Add operand type invariant to `torch.overwrite.tensor.contents` (#606)

This commit adds the invariant to the op `torch.overwrite.tensor.contents` that
both of its operands have the same shape and size. In order to
maintain the invariant, special handling of this op is added to the
`RefineTypes` pass.

include/torch-mlir/Dialect/Torch/IR/TorchOps.td

@@ -884,8 +884,10 @@ def Torch_CopyToValueTensorOp : Torch_Op<"copy.to_vtensor", [
   let verifier = "return ::verify(*this);";
 }
 
-def Torch_OverwriteTensorOp : Torch_Op<"overwrite.tensor", [
+def Torch_OverwriteTensorContentsOp : Torch_Op<"overwrite.tensor.contents", [
-    AllowsTypeRefinement
+    TypesMatchWith<"overwritten tensor type is corresponding !torch.tensor of value tensor type",
+                   "value", "overwritten",
+                   "$_self.cast<ValueTensorType>().getWithoutValueSemantics()">
   ]> {
   let summary = "Ovewrite the contents of tensor with values from another.";
   let description = [{
@@ -895,10 +897,12 @@ def Torch_OverwriteTensorOp : Torch_Op<"overwrite.tensor", [
     Immediately after this op has completed, indexing `overwritten` will result
     in identical values as indexing into `value`. Of course, later ops
     might mutate `overwritten`, so this relationship need not hold for the
-    entire program.
+    entire program. This op only updates the tensor data (not metadata).
+    In other words, it cannot change the (dynamic) shape of the overwritten tensor.
 
-    This op has undefined behavior if the two tensors have different
+    This op does not have the AllowsTypeRefinement trait because the types of the
-    shapes or dtypes.
+    two operands are coupled. Only places that know how to simultaneously update
+    both types should be changing the type of this op.
   }];
   let arguments = (ins
     Torch_ValueTensorType:$value,

lib/Dialect/Torch/Transforms/MaximizeValueSemantics.cpp

@@ -47,7 +47,7 @@ public:
       if (user->getBlock() != copy->getBlock())
         return failure();
       // We can only analyze these ops or view-like ops.
-      if (isa<CopyToValueTensorOp, OverwriteTensorOp>(user))
+      if (isa<CopyToValueTensorOp, OverwriteTensorContentsOp>(user))
         foundNonViewLikeOpUser = true;
       else if (!isViewLikeOp(user))
         return failure();
@@ -71,9 +71,10 @@ public:
     for (Operation *user : users) {
       if (auto copyToValueTensor = dyn_cast<CopyToValueTensorOp>(user)) {
         rewriter.replaceOp(copyToValueTensor, {currentlyHeldValueTensor});
-      } else if (auto overwriteTensor = dyn_cast<OverwriteTensorOp>(user)) {
+      } else if (auto overwriteTensorContents =
-        currentlyHeldValueTensor = overwriteTensor.value();
+                     dyn_cast<OverwriteTensorContentsOp>(user)) {
-        rewriter.eraseOp(overwriteTensor);
+        currentlyHeldValueTensor = overwriteTensorContents.value();
+        rewriter.eraseOp(overwriteTensorContents);
       } else if (isViewLikeOp(user)) {
         // This case currently only handles view-like ops that have one tensor
         // input and one tensor output.

lib/Dialect/Torch/Transforms/ReduceOpVariants.cpp

@@ -18,6 +18,24 @@ using namespace mlir;
 using namespace mlir::torch;
 using namespace mlir::torch::Torch;
 
+// Create an overwrite in a manner that preserves the
+// `OverwriteTensorContentsOp` invariant that both arguments
+// must have the same shape and dtype.
+static void createOverwriteTensorContents(PatternRewriter &rewriter,
+                                          Location loc, Value overwriterTensor,
+                                          Value overwrittenTensor) {
+  Type overwriterTensorType = overwriterTensor.getType();
+  Type overwrittenTensorType = overwrittenTensor.getType()
+                                   .dyn_cast<NonValueTensorType>()
+                                   .getWithValueSemantics();
+  if (overwriterTensorType != overwrittenTensorType) {
+    overwriterTensor = rewriter.create<TensorStaticInfoCastOp>(
+        loc, overwrittenTensorType, overwriterTensor);
+  }
+  rewriter.create<OverwriteTensorContentsOp>(loc, overwriterTensor,
+                                             overwrittenTensor);
+}
+
 namespace {
 // Convert value semantic ops operating on mutable arrays to instead operate on
 // immutable tensors.
@@ -143,7 +161,7 @@ public:
 
