From 063a35098b511d5c25964d756ecd9561245a7f2f Mon Sep 17 00:00:00 2001 From: Allen Guo Date: Fri, 6 May 2022 10:29:06 +0800 Subject: [PATCH] update UTs 1 (#42517) --- .../ipu/test_fp16_inference_io_ipu.py | 160 ---------------- .../unittests/ipu/test_fp16_support_ipu.py | 180 ++++-------------- .../tests/unittests/ipu/test_gather_op_ipu.py | 75 ++------ .../tests/unittests/ipu/test_gelu_op_ipu.py | 69 ++----- .../unittests/ipu/test_gradient_clip_ipu.py | 123 +++++------- .../unittests/ipu/test_greater_op_ipu.py | 77 ++------ .../unittests/ipu/test_groupnorm_op_ipu.py | 120 ++++-------- .../unittests/ipu/test_instancenorm_op_ipu.py | 113 +++-------- .../tests/unittests/ipu/test_ipu_shard_api.py | 111 ----------- .../unittests/ipu/test_layernorm_op_ipu.py | 155 +++++---------- .../unittests/ipu/test_log_softmax_op_ipu.py | 69 ++----- .../unittests/ipu/test_logical_not_op_ipu.py | 82 +++----- .../unittests/ipu/test_logical_x_op_ipu.py | 79 +++----- .../unittests/ipu/test_lookuptable_op_ipu.py | 106 +++-------- .../ipu/test_lookuptable_v2_op_ipu.py | 107 +++-------- .../unittests/ipu/test_lr_sheduler_ipu.py | 110 +++++------ .../tests/unittests/ipu/test_matmul_op_ipu.py | 82 ++------ .../unittests/ipu/test_matmul_serilize_ipu.py | 89 ++++----- .../unittests/ipu/test_matmul_v2_op_ipu.py | 81 ++------ 19 files changed, 487 insertions(+), 1501 deletions(-) delete mode 100644 python/paddle/fluid/tests/unittests/ipu/test_fp16_inference_io_ipu.py delete mode 100644 python/paddle/fluid/tests/unittests/ipu/test_ipu_shard_api.py diff --git a/python/paddle/fluid/tests/unittests/ipu/test_fp16_inference_io_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_fp16_inference_io_ipu.py deleted file mode 100644 index cd29ff705b..0000000000 --- a/python/paddle/fluid/tests/unittests/ipu/test_fp16_inference_io_ipu.py +++ /dev/null @@ -1,160 +0,0 @@ -# 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 shutil - -import numpy as np -import paddle -import paddle.fluid as fluid -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_op_attrs() - - def set_atol(self): - self.atol = 1e-6 - self.rtol = 1e-5 - self.atol_fp16 = 1e-2 - self.rtol_fp16 = 1e-3 - - def set_data_feed(self): - data = np.random.uniform(size=[1, 3, 10, 10]) - self.feed_fp32 = {"in_0": data.astype(np.float32)} - self.feed_fp16 = {"in_0": data.astype(np.float16)} - - def set_feed_attr(self): - self.feed_shape = [x.shape for x in self.feed_fp32.values()] - self.feed_list = list(self.feed_fp32.keys()) - - def set_op_attrs(self): - self.attrs = {} - self.attrs['steps'] = 100 - self.attrs['save_at_step'] = 20 - self.attrs['is_training'] = True - self.attrs['opt_type'] = 'sgd' - self.attrs['path'] = 'model' - self.attrs['model_name'] = 'test' - - def _test_save(self): - 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 - generator = paddle.fluid.unique_name.UniqueNameGenerator() - self.full_name = '/'.join( - [self.attrs['path'], self.attrs['model_name']]) - - with paddle.fluid.unique_name.guard(generator): - 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='float32') - - scale = paddle.fluid.layers.scale( - x, scale=1.0, bias=0.0, bias_after_scale=True) - conv = paddle.static.nn.conv2d( - scale, - num_filters=3, - filter_size=3, - bias_attr=False, - name='conv2d') - loss = paddle.mean(conv) - - if self.attrs['is_training']: - if self.attrs['opt_type'] == 'sgd': - sgd = paddle.optimizer.SGD(learning_rate=1e-2) - sgd.minimize(loss) - elif self.attrs['opt_type'] == 'adam': - adam = paddle.optimizer.Adam(learning_rate=1e-2) - adam.minimize(loss) - elif self.attrs['opt_type'] == 'lamb': - lamb = paddle.optimizer.Lamb(learning_rate=1e-2) - lamb.minimize(loss) - - fetch_list = [loss.name] - - place = paddle.IPUPlace() - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=True) - ipu_strategy.set_precision_config(enable_fp16=True) - program = paddle.static.IpuCompiledProgram( - main_prog, ipu_strategy=ipu_strategy).compile( - self.feed_list, fetch_list) - - for _ in range(self.attrs['steps']): - exe.run(program, feed=self.feed_fp16, fetch_list=fetch_list) - - paddle.static.save_inference_model( - self.full_name, x, loss, exe, program=program.org_program) - - def _test_load(self, run_ipu): - if run_ipu: - place = paddle.IPUPlace() - else: - place = paddle.CPUPlace() - exe = paddle.static.Executor(place) - - [inference_program, feed_target_names, fetch_targets] = ( - paddle.static.load_inference_model(self.full_name, exe)) - - if run_ipu: - feed_list = feed_target_names - fetch_list = [fetch_targets[0].name] - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=False) - ipu_strategy.set_precision_config(enable_fp16=True) - program = paddle.static.IpuCompiledProgram( - inference_program, - ipu_strategy=ipu_strategy).compile(feed_list, fetch_list) - else: - program = inference_program - - feed = self.feed_fp16 if run_ipu else self.feed_fp32 - result = [] - for i in range(10): - feed["in_0"] += np.array([1.1 * i]).astype(feed["in_0"].dtype) - out = exe.run(program, feed=feed, fetch_list=[fetch_targets]) - result.append(out) - - return np.array(result) - - def test_base(self): - self._test_save() - cpu_res = self._test_load(False) - ipu_res = self._test_load(True).astype(np.float32) - - self.assertTrue( - np.allclose( - cpu_res, ipu_res, rtol=self.rtol_fp16, atol=self.atol_fp16)) - - shutil.rmtree(self.attrs['path'], True) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ipu/test_fp16_support_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_fp16_support_ipu.py index 71742deefc..1d3b17dbc2 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_fp16_support_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_fp16_support_ipu.py @@ -16,9 +16,8 @@ import unittest import numpy as np import paddle -import paddle.fluid as fluid import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -31,10 +30,6 @@ class TestBase(IPUOpTest): self.set_feed_attr() self.set_op_attrs() - @property - def fp16_enabled(self): - return True - def set_atol(self): self.atol = 5e-6 self.rtol = 1e-5 @@ -54,80 +49,32 @@ class TestBase(IPUOpTest): def set_op_attrs(self): self.attrs = {} - def _test_base(self, exec_mode): - scope = fluid.core.Scope() - main_prog = paddle.static.Program() - startup_prog = paddle.static.Program() - main_prog.random_seed = self.SEED - startup_prog.random_seed = self.SEED - - with fluid.