[Dy2St]Add backend for to_static API

python/paddle/fluid/tests/unittests/test_input_spec.py

@@ -349,7 +349,7 @@ def test_run(self):
         )
         x = paddle.randn([2, 10])
         out = net(x)
-        np.testing.assert_equal(out.shape, [2, 5])
+        np.testing.assert_equal(net.forward._input_spec, None)
 
 
 if __name__ == '__main__':

python/paddle/jit/api.py

@@ -218,8 +218,23 @@ def ignore_module(modules: list[Any]):
     add_ignore_module(modules)
 
 
+def _check_and_set_backend(backend, build_strategy):
+    if backend not in ['CINN', None]:
+        raise ValueError(
+            "The backend of to_static should be 'CINN' or None, but received {}.".format(
+                backend
+            )
+        )
+    if backend == 'CINN':
+        build_strategy.build_cinn_pass = True
+
+
 def to_static(
-    function=None, input_spec=None, build_strategy=None, property=False
+    function=None,
+    input_spec=None,
+    build_strategy=None,
+    backend=None,
+    **kwargs,
 ):
     """
     Converts imperative dygraph APIs into declarative function APIs. Decorator
@@ -228,7 +243,6 @@ def to_static(
     Tensor(s) to do imperative training, inference, or other operations. If the
     decorated function calls other imperative function, the called one will be
     converted into declarative function as well.
-
     Args:
         function (callable): callable imperative function.
         input_spec(list[InputSpec]|tuple[InputSpec]): list/tuple of InputSpec to specific the shape/dtype/name
@@ -238,7 +252,8 @@ def to_static(
             in the computational graph and memory optimization during the execution
             of the computational graph. For more information about build_strategy,
             please refer to :code:`paddle.static.BuildStrategy`. The default is None.
-        property(bool, Optional): whether the fucntion is python property. The default is False.
+        backend(str, Optional): Specifies compilation backend, which can be `CINN` or None. When backend is `CINN`, CINN compiler will be used to speed up training and inference.
+        kwargs: Support keys including `property`, set `property` to True if the fucntion is python property.
 
 
     Returns:
@@ -263,6 +278,7 @@ def func(x):
             print(x_v) # [[2. 2.]]
 
     """
+    property = kwargs.get("property", False)
 
     def decorated(python_func):
         """
@@ -279,6 +295,7 @@ def decorated(python_func):
                 input_spec=input_spec,
                 build_strategy=build_strategy,
                 property=property,
+                backend=backend,
             ),
         )
 
@@ -291,6 +308,7 @@ def decorated(python_func):
                 type(build_strategy).__name__
             )
         )
+    _check_and_set_backend(backend, build_strategy)
 
     # for usage: `to_static(foo, ...)`
     if function is not None:

python/paddle/jit/dy2static/partial_program.py

@@ -27,7 +27,12 @@
 from paddle.optimizer.lr import LRScheduler
 
 from . import logging_utils
-from .utils import RETURN_NO_VALUE_MAGIC_NUM, _out_grad_names, _param_grad_names
+from .utils import (
+    RETURN_NO_VALUE_MAGIC_NUM,
+    _out_grad_names,
+    _param_grad_names,
+    dy2st_prim_guard,
+)
 
 __all__ = []
 
@@ -197,6 +202,7 @@ def __init__(
         # program_id -> list(scope)
         self._scope_cache = {}
         self._hooker = None
+        self._backend = kwargs.get('backend', None)
 
     def __call__(self, inputs):
         """
@@ -636,10 +642,9 @@ def _append_backward_desc(self, main_program):
 
         start_idx = len(program.block(0).ops) + len(self._outputs.tolist())
         if targets:
-            # TODO(CZ): later when use cinn, set_prim_all_enabled and check_and_set_prim_all_enabled will be set at else branch.
-            core.check_and_set_prim_all_enabled()
             start_idx = len(program.block(0).ops) + len(self._outputs.tolist())
-            backward.gradients(targets=targets, inputs=[])
+            with dy2st_prim_guard(self._backend):
+                backward.gradients(targets=targets, inputs=[])
 
