wehub-resource-sync
686 lines
21 KiB
Python
686 lines
21 KiB
Python
# Copyright (c) 2024 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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from copy import deepcopy
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import numpy as np
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from op_test import (
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OpTest,
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get_device,
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get_device_place,
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get_devices,
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get_places,
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is_custom_device,
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)
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import paddle
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from paddle import base
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from paddle.framework import core
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RTOL = 1e-06
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ATOL = 1e-06
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def radam_step(inputs, attributes, dtype="float32"):
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param = inputs['param']
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grad = inputs['grad']
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lr = inputs['learning_rate']
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# accumulators
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beta1_pow = inputs['beta1_pow']
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beta2_pow = inputs['beta2_pow']
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rho = inputs['rho']
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moment1 = inputs['moment1']
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moment2 = inputs['moment2']
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# attrs
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epsilon = attributes['epsilon']
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beta1 = attributes['beta1']
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beta2 = attributes['beta2']
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rho_inf = 2 / (1 - beta2) - 1
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beta1_pow *= beta1
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beta2_pow *= beta2
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rho = (rho * (beta2 - beta2_pow) + beta2_pow) / (1 - beta2_pow)
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moment1 = beta1 * moment1 + (1.0 - beta1) * grad
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moment2 = beta2 * moment2 + (1.0 - beta2) * grad * grad
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moment1_hat = moment1 / (1 - beta1_pow)
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rho_t = rho_inf - 2 * rho
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if rho_t.reshape(-1)[0] > 5:
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l_t = np.sqrt(1 - beta2_pow) / (np.sqrt(moment2) + epsilon)
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r_t = np.sqrt(
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((rho_t - 4) * (rho_t - 2) * rho_inf)
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/ ((rho_inf - 4) * (rho_inf - 2) * rho_t)
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)
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param = param - lr * moment1_hat * r_t * l_t
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else:
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param = param - lr * moment1_hat
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# get accumulators
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return (
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param.astype(dtype),
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beta1_pow.astype(dtype),
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beta2_pow.astype(dtype),
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rho.astype(dtype),
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moment1.astype(dtype),
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moment2.astype(dtype),
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)
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def radam_wrapper(
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param,
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grad,
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lr,
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beta1_pow,
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beta2_pow,
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rho,
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moment1,
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moment2,
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master_param=None,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-8,
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multi_precision=False,
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):
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_, _, _, _, _, _, _ = paddle._C_ops.radam_(
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param,
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grad,
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lr,
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beta1_pow,
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beta2_pow,
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rho,
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moment1,
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moment2,
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master_param,
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beta1,
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beta2,
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epsilon,
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multi_precision,
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)
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class TestRAdamOp(OpTest):
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def _init_param(self):
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self.beta1 = 0.78
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self.beta2 = 0.915
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self.epsilon = 1e-8
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def setUp(self):
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'''Test RAdam Op with supplied attributes'''
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np.random.seed(2024)
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self.op_type = "radam"
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self.python_api = radam_wrapper
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self.python_out_sig = ['out']
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param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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learning_rate = np.array(0.003).astype("float32")
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self._init_param()
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# accumulators
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beta1_pow = (np.ones((102, 105)) * (self.beta1**3)).astype("float32")
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beta2_pow = (np.ones((102, 105)) * (self.beta2**3)).astype("float32")
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rho_inf = 2 / (1 - self.beta2) - 1
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rho = (np.ones((102, 105)) * self._init_rho(rho_inf)).astype("float32")
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moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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# The second moment is positive
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moment2 = np.random.random((102, 105)).astype("float32")
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self.inputs = {
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"param": param,
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"grad": grad,
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"beta1_pow": beta1_pow,
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"beta2_pow": beta2_pow,
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"rho": rho,
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"moment1": moment1,
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"moment2": moment2,