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='float32') - - conv1 = paddle.static.nn.conv2d( - x, num_filters=3, filter_size=3, bias_attr=False) - conv2 = paddle.static.nn.conv2d( - x, num_filters=3, filter_size=3, bias_attr=False) - add1 = conv1 + conv2 - conv3 = paddle.static.nn.conv2d( - add1, num_filters=8, filter_size=8, bias_attr=False) - out = paddle.fluid.layers.relu(conv3, **self.attrs) - - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() - - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_fp32 - if exec_mode > ExecutionMode.IPU_FP32: - feed = self.feed_fp16 - - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return result[0] - - def test_base(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - output_dict[mode] = self._test_base(mode).flatten() - - self.check(output_dict) - - -class TestIntInput(IPUOpTest): - def setUp(self): - self.set_atol() - self.set_training() - self.set_data_feed() - self.set_feed_attr() - self.set_op_attrs() - - @property - def fp16_enabled(self): - return True - + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + conv1 = paddle.static.nn.conv2d( + x, num_filters=3, filter_size=3, bias_attr=False) + conv2 = paddle.static.nn.conv2d( + x, num_filters=3, filter_size=3, bias_attr=False) + add1 = conv1 + conv2 + conv3 = paddle.static.nn.conv2d( + add1, num_filters=8, filter_size=8, bias_attr=False) + out = paddle.fluid.layers.relu(conv3, **self.attrs) + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) + + def test(self): + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() + + +class TestIntInput(TestBase): def set_data_feed(self): embedding = np.random.uniform(size=[10, 20]) indice = np.array([1, 3, 5]).astype(np.int32) @@ -140,71 +87,14 @@ class TestIntInput(IPUOpTest): "indice": indice, } - def set_feed_attr(self): - self.feed_shape = [x.shape for x in self.feed_fp32.values()] - self.feed_list = list(self.feed_fp32.keys()) - self.feed_dtype = [x.dtype for x in self.feed_fp32.values()] - - def set_op_attrs(self): - self.attrs = {} - - def _test_base(self, exec_mode): - scope = fluid.core.Scope() - main_prog = paddle.static.Program() - startup_prog = paddle.static.Program() - main_prog.random_seed = self.SEED - startup_prog.random_seed = self.SEED - - with fluid.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='float32') - y = paddle.static.data( - name=self.feed_list[1], - shape=self.feed_shape[1], - dtype='int32') - - out = paddle.fluid.layers.gather(x, index=y) - - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() - - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_fp32 - if exec_mode > ExecutionMode.IPU_FP32: - feed = self.feed_fp16 - - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return np.array(result) - - def test_base(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - output_dict[mode] = self._test_base(mode).flatten() - - self.check(output_dict) + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + y = paddle.static.data( + name=self.feed_list[1], shape=self.feed_shape[1], dtype='int32') + out = paddle.fluid.layers.gather(x, index=y) + self.fetch_list = [out.name] if __name__ == "__main__": diff --git a/python/paddle/fluid/tests/unittests/ipu/test_gather_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_gather_op_ipu.py index 01a56fd14b..bbf3ec0ffd 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_gather_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_gather_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -30,10 +30,6 @@ class TestBase(IPUOpTest): self.set_feed_attr() self.set_op_attrs() - @property - def fp16_enabled(self): - return True - def set_data_feed(self): x = np.random.uniform(size=[10, 20]) y = np.array([1, 3, 5]) @@ -47,63 +43,24 @@ class TestBase(IPUOpTest): def set_op_attrs(self): self.attrs = {} - def _test_base(self, exec_mode): - 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='float32') - y = paddle.static.data( - name=self.feed_list[1], - shape=self.feed_shape[1], - dtype='int32') - - out = paddle.fluid.layers.gather(x, index=y, **self.attrs) - - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + y = paddle.static.data( + name=self.feed_list[1], shape=self.feed_shape[1], dtype='int32') + out = paddle.fluid.layers.gather(x, index=y, **self.attrs) + self.fetch_list = [out.name] - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_fp32 - if exec_mode > ExecutionMode.IPU_FP32: - feed = self.feed_fp16 - - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return result[0] + def run_model(self, exec_mode): + self.run_op_test(exec_mode) def test(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - output_dict[mode] = self._test_base(mode).flatten() - - self.check(output_dict) + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() class TestCase1(TestBase): diff --git a/python/paddle/fluid/tests/unittests/ipu/test_gelu_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_gelu_op_ipu.py index 602289f3f1..e972146387 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_gelu_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_gelu_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -30,10 +30,6 @@ class TestBase(IPUOpTest): self.set_feed_attr() self.set_op_attrs() - @property - def fp16_enabled(self): - return True - def set_data_feed(self): data = np.random.uniform(size=[1, 3, 10, 10]) self.feed_fp32 = {'in_0': data.astype(np.float32)} @@ -46,59 +42,22 @@ class TestBase(IPUOpTest): def set_op_attrs(self): self.attrs = {"approximate": False} - def _test_base(self, exec_mode): - 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='float32') - - out = paddle.fluid.layers.gelu(x, **self.attrs) - - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + out = paddle.fluid.layers.gelu(x, **self.attrs) + self.fetch_list = [out.name] - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_fp32 - if exec_mode > ExecutionMode.IPU_FP32: - feed = self.feed_fp16 - - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return result[0] + def run_model(self, exec_mode): + self.run_op_test(exec_mode) def test(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - output_dict[mode] = self._test_base(mode).flatten() - - self.check(output_dict) + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() class TestCase1(TestBase): diff --git a/python/paddle/fluid/tests/unittests/ipu/test_gradient_clip_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_gradient_clip_ipu.py index 281baeca09..b7567f60cc 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_gradient_clip_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_gradient_clip_ipu.py @@ -28,19 +28,26 @@ class