[Dy2St] pir dy2st unittest verification - Part 8 (PaddlePaddle#59120)

---------

Co-authored-by: SigureMo <sigure.qaq@gmail.com>

python/paddle/jit/dy2static/convert_operators.py

@@ -556,7 +556,7 @@ def convert_len(var):
           operations are added in `len` transformation, such as appending
           `shape_op` in var.block.
     """
-    if isinstance(var, (Variable, OpResult)):
+    if isinstance(var, Variable):
         assert var.ndim > 0, "len() of a 0-D tensor is wrong"
         if var.type in [
             core.VarDesc.VarType.LOD_TENSOR,
@@ -575,8 +575,24 @@ def convert_len(var):
                 'len(var) only supports LoDTensor/LoDTensorArray/SelectedRows, but received %s.'
                 % type(var)
             )
+    elif isinstance(var, OpResult):
+        assert var.ndim > 0, "len() of a 0-D tensor is wrong"
+        if var.is_dense_tensor_type() or var.is_selected_row_type():
+            # Note: Length of var may be known ahead of time in dygraph,
+            # but it probably represents batch size which can be variant.
+            # so we return a variable dynamically inferred from var.shape.
+            if var.shape[0] > 0 and var.is_dense_tensor_type():
+                return var.shape[0]
+            return paddle.shape(var)[0]
+        elif var.is_dense_tensor_array_type():
+            return paddle.tensor.array_length(var)
+        else:
+            raise TypeError(
+                'len(var) only supports DenseTensor/DenseTensorArray/SelectedRows, '
+                + f'but received {type(var)}.'
+            )
     else:
-        if isinstance(var, (VariableTuple)):
+        if isinstance(var, VariableTuple):
             return var.__len__()
         return len(var)
 
@@ -625,11 +641,11 @@ def convert_range(*args):
     has_variable = any(isinstance(x, (Variable, OpResult)) for x in args)
     if has_variable:
         if len(args) == 1:
-            return paddle.arange(0, args[0], 1, paddle.int64)
+            return paddle.arange(0, args[0], 1, "int64")
         if len(args) == 2:
-            return paddle.arange(args[0], args[1], 1, paddle.int64)
+            return paddle.arange(args[0], args[1], 1, "int64")
         if len(args) == 3:
-            return paddle.arange(args[0], args[1], args[2], paddle.int64)
+            return paddle.arange(args[0], args[1], args[2], "int64")
     return range(*args)
 
 

python/paddle/jit/dy2static/pir_partial_program.py

@@ -390,7 +390,7 @@ class PartialProgramLayer:
         **1. This is a very low level API. Users should not use this API
              directly. Please use `partial_program_from(concrete_program)`
              to create it.
-        **2. LoDTensorArray is not currently supported in the output.
+        **2. TensorArray is not currently supported in the output.
 
     Args:
         main_program(Program): The main program that contains ops need to be executed.

python/paddle/jit/sot/infer_meta.py

@@ -105,7 +105,10 @@ class VariableCreator:
     """
 
     def __init__(self):
-        self.var_cache = {}
+        # TODO(dev): Remove the program and var_cache shims after PIR become default state.
+        # self.var_cache = {}
+        # self.main_program = paddle.static.Program()
+        # self.startup_program = paddle.static.Program()
         self.var_name_generator = UniqueNameGenerator("infer_meta_variable_")
 
     def gen_name(self, meta):
@@ -114,6 +117,21 @@ def gen_name(self, meta):
             name += f"_{l}"
         return name
 
+    @property
+    def var_cache(self):
+        if paddle.framework.use_pir_api():
+            return self.pir_var_cache
+        else:
+            return self.legacy_var_cache
+
+    @cached_property
+    def legacy_var_cache(self):
+        return {}
+
+    @cached_property
+    def pir_var_cache(self):
+        return {}
+
     @cached_property
     def legacy_programs(self):
         # Just for PIR and legacy IR compatibility.
@@ -133,13 +151,13 @@ def main_program(self):
 
