wehub-resource-sync
362 lines
14 KiB
Python
362 lines
14 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_test import get_device_place, is_custom_device
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import paddle
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from paddle import base
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from paddle.base import core
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from paddle.base.dygraph.base import switch_to_static_graph
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class LAMBOptimizer(paddle.optimizer.Lamb):
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def _append_optimize_op(self, block, param_and_grad):
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assert isinstance(block, (base.framework.Block, paddle.pir.Block))
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block.program._use_lamb = True
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m = moment1 = self._get_accumulator(
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self._moment1_acc_str, param_and_grad[0]
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)
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v = self._get_accumulator(self._moment2_acc_str, param_and_grad[0])
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beta_1_pow_acc = self._get_accumulator(
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self._beta1_pow_acc_str, param_and_grad[0]
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)
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beta_2_pow_acc = self._get_accumulator(
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self._beta2_pow_acc_str, param_and_grad[0]
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)
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beta_1 = paddle.tensor.fill_constant(
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dtype='float32', shape=[1], value=self._beta1, name='lamb_beta_1'
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)
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beta_2 = paddle.tensor.fill_constant(
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dtype='float32', shape=[1], value=self._beta2, name='lamb_beta_2'
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)
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epsilon = paddle.tensor.fill_constant(
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dtype='float32', shape=[1], value=self._epsilon, name='epsilon'
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)
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one = paddle.ones(shape=[1]).astype('float32')
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zero = paddle.zeros(shape=[1]).astype('float32')
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next_m = paddle.multiply(m, beta_1) + paddle.multiply(
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param_and_grad[1], one - beta_1
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)
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next_v = paddle.multiply(v, beta_2) + paddle.multiply(
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paddle.pow(param_and_grad[1], 2), one - beta_2
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)
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beta1_correction = one - beta_1_pow_acc
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beta2_correction = one - beta_2_pow_acc
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next_m_unbiased = next_m / beta1_correction
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next_v_unbiased = next_v / beta2_correction
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update = next_m_unbiased / (paddle.sqrt(next_v_unbiased) + epsilon)
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if (
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self._exclude_from_weight_decay_fn is not None
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and self._exclude_from_weight_decay_fn(param_and_grad[0])
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):
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self._lamb_weight_decay = 0.0
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update += self._lamb_weight_decay * param_and_grad[0]
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w_norm = paddle.norm(param_and_grad[0], p=2)
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g_norm = paddle.norm(update, p=2)
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learning_rate = self._create_param_lr(param_and_grad)
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ratio = paddle.where(
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paddle.greater_than(w_norm, zero),
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paddle.where(
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paddle.greater_than(g_norm, zero), (w_norm / g_norm), one
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),
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one,
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)
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update_with_lr = ratio * learning_rate * update
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next_param = param_and_grad[0] - update_with_lr
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beta_1_pow_acc *= beta_1
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beta_2_pow_acc *= beta_2
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paddle.assign(next_m, m)
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paddle.assign(next_v, v)
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paddle.assign(next_param, param_and_grad[0])
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class TestLambOpV2(unittest.TestCase):
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def test_lamb_op(self):
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shape = [2, 4, 8, 8]
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data = paddle.to_tensor(np.random.random(size=shape).astype("float32"))
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conv = paddle.nn.Conv2D(4, 6, (3, 3))
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data = conv(data)
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loss = paddle.mean(data)
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opt = paddle.optimizer.Lamb(
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learning_rate=1e-5, epsilon=1e-8, parameters=conv.parameters()
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)
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loss.backward()
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opt.minimize(loss)
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assert loss.numpy() is not None
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class TestLambOpWithCombinedOp(unittest.TestCase):
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def test_lamb_op_with_multi_steps(self):
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paddle.enable_static()
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def _build_static_model(main, startup, seed=100):
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with base.program_guard(main, startup):
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paddle.seed(seed)
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x = paddle.static.data(
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name='X', shape=[-1, 13], dtype='float32'
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)
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y = paddle.static.data(name='Y', shape=[-1, 1], dtype='float32')
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linear = paddle.nn.Linear(
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in_features=x.shape[-1], out_features=1
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)
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prediction = linear(x)
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loss = paddle.nn.functional.square_error_cost(
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input=prediction, label=y
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)
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avg_loss = paddle.mean(loss)
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return avg_loss
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place = base.CPUPlace()
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num_steps = 10
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for i in range(num_steps):
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feed_x = np.random.random(size=(10, 13)).astype('float32')
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feed_y = np.random.random(size=(10, 1)).astype('float32')
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with base.program_guard(main_program, startup_program):
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avg_loss = _build_static_model(main_program, startup_program)
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lamb_kernel = paddle.optimizer.Lamb(learning_rate=0.2)
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lamb_kernel.minimize(avg_loss)
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executor = base.Executor(place)
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executor.run(startup_program)
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output = executor.run(
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program=main_program,
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feed={'X': feed_x, 'Y': feed_y},
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fetch_list=[avg_loss],
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)
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with base.program_guard(main, startup):
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loss = _build_static_model(main, startup)
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lamb = LAMBOptimizer(learning_rate=0.2)
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lamb.minimize(loss)
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exe = base.Executor(place)
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exe.run(startup)
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out = exe.run(
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program=main,
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feed={'X': feed_x, 'Y': feed_y},
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fetch_list=[loss],
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)
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np.testing.assert_allclose(out, output, rtol=1e-05)
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class TestLambOpV2Group(TestLambOpV2):
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def test_lamb_op(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.Lamb(
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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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'lamb_weight_decay': 0.001,
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'beta1': 0.9,
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'beta2': 0.99,
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},
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],
