save dtype of inputs (#28301)

python/paddle/hapi/model.py

@@ -200,16 +200,22 @@ def prepare_distributed_context(place=None):
     return strategy
 
 
-def _update_input_shapes(inputs):
+def _update_input_info(inputs):
     "Get input shape list by given inputs in Model initialization."
     shapes = None
+    dtypes = None
     if isinstance(inputs, Input):
         shapes = [list(inputs.shape)]
+        dtypes = [inputs.dtype]
     elif isinstance(inputs, list):
         shapes = [list(input.shape) for input in inputs]
+        dtypes = [input.dtype for input in inputs]
     elif isinstance(inputs, dict):
         shapes = [list(inputs[name].shape) for name in inputs]
-    return shapes
+        dtypes = [inputs[name].dtype for name in inputs]
+    else:
+        return None
+    return shapes, dtypes
 
 
 class StaticGraphAdapter(object):
@@ -617,7 +623,7 @@ class DynamicGraphAdapter(object):
             'test_batch': 0
         }
 
-        self._input_shapes = None
+        self._input_info = None
         if self._nranks > 1:
             stradegy = fluid.dygraph.parallel.ParallelStrategy()
             stradegy.nranks = ParallelEnv().nranks
@@ -642,7 +648,7 @@ class DynamicGraphAdapter(object):
         self.model.network.train()
         self.mode = 'train'
         inputs = to_list(inputs)
-        self._input_shapes = _update_input_shapes(inputs)
+        self._input_info = _update_input_info(inputs)
         labels = labels or []
         labels = [to_variable(l) for l in to_list(labels)]
 
@@ -679,7 +685,7 @@ class DynamicGraphAdapter(object):
         self.model.network.eval()
         self.mode = 'eval'
         inputs = to_list(inputs)
-        self._input_shapes = _update_input_shapes(inputs)
+        self._input_info = _update_input_info(inputs)
         labels = labels or []
         labels = [to_variable(l) for l in to_list(labels)]
 
@@ -728,7 +734,7 @@ class DynamicGraphAdapter(object):
         self.model.network.eval()
         self.mode = 'test'
         inputs = [to_variable(x) for x in to_list(inputs)]
-        self._input_shapes = _update_input_shapes(inputs)
+        self._input_info = _update_input_info(inputs)
         outputs = self.model.network.forward(*inputs)
         if self._nranks > 1 and isinstance(self.model._place, fluid.CUDAPlace):
             outputs = [_all_gather(o, self._nranks) for o in to_list(outputs)]
@@ -875,7 +881,7 @@ class Model(object):
         self._loss = None
         self._loss_weights = None
         self._optimizer = None
-        self._input_shapes = None
+        self._input_info = None
         self._is_shape_inferred = False
         self._test_dataloader = None
 
@@ -884,7 +890,7 @@ class Model(object):
                 raise TypeError(
                     "'inputs' must be list or dict, and couldn't be None.")
         elif inputs:
-            self._input_shapes = _update_input_shapes(inputs)
+            self._input_info = _update_input_info(inputs)
 
         self._inputs = self._verify_spec(inputs, is_input=True)
         self._labels = self._verify_spec(labels)
@@ -941,7 +947,7 @@ class Model(object):
               print(loss)
         """
         loss = self._adapter.train_batch(inputs, labels)
-        if fluid.in_dygraph_mode() and self._input_shapes is None:
+        if fluid.in_dygraph_mode() and self._input_info is None:
             self._update_inputs()
         return loss
 
@@ -992,7 +998,7 @@ class Model(object):
               print(loss)
         """
         loss = self._adapter.eval_batch(inputs, labels)
-        if fluid.in_dygraph_mode() and self._input_shapes is None:
+        if fluid.in_dygraph_mode() and self._input_info is None:
             self._update_inputs()
         return loss
 
@@ -1036,7 +1042,7 @@ class Model(object):
               print(out)
         """
         loss = self._adapter.predict_batch(inputs)
-        if fluid.in_dygraph_mode() and self._input_shapes is None:
+        if fluid.in_dygraph_mode() and self._input_info is None:
             self._update_inputs()
         return loss
 
@@ -1750,14 +1756,15 @@ class Model(object):
         if fluid.in_dygraph_mode():
             with fluid.framework._dygraph_guard(None):
                 layer = self.network
-                if self._input_shapes is None:  # No provided or inferred
+                if self._input_info is None:  # No provided or inferred
                     raise RuntimeError(
                         "Saving inference model needs 'inputs' or running before saving. Please specify 'inputs' in Model initialization or input training data and perform a training for shape derivation."
                     )
                 if self._is_shape_inferred:
                     warnings.warn(
                         "'inputs' was not specified when Model initialization, so the input shape to be saved will be the shape derived from the user's actual inputs. The input shape to be saved is %s. For saving correct input shapes, please provide 'inputs' for Model initialization."
-                        % self._input_shapes)
+                        % self._input_info[0])
+
                 layer.forward = paddle.jit.to_static(
                     layer.forward, input_spec=self._inputs)
 
@@ -1945,7 +1952,7 @@ class Model(object):
             _input_size = self._inputs
         return summary(self.network, _input_size, dtype)
 
-    def _verify_spec(self, specs, shapes=None, is_input=False):
+    def _verify_spec(self, specs, shapes=None, dtypes=None, is_input=False):
         out_specs = []
 
         if specs is None:
@@ -1954,10 +1961,12 @@ class Model(object):
 
             if is_input:
                 arg_names = extract_args(self.network.forward)[1:]
-                if shapes is not None and fluid.in_dygraph_mode():
+                # While Saving inference model in dygraph, and providing inputs only in running.
+                if shapes is not None and dtypes is not None and fluid.in_dygraph_mode(
+                ):
                     out_specs = [
                         Input(
-                            name=n, shape=shapes[i])
+                            name=n, dtype=dtypes[i], shape=shapes[i])
                         for i, n in enumerate(arg_names)
                     ]
                 else:
@@ -2000,6 +2009,8 @@ class Model(object):
 
     def _update_inputs(self):
         "Update self._inputs according to given inputs."
-        self._input_shapes = self._adapter._input_shapes
+        self._input_info = self._adapter._input_info
+        if self._input_info is not None and len(self._input_info) == 2:
+            self._inputs = self._verify_spec(None, self._input_info[0],
+                                             self._input_info[1], True)
             self._is_shape_inferred = True
-        self._inputs = self._verify_spec(None, self._input_shapes, True)