add casting of int types for resources

cruise-automation · Sep 20, 2024 · 60fdf0d · 60fdf0d
1 parent 0e8e84a
commit 60fdf0d
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Showing 4 changed files with 17 additions and 43 deletions.
diff --git a/include/mlir-tcp/Dialect/IR/TcpOps.td b/include/mlir-tcp/Dialect/IR/TcpOps.td
@@ -178,6 +178,7 @@ def Tcp_ConstOp : Tcp_Op<"const", [ConstantLike, Pure]> {
   let assemblyFormat = "attr-dict `:` type($out)";
 
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 def Tcp_BroadcastOp : Tcp_Op<"broadcast", [

diff --git a/lib/Conversion/TorchToTcp/DataMovement.cpp b/lib/Conversion/TorchToTcp/DataMovement.cpp
@@ -311,8 +311,6 @@ class ConvertAtenIndexTensorHackedTwin
     indices = getTypeConvertedValues(rewriter, op.getLoc(), getTypeConverter(),
                                      indices);
 
-
-
     for(unsigned int i = 0; i < indices.size(); i++) {
       auto idx = indices[i];
       auto ttype = cast<RankedTensorType>(idx.getType());
@@ -323,10 +321,9 @@ class ConvertAtenIndexTensorHackedTwin
           outShape, cast<RankedTensorType>(self.getType()).getElementType());
 
       auto expandedShape = torch_to_tcp::broadcastRankInLeadingDims(rewriter, idx, outShape.size() - ttype.getRank());
+
       SmallVector<Value> broadcastValues;
       SmallVector<int64_t> broadcastAxes;
-
-
       for(unsigned int j = 0; j < selfType.getRank(); j++) {
         if(j != i) {
           broadcastAxes.push_back(j);
@@ -348,45 +345,6 @@ class ConvertAtenIndexTensorHackedTwin
         self = gather.getResult();
     }
 
-    /*for (unsigned int i = 0; i < indices.size(); i++) {
-      auto idx = indices[i];
-      int numNonOneAxis = 0;
-      auto ttype = cast<RankedTensorType>(idx.getType());
-      if(ttype.getRank() != indices.size() - i) {
-        // there is a version of this op, where everything comes in as a single dim and then is should instead select the different indicies from each?
-        // so the difference would be if it keeps the dim or shrinks it.  But not 100% clear on what the definition of the different semantics are
-        return op.emitError("unsure what to do");
-      }
-      for (int j = 0; j < ttype.getRank(); j++)
-        if (ttype.getShape()[j] != 1)
-          numNonOneAxis++;
-      if (numNonOneAxis > 1)
-        return op.emitError(
-            "Expected the input shape to have a single non-one axis");
-      // convert it to a 1-dim vector
-      if (ttype.getRank() != 1) {
-        ReassociationIndices reassocIndices;
-        for (int j = 0; j < ttype.getRank(); j++)
-          reassocIndices.push_back(j);
-        SmallVector<ReassociationIndices> ri = {reassocIndices};
-        auto reshape =
-            rewriter.create<tensor::CollapseShapeOp>(op.getLoc(), idx, ri);
-        idx = reshape.getResult();
-      }
-
-      SmallVector<int64_t> outShape(
-          cast<RankedTensorType>(self.getType()).getShape());
-      outShape[i] = ttype.getNumElements();
-      auto outType = RankedTensorType::get(
-          outShape, cast<RankedTensorType>(self.getType()).getElementType());
-
-      auto gather = rewriter.create<tcp::GatherOp>(
-          op.getLoc(), outType, self, idx, rewriter.getIndexAttr(i));
-      self = gather.getResult();
-    }*/
-
-    // assert(op.getType() == self.getType());
-
     rewriter.replaceOp(op, self);
     return success();
   }

diff --git a/lib/Conversion/TorchToTcp/Misc.cpp b/lib/Conversion/TorchToTcp/Misc.cpp
@@ -16,6 +16,7 @@
 #include "Utils.h"
 #include "mlir/Dialect/Arith/IR/Arith.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/IR/DialectResourceBlobManager.h"
 #include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
 #include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
 #include "torch-mlir/Dialect/Torch/Utils/Utils.h"
@@ -162,6 +163,14 @@ class ConvertValueTensorLiteralOp
       rewriter.replaceOpWithNewOp<tcp::ConstOp>(op, resultType, denseIntAttr);
       return success();
     }
+    if (auto elements = dyn_cast<DenseResourceElementsAttr>(op.getValueAttr())) {
+      if(resultType.getElementType().isInteger() &&
+        resultType != adaptor.getValue().getType()) {
+      auto attr = DenseResourceElementsAttr::get(resultType, elements.getRawHandle());
+      rewriter.replaceOpWithNewOp<tcp::ConstOp>(op, resultType, attr);
+      return success();
+    }
+    }
 
     rewriter.replaceOpWithNewOp<tcp::ConstOp>(op, resultType,
                                               adaptor.getValue());

diff --git a/lib/Dialect/IR/TcpOps.cpp b/lib/Dialect/IR/TcpOps.cpp
@@ -127,6 +127,12 @@ LogicalResult IsolatedGroupOp::verify() {
 
 OpFoldResult ConstOp::fold(FoldAdaptor) { return getValueAttr(); }
 
+LogicalResult ConstOp::verify() {
+  if(getValueAttr().getType() != getType()) 
+    return emitOpError("can not be used to cast types");
+  return success();
+}
+
 LogicalResult CastOp::verify() {
   auto inputType = getIn().getType().cast<RankedTensorType>();
   auto outputType = getOut().getType().cast<RankedTensorType>();