Add support for index.Tensor on dimensions other than the first

This patch still only supports a single indexing tensor.
llvm · Jul 16, 2022 · c354787 · c354787
1 parent baa4383
commit c354787
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Showing 2 changed files with 52 additions and 13 deletions.
diff --git a/lib/Conversion/TorchToLinalg/IndirectDataMovement.cpp b/lib/Conversion/TorchToLinalg/IndirectDataMovement.cpp
@@ -262,19 +262,28 @@ class ConvertAtenIndexTensorOp : public OpConversionPattern<AtenIndexTensorOp> {
       return rewriter.notifyMatchFailure(
           op, "unimplemented: the indices list is not from a list construct");
     }
-    if (indicesTuple.size() != 1) {
-      return rewriter.notifyMatchFailure(
-          op, "unimplemented: only one index tensor is supported");
-    }
 
     SmallVector<Value> indicesVal =
         getTypeConvertedValues(rewriter, loc, getTypeConverter(), indicesTuple);
-    Value indexTensor = indicesVal[0];
-    if (failed(checkNotNone(rewriter, op, indexTensor))) {
+
+    int indexTensorDim = -1;
+    for (auto i : llvm::seq(0, (int)indicesVal.size())) {
+        Value index = indicesVal[i];
+        if (!index || failed(checkNotNone(rewriter, op, index)))
+          continue;
+        if (indexTensorDim >= 0) {
+          return rewriter.notifyMatchFailure(
+              op, "unimplemented: only one index tensor allowed");
+        }
+        indexTensorDim = i;
+    }
+
+    if (indexTensorDim == -1) {
       return rewriter.notifyMatchFailure(
           op, "unimplemented: index tensor must not be None");
     }
 
+    Value indexTensor = indicesVal[indexTensorDim];
     RankedTensorType inputType = input.getType().cast<RankedTensorType>();
     RankedTensorType indexTensorType =
         indexTensor.getType().cast<RankedTensorType>();
@@ -289,10 +298,13 @@ class ConvertAtenIndexTensorOp : public OpConversionPattern<AtenIndexTensorOp> {
     // index tensor and that it is indexing the first dimension of the
     // input tensor. The calculation for arbitrary inputs is much more complex.
     SmallVector<Value> resultShape;
+    for (auto i : llvm::seq(0, indexTensorDim)) {
+      resultShape.push_back(getDimOp(rewriter, loc, input, i));
+    }
     for (auto i : llvm::seq(0, indexTensorRank)) {
       resultShape.push_back(getDimOp(rewriter, loc, indexTensor, i));
     }
-    for (auto i : llvm::seq(1, inputRank)) {
+    for (auto i : llvm::seq(indexTensorDim + 1, inputRank)) {
       resultShape.push_back(getDimOp(rewriter, loc, input, i));
     }
     int resultRank = resultShape.size();
@@ -302,7 +314,7 @@ class ConvertAtenIndexTensorOp : public OpConversionPattern<AtenIndexTensorOp> {
     SmallVector<AffineExpr> indicesExpr, resultExpr;
     SmallVector<StringRef> iteratorTypes;
 
-    for (auto i : llvm::seq(0, indexTensorRank))
+    for (auto i : llvm::seq(indexTensorDim, indexTensorDim + indexTensorRank))
       indicesExpr.push_back(rewriter.getAffineDimExpr(i));
     for (auto i : llvm::seq(0, resultRank)) {
       resultExpr.push_back(rewriter.getAffineDimExpr(i));
@@ -316,11 +328,16 @@ class ConvertAtenIndexTensorOp : public OpConversionPattern<AtenIndexTensorOp> {
                 loc, initTensor.getType(), indexTensor, initTensor,
                 indexingMaps, iteratorTypes,
                 [&](OpBuilder &b, Location loc, ValueRange args) {
-                  SmallVector<Value> extractionIndices{
-                      castIntToIndex(b, loc, args[0])};
-                  for (auto i : llvm::seq(1, inputRank)) {
-                    extractionIndices.push_back(b.create<linalg::IndexOp>(
-                        loc, i + indexTensorRank - 1));
+                  Value index = castIntToIndex(b, loc, args[0]);
+                  SmallVector<Value> extractionIndices;
+                  int extra_dims = 0;
+                  for (auto i : llvm::seq(0, inputRank)) {
+                    if (i == indexTensorDim) {
+                      extractionIndices.push_back(index);
+                      extra_dims += indexTensorRank - 1;
+                    } else {
+                      extractionIndices.push_back(b.create<linalg::IndexOp>(loc, i + extra_dims));
+                    }
                   }
                   Value extractedElement = b.create<tensor::ExtractOp>(
                       loc, input, extractionIndices);

diff --git a/python/torch_mlir_e2e_test/test_suite/basic.py b/python/torch_mlir_e2e_test/test_suite/basic.py
@@ -1632,6 +1632,28 @@ def IndexTensorModule3dInput_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class IndexTensorSelectDimModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1], torch.float32, True),
+        ([-1, -1], torch.int64, True),
+    ])
+    def forward(self, a, ind):
+        return torch.ops.aten.index(a, (None, ind, None))
+
+
+@register_test_case(module_factory=lambda: IndexTensorSelectDimModule())
+def IndexTensorSelectDimModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(2, 4, 6), torch.randint(3, (2, 3)))
+
+# ==============================================================================
+
+
 class SquareModule(torch.nn.Module):
 
     def __init__(self):