Fix rebase errors. (#378)
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@ -58,17 +58,17 @@ class ONNX_Op<string mnemonic, list<OpTrait> traits = []> :
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include "dialect/onnx/onnxop.inc"
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def ONNXFullGemmOp: ONNX_Op<"full_gemm",
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[NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
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let summary = "ONNX general matrix multiply operation";
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let description = [{
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def ONNXFullGemmOp: ONNX_Op<"FullGemm",
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[NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
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let summary = "ONNX general matrix multiply operation";
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let description = [{
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The "onnx.gemm" generic matrix multiplication with bias.
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The "onnx.gemm" generic matrix multiplication with bias.
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}];
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}];
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let arguments = (ins AnyTensor:$lhs_in, AnyTensor:$rhs_in, AnyTensor:$bias_in);
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let results = (outs AnyTensor);
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let arguments = (ins AnyTensor:$lhs_in, AnyTensor:$rhs_in, AnyTensor:$bias_in);
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let results = (outs AnyTensor);
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}
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#endif // ONNX_OPS
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@ -30,7 +30,7 @@ def HasOneUse : Constraint<CPred<"$0->hasOneUse()">>;
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// Pattern-Match and Rewrite
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//===----------------------------------------------------------------------===//
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// onnx.add(onnx.matmul(%X, %Y), %Z) = onnx.full_gemm(%X, %Y, %Z)
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// onnx.add(onnx.matmul(%X, %Y), %Z) = onnx.FullGemm(%X, %Y, %Z)
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def MulAddToGemmOptPattern : Pat<(ONNXAddOp (ONNXMatMulOp:$res $m1, $m2), $m3),
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(ONNXFullGemmOp $m1, $m2, $m3),
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[(HasOneUse $res)]>;
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@ -82,10 +82,10 @@ class ShapeInferencePass : public mlir::FunctionPass<ShapeInferencePass> {
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// All operations which do not return a ranked tensor type have dynamic
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// shaped outputs. All those operation need to implement the inferShape()
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// method.
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if (op->getName().getStringRef() != "onnx.add" &&
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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.full_gemm")
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if (op->getName().getStringRef() != "onnx.Add" &&
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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.FullGemm")
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return false;
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return llvm::any_of(op->getResultTypes(),
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[](Type result_type) { return !result_type.isa<RankedTensorType>(); });
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@ -1,6 +1,14 @@
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import os
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import sys
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import re
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import platform
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import subprocess
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import lit.util
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import lit.formats
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from lit.llvm import llvm_config
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from lit.llvm.subst import FindTool
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from lit.llvm.subst import ToolSubst
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# name: The name of this test suite.
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@ -2,7 +2,7 @@
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import lit.llvm
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config.llvm_tools_dir = "@MLIR_TOOLS_DIR@"
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config.mlir_obj_root = "@MLIR_BUILD_DIR@"
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config.mlir_obj_root = "@LLVM_BUILD@"
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config.mlir_tools_dir = "@MLIR_TOOLS_DIR@"
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config.suffixes = ['.mlir']
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@ -2,13 +2,10 @@
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//CHECK: module {
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module {
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func @test_sigmoid() {
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%0 = "frontend.input t1"() : () -> tensor<10x10xf32>
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%1 = "frontend.input t2"() : () -> tensor<10x10xf32>
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%2 = "frontend.input t3"() : () -> tensor<10x10xf32>
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// CHECK: %{{[0-9]+}} = "onnx.full_gemm"(%{{.*}}, %{{.*}}, %{{.*}}) : (tensor<10x10xf32>, tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%3 = "onnx.MatMul"(%0, %1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%4 = "onnx.Add"(%3, %2) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%5 = "frontend.output t4"(%4) : (tensor<10x10xf32>) -> tensor<10x10xf32>
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func @test_sigmoid(%a0: tensor<10x10xf32>, %a1: tensor<10x10xf32>, %a2: tensor<10x10xf32>) -> tensor<10x10xf32> {
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// CHECK: %{{[0-9]+}} = "onnx.FullGemm"(%{{.*}}, %{{.*}}, %{{.*}}) : (tensor<10x10xf32>, tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%0 = "onnx.MatMul"(%a0, %a1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%1 = "onnx.Add"(%0, %a2) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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"std.return"(%1) : (tensor<10x10xf32>) -> ()
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}
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}
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