             if self._hooker:
                 program, start_idx = self._hooker.after_append_backward(

python/paddle/jit/dy2static/program_translator.py

@@ -47,6 +47,7 @@
     ALREADY_D2S,
     ast_to_func,
     ast_to_source_code,
+    dy2st_prim_guard,
     func_to_source_code,
     input_specs_compatible,
     is_paddle_func,
@@ -333,7 +334,7 @@ def __init__(self, function, input_spec=None, **kwargs):
             self._class_instance = None
 
         if input_spec is not None and prim_or_cinn_is_enabled(
-            kwargs.get("build_strategy", None)
+            kwargs.get("build_strategy", None), kwargs.get("backend", None)
         ):
             from paddle.static import InputSpec
 
@@ -1183,11 +1184,9 @@ def __init__(self):
     def _build_once(self, cache_key):
         # TODO(Aurelius84): Need a gloabl FLAGS to enable/disable to_prim
         enable_prim = cache_key.kwargs['build_strategy'].build_cinn_pass
-        # TODO(CZ): later when use cinn, set_prim_all_enabled and check_and_set_prim_all_enabled will be set at else branch.
 
         # NOTE(xiongkun): Need a global FLAGS to enable/disable fallback
         enable_fallback = enable_prim
-        core.check_and_set_prim_all_enabled()
         try:
             concrete_program = ConcreteProgram.from_func_spec(
                 func_spec=cache_key.function_spec,
@@ -1215,7 +1214,8 @@ def _build_once(self, cache_key):
             else:
                 raise
 
-        if prim_or_cinn_is_enabled(cache_key.kwargs['build_strategy']):
+        backend = cache_key.kwargs['backend']
+        if prim_or_cinn_is_enabled(cache_key.kwargs['build_strategy'], backend):
             for var in concrete_program.main_program.list_vars():
                 if -1 in var.shape:
                     warnings.warn(
@@ -1227,10 +1227,11 @@ def _build_once(self, cache_key):
         partial_program = partial_program_from(
             concrete_program, cache_key.class_instance is not None
         )
-        if core._is_fwd_prim_enabled():
-            partial_program.set_hooker(
-                PrimHooker(concrete_program.main_program)
-            )
+        with dy2st_prim_guard(backend):
+            if core._is_fwd_prim_enabled():
+                partial_program.set_hooker(
+                    PrimHooker(concrete_program.main_program, backend)
+                )
         return concrete_program, partial_program
 
     def __getitem__(self, item):
@@ -1290,39 +1291,46 @@ def clear(self):
 
 
 class PrimHooker(PartialProgramLayerHook):
-    def __init__(self, original_program):
+    def __init__(self, original_program, backend):
         if len(original_program.blocks) > 1:
             raise ValueError(
                 'The primitive mode only support one block currently.'
             )
+        self.backend = backend
         self.custom_vjps = set()
-        if core._is_all_prim_enabled():
-            self.custom_vjps = {
-                op.type
-                for op in original_program.block(0).ops
-                if core.has_comp_grad_op_maker(op.type)
-            }
+        with dy2st_prim_guard(self.backend):
+            if core._is_all_prim_enabled():
+                self.custom_vjps = {
+                    op.type
+                    for op in original_program.block(0).ops
+                    if core.has_comp_grad_op_maker(op.type)
+                }
 
     def before_append_backward(self, forward_program):
-        if core._is_fwd_prim_enabled():
-            _to_prim(forward_program.blocks, blacklist=self.custom_vjps)
-        return forward_program
+        with dy2st_prim_guard(self.backend):
+            if core._is_fwd_prim_enabled():
+                _to_prim(forward_program.blocks, blacklist=self.custom_vjps)
+            return forward_program
 
