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42 lines
1.4 KiB
42 lines
1.4 KiB
3 years ago
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import torch
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import torch.nn as nn
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from torch.optim.adam import Adam
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from torch.optim import AdamW
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from colossalai.nn.optimizer.hybrid_adam import HybridAdam
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from colossalai.testing import parameterize
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RE = 1024
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@parameterize('adamw', [False, True])
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@parameterize('device', ['cpu', 'cuda:0'])
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@parameterize('p_dtype', [torch.float])
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@parameterize('g_dtype', [torch.float, torch.half])
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def test_adam(adamw, device, p_dtype, g_dtype):
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rng_state = torch.get_rng_state()
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p = nn.Parameter(torch.rand(64).to(device, p_dtype))
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torch.set_rng_state(rng_state)
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2 years ago
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p_copy = nn.Parameter(torch.rand(64).to(device).float())
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3 years ago
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if adamw:
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optim = HybridAdam([p], lr=1e-3, adamw_mode=True)
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torch_optim = AdamW([p_copy], lr=1e-3)
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else:
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optim = HybridAdam([p], lr=1e-3)
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torch_optim = Adam([p_copy], lr=1e-3)
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print(f"adaw mode {adamw}, device {device}, p_dtype {p_dtype}, g_dtype {g_dtype}")
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for i in range(RE):
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p.grad = torch.rand(64).to(device, p_dtype)
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p_copy.grad = p.grad.clone().float()
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p.grad.data = p.grad.data.to(g_dtype)
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optim.step()
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torch_optim.step()
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if torch.isnan(p.data).any() or torch.isnan(p_copy.data).any():
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continue
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assert torch.allclose(p.data, p_copy.data, 1e-4, 1e-2), \
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2 years ago
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f"adaw mode {adamw}, device {device}, p_dtype {p_dtype}, g_dtype {g_dtype}"
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