[Torch] fix aten.linear's decomposition (#3391)

* support aten.linear with more rank.

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -5513,38 +5513,57 @@ public:
     Value bias = op.getBias();
 
     BaseTensorType inputType = cast<BaseTensorType>(input.getType());
-    if (!inputType.hasSizes() || inputType.getSizes().size() < 2)
-      return rewriter.notifyMatchFailure(
-          op, "expected input to be rank 2 or greater");
+    if (!inputType.hasSizes())
+      return rewriter.notifyMatchFailure(op, "expected input to have sizes");
 
     BaseTensorType weightType = cast<BaseTensorType>(weight.getType());
-    // `weight` must be a rank 2 matrix.
-    if (!weightType.hasSizes() || weightType.getSizes().size() != 2)
-      return rewriter.notifyMatchFailure(op, "expected weight to be a rank 2");
+    if (!weightType.hasSizes())
+      return rewriter.notifyMatchFailure(op, "expected weight to have sizes");
 
-    SmallVector<int64_t> transposeShape =
-        llvm::to_vector(llvm::reverse(weightType.getSizes()));
-    Type transposeType = weightType.getWithSizesAndDtype(
-        llvm::ArrayRef(transposeShape), weightType.getOptionalDtype());
-    Value transposeWeight =
-        rewriter.create<AtenTOp>(loc, transposeType, weight);
+    auto transposeWeight = [&]() -> Value {
+      SmallVector<int64_t> transposeShape =
+          llvm::to_vector(llvm::reverse(weightType.getSizes()));
+      Type transposeType = weightType.getWithSizesAndDtype(
+          llvm::ArrayRef(transposeShape), weightType.getOptionalDtype());
+      Value transposeWeight =
+          rewriter.create<AtenTOp>(loc, transposeType, weight);
+      return transposeWeight;
+    };
 
-    Value matmul = rewriter.create<AtenMatmulOp>(loc, op.getType(), input,
-                                                 transposeWeight);
     if (bias.getType().isa<Torch::NoneType>()) {
-      rewriter.replaceOp(op, matmul);
+      auto weightRank = weightType.getSizes().size();
+      if (weightRank > 2 || weightRank <= 0)
+        return rewriter.notifyMatchFailure(
+            op, "expected weight's rank <= 2 && >= 1");
+      if (weightRank == 1) {
+        rewriter.replaceOpWithNewOp<AtenMatmulOp>(op, op.getType(), input,
+                                                  weight);
+        return success();
+      } else if (weightRank == 2) {
+        rewriter.replaceOpWithNewOp<AtenMatmulOp>(op, op.getType(), input,
+                                                  transposeWeight());
+        return success();
+      }
+      llvm_unreachable("unsupported weightRank");
+    } else {
+      BaseTensorType biasType = cast<BaseTensorType>(bias.getType());
+      if (!biasType.hasSizes() || biasType.getSizes().size() != 1)
+        return rewriter.notifyMatchFailure(op, "expected bias to be rank 1");
+
+      // `weight` must be a rank 2 matrix.
+      auto weightRank = weightType.getSizes().size();
+      if (weightRank != 2)
+        return rewriter.notifyMatchFailure(op,
+                                           "expected weight to be a rank 2");
+
+      Value matmul = rewriter.create<AtenMatmulOp>(loc, op.getType(), input,
+                                                   transposeWeight());
+      Value alpha =
+          rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
+      rewriter.replaceOpWithNewOp<AtenAddTensorOp>(op, op.getType(), matmul,
+                                                   op.getBias(), alpha);
       return success();
     }
-
-    BaseTensorType biasType = cast<BaseTensorType>(bias.getType());
-    if (!biasType.hasSizes() || biasType.getSizes().size() != 1)
-      return rewriter.notifyMatchFailure(op, "expected bias to be rank 1");
-
-    Value alpha =
-        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1));
-    rewriter.replaceOpWithNewOp<AtenAddTensorOp>(op, op.getType(), matmul,
-                                                 op.getBias(), alpha);
-    return success();
   }
 };
 } // namespace