     def after_append_backward(self, whole_program, backward_start_idx):
-        backward_length = len(whole_program.block(0).ops) - backward_start_idx
-        if core._is_fwd_prim_enabled() and len(self.custom_vjps) != 0:
-            # only process backward part of block
-            _to_prim(whole_program.blocks, backward_length=backward_length)
-        new_start_index = len(whole_program.block(0).ops) - backward_length
-        if backward_length > 0:
-            # only process forward part of block
-            _to_prim(whole_program.blocks, start_idx=new_start_index)
-        return whole_program, new_start_index
+        with dy2st_prim_guard(self.backend):
+            backward_length = (
+                len(whole_program.block(0).ops) - backward_start_idx
+            )
+            if core._is_fwd_prim_enabled() and len(self.custom_vjps) != 0:
+                # only process backward part of block
+                _to_prim(whole_program.blocks, backward_length=backward_length)
+            new_start_index = len(whole_program.block(0).ops) - backward_length
+            if backward_length > 0:
+                # only process forward part of block
+                _to_prim(whole_program.blocks, start_idx=new_start_index)
+            return whole_program, new_start_index
 
     def after_infer(self, infer_program):
-        if core._is_fwd_prim_enabled():
-            _to_prim(infer_program.block(0))
-        return infer_program
+        with dy2st_prim_guard(self.backend):
+            if core._is_fwd_prim_enabled():
+                _to_prim(infer_program.block(0))
+            return infer_program
 
 
 class ProgramTranslator:

python/paddle/jit/dy2static/utils.py

@@ -35,6 +35,7 @@
 from paddle.fluid import core, unique_name
 from paddle.fluid.data_feeder import convert_dtype
 from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.wrapped_decorator import signature_safe_contextmanager
 from paddle.utils import gast
 
 from .ast_utils import ast_to_source_code
@@ -1491,7 +1492,10 @@ def _out_grad_names(program_desc, fwd_end_op_index, out_size):
     return names
 
 
-def prim_or_cinn_is_enabled(build_strategy):
+def prim_or_cinn_is_enabled(build_strategy, backend):
+    if backend == 'CINN':
+        return True
+
     if build_strategy is not None and build_strategy.build_cinn_pass:
         return True
 
@@ -1527,3 +1531,18 @@ def name_judge():
         return True
     else:
         return False
+
+
+@signature_safe_contextmanager
+def dy2st_prim_guard(backend):
+    core.check_and_set_prim_all_enabled()
+    orign_fwd = core._is_fwd_prim_enabled()
+    orign_bwd = core._is_bwd_prim_enabled()
+
+    if backend == 'CINN':
+        core._set_prim_all_enabled(True)
+    try:
+        yield
+    finally:
+        core._set_prim_forward_enabled(orign_fwd)
+        core._set_prim_backward_enabled(orign_bwd)

test/dygraph_to_static/test_cinn_prim.py

@@ -163,5 +163,20 @@ def test_cinn_prim(self):
             )
 
 
+class TestBackend(unittest.TestCase):
+    def test_backend(self):
+        x = paddle.randn([2, 4])
+        out1 = self.forward(x, 'CINN')
+        out2 = self.forward(x, None)
+        np.testing.assert_allclose(out1, out2, rtol=1e-6)
+
+    def forward(self, x, beckend=None):
+        paddle.seed(2022)
+        net = PrimeNet()
+        net = paddle.jit.to_static(net, backend=beckend)
+        out = net(x)
+        return out
+
+
 if __name__ == '__main__':
     unittest.main()

test/dygraph_to_static/test_partial_program_hook.py

@@ -44,7 +44,7 @@ def f():
             f
         ).get_concrete_program()
         self._hook = program_translator.PrimHooker(
-            concrete_program.main_program
+            concrete_program.main_program, None
         )
         self._forward = partial_program.forward_program
         self._whole = partial_program._train_program