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"learning_rate": learning_rate,
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}
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self.attrs = {
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"epsilon": self.epsilon,
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"beta1": self.beta1,
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"beta2": self.beta2,
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}
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(
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param_out,
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beta1_pow_out,
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beta2_pow_out,
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rho_out,
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moment1_out,
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moment2_out,
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) = radam_step(
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deepcopy(self.inputs), deepcopy(self.attrs), dtype="float32"
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)
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self.outputs = {
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"param_out": param_out,
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"beta1_pow_out": beta1_pow_out,
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"beta2_pow_out": beta2_pow_out,
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"rho_out": rho_out,
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"moment1_out": moment1_out,
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"moment2_out": moment2_out,
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}
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def _init_rho(self, rho_inf):
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return np.array((rho_inf - 5) / 2 + 5.0).astype("float32")
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def test_check_output(self):
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self.check_output(check_pir=True, rtol=RTOL, atol=ATOL)
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class TestRAdamOpWithDefault(TestRAdamOp):
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def _init_param(self):
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self.beta1 = 0.9
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self.beta2 = 0.999
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self.epsilon = 1.0e-8
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class TestRAdamOpRhoSmall(TestRAdamOp):
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def _init_rho(self, rho_inf):
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return np.array((rho_inf - 5) / 2 - 5.0).astype("float32")
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestRAdamOpGPU(TestRAdamOp):
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def test_check_output(self):
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self.check_output_with_place(
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get_device_place(), check_pir=True, rtol=RTOL, atol=ATOL
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)
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class TestRAdamOpGPURhoSmall(TestRAdamOpGPU):
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def _init_rho(self, rho_inf):
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return np.array((rho_inf - 5) / 2 - 5.0).astype("float32")
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class TestRAdamOpMultipleSteps(TestRAdamOp):
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num_steps = 10
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def test_check_output(self):
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for _ in range(self.num_steps):
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(
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param_out,
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beta1_pow_out,
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beta2_pow_out,
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rho_out,
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moment1_out,
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moment2_out,
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) = radam_step(
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deepcopy(self.inputs), deepcopy(self.attrs), dtype="float32"
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)
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self.outputs = {
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"param_out": param_out,
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"beta1_pow_out": beta1_pow_out,
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"beta2_pow_out": beta2_pow_out,
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"rho_out": rho_out,
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"moment1_out": moment1_out,
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"moment2_out": moment2_out,
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}
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# Verify output for this step
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self.check_output()
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# Output of this step becomes input for next step
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self.inputs['param'] = param_out
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self.inputs['beta1_pow'] = beta1_pow_out
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self.inputs['beta2_pow'] = beta2_pow_out
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self.inputs['rho'] = rho_out
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self.inputs['moment1'] = moment1_out
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self.inputs['moment2'] = moment2_out
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# Randomize gradient for next step
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self.inputs['grad'] = np.random.uniform(-1, 1, (102, 105)).astype(
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"float32"
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)
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class TestRAdamAPI(unittest.TestCase):
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def test_radam_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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radam = paddle.optimizer.RAdam(
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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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for _ in range(2):
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out = linear(a)
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out.backward()
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radam.step()
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radam.clear_gradients()
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def test_radam_apply_gradients(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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radam = paddle.optimizer.RAdam(
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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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for _ in range(2):
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out = linear(a)
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loss = paddle.mean(out)
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param_grads = radam.backward(loss)
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radam.apply_gradients(param_grads)
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radam.clear_gradients()
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def test_radam_static(self):
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paddle.enable_static()
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place = base.CPUPlace()
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shape = [2, 3, 8, 8]
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exe = base.Executor(place)
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train_prog = base.Program()
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startup = base.Program()
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with (
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base.program_guard(train_prog, startup),
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base.unique_name.guard(),
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):
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data = paddle.static.data(name="data", shape=shape)
