mirror of https://github.com/hpcaitech/ColossalAI
119 lines
3.4 KiB
Python
119 lines
3.4 KiB
Python
import math
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import torch
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from colossalai.testing import parameterize
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def torch_adam_update(
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step,
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lr,
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beta1,
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beta2,
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eps,
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weight_decay,
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param,
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grad,
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exp_avg,
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exp_avg_sq,
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use_adamw,
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):
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bias_correction1 = 1 - beta1**step
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bias_correction2 = 1 - beta2**step
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if weight_decay != 0:
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if use_adamw:
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# Perform stepweight decay
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param.mul_(1 - lr * weight_decay)
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else:
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grad = grad.add(param, alpha=weight_decay)
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# Decay the first and second moment running average coefficient
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)
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step_size = lr / bias_correction1
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param.addcdiv_(exp_avg, denom, value=-step_size)
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def assertLess(data_diff, threshold, msg):
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assert data_diff < threshold, msg
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def assertTrue(condition, msg):
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assert condition, msg
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@parameterize('adamw', [True, False])
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@parameterize('step', [1, 2])
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@parameterize('p_dtype', [torch.float, torch.half])
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@parameterize('g_dtype', [torch.float, torch.half])
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def test_cpu_adam(adamw, step, p_dtype, g_dtype):
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lr = 1e-3
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beta1, beta2 = 0.9, 0.999
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eps = 1e-8
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weight_decay = 0
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for i in range(1024):
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p_data = torch.rand(64, dtype=p_dtype)
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p_data_copy = p_data.clone().float()
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p_grad = torch.rand(64, dtype=g_dtype)
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p_grad_copy = p_grad.clone().float()
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exp_avg = torch.rand(p_data.shape)
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exp_avg_copy = exp_avg.clone()
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exp_avg_sq = torch.rand(p_data.shape)
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exp_avg_sq_copy = exp_avg_sq.clone()
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try:
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import cpu_adam
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cpu_adam_op = cpu_adam
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except:
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raise ImportError("Import cpu adam error, please install colossal from source code")
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cpu_adam_op.create_adam(0, lr, beta1, beta2, eps, weight_decay, adamw, False)
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cpu_adam_op.adam_update(
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0,
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step,
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lr,
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beta1,
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beta2,
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eps,
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weight_decay,
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True,
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p_data.view(-1), # fp32 data
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p_grad.view(-1), # fp32 grad
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exp_avg.view(-1),
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exp_avg_sq.view(-1),
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-1,
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)
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torch_adam_update(
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step,
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lr,
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beta1,
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beta2,
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eps,
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weight_decay,
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p_data_copy, # fp32 data
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p_grad_copy, # fp32 grad
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exp_avg_copy,
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exp_avg_sq_copy,
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adamw,
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)
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var = p_data_copy - p_data
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data_diff = torch.max(torch.abs(var))
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threshold = 1e-3
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assertLess(
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data_diff,
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threshold,
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f"p_data diff {data_diff}. failed check, step {step}, lr {lr}, eps "
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f"{eps} beta1 {beta1} beta2 {beta2} weight_decay {weight_decay} p_dtype {p_dtype}, g_dtype {g_dtype}",
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)
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max_grad_diff = torch.max(torch.abs(p_grad_copy - p_grad))
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assertTrue(max_grad_diff < threshold, f"diff {max_grad_diff}")
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max_exp_avg_diff = torch.max(torch.abs(exp_avg_copy - exp_avg))
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assertTrue(max_exp_avg_diff < threshold, f"max_exp_avg_diff {max_exp_avg_diff}")
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max_exp_avg_sq_diff = torch.max(torch.abs(exp_avg_sq_copy - exp_avg_sq))
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assertTrue(max_exp_avg_sq_diff < threshold, f"max_exp_avg_sq_diff {max_exp_avg_sq_diff}")
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