TestBase(IPUOpTest): self.set_data_feed() self.set_feed_attr() self.set_attrs() + self.set_training() + + @property + def fp16_enabled(self): + return False def set_atol(self): + super().set_atol() self.atol = 1e-6 + self.rtol = 1e-5 def set_data_feed(self): - self.feed = { + self.feed_fp32 = { "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()] + self.feed_shape = [x.shape for x in self.feed_fp32.values()] + self.feed_list = list(self.feed_fp32.keys()) + self.feed_dtype = [x.dtype for x in self.feed_fp32.values()] def set_attrs(self): self.attrs = { @@ -48,76 +55,48 @@ class TestBase(IPUOpTest): "weight_decay": 0.0, } - 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'] - # Only support ClipGradByGlobalNorm - clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0) - if self.attrs['optimizer'] == 'sgd': - opt = paddle.optimizer.SGD(learning_rate=1e-1, - weight_decay=weight_decay, - grad_clip=clip) - elif self.attrs['optimizer'] == 'adam': - opt = paddle.optimizer.Adam( - learning_rate=1e-1, - weight_decay=weight_decay, - grad_clip=clip) - elif self.attrs['optimizer'] == 'lamb': - opt = paddle.optimizer.Lamb( - learning_rate=1e-1, - lamb_weight_decay=weight_decay, - grad_clip=clip) - else: - raise ValueError( - f"Not supported optimizer {self.attrs['optimizer']} for test" - ) - 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) - program = paddle.static.IpuCompiledProgram( - main_prog, ipu_strategy=ipu_strategy).compile(feed_list, - fetch_list) - else: - program = main_prog - - result = [] - 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 set_training(self): + self.is_training = True + self.epoch = 100 + + @IPUOpTest.static_graph + def build_model(self): + image = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + conv1 = paddle.static.nn.conv2d( + image, num_filters=3, filter_size=3, bias_attr=False) + loss = paddle.mean(conv1) + self.fetch_list = [loss.name] + + weight_decay = self.attrs['weight_decay'] + # Only support ClipGradByGlobalNorm + clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0) + if self.attrs['optimizer'] == 'sgd': + opt = paddle.optimizer.SGD(learning_rate=1e-1, + weight_decay=weight_decay, + grad_clip=clip) + elif self.attrs['optimizer'] == 'adam': + opt = paddle.optimizer.Adam( + learning_rate=1e-1, weight_decay=weight_decay, grad_clip=clip) + elif self.attrs['optimizer'] == 'lamb': + opt = paddle.optimizer.Lamb( + learning_rate=1e-1, + lamb_weight_decay=weight_decay, + grad_clip=clip) + else: + raise ValueError( + f"Not supported optimizer {self.attrs['optimizer']} for test") + opt.minimize(loss) + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) 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(np.allclose(ipu_loss, cpu_loss, atol=self.atol)) + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() class TestAdam(TestBase): diff --git a/python/paddle/fluid/tests/unittests/ipu/test_greater_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_greater_op_ipu.py index 934ad10142..c499bb0bd5 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_greater_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_greater_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -28,73 +28,30 @@ class TestGreaterThan(IPUOpTest): self.set_training() self.set_test_op() - @property - def fp16_enabled(self): - return True - def set_test_op(self): self.op = paddle.fluid.layers.greater_than def set_op_attrs(self): self.attrs = {} - def _test_base(self, exec_mode): - 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='float32') - y = paddle.static.data( - name=self.feed_list[1], - shape=self.feed_shape[1], - dtype='float32') - - out = self.op(x, y, **self.attrs) - - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() - - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_fp32 - if exec_mode > ExecutionMode.IPU_FP32: - feed = self.feed_fp16 - - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return result[0] + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + y = paddle.static.data( + name=self.feed_list[1], shape=self.feed_shape[1], dtype='float32') + out = self.op(x, y, **self.attrs) + self.fetch_list = [out.name] - def run_test_base(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - output_dict[mode] = self._test_base(mode).flatten().astype(np.int32) + def run_model(self, exec_mode): + self.run_op_test(exec_mode) - self.check(output_dict) + def run_test_base(self): + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() def set_feed_attr(self): self.feed_shape = [x.shape for x in self.feed_fp32.values()] diff --git a/python/paddle/fluid/tests/unittests/ipu/test_groupnorm_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_groupnorm_op_ipu.py index 102e764cb2..bb984a8d90 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_groupnorm_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_groupnorm_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -30,10 +30,6 @@ class TestBase(IPUOpTest): self.set_feed_attr() self.set_op_attrs() - @property - def fp16_enabled(self): - return True - def set_atol(self): self.atol = 3e-6 self.rtol = 1e-6 @@ -56,86 +52,36 @@ class TestBase(IPUOpTest): "data_layout": 'NCHW', } - def _test_base(self, exec_mode): - 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='float32') - - if self.is_training: - ch = self.feed_shape[0][1] - conv1 = paddle.static.nn.conv2d( - x, num_filters=ch, filter_size=3, bias_attr=False) - scale = paddle.ParamAttr(trainable=True) - bias = paddle.ParamAttr(trainable=True) - out = paddle.fluid.layers.nn.group_norm( - conv1, param_attr=scale, bias_attr=bias, **self.attrs) - loss = paddle.mean(out) - adam = paddle.optimizer.Adam(learning_rate=1e-2) - adam.minimize(loss) - else: - out = paddle.fluid.layers.nn.group_norm( - x, param_attr=True, bias_attr=True, **self.attrs) - - if self.is_training: - fetch_list = [loss.name] - else: - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() - - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_fp32 - if exec_mode > ExecutionMode.IPU_FP32: - feed = self.feed_fp16 - - if self.is_training: - result = [] - for _ in range(self.epoch): - loss_res = exe.run(program, - feed=feed, - fetch_list=fetch_list) - result.append(loss_res[0]) - return np.array(result) - else: - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return result[0] - - def test_base(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - if mode > ExecutionMode.IPU_FP32 and self.is_training: - break - output_dict[mode] = self._test_base(mode).flatten() - - self.check(output_dict) + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + if self.is_training: + ch = self.feed_shape[0][1] + conv1 = paddle.static.nn.conv2d( + x, num_filters=ch, filter_size=3, bias_attr=False) + scale = paddle.ParamAttr(trainable=True) + bias = paddle.ParamAttr(trainable=True) + out = paddle.fluid.layers.nn.group_norm( + conv1, param_attr=scale, bias_attr=bias, **self.attrs) + loss = paddle.mean(out) + adam = paddle.optimizer.Adam(learning_rate=1e-2) + adam.minimize(loss) + self.fetch_list = [loss.name] + else: + out = paddle.fluid.layers.nn.group_norm( + x, param_attr=True, bias_attr=True, **self.attrs) + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) + + def test(self): + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() class TestCase1(TestBase): @@ -150,7 +96,7 @@ class TestCase1(TestBase): class TestTrainCase1(TestBase): def set_training(self): self.is_training = True - self.epoch = 10 + self.epoch = 20 @unittest.skipIf(IPUOpTest.use_ipumodel(), "skip for ipumodel") @@ -170,7 +116,7 @@ class TestTrainCase2(TestBase): def set_training(self): self.is_training = True - self.epoch = 10 + self.epoch = 20 if __name__ == "__main__": diff --git a/python/paddle/fluid/tests/unittests/ipu/test_instancenorm_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_instancenorm_op_ipu.py index ed8f3950ac..fa425cbf9f 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_instancenorm_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_instancenorm_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -30,10 +30,6 @@ class TestBase(IPUOpTest): self.set_feed_attr() self.set_op_attrs() - @property - def fp16_enabled(self): - return True - def set_atol(self): self.atol = 1e-6 self.rtol = 1e-5 @@ -52,86 +48,37 @@ class TestBase(IPUOpTest): def set_op_attrs(self): self.attrs = {"epsilon": 1e-05} - def _test_base(self, exec_mode): - 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='float32') - - if self.is_training: - ch = self.feed_shape[0][1] - conv1 = paddle.static.nn.conv2d( - x, num_filters=ch, filter_size=3, bias_attr=False) - scale = paddle.ParamAttr(trainable=True) - bias = paddle.ParamAttr(trainable=True) - out = paddle.fluid.layers.nn.instance_norm( - conv1, param_attr=scale, bias_attr=bias, **self.attrs) - loss = paddle.mean(out) - adam = paddle.optimizer.Adam(learning_rate=1e-2) - adam.minimize(loss) - else: - out = paddle.fluid.layers.nn.instance_norm( - x, param_attr=True, bias_attr=True, **self.attrs) - - if self.is_training: - fetch_list = [loss.name] - else: - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() - - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_fp32 - if exec_mode > ExecutionMode.IPU_FP32: - feed = self.feed_fp16 - - if self.is_training: - result = [] - for _ in range(self.epoch): - loss_res = exe.run(program, - feed=feed, - fetch_list=fetch_list) - result.append(loss_res) - return np.array(result) - else: - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return result[0] + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + + if self.is_training: + ch = self.feed_shape[0][1] + conv1 = paddle.static.nn.conv2d( + x, num_filters=ch, filter_size=3, bias_attr=False) + scale = paddle.ParamAttr(trainable=True) + bias = paddle.ParamAttr(trainable=True) + out = paddle.fluid.layers.nn.instance_norm( + conv1, param_attr=scale, bias_attr=bias, **self.attrs) + loss = paddle.mean(out) + adam = paddle.optimizer.Adam(learning_rate=1e-2) + adam.minimize(loss) + self.fetch_list = [loss.name] + else: + out = paddle.fluid.layers.nn.instance_norm( + x, param_attr=True, bias_attr=True, **self.attrs) + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) def test(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - if mode > ExecutionMode.IPU_FP32 and self.is_training: - break - output_dict[mode] = self._test_base(mode).flatten() - - self.check(output_dict) + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() class TestTrainCase1(TestBase): diff --git a/python/paddle/fluid/tests/unittests/ipu/test_ipu_shard_api.py b/python/paddle/fluid/tests/unittests/ipu/test_ipu_shard_api.py deleted file mode 100644 index a306a3f772..0000000000 --- a/python/paddle/fluid/tests/unittests/ipu/test_ipu_shard_api.py +++ /dev/null @@ -1,111 +0,0 @@ -# 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 - -paddle.enable_static() - - -@unittest.skipIf(not paddle.is_compiled_with_ipu(), - "core is not compiled with IPU") -class TestIpuShard(unittest.TestCase): - def _test(self): - # build graph - a = paddle.static.data(name='data', shape=[None, 1], dtype='int32') - b = a + 2 # scale : scale * x + bias, ipu_index : no - - with paddle.static.ipu_shard_guard(index=1): - c = b + 1 # scale, ipu_index : 1 - with paddle.static.ipu_shard_guard(index=2): - d = c * 2 # scale, ipu_index : 2 - with paddle.static.ipu_shard_guard(index=3): - e = d + 3 # scale, ipu_index : 3 - with paddle.static.ipu_shard_guard(index=1): - e = e + 3 # scale, ipu_index : 1 - with paddle.static.ipu_shard_guard(index=2): - e = e + 3 # scale, ipu_index : 2 - - with paddle.static.ipu_shard_guard(index=1): - f = paddle.tensor.pow(e, 2.0) # pow, ipu_index : 1 - - with paddle.static.ipu_shard_guard(index=2): - g = f - 1 # scale, ipu_index : 2 - - h = g + 1 # scale, ipu_index : no - - ipu_index_list = [] - main_prog = paddle.static.default_main_program() - for op in main_prog.global_block().ops: - if op.desc.has_attr("ipu_index"): - ipu_index_list.append(op.desc.attr("ipu_index")) - - return ipu_index_list - - def test_ipu_shard(self): - ipu_index_list = self._test() - expected_ipu_index_list = [1, 2, 3, 1, 2, 1, 2] - self.assertTrue( - np.allclose( - ipu_index_list, expected_ipu_index_list, atol=0)) - - -@unittest.skipIf(not paddle.is_compiled_with_ipu(), - "core is not compiled with IPU") -class TestIpuPipeline(unittest.TestCase): - def _test(self): - # build graph - a = paddle.static.data(name='data', shape=[None, 1], dtype='int32') - b = a + 2 # scale : scale * x + bias, ipu_stage : no - - with paddle.static.ipu_shard_guard(stage=1): - c = b + 1 # scale, ipu_stage : 1 - with paddle.static.ipu_shard_guard(stage=2): - d = c * 2 # scale, ipu_stage : 2 - with paddle.static.ipu_shard_guard(stage=3): - e = d + 3 # scale, ipu_stage : 3 - with paddle.static.ipu_shard_guard(stage=1): - e = e + 3 # scale, ipu_stage : 1 - with paddle.static.ipu_shard_guard(stage=2): - e = e + 3 # scale, ipu_stage : 2 - - with paddle.static.ipu_shard_guard(stage=1): - f = paddle.tensor.pow(e, 2.0) # pow, ipu_stage : 1 - - with paddle.static.ipu_shard_guard(stage=2): - g = f - 1 # scale, ipu_stage : 2 - - h = g + 1 # scale, ipu_stage : no - - ipu_index_list = [] - main_prog = paddle.static.default_main_program() - for op in main_prog.global_block().ops: - if op.desc.has_attr("ipu_stage"): - ipu_index_list.append(op.desc.attr("ipu_stage")) - - return ipu_index_list - - def test_ipu_shard(self): - ipu_index_list = self._test() - expected_ipu_index_list = [1, 2, 3, 1, 2, 1, 2] - - self.assertTrue( - np.allclose( - ipu_index_list, expected_ipu_index_list, atol=0)) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ipu/test_layernorm_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_layernorm_op_ipu.py index a52946bba1..cab2fa3fde 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_layernorm_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_layernorm_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -30,10 +30,6 @@ class TestBase(IPUOpTest): self.set_feed_attr() self.set_op_attrs() - @property - def fp16_enabled(self): - return True - def set_atol(self): self.atol = 1e-6 self.rtol = 1e-5 @@ -59,89 +55,48 @@ class TestBase(IPUOpTest): } self.optimizer = None - def _test_base(self, exec_mode): - 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='float32') - - if self.is_training: - ch = self.feed_shape[0][1] - conv1 = paddle.static.nn.conv2d( - x, num_filters=ch, filter_size=3, bias_attr=False) - scale = paddle.ParamAttr(trainable=True) - bias = paddle.ParamAttr(trainable=True) - out = paddle.fluid.layers.nn.layer_norm( - conv1, param_attr=scale, bias_attr=bias, **self.attrs) - else: - scale = self.attrs['scale'] - bias = self.attrs['shift'] - out = paddle.fluid.layers.nn.layer_norm( - x, param_attr=scale, bias_attr=bias, **self.attrs) - loss = paddle.mean(out) - - fetch_list = [loss.name] - - if self.is_training: - optimizer = None - if self.optimizer == 'sgd': - optimizer = paddle.optimizer.SGD(learning_rate=1e-2) - elif self.optimizer == 'adam': - optimizer = paddle.optimizer.Adam(learning_rate=1e-2) - elif self.optimizer == 'lamb': - optimizer = paddle.optimizer.Lamb( - learning_rate=1e-2, lamb_weight_decay=0.0) - if optimizer is not None: - optimizer.minimize(loss) - - if exec_mode: - place = paddle.IPUPlace() - else: - place = paddle.CPUPlace() - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - program = paddle.static.IpuCompiledProgram( - main_prog, - ipu_strategy=ipu_strategy).compile(feed_list, fetch_list) - else: - program = main_prog - - if self.is_training: - result = [] - for _ in range(self.epoch): - loss_res = exe.run(program, - feed=self.feed_fp32, - fetch_list=fetch_list) - result.append(loss_res[0]) - return np.array(result) - else: - result = exe.run(program, - feed=self.feed_fp32, - fetch_list=fetch_list) - return result[0] - - def test_base(self): - res0 = self._test_base(False) - res1 = self._test_base(True) - - self.assertTrue( - np.allclose( - res0.flatten(), res1.flatten(), atol=self.atol)) - - self.assertTrue(res0.shape == res1.shape) + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + if self.is_training: + ch = self.feed_shape[0][1] + conv1 = paddle.static.nn.conv2d( + x, num_filters=ch, filter_size=3, bias_attr=False) + scale = paddle.ParamAttr(trainable=True) + bias = paddle.ParamAttr(trainable=True) + out = paddle.fluid.layers.nn.layer_norm( + conv1, param_attr=scale, bias_attr=bias, **self.attrs) + loss = paddle.mean(out) + self.fetch_list = [loss.name] + else: + scale = self.attrs['scale'] + bias = self.attrs['shift'] + out = paddle.fluid.layers.nn.layer_norm( + x, param_attr=scale, bias_attr=bias, **self.attrs) + self.fetch_list = [out.name] + + if self.is_training: + optimizer = None + if self.optimizer == 'sgd': + optimizer = paddle.optimizer.SGD(learning_rate=1e-2) + elif self.optimizer == 'adam': + optimizer = paddle.optimizer.Adam(learning_rate=1e-2) + elif self.optimizer == 'lamb': + optimizer = paddle.optimizer.Lamb( + learning_rate=1e-2, lamb_weight_decay=0.0) + if optimizer is not None: + optimizer.minimize(loss) + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) + + def test(self): + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() @unittest.skip('raise error') @@ -188,33 +143,17 @@ class TestTrainCase1(TestBase): self.optimizer = 'sgd' def set_atol(self): + super().set_atol() self.atol = 1e-6 def set_training(self): self.is_training = True - self.epoch = 10 - - -class TestTrainCase2(TestBase): - def set_atol(self): - self.atol = 5e-4 - - def set_op_attrs(self): - self.attrs = { - "scale": True, - "shift": True, - "begin_norm_axis": 2, - "epsilon": 1e-05 - } - self.optimizer = 'adam' - - def set_training(self): - self.is_training = True - self.epoch = 10 + self.epoch = 20 class TestTrainCase3(TestBase): def set_atol(self): + super().set_atol() self.atol = 5e-3 def set_op_attrs(self): @@ -228,7 +167,7 @@ class TestTrainCase3(TestBase): def set_training(self): self.is_training = True - self.epoch = 10 + self.epoch = 20 # not support `layer_norm(x, param_attr=False, bias_attr=False, **self.attrs)` diff --git a/python/paddle/fluid/tests/unittests/ipu/test_log_softmax_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_log_softmax_op_ipu.py index fad7516e44..c0e4865b3a 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_log_softmax_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_log_softmax_op_ipu.py @@ -18,7 +18,7 @@ import numpy as np import paddle import paddle.nn.functional as F import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -31,10 +31,6 @@ class TestBase(IPUOpTest): self.set_feed_attr() self.set_op_attrs() - @property - def fp16_enabled(self): - return True - def set_data_feed(self): data = np.random.uniform(size=[1, 3, 10, 10]) self.feed_fp32 = {'in_0': data.astype(np.float32)} @@ -49,59 +45,22 @@ class TestBase(IPUOpTest): def set_op_attrs(self): self.attrs = {"axis": -1} - def _test_base(self, exec_mode): - 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='float32') - - out = F.log_softmax(x, **self.attrs) - - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + out = F.log_softmax(x, **self.attrs) + self.fetch_list = [out.name] - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_fp32 - if exec_mode > ExecutionMode.IPU_FP32: - feed = self.feed_fp16 - - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return result[0] + def run_model(self, exec_mode): + self.run_op_test(exec_mode) def test(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - output_dict[mode] = self._test_base(mode).flatten() - - self.check(output_dict) + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() class