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lamb_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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class TestLambOpMultiPrecision(unittest.TestCase):
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def check_main(self, x_np, place, multi_precision=False, seed=10, n=10):
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with paddle.pir_utils.OldIrGuard():
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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paddle.seed(seed)
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with paddle.static.amp.fp16_guard():
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x = paddle.static.data(
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name='x', shape=[None, 10], dtype='float32'
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)
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linear = paddle.nn.Linear(10, 2)
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hidden = linear(x)
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loss = paddle.mean(hidden)
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original_optimizer = paddle.optimizer.Lamb(learning_rate=1e-3)
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original_optimizer._multi_precision = multi_precision
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if multi_precision:
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optimizer = paddle.static.amp.decorate(
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original_optimizer,
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use_pure_fp16=True,
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use_fp16_guard=True,
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)
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else:
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optimizer = original_optimizer
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optimizer.minimize(loss)
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weight, bias = linear.weight, linear.bias
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exe = paddle.static.Executor(place)
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scope = paddle.static.Scope()
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if x.dtype in (core.VarDesc.VarType.FP16, core.DataType.FLOAT16):
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x_np = x_np.astype(np.float16)
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def get_parameter(var):
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name = var if isinstance(var, (str, bytes)) else var.name
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params = original_optimizer._get_parameter(name, scope)
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assert isinstance(params, (list, tuple))
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params = list(params)
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assert len(params) == 2
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if multi_precision:
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params[0] = np.array(params[0])
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params[1] = np.array(params[1])
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np.testing.assert_array_equal(
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params[0], params[1].astype(np.float16)
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)
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return params[0].astype(np.float32)
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else:
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self.assertIsNotNone(params[0])
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self.assertIsNone(params[1])
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params[0] = np.array(params[0])
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return params[0]
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with paddle.static.scope_guard(scope):
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exe.run(startup_prog)
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if multi_precision:
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optimizer.amp_init(place)
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weight_np, bias_np = None, None
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for i in range(n):
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feed_dict = {'x': x_np}
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weight_np, bias_np = exe.run(
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main_prog, feed=feed_dict, fetch_list=[weight, bias]
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)
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weight_np = weight_np.astype('float32')
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bias_np = bias_np.astype('float32')
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np.testing.assert_array_equal(
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weight_np, get_parameter(weight)
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)
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np.testing.assert_array_equal(bias_np, get_parameter(bias))
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return weight_np, bias_np
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def check_amp_in_pir(
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self, x_np, place, multi_precision=True, seed=10, n=10
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):
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with paddle.pir_utils.IrGuard():
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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paddle.seed(seed)
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x = paddle.static.data(
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name='x', shape=[None, 10], dtype='float32'
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)
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linear = paddle.nn.Linear(10, 2)
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original_optimizer = paddle.optimizer.Lamb(
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learning_rate=0.001, parameters=linear.parameters()
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)
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linear, optimizer = paddle.amp.decorate(
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models=linear,
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optimizers=original_optimizer,
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level='O2',
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)
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with paddle.amp.auto_cast(
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level='O2', dtype='float16', use_promote=True
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):
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out = linear(x)
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loss = paddle.mean(out)
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optimizer.minimize(loss)
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weight, bias = linear.weight, linear.bias
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exe = paddle.static.Executor(place)
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def get_parameter(var):
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name = var if isinstance(var, (str, bytes)) else var.name
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params = original_optimizer._get_parameter(name)
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assert isinstance(params, (list, tuple))
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params = list(params)
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assert len(params) == 2
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if multi_precision:
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params[0] = np.array(params[0])
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params[1] = np.array(params[1])
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np.testing.assert_array_equal(
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params[0], params[1].astype(np.float16)
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)
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return params[0].astype(np.float32)
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else:
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self.assertIsNotNone(params[0])
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self.assertIsNone(params[1])
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params[0] = np.array(params[0])
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return params[0]
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exe.run(startup_prog)
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if multi_precision:
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optimizer.amp_init(place)
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weight_np, bias_np = None, None
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for i in range(n):
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feed_dict = {'x': x_np}
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weight_np, bias_np = exe.run(
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main_prog, feed=feed_dict, fetch_list=[weight, bias]
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)
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weight_np = weight_np.astype('float32')
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bias_np = bias_np.astype('float32')
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np.testing.assert_array_equal(weight_np, get_parameter(weight))
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np.testing.assert_array_equal(bias_np, get_parameter(bias))
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return weight_np, bias_np
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@switch_to_static_graph
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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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place = get_device_place()
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x_np = np.random.random(size=[5, 10]).astype('float32')
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weight_1, bias_1 = self.check_main(x_np, place, multi_precision=False)
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weight_2, bias_2 = self.check_main(x_np, place, multi_precision=True)
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weight_3, bias_3 = self.check_amp_in_pir(x_np, place)
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self.assertTrue(np.all(np.abs(weight_1 - weight_2) < 1e-3))
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self.assertTrue(np.all(np.abs(bias_1 - bias_2) < 1e-7))
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self.assertTrue(np.all(np.abs(weight_1 - weight_3) < 1e-3))
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self.assertTrue(np.all(np.abs(bias_1 - bias_3) < 1e-7))
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if __name__ == "__main__":
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unittest.main()
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