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[TorchToLinalg] Fix multi batch matmul conversion to Linalg #4319

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[TorchToLinalg] Fix multi batch matmul conversion to Linalg #4319

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0217138
[TorchToLinalg] Fix multi batch matmul conversion to Linalg
mingzheTerapines Sep 22, 2025
6393bb7
use upstream utils functions and add lit tests
mingzheTerapines Sep 26, 2025
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14 changes: 9 additions & 5 deletions lib/Conversion/TorchToLinalg/Linear.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -505,9 +505,11 @@ class ConvertAtenMatmulOp : public OpConversionPattern<AtenMatmulOp> {

// Broadcast the batch dimensions of both the matrices.
Value broadcastedLhs, broadcastedRhs;
// TODO: Improve usage of static shape information.
SmallVector<int64_t> lhsTargetShape(lhsBroadcastToShape.size(),
ShapedType::kDynamic);
SmallVector<int64_t> lhsTargetShape =
llvm::to_vector(llvm::map_range(lhsBroadcastToShape, [](Value v) {
return getConstantIntValue(v).value_or(ShapedType::kDynamic);
}));

auto lhsBroadcastType = RankedTensorType::get(
lhsTargetShape, lhsType.getElementType(), lhsType.getEncoding());
if (failed(torch_to_linalg::broadcastToGivenShape(
Expand All @@ -516,8 +518,10 @@ class ConvertAtenMatmulOp : public OpConversionPattern<AtenMatmulOp> {
return rewriter.notifyMatchFailure(
op, "unable to perform broadcast operation");
}
SmallVector<int64_t> rhsTargetShape(rhsBroadcastToShape.size(),
ShapedType::kDynamic);
SmallVector<int64_t> rhsTargetShape =
llvm::to_vector(llvm::map_range(rhsBroadcastToShape, [](Value v) {
return getConstantIntValue(v).value_or(ShapedType::kDynamic);
}));
auto rhsBroadcastType = RankedTensorType::get(
rhsTargetShape, rhsType.getElementType(), rhsType.getEncoding());
if (failed(torch_to_linalg::broadcastToGivenShape(
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64 changes: 64 additions & 0 deletions test/Conversion/TorchToLinalg/basic.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,70 @@ func.func @torch.aten.matmul.2d(%arg0: !torch.vtensor<[8,16],f32>, %arg1: !torch

// -----

// CHECK-LABEL: func.func @torch.aten.matmul.4d
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[1,2,32,400],f32>,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[1,2,400,32],f32>) -> !torch.vtensor<[1,2,400,400],f32> {
// CHECK: %[[VAL_0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[1,2,32,400],f32> -> tensor<1x2x32x400xf32>
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[1,2,400,32],f32> -> tensor<1x2x400x32xf32>
// CHECK: %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_6:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_7:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_8:.*]] = arith.constant 1 : index
Comment on lines +51 to +57
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This might be a bit flaky and adds a bunch of checks we don't actually care about in the logic of the conversion.

I'd just verify the relevant ops. E.g. check the collapse, expand, and linalg batch mm ops. The specific values for fill ops etc. aren't super important to check here.