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hidden = paddle.static.nn.fc(x=data, size=10)
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loss = paddle.mean(hidden)
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beta1 = 0.85
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beta2 = 0.95
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opt = paddle.optimizer.RAdam(
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learning_rate=1e-5,
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beta1=beta1,
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beta2=beta2,
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weight_decay=0.01,
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epsilon=1e-8,
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)
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opt.minimize(loss)
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exe.run(startup)
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data_np = np.random.random(shape).astype("float32")
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rets = exe.run(train_prog, feed={"data": data_np}, fetch_list=[loss])
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assert rets[0] is not None
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paddle.disable_static()
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def test_pir_radam(self):
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with paddle.pir_utils.IrGuard():
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place = base.CPUPlace()
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shape = [2, 3, 8, 8]
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exe = base.Executor(place)
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train_prog = paddle.static.Program()
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startup = paddle.static.Program()
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with (
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paddle.static.program_guard(train_prog, startup),
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base.unique_name.guard(),
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):
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data = paddle.static.data(name="data", shape=shape)
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hidden = paddle.static.nn.fc(x=data, size=10)
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loss = paddle.mean(hidden)
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beta1 = 0.85
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beta2 = 0.95
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opt = paddle.optimizer.RAdam(
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learning_rate=1e-5,
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beta1=beta1,
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beta2=beta2,
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weight_decay=0.01,
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epsilon=1e-8,
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)
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opt.minimize(loss)
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exe.run(startup)
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data_np = np.random.random(shape).astype("float32")
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rets = exe.run(
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train_prog, feed={"data": data_np}, fetch_list=[loss]
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)
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assert rets[0] is not None
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def test_radam_invalid_input(self):
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paddle.disable_static()
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linear = paddle.nn.Linear(10, 10)
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with self.assertRaises(ValueError):
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_ = paddle.optimizer.RAdam(
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learning_rate=-1, parameters=linear.parameters()
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)
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with self.assertRaises(ValueError):
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_ = paddle.optimizer.RAdam(
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0.1, beta1=-1, parameters=linear.parameters()
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)
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with self.assertRaises(ValueError):
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_ = paddle.optimizer.RAdam(
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0.1, beta2=-1, parameters=linear.parameters()
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)
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with self.assertRaises(ValueError):
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_ = paddle.optimizer.RAdam(
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0.1, beta1=2.0, parameters=linear.parameters()
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)
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with self.assertRaises(ValueError):
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_ = paddle.optimizer.RAdam(
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0.1, beta2=2.0, parameters=linear.parameters()
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)
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with self.assertRaises(ValueError):
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_ = paddle.optimizer.RAdam(
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0.1, epsilon=-1, parameters=linear.parameters()
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)
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class TestRAdamAPIWeightDecay(unittest.TestCase):
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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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radam = paddle.optimizer.RAdam(
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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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for _ in range(2):
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out = linear(a)
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out.backward()
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radam.step()
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radam.clear_gradients()
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class TestRAdamAPIGroup(TestRAdamAPI):
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def test_radam_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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radam = paddle.optimizer.RAdam(
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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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{'params': linear_2.parameters(), 'weight_decay': 0.001},
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],
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weight_decay=0.01,
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)
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for _ in range(2):
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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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radam.step()
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radam.clear_gradients()
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class TestRAdamMultiPrecision(unittest.TestCase):
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def _test_radam_dygraph_place_amp(self, place, use_amp=False):
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paddle.disable_static()
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paddle.seed(10)
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paddle.set_device(place)
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input = paddle.randn((5, 5))
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model = paddle.nn.Linear(5, 5)
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optimizer = paddle.optimizer.RAdam(
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parameters=[
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{
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'params': model.parameters(),
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'weight_decay': 0.001,
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'beta1': 0.1,
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'beta2': 0.99,
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}