     auto tensor =
         rewriter.create<CopyToValueTensorOp>(loc, newOp->getResult(0));
-    rewriter.create<OverwriteTensorOp>(loc, tensor, op->getOperand(0));
+    createOverwriteTensorContents(rewriter, loc, tensor, op->getOperand(0));
     rewriter.replaceOp(op, op->getOperand(0));
     return success();
   }
@@ -180,7 +198,8 @@ public:
     Operation *newOp = rewriter.createOperation(state);
     auto tensor =
         rewriter.create<CopyToValueTensorOp>(op->getLoc(), newOp->getResult(0));
-    rewriter.create<OverwriteTensorOp>(op->getLoc(), tensor, op->getOperand(0));
+    createOverwriteTensorContents(rewriter, op->getLoc(), tensor,
+                                  op->getOperand(0));
     rewriter.replaceOp(op, op->getOperand(0));
 
     return success();

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -2105,7 +2105,44 @@ void optimize(FuncOp func, TypeAnalyzer &analyzer) {
             if (isSafeToRefineOperandInPlace(use, refinedType)) {
               use->set(newTypedValue);
               continue;
+            } else if (auto overwriteTensorContents =
+                           dyn_cast<OverwriteTensorContentsOp>(
+                               use->getOwner())) {
+              // `OverwriteTensorContentsOp` has special handling here because
+              // it requires that both of its operands always have the same
+              // shape and dtype.
+              //
+              // WARNING: In order to simplify the implementation, the type
+              // used for both operands is the type of the overwritten tensor.
+              // A better way of doing this would be to join the two operand
+              // types to create the most specific type possible and use that
+              // for both arguments, allowing static sizes to always propagate.
+              const unsigned overwriterOperandIndex = 0;
+              const unsigned overwrittenOperandIndex = 1;
+              unsigned operandNumber = use->getOperandNumber();
+              if (operandNumber != overwrittenOperandIndex)
+                continue;
+
+              Location loc = overwriteTensorContents.getLoc();
+              Value overwriterTensor = overwriteTensorContents.value();
+              Type overwriterTensorType = overwriterTensor.getType();
+              Type overwrittenTensorType = newTypedValue.getType()
+                                               .dyn_cast<NonValueTensorType>()
+                                               .getWithValueSemantics();
+              if (overwriterTensorType == overwrittenTensorType)
+                continue;
+
+              {
+                OpBuilder::InsertionGuard guard(b);
+                b.setInsertionPoint(overwriteTensorContents);
+                Value castedOverwriterTensor = b.create<TensorStaticInfoCastOp>(
+                    loc, overwrittenTensorType, overwriterTensor);
+                overwriteTensorContents.setOperand(overwriterOperandIndex,
+                                                   castedOverwriterTensor);
+              }
+              continue;
             }
+
             // If needed, create a value of the original type to appease users
             // that cannot accept the new type.
             if (!oldTypedValue) {

test/Dialect/Torch/invalid.mlir

@@ -169,3 +169,13 @@ builtin.func @torch.prim.ListConstruct() {
   torch.prim.ListConstruct %int2 : (!torch.int) -> !torch.list<!torch.tensor>
   return
 }
+
+// -----
+
+builtin.func @torch.overwrite.tensor.contents(%arg0: !torch.vtensor<[1],f32>, %arg1: !torch.vtensor<[?],f32>) -> !torch.vtensor<[1],f32> {
+  %0 = torch.copy.to_tensor %arg0 : !torch.tensor<[1],f32>
+  // expected-error@+1 {{'torch.overwrite.tensor.contents' op failed to verify that overwritten tensor type is corresponding !torch.tensor of value tensor type}}
+  torch.overwrite.tensor.contents %arg1 overwrites %0 : !torch.vtensor<[?],f32>, !torch.tensor<[1],f32>
+  %1 = torch.copy.to_vtensor %0 : !torch.vtensor<[1],f32>
+  return %1 : !torch.vtensor<[1],f32>
+}

