[0D-Tensor] CINN supports transpose, add special case to expand_zero_dim_pass (#55379)

paddle/cinn/frontend/pass/expand_zero_dim_pass.cc

@@ -34,33 +34,73 @@ class ExpandZeroDimPass : public ProgramPass {
                  const std::unordered_set<std::string>& fetch_ids,
                  const common::Target& target) override {
     NetBuilder builder("expand_zero_dim_builder");
-    for (auto var : program->GetInputs()) {
-      if (var->shape.empty()) {
-        var->shape.push_back(1);
-      }
-      builder.CreateInput(var);
-    }
     for (int i = 0; i < program->size(); ++i) {
       auto& instr = (*program)[i];
+      if (instr->op_type == "transpose") {
+        builder.AppendInstruction(HandleTranspose(instr));
+        continue;
+      }
       for (auto& input : instr->inputs) {
         if (input->shape.empty()) {
-          VLOG(4) << "Change input 0D-Tensor " << input->id << " to 1D-Tensor";
+          VLOG(4) << "Change " << instr->op_type << "'s input 0D-Tensor "
+                  << input->id << " to 1D-Tensor";
           input->shape.push_back(1);
         }
       }
       for (auto& output : instr->outputs) {
         if (output->shape.empty()) {
-          VLOG(4) << "Change output 0D-Tensor " << output->id
-                  << " to 1D-Tensor";
+          VLOG(4) << "Change " << instr->op_type << "'s output 0D-Tensor "
+                  << output->id << " to 1D-Tensor";
           output->shape.push_back(1);
         }
       }
       builder.AppendInstruction(instr);
     }
+    for (auto var : program->GetInputs()) {
+      if (var->shape.empty()) {
+        VLOG(4) << "Change program's input 0D-Tensor " << var->id
+                << " to 1D-Tensor";
+        var->shape.push_back(1);
+      }
+      builder.CreateInput(var);
+    }
     *program = builder.Build();
   }
 
   void Clear() override {}
+
+ private:
+  // Before: out-0D = transpose(x-0D, [])
+  // After:  out-1D = transpose(x-1D, [1])
+  Instruction HandleTranspose(const Instruction& instr) {
+    Instruction new_instr = instr;
+    bool has_0d_input = false;
+    for (auto& input : new_instr->inputs) {
+      if (input->shape.empty()) {
+        VLOG(4) << "Change transpose's input 0D-Tensor " << input->id
+                << " to 1D-Tensor";
+        input->shape.push_back(1);
+        has_0d_input = true;
+      }
+    }
+    for (auto& output : new_instr->outputs) {
+      if (output->shape.empty()) {
+        VLOG(4) << "Change transpose's output 0D-Tensor " << output->id
+                << " to 1D-Tensor";
+        output->shape.push_back(1);
+      }
+    }
+    if (has_0d_input) {
+      std::vector<int32_t> axis =
+          new_instr.GetAttrs<std::vector<int32_t>>("axis");
+      CHECK(axis.empty()) << "transpose's axis should be empty when inputs "
+                             "0D-Tensor! Please check setting.\n";
+      axis.push_back(0);
+      VLOG(4) << "Change Transpose's attribute axis from [] to [1]";
+      new_instr.SetAttr<std::vector<int32_t>>("axis", axis);
+    }
+    return new_instr;
+  }
 };
 
 }  // namespace pass

paddle/cinn/hlir/op/transform.cc

@@ -1137,8 +1137,8 @@ std::vector<framework::shape_t> InferShapeForTranspose(
     const std::vector<framework::shape_t> &inputs_shape,
     const framework::AttrMapType &attrs) {
   std::vector<framework::shape_t> result;
-  CHECK(!inputs_shape.empty() && !inputs_shape[0].empty())
-      << "The input's shape size is 0! Please check again.";
+  CHECK(!inputs_shape.empty())
+      << "The input's shape is empty! Please check again.";
   if (attrs.find("axis") != attrs.end()) {
     auto axis = absl::get<std::vector<int>>(attrs.at("axis"));
     CHECK_EQ(axis.size(), inputs_shape[0].size())

test/cinn/ops/test_zero_dim_tensor.py

@@ -630,6 +630,44 @@ class TestScaleOp(OpTest):
         self.check_outputs_and_grads()
 
 
+@OpTestTool.skip_if(
+    not is_compiled_with_cuda(), "x86 test will be skipped due to timeout."
+)
+class TestTransposeOp(OpTest):
+    def setUp(self):
+        np.random.seed(2023)
+        self.dtype = "float32"
+        self.init_input()
+
+    def init_input(self):
+        self.inputs = {
+            "x": np.random.randint(-10, 10, []).astype(self.dtype),
+        }
+        self.target_shape = ()
+
+    def build_paddle_program(self, target):
+        x = paddle.to_tensor(self.inputs["x"], stop_gradient=False)
+        out = paddle.transpose(x, [])
+
+        self.paddle_outputs = [out]
+
+    def build_cinn_program(self, target):
+        builder = NetBuilder("transpose_op")
+        x = builder.create_input(
+            cinn_dtype_convert(self.dtype), self.inputs["x"].shape, "x"
+        )
+        out = builder.transpose(x, [])
+
+        prog = builder.build()
+        res = self.get_cinn_output(prog, target, [x], [self.inputs["x"]], [out])
+
+        self.cinn_outputs = res
+        self.assertEqual(res[0].shape, self.target_shape)
+
+    def test_check_results(self):
+        self.check_outputs_and_grads()
+
+
 @OpTestTool.skip_if(
     not is_compiled_with_cuda(), "x86 test will be skipped due to timeout."
 )