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https://github.com/zebrajr/tensorflow.git
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[XLA][codegen] Migrate triton operations that have shared dialect lowerings are implemented for.
These were missed in previous commits. Addresses transpose and bitcast. PiperOrigin-RevId: 826158776
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commit
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@ -137,8 +137,8 @@ cc_library(
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"@llvm-project//mlir:IR",
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"@llvm-project//mlir:LLVMDialect",
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"@llvm-project//mlir:MathDialect",
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"@llvm-project//mlir:NVVMDialect",
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"@llvm-project//mlir:Support",
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"@llvm-project//mlir:TensorDialect",
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"@triton//:TritonDialects",
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],
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)
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@ -428,6 +428,7 @@ cc_library(
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"@llvm-project//mlir:MathDialect",
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"@llvm-project//mlir:Support",
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"@local_tsl//tsl/platform:tensor_float_32_hdr_lib",
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"@stablehlo//:stablehlo_ops",
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"@triton//:TritonDialects",
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],
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)
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@ -36,6 +36,7 @@ limitations under the License.
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#include "mlir/IR/TypeUtilities.h"
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#include "mlir/IR/Value.h"
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#include "mlir/Support/LLVM.h"
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#include "stablehlo/dialect/StablehloOps.h"
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#include "xla/backends/gpu/codegen/triton/emitter_helpers.h"
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#include "xla/codegen/emitter_loc_op_builder.h"
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#include "xla/hlo/ir/hlo_instruction.h"
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@ -160,8 +161,8 @@ absl::StatusOr<Value> ScaledDot(EmitterLocOpBuilder b,
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Value rhs_scale;
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if (rhs_dot_elem_type != ttir::ScaleDotElemType::BF16) {
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rhs_scale = Bitcast(b, operands.rhs_scale, b.getI8Type());
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rhs_scale =
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b.create<ttir::TransOp>(rhs_scale, mlir::ArrayRef<int32_t>{1, 0});
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rhs_scale = b.create<mlir::stablehlo::TransposeOp>(
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rhs_scale, b.getDenseI64ArrayAttr({1, 0}));
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}
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// make type with the same shape as the scale but with i8 type
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@ -35,6 +35,7 @@ limitations under the License.
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
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#include "mlir/Dialect/Math/IR/Math.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/BuiltinAttributes.h"
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#include "mlir/IR/BuiltinOps.h"
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@ -501,7 +502,7 @@ absl::StatusOr<Value> EmitElementwise(EmitterLocOpBuilder& b,
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mh::ComparisonDirection::NE),
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inputs[1], inputs[2]);
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case HloOpcode::kReducePrecision:
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return mh::reducePrecision<mt::BitcastOp>(
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return mh::reducePrecision<mlir::tensor::BitcastOp>(
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b.getLoc(), inputs[0], hlo.exponent_bits(), hlo.mantissa_bits(), &b);
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default:
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return absl::InvalidArgumentError(
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