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>
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Tung D. Le
GitHub
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@ -1558,33 +1558,6 @@ ONNX Gather operation
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1. `output`: memref of any type values or tensor of any type values
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### onnx.GemmNoBias (ONNXGemmNoBiasOp)
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ONNX general matrix multiply operation without bias.
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#### Description:
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The "onnx.Gemm" generic matrix multiplication without bias.
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#### Operands:
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1. `A`: memref of any type values or tensor of any type values
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1. `B`: memref of any type values or tensor of any type values
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#### Attributes:
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| Attribute | MLIR Type | Description |
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| :-------: | :-------: | ----------- |
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| `alpha` | `FloatAttr` | 32-bit float attribute attribute |
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| `beta` | `FloatAttr` | 32-bit float attribute attribute |
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| `transA` | `IntegerAttr` | 64-bit integer attribute attribute |
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| `transB` | `IntegerAttr` | 64-bit integer attribute attribute |
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#### Results:
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1. `o_Y`: memref of any type values or tensor of any type values
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### onnx.Gemm (ONNXGemmOp)
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ONNX Gemm operation
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@ -17,9 +17,7 @@ struct ONNXGemmOpLowering : public ConversionPattern {
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matchAndRewrite(Operation *op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const final {
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auto loc = op->getLoc();
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// The first predicate is unnecessary when we remove ONXGemmNoBiasOp.
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bool hasBias = (operands.size() == 3) &&
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(!op->getOperand(2).getType().isa<NoneType>());
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bool hasBias = !op->getOperand(2).getType().isa<NoneType>();
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Value A, B, C;
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A = operands[0];
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@ -215,5 +213,4 @@ struct ONNXGemmOpLowering : public ConversionPattern {
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void populateLoweringONNXGemmOpPattern(OwningRewritePatternList &patterns,
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MLIRContext *ctx) {
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patterns.insert<ONNXGemmOpLowering<ONNXGemmOp>>(ctx);
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patterns.insert<ONNXGemmOpLowering<ONNXGemmNoBiasOp>>(ctx);
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}
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@ -90,25 +90,6 @@ def ONNXEntryPointOp: ONNX_Op<"EntryPoint"> {
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// or outputs. This decision affects only ONNX operations with optional
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// arguments not ONNX operations with variadic operands.
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def ONNXGemmNoBiasOp: ONNX_Op<"GemmNoBias",
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[NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
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let summary = "ONNX general matrix multiply operation without bias.";
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let description = [{
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The "onnx.Gemm" generic matrix multiplication without bias.
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}];
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let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A,
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AnyTypeOf<[AnyMemRef, AnyTensor]>:$B,
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DefaultValuedAttr<F32Attr, "1.0">:$alpha,
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DefaultValuedAttr<F32Attr, "1.0">:$beta,
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DefaultValuedAttr<I64Attr, "0">:$transA,
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DefaultValuedAttr<I64Attr, "0">:$transB);
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let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$o_Y);
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}
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def ONNXConvNoBiasOp:ONNX_Op<"ConvNoBias",
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[NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
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let hasCanonicalizer = 1;
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@ -565,32 +565,6 @@ void ONNXGemmOp::inferShapes() {
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getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
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}
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// GemmNoBias
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void ONNXGemmNoBiasOp::inferShapes() {
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// Cannot infer shape if no shape exists.
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if (!getOperand(0).getType().isa<RankedTensorType>() ||
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!getOperand(1).getType().isa<RankedTensorType>())
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return;
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auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
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auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
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int64_t M, N, K_A, K_B;
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M = (transA() == 0) ? lhsTy.getShape()[0] : lhsTy.getShape()[1];
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K_A = (transA() == 0) ? lhsTy.getShape()[1] : lhsTy.getShape()[0];
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N = (transB() == 0) ? rhsTy.getShape()[1] : rhsTy.getShape()[0];
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K_B = (transB() == 0) ? rhsTy.getShape()[0] : rhsTy.getShape()[1];
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if ((K_A != -1) and (K_B != -1) and (K_A != K_B)) {
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emitError("Tensor shapes mismatched.");
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}
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SmallVector<int64_t, 2> dims;
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dims.emplace_back(M);
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dims.emplace_back(N);
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getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
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}
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/// BatchNormalizationTestMode
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void ONNXBatchNormalizationTestModeOp::inferShapes() {
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// Cannot infer shape if no shape exists.
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@ -118,7 +118,6 @@ public:
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op->getName().getStringRef() != "onnx.Identity" &&
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op->getName().getStringRef() != "onnx.MatMul" &&
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op->getName().getStringRef() != "onnx.Gemm" &&
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op->getName().getStringRef() != "onnx.GemmNoBias" &&
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op->getName().getStringRef() != "onnx.Reshape" &&
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op->getName().getStringRef() != "onnx.Transpose" &&
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op->getName().getStringRef() != "onnx.ReduceMax" &&
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@ -806,35 +806,6 @@ func @test_gemm(%arg0 : tensor<5x10xf32>, %arg1 : tensor<5x10xf32>, %arg2: tenso
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// CHECK: }
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}
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func @test_gemm_no_bias(%arg0 : tensor<5x10xf32>, %arg1 : tensor<5x10xf32>) -> tensor<*xf32> {
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%0 ="onnx.GemmNoBias"(%arg0, %arg1) {alpha = 1.0 : f32, beta = 5.0 : f32, transA = 1, transB = 0} : (tensor<5x10xf32>, tensor<5x10xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_gemm_no_bias
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// CHECK: [[RES:%.+]] = alloc() : memref<10x10xf32>
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// CHECK: [[ALPHA:%.+]] = constant 1.000000e+00 : f32
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// CHECK: [[BETA:%.+]] = constant 5.000000e+00 : f32
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// CHECK: [[DEF_LOOPS:%.+]]:3 = krnl.define_loops 3
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// CHECK: [[OPT_LOOPS:%.+]]:3 = krnl.optimize_loops {
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// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1, [[DEF_LOOPS]]#2
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// CHECK: } : () -> (!krnl.loop, !krnl.loop, !krnl.loop)
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// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to 10, [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
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// CHECK: krnl.iterate([[OPT_LOOPS]]#2) with ([[DEF_LOOPS]]#2 -> %arg4 = 0 to 5) {
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// CHECK: [[A:%.+]] = load %arg0[%arg4, %arg2] : memref<5x10xf32>
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// CHECK: [[B:%.+]] = load %arg1[%arg4, %arg3] : memref<5x10xf32>
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// CHECK: [[Y:%.+]] = load [[RES]][%arg2, %arg3] : memref<10x10xf32>
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// CHECK: [[AB:%.+]] = mulf [[A]], [[B]] : f32
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// CHECK: [[SUM:%.+]] = addf [[Y]], [[AB]] : f32
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// CHECK: store [[SUM]], [[RES]][%arg2, %arg3] : memref<10x10xf32>
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// CHECK: }
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// CHECK: [[LOAD_Y:%.+]] = load [[RES]][%arg2, %arg3] : memref<10x10xf32>
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// CHECK: [[ALPHA_AB:%.+]] = mulf [[ALPHA]], [[LOAD_Y]] : f32
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// CHECK: store [[ALPHA_AB]], [[RES]][%arg2, %arg3] : memref<10x10xf32>
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// CHECK: }
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// CHECK: return [[RES]] : memref<10x10xf32>
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// CHECK: }
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}
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func @test_sqrt(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
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%0 = "onnx.Sqrt"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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