WildfireDetection/Swin-Transformer/apex/tests/L0/run_optimizers/test_fused_optimizer.py

from itertools import product
import random
import unittest

import torch

import apex


class TestFusedOptimizer(unittest.TestCase):
    def setUp(self, max_abs_diff=1e-3, max_rel_diff=1, iters=7):
        self.max_abs_diff = max_abs_diff
        self.max_rel_diff = max_rel_diff
        self.iters = iters
        torch.manual_seed(9876)

    def tearDown(self):
        pass

    def gen_param_optim(self, tensors, options, tst_options=None):

        # Adding this to make backward compatible with existing tests. Just in
        # case "tst_options" are not provided, it gets a copy of options
        # which contains the parameters for the reference optimizer
        if tst_options == None:
            tst_options = options

        ref_param = []
        tst_param = []
        for tensor in tensors:
            ref_param.append(torch.nn.Parameter(tensor.clone()))
            tst_param.append(torch.nn.Parameter(tensor.clone()))

        ref_optim = self.ref_optim(ref_param, **options)
        tst_optim = self.fused_optim(tst_param, **tst_options)

        return (ref_param, tst_param, ref_optim, tst_optim)

    def gen_grad(self, ref_param, tst_param):
        for p_ref, p_tst in zip(ref_param, tst_param):
            p_ref.grad = torch.rand_like(p_ref)
            p_tst.grad = p_ref.grad

    def gen_mixed_grad(self, ref_param, tst_param, scale=1.0):
        half_grads = []
        for p_ref, p_tst in zip(ref_param, tst_param):
            half_grads.append(torch.rand_like(p_ref).half())
            p_ref.grad = half_grads[-1].float() / scale
        return half_grads

    def get_max_diff(self, ref_param, tst_param):
        max_abs_diff = max_rel_diff = 0
        for p_ref, p_tst in zip(ref_param, tst_param):
            max_abs_diff_p = (p_ref - p_tst).abs().max().item()
            max_rel_diff_p = ((p_ref - p_tst) / p_ref).abs().max().item()

            if max_abs_diff_p > max_abs_diff:  max_abs_diff = max_abs_diff_p
            if max_rel_diff_p > max_rel_diff:  max_rel_diff = max_rel_diff_p

        return max_abs_diff, max_rel_diff

    def gen_single_type_test(self, param_type=torch.float, device='cuda', *, skip_assert: bool = False):
        nelem = 278011

        # Some ref and test optimizers may require different set of options.
        # This is a quick workaround to add that functionality while making
        # minimum changes in existing code.
        # If there is no "tst_options" field provided, safe to initialize
        # the test optimizer with the parameters of reference optimizer.
        if not hasattr(self, 'tst_options'):
            self.tst_options = self.options

        tensor = torch.rand(nelem, dtype=param_type, device=device)

        ref_param, tst_param, ref_optim, tst_optim = \
            self.gen_param_optim([tensor], self.options, self.tst_options)

        for i in range(self.iters):
            self.gen_grad(ref_param, tst_param)
            ref_optim.step()
            tst_optim.step()
            if skip_assert:
                return
            max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
            self.assertLessEqual(max_rel_diff, self.max_rel_diff)


class TestFusedAdam(TestFusedOptimizer):

    def setUp(self):
        super().setUp()
        self.options = {'lr':5e-4, 'betas':(0.9, 0.999), 'eps':1e-08,
            'weight_decay': 0, 'amsgrad': False}
        self.ref_optim = torch.optim.Adam
        self.fused_optim = apex.optimizers.FusedAdam

    def test_float(self):
        self.gen_single_type_test(param_type=torch.float)

    # NOTE(mkozuki): Current threshold values look too small for BFloat16.
    # TODO(mkozuki): Refactor `TestFusedOptimizer`
    def test_half(self):
        self.gen_single_type_test(param_type=torch.float16, skip_assert=True)

    def test_bfloat16(self):
        self.gen_single_type_test(param_type=torch.bfloat16, skip_assert=True)

