[onnx] Lowering `onnx.dequantize_linear` to `torch` (#2759)

We can make the per-tensor version of the operation to the dequantize
operation via marking with the make quantized tensor component. This
introductions the `qint*` and `quint*` tensor type that can be lowered
to teh appropriate dequantization behavior during the torch-to-linalg
conversion.

lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp

@@ -1156,6 +1156,59 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
             binder.op, resultType, transposedInput, reshapeSizesList);
         return success();
       });
+  patterns.onOp(
+      "DequantizeLinear", 1,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Torch::ValueTensorType resultType;
+        llvm::SmallVector<Value> operands;
+        if (binder.tensorOperands(operands, 3) ||
+            binder.tensorResultType(resultType))
+          return failure();
+
+        Value operand = operands[0];
+        Value scale = operands[1];
+        Value zeropoint = operands[2];
+
+        auto operandTy = operand.getType().cast<Torch::ValueTensorType>();
+
+        auto scaleTy = scale.getType().dyn_cast<Torch::ValueTensorType>();
+        if (!scaleTy || !scaleTy.hasSizes())
+          return rewriter.notifyMatchFailure(binder.op, "requires known rank");
+        if (!resultType.hasDtype())
+          return rewriter.notifyMatchFailure(binder.op,
+                                             "requires known resulty dtype");
+
+        if (scaleTy.getSizes().size() == 0) {
+          Type qTy = operandTy.getDtype();
+
+          if (qTy.isUnsignedInteger(8)) {
+            qTy = rewriter.getType<Torch::QUInt8Type>();
+          } else if (qTy.isSignedInteger(8)) {
+            qTy = rewriter.getType<Torch::QInt8Type>();
+          } else if (qTy.isSignedInteger(32)) {
+            qTy = rewriter.getType<Torch::QInt32Type>();
+          } else {
+            return rewriter.notifyMatchFailure(binder.op,
+                                               "unsupported result dtype");
+          }
+
+          auto qTensorTy = rewriter.getType<Torch::ValueTensorType>(
+              resultType.getOptionalSizes(), qTy);
+          scale = rewriter.create<Torch::AtenItemOp>(
+              binder.getLoc(), rewriter.getType<Torch::FloatType>(), scale);
+          zeropoint = rewriter.create<Torch::AtenItemOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(), zeropoint);
+
+          auto quantize =
+              rewriter.create<Torch::Aten_MakePerTensorQuantizedTensorOp>(
+                  binder.getLoc(), qTensorTy, operand, scale, zeropoint);
+          rewriter.replaceOpWithNewOp<Torch::AtenDequantizeSelfOp>(
+              binder.op, resultType, quantize);
+          return success();
+        }
+
+        return failure();
+      });
   patterns.onOp("Div", 14,
                 [](OpBinder binder, ConversionPatternRewriter &rewriter) {
                   Torch::ValueTensorType resultType;

test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir

@@ -1,4 +1,4 @@
-// RUN: torch-mlir-opt <%s -convert-torch-onnx-to-torch | FileCheck %s
+// RUN: torch-mlir-opt <%s --split-input-file -convert-torch-onnx-to-torch | FileCheck %s
 // Generally, the test cases accumulated here come from running the importer
 // over all included backend tests that involve simple ops with no model
 // level constants. This is a pragmatic choice which lets us have a lot
@@ -438,6 +438,48 @@ func.func @test_cos(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5
   return %0 : !torch.vtensor<[3,4,5],f32>
 }
 
+// -----
+
+// CHECK-LABEL: @test_dequantizelinear_si8
+func.func @test_dequantizelinear_si8(%arg0: !torch.vtensor<[6],si8>, %arg1: !torch.vtensor<[],f32>, %arg2: !torch.vtensor<[],si8>) -> !torch.vtensor<[6],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 19 : si64} {
+  %0 = torch.operator "onnx.DequantizeLinear"(%arg0, %arg1, %arg2) : (!torch.vtensor<[6],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[6],f32>
+  // CHECK: %[[SCALE:.+]] = torch.aten.item %arg1 : !torch.vtensor<[],f32> -> !torch.float
+  // CHECK: %[[ZP:.+]] = torch.aten.item %arg2 : !torch.vtensor<[],si8> -> !torch.int
+  // CHECK: %[[MAKE:.+]] = torch.aten._make_per_tensor_quantized_tensor %arg0, %[[SCALE]], %[[ZP]]
+  // CHECK: %[[DEQ:.+]] = torch.aten.dequantize.self %[[MAKE]]
+  // CHECK: return %[[DEQ]]
+  return %0 : !torch.vtensor<[6],f32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_dequantizelinear_ui8
+func.func @test_dequantizelinear_ui8(%arg0: !torch.vtensor<[6],ui8>, %arg1: !torch.vtensor<[],f32>, %arg2: !torch.vtensor<[],ui8>) -> !torch.vtensor<[6],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 19 : si64} {
+  %0 = torch.operator "onnx.DequantizeLinear"(%arg0, %arg1, %arg2) : (!torch.vtensor<[6],ui8>, !torch.vtensor<[],f32>, !torch.vtensor<[],ui8>) -> !torch.vtensor<[6],f32>
+  // CHECK: %[[SCALE:.+]] = torch.aten.item %arg1 : !torch.vtensor<[],f32> -> !torch.float
+  // CHECK: %[[ZP:.+]] = torch.aten.item %arg2 : !torch.vtensor<[],ui8> -> !torch.int
+  // CHECK: %[[MAKE:.+]] = torch.aten._make_per_tensor_quantized_tensor %arg0, %[[SCALE]], %[[ZP]]
+  // CHECK: %[[DEQ:.+]] = torch.aten.dequantize.self %[[MAKE]]
+  // CHECK: return %[[DEQ]]
+  return %0 : !torch.vtensor<[6],f32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_dequantizelinear_i32
+func.func @test_dequantizelinear_i32(%arg0: !torch.vtensor<[6],si32>, %arg1: !torch.vtensor<[],f32>, %arg2: !torch.vtensor<[],si32>) -> !torch.vtensor<[6],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 19 : si64} {
+  %0 = torch.operator "onnx.DequantizeLinear"(%arg0, %arg1, %arg2) : (!torch.vtensor<[6],si32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[6],f32>
+  // CHECK: %[[SCALE:.+]] = torch.aten.item %arg1 : !torch.vtensor<[],f32> -> !torch.float
+  // CHECK: %[[ZP:.+]] = torch.aten.item %arg2 : !torch.vtensor<[],si32> -> !torch.int
+  // CHECK: %[[MAKE:.+]] = torch.aten._make_per_tensor_quantized_tensor %arg0, %[[SCALE]], %[[ZP]]
+  // CHECK: %[[DEQ:.+]] = torch.aten.dequantize.self %[[MAKE]]
+  // CHECK: return %[[DEQ]]
+  return %0 : !torch.vtensor<[6],f32>
+}
+
+// -----
+
+
 // CHECK-LABEL: @test_div_bcast
 func.func @test_div_bcast(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 14 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   // CHECK: torch.aten.div.Tensor %arg0, %arg1 : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[5],f32> -> !torch.vtensor<[3,4,5],f32>