[PIR+CINN]Fix Convert0DTo1D Pass bug in CombineOp

paddle/cinn/hlir/dialect/operator/transforms/group_merge/convert_0d_to_1d_pass.cc

@@ -58,13 +58,48 @@ class FullOpPattern : public pir::OpRewritePattern<paddle::dialect::FullOp> {
   }
 };
 
+class CombineOpPattern : public pir::OpRewritePattern<pir::CombineOp> {
+ public:
+  using pir::OpRewritePattern<pir::CombineOp>::OpRewritePattern;
+
+  bool Match(pir::CombineOp op) const override {
+    auto out_type = op.result(0).type().dyn_cast<pir::VectorType>();
+    for (auto type : out_type.data()) {
+      if (HasZeroDim(type)) return true;
+    }
+    return false;
+  }
+
+  void Rewrite(pir::CombineOp op,
+               pir::PatternRewriter &rewriter) const override {
+    pir::Builder builder(rewriter.ir_context());
+
+    const std::vector<pir::Type> inputs_type = [&]() {
+      std::vector<pir::Type> types;
+      for (auto value : op->operands_source()) {
+        types.push_back(value.type());
+      }
+      return types;
+    }();
+    op.result(0).set_type(builder.vec_type(inputs_type));
+  }
+
+ private:
+  bool HasZeroDim(pir::Type type) const {
+    if (!type) return false;
+    const auto dense_tensor_type = type.dyn_cast<pir::DenseTensorType>();
+    return dense_tensor_type && (dense_tensor_type.dims().size() == 0U);
+  }
+};
+
 class Convert0DTo1DPass : public pir::PatternRewritePass {
  public:
   Convert0DTo1DPass() : pir::PatternRewritePass("convert_0D_to_1D", 1) {}
 
   pir::RewritePatternSet InitializePatterns(pir::IrContext *context) override {
     pir::RewritePatternSet ps(context);
     ps.Add<FullOpPattern>(context);
+    ps.Add<CombineOpPattern>(context);
 
     return ps;
   }

test/ir/pir/cinn/sub_graphs/test_sub_graph_23.py

@@ -31,8 +31,7 @@ def forward(
         var_0,  # (shape: [11, 24, 56, 56], dtype: paddle.float32, stop_gradient: False)
         var_1,  # (shape: [11, 24, 56, 56], dtype: paddle.float32, stop_gradient: False)
     ):
-        var_2 = paddle.tensor.attribute.shape(var_0)
-        var_3 = var_2[0]
+        var_3 = var_0.shape[0]
         var_4 = paddle.tensor.random.rand(shape=[var_3, 1, 1, 1])
         var_5 = 0.975 + var_4
         var_6 = paddle.tensor.ops.floor(var_5)
@@ -65,16 +64,15 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
         outs = net(*self.inputs)
         return outs
 
-    # NOTE prim + cinn lead to error
     def test_ast_prim_cinn(self):
         st_out = self.train(self.net, to_static=True)
         cinn_out = self.train(
-            self.net, to_static=True, with_prim=True, with_cinn=False
+            self.net, to_static=True, with_prim=True, with_cinn=True
         )
         for st, cinn in zip(
             paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
         ):
-            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-6)
 
 
 if __name__ == '__main__':