TestCase1(TestBase): diff --git a/python/paddle/fluid/tests/unittests/ipu/test_logical_not_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_logical_not_op_ipu.py index 3f8472890d..725d2b3429 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_logical_not_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_logical_not_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -29,68 +29,32 @@ class TestBase(IPUOpTest): self.set_data_feed() self.set_feed_attr() - @property - def fp16_enabled(self): - return True - def set_data_feed(self): data = np.random.uniform(size=[2, 20, 30528]) - self.feed = {"in_0": data.astype('bool')} + self.feed_fp32 = {"in_0": data.astype('bool')} + self.feed_fp16 = {"in_0": data.astype('bool')} 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 _test_base(self, exec_mode): - 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="bool") - - out = paddle.fluid.layers.logical_not(x) - - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() - - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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] - - def test_base(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - output_dict[mode] = self._test_base(mode).astype(np.int32) - - self.check(output_dict, check_shape=True) + self.feed_shape = [x.shape for x in self.feed_fp32.values()] + self.feed_list = list(self.feed_fp32.keys()) + self.feed_dtype = [x.dtype for x in self.feed_fp32.values()] + + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype="bool") + out = paddle.fluid.layers.logical_not(x) + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) + + def test(self): + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() if __name__ == "__main__": diff --git a/python/paddle/fluid/tests/unittests/ipu/test_logical_x_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_logical_x_op_ipu.py index 05572a72ea..55a2c08c1b 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_logical_x_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_logical_x_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -38,69 +38,38 @@ class TestLogicalAnd(IPUOpTest): def set_op_attrs(self): self.attrs = {} - def _test_base(self, exec_mode): - 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]) - y = paddle.static.data( - name=self.feed_list[1], - shape=self.feed_shape[1], - dtype=self.feed_dtype[1]) - - out = self.op(x, y, **self.attrs) - - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() - - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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] + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], + shape=self.feed_shape[0], + dtype=self.feed_dtype[0]) + y = paddle.static.data( + name=self.feed_list[1], + shape=self.feed_shape[1], + dtype=self.feed_dtype[1]) + out = self.op(x, y, **self.attrs) + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) def run_test_base(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - output_dict[mode] = self._test_base(mode).astype(np.int32) - - self.check(output_dict, check_shape=True) + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() 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_shape = [x.shape for x in self.feed_fp32.values()] + self.feed_list = list(self.feed_fp32.keys()) self.feed_dtype = ['bool', 'bool'] def set_data_feed0(self): x = np.random.choice([True, False], size=(1, 3, 5, 5)) y = np.random.choice([True, False], size=(1, 3, 5, 5)) - self.feed = { + self.feed_fp32 = { "x": x.astype('bool'), "y": y.astype('bool'), } diff --git a/python/paddle/fluid/tests/unittests/ipu/test_lookuptable_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_lookuptable_op_ipu.py index 4a877ddce4..80636348cf 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_lookuptable_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_lookuptable_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -30,19 +30,15 @@ class TestBase(IPUOpTest): self.set_feed_attr() self.set_op_attrs() - @property - def fp16_enabled(self): - return True - def set_data_feed(self): data = np.array([[[1], [3]], [[2], [4]], [[4], [127]]]) - self.feed_cpu = {"x": data.astype(np.int64)} - self.feed_ipu = {"x": data.astype(np.int32)} + self.feed_fp32 = {"x": data.astype(np.int64)} + self.feed_fp16 = {"x": data.astype(np.int32)} def set_feed_attr(self): - self.feed_shape = [x.shape for x in self.feed_cpu.values()] - self.feed_list = list(self.feed_cpu.keys()) - self.feed_dtype = [x.dtype for x in self.feed_cpu.values()] + self.feed_shape = [x.shape for x in self.feed_fp32.values()] + self.feed_list = list(self.feed_fp32.keys()) + self.feed_dtype = [x.dtype for x in self.feed_fp32.values()] def set_op_attrs(self): self.attrs = { @@ -53,76 +49,30 @@ class TestBase(IPUOpTest): "dtype": 'float32' } - def _test_base(self, exec_mode): - 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='int64') - - out = paddle.fluid.layers.embedding(x, **self.attrs) - - if self.is_training: - loss = paddle.mean(out) - adam = paddle.optimizer.Adam(learning_rate=1e-2) - adam.minimize(loss) - fetch_list = [loss.name] - else: - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() - - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_cpu - if exec_mode > ExecutionMode.CPU_FP32: - feed = self.feed_ipu - - if self.is_training: - result = [] - for _ in range(self.epoch): - loss_res = exe.run(program, - feed=feed, - fetch_list=fetch_list) - result.append(loss_res[0]) - return np.array(result) - else: - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return result[0] + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='int64') + out = paddle.fluid.layers.embedding(x, **self.attrs) + if self.is_training: + loss = paddle.mean(out) + adam = paddle.optimizer.Adam(learning_rate=1e-2) + adam.minimize(loss) + self.fetch_list = [loss.name] + else: + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + if self.is_ipu_mode(exec_mode): + self.feed_fp32['x'] = self.feed_fp32['x'].astype(np.int32) + self.run_op_test(exec_mode) def test(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and (not self.fp16_enabled or - self.is_training): - break - - output_dict[mode] = self._test_base(mode).flatten() - - self.check(output_dict) + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() class TestTrainCase1(TestBase): diff --git a/python/paddle/fluid/tests/unittests/ipu/test_lookuptable_v2_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_lookuptable_v2_op_ipu.py index