Support for tt.dot_scaled operator

lib/Dialect/TritonGPU/IR/Ops.cpp

@@ -1,3 +1,4 @@
+#include "intel/include/Dialect/TritonIntelGPU/IR/Attributes.h"
 #include "mlir/IR/BuiltinTypes.h"
 #include "triton/Dialect/Triton/IR/Dialect.h"
 #include "triton/Dialect/Triton/IR/Utility.h"
@@ -114,17 +115,36 @@ LogicalResult UpcastMXFPOp::inferReturnTypes(
       retTy = RankedTensorType::get(xShape, FloatType::getBF16(ctx));
     } else {
       auto oldEncoding = cast<DotOperandEncodingAttr>(encoding);
-      auto newVEncoding = DotOperandEncodingAttr::get(
-          ctx, oldEncoding.getOpIdx(), oldEncoding.getParent(),
-          oldEncoding.getKWidth() * 2);
-      // Figure out the K dimension for the input A/B, given that the return
-      // type is upcasted A/B type so we need to update the proper dim size.
+
       const int opIdx = oldEncoding.getOpIdx();
       const bool hasBatch = xShape.size() == 3;
       const int kIdx = (opIdx == 0 ? 1 : 0) + hasBatch;
       newShape[kIdx] *= 2;
-      retTy = RankedTensorType::get(newShape, FloatType::getBF16(ctx),
-                                    newVEncoding);
+      Type elemType = FloatType::getBF16(ctx);
+
+      // Note: For Intel the dot operands layout's kWidth parameter must
+      // match the parent's DPAS layout opsPerChannel so we need to materialize
+      // a new DPAS layout.
+      Attribute newVEncoding;
+      if (auto dpasEncoding =
+              dyn_cast<intel::DpasEncodingAttr>(oldEncoding.getParent())) {
+        auto newDpasEncoding = intel::DpasEncodingAttr::get(
+            ctx, dpasEncoding.getRepeatCount(), dpasEncoding.getSystolicDepth(),
+            dpasEncoding.getExecutionSize(),
+            intel::DpasEncodingAttr::getOpsPerChannel(elemType),
+            dpasEncoding.getWarpsPerCTA(), dpasEncoding.getRepCluster(),
+            dpasEncoding.getSubGroupSize());
+        newVEncoding = DotOperandEncodingAttr::get(
+            ctx, oldEncoding.getOpIdx(), newDpasEncoding,
+            newDpasEncoding.getOpsPerChannel());
+      } else {
+        // Figure out the K dimension for the input A/B, given that the return
+        // type is upcasted A/B type so we need to update the proper dim size.
+        newVEncoding = DotOperandEncodingAttr::get(ctx, oldEncoding.getOpIdx(),
+                                                   oldEncoding.getParent(),
+                                                   oldEncoding.getKWidth() * 2);
+      }
+      retTy = RankedTensorType::get(newShape, elemType, newVEncoding);
     }
     inferredReturnTypes.push_back(retTy);
   } else {