     @property
     def startup_program(self):
-        if paddle.base.framework.use_pir_api():
+        if paddle.framework.use_pir_api():
             return self.pir_programs[1]
         else:
             return self.legacy_programs[1]
 
     def create_var(self, meta):
-        if paddle.base.framework.use_pir_api():
+        if paddle.framework.use_pir_api():
             with paddle.static.program_guard(
                 self.main_program, self.startup_program
             ):

test/dygraph_to_static/test_backward_without_params.py

@@ -15,7 +15,10 @@
 import unittest
 
 import numpy as np
-from dygraph_to_static_utils_new import Dy2StTestBase, test_legacy_and_pir
+from dygraph_to_static_utils_new import (
+    Dy2StTestBase,
+    test_legacy_and_pir_exe_and_pir_api,
+)
 
 import paddle
 
@@ -30,7 +33,7 @@ def forward(self, x):
 
 
 class TestBackwardWithoutParams(Dy2StTestBase):
-    @test_legacy_and_pir
+    @test_legacy_and_pir_exe_and_pir_api
     def test_run(self):
         net = paddle.jit.to_static(Net())
 
@@ -54,7 +57,7 @@ def forward(self, x):
 
 
 class TestZeroSizeNet(Dy2StTestBase):
-    @test_legacy_and_pir
+    @test_legacy_and_pir_exe_and_pir_api
     def test_run(self):
         net = paddle.jit.to_static(ZeroSizeNet())
         x = paddle.ones([2, 2])

test/dygraph_to_static/test_cache_program.py

@@ -16,12 +16,15 @@
 from collections import Counter
 
 import numpy as np
-from dygraph_to_static_utils_new import Dy2StTestBase
+from dygraph_to_static_utils_new import (
+    Dy2StTestBase,
+    test_ast_only,
+    test_legacy_and_pir_exe_and_pir_api,
+)
 from test_fetch_feed import Linear, Pool2D
 
 import paddle
 from paddle import base
-from paddle.jit.api import to_static
 from paddle.jit.dy2static import convert_to_static
 
 
@@ -31,41 +34,55 @@ def setUp(self):
         self.dygraph_class = Pool2D
         self.data = np.random.random((1, 2, 4, 4)).astype('float32')
 
+    @test_legacy_and_pir_exe_and_pir_api
+    @test_ast_only
     def test_cache(self):
         prev_ops, cur_ops = Counter(), Counter()
         prev_out, cur_out = None, None
-        with base.dygraph.guard(base.CPUPlace()):
-            static_net = self.dygraph_class()
-            for batch_id in range(self.batch_num):
-                out = static_net(paddle.to_tensor(self.data))
-                # Check outputs
-                prev_out = cur_out
-                cur_out = out
-                # Check forward ops
-                prev_ops = cur_ops
+        static_net = paddle.jit.to_static(self.dygraph_class())
+        for batch_id in range(self.batch_num):
+            out = static_net(paddle.to_tensor(self.data))
+            # Check outputs
+            prev_out = cur_out
+            cur_out = out
+            # Check forward ops
+            prev_ops = cur_ops
+
+            if paddle.framework.use_pir_api():
                 cur_ops = Counter(
-                    [op.type for op in base.default_main_program().block(0).ops]
+                    [
+                        op.name()
+                        for op in static_net.forward.concrete_program.main_program.global_block().ops
+                    ]
                 )
-                if batch_id > 0:
-                    prev_out_numpy = (
-                        prev_out[0].numpy()
-                        if isinstance(prev_out, (tuple, list))
-                        else prev_out.numpy()
-                    )
-                    cur_out_numpy = (
-                        cur_out[0].numpy()
-                        if isinstance(cur_out, (tuple, list))
-                        else cur_out.numpy()
-                    )
-                    np.testing.assert_allclose(
-                        prev_out_numpy,
-                        cur_out_numpy,
-                        rtol=1e-05,
-                        err_msg='Output in previous batch is {}\n Output in current batch is \n{}'.format(
-                            prev_out_numpy, cur_out_numpy
-                        ),
-                    )
-                    self.assertEqual(prev_ops, cur_ops)
+
+            else:
+                cur_ops = Counter(
+                    [
+                        op.type
+                        for op in static_net.forward.concrete_program.main_program.global_block().ops
+                    ]
+                )
+            if batch_id > 0:
+                prev_out_numpy = (
+                    prev_out[0].numpy()
+                    if isinstance(prev_out, (tuple, list))
+                    else prev_out.numpy()
+                )
+                cur_out_numpy = (
+                    cur_out[0].numpy()
+                    if isinstance(cur_out, (tuple, list))
+                    else cur_out.numpy()
+                )
+                np.testing.assert_allclose(
+                    prev_out_numpy,
+                    cur_out_numpy,
+                    rtol=1e-05,
+                    err_msg='Output in previous batch is {}\n Output in current batch is \n{}'.format(
+                        prev_out_numpy, cur_out_numpy
+                    ),
+                )
+                self.assertEqual(prev_ops, cur_ops)
 
 
 class TestCacheProgram2(TestCacheProgram):
@@ -90,23 +107,23 @@ def train_dygraph(self):
     def train(self, to_static=False):
         paddle.jit.enable_to_static(to_static)
 
-        with base.dygraph.guard(base.CPUPlace()):
-            dygraph_net = self.dygraph_class()
-            adam = paddle.optimizer.Adam(
-                learning_rate=0.001, parameters=dygraph_net.parameters()
-            )
-            loss_data = []
-            for batch_id in range(self.batch_num):
-                input = base.dygraph.to_variable(self.data)
-                pred, avg_loss = dygraph_net(input)
-
-                loss_data.append(avg_loss.numpy())
-                avg_loss.backward()
-                adam.minimize(avg_loss)
-                dygraph_net.clear_gradients()
+        static_net = paddle.jit.to_static(self.dygraph_class())
+        adam = paddle.optimizer.Adam(
+            learning_rate=0.001, parameters=static_net.parameters()
+        )
+        loss_data = []
+        for batch_id in range(self.batch_num):
+            input = paddle.to_tensor(self.data)
+            pred, avg_loss = static_net(input)
+
+            loss_data.append(avg_loss.numpy())
+            avg_loss.backward()
+            adam.minimize(avg_loss)
+            static_net.clear_gradients()
 
         return loss_data
 
+    @test_legacy_and_pir_exe_and_pir_api
     def test_with_optimizer(self):
         dygraph_loss = self.train_dygraph()
         static_loss = self.train_static()
@@ -125,14 +142,14 @@ def simple_func(x):
 
 
 class TestConvertWithCache(Dy2StTestBase):
+    @test_legacy_and_pir_exe_and_pir_api
     def test_cache(self):
         static_func = convert_to_static(simple_func)
         # Get transformed function from cache.
         cached_func = convert_to_static(simple_func)
         self.assertTrue(id(static_func), id(cached_func))
 
 
-@to_static
 def sum_even_until_limit(max_len, limit):
     ret_sum = base.dygraph.to_variable(np.zeros(1).astype('int32'))
     for i in range(max_len):
@@ -156,12 +173,11 @@ def sum_under_while(limit):
 
 class TestToOutputWithCache(Dy2StTestBase):
     def test_output(self):
-        with base.dygraph.guard():
-            ret = sum_even_until_limit(80, 10)
-            self.assertEqual(ret.numpy(), 30)
+        ret = paddle.jit.to_static(sum_even_until_limit)(80, 10)
+        self.assertEqual(ret.numpy(), 30)
 
-            ret = to_static(sum_under_while)(100)
-            self.assertEqual(ret.numpy(), 5050)
+        ret = paddle.jit.to_static(sum_under_while)(100)
+        self.assertEqual(ret.numpy(), 5050)
 
 
 if __name__ == '__main__':