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],
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)
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optimizer._multi_precision = use_amp
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for _ in range(2):
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if place == get_device() and use_amp:
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model = paddle.amp.decorate(models=model, level='O2')
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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if place == get_device() and use_amp:
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with paddle.amp.auto_cast(level='O2'):
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output = model(input)
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loss = paddle.mean(output)
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scaled = scaler.scale(loss)
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scaled.backward()
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scaler.step(optimizer)
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optimizer.clear_grad()
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else:
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output = model(input)
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loss = paddle.mean(output)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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def test_main(self):
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for place in get_devices():
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use_amp_list = [True, False]
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for use_amp in use_amp_list:
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self._test_radam_dygraph_place_amp(place, use_amp)
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class TestNdamaxMultiPrecision2_0(unittest.TestCase):
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def dygraph_radam_mp(self, mp, use_amp):
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paddle.disable_static()
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paddle.seed(2024)
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paddle.set_device(get_device())
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input = paddle.randn((2, 2))
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model = paddle.nn.Linear(2, 2)
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optimizer = paddle.optimizer.RAdam(0.1, parameters=model.parameters())
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optimizer._multi_precision = mp
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if use_amp:
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model = paddle.amp.decorate(models=model, level='O2')
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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for idx in range(5):
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if use_amp:
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with paddle.amp.auto_cast(level='O2'):
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output = model(input)
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loss = paddle.mean(output)
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scaled = scaler.scale(loss)
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scaled.backward()
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scaler.minimize(optimizer, scaled)
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optimizer.clear_grad()
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else:
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output = model(input)
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loss = paddle.mean(output)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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return output, model.parameters()
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def static_radam_mp(self, mp, use_amp):
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paddle.seed(2024)
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paddle.enable_static()
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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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linear = paddle.nn.Linear(2, 10)
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optimizer = paddle.optimizer.NAdam(
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learning_rate=0.1, parameters=linear.parameters()
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)
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if use_amp:
|
|
data = paddle.static.data('X', [2, 2], 'float16')
|
|
if paddle.framework.in_pir_mode():
|
|
linear, optimizer = paddle.amp.decorate(
|
|
models=linear,
|
|
optimizers=optimizer,
|
|
level='O2',
|
|
master_grad=False,
|
|
)
|
|
else:
|
|
optimizer = paddle.static.amp.decorate(
|
|
optimizer,
|
|
init_loss_scaling=128.0,
|
|
use_dynamic_loss_scaling=True,
|
|
use_pure_fp16=True,
|
|
use_fp16_guard=False,
|
|
)
|
|
else:
|
|
data = paddle.static.data('X', [2, 2], 'float32')
|
|
|
|
out = linear(data)
|
|
loss = paddle.mean(out)
|
|
optimizer.minimize(loss)
|
|
|
|
exe.run(startup_program)
|
|
|
|
np.random.seed(2024)
|
|
if use_amp:
|
|
optimizer.amp_init(
|
|
place=get_device_place(), scope=paddle.static.global_scope()
|
|
)
|
|
x = np.random.random(size=(2, 2)).astype('float16')
|
|
else:
|
|
x = np.random.random(size=(2, 2)).astype('float32')
|
|
out = []
|
|
for idx in range(5):
|
|
(loss_data,) = exe.run(
|
|
train_program, feed={"X": x}, fetch_list=[loss]
|
|
)
|
|
out.append(loss_data)
|
|
|
|
return out
|
|
|
|
def test_main(self):
|
|
if not (paddle.is_compiled_with_cuda() or is_custom_device()):
|
|
return
|
|
"Test dygraph mode"
|
|
output1_dy, params1_dy = self.dygraph_radam_mp(use_amp=True, mp=True)
|
|
output2_dy, params2_dy = self.dygraph_radam_mp(use_amp=False, mp=False)
|
|
np.testing.assert_allclose(
|
|
output1_dy.astype('float32').numpy(),
|
|
output2_dy.astype('float32').numpy(),
|
|
rtol=1e-05,
|
|
atol=0.1,
|
|
)
|
|
for idx in range(len(params1_dy)):
|
|
np.testing.assert_allclose(
|
|
params1_dy[idx].astype('float32').numpy(),
|
|
params2_dy[idx].astype('float32').numpy(),
|
|
rtol=1e-05,
|
|
atol=0.1,
|
|
)
|
|
"Test static mode"
|
|
output1_st = self.static_radam_mp(use_amp=True, mp=True)
|
|
output2_st = self.static_radam_mp(use_amp=False, mp=False)
|
|
for idx in range(len(output1_st)):
|
|
np.testing.assert_allclose(
|
|
output1_st[idx].astype('float32'),
|
|
output2_st[idx].astype('float32'),
|
|
rtol=1e-05,
|
|
atol=0.1,
|
|
)
|
|
|
|
|
|
class TestRAdamGroupWithLR(TestRAdamAPI):
|
|
def test_radam(self):
|
|
paddle.disable_static()
|
|
value = np.arange(26).reshape(2, 13).astype("float32")
|
|
a = paddle.to_tensor(value)
|
|
linear_1 = paddle.nn.Linear(13, 5)
|
|
linear_2 = paddle.nn.Linear(5, 3)
|
|
radam = paddle.optimizer.RAdam(
|
|
learning_rate=paddle.optimizer.lr.PiecewiseDecay(
|
|
boundaries=[3, 6], values=[0.1, 0.2, 0.3]
|
|
),
|
|
parameters=[
|
|
{
|
|
'params': linear_1.parameters(),
|
|
'learning_rate': 0.1,
|
|
},
|
|
{
|
|
'params': linear_2.parameters(),
|
|
'weight_decay': 0.001,
|
|
},
|
|
],
|
|
weight_decay=0.01,
|
|
)
|
|
|
|
for _ in range(2):
|
|
out = linear_1(a)
|
|
out = linear_2(out)
|
|
out.backward()
|
|
radam.step()
|
|
radam.clear_gradients()
|
|
|
|
|
|
def main_test_func(place, dtype):
|
|
paddle.enable_static()
|
|
main = base.Program()
|
|
startup = base.Program()
|
|
with (
|
|
base.program_guard(main, startup),
|
|
base.scope_guard(base.Scope()),
|
|
):
|
|
x = paddle.static.data(name='x', shape=[None, 13], dtype=dtype)
|
|
y = paddle.static.data(name='y', shape=[None, 1], dtype=dtype)
|
|
y_predict = paddle.static.nn.fc(x, size=1)
|
|
cost = paddle.nn.functional.square_error_cost(input=y_predict, label=y)
|
|
avg_cost = paddle.mean(cost)
|
|
|
|
radam_optimizer = paddle.optimizer.RAdam(0.01)
|
|
radam_optimizer.minimize(avg_cost)
|
|
|
|
fetch_list = [avg_cost]
|
|
train_reader = list(
|
|
zip(
|
|
np.random.rand(101, 13),
|
|
np.random.randint(12, size=(101, 1)),
|
|
)
|
|
)
|
|
feeder = base.DataFeeder(place=place, feed_list=[x, y])
|
|
exe = base.Executor(place)
|
|
exe.run(base.default_startup_program())
|
|
for data in train_reader:
|
|
exe.run(main, feed=feeder.feed([data]), fetch_list=fetch_list)
|
|
|
|
paddle.disable_static()
|
|
|
|
|
|
class RAdamFp32Test(unittest.TestCase):
|
|
def setUp(self):
|
|
self.dtype = "float32"
|
|
|
|
def test_main(self):
|
|
for p in get_places():
|
|
main_test_func(p, self.dtype)
|
|
|
|
|
|
class RAdamFp64Test(RAdamFp32Test):
|
|
def setUp(self):
|
|
self.dtype = 'float64'
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|