Add support of GemmNoBias (#91)

* Add support of GemmNoBias

* Fix a wrong indentation

src/conversion/onnx_to_krnl/rewrite_patterns/math/gemm.inc

@@ -8,31 +8,34 @@
 //
 //===----------------------------------------------------------------------===//
 
+template <typename GemmOp>
 struct ONNXGemmOpLowering : public ConversionPattern {
   ONNXGemmOpLowering(MLIRContext *ctx)
-      : ConversionPattern(mlir::ONNXGemmOp::getOperationName(), 1, ctx) {}
+      : ConversionPattern(GemmOp::getOperationName(), 1, ctx) {}
 
   PatternMatchResult
   matchAndRewrite(Operation *op, ArrayRef<Value> operands,
                   ConversionPatternRewriter &rewriter) const final {
     auto loc = op->getLoc();
+    auto has_bias = (operands.size() == 3);
 
     Value A, B, C;
     A = operands[0];
     B = operands[1];
+    if (has_bias)
       C = operands[2];
 
     auto memRefType = convertToMemRefType(*op->result_type_begin());
 
     auto alphaAttr = FloatAttr::get(memRefType.getElementType(),
-        llvm::dyn_cast<ONNXGemmOp>(op).alpha().convertToFloat());
+        llvm::dyn_cast<GemmOp>(op).alpha().convertToFloat());
     auto betaAttr = FloatAttr::get(memRefType.getElementType(),
-        llvm::dyn_cast<ONNXGemmOp>(op).beta().convertToFloat());
+        llvm::dyn_cast<GemmOp>(op).beta().convertToFloat());
     auto alpha = rewriter.create<ConstantOp>(loc, alphaAttr);
     auto beta = rewriter.create<ConstantOp>(loc, betaAttr);
 
-    bool isTransA = (llvm::dyn_cast<ONNXGemmOp>(op).transA() != 0);
+    bool isTransA = (llvm::dyn_cast<GemmOp>(op).transA() != 0);
-    bool isTransB = (llvm::dyn_cast<ONNXGemmOp>(op).transB() != 0);
+    bool isTransB = (llvm::dyn_cast<GemmOp>(op).transB() != 0);
 
     // Insert an allocation and deallocation for the result of this operation.
     Value alloc;
@@ -116,6 +119,7 @@ struct ONNXGemmOpLowering : public ConversionPattern {
     // GemmOp supports unidirectional broadcasting from C to A*B.
     // Hence, it must be enough to get broadcasting information for C only.
     std::map<int, Value> broadcastedDimInfo;
+    if (has_bias) {
       auto shape = C.getType().cast<MemRefType>().getShape();
       for (int i = 0; i < shape.size(); ++i) {
         if (shape[i] < 0) {
@@ -126,6 +130,7 @@ struct ONNXGemmOpLowering : public ConversionPattern {
           broadcastedDimInfo.insert(std::make_pair(i, isBroadcasted));
         }
       }
+    }
 
     auto outerIterateOp = rewriter.create<KrnlIterateOp>(loc, outerPack);
 
@@ -155,14 +160,18 @@ struct ONNXGemmOpLowering : public ConversionPattern {
     auto matmulIterateOp = rewriter.create<KrnlIterateOp>(loc, reductionPack);
 
     // Compute beta*C, and add up to alpha*A*B (unidirectional broadcasting)
-    auto loopCIVs = getLoopIVsForBroadcasting(
-        loc, rewriter, loopMNIVs, C, broadcastedDimInfo);
-    auto loadedC = rewriter.create<LoadOp>(loc, C, loopCIVs);
     auto loadedAB = rewriter.create<LoadOp>(loc, alloc, loopMNIVs);
     auto alphaAB = rewriter.create<MulFOp>(loc, alpha, loadedAB);
+    if (has_bias) {
+      auto loopCIVs = getLoopIVsForBroadcasting(loc, rewriter, loopMNIVs, C,
+                                                broadcastedDimInfo);
+      auto loadedC = rewriter.create<LoadOp>(loc, C, loopCIVs);
       auto betaC = rewriter.create<MulFOp>(loc, beta, loadedC);
       auto Y = rewriter.create<AddFOp>(loc, alphaAB, betaC);
       rewriter.create<StoreOp>(loc, Y, alloc, loopMNIVs);
+    } else {
+      rewriter.create<StoreOp>(loc, alphaAB, alloc, loopMNIVs);
+    }
 
     // Insert instructions to do matrix multiplication: A*B
     Block &matmulIterationBlock = matmulIterateOp.bodyRegion().front();
@@ -203,5 +212,6 @@ struct ONNXGemmOpLowering : public ConversionPattern {
 
 void populateLoweringONNXGemmOpPattern(
     OwningRewritePatternList &patterns, MLIRContext *ctx) {
-  patterns.insert<ONNXGemmOpLowering>(ctx);
+  patterns.insert<ONNXGemmOpLowering<ONNXGemmOp>>(ctx);
+  patterns.insert<ONNXGemmOpLowering<ONNXGemmNoBiasOp>>(ctx);
 }

