Remove special GemmNoBias since we can handle it using NoneType bias (#100)

* Remove special GemmNoBias since we can handle it using NoneType bias

* Remove GemmNoBias from onnx.md

Co-authored-by: Tian Jin <tjingrant@gmail.com>

doc/Dialects/onnx.md

@@ -1558,33 +1558,6 @@ ONNX Gather operation
 
 1. `output`: memref of any type values or tensor of any type values
 
-### onnx.GemmNoBias (ONNXGemmNoBiasOp)
-ONNX general matrix multiply operation without bias.
-
-#### Description:
-
-
-The "onnx.Gemm" generic matrix multiplication without bias.
-
-
-#### Operands:
-
-1. `A`: memref of any type values or tensor of any type values
-1. `B`: memref of any type values or tensor of any type values
-
-#### Attributes:
-
-| Attribute | MLIR Type | Description |
-| :-------: | :-------: | ----------- |
-| `alpha` | `FloatAttr` | 32-bit float attribute attribute |
-| `beta` | `FloatAttr` | 32-bit float attribute attribute |
-| `transA` | `IntegerAttr` | 64-bit integer attribute attribute |
-| `transB` | `IntegerAttr` | 64-bit integer attribute attribute |
-
-#### Results:
-
-1. `o_Y`: memref of any type values or tensor of any type values
-
 ### onnx.Gemm (ONNXGemmOp)
 ONNX Gemm operation
 

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

@@ -17,9 +17,7 @@ struct ONNXGemmOpLowering : public ConversionPattern {
   matchAndRewrite(Operation *op, ArrayRef<Value> operands,
                   ConversionPatternRewriter &rewriter) const final {
     auto loc = op->getLoc();
-    // The first predicate is unnecessary when we remove ONXGemmNoBiasOp.
+    bool hasBias = !op->getOperand(2).getType().isa<NoneType>();
-    bool hasBias = (operands.size() == 3) &&
-                   (!op->getOperand(2).getType().isa<NoneType>());
 
     Value A, B, C;
     A = operands[0];
@@ -215,5 +213,4 @@ struct ONNXGemmOpLowering : public ConversionPattern {
 void populateLoweringONNXGemmOpPattern(OwningRewritePatternList &patterns,
                                        MLIRContext *ctx) {
   patterns.insert<ONNXGemmOpLowering<ONNXGemmOp>>(ctx);
-  patterns.insert<ONNXGemmOpLowering<ONNXGemmNoBiasOp>>(ctx);
 }

src/dialect/onnx/onnx.td

@@ -90,25 +90,6 @@ def ONNXEntryPointOp: ONNX_Op<"EntryPoint"> {
 // or outputs. This decision affects only ONNX operations with optional
 // arguments not ONNX operations with variadic operands.
 
-def ONNXGemmNoBiasOp: ONNX_Op<"GemmNoBias",
-    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
-  let summary = "ONNX general matrix multiply operation without bias.";
-  let description = [{
-
-    The "onnx.Gemm" generic matrix multiplication without bias.
-
-  }];
-
-  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A,
-           AnyTypeOf<[AnyMemRef, AnyTensor]>:$B,
-           DefaultValuedAttr<F32Attr, "1.0">:$alpha,
-           DefaultValuedAttr<F32Attr, "1.0">:$beta,
-           DefaultValuedAttr<I64Attr, "0">:$transA,
-           DefaultValuedAttr<I64Attr, "0">:$transB);
-
-  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$o_Y);
-}
-
 def ONNXConvNoBiasOp:ONNX_Op<"ConvNoBias",
     [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
   let hasCanonicalizer = 1;

src/dialect/onnx/onnx_ops.cpp

@@ -565,32 +565,6 @@ void ONNXGemmOp::inferShapes() {
   getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
 }
 
-// GemmNoBias
-
-void ONNXGemmNoBiasOp::inferShapes() {
-  // Cannot infer shape if no shape exists.
-  if (!getOperand(0).getType().isa<RankedTensorType>() ||
-      !getOperand(1).getType().isa<RankedTensorType>())
-    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(M);
-  dims.emplace_back(N);
-  getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
-}
-
 /// BatchNormalizationTestMode
 void ONNXBatchNormalizationTestModeOp::inferShapes() {
   // Cannot infer shape if no shape exists.

src/pass/shape_inference_pass.cpp

@@ -118,7 +118,6 @@ public:
         op->getName().getStringRef() != "onnx.Identity" &&
         op->getName().getStringRef() != "onnx.MatMul" &&
         op->getName().getStringRef() != "onnx.Gemm" &&
-        op->getName().getStringRef() != "onnx.GemmNoBias" &&
         op->getName().getStringRef() != "onnx.Reshape" &&
         op->getName().getStringRef() != "onnx.Transpose" &&
         op->getName().getStringRef() != "onnx.ReduceMax" &&

test/mlir/onnx/onnx_lowering.mlir

@@ -806,35 +806,6 @@ func @test_gemm(%arg0 : tensor<5x10xf32>, %arg1 : tensor<5x10xf32>, %arg2: tenso
   // 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: }
-}
-
 func @test_sqrt(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
   %0 = "onnx.Sqrt"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()