da8048fb32..7f021a615a 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_lookuptable_v2_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_lookuptable_v2_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -30,19 +30,15 @@ class TestBase(IPUOpTest): self.set_feed_attr() self.set_op_attrs() - @property - def fp16_enabled(self): - return True - def set_data_feed(self): x = np.array([[[1], [3]], [[2], [4]], [[4], [127]]]) - self.feed_cpu = {"x": x.astype(np.int64)} - self.feed_ipu = {"x": x.astype(np.int32)} + self.feed_fp32 = {"x": x.astype(np.int64)} + self.feed_fp16 = {"x": x.astype(np.int32)} def set_feed_attr(self): - self.feed_shape = [x.shape for x in self.feed_cpu.values()] - self.feed_list = list(self.feed_cpu.keys()) - self.feed_dtype = [x.dtype for x in self.feed_cpu.values()] + self.feed_shape = [x.shape for x in self.feed_fp32.values()] + self.feed_list = list(self.feed_fp32.keys()) + self.feed_dtype = [x.dtype for x in self.feed_fp32.values()] def set_op_attrs(self): self.attrs = { @@ -53,76 +49,31 @@ class TestBase(IPUOpTest): "weight_attr": None } - def _test_base(self, exec_mode): - 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='int64') - - embedding = paddle.nn.Embedding(**self.attrs) - out = embedding(x) - - if self.is_training: - loss = paddle.mean(out) - adam = paddle.optimizer.Adam(learning_rate=1e-2) - adam.minimize(loss) - fetch_list = [loss.name] - else: - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() - - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_cpu - if exec_mode > ExecutionMode.CPU_FP32: - feed = self.feed_ipu - - if self.is_training: - result = [] - for _ in range(self.epoch): - loss_res = exe.run(program, - feed=feed, - fetch_list=fetch_list) - result.append(loss_res[0]) - return np.array(result) - else: - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return result[0] + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='int64') + embedding = paddle.nn.Embedding(**self.attrs) + out = embedding(x) + if self.is_training: + loss = paddle.mean(out) + adam = paddle.optimizer.Adam(learning_rate=1e-2) + adam.minimize(loss) + self.fetch_list = [loss.name] + else: + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + if self.is_ipu_mode(exec_mode): + self.feed_fp32['x'] = self.feed_fp32['x'].astype(np.int32) + self.run_op_test(exec_mode) def test(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and (not self.fp16_enabled or - self.is_training): - break - output_dict[mode] = self._test_base(mode).flatten() - - self.check(output_dict) + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() class TestTrainCase1(TestBase): diff --git a/python/paddle/fluid/tests/unittests/ipu/test_lr_sheduler_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_lr_sheduler_ipu.py index 58f018e2ae..6641efde69 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_lr_sheduler_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_lr_sheduler_ipu.py @@ -12,89 +12,75 @@ # See the License for the specific language governing permissions and # limitations under the License. -from __future__ import print_function - import numpy as np import unittest -import sys import paddle -import paddle.fluid as fluid import paddle.static from paddle.optimizer.lr import LRScheduler - -paddle.enable_static() -SEED = 2021 +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest class LR_New(LRScheduler): - def __init__(self, learning_rate=1.0, last_epoch=-1, verbose=False): + def __init__(self, learning_rate=1e-5, last_epoch=-1, verbose=False): super(LR_New, self).__init__(learning_rate, last_epoch, verbose) def get_lr(self): - self.base_lr = self.base_lr + 1 + self.base_lr = self.base_lr + 1e-4 self.last_epoch = self.last_epoch + 1 return self.base_lr @unittest.skipIf(not paddle.is_compiled_with_ipu(), "core is not compiled with IPU") -class TestConvNet(unittest.TestCase): - def _test(self, run_ipu=True): - scope = fluid.core.Scope() - main_prog = paddle.static.Program() - startup_prog = paddle.static.Program() - main_prog.random_seed = SEED - startup_prog.random_seed = SEED - np.random.seed(SEED) - - np_image = np.random.rand(1, 3, 10, 10).astype(np.float32) - - with fluid.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) - - sgd = paddle.optimizer.SGD(learning_rate=LR_New()) - sgd.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) - program = paddle.static.IpuCompiledProgram( - main_prog, ipu_strategy=ipu_strategy).compile(feed_list, - fetch_list) - else: - program = main_prog - - result = [] - for epoch in range(100): - if hasattr(program, "lr_sheduler"): - program.lr_sheduler.step() - loss_res = exe.run(program, - feed={image.name: np_image}, - fetch_list=[loss]) - result.append(loss_res) - - return np.array(result) +class TestConvNet(IPUOpTest): + @IPUOpTest.static_graph + def build_model(self): + 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) + + opt = paddle.optimizer.Lamb(learning_rate=LR_New()) + opt.minimize(loss) + self.feed_list = [image.name] + self.fetch_list = [loss.name] + + def run_model(self, run_ipu=True): + self.build_model() + if run_ipu: + place = paddle.IPUPlace() + else: + place = paddle.CPUPlace() + exe = paddle.static.Executor(place) + exe.run(self.startup_prog) + if run_ipu: + ipu_strategy = paddle.static.IpuStrategy() + ipu_strategy.set_graph_config(is_training=True) + program = paddle.static.IpuCompiledProgram( + self.main_prog, ipu_strategy=ipu_strategy).compile( + self.feed_list, self.fetch_list) + else: + program = self.main_prog + + result = [] + for _ in range(100): + if hasattr(program, "lr_sheduler"): + program.lr_sheduler.step() + loss_res = exe.run(program, + feed=self.feed, + fetch_list=self.fetch_list) + result.append(loss_res) + return np.array(result) def test_training(self): + data = np.random.rand(1, 3, 10, 10).astype(np.float32) + self.feed = {'image': data} # cpu and ipu dimenstion mismatch, cpu:(100, 1, 1), ipu:(100, 1) - ipu_loss = self._test(True).flatten() - cpu_loss = self._test(False).flatten() + ipu_loss = self.run_model(True).flatten() + cpu_loss = self.run_model(False).flatten() - self.assertTrue(np.allclose(ipu_loss, cpu_loss, atol=1e-4)) + self.assertTrue(np.allclose(ipu_loss, cpu_loss, atol=1e-10)) if __name__ == "__main__": diff --git a/python/paddle/fluid/tests/unittests/ipu/test_matmul_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_matmul_op_ipu.py index 6929ded6eb..e7e4c000e1 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_matmul_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_matmul_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -30,10 +30,6 @@ class TestBase(IPUOpTest): self.set_feed_attr() self.set_op_attrs() - @property - def fp16_enabled(self): - return True - def set_data_feed(self): x = np.random.uniform(size=[20, 30]) y = np.random.uniform(size=[30, 20]) @@ -52,63 +48,25 @@ class TestBase(IPUOpTest): "alpha": 1.0, } - def _test_base(self, exec_mode): - 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='float32') - y = paddle.static.data( - name=self.feed_list[1], - shape=self.feed_shape[1], - dtype='float32') - - out = paddle.fluid.layers.matmul(x, y, **self.attrs) - - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() - - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_fp32 - if exec_mode > ExecutionMode.IPU_FP32: - feed = self.feed_fp16 - - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return result[0] - - def test_base(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - output_dict[mode] = self._test_base(mode).flatten() - - self.check(output_dict) + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + y = paddle.static.data( + name=self.feed_list[1], shape=self.feed_shape[1], dtype='float32') + + out = paddle.fluid.layers.matmul(x, y, **self.attrs) + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) + + def test(self): + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() class TestCase1(TestBase): diff --git a/python/paddle/fluid/tests/unittests/ipu/test_matmul_serilize_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_matmul_serilize_ipu.py index ddb0640054..0a273e91dd 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_matmul_serilize_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_matmul_serilize_ipu.py @@ -26,7 +26,7 @@ def set_serialize_factor(serialize_factor): op._set_attr('serialize_factor', serialize_factor) -@unittest.skipIf(not paddle.is_compiled_with_ipu() or IPUOpTest.use_ipumodel(), +@unittest.skipIf(not paddle.is_compiled_with_ipu(), "core is not compiled with IPU") class TestBase(IPUOpTest): def setUp(self): @@ -38,8 +38,8 @@ class TestBase(IPUOpTest): def set_data_feed(self): self.feed = { - "x": np.random.uniform(size=[2048, 3072]).astype('float32'), - "y": np.random.uniform(size=[3072, 2048]).astype('float32'), + "x": np.random.uniform(size=[16, 32]).astype('float32'), + "y": np.random.uniform(size=[32, 16]).astype('float32'), } def set_feed_attr(self): @@ -50,58 +50,47 @@ class TestBase(IPUOpTest): def set_op_attrs(self): self.attrs = {"transpose_x": False, "transpose_y": False} - 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]) - y = paddle.static.data( - name=self.feed_list[1], - shape=self.feed_shape[1], - dtype=self.feed_dtype[1]) - - # decrator maybe the best choice, but need to modify api - out = paddle.matmul(x, y, **self.attrs) - set_serialize_factor(4) - - 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) - 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] + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], + shape=self.feed_shape[0], + dtype=self.feed_dtype[0]) + y = paddle.static.data( + name=self.feed_list[1], + shape=self.feed_shape[1], + dtype=self.feed_dtype[1]) + # decrator maybe the best choice, but need to modify api + out = paddle.matmul(x, y, **self.attrs) + set_serialize_factor(4) + self.fetch_list = [out.name] + + def run_model(self, run_ipu): + self.build_model() + if run_ipu: + place = paddle.IPUPlace() + else: + place = paddle.CPUPlace() + exe = paddle.static.Executor(place) + exe.run(self.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) + program = paddle.static.IpuCompiledProgram( + self.main_prog, + ipu_strategy=ipu_strategy).compile(feed_list, self.fetch_list) + else: + program = self.main_prog + result = exe.run(program, feed=self.feed, fetch_list=self.fetch_list) + return result[0] def test_base(self): - res0 = self._test_base(False) - res1 = self._test_base(True) - + res0 = self.run_model(False) + res1 = self.run_model(True) self.assertTrue( np.allclose( res0.flatten(), res1.flatten(), atol=self.atol)) - self.assertTrue(res0.shape == res1.shape) diff --git a/python/paddle/fluid/tests/unittests/ipu/test_matmul_v2_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_matmul_v2_op_ipu.py index 9f1c115403..725f3243e0 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_matmul_v2_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_matmul_v2_op_ipu.py @@ -17,7 +17,7 @@ import unittest import numpy as np import paddle import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -30,10 +30,6 @@ class TestBase(IPUOpTest): self.set_feed_attr() self.set_op_attrs() - @property - def fp16_enabled(self): - return True - def set_data_feed(self): x = np.random.uniform(size=[2, 3]) y = np.random.uniform(size=[3, 2]) @@ -48,63 +44,24 @@ class TestBase(IPUOpTest): def set_op_attrs(self): self.attrs = {"transpose_x": False, "transpose_y": False} - def _test_base(self, exec_mode): - 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='float32') - y = paddle.static.data( - name=self.feed_list[1], - shape=self.feed_shape[1], - dtype='float32') - - out = paddle.matmul(x, y, **self.attrs) - - fetch_list = [out.name] - - if exec_mode == ExecutionMode.CPU_FP32: - place = paddle.CPUPlace() - else: - place = paddle.IPUPlace() - - exe = paddle.static.Executor(place) - exe.run(startup_prog) - - if exec_mode != ExecutionMode.CPU_FP32: - feed_list = self.feed_list - ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.set_graph_config(is_training=self.is_training) - if exec_mode == ExecutionMode.IPU_POPART_FP16: - 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 - - feed = self.feed_fp32 - if exec_mode > ExecutionMode.IPU_FP32: - feed = self.feed_fp16 - - result = exe.run(program, feed=feed, fetch_list=fetch_list) - return result[0] - - def test_base(self): - output_dict = {} - for mode in ExecutionMode: - if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: - break - output_dict[mode] = self._test_base(mode).flatten() - - self.check(output_dict) + @IPUOpTest.static_graph + def build_model(self): + x = paddle.static.data( + name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') + y = paddle.static.data( + name=self.feed_list[1], shape=self.feed_shape[1], dtype='float32') + out = paddle.matmul(x, y, **self.attrs) + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) + + def test(self): + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model() + self.run_model(m) + self.check() class TestCase1(TestBase): -- GitLab