test/Dialect/Torch/maximize-value-semantics.mlir

@@ -17,7 +17,7 @@ func @torch.copy.tensor$basic(%arg0: !torch.vtensor) -> (!torch.vtensor, !torch.
 func @one_mutation_in_a_block(%arg0: !torch.vtensor, %arg1: !torch.vtensor) -> (!torch.vtensor, !torch.vtensor) {
   %0 = torch.copy.to_tensor %arg0 : !torch.tensor
   %equal_to_arg0 = torch.copy.to_vtensor %0 : !torch.vtensor
-  torch.overwrite.tensor %arg1 overwrites %0 : !torch.vtensor, !torch.tensor
+  torch.overwrite.tensor.contents %arg1 overwrites %0 : !torch.vtensor, !torch.tensor
   %equal_to_arg1 = torch.copy.to_vtensor %0 : !torch.vtensor
   return %equal_to_arg0, %equal_to_arg1 : !torch.vtensor, !torch.vtensor
 }
@@ -34,12 +34,12 @@ func @multiple_mutations_in_a_block(%arg0: !torch.vtensor, %arg1: !torch.vtensor
   %equal_to_arg0 = torch.copy.to_vtensor %tensor : !torch.vtensor
 
   // Overwrite with %arg1
-  torch.overwrite.tensor %arg1 overwrites %tensor : !torch.vtensor, !torch.tensor
+  torch.overwrite.tensor.contents %arg1 overwrites %tensor : !torch.vtensor, !torch.tensor
   %equal_to_arg1 = torch.copy.to_vtensor %tensor : !torch.vtensor
   %equal_to_arg1_again = torch.copy.to_vtensor %tensor : !torch.vtensor
 
   // Overwrite with %arg2
-  torch.overwrite.tensor %arg2 overwrites %tensor : !torch.vtensor, !torch.tensor
+  torch.overwrite.tensor.contents %arg2 overwrites %tensor : !torch.vtensor, !torch.tensor
   %equal_to_arg2 = torch.copy.to_vtensor %tensor : !torch.vtensor
 
   return %equal_to_arg0, %equal_to_arg1, %equal_to_arg1_again, %equal_to_arg2 : !torch.vtensor, !torch.vtensor, !torch.vtensor, !torch.vtensor
@@ -52,7 +52,7 @@ func @multiple_mutations_in_a_block(%arg0: !torch.vtensor, %arg1: !torch.vtensor
 // CHECK:           return %[[RESULT]] : !torch.vtensor
 func @mutation_followed_by_view_like_ops(%value_t: !torch.vtensor, %overwriter: !torch.vtensor, %int_list: !torch.list<!torch.int>) -> !torch.vtensor {
   %t = torch.copy.to_tensor %value_t : !torch.tensor
-  torch.overwrite.tensor %overwriter overwrites %t : !torch.vtensor, !torch.tensor
+  torch.overwrite.tensor.contents %overwriter overwrites %t : !torch.vtensor, !torch.tensor
   %view = torch.aten.view %t, %int_list : !torch.tensor, !torch.list<!torch.int> -> !torch.tensor
   %result = torch.aten.permute %view, %int_list : !torch.tensor, !torch.list<!torch.int> -> !torch.tensor
   %value_result = torch.copy.to_vtensor %result : !torch.vtensor
@@ -60,10 +60,10 @@ func @mutation_followed_by_view_like_ops(%value_t: !torch.vtensor, %overwriter:
 }
 
 // CHECK-LABEL:   func @unmodeled_mutation(
-// CHECK:           torch.overwrite.tensor
+// CHECK:           torch.overwrite.tensor.contents
 func @unmodeled_mutation(%arg0: !torch.vtensor, %arg1: !torch.vtensor) -> !torch.vtensor {
   %0 = torch.copy.to_tensor %arg0 : !torch.tensor
-  torch.overwrite.tensor %arg1 overwrites %0 : !torch.vtensor, !torch.tensor
+  torch.overwrite.tensor.contents %arg1 overwrites %0 : !torch.vtensor, !torch.tensor
   "some.op"(%0) : (!torch.tensor) -> ()
   %result = torch.copy.to_vtensor %0 : !torch.vtensor
   return %result : !torch.vtensor
@@ -76,7 +76,7 @@ func @unimplemented_control_flow(%arg0: !torch.vtensor, %arg1: !torch.vtensor, %
   %tensor = torch.copy.to_tensor %arg0 : !torch.tensor
   %equal_to_arg0 = torch.copy.to_vtensor %tensor : !torch.vtensor
   torch.prim.If %cond -> () {
-    torch.overwrite.tensor %arg1 overwrites %tensor : !torch.vtensor, !torch.tensor
+    torch.overwrite.tensor.contents %arg1 overwrites %tensor : !torch.vtensor, !torch.tensor
     torch.prim.If.yield
   } else {
     torch.prim.If.yield