test/TritonIntelGPU/accelerate-matmul-pvc.mlir

@@ -201,3 +201,32 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
     tt.return
   }
 }
+
+// -----
+
+#blocked = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [1, 16], warpsPerCTA = [1, 4], order = [1, 0]}>
+#blocked1 = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [16, 1], warpsPerCTA = [4, 1], order = [1, 0]}>
+#blocked2 = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [1, 16], warpsPerCTA = [2, 2], order = [1, 0]}>
+
+module attributes {"triton_gpu.target" = "xpu", "triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32, "triton_gpu.threads-per-warp" = 16 : i32, "triton_intel_gpu.min_sg_size" = 16 : i32, "triton_intel_gpu.support_dpas"} {
+  // CHECK: [[BLOCKED:#.+]] = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [1, 16], warpsPerCTA = [2, 2], order = [1, 0]}>
+  // CHECK: [[BLOCKED1:#.+]] = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [16, 1], warpsPerCTA = [4, 1], order = [1, 0]}>
+  // CHECK: [[BLOCKED2:#.+]] = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [1, 16], warpsPerCTA = [1, 4], order = [1, 0]}>
+  // CHECK: [[DPAS:#.+]] = #triton_intel_gpu.dpas<{repeatCount = 8, systolicDepth = 8, executionSize = 16, opsPerChan = 2, threadsPerWarp = 16, warpsPerCTA = [4, 1], repCluster = [1, 1], A = [8, 16], B = [16, 16], C = [8, 16]}>
+  // CHECK: [[DPAS1:#.+]] = #triton_intel_gpu.dpas<{repeatCount = 8, systolicDepth = 8, executionSize = 16, opsPerChan = 4, threadsPerWarp = 16, warpsPerCTA = [4, 1], repCluster = [1, 1], A = [8, 32], B = [32, 16], C = [8, 16]}>
+  // CHECK: dot_scaled
+  tt.func @dot_scaled(%a: tensor<128x32xi8, #blocked2>, %scale: tensor<128x2xi8, #blocked1>, %b: tensor<64x128xbf16, #blocked>) -> tensor<128x128xf32, #blocked> {
+    // CHECK: [[CST:%.*]] = arith.constant dense<0.000000e+00> : tensor<128x128xf32, [[BLOCKED2]]>
+    // CHECK: [[C:%.*]] = triton_gpu.convert_layout [[CST]] : tensor<128x128xf32, [[BLOCKED2]]> -> tensor<128x128xf32, [[DPAS]]>
+    // CHECK: [[CVT_ARG0:%.*]] = triton_gpu.convert_layout %arg0 : tensor<128x32xi8, [[BLOCKED]]> -> tensor<128x32xi8, #triton_gpu.dot_op<{opIdx = 0, parent = [[DPAS1]], kWidth = 4}>>
+    // CHECK: [[CVT_ARG1:%.*]] = triton_gpu.convert_layout %arg1 : tensor<128x2xi8, [[BLOCKED1]]> -> tensor<128x2xi8, [[BLOCKED1]]>
+    // CHECK: [[A:%.*]] = triton_gpu.upcast_mxfp [[CVT_ARG0]], [[CVT_ARG1]] fp_type = e2m1 : tensor<128x32xi8, #triton_gpu.dot_op<{opIdx = 0, parent = [[DPAS1]], kWidth = 4}>>, tensor<128x2xi8, [[BLOCKED1]]> -> tensor<128x64xbf16, #triton_gpu.dot_op<{opIdx = 0, parent = [[DPAS]], kWidth = 2}>>
+    // CHECK: [[B:%.*]] = triton_gpu.convert_layout %arg2 : tensor<64x128xbf16, [[BLOCKED2]]> -> tensor<64x128xbf16, #triton_gpu.dot_op<{opIdx = 1, parent = [[DPAS]], kWidth = 2}>>
+    // CHECK: [[D:%.*]] = tt.dot [[A]], [[B]], [[C]] : tensor<128x64xbf16, #triton_gpu.dot_op<{opIdx = 0, parent = [[DPAS]], kWidth = 2}>> * tensor<64x128xbf16, #triton_gpu.dot_op<{opIdx = 1, parent = [[DPAS]], kWidth = 2}>> -> tensor<128x128xf32, [[DPAS]]>
+    // CHECK: [[RES:%.*]] = triton_gpu.convert_layout {{.*}} : tensor<128x128xf32, [[DPAS]]> -> tensor<128x128xf32, [[BLOCKED2]]>
+
+    %cst = arith.constant dense<0.000000e+00> : tensor<128x128xf32, #blocked>
+    %result = tt.dot_scaled %a scale %scale, %b, %cst lhs = e2m1 rhs = bf16 : tensor<128x32xi8, #blocked2>, tensor<128x2xi8, #blocked1> * tensor<64x128xbf16, #blocked> -> tensor<128x128xf32, #blocked>
+    tt.return %result : tensor<128x128xf32, #blocked>
+  }
+}

third_party/intel/include/Dialect/TritonIntelGPU/IR/Attributes.h

@@ -3,6 +3,10 @@
 
 #include "triton/Dialect/TritonGPU/IR/Attributes.h"
 
+namespace mlir {
+class ModuleOp;
+}
+
 #define GET_ATTRDEF_CLASSES
 #include "intel/include/Dialect/TritonIntelGPU/IR/TritonIntelGPUAttrDefs.h.inc"
 

third_party/intel/include/Dialect/TritonIntelGPU/IR/TritonIntelGPUAttrDefs.td

@@ -74,7 +74,6 @@ along the row (resp. col) dimension.
   );
 
   let extraClassDeclaration = extraDistributedDeclaration # [{
-
     SmallVector<unsigned> getDPASInstShapeA() const;
     SmallVector<unsigned> getDPASInstShapeB() const;
     SmallVector<unsigned> getDPASInstShapeC() const;
@@ -91,7 +90,30 @@ along the row (resp. col) dimension.
       return true;
     }
 