// CHECK: %[[VAL_9:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_10:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_11:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_12:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_13:.*]] = arith.constant 400 : index
// CHECK: %[[VAL_14:.*]] = arith.constant 3 : index
// CHECK: %[[VAL_15:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_16:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_17:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_18:.*]] = arith.constant 3 : index
// CHECK: %[[VAL_19:.*]] = arith.constant 400 : index
// CHECK: %[[VAL_20:.*]] = arith.constant 32 : i64
// CHECK: %[[VAL_21:.*]] = arith.constant 32 : i64
// CHECK: %[[VAL_22:.*]] = arith.cmpi eq, %[[VAL_20]], %[[VAL_21]] : i64
// CHECK: cf.assert %[[VAL_22]], "mismatching contracting dimension"
// CHECK: %[[VAL_23:.*]] = arith.constant 1 : i64
// CHECK: %[[VAL_24:.*]] = arith.constant 1 : i64
// CHECK: %[[VAL_25:.*]] = arith.constant 2 : i64
// CHECK: %[[VAL_26:.*]] = arith.constant 2 : i64
// CHECK: %[[VAL_27:.*]] = arith.constant 400 : i64
// CHECK: %[[VAL_28:.*]] = arith.constant 32 : i64
// CHECK: %[[VAL_29:.*]] = arith.constant 32 : i64
// CHECK: %[[VAL_30:.*]] = arith.constant 400 : i64
// CHECK: %[[VAL_31:.*]] = arith.constant 0 : i64
// CHECK: %[[VAL_32:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_33:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_34:.*]] = tensor.empty() : tensor<1x2x400x32xf32>
// CHECK: %[[VAL_35:.*]] = tensor.cast %[[VAL_1]] : tensor<1x2x400x32xf32> to tensor<1x2x400x32xf32>
// CHECK: %[[VAL_36:.*]] = arith.constant 0 : i64
// CHECK: %[[VAL_37:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_38:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_39:.*]] = tensor.empty() : tensor<1x2x32x400xf32>
// CHECK: %[[VAL_40:.*]] = tensor.cast %[[VAL_0]] : tensor<1x2x32x400xf32> to tensor<1x2x32x400xf32>
// CHECK: %[[VAL_41:.*]] = tensor.collapse_shape %[[VAL_35]] {{\[\[}}0, 1], [2], [3]] : tensor<1x2x400x32xf32> into tensor<2x400x32xf32>
// CHECK: %[[VAL_42:.*]] = tensor.collapse_shape %[[VAL_40]] {{\[\[}}0, 1], [2], [3]] : tensor<1x2x32x400xf32> into tensor<2x32x400xf32>
// CHECK: %[[VAL_43:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_44:.*]] = tensor.empty() : tensor<2x400x400xf32>
// CHECK: %[[VAL_45:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_46:.*]] = linalg.fill ins(%[[VAL_45]] : f32) outs(%[[VAL_44]] : tensor<2x400x400xf32>) -> tensor<2x400x400xf32>
// CHECK: %[[VAL_47:.*]] = linalg.batch_matmul ins(%[[VAL_41]], %[[VAL_42]] : tensor<2x400x32xf32>, tensor<2x32x400xf32>) outs(%[[VAL_46]] : tensor<2x400x400xf32>) -> tensor<2x400x400xf32>
// CHECK: %[[VAL_48:.*]] = tensor.expand_shape %[[VAL_47]] {{\[\[}}0, 1], [2], [3]] output_shape [1, 2, 400, 400] : tensor<2x400x400xf32> into tensor<1x2x400x400xf32>
// CHECK: %[[VAL_49:.*]] = tensor.cast %[[VAL_48]] : tensor<1x2x400x400xf32> to tensor<1x2x400x400xf32>
// CHECK: %[[VAL_50:.*]] = torch_c.from_builtin_tensor %[[VAL_49]] : tensor<1x2x400x400xf32> -> !torch.vtensor<[1,2,400,400],f32>
// CHECK: return %[[VAL_50]] : !torch.vtensor<[1,2,400,400],f32>
// CHECK: }
func.func @torch.aten.matmul.4d(%arg0: !torch.vtensor<[1,2,32,400],f32>, %arg1: !torch.vtensor<[1,2,400,32],f32>) -> !torch.vtensor<[1,2,400,400],f32> {
%0 = torch.aten.matmul %arg1, %arg0 : !torch.vtensor<[1,2,400,32],f32>, !torch.vtensor<[1,2,32,400],f32> -> !torch.vtensor<[1,2,400,400],f32>
return %0 : !torch.vtensor<[1,2,400,400],f32>
}

// -----

// CHECK-LABEL: func.func @torch.aten.mm$basic_strict(
// CHECK-NOT: assert
func.func @torch.aten.mm$basic_strict(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,2],f32>
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