From 86effa0ce1309ea27f29af6a28dd5bb3d4aa1ac5 Mon Sep 17 00:00:00 2001 From: Allen Guo Date: Wed, 9 Mar 2022 11:23:02 +0800 Subject: [PATCH] [IPU] update ipu unittests p3 (#40072) * update ipu UTs part3 * rename uts * sync api changes * update uts for new api * update use_ipumodel() * split pr --- .../unittests/ipu/test_matmul_v2_op_ipu.py | 186 ++++++++++++++++++ .../tests/unittests/ipu/test_mean_op_ipu.py | 109 ++++------ ...pipeline.py => test_model_pipeline_ipu.py} | 16 +- .../tests/unittests/ipu/test_mul_op_ipu.py | 112 +++++------ .../unittests/ipu/test_pool_avg_op_ipu.py | 84 ++++---- .../unittests/ipu/test_pool_max_op_ipu.py | 128 ++++++------ .../tests/unittests/ipu/test_pow_op_ipu.py | 140 +++++++------ .../tests/unittests/ipu/test_print_op_ipu.py | 143 ++++++++++++++ .../unittests/ipu/test_reduce_x_op_ipu.py | 124 ++++++------ .../ipu/test_reshape_inplace_op_ipu.py | 84 ++++---- .../unittests/ipu/test_reshape_op_ipu.py | 83 ++++---- ...est_save_load.py => test_save_load_ipu.py} | 105 +++++++--- .../tests/unittests/ipu/test_scale_op_ipu.py | 152 +++++++------- 13 files changed, 934 insertions(+), 532 deletions(-) create mode 100644 python/paddle/fluid/tests/unittests/ipu/test_matmul_v2_op_ipu.py rename python/paddle/fluid/tests/unittests/ipu/{test_ipu_model_pipeline.py => test_model_pipeline_ipu.py} (86%) create mode 100644 python/paddle/fluid/tests/unittests/ipu/test_print_op_ipu.py rename python/paddle/fluid/tests/unittests/ipu/{test_save_load.py => test_save_load_ipu.py} (58%) 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 new file mode 100644 index 0000000000..9f1c115403 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ipu/test_matmul_v2_op_ipu.py @@ -0,0 +1,186 @@ +# 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, ExecutionMode + + +@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_training() + self.set_data_feed() + 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]) + + self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)} + self.feed_fp16 = {"x": x.astype(np.float16), "y": y.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 = {"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) + + +class TestCase1(TestBase): + def set_op_attrs(self): + self.attrs = { + "transpose_x": True, + "transpose_y": True, + } + + +class TestCase3(TestBase): + def set_data_feed(self): + x = np.random.uniform(size=[5, 4, 2, 3]) + y = np.random.uniform(size=[5, 4, 3, 2]) + + self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)} + self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)} + + +class TestCase4(TestBase): + def set_data_feed(self): + x = np.random.uniform(size=[4, 2, 3]) + y = np.random.uniform(size=[4, 3, 2]) + + self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)} + self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)} + + +class TestCase5(TestBase): + def set_data_feed(self): + x = np.random.uniform(size=[4, 2, 3]) + y = np.random.uniform(size=[3, 2]) + + self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)} + self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)} + + +class TestCase6(TestBase): + def set_data_feed(self): + x = np.random.uniform(size=[3]) + y = np.random.uniform(size=[3]) + + self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)} + self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)} + + +@unittest.skip("not supported") +class TestCase6_2(TestCase6): + def set_data_feed(self): + x = np.random.uniform(size=[3]) + y = np.random.uniform(size=[3]) + + self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)} + self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)} + + def set_op_attrs(self): + self.attrs = {"transpose_x": True, "transpose_y": True} + + +class TestCase7(TestBase): + def set_data_feed(self): + x = np.random.uniform(size=[3, 1]) + y = np.random.uniform(size=[1, 2]) + + self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)} + self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)} + + +@unittest.skip("dim > 4 is not supported") +class TestCase8(TestBase): + def set_data_feed(self): + self.feed = { + "x": np.random.uniform(size=[6, 5, 4, 2, 3]).astype('float32'), + "y": np.random.uniform(size=[6, 5, 4, 3, 2]).astype('float32'), + } + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ipu/test_mean_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_mean_op_ipu.py index f04d712755..b9dd7358b7 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_mean_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_mean_op_ipu.py @@ -16,13 +16,8 @@ import unittest import numpy as np import paddle -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.optimizer import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest - -paddle.enable_static() +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -31,97 +26,79 @@ class TestBase(IPUOpTest): def setUp(self): self.set_atol() self.set_training() - self.set_feed() - self.set_attrs() + self.set_data_feed() + self.set_feed_attr() + self.set_op_attrs() - def set_feed(self): - self.feed_shape = [] - self.feed_shape.append([1, 3, 10, 10]) + @property + def fp16_enabled(self): + return True - self.feed = {} - self.feed["in_0"] = np.random.uniform( - size=self.feed_shape[0]).astype(np.float32) + 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)} - self.feed_list = list(self.feed.keys()) + 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_attrs(self): + def set_op_attrs(self): self.attrs = {} - self.attrs['axis'] = None - self.attrs['keepdim'] = False - def _test_base(self, run_ipu=True): - scope = fluid.core.Scope() + def _test_base(self, exec_mode): + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() - SEED = self.SEED - main_prog.random_seed = SEED - startup_prog.random_seed = SEED + main_prog.random_seed = self.SEED + startup_prog.random_seed = self.SEED - with fluid.scope_guard(scope): + 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.mean(x, **self.attrs) - fetch_list = [out.name] + out = paddle.fluid.layers.mean(x) - if run_ipu: - place = paddle.IPUPlace() - 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 run_ipu: + if exec_mode != ExecutionMode.CPU_FP32: feed_list = self.feed_list ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig(is_training=self.is_training) - program = compiler.IPUCompiledProgram( + 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) + 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): - res0 = self._test_base(True) - res1 = self._test_base(False) - - self.assertTrue( - np.allclose( - res0.flatten(), res1.flatten(), atol=self.atol)) - + 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() -class TestCase1(TestBase): - def set_attrs(self): - self.attrs = {} - self.attrs['axis'] = 1 - self.attrs['keepdim'] = False - - -class TestCase2(TestBase): - def set_attrs(self): - self.attrs = {} - self.attrs['axis'] = 2 - self.attrs['keepdim'] = False - - -class TestCase3(TestBase): - def set_attrs(self): - self.attrs = {} - self.attrs['axis'] = 2 - self.attrs['keepdim'] = True - - -class TestCase4(TestBase): - def set_attrs(self): - self.attrs = {} - self.attrs['axis'] = None - self.attrs['keepdim'] = True + self.check(output_dict) if __name__ == "__main__": diff --git a/python/paddle/fluid/tests/unittests/ipu/test_ipu_model_pipeline.py b/python/paddle/fluid/tests/unittests/ipu/test_model_pipeline_ipu.py similarity index 86% rename from python/paddle/fluid/tests/unittests/ipu/test_ipu_model_pipeline.py rename to python/paddle/fluid/tests/unittests/ipu/test_model_pipeline_ipu.py index e1ed7603ed..7e70239964 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_ipu_model_pipeline.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_model_pipeline_ipu.py @@ -17,8 +17,7 @@ from __future__ import print_function import numpy as np import unittest import paddle -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler +import paddle.static paddle.enable_static() SEED = 2021 @@ -28,7 +27,7 @@ SEED = 2021 "core is not compiled with IPU") class TestCastNet(unittest.TestCase): def _test(self, run_ipu=True): - scope = fluid.core.Scope() + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() main_prog.random_seed = SEED @@ -37,14 +36,14 @@ class TestCastNet(unittest.TestCase): np_image = np.random.rand(1, 3, 10, 10).astype(np.float32) - with fluid.scope_guard(scope): + 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') - with fluid.ipu_shard(ipu_index=0): + with paddle.static.ipu_shard_guard(index=0): conv1 = paddle.static.nn.conv2d( image, num_filters=3, filter_size=3, bias_attr=False) - with fluid.ipu_shard(ipu_index=1): + with paddle.static.ipu_shard_guard(index=1): conv2 = paddle.static.nn.conv2d( conv1, num_filters=3, filter_size=3, bias_attr=False) loss = paddle.mean(conv2) @@ -60,9 +59,10 @@ class TestCastNet(unittest.TestCase): feed_list = [image.name] fetch_list = [loss.name] ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig( + ipu_strategy.set_graph_config( num_ipus=2, is_training=False, enable_manual_shard=True) - program = compiler.IPUCompiledProgram( + ipu_strategy.set_pipelining_config(enable_pipelining=False) + program = paddle.static.IpuCompiledProgram( main_prog, ipu_strategy=ipu_strategy).compile(feed_list, fetch_list) else: diff --git a/python/paddle/fluid/tests/unittests/ipu/test_mul_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_mul_op_ipu.py index 78a2589d9a..7a9135626d 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_mul_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_mul_op_ipu.py @@ -16,14 +16,8 @@ import unittest import numpy as np import paddle -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.optimizer import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest, - np_dtype_to_fluid_str) - -paddle.enable_static() +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -32,90 +26,98 @@ class TestBase(IPUOpTest): def setUp(self): self.set_atol() self.set_training() - self.set_feed() + self.set_data_feed() self.set_feed_attr() - self.set_attrs() + self.set_op_attrs() - def set_feed(self): - self.feed = { - "x": np.random.uniform(size=[2, 5]).astype('float32'), - "y": np.random.uniform(size=[5, 3]).astype('float32'), - } + @property + def fp16_enabled(self): + return True + + def set_data_feed(self): + x = np.random.uniform(size=[2, 5]) + y = np.random.uniform(size=[5, 3]) + self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)} + self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)} 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 = [ - np_dtype_to_fluid_str(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): + def set_op_attrs(self): self.attrs = { "x_num_col_dims": 1, "y_num_col_dims": 1, } - def _test_base(self, run_ipu=True): - scope = fluid.core.Scope() + def _test_base(self, exec_mode): + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() - SEED = self.SEED - main_prog.random_seed = SEED - startup_prog.random_seed = SEED + main_prog.random_seed = self.SEED + startup_prog.random_seed = self.SEED - with fluid.scope_guard(scope): + 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]) + dtype='float32') y = paddle.static.data( name=self.feed_list[1], shape=self.feed_shape[1], - dtype=self.feed_dtype[1]) + dtype='float32') + out = paddle.fluid.layers.mul(x, y, **self.attrs) - fetch_list = [out.name] + fetch_list = [out.name] - if run_ipu: - place = paddle.IPUPlace() - else: + if exec_mode == ExecutionMode.CPU_FP32: place = paddle.CPUPlace() + else: + place = paddle.IPUPlace() + exe = paddle.static.Executor(place) exe.run(startup_prog) - if run_ipu: + if exec_mode != ExecutionMode.CPU_FP32: feed_list = self.feed_list ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig(is_training=self.is_training) - program = compiler.IPUCompiledProgram( + 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) + 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): - res0 = self._test_base(False) - res1 = self._test_base(True) + 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.assertTrue( - np.allclose( - res0.flatten(), res1.flatten(), atol=self.atol)) - - self.assertTrue(res0.shape == res1.shape) + self.check(output_dict) class TestCase1(TestBase): - def set_feed(self): - self.feed = { - "x": np.random.uniform(size=[1, 2, 5]).astype('float32'), - "y": np.random.uniform(size=[5, 3]).astype('float32'), - } + def set_data_feed(self): + x = np.random.uniform(size=[1, 2, 5]) + y = np.random.uniform(size=[5, 3]) + self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)} + self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)} - def set_attrs(self): + def set_op_attrs(self): self.attrs = { "x_num_col_dims": 2, "y_num_col_dims": 1, @@ -123,13 +125,13 @@ class TestCase1(TestBase): class TestCase2(TestBase): - def set_feed(self): - self.feed = { - "x": np.random.uniform(size=[3, 4, 2, 9]).astype('float32'), - "y": np.random.uniform(size=[3, 6, 1, 2, 3]).astype('float32'), - } + def set_data_feed(self): + x = np.random.uniform(size=[3, 4, 2, 9]) + y = np.random.uniform(size=[3, 6, 1, 2, 3]) + self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)} + self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)} - def set_attrs(self): + def set_op_attrs(self): self.attrs = { 'x_num_col_dims': 2, 'y_num_col_dims': 2, diff --git a/python/paddle/fluid/tests/unittests/ipu/test_pool_avg_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_pool_avg_op_ipu.py index e81591ad68..4288b82832 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_pool_avg_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_pool_avg_op_ipu.py @@ -16,14 +16,8 @@ import unittest import numpy as np import paddle -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.optimizer import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest, - np_dtype_to_fluid_str) - -paddle.enable_static() +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -32,23 +26,25 @@ class TestBase(IPUOpTest): def setUp(self): self.set_atol() self.set_training() - self.set_feed() + self.set_data_feed() self.set_feed_attr() - self.set_attrs() + self.set_op_attrs() - def set_feed(self): - self.feed = { - "x": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'), - } + @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)} + self.feed_fp16 = {'in_0': data.astype(np.float16)} 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 = [ - np_dtype_to_fluid_str(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): + def set_op_attrs(self): self.attrs = { "pool_size": 3, "pool_type": 'avg', @@ -60,53 +56,59 @@ class TestBase(IPUOpTest): "data_format": 'NCHW', } - def _test_base(self, run_ipu=True): - scope = fluid.core.Scope() + def _test_base(self, exec_mode): + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() - SEED = self.SEED - main_prog.random_seed = SEED - startup_prog.random_seed = SEED + main_prog.random_seed = self.SEED + startup_prog.random_seed = self.SEED - with fluid.scope_guard(scope): + 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]) + dtype='float32') + out = paddle.fluid.layers.pool2d(x, **self.attrs) - fetch_list = [out.name] + fetch_list = [out.name] - if run_ipu: - place = paddle.IPUPlace() - else: + if exec_mode == ExecutionMode.CPU_FP32: place = paddle.CPUPlace() + else: + place = paddle.IPUPlace() + exe = paddle.static.Executor(place) exe.run(startup_prog) - if run_ipu: + if exec_mode != ExecutionMode.CPU_FP32: feed_list = self.feed_list ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig(is_training=self.is_training) - program = compiler.IPUCompiledProgram( + 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) + 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): - res0 = self._test_base(False) - res1 = self._test_base(True) - - self.assertTrue( - np.allclose( - res0.flatten(), res1.flatten(), atol=self.atol)) + 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.assertTrue(res0.shape == res1.shape) + self.check(output_dict) class TestCase1(TestBase): diff --git a/python/paddle/fluid/tests/unittests/ipu/test_pool_max_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_pool_max_op_ipu.py index a7c45c6686..911a163b8a 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_pool_max_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_pool_max_op_ipu.py @@ -16,14 +16,8 @@ import unittest import numpy as np import paddle -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.optimizer import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest, - np_dtype_to_fluid_str) - -paddle.enable_static() +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -32,23 +26,25 @@ class TestBase(IPUOpTest): def setUp(self): self.set_atol() self.set_training() - self.set_feed() + self.set_data_feed() self.set_feed_attr() - self.set_attrs() + self.set_op_attrs() - def set_feed(self): - self.feed = { - "x": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'), - } + @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)} + self.feed_fp16 = {'in_0': data.astype(np.float16)} 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 = [ - np_dtype_to_fluid_str(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): + def set_op_attrs(self): self.attrs = { "pool_size": 3, "pool_type": 'max', @@ -60,120 +56,126 @@ class TestBase(IPUOpTest): "data_format": 'NCHW', } - def _test_base(self, run_ipu=True): - scope = fluid.core.Scope() + def _test_base(self, exec_mode): + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() - SEED = self.SEED - main_prog.random_seed = SEED - startup_prog.random_seed = SEED + main_prog.random_seed = self.SEED + startup_prog.random_seed = self.SEED - with fluid.scope_guard(scope): + 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]) + dtype='float32') + out = paddle.fluid.layers.pool2d(x, **self.attrs) - fetch_list = [out.name] + fetch_list = [out.name] - if