bf16xbf16_to_bf16",
srcs = if_cuda_is_configured(["cutlass_gemm_kernel_bf16xbf16_to_bf16.cu.cc"]),
copts = ["-Wno-unknown-attributes -mllvm -unroll-threshold=100000"],
copts = ["-mllvm", "-unroll-threshold=100000"] + select({
"@xla//xla/tsl:windows": [],
"//conditions:default": ["-Wno-unknown-attributes"],
}),
deps = if_cuda_is_configured([
":cutlass_gemm_adaptor",
"@cutlass_archive//:cutlass",
Expand All @@ -378,7 +384,10 @@ cuda_library(
cuda_library(
name = "cutlass_gemm_kernel_bf16xbf16_to_bf16_sm80",
srcs = if_cuda_is_configured(["cutlass_gemm_kernel_bf16xbf16_to_bf16_sm80.cu.cc"]),
copts = ["-Wno-unknown-attributes -mllvm -unroll-threshold=100000"],
copts = ["-mllvm", "-unroll-threshold=100000"] + select({
"@xla//xla/tsl:windows": [],
"//conditions:default": ["-Wno-unknown-attributes"],
}),
deps = if_cuda_is_configured([
":cutlass_gemm_adaptor",
"@cutlass_archive//:cutlass",
Expand All @@ -389,7 +398,10 @@ cuda_library(
cuda_library(
name = "cutlass_gemm_kernel_bf16xbf16_to_bf16_sm90",
srcs = if_cuda_is_configured(["cutlass_gemm_kernel_bf16xbf16_to_bf16_sm90.cu.cc"]),
copts = ["-Wno-ctad-maybe-unsupported -Wno-unknown-attributes -mllvm -unroll-threshold=100000"],
copts = ["-mllvm", "-unroll-threshold=100000"] + select({
"@xla//xla/tsl:windows": [],
"//conditions:default": ["-Wno-ctad-maybe-unsupported", "-Wno-unknown-attributes"],
}),
deps = if_cuda_is_configured([
":cutlass_gemm_adaptor",
":cutlass_gemm_epilogue",
Expand All @@ -401,7 +413,10 @@ cuda_library(
cuda_library(
name = "cutlass_gemm_kernel_f32xf32_to_f32",
srcs = if_cuda_is_configured(["cutlass_gemm_kernel_f32xf32_to_f32.cu.cc"]),
copts = ["-Wno-unknown-attributes"],
copts = select({
"@xla//xla/tsl:windows": [],
"//conditions:default": ["-Wno-unknown-attributes"],
}),
deps = if_cuda_is_configured([
":cutlass_gemm_adaptor",
"@cutlass_archive//:cutlass",
Expand Down
5 changes: 3 additions & 2 deletions xla/service/gpu/kernels/cutlass_gemm_adaptor.cu.h
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Expand Up @@ -199,8 +199,9 @@ namespace adaptor_3x {
template <typename Tag>
static std::optional<Dim3> ClusterDim() {
typename Traits<Tag>::Kernel::DispatchPolicy::ClusterShape cluster;
return Dim3{cute::get<0>(cluster), cute::get<1>(cluster),
cute::get<2>(cluster)};
return Dim3{static_cast<uint32_t>(cute::get<0>(cluster)),
static_cast<uint32_t>(cute::get<1>(cluster)),
static_cast<uint32_t>(cute::get<2>(cluster))};
}

template <typename Tag>
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4 changes: 4 additions & 0 deletions xla/service/gpu/kernels/cutlass_gemm_custom_kernel.cc
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Expand Up @@ -101,7 +101,11 @@ KernelArgsPacking ArgsPacking(int32_t m, int32_t n, int32_t k,
// object constructed in the storage. For now we ignore it, and it's textbook
// definition of UB, but for CUTLASS kernels we use today it's perfectly safe.
struct Params {
#if defined(_MSC_VER)
alignas(64) std::byte storage[1024];
#else
alignas(128) std::byte storage[1024];
#endif
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This results in the following error on Windows:

error C2719: '<args_0>': formal parameter with requested alignment of 128 won't be aligned

cc @dimvar who previously made the change from 64 to 128.

};

return [=](const se::Kernel& kernel, const se::KernelArgs& args) -> Packed {
Expand Down
2 changes: 1 addition & 1 deletion xla/service/gpu/model/gpu_collective_performance_model.cc
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Expand Up @@ -133,7 +133,7 @@ float GpuPerformanceWithCollectiveModel::GetNvlinkBw(
}

/*static*/ bool GpuPerformanceWithCollectiveModel::InitNvml() {
#if GOOGLE_CUDA
#if GOOGLE_CUDA && defined(PLATFORM_POSIX)
void* libhandle = dlopen("libnvidia-ml.so.1", RTLD_NOW);
CHECK(libhandle != nullptr) << "Failed to open libnvidia-ml.so.1";

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