-    SmallVector<unsigned> getContigPerThread();
+    SmallVector<unsigned> getContigPerThread() const;
+
+    struct DPASCapability {
+      explicit DPASCapability(unsigned minSGSize) : executionSize(minSGSize) {}
+      DPASCapability() = default;
+
+      bool isPVC() const {
+        return executionSize == 16;
+      }
+      bool isFalconShore() const {
+        return executionSize == 16;
+      }
+      bool isATSM() const {
+        return executionSize == 8;
+      }
+
+      static constexpr unsigned systolicDepth = 8u;
+      static constexpr unsigned repeatCount = 8u;
+      static constexpr unsigned opsChanBitWidths = 32u;
+      unsigned executionSize = 0u;
+    };
+
+    static DPASCapability getDPASCapability(mlir::ModuleOp mod);
+    static unsigned getOpsPerChannel(Type elemType);
   }];
 
   let hasCustomAssemblyFormat = 1;

third_party/intel/lib/Dialect/TritonIntelGPU/IR/Dialect.cpp

@@ -357,7 +357,7 @@ SmallVector<unsigned> DpasEncodingAttr::getElemsPerThreadForOperands(
   return elemsPerThread;
 };
 
-SmallVector<unsigned> DpasEncodingAttr::getContigPerThread() {
+SmallVector<unsigned> DpasEncodingAttr::getContigPerThread() const {
   size_t rank = getWarpsPerCTA().size();
   assert(rank == 2 || rank == 3);
   SmallVector<unsigned> contigPerThread(rank, 1);
@@ -381,6 +381,30 @@ SmallVector<unsigned> DpasEncodingAttr::getContigPerThread() {
                            "be smaller than the threads required per row.");
 }
 
+DpasEncodingAttr::DPASCapability
+DpasEncodingAttr::getDPASCapability(ModuleOp mod) {
+  assert(mod && "expected a valid module");
+
+  if (auto minSGSizeAttr = mod->getAttrOfType<IntegerAttr>(
+          triton::gpu::intel::TritonIntelGPUDialect::getMinSGSizeAttrName())) {
+    unsigned minSGSize = minSGSizeAttr.getInt();
+    assert(minSGSize == 8 || minSGSize == 16 && "unsupported minSGSize");
+    return DPASCapability(minSGSize);
+  }
+
+  return DPASCapability();
+}
+
+unsigned DpasEncodingAttr::getOpsPerChannel(Type elemType) {
+  assert(elemType.isIntOrFloat() && "unsupported type for DpasEncodingAttr");
+
+  unsigned dpasElemBitWidths = elemType.getIntOrFloatBitWidth();
+  if (elemType.isFloat8E5M2() || elemType.isFloat8E4M3FN())
+    dpasElemBitWidths *= 2; // We are upcasting FP8 to FP16.
+
+  return DPASCapability::opsChanBitWidths / dpasElemBitWidths;
+}
+
 LogicalResult DpasEncodingAttr::verify(
     ::llvm::function_ref<::mlir::InFlightDiagnostic()> emitError,
     unsigned repeatCount, unsigned systolicDepth, unsigned executionSize,
@@ -469,18 +493,14 @@ void DpasEncodingAttr::print(AsmPrinter &printer) const {
   llvm::ArrayRef<unsigned> rC = shapeC;
   auto warpsPerCTA = getWarpsPerCTA();
   auto repCluster = getRepCluster();
-  printer << "<{"
-          << "repeatCount = " << getRepeatCount() << ", "
+  printer << "<{" << "repeatCount = " << getRepeatCount() << ", "
           << "systolicDepth = " << getSystolicDepth() << ", "
           << "executionSize = " << getExecutionSize() << ", "
           << "opsPerChan = " << getOpsPerChannel() << ", "
           << "threadsPerWarp = " << getSubGroupSize() << ", "
           << "warpsPerCTA = [" << llvm::ArrayRef<unsigned>(warpsPerCTA) << "], "
-          << "repCluster = [" << repCluster << "], "
-          << "A = [" << rA << "], "
-          << "B = [" << rB << "], "
-          << "C = [" << rC << "]"
-          << "}>";
+          << "repCluster = [" << repCluster << "], " << "A = [" << rA << "], "
+          << "B = [" << rB << "], " << "C = [" << rC << "]" << "}>";
 }
 
 std::optional<LinearLayout>
@@ -553,13 +573,10 @@ Attribute WarpEncodingAttr::parse(AsmParser &parser, Type type) {
 void WarpEncodingAttr::print(mlir::AsmPrinter &printer) const {
   auto threadsPerWarp = getThreadsPerWarp();
   auto sizePerThread = getSizePerThread();
-  printer << "<{"
-          << "sizePerThread = [" << llvm::ArrayRef<unsigned>(sizePerThread)
-          << "]"
+  printer << "<{" << "sizePerThread = ["
+          << llvm::ArrayRef<unsigned>(sizePerThread) << "]"
           << ", threadsPerWarp = [" << llvm::ArrayRef<unsigned>(threadsPerWarp)
-          << "]"
-          << ", order = [" << getOrder() << "]"
-          << "}>";
+          << "]" << ", order = [" << getOrder() << "]" << "}>";
 }
 
 //===----------------------------------------------------------------------===//