run_ipu: - place = paddle.IPUPlace() - else: + if exec_mode == ExecutionMode.CPU_FP32: place = paddle.CPUPlace() + else: + place = paddle.IPUPlace() + exe = paddle.static.Executor(place) exe.run(startup_prog) - if run_ipu: + if exec_mode != ExecutionMode.CPU_FP32: feed_list = self.feed_list ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig(is_training=self.is_training) - program = compiler.IPUCompiledProgram( + 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) + 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): - res0 = self._test_base(False) - res1 = self._test_base(True) - - self.assertTrue( - np.allclose( - res0.flatten(), res1.flatten(), atol=self.atol)) + 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.assertTrue(res0.shape == res1.shape) + self.check(output_dict) class TestCase1(TestBase): - def set_attrs(self): - super().set_attrs() + def set_op_attrs(self): + super().set_op_attrs() self.attrs['pool_size'] = 3 class TestCase1_2(TestBase): - def set_attrs(self): - super().set_attrs() + def set_op_attrs(self): + super().set_op_attrs() self.attrs['pool_size'] = [3, 1] class TestCase2(TestBase): - def set_attrs(self): - super().set_attrs() + def set_op_attrs(self): + super().set_op_attrs() self.attrs['pool_stride'] = 2 class TestCase2_2(TestBase): - def set_attrs(self): - super().set_attrs() + def set_op_attrs(self): + super().set_op_attrs() self.attrs['pool_stride'] = [2, 1] class TestCase3(TestBase): - def set_attrs(self): - super().set_attrs() + def set_op_attrs(self): + super().set_op_attrs() self.attrs['pool_padding'] = [1, 1] class TestCase3_2(TestBase): - def set_attrs(self): - super().set_attrs() + def set_op_attrs(self): + super().set_op_attrs() self.attrs['pool_padding'] = [1, 1, 2, 2] @unittest.skip('auto_pad is not currently supported') class TestCase3_3(TestBase): - def set_attrs(self): - super().set_attrs() + def set_op_attrs(self): + super().set_op_attrs() self.attrs['pool_padding'] = 'VALID' @unittest.skip('auto_pad is not currently supported') class TestCase3_4(TestBase): - def set_attrs(self): - super().set_attrs() + def set_op_attrs(self): + super().set_op_attrs() self.attrs['pool_padding'] = 'SAME' class TestCase4(TestBase): - def set_attrs(self): - super().set_attrs() + def set_op_attrs(self): + super().set_op_attrs() self.attrs['global_pooling'] = True class TestCase5(TestBase): - def set_attrs(self): - super().set_attrs() + def set_op_attrs(self): + super().set_op_attrs() self.attrs['ceil_mode'] = True class TestCase6(TestBase): - def set_attrs(self): - super().set_attrs() + def set_op_attrs(self): + super().set_op_attrs() self.attrs['exclusive'] = False diff --git a/python/paddle/fluid/tests/unittests/ipu/test_pow_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_pow_op_ipu.py index 5059de7ba7..b3562d722c 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_pow_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_pow_op_ipu.py @@ -16,14 +16,8 @@ import unittest import numpy as np import paddle -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.optimizer import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest, - np_dtype_to_fluid_str) - -paddle.enable_static() +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -32,124 +26,146 @@ class TestBase(IPUOpTest): def setUp(self): self.set_atol() self.set_training() - self.set_feed() + self.set_data_feed() self.set_feed_attr() - self.set_attrs() + self.set_op_attrs() - def set_feed(self): - self.feed = { - "x": np.random.uniform(size=[1, 3, 2, 2]).astype('float32'), - } + @property + def fp16_enabled(self): + return True + + def set_data_feed(self): + data = np.random.uniform(size=[1, 3, 2, 2]) + self.feed_fp32 = {"x": data.astype(np.float32)} + self.feed_fp16 = {"x": data.astype(np.float16)} 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 = [ - np_dtype_to_fluid_str(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): + def set_op_attrs(self): self.attrs = {"factor": 2.0} - def _test_base(self, run_ipu=True): - scope = fluid.core.Scope() + def _test_base(self, exec_mode): + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() - SEED = self.SEED - main_prog.random_seed = SEED - startup_prog.random_seed = SEED + main_prog.random_seed = self.SEED + startup_prog.random_seed = self.SEED - with fluid.scope_guard(scope): + 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]) + dtype='float32') + out = paddle.fluid.layers.pow(x, **self.attrs) - fetch_list = [out.name] + fetch_list = [out.name] - if run_ipu: - place = paddle.IPUPlace() - else: + if exec_mode == ExecutionMode.CPU_FP32: place = paddle.CPUPlace() + else: + place = paddle.IPUPlace() + exe = paddle.static.Executor(place) exe.run(startup_prog) - if run_ipu: + if exec_mode != ExecutionMode.CPU_FP32: feed_list = self.feed_list ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig(is_training=self.is_training) - program = compiler.IPUCompiledProgram( + 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) + 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): - res0 = self._test_base(False) - res1 = self._test_base(True) + 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.assertTrue( - np.allclose( - res0.flatten(), res1.flatten(), atol=self.atol)) - - self.assertTrue(res0.shape == res1.shape) + self.check(output_dict) class TestCase1(TestBase): - def set_feed(self): - self.feed = { - "x": np.random.uniform(size=[1, 3, 2, 2]).astype('float32'), - "y": np.array([2.0]).astype('float32'), + def set_data_feed(self): + data1 = np.random.uniform(size=[1, 3, 2, 2]) + data2 = np.array([2.0]) + + self.feed_fp32 = { + "x": data1.astype(np.float32), + "y": data2.astype(np.float32) + } + self.feed_fp16 = { + "x": data1.astype(np.float16), + "y": data2.astype(np.float16) } - def set_attrs(self): + def set_op_attrs(self): self.attrs = {} - def _test_base(self, run_ipu=True): - scope = fluid.core.Scope() + def _test_base(self, exec_mode): + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() - SEED = self.SEED - main_prog.random_seed = SEED - startup_prog.random_seed = SEED + main_prog.random_seed = self.SEED + startup_prog.random_seed = self.SEED - with fluid.scope_guard(scope): + 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]) + dtype='float32') factor = paddle.static.data( name=self.feed_list[1], shape=self.feed_shape[1], - dtype=self.feed_dtype[1]) + dtype='float32') + out = paddle.fluid.layers.pow(x, factor=factor, **self.attrs) - fetch_list = [out.name] + fetch_list = [out.name] - if run_ipu: - place = paddle.IPUPlace() - else: + if exec_mode == ExecutionMode.CPU_FP32: place = paddle.CPUPlace() + else: + place = paddle.IPUPlace() + exe = paddle.static.Executor(place) exe.run(startup_prog) - if run_ipu: + if exec_mode != ExecutionMode.CPU_FP32: feed_list = self.feed_list ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig(is_training=self.is_training) - program = compiler.IPUCompiledProgram( + 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) + 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] diff --git a/python/paddle/fluid/tests/unittests/ipu/test_print_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_print_op_ipu.py new file mode 100644 index 0000000000..c9454e5945 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ipu/test_print_op_ipu.py @@ -0,0 +1,143 @@ +# 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_training() + self.set_data_feed() + self.set_feed_attr() + self.set_op_attrs() + + def set_data_feed(self): + self.feed = { + "x": np.random.uniform(size=[1, 3, 3, 3]).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_op_attrs(self): + self.attrs = {} + + 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.fluid.layers.conv2d( + x, num_filters=3, filter_size=3) + out = paddle.fluid.layers.Print(out, **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 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 + + if self.is_training: + result = [] + for _ in range(self.epoch): + loss_res = exe.run(program, + feed=self.feed, + fetch_list=fetch_list) + result.append(loss_res[0]) + return np.array(result) + else: + result = exe.run(program, feed=self.feed, fetch_list=fetch_list) + return result[0] + + def test(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) + + +class TestCase1(TestBase): + def set_op_attrs(self): + self.attrs = {"message": "input_data"} + + +class TestTrainCase1(TestBase): + def set_op_attrs(self): + # "forward" : print forward + # "backward" : print forward and backward + # "both": print forward and backward + self.attrs = {"message": "input_data2", "print_phase": "both"} + + def set_training(self): + self.is_training = True + self.epoch = 2 + + +@unittest.skip("attrs are not supported") +class TestCase2(TestBase): + def set_op_attrs(self): + self.attrs = { + "first_n": 10, + "summarize": 10, + "print_tensor_name": True, + "print_tensor_type": True, + "print_tensor_shape": True, + "print_tensor_layout": True, + "print_tensor_lod": True + } + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ipu/test_reduce_x_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_reduce_x_op_ipu.py index ac8ad08e8b..929ee51b65 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_reduce_x_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_reduce_x_op_ipu.py @@ -16,14 +16,8 @@ import unittest import numpy as np import paddle -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.optimizer import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest, - np_dtype_to_fluid_str) - -paddle.enable_static() +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -32,125 +26,137 @@ class TestMean(IPUOpTest): def setUp(self): self.set_atol() self.set_training() - self.init_op() + self.set_test_op() + + @property + def fp16_enabled(self): + return True - def init_op(self): + def set_test_op(self): self.op = paddle.fluid.layers.reduce_mean 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 = [ - np_dtype_to_fluid_str(x.dtype) for x in self.feed.values() - ] - - def _test_base(self, run_ipu=True): - scope = fluid.core.Scope() + 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 _test_base(self, exec_mode): + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() - SEED = self.SEED - main_prog.random_seed = SEED - startup_prog.random_seed = SEED + main_prog.random_seed = self.SEED + startup_prog.random_seed = self.SEED - with fluid.scope_guard(scope): + 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 = self.op(x, **self.attrs) - fetch_list = [out.name] + fetch_list = [out.name] - if run_ipu: - place = paddle.IPUPlace() - else: + if exec_mode == ExecutionMode.CPU_FP32: place = paddle.CPUPlace() + else: + place = paddle.IPUPlace() + exe = paddle.static.Executor(place) exe.run(startup_prog) - if run_ipu: + if exec_mode != ExecutionMode.CPU_FP32: feed_list = self.feed_list ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig(is_training=self.is_training) - program = compiler.IPUCompiledProgram( + 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) + 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_test_base(self): - res0 = self._test_base(True) - res1 = self._test_base(False) - - self.assertTrue( - np.allclose( - res0.flatten(), res1.flatten(), atol=self.atol)) - - def set_feed0(self): - self.feed = {} - self.feed["in_0"] = np.random.uniform(size=[2, 4]).astype(np.float32) + 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) + + def set_data_feed0(self): + data = np.random.uniform(size=[2, 4]) + self.feed_fp32 = {"in_0": data.astype(np.float32)} + self.feed_fp16 = {"in_0": data.astype(np.float16)} self.set_feed_attr() - def set_feed1(self): - self.feed = {} - self.feed["in_0"] = np.random.uniform(size=[2, 2, 2]).astype(np.float32) + def set_data_feed1(self): + data = np.random.uniform(size=[2, 2, 2]) + self.feed_fp32 = {"in_0": data.astype(np.float32)} + self.feed_fp16 = {"in_0": data.astype(np.float16)} self.set_feed_attr() - def set_attr0(self): + def set_op_attr0(self): self.attrs = {} self.attrs['dim'] = None self.attrs['keep_dim'] = False def test_case0(self): - self.set_feed0() - self.set_attr0() + self.set_data_feed0() + self.set_op_attr0() self.run_test_base() def test_case1(self): - self.set_feed0() - self.set_attr0() + self.set_data_feed0() + self.set_op_attr0() self.attrs['dim'] = 0 self.run_test_base() def test_case2(self): - self.set_feed0() - self.set_attr0() + self.set_data_feed0() + self.set_op_attr0() self.attrs['dim'] = -1 self.run_test_base() def test_case3(self): - self.set_feed0() - self.set_attr0() + self.set_data_feed0() + self.set_op_attr0() self.attrs['dim'] = 1 self.run_test_base() def test_case4(self): - self.set_feed0() + self.set_data_feed0() self.attrs = {} self.attrs['dim'] = 1 self.attrs['keep_dim'] = True self.run_test_base() def test_case5(self): - self.set_feed1() + self.set_data_feed1() self.attrs = {} self.attrs['dim'] = [1, 2] self.attrs['keep_dim'] = False self.run_test_base() def test_case6(self): - self.set_feed1() + self.set_data_feed1() self.attrs = {} self.attrs['dim'] = [0, 1] self.attrs['keep_dim'] = False self.run_test_base() def test_case7(self): - self.set_feed1() + self.set_data_feed1() self.attrs = {} self.attrs['dim'] = [0, 1] self.attrs['keep_dim'] = True @@ -158,22 +164,22 @@ class TestMean(IPUOpTest): class TestMax(TestMean): - def init_op(self): + def set_test_op(self): self.op = paddle.fluid.layers.reduce_max class TestMin(TestMean): - def init_op(self): + def set_test_op(self): self.op = paddle.fluid.layers.reduce_min class TestProd(TestMean): - def init_op(self): + def set_test_op(self): self.op = paddle.fluid.layers.reduce_prod class TestSum(TestMean): - def init_op(self): + def set_test_op(self): self.op = paddle.fluid.layers.reduce_sum diff --git a/python/paddle/fluid/tests/unittests/ipu/test_reshape_inplace_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_reshape_inplace_op_ipu.py index f312b7b69a..9ddf5c7537 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_reshape_inplace_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_reshape_inplace_op_ipu.py @@ -16,14 +16,8 @@ import unittest import numpy as np import paddle -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.optimizer import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest, - np_dtype_to_fluid_str) - -paddle.enable_static() +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -32,76 +26,84 @@ class TestBase(IPUOpTest): def setUp(self): self.set_atol() self.set_training() - self.set_feed() + self.set_data_feed() self.set_feed_attr() - self.set_attrs() + self.set_op_attrs() - def set_feed(self): - self.feed = { - "x": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'), - } + @property + def fp16_enabled(self): + return True + + def set_data_feed(self): + data = np.random.uniform(size=[1, 3, 10, 10]) + self.feed_fp32 = {"x": data.astype(np.float32)} + self.feed_fp16 = {"x": data.astype(np.float16)} 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 = [ - np_dtype_to_fluid_str(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): + def set_op_attrs(self): self.attrs = { "shape": [30, 10], "inplace": True, } - def _test_base(self, run_ipu=True): - scope = fluid.core.Scope() + def _test_base(self, exec_mode): + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() - SEED = self.SEED - main_prog.random_seed = SEED - startup_prog.random_seed = SEED + main_prog.random_seed = self.SEED + startup_prog.random_seed = self.SEED - with fluid.scope_guard(scope): + 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]) + dtype='float32') + add = paddle.fluid.layers.elementwise_add(x, x) out = paddle.fluid.layers.reshape(add, **self.attrs) - fetch_list = [out.name] + fetch_list = [out.name] - if run_ipu: - place = paddle.IPUPlace() - else: + if exec_mode == ExecutionMode.CPU_FP32: place = paddle.CPUPlace() + else: + place = paddle.IPUPlace() + exe = paddle.static.Executor(place) exe.run(startup_prog) - if run_ipu: + if exec_mode != ExecutionMode.CPU_FP32: feed_list = self.feed_list ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig(is_training=self.is_training) - program = compiler.IPUCompiledProgram( + 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) + 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): - res0 = self._test_base(True) - res1 = self._test_base(False) - - self.assertTrue( - np.allclose( - res0.flatten(), res1.flatten(), atol=self.atol)) + output_dict = {} + for mode in ExecutionMode: + if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: + break + output_dict[mode] = self._test_base(mode) - self.assertTrue(res0.shape == res1.shape) + self.check(output_dict, check_shape=True) class TestCase1(TestBase): diff --git a/python/paddle/fluid/tests/unittests/ipu/test_reshape_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_reshape_op_ipu.py index 5163838bc0..1197719317 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_reshape_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_reshape_op_ipu.py @@ -16,13 +16,8 @@ import unittest import numpy as np import paddle -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.optimizer import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest - -paddle.enable_static() +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -31,82 +26,92 @@ class TestBase(IPUOpTest): def setUp(self): self.set_atol() self.set_training() - self.set_feed() - self.set_attrs() + self.set_data_feed() + self.set_feed_attr() + self.set_op_attrs() - def set_feed(self): - self.feed_shape = [] - self.feed_shape.append([2, 4, 6]) + @property + def fp16_enabled(self): + return True - self.feed = {} - self.feed["in_0"] = np.random.uniform( - size=self.feed_shape[0]).astype(np.float32) + def set_data_feed(self): + data = np.random.uniform(size=[2, 4, 6]) + self.feed_fp32 = {"in_0": data.astype(np.float32)} + self.feed_fp16 = {"in_0": data.astype(np.float16)} - self.feed_list = list(self.feed.keys()) + 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_attrs(self): + def set_op_attrs(self): self.attrs = {} self.attrs['shape'] = [6, 8] self.attrs['inplace'] = False - def _test_base(self, run_ipu=True): - scope = fluid.core.Scope() + def _test_base(self, exec_mode): + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() - SEED = self.SEED - main_prog.random_seed = SEED - startup_prog.random_seed = SEED + main_prog.random_seed = self.SEED + startup_prog.random_seed = self.SEED - with fluid.scope_guard(scope): + 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.reshape(x=x, **self.attrs) - fetch_list = [out.name] + fetch_list = [out.name] - if run_ipu: - place = paddle.IPUPlace() - else: + if exec_mode == ExecutionMode.CPU_FP32: place = paddle.CPUPlace() + else: + place = paddle.IPUPlace() + exe = paddle.static.Executor(place) exe.run(startup_prog) - if run_ipu: + if exec_mode != ExecutionMode.CPU_FP32: feed_list = self.feed_list ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig(is_training=self.is_training) - program = compiler.IPUCompiledProgram( + 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) + 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): - res0 = self._test_base(True) - res1 = self._test_base(False) - - self.assertTrue( - np.allclose( - res0.flatten(), res1.flatten(), atol=self.atol)) + output_dict = {} + for mode in ExecutionMode: + if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled: + break + output_dict[mode] = self._test_base(mode) - self.assertTrue(res0.shape == res1.shape) + self.check(output_dict, check_shape=True) class TestCase1(TestBase): - def set_attrs(self): + def set_op_attrs(self): self.attrs = {} self.attrs['shape'] = [2, 3, -1, 2] self.attrs['inplace'] = False class TestCase2(TestBase): - def set_attrs(self): + def set_op_attrs(self): self.attrs = {} self.attrs['shape'] = [-1, 0, 3, 2] self.attrs['inplace'] = False diff --git a/python/paddle/fluid/tests/unittests/ipu/test_save_load.py b/python/paddle/fluid/tests/unittests/ipu/test_save_load_ipu.py similarity index 58% rename from python/paddle/fluid/tests/unittests/ipu/test_save_load.py rename to python/paddle/fluid/tests/unittests/ipu/test_save_load_ipu.py index 24bb8e1118..3a69487306 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_save_load.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_save_load_ipu.py @@ -12,55 +12,52 @@ # See the License for the specific language governing permissions and # limitations under the License. +import tempfile import unittest -import shutil import numpy as np import paddle -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.optimizer import paddle.static from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest -paddle.enable_static() - @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_feed() - self.set_attrs() - - def set_feed(self): - self.feed_shape = [] - self.feed_shape.append([1, 3, 10, 10]) + self.set_data_feed() + self.set_feed_attr() + self.set_op_attrs() - self.feed = {} - self.feed["in_0"] = np.random.uniform( - size=self.feed_shape[0]).astype(np.float32) + 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)} - self.feed_list = list(self.feed.keys()) + 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_attrs(self): + 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['enable_fp16'] = False + self.attrs['model_path'] = tempfile.TemporaryDirectory() def _test_base(self, save_otherwise_load): - scope = fluid.core.Scope() + 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 = fluid.unique_name.UniqueNameGenerator() + generator = paddle.fluid.unique_name.UniqueNameGenerator() - with fluid.unique_name.guard(generator): - with fluid.scope_guard(scope): + 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], @@ -91,12 +88,17 @@ class TestBase(IPUOpTest): exe.run(startup_prog) if not save_otherwise_load: - paddle.static.load(main_prog, "model/model") + paddle.static.load(main_prog, self.attrs['model_path'].name) ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig( + ipu_strategy.set_graph_config( is_training=self.attrs['is_training']) - program = compiler.IPUCompiledProgram( + ipu_strategy.set_precision_config( + enable_fp16=self.attrs['enable_fp16']) + ipu_strategy.set_options({ + 'save_per_n_step': self.attrs['save_at_step'] + }) + program = paddle.static.IpuCompiledProgram( main_prog, ipu_strategy=ipu_strategy).compile( self.feed_list, fetch_list) @@ -104,16 +106,17 @@ class TestBase(IPUOpTest): run_steps = self.attrs['steps'] if save_otherwise_load \ else self.attrs['steps'] - self.attrs['save_at_step'] + feed = self.feed_fp16 if self.attrs[ + 'enable_fp16'] else self.feed_fp32 for i in range(run_steps): - tmp = exe.run(program, - feed=self.feed, - fetch_list=fetch_list) + tmp = exe.run(program, feed=feed, fetch_list=fetch_list) # currently, we update opt state every sess.run, # will optimize if save_otherwise_load and \ i == self.attrs['save_at_step'] - 1: - paddle.static.save(main_prog, "model/model") + paddle.static.save(main_prog, + self.attrs['model_path'].name) if save_otherwise_load and i >= self.attrs['save_at_step']: result.append(tmp) @@ -129,25 +132,65 @@ class TestBase(IPUOpTest): self.assertTrue( np.allclose( res0.flatten(), res1.flatten(), atol=self.atol)) - shutil.rmtree("model", True) + self.attrs['model_path'].cleanup() class TestAdam(TestBase): - def set_attrs(self): + 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'] = 'adam' + self.attrs['enable_fp16'] = False + self.attrs['model_path'] = tempfile.TemporaryDirectory() class TestLamb(TestBase): - def set_attrs(self): + 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'] = 'lamb' + self.attrs['enable_fp16'] = False + self.attrs['model_path'] = tempfile.TemporaryDirectory() + + +@unittest.skipIf(IPUOpTest.use_ipumodel(), "skip for ipumodel") +class TestSGDFP16(TestBase): + 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['enable_fp16'] = True + self.attrs['model_path'] = tempfile.TemporaryDirectory() + + +@unittest.skipIf(IPUOpTest.use_ipumodel(), "skip for ipumodel") +class TestAdamFP16(TestBase): + 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'] = 'adam' + self.attrs['enable_fp16'] = True + self.attrs['model_path'] = tempfile.TemporaryDirectory() + + +@unittest.skipIf(IPUOpTest.use_ipumodel(), "skip for ipumodel") +class TestLambFP16(TestBase): + 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'] = 'lamb' + self.attrs['enable_fp16'] = True + self.attrs['model_path'] = tempfile.TemporaryDirectory() if __name__ == "__main__": diff --git a/python/paddle/fluid/tests/unittests/ipu/test_scale_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_scale_op_ipu.py index 6ad2a89a73..49714eba8d 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_scale_op_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_scale_op_ipu.py @@ -16,14 +16,8 @@ import unittest import numpy as np import paddle -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.optimizer import paddle.static -from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest, - np_dtype_to_fluid_str) - -paddle.enable_static() +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode @unittest.skipIf(not paddle.is_compiled_with_ipu(), @@ -32,80 +26,88 @@ class TestBase(IPUOpTest): def setUp(self): self.set_atol() self.set_training() - self.set_feed() + self.set_data_feed() self.set_feed_attr() - self.set_attrs() + self.set_op_attrs() - def set_feed(self): - self.feed = { - "x": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'), - } + @property + def fp16_enabled(self): + return False + + def set_data_feed(self): + data = np.random.uniform(size=[1, 3, 10, 10]) + self.feed_fp32 = {"x": data.astype(np.float32)} + self.feed_fp16 = {"x": data.astype(np.float16)} 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 = [ - np_dtype_to_fluid_str(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): + def set_op_attrs(self): self.attrs = { "scale": 1.0, "bias": 0.0, "bias_after_scale": True, } - def _test_base(self, run_ipu=True): - scope = fluid.core.Scope() + def _test_base(self, exec_mode): + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() - SEED = self.SEED - main_prog.random_seed = SEED - startup_prog.random_seed = SEED + main_prog.random_seed = self.SEED + startup_prog.random_seed = self.SEED - with fluid.scope_guard(scope): + 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]) + dtype='float32') + out = paddle.fluid.layers.scale(x, **self.attrs) - fetch_list = [out.name] + fetch_list = [out.name] - if run_ipu: - place = paddle.IPUPlace() - else: + if exec_mode == ExecutionMode.CPU_FP32: place = paddle.CPUPlace() + else: + place = paddle.IPUPlace() + exe = paddle.static.Executor(place) exe.run(startup_prog) - if run_ipu: + if exec_mode != ExecutionMode.CPU_FP32: feed_list = self.feed_list ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig(is_training=self.is_training) - program = compiler.IPUCompiledProgram( + 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) + 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): - res0 = self._test_base(False) - res1 = self._test_base(True) - - self.assertTrue( - np.allclose( - res0.flatten(), res1.flatten(), atol=self.atol)) + 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.assertTrue(res0.shape == res1.shape) + self.check(output_dict) class TestCase1(TestBase): - def set_attrs(self): + def set_op_attrs(self): self.attrs = { "scale": 5.0, "bias": 0.0, @@ -114,7 +116,7 @@ class TestCase1(TestBase): class TestCase2(TestBase): - def set_attrs(self): + def set_op_attrs(self): self.attrs = { "scale": 1.0, "bias": 0.5, @@ -123,7 +125,16 @@ class TestCase2(TestBase): class TestCase3(TestBase): - def set_attrs(self): + def set_op_attrs(self): + self.attrs = { + "scale": 5.0, + "bias": 0.7, + "bias_after_scale": True, + } + + +class TestCase4(TestBase): + def set_op_attrs(self): self.attrs = { "scale": 1.0, "bias": 0.0, @@ -131,59 +142,66 @@ class TestCase3(TestBase): } -class TestCase4(TestBase): - def set_feed(self): - self.feed = { - "x": np.random.uniform(size=[3, 3, 10, 10]).astype('float32'), - "y": np.array([3.0]).astype('float32'), - } +class TestCase5(TestBase): + def set_data_feed(self): + x = np.random.uniform(size=[3, 3, 10, 10]) + y = np.array([3.0]) + self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)} + self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)} - def set_attrs(self): + def set_op_attrs(self): self.attrs = { "bias": 0.0, "bias_after_scale": True, } - def _test_base(self, run_ipu=True): - scope = fluid.core.Scope() + def _test_base(self, exec_mode): + scope = paddle.static.Scope() main_prog = paddle.static.Program() startup_prog = paddle.static.Program() - SEED = self.SEED - main_prog.random_seed = SEED - startup_prog.random_seed = SEED + main_prog.random_seed = self.SEED + startup_prog.random_seed = self.SEED - with fluid.scope_guard(scope): + 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]) + dtype='float32') y = paddle.static.data( name=self.feed_list[1], shape=self.feed_shape[1], - dtype=self.feed_dtype[1]) + dtype='float32') + out = paddle.fluid.layers.scale(x, scale=y, **self.attrs) - fetch_list = [out.name] + fetch_list = [out.name] - if run_ipu: - place = paddle.IPUPlace() - else: + if exec_mode == ExecutionMode.CPU_FP32: place = paddle.CPUPlace() + else: + place = paddle.IPUPlace() + exe = paddle.static.Executor(place) exe.run(startup_prog) - if run_ipu: + if exec_mode != ExecutionMode.CPU_FP32: feed_list = self.feed_list ipu_strategy = paddle.static.IpuStrategy() - ipu_strategy.SetGraphConfig(is_training=self.is_training) - program = compiler.IPUCompiledProgram( + 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) + 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] -- GitLab