    @unittest.skipIf(torch.cuda.device_count()<2, "more than 1 GPU required")
    def test_multi_device(self):
        devices = ("cuda:0", "cuda:1")
        for current_dev, tensor_dev in product(devices, devices):
            with torch.cuda.device(current_dev):
                self.gen_single_type_test(param_type=torch.float, device=tensor_dev)

    @unittest.skip('Disable until 8/1/2019 adam/adamw upstream picked')
    def test_multi_params(self):
        sizes = [[4096, 1024], [4096], [4096, 2048], [32320, 1024], [1]]

        tensors = []
        for size in sizes:
            tensors.append(torch.rand(size, dtype=torch.float, device='cuda'))
        ref_param, tst_param, ref_optim, tst_optim = \
            self.gen_param_optim(tensors, self.options)

        for i in range(self.iters):
            self.gen_grad(ref_param, tst_param)
            ref_optim.step()
            tst_optim.step()
            max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
            self.assertLessEqual(max_rel_diff, self.max_rel_diff)

    @unittest.skip('No longer support fuse scaling')
    def test_scale(self):
        nelem = 278011
        tensor = torch.rand(nelem, dtype=torch.float, device='cuda')
        ref_param, tst_param, ref_optim, tst_optim = \
            self.gen_param_optim([tensor], self.options)

        for i in range(self.iters):
            scale = random.random() * 1000
            half_grads = self.gen_mixed_grad(ref_param, tst_param, scale)
            ref_optim.step()
            tst_optim.step(grads=half_grads, scale=scale)
            max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)

            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
            self.assertLessEqual(max_rel_diff, self.max_rel_diff)

    @unittest.skip('No longer support output fp16 param')
    def test_fp16_output(self):
        nelem = 278011

        tensor = torch.rand(nelem, dtype=torch.float, device='cuda')
        ref_param, tst_param, ref_optim, tst_optim = \
            self.gen_param_optim([tensor], self.options)

        fp16_param = torch.nn.Parameter(tensor.clone().half())

        for i in range(self.iters):
            half_grads = self.gen_mixed_grad(ref_param, tst_param)
            ref_optim.step()
            tst_optim.step(grads=half_grads, output_params=[fp16_param])

            max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
            self.assertLessEqual(max_rel_diff, self.max_rel_diff)

            max_abs_diff, max_rel_diff = self.get_max_diff(tst_param, \
                [fp16_param.float()])
            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
            self.assertLessEqual(max_rel_diff, self.max_rel_diff)

    def test_adam_option(self):
        nelem = 1
        adam_option = {'lr':0.01, 'betas':(0.6, 0.9), 'eps':3e-06,
            'weight_decay':0, 'amsgrad':False}

        tensor = torch.rand(nelem, dtype=torch.float, device='cuda')
        ref_param, tst_param, ref_optim, tst_optim = \
            self.gen_param_optim([tensor], adam_option)

        for i in range(self.iters):
            self.gen_grad(ref_param, tst_param)
            ref_optim.step()
            tst_optim.step()
            max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)

            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
            self.assertLessEqual(max_rel_diff, self.max_rel_diff)
            
    def test_frozen_model(self):
        nelem = 1
        adam_option = {'lr':0.01, 'betas':(0.6, 0.9), 'eps':3e-06,
            'weight_decay':0, 'amsgrad':False}

        tensor = torch.rand(nelem, dtype=torch.float, device='cuda')
        ref_param, tst_param, ref_optim, tst_optim = \
            self.gen_param_optim([tensor], adam_option)

        #Add an empty param group which may occur for pipeline parallel p-tuning
        tst_optim.add_param_group({"params": []})

        for i in range(self.iters):
            self.gen_grad(ref_param, tst_param)
            ref_optim.step()
            tst_optim.step()
            max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)