projects/pt1/e2e_testing/xfail_sets.py

@@ -814,6 +814,12 @@ FX_IMPORTER_STABLEHLO_CRASHING_SET = {
 }
 
 STABLEHLO_PASS_SET = {
+    "AtenLinear1D_basic",
+    "AtenLinear2D_basic",
+    "AtenLinear3DBias_basic",
+    "AtenLinearMatVec_basic",
+    "AtenLinearVecMatBias_basic",
+    "AtenLinearVecMat_basic",
     "ReduceAminSingleDim_basic",
     "AtenDotModule_basic",
     "AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
@@ -1447,6 +1453,8 @@ STABLEHLO_CRASHING_SET = set()
 # Write the TOSA set as a "passing" set as it is very early in development
 # and very few tests work yet.
 TOSA_PASS_SET = {
+    "AtenLinear2D_basic",
+    "AtenLinear3DBias_basic",
     "ElementwiseAddScalar_NumToTensorFloat_Module_basic",
     "ElementwiseDivTensorFloatModule_basic",
     "ElementwiseMulTensorFloatModule_basic",
@@ -1911,6 +1919,9 @@ MAKE_FX_TOSA_PASS_SET = (
     TOSA_PASS_SET
     | {
         ### Tests additionally passing in make_fx_tosa
+        "AtenLinear1D_basic",
+        "AtenLinearMatVec_basic",
+        "AtenLinearVecMatBias_basic",
         "MaxPool1dEmptyStrideStaticModule_basic",
         "MaxPool1dStaticCeilModeTrueModule_basic",
         "MaxPool1dStaticModule_basic",

projects/pt1/python/torch_mlir_e2e_test/test_suite/matmul.py

@@ -622,6 +622,131 @@ def AtenMatmulQMixedSigni8Transpose_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class AtenLinear1D(torch.nn.Module):
+    @export
+    @annotate_args(
+        [
+            None,
+            ([3], torch.float32, True),
+            ([3], torch.float32, True),
+        ]
+    )
+    def forward(self, a, b):
+        return torch.ops.aten.linear(a, b)
+
+
+@register_test_case(module_factory=lambda: AtenLinear1D())
+def AtenLinear1D_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3), tu.rand(3))
+
+
+# ==============================================================================
+
+
+class AtenLinearMatVec(torch.nn.Module):
+    @export
+    @annotate_args(
+        [
+            None,
+            ([3, 4], torch.float32, True),
+            ([4], torch.float32, True),
+        ]
+    )
+    def forward(self, a, b):
+        return torch.ops.aten.linear(a, b)
+
+
+@register_test_case(module_factory=lambda: AtenLinearMatVec())
+def AtenLinearMatVec_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4), tu.rand(4))
+
+
+# ==============================================================================
+
+
+class AtenLinearVecMat(torch.nn.Module):
+    @export
+    @annotate_args(
+        [
+            None,
+            ([4], torch.float32, True),
+            ([3, 4], torch.float32, True),
+        ]
+    )
+    def forward(self, a, b):
+        return torch.ops.aten.linear(a, b)
+
+
+@register_test_case(module_factory=lambda: AtenLinearVecMat())
+def AtenLinearVecMat_basic(module, tu: TestUtils):
+    module.forward(tu.rand(4), tu.rand(3, 4))
+
+
+class AtenLinearVecMatBias(torch.nn.Module):
+    @export
+    @annotate_args(
+        [
+            None,
+            ([4], torch.float32, True),
+            ([3, 4], torch.float32, True),
+            ([3], torch.float32, True),
+        ]
+    )
+    def forward(self, a, b, c):
+        return torch.ops.aten.linear(a, b, c)
+
+
+@register_test_case(module_factory=lambda: AtenLinearVecMatBias())
+def AtenLinearVecMatBias_basic(module, tu: TestUtils):
+    module.forward(tu.rand(4), tu.rand(3, 4), tu.rand(3))
+
+
+# ==============================================================================
+
+
+class AtenLinear2D(torch.nn.Module):
+    @export
+    @annotate_args(
+        [
+            None,
+            ([3, 4], torch.float32, True),
+            ([5, 4], torch.float32, True),
+        ]
+    )
+    def forward(self, a, b):
+        return torch.ops.aten.linear(a, b)
+
+
+@register_test_case(module_factory=lambda: AtenLinear2D())
+def AtenLinear2D_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4), tu.rand(5, 4))
+
+
+# ==============================================================================
+
+
+class AtenLinear3DBias(torch.nn.Module):
+    @export
+    @annotate_args(
+        [
+            None,
+            ([3, 6, 4], torch.float32, True),
+            ([5, 4], torch.float32, True),
+            ([5], torch.float32, True),
+        ]
+    )
+    def forward(self, a, b, c):
+        return torch.ops.aten.linear(a, b, c)
+
+
+@register_test_case(module_factory=lambda: AtenLinear3DBias())
+def AtenLinear3DBias_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 6, 4), tu.rand(5, 4), tu.rand(5))
+
+
+# ==============================================================================
+
+
 class AtenLinalgCrossInt(torch.nn.Module):
     @export
     @annotate_args(