Upload apex-master/tests/L0/run_optimizers/test_fused_optimizer.py with huggingface_hub

apex-master/tests/L0/run_optimizers/test_fused_optimizer.py

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+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()