            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
            self.assertLessEqual(max_rel_diff, self.max_rel_diff)

            
class TestFusedAdagrad(TestFusedOptimizer):
    def __init__(self, *args, **kwargs):
        super(TestFusedAdagrad, self).__init__(*args, **kwargs)
        self.options = {"lr": 5e-4, "eps": 1e-08, "weight_decay": 1.0e-5}
        self.ref_optim = torch.optim.Adagrad
        self.fused_optim = apex.optimizers.FusedAdagrad

    def test_float(self):
        self.gen_single_type_test(param_type=torch.float)

    @unittest.skip("PyTorch optimizer is not numerically correct for fp16")
    def test_half(self):
        self.gen_single_type_test(param_type=torch.float16)

    @unittest.skipIf(torch.cuda.device_count()<2, "more than 1 GPU required")
    def test_multi_device(self):
        devices = ("cuda:0", "cuda:1")
        for current_dev, tensor_dev in product(devices, devices):
            with torch.cuda.device(current_dev):
                self.gen_single_type_test(param_type=torch.float, device=tensor_dev)


    def test_multi_params(self):
        sizes = [[4096, 1024], [4096], [4096, 2048], [32320, 1024], [1]]
        adagrad_option = {"lr": 5e-4, "eps": 1e-08, "weight_decay": 0}

        tensors = []
        for size in sizes:
            tensors.append(torch.rand(size, dtype=torch.float, device="cuda"))
        ref_param, tst_param, ref_optim, tst_optim = self.gen_param_optim(
            tensors, adagrad_option
        )

        for _ in range(self.iters):
            self.gen_grad(ref_param, tst_param)
            ref_optim.step()
            tst_optim.step()
            max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
            self.assertLessEqual(max_rel_diff, self.max_rel_diff)

    @unittest.skipIf(torch.cuda.device_count()<2, "more than 1 GPU required")
    def test_multi_params_different_devices_throws(self):
        sizes = [[4096, 1024], [4096], [4096, 2048], [32320, 1024], [1]]
        adagrad_option = {"lr": 5e-4, "eps": 1e-08, "weight_decay": 0}

        tensors = []
        for i, size in enumerate(sizes):
            tensors.append(torch.rand(size, dtype=torch.float, device="cuda:"+str(i % 2)))
        ref_param, tst_param, ref_optim, tst_optim = self.gen_param_optim(
            tensors, adagrad_option
        )
        self.gen_grad(ref_param, tst_param)
        with self.assertRaisesRegex(RuntimeError, "not on the same device"):
            tst_optim.step()

    def test_adagrad_option(self):
        nelem = 1
        adagrad_option = {"lr": 0.01, "eps": 3e-06, "weight_decay": 0}

        tensor = torch.rand(nelem, dtype=torch.float, device="cuda")
        ref_param, tst_param, ref_optim, tst_optim = self.gen_param_optim(
            [tensor], adagrad_option
        )

        for _ in range(self.iters):
            self.gen_grad(ref_param, tst_param)
            ref_optim.step()
            tst_optim.step()
            max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)

            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
            self.assertLessEqual(max_rel_diff, self.max_rel_diff)


class TestFusedSGD(TestFusedOptimizer):
    def __init__(self, *args, **kwargs):
        super(TestFusedSGD, self).__init__(*args, **kwargs)
        self.options = {"lr": .25, "momentum": .125}
        self.ref_optim = torch.optim.SGD
        self.fused_optim = apex.optimizers.FusedSGD

    def test_float(self):
        self.gen_single_type_test(param_type=torch.float)

    def test_half(self):
        self.gen_single_type_test(param_type=torch.float16)

    @unittest.skipIf(torch.cuda.device_count()<2, "more than 1 GPU required")
    def test_multi_device(self):
        devices = ("cuda:0", "cuda:1")
        for current_dev, tensor_dev in product(devices, devices):
            with torch.cuda.device(current_dev):
                self.gen_single_type_test(param_type=torch.float, device=tensor_dev)

if __name__ == '__main__':
    unittest.main()