remove scale op when save inference model (#67570)

yuanlehome · web-flow · commit c7791fb222b0 · 2024-08-27T21:35:57.000+08:00
diff --git a/python/paddle/static/io.py b/python/paddle/static/io.py
@@ -30,7 +30,6 @@
     Variable,
     core,
     default_main_program,
-    program_guard,
     unique_name,
 )
 from paddle.base.executor import Executor, global_scope
@@ -245,6 +244,11 @@ def normalize_program(
             "fetch_vars type must be a Variable or a list of Variable."
         )
 
+    if len(program.global_block().ops) == 0:
+        raise ValueError(
+            "program must not be empty. at least one operator is required!"
+        )
+
     # remind users to set auc_states to 0 if auc op were found.
     for op in program.global_block().ops:
         # clear device of Op
@@ -256,17 +260,6 @@ def normalize_program(
             )
             break
 
-    # fix the bug that the activation op's output as target will be pruned.
-    # will affect the inference performance.
-    # TODO(Superjomn) add an IR pass to remove 1-scale op.
-    with program_guard(program):
-        uniq_fetch_vars = []
-        for i, var in enumerate(fetch_vars):
-            if var.dtype != paddle.bool:
-                var = paddle.scale(var, 1.0, name=f"save_infer_model/scale_{i}")
-            uniq_fetch_vars.append(var)
-        fetch_vars = uniq_fetch_vars
-
     # serialize program
     copy_program = program.clone()
     global_block = copy_program.global_block()
diff --git a/test/legacy_test/test_jit_save_load.py b/test/legacy_test/test_jit_save_load.py
@@ -257,7 +257,7 @@ def forward(self, x):
         return self._linear_0(x)
 
     def forward_no_param(self, x):
-        return x
+        return x * 1.0
 
     def forward_general(self, x):
         return self._linear_0(x) + self._linear_1(x) * self._scale
@@ -1437,10 +1437,13 @@ def test_save_load_empty_layer(self):
         layer = EmptyLayer()
         x = paddle.to_tensor(np.random.random(10).astype('float32'))
         out = layer(x)
-        paddle.jit.save(layer, self.model_path)
-        load_layer = paddle.jit.load(self.model_path)
-        load_out = load_layer(x)
-        np.testing.assert_array_equal(out, load_out)
+        try:
+            paddle.jit.save(layer, self.model_path)
+        except ValueError as e:
+            self.assertTrue(
+                'program must not be empty. at least one operator is required!'
+                in str(e)
+            )
 
 
 class TestJitSaveLoadNoParamLayer(unittest.TestCase):
@@ -2085,7 +2088,7 @@ class InputSepcLayer(paddle.nn.Layer):
     # A layer with InputSpec to test InputSpec compatibility
 
     def forward(self, x, y):
-        return x, y
+        return x * 1.0, y * 1.0
 
 
 class TestInputSpecCompatibility(unittest.TestCase):
diff --git a/test/legacy_test/test_static_save_load.py b/test/legacy_test/test_static_save_load.py
@@ -1204,9 +1204,7 @@ def test_no_params(self):
             else:
                 self.assertEqual(fetch_targets[0].shape, (10, 10))
                 ops = [op.type for op in inference_program.block(0).ops]
-                self.assertEqual(
-                    ops, ['feed', 'elementwise_add', 'scale', 'fetch']
-                )
+                self.assertEqual(ops, ['feed', 'elementwise_add', 'fetch'])
 
 
 if __name__ == '__main__':
