MT model compilation minor changes

This contains the following changes: - Fix optional knowledge propagation. The initial knowledge should always be NotNone for the operations we implemented. - Add Folder for `prim.dtype`
llvm · Sep 9, 2021 · ebe6f9b · ebe6f9b
1 parent 5f3eb63
commit ebe6f9b
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Showing 6 changed files with 151 additions and 52 deletions.
diff --git a/frontends/pytorch/python/torch_mlir_utils/codegen/torch_ods_gen.py b/frontends/pytorch/python/torch_mlir_utils/codegen/torch_ods_gen.py
@@ -398,7 +398,7 @@ def emit(key, **kwargs):
         emit("prim::layout : (Tensor) -> (int)")
         emit("prim::TupleIndex : (Any, int) -> (Any)")
         emit("prim::device : (Tensor) -> (Device)")
-        emit("prim::dtype : (Tensor) -> (int)")
+        emit("prim::dtype : (Tensor) -> (int)", has_folder=True)
         emit("prim::TupleUnpack : (Any) -> (...)", has_canonicalizer=True)
         emit("prim::NumToTensor.Scalar : (Scalar) -> (Tensor)")
         emit("prim::min.self_int : (int[]) -> (int)")

diff --git a/include/npcomp/Dialect/Torch/IR/GeneratedAtenOps.td b/include/npcomp/Dialect/Torch/IR/GeneratedAtenOps.td
@@ -791,6 +791,40 @@ def Torch_AtenTriu_Op : Torch_Op<"aten.triu_", [
   let assemblyFormat = "$self `,` $diagonal attr-dict `:` type($self) `,` type($diagonal) `->` type($result)";
 }
 
+def Torch_AtenIndexPutOp : Torch_Op<"aten.index_put", [
+    AllowsTypeRefinement,
+    HasValueSemantics
+  ]> {
+  let summary = "Generated op for `aten::index_put : (Tensor, Tensor?[], Tensor, bool) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchOptionalTensorListType:$indices,
+    AnyTorchTensorType:$values,
+    Torch_BoolType:$accumulate
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let assemblyFormat = "$self `,` $indices `,` $values `,` $accumulate attr-dict `:` type($self) `,` type($indices) `,` type($values) `,` type($accumulate) `->` type($result)";
+}
+
+def Torch_AtenIndexPut_Op : Torch_Op<"aten.index_put_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::index_put_ : (Tensor, Tensor?[], Tensor, bool) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchOptionalTensorListType:$indices,
+    AnyTorchTensorType:$values,
+    Torch_BoolType:$accumulate
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let assemblyFormat = "$self `,` $indices `,` $values `,` $accumulate attr-dict `:` type($self) `,` type($indices) `,` type($values) `,` type($accumulate) `->` type($result)";
+}
+
 def Torch_AtenLinearOp : Torch_Op<"aten.linear", [
     AllowsTypeRefinement,
     HasValueSemantics
@@ -1404,22 +1438,6 @@ def Torch_AtenIndexTensorOp : Torch_Op<"aten.index.Tensor", [
   let assemblyFormat = "$self `,` $indices attr-dict `:` type($self) `,` type($indices) `->` type($result)";
 }
 
-def Torch_AtenIndexPut_Op : Torch_Op<"aten.index_put_", [
-    AllowsTypeRefinement
-  ]> {
-  let summary = "Generated op for `aten::index_put_ : (Tensor, Tensor?[], Tensor, bool) -> (Tensor)`";
-  let arguments = (ins
-    AnyTorchTensorType:$self,
-    AnyTorchOptionalTensorListType:$indices,
-    AnyTorchTensorType:$values,
-    Torch_BoolType:$accumulate
-  );
-  let results = (outs
-    AnyTorchTensorType:$result
-  );
-  let assemblyFormat = "$self `,` $indices `,` $values `,` $accumulate attr-dict `:` type($self) `,` type($indices) `,` type($values) `,` type($accumulate) `->` type($result)";
-}
-
 def Torch_AtenIndexSelectOp : Torch_Op<"aten.index_select", [
     AllowsTypeRefinement,
     HasValueSemantics

diff --git a/include/npcomp/Dialect/Torch/IR/GeneratedPrimOps.td b/include/npcomp/Dialect/Torch/IR/GeneratedPrimOps.td
@@ -70,6 +70,7 @@ def Torch_PrimDtypeOp : Torch_Op<"prim.dtype", [
     Torch_IntType:$result
   );
   let assemblyFormat = "$a attr-dict `:` type($a) `->` type($result)";
+  let hasFolder = 1;
 }
 
 def Torch_PrimTupleUnpackOp : Torch_Op<"prim.TupleUnpack", [

diff --git a/lib/Dialect/Torch/IR/TorchOps.cpp b/lib/Dialect/Torch/IR/TorchOps.cpp
@@ -18,6 +18,20 @@ using namespace mlir;
 using namespace mlir::NPCOMP;
 using namespace mlir::NPCOMP::Torch;
 
+// see https://github.com/pytorch/pytorch/blob/master/c10/core/ScalarType.h#L28
+static int64_t getDtypeIntegerFromMlirType(Type dtype) {
+  if (dtype.isa<Float32Type>())
+    return 6;
+
+  if (auto integerType = dtype.dyn_cast<IntegerType>()) {
+    if (integerType.isSignedInteger(64))
+      return 4;
+    if (integerType.isSignlessInteger(1))
+      return 11;
+  }
+  return -1;
+}
+
 //===----------------------------------------------------------------------===//
 // Utilities
 //===----------------------------------------------------------------------===//
@@ -129,6 +143,10 @@ bool isValidSubtype(Type subtype, Type type) {
       type ==
           NonValueTensorType::getWithLeastStaticInformation(type.getContext()))
     return true;
+
+  if (subtype.isa<ValueTensorType>() && type.isa<ValueTensorType>() &&
+      type == ValueTensorType::getWithLeastStaticInformation(type.getContext()))
+    return true;
   return false;
 }
 
@@ -972,5 +990,18 @@ OpFoldResult AtenMulIntOp::fold(ArrayRef<Attribute> operands) {
   return nullptr;
 }
 
+//===----------------------------------------------------------------------===//
+// PrimDtypeOp
+//===----------------------------------------------------------------------===//
+
+OpFoldResult PrimDtypeOp::fold(ArrayRef<Attribute> operands) {
+  BaseTensorType tensorType = a().getType().cast<BaseTensorType>();
+  if (tensorType.hasDtype()) {
+    int64_t dtypeInt = getDtypeIntegerFromMlirType(tensorType.getDtype());
+    if (dtypeInt != -1)
+      return getI64IntegerAttr(getContext(), dtypeInt);
+  }
+  return nullptr;
+}
 #define GET_OP_CLASSES
 #include "npcomp/Dialect/Torch/IR/TorchOps.cpp.inc"