[tosa] Support for Torch.squeeze (#487)

e2e_testing/torchscript/xfail_sets.py

@@ -38,4 +38,7 @@ TOSA_PASS_SET = {
     "BoolTensorReturnTrueModule_basic",
     "BoolTensorReturnMixedModule_basic",
     "ElementwiseRsqrtModule_basic",
+    "SqueezeModule_static",
+    "SqueezeModule_noUnitDim",
+    "SqueezeModule_allUnitDim",
 }

lib/Conversion/TorchToTosa/TorchToTosa.cpp

@@ -526,6 +526,41 @@ LogicalResult ConvertAtenOp<AtenArgmaxOp>::matchAndRewrite(
   return success();
 }
 
+template <>
+LogicalResult ConvertAtenOp<AtenSqueezeOp>::matchAndRewrite(
+    AtenSqueezeOp op, OpAdaptor adaptor,
+    ConversionPatternRewriter &rewriter) const {
+
+  Value self = adaptor.self();
+  auto selfTy = self.getType().template cast<RankedTensorType>();
+
+  if (!selfTy)
+    return op.emitError("Only ranked tensor types supported in TOSA argmax");
+
+  auto selfShape = selfTy.getShape();
+
+  SmallVector<int64_t> newOutputShape;
+  for (auto &dim : selfShape) {
+    if (dim != 1)
+      newOutputShape.push_back(dim);
+  }
+
+  auto resultTy = getTypeConverter()
+                      ->convertType(op.getResult().getType())
+                      .cast<RankedTensorType>();
+  auto resultElemTy = resultTy.getElementType();
+
+  auto newOutputTy = RankedTensorType::get(newOutputShape, resultElemTy);
+
+  auto reshapeOp = rewriter.create<tosa::ReshapeOp>(
+      op->getLoc(), getTypeConverter()->convertType(newOutputTy), self,
+      rewriter.getI64ArrayAttr(newOutputShape));
+  rewriter.replaceOpWithNewOp<tensor::CastOp>(
+      op, getTypeConverter()->convertType(newOutputTy), reshapeOp);
+
+  return success();
+}
+
 } // namespace
 
 // -----------------------------------------------------------------------------
@@ -624,6 +659,7 @@ public:
     INSERT_ATENOP_PATTERN(AtenMulTensorOp);
     INSERT_ATENOP_PATTERN(AtenDivTensorOp);
     INSERT_ATENOP_PATTERN(AtenArgmaxOp);
+    INSERT_ATENOP_PATTERN(AtenSqueezeOp);
 #undef INSERT_ATENOP_PATTERN
 
     if (failed(applyPartialConversion(getOperation(), target,