src/dialect/onnx/onnx_ops.cpp

@@ -586,9 +586,20 @@ void ONNXGemmNoBiasOp::inferShapes() {
     return;
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
+
+  int64_t M, N, K_A, K_B;
+  M = (transA() == 0) ? lhsTy.getShape()[0] : lhsTy.getShape()[1];
+  K_A = (transA() == 0) ? lhsTy.getShape()[1] : lhsTy.getShape()[0];
+  N = (transB() == 0) ? rhsTy.getShape()[1] : rhsTy.getShape()[0];
+  K_B = (transB() == 0) ? rhsTy.getShape()[0] : rhsTy.getShape()[1];
+
+  if ((K_A != -1) and (K_B != -1) and (K_A != K_B)) {
+    emitError("Tensor shapes mismatched.");
+  }
+
   SmallVector<int64_t, 2> dims;
-  dims.emplace_back(lhsTy.getShape()[0]);
+  dims.emplace_back(M);
-  dims.emplace_back(rhsTy.getShape()[1]);
+  dims.emplace_back(N);
   getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
 }
 

test/backend/test.py

@@ -98,7 +98,7 @@ test_to_enable = [
     "test_gemm_alpha_cpu",
     "test_gemm_beta_cpu",
     "test_gemm_default_matrix_bias_cpu",
-    # "test_gemm_default_no_bias_cpu", <- error, need support for optional operands
+    "test_gemm_default_no_bias_cpu",
     "test_gemm_default_scalar_bias_cpu",
     "test_gemm_default_single_elem_vector_bias_cpu",
     "test_gemm_default_vector_bias_cpu",

test/mlir/onnx/onnx_lowering.mlir

@@ -795,12 +795,42 @@ func @test_gemm(%arg0 : tensor<5x10xf32>, %arg1 : tensor<5x10xf32>, %arg2: tenso
   // CHECK: [[SUM:%.+]] = addf [[Y]], [[AB]] : f32
   // CHECK: store [[SUM]], [[RES]][%arg3, %arg4] : memref<10x10xf32>
   // CHECK: }
-  // CHECK: [[C:%.+]] = load %arg2[%arg4] : memref<10xf32>
   // CHECK: [[LOAD_Y:%.+]] = load [[RES]][%arg3, %arg4] : memref<10x10xf32>
   // CHECK: [[ALPHA_AB:%.+]] = mulf [[ALPHA]], [[LOAD_Y]] : f32
+  // CHECK: [[C:%.+]] = load %arg2[%arg4] : memref<10xf32>
   // CHECK: [[BETA_C:%.+]] = mulf [[BETA]], [[C]] : f32
   // CHECK: [[Y_RES:%.+]] = addf [[ALPHA_AB]], [[BETA_C]] : f32
   // CHECK: store [[Y_RES]], [[RES]][%arg3, %arg4] : memref<10x10xf32>
+  // CHECK: }
+  // CHECK: return [[RES]] : memref<10x10xf32>
+  // CHECK: }
+}
+
+func @test_gemm_no_bias(%arg0 : tensor<5x10xf32>, %arg1 : tensor<5x10xf32>) -> tensor<*xf32> {
+  %0 ="onnx.GemmNoBias"(%arg0, %arg1) {alpha = 1.0 : f32, beta = 5.0 : f32, transA = 1, transB = 0} : (tensor<5x10xf32>, tensor<5x10xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+
+  // CHECK-LABEL: test_gemm_no_bias
+  // CHECK: [[RES:%.+]] = alloc() : memref<10x10xf32>
+  // CHECK: [[ALPHA:%.+]] = constant 1.000000e+00 : f32
+  // CHECK: [[BETA:%.+]] = constant 5.000000e+00 : f32
+  // CHECK: [[DEF_LOOPS:%.+]]:3 = krnl.define_loops 3
+  // CHECK: [[OPT_LOOPS:%.+]]:3 = krnl.optimize_loops  {
+  // CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1, [[DEF_LOOPS]]#2
+  // CHECK: } : () -> (!krnl.loop, !krnl.loop, !krnl.loop)
+  // CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to 10, [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
+  // CHECK: krnl.iterate([[OPT_LOOPS]]#2) with ([[DEF_LOOPS]]#2 -> %arg4 = 0 to 5) {
+  // CHECK: [[A:%.+]] = load %arg0[%arg4, %arg2] : memref<5x10xf32>
+  // CHECK: [[B:%.+]] = load %arg1[%arg4, %arg3] : memref<5x10xf32>
+  // CHECK: [[Y:%.+]] = load [[RES]][%arg2, %arg3] : memref<10x10xf32>
+  // CHECK: [[AB:%.+]] = mulf [[A]], [[B]] : f32
+  // CHECK: [[SUM:%.+]] = addf [[Y]], [[AB]] : f32
+  // CHECK: store [[SUM]], [[RES]][%arg2, %arg3] : memref<10x10xf32>
+  // CHECK: }
+  // CHECK: [[LOAD_Y:%.+]] = load [[RES]][%arg2, %arg3] : memref<10x10xf32>
+  // CHECK: [[ALPHA_AB:%.+]] = mulf [[ALPHA]], [[LOAD_Y]] : f32
+  // CHECK: store [[ALPHA_AB]], [[RES]][%arg2, %arg3] : memref<10x10xf32>
+  // CHECK: }
   // CHECK: return [[RES]] : memref<10x10xf32>
   // CHECK: }
 }