[IPU] add more ipu UTs (#41176)

* add ipu uts

* fix ut

* split PR

* fix ut

* rm ut

python/paddle/fluid/tests/unittests/ipu/test_cast_op_ipu.py

@@ -94,6 +94,105 @@ def test_base(self):
         self.assertTrue(res0.shape == res1.shape)
 
 
+class TestEnableFp16(TestBase):
+    def set_atol(self):
+        self.atol = 1e-10
+
+    def set_data_feed(self):
+        self.feed = {"x": np.array([1, 200, 3000, 40000]).astype('int32'), }
+
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs['dtype'] = 'float32'
+
+    def _test_base(self, run_ipu=True):
+        scope = paddle.static.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
+
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(main_prog, startup_prog):
+                x = paddle.static.data(
+                    name=self.feed_list[0],
+                    shape=self.feed_shape[0],
+                    dtype=self.feed_dtype[0])
+                out = paddle.cast(x, **self.attrs)
+                fetch_list = [out.name]
+
+            if run_ipu:
+                place = paddle.IPUPlace()
+            else:
+                place = paddle.CPUPlace()
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+
+            if run_ipu:
+                feed_list = self.feed_list
+                ipu_strategy = paddle.static.IpuStrategy()
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
+                    main_prog,
+                    ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
+            else:
+                program = main_prog
+
+            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            return result[0]
+
+
+class TestDisableTransferCast(TestEnableFp16):
+    def set_atol(self):
+        self.atol = 1e-10
+
+    def set_data_feed(self):
+        self.feed = {"x": np.array([1, 200, 3000, 40000]).astype('int32'), }
+
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs['dtype'] = 'float32'
+
+    def _test_base(self, run_ipu=True):
+        scope = paddle.static.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
+
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(main_prog, startup_prog):
+                x = paddle.static.data(
+                    name=self.feed_list[0],
+                    shape=self.feed_shape[0],
+                    dtype=self.feed_dtype[0])
+                out = paddle.cast(x, **self.attrs)
+                fetch_list = [out.name]
+
+            if run_ipu:
+                place = paddle.IPUPlace()
+            else:
+                place = paddle.CPUPlace()
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+
+            if run_ipu:
+                feed_list = self.feed_list
+                ipu_strategy = paddle.static.IpuStrategy()
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                ipu_strategy.set_precision_config(enable_fp16=True)
+                ipu_strategy.set_options({"transfer_cast_op": False})
+                program = paddle.static.IpuCompiledProgram(
+                    main_prog,
+                    ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
+            else:
+                program = main_prog
+
+            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            return result[0]
+
+
 class TestCase2(TestBase):
     def set_atol(self):
         self.atol = 1e-10

python/paddle/fluid/tests/unittests/ipu/test_eval_model_ipu.py

@@ -0,0 +1,126 @@
+#  Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestBase(IPUOpTest):
+    def setUp(self):
+        self.set_atol()
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_attrs()
+
+    def set_atol(self):
+        self.atol = 1e-4
+
+    def set_data_feed(self):
+        self.feed = {
+            "image": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'),
+        }
+
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed.values()]
+        self.feed_list = list(self.feed.keys())
+        self.feed_dtype = [x.dtype for x in self.feed.values()]
+
+    def set_attrs(self):
+        self.attrs = {
+            "optimizer": 'lamb',
+            "weight_decay": 2.0,
+        }
+
+    def _test_optimizer(self, run_ipu=True):
+        scope = paddle.static.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
+        np.random.seed(self.SEED)
+
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(main_prog, startup_prog):
+                image = paddle.static.data(
+                    name='image', shape=[1, 3, 10, 10], dtype='float32')
+                conv1 = paddle.static.nn.conv2d(
+                    image, num_filters=3, filter_size=3, bias_attr=False)
+                loss = paddle.mean(conv1)
+
+                weight_decay = self.attrs['weight_decay']
+                opt = paddle.optimizer.SGD(learning_rate=1e-1,
+                                           weight_decay=weight_decay)
+                if self.attrs['optimizer'] == 'adam':
+                    opt = paddle.optimizer.Adam(
+                        learning_rate=1e-1, weight_decay=weight_decay)
+                elif self.attrs['optimizer'] == 'lamb':
+
+                    opt = paddle.optimizer.Lamb(
+                        learning_rate=1e-1, lamb_weight_decay=weight_decay)
+                opt.minimize(loss)
+
+            if run_ipu:
+                place = paddle.IPUPlace()
+            else:
+                place = paddle.CPUPlace()
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+
+            if run_ipu:
+                feed_list = [image.name]
+                fetch_list = [loss.name]
+                ipu_strategy = paddle.static.IpuStrategy()
+                ipu_strategy.set_graph_config(is_training=True)
+                ipu_strategy.set_options({"runtime_options.enable_eval": True})
+                program = paddle.static.IpuCompiledProgram(
+                    main_prog, ipu_strategy=ipu_strategy).compile(feed_list,
+                                                                  fetch_list)
+            else:
+                program = main_prog
+
+            result = []
+            if run_ipu:
+                for epoch in range(200):
+                    if epoch == 100:
+                        ipu_strategy.set_options({
+                            "runtime_options.enable_eval": False
+                        })
+                    loss_res = exe.run(program,
+                                       feed=self.feed,
+                                       fetch_list=[loss])
+                    result.append(loss_res)
+            else:
+                for epoch in range(100):
+                    loss_res = exe.run(program,
+                                       feed=self.feed,
+                                       fetch_list=[loss])
+                    result.append(loss_res)
+            return np.array(result)
+
+    def test(self):
+        # cpu and ipu dimenstion mismatch, cpu:(100, 1, 1), ipu:(100, 1)
+        ipu_loss = self._test_optimizer(True).flatten()
+        cpu_loss = self._test_optimizer(False).flatten()
+        self.assertTrue(ipu_loss[0] == ipu_loss[99])
+        self.assertTrue(np.allclose(ipu_loss[100:], cpu_loss, atol=self.atol))
+
+
+if __name__ == "__main__":
+    unittest.main()