test/Dialect/Torch/reduce-op-variants.mlir

@@ -95,7 +95,7 @@ func @convert_to_value_semantic_tensors_optional(%t: !torch.tensor,
 // being applied in sequence.
 // CHECK:           %[[ARRAY_RESULT:.*]] = torch.copy.to_tensor %[[TENSOR_RESULT]] : !torch.tensor<[2,2],f32>
 // CHECK:           %[[TENSOR_AGAIN:.*]] = torch.copy.to_vtensor %[[ARRAY_RESULT]] : !torch.vtensor<[2,2],f32>
-// CHECK:           torch.overwrite.tensor %[[TENSOR_AGAIN]] overwrites %[[ARG0]] : !torch.vtensor<[2,2],f32>, !torch.tensor<[2,2],f32>
+// CHECK:           torch.overwrite.tensor.contents %[[TENSOR_AGAIN]] overwrites %[[ARG0]] : !torch.vtensor<[2,2],f32>, !torch.tensor<[2,2],f32>
 // CHECK:           return %[[ARG0]], %[[ARG0]] : !torch.tensor<[2,2],f32>, !torch.tensor<[2,2],f32>
 func @reduce_trailing_underscore_inplace_variant(%arg0: !torch.tensor<[2,2],f32>, %arg1: !torch.tensor<[2,2],f32>) -> (!torch.tensor<[2,2],f32>, !torch.tensor<[2,2],f32>) {
   %c1 = torch.constant.int 1
@@ -138,7 +138,7 @@ func @convert_to_value_semantic_tensors_optional_list(%self: !torch.tensor<[5],f
 // CHECK-SAME:         !torch.vtensor, !torch.float, !torch.float, !torch.none -> !torch.vtensor
 // CHECK:           %[[RET:.*]] = torch.copy.to_tensor %[[VRET]] : !torch.tensor
 // CHECK:           %[[COPY_VTENSOR:.*]] = torch.copy.to_vtensor %[[RET]] : !torch.vtensor
-// CHECK:           torch.overwrite.tensor %[[COPY_VTENSOR]] overwrites %[[T]] : !torch.vtensor, !torch.tensor
+// CHECK:           torch.overwrite.tensor.contents %[[COPY_VTENSOR]] overwrites %[[T]] : !torch.vtensor, !torch.tensor
 // CHECK:           return %[[T]] : !torch.tensor
 func @torch.aten.uniform_(%t: !torch.tensor, %min: !torch.float, %max: !torch.float, %generator: !torch.none) -> !torch.tensor {
   %ret = torch.aten.uniform_ %t, %min, %max, %generator: !torch.tensor, !torch.float, !torch.float, !torch.none -> !torch.tensor
@@ -153,7 +153,7 @@ func @torch.aten.uniform_(%t: !torch.tensor, %min: !torch.float, %max: !torch.fl
 // CHECK:           %[[VRET:.*]] = torch.pseudo.aten.bernoulli.float %[[T_VTENSOR]], %[[P]], %[[GENERATOR]] : !torch.vtensor, !torch.float, !torch.none -> !torch.vtensor
 // CHECK:           %[[RET:.*]] = torch.copy.to_tensor %[[VRET]] : !torch.tensor
 // CHECK:           %[[COPY_VTENSOR:.*]] = torch.copy.to_vtensor %[[RET]] : !torch.vtensor
-// CHECK:           torch.overwrite.tensor %[[COPY_VTENSOR]] overwrites %[[T]] : !torch.vtensor, !torch.tensor
+// CHECK:           torch.overwrite.tensor.contents %[[COPY_VTENSOR]] overwrites %[[T]] : !torch.vtensor, !torch.tensor
 // CHECK:           return %[[T]] : !torch.tensor
 func @torch.aten.bernoulli_.float(%t: !torch.tensor) -> !torch.tensor {
   %generator = torch.constant.none
@@ -169,7 +169,7 @@ func @torch.aten.bernoulli_.float(%t: !torch.tensor) -> !torch.tensor {
 // CHECK:           %[[VRET:.*]] = torch.pseudo.aten.fill.Scalar %[[T_VTENSOR]], %[[VALUE]] : !torch.vtensor, !torch.int -> !torch.vtensor
 // CHECK:           %[[RET:.*]] = torch.copy.to_tensor %[[VRET]] : !torch.tensor
 // CHECK:           %[[COPY_VTENSOR:.*]] = torch.copy.to_vtensor %[[RET]] : !torch.vtensor
-// CHECK:           torch.overwrite.tensor %[[COPY_VTENSOR]] overwrites %[[T]] : !torch.vtensor, !torch.tensor
+// CHECK:           torch.overwrite.tensor.contents %[[COPY_VTENSOR]] overwrites %[[T]] : !torch.vtensor, !torch.tensor
 // CHECK:           return %[[T]] : !torch.tensor
 func @torch.aten.fill_.Scalar(%t: !torch.tensor) -> !torch.tensor {
   %value = torch.constant.int 1

