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132 lines
5.7 KiB
132 lines
5.7 KiB
# This test checks adam kernels
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# Baseline is pure fp32 torch adam optimizer
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import math
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from abc import abstractmethod
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from typing import Type
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import pytest
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import torch
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from torch import Tensor
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from colossalai.utils import get_current_device, multi_tensor_applier
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_FUSED_ALLOWED_P_G_TYPES = [(torch.float, torch.half), (torch.float, torch.float), (torch.half, torch.float),
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(torch.half, torch.half), (torch.bfloat16, torch.float), (torch.float, torch.bfloat16),
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(torch.bfloat16, torch.bfloat16)]
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_CPU_ALLOWED_P_G_TYPES = [(torch.float, torch.half), (torch.float, torch.float), (torch.half, torch.float),
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(torch.half, torch.half)]
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class AdamKernel:
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def __init__(self, lr: float, beta1: float, beta2: float, eps: float, weight_decay: float, use_adamw: bool) -> None:
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self.lr = lr
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self.beta1 = beta1
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self.beta2 = beta2
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self.eps = eps
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self.weight_decay = weight_decay
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self.use_adamw = use_adamw
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@abstractmethod
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def update(self, step: int, param: Tensor, grad: Tensor, exp_avg: Tensor, exp_avg_sq: Tensor):
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pass
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class TorchAdamKernel(AdamKernel):
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def update(self, step: int, param: Tensor, grad: Tensor, exp_avg: Tensor, exp_avg_sq: Tensor):
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bias_correction1 = 1 - self.beta1**step
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bias_correction2 = 1 - self.beta2**step
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if self.weight_decay != 0:
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if self.use_adamw:
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# Perform stepweight decay
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param.mul_(1 - self.lr * self.weight_decay)
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else:
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grad = grad.add(param, alpha=self.weight_decay)
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# Decay the first and second moment running average coