wehub-resource-sync
493 lines
15 KiB
Python
493 lines
15 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op import Operator
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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convert_uint16_to_float,
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get_device_place,
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get_places,
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is_custom_device,
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)
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from utils import dygraph_guard
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import paddle
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from paddle.base import core
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paddle.enable_static()
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def sgd_wrapper(
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param, learning_rate, grad, master_param=None, multi_precision=False
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):
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paddle._C_ops.sgd_(
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param, learning_rate, grad, master_param, multi_precision
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)
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class TestSGDOp(OpTest):
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def setUp(self):
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self.op_type = "sgd"
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self.python_api = sgd_wrapper
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self.python_out_sig = ['Out']
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self.conf()
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w = np.random.random((self.h, self.w)).astype("float32")
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g = np.random.random((self.h, self.w)).astype("float32")
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lr = np.array([0.1]).astype("float32")
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self.inputs = {'Param': w, 'Grad': g, 'LearningRate': lr}
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self.outputs = {'ParamOut': w - lr * g}
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def conf(self):
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self.h = 102
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self.w = 105
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def test_check_output(self):
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self.check_output(check_pir=True)
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class TestSGDOpCase8X(TestSGDOp):
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def conf(self):
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self.h = 10
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self.w = 64
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class TestSparseSGDOp(unittest.TestCase):
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def check_with_place(self, place):
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scope = core.Scope()
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# create and initialize Grad Variable
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height = 10
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rows = [0, 4, 7]
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self.conf()
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grad_selected_rows = scope.var('Grad').get_selected_rows()
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grad_selected_rows.set_height(height)
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grad_selected_rows.set_rows(rows)
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np_array = np.ones((len(rows), self.row_numel)).astype("float32")
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np_array[0, 0] = 2.0
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np_array[2, 8] = 4.0
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grad_tensor = grad_selected_rows.get_tensor()
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grad_tensor.set(np_array, place)
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# create and initialize Param Variable
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param = scope.var('Param').get_tensor()
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param_array = np.full((height, self.row_numel), 5.0).astype("float32")
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param.set(param_array, place)
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# create and initialize LearningRate Variable
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lr = scope.var('LearningRate').get_tensor()
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lr_array = np.full((1), 2.0).astype("float32")
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lr.set(lr_array, place)
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# create and run sgd operator
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sgd_op = Operator(
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"sgd",
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Param='Param',
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Grad='Grad',
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ParamOut='Param',
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LearningRate='LearningRate',
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)
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sgd_op.run(scope, place)
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# get and compare result
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result_array = np.array(param)
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# rows[0] = 0, 5.0 - 2.0 * 2.0
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self.assertAlmostEqual(1.0, result_array[rows[0], 0])
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# rows[0] = 0, 5.0 - 2.0 * 1.0
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self.assertAlmostEqual(3.0, result_array[rows[0], 2])
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# 5.0 - 2.0 * 0.0
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self.assertAlmostEqual(5.0, result_array[1, 0])
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# rows[1] = 4, 5.0 - 2.0 * 1.0
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self.assertAlmostEqual(3.0, result_array[rows[1], 10])
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# 5.0 - 2.0 * 0.0
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self.assertAlmostEqual(5.0, result_array[5, 8])
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# rows[2] = 7, 5.0 - 2.0 * 1.0
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self.assertAlmostEqual(3.0, result_array[rows[2], 1])
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# rows[2] = 7, 5.0 - 2.0 * 4.0
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self.assertAlmostEqual(-3.0, result_array[rows[2], 8])
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def test_sparse_sgd(self):
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for place in get_places():
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self.check_with_place(place)
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def conf(self):
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self.row_numel = 12
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class TestSparseSGDOpCase8X(TestSparseSGDOp):
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def conf(self):
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self.row_numel = 16
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class TestSGDOpOptimizeSelectedRows(unittest.TestCase):
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def check_with_place(self, place):
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scope = core.Scope()
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row_width = 12
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# create and initialize Grad Variable
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grad_height = 10
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grad_rows = [0, 4, 7]
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grad_selected_rows = scope.var('Grad').get_selected_rows()
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grad_selected_rows.set_height(grad_height)
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grad_selected_rows.set_rows(grad_rows)
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grad_array = np.ones((len(grad_rows), row_width)).astype("float32")
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grad_array[0, 0] = 2.0
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grad_array[2, 8] = 4.0
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grad_tensor = grad_selected_rows.get_tensor()
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grad_tensor.set(grad_array, place)
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# create and initialize Param Variable
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# create and initialize W Variable
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param_rows = [0, 1, 2, 3, 4, 5, 6, 7]
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# init Param
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w_selected_rows = scope.var('Param').get_selected_rows()
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w_selected_rows.set_height(len(param_rows))
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w_selected_rows.set_rows(param_rows)
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w_selected_rows.sync_index()
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w_array = np.ones((len(param_rows), row_width)).astype("float32")
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for i in range(len(param_rows)):
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w_array[i] *= i
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w_tensor = w_selected_rows.get_tensor()
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w_tensor.set(w_array, place)
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w_before_optimize = np.array(w_tensor)
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# create and initialize LearningRate Variable
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lr_value = 0.1
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lr = scope.var('LearningRate').get_tensor()
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lr_array = np.full((1), lr_value).astype("float32")
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lr.set(lr_array, place)
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# optimize with Python
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w_after_optimize = np.copy(w_before_optimize)
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for index, id in enumerate(grad_rows):
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w_after_optimize[id] = (
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w_before_optimize[id] - lr_value * grad_array[index]
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)
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# create and run sgd operator
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sgd_op = Operator(
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"sgd",
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Param='Param',
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Grad='Grad',
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ParamOut='Param',
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LearningRate='LearningRate',
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)
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sgd_op.run(scope, place)
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# get and compare result
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result_array = np.array(w_tensor)
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assert (result_array == w_after_optimize).all()
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def test_sparse_parameter_sgd(self):
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places = [core.CPUPlace()]
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# do not support GPU kernel currently
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for place in places:
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self.check_with_place(place)
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class TestSGDV2(unittest.TestCase):
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def test_sgd_dygraph(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_tensor(value)
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linear = paddle.nn.Linear(13, 5)
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# This can be any optimizer supported by dygraph.
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adam = paddle.optimizer.SGD(
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learning_rate=0.01,
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parameters=linear.parameters(),
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weight_decay=0.01,
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)
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out = linear(a)
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out.backward()
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adam.step()
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adam.clear_gradients()
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def test_raise_error(self):
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self.assertRaises(ValueError, paddle.optimizer.SGD, learning_rate=None)
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def test_sgd_group_dygraph(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_tensor(value)
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linear_1 = paddle.nn.Linear(13, 5)
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linear_2 = paddle.nn.Linear(5, 3)
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# This can be any optimizer supported by dygraph.
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adam = paddle.optimizer.SGD(
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learning_rate=0.01,
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parameters=[
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{'params': linear_1.parameters()},
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{
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'params': linear_2.parameters(),
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'weight_decay': 0.001,
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'learning_rate': 0.1,
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},
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],
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weight_decay=0.01,
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)
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out = linear_1(a)
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out = linear_2(out)
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out.backward()
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adam.step()
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adam.clear_gradients()
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def test_weight_decay_int(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_tensor(value)
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linear = paddle.nn.Linear(13, 5)
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# This can be any optimizer supported by dygraph.
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adam = paddle.optimizer.SGD(
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learning_rate=0.01,
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parameters=linear.parameters(),
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weight_decay=1,
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)
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out = linear(a)
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out.backward()
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adam.step()
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adam.clear_gradients()
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class TestSGDSimple(unittest.TestCase):
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def setUp(self) -> None:
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self.data = np.random.random(size=(2, 2)).astype('float32')
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def run_static(self):
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with paddle.pir_utils.IrGuard():
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paddle.seed(10)
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np.random.seed(10)
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exe = paddle.static.Executor(get_device_place())
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train_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(train_program, startup_program):
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input = paddle.static.data(
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shape=[2, 2], name='input', dtype='float32'
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)
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model = paddle.nn.Linear(2, 2)
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output = model(input)
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loss = paddle.mean(output)
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optimizer = paddle.optimizer.SGD()
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optimizer.minimize(loss)
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exe.run(startup_program)
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out = []
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for _ in range(5):
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(loss_data,) = exe.run(
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train_program, feed={"input": self.data}, fetch_list=[loss]
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)
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out.append(loss_data)
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return out
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def run_dygraph(self):
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with dygraph_guard():
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paddle.seed(10)
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np.random.seed(10)
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out = []
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model = paddle.nn.Linear(2, 2)
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optimizer = paddle.optimizer.SGD(parameters=model.parameters())
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for _ in range(5):
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output = model(paddle.to_tensor(self.data))
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loss = paddle.mean(output)
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out.append(loss.numpy())
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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return out
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def test_main(self):
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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out1 = self.run_dygraph()
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out2 = self.run_static()
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np.testing.assert_allclose(out1, out2)
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class TestSGDSparseBF16(unittest.TestCase):
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"""Test SGD with bfloat16 sparse grad on CPU (no oneDNN)."""
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def test_sparse_grad_sgd_bf16(self):
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paddle.enable_static()
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scope = core.Scope()
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place = core.CPUPlace()
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height = 10
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rows = [0, 4, 7]
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row_numel = 12
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# Grad: SelectedRows in bfloat16
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grad_selected_rows = scope.var('Grad').get_selected_rows()
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grad_selected_rows.set_height(height)
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grad_selected_rows.set_rows(rows)
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grad_np = np.random.random((len(rows), row_numel)).astype('float32')
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grad_tensor = grad_selected_rows.get_tensor()
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grad_tensor.set(convert_float_to_uint16(grad_np), place)
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# Param: dense bfloat16
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param_np = np.random.random((height, row_numel)).astype('float32')
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param_var = scope.var('Param').get_tensor()
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param_var.set(convert_float_to_uint16(param_np), place)
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# LearningRate: float32
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lr_value = 0.1
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lr_var = scope.var('LearningRate').get_tensor()
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lr_var.set(np.array([lr_value]).astype('float32'), place)
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sgd_op = Operator(
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"sgd",
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Param='Param',
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Grad='Grad',
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ParamOut='Param',
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LearningRate='LearningRate',
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)
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sgd_op.run(scope, place)
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reference = np.copy(param_np)
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for idx, row_id in enumerate(rows):
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reference[row_id] -= lr_value * grad_np[idx]
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result = convert_uint16_to_float(np.array(param_var))
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np.testing.assert_allclose(result, reference, atol=5e-3, rtol=1e-1)
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paddle.disable_static()
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class TestSGDDenseBF16OneDNN(unittest.TestCase):
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"""Test SGD with bfloat16 dense grad on CPU with oneDNN."""
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def test_dense_sgd_bf16_onednn(self):
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paddle.enable_static()
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scope = core.Scope()
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place = core.CPUPlace()
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h, w = 10, 12
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param_np = np.random.random((h, w)).astype('float32')
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grad_np = np.random.random((h, w)).astype('float32')
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lr_value = 0.1
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param_var = scope.var('Param').get_tensor()
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param_var.set(convert_float_to_uint16(param_np), place)
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grad_var = scope.var('Grad').get_tensor()
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grad_var.set(convert_float_to_uint16(grad_np), place)
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lr_var = scope.var('LearningRate').get_tensor()
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lr_var.set(np.array([lr_value]).astype('float32'), place)
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sgd_op = Operator(
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"sgd",
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Param='Param',
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Grad='Grad',
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ParamOut='Param',
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LearningRate='LearningRate',
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use_onednn=True,
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)
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sgd_op.run(scope, place)
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reference = param_np - lr_value * grad_np
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result = convert_uint16_to_float(np.array(param_var))
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np.testing.assert_allclose(result, reference, atol=5e-3, rtol=1e-1)
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paddle.disable_static()
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def test_sparse_grad_sgd_bf16_onednn(self):
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paddle.enable_static()
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scope = core.Scope()
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place = core.CPUPlace()
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height = 10
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rows = [0, 4, 7]
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row_numel = 12
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# Grad: SelectedRows in bfloat16
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grad_selected_rows = scope.var('Grad').get_selected_rows()
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grad_selected_rows.set_height(height)
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grad_selected_rows.set_rows(rows)
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grad_np = np.random.random((len(rows), row_numel)).astype('float32')
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grad_tensor = grad_selected_rows.get_tensor()
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grad_tensor.set(convert_float_to_uint16(grad_np), place)
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# Param: dense bfloat16
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param_np = np.random.random((height, row_numel)).astype('float32')
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param_var = scope.var('Param').get_tensor()
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param_var.set(convert_float_to_uint16(param_np), place)
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# LearningRate: float32
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lr_value = 0.1
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lr_var = scope.var('LearningRate').get_tensor()
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lr_var.set(np.array([lr_value]).astype('float32'), place)
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sgd_op = Operator(
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"sgd",
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Param='Param',
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Grad='Grad',
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ParamOut='Param',
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LearningRate='LearningRate',
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use_onednn=True,
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)
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sgd_op.run(scope, place)
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reference = np.copy(param_np)
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for idx, row_id in enumerate(rows):
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reference[row_id] -= lr_value * grad_np[idx]
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result = convert_uint16_to_float(np.array(param_var))
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np.testing.assert_allclose(result, reference, atol=5e-3, rtol=1e-1)
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paddle.disable_static()
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class TestSGDGradFP32(unittest.TestCase):
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def setUp(self):
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np.random.seed(2023)
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self.h = 102
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self.w = 105
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def get_available_places(self):
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places = [paddle.CPUPlace()]
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if paddle.is_compiled_with_cuda():
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places.append(paddle.CUDAPlace(0))
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if paddle.is_compiled_with_xpu():
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places.append(paddle.XPUPlace(0))
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return places
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def test_sgd_execution(self):
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for place in self.get_available_places():
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paddle.disable_static()
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if isinstance(place, paddle.CPUPlace):
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param_dtype = 'bfloat16'
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else:
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param_dtype = 'float16'
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param = paddle.randn([self.h, self.w], dtype=param_dtype).to(place)
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grad = paddle.randn([self.h, self.w], dtype='float32').to(place)
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lr = paddle.to_tensor([0.1], dtype='float32', place=place)
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sgd_wrapper(param, lr, grad)
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if __name__ == "__main__":
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unittest.main()
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