[linalg] Add handling for leadin and trailing size-1 dims in ViewOp

This commit adds to the lowering of `aten.view` handling for the
following cases:

- `(..., a.size(i))` -> `(..., a.size(i), 1, ..., 1)`
- `(..., a.size(i), 1, ..., 1)` -> `(..., a.size(i))`
- `(a.size(i), ...)` -> `(1, ..., 1, a.size(i), ...)`
- `(1, ..., 1, a.size(i), ...)` -> `(a.size(i), ...)`

lib/Conversion/TorchToLinalg/DataMovement.cpp

@@ -193,6 +193,9 @@ public:
                                              ArrayRef<int64_t> yDims,
                                              SmallVector<int64_t> &xIndices,
                                              SmallVector<int64_t> &yIndices) {
+    if (xDims.empty() || yDims.empty())
+      return failure();
+
     auto isValidReduction = [](int64_t expectedReductionProduct,
                                ArrayRef<int64_t> arrayToReduce) -> bool {
       if (llvm::count(arrayToReduce, kUnknownSize) > 0 ||
@@ -262,6 +265,8 @@ public:
   // all the dimensions in `outputShape`.
   static void calculateSingleDynamicSize(MutableArrayRef<int64_t> inputShape,
                                          MutableArrayRef<int64_t> outputShape) {
+    if (inputShape.empty() || outputShape.empty())
+      return;
     int64_t inputDynamicDimCount = llvm::count(inputShape, kUnknownSize);
     int64_t outputDynamicDimCount = llvm::count(outputShape, kUnknownSize);
     if (inputDynamicDimCount + outputDynamicDimCount != 1)
@@ -488,12 +493,29 @@ public:
         outputDim = outputAssociations.back().back() + 1;
       }
 
-      // Append the associations for the dims matching `aten.size.int`
-      if (nextUnchangedInput != inputRank &&
-          nextUnchangedOutput != resultRank) {
+      // Handle any leading or trailing size-1 dimensions and append the
+      // associations for the dims matching `aten.size.int`.
+      if (nextUnchangedInput != inputRank) {
+        assert(nextUnchangedOutput != resultRank &&
+               "`nextUnchangedInput` and `nextUnchangedOutput` should equal "
+               "the respective input and output rank at the same time");
         inputAssociations.emplace_back();
         outputAssociations.emplace_back();
+      }
+      while (inputDim <= nextUnchangedInput && inputDim < inputRank) {
+        if (inputDim != nextUnchangedInput && inputShape[inputDim] != 1) {
+          return rewriter.notifyMatchFailure(
+              op, "unimplemented: only collapsing of static size-1 into "
+                  "unchanged dim supported");
+        }
         inputAssociations.back().push_back(inputDim++);
+      }
+      while (outputDim <= nextUnchangedOutput && outputDim < resultRank) {
+        if (outputDim != nextUnchangedOutput && outputShape[outputDim] != 1) {
+          return rewriter.notifyMatchFailure(
+              op, "unimplemented: only expanding of static size-1 out of "
+                  "unchanged dim supported");
+        }
         outputAssociations.back().push_back(outputDim++);
       }
     }

python/torch_mlir_e2e_test/test_suite/reshape_like.py

@@ -672,6 +672,108 @@ class ViewNegativeStaticModule(torch.nn.Module):
 def ViewNegativeStaticModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(1, 128))
 
+class ViewSizeDimFollowedByExpandedOnesModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.float32, True),
+    ])
+
+    def forward(self, a):
+        return a.view(a.size(0), 1, 1, 1)
+
+@register_test_case(module_factory=lambda: ViewSizeDimFollowedByExpandedOnesModule())
+def ViewSizeDimFollowedByExpandedOnesModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(128))
+
+class ViewSizeDimFollowedByCollapsedOnesModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, 1, 1, 1], torch.float32, True),
+    ])
+
+    def forward(self, a):
+        return a.view(a.size(0))
+
+@register_test_case(module_factory=lambda: ViewSizeDimFollowedByCollapsedOnesModule())
+def ViewSizeDimFollowedByCollapsedOnesModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(128, 1, 1, 1))
+
+class ViewSizeDimLedByExpandedOnesModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.float32, True),
+    ])
+
+    def forward(self, a):
+        return a.view(1, 1, 1, a.size(0))
+
+@register_test_case(module_factory=lambda: ViewSizeDimLedByExpandedOnesModule())
+def ViewSizeDimLedByExpandedOnesModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(128))
+
+class ViewSizeDimLedByCollapsedOnesModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([1, 1, 1, -1], torch.float32, True),
+    ])
+
+    def forward(self, a):
+        return a.view(a.size(3))
+
+@register_test_case(module_factory=lambda: ViewSizeDimLedByCollapsedOnesModule())
+def ViewSizeDimLedByCollapsedOnesModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 1, 1, 128))
+
+class ViewSizeDimLedAndFollowedByExpandedOnesModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.float32, True),
+    ])
+
+    def forward(self, a):
+        return a.view(1, 1, 1, a.size(0), 1, 1, 1)
+
+@register_test_case(module_factory=lambda: ViewSizeDimLedAndFollowedByExpandedOnesModule())
+def ViewSizeDimLedAndFollowedByExpandedOnesModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(128))
+
+class ViewSizeDimLedAndFollowedByCollapsedOnesModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([1, 1, 1, -1, 1, 1, 1], torch.float32, True),
+    ])
+
+    def forward(self, a):
+        return a.view(a.size(3))
+
+@register_test_case(module_factory=lambda: ViewSizeDimLedAndFollowedByCollapsedOnesModule())
+def ViewSizeDimLedAndFollowedByCollapsedOnesModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 1, 1, 128, 1, 1, 1))
+
 # ==============================================================================
 
 class ReshapeAliasExpandModule(torch.nn.Module):