test/Dialect/Torch/refine-types.mlir

@@ -1157,3 +1157,35 @@ func @torch.aten.BinaryBroadcasting(%arg0: !torch.vtensor<[5,4,3,3,1],f32>, %arg
   %0 = torch.aten.add.Tensor %arg0, %arg1, %arg2: !torch.vtensor<[5,4,3,3,1],f32>, !torch.vtensor<[?,3,1,2],f32>, !torch.int -> !torch.tensor
   return %0 : !torch.tensor
 }
+
+// -----
+// CHECK-LABEL:   func @torch.overwrite.tensor.contents$dynamic_overwrites_static(
+// CHECK-SAME:                                                           %[[STATIC:.*]]: !torch.vtensor<[2],f32>,
+// CHECK-SAME:                                                           %[[DYNAMIC:.*]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[2],f32> {
+// CHECK:           %[[CAST:.*]] = torch.tensor_static_info_cast %[[DYNAMIC_COPY:.*]] : !torch.vtensor<[?],f32> to !torch.vtensor<[2],f32>
+// CHECK:           torch.overwrite.tensor.contents %[[CAST]] overwrites %[[STATIC_COPY:.*]] : !torch.vtensor<[2],f32>, !torch.tensor<[2],f32>
+func @torch.overwrite.tensor.contents$dynamic_overwrites_static(%static: !torch.vtensor<[2],f32>, %dynamic: !torch.vtensor<[?],f32>) -> !torch.vtensor<[2],f32> {
+  %static_no_type = torch.tensor_static_info_cast %static : !torch.vtensor<[2],f32> to !torch.vtensor
+  %static_copy = torch.copy.to_tensor %static_no_type : !torch.tensor
+  %dynamic_no_type = torch.tensor_static_info_cast %dynamic : !torch.vtensor<[?],f32> to !torch.vtensor
+  torch.overwrite.tensor.contents %dynamic_no_type overwrites %static_copy : !torch.vtensor, !torch.tensor
+  %static_value_copy = torch.copy.to_vtensor %static_copy : !torch.vtensor
+  %result = torch.tensor_static_info_cast %static_value_copy : !torch.vtensor to !torch.vtensor<[2],f32>
+  return %result : !torch.vtensor<[2],f32>
+}
+
+// -----
+// CHECK-LABEL:   func @torch.overwrite.tensor.contents$static_overwrites_dynamic(
+// CHECK-SAME:                                                           %[[STATIC:.*]]: !torch.vtensor<[2],f32>,
+// CHECK-SAME:                                                           %[[DYNAMIC:.*]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> {
+// CHECK:           %[[CAST:.*]] = torch.tensor_static_info_cast %[[STATIC_COPY:.*]] : !torch.vtensor<[2],f32> to !torch.vtensor<[?],f32>
+// CHECK:           torch.overwrite.tensor.contents %[[CAST]] overwrites %[[DYNAMIC_COPY:.*]] : !torch.vtensor<[?],f32>, !torch.tensor<[?],f32>
+func @torch.overwrite.tensor.contents$static_overwrites_dynamic(%static: !torch.vtensor<[2],f32>, %dynamic: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> {
+  %static_no_type = torch.tensor_static_info_cast %static : !torch.vtensor<[2],f32> to !torch.vtensor
+  %dynamic_no_type = torch.tensor_static_info_cast %dynamic : !torch.vtensor<[?],f32> to !torch.vtensor
+  %dynamic_copy = torch.copy.to_tensor %dynamic_no_type : !torch.tensor
+  torch.overwrite.tensor.contents %static_no_type overwrites %dynamic_copy : !torch.vtensor, !torch.tensor
+  %dynamic_value_copy = torch.copy.to_vtensor %dynamic_copy : !torch.vtensor
+  %result = torch.tensor_static_info_cast %dynamic_value_copy : !torch.vtensor to !torch.vtensor<[?],f32>
+  return %result : !torch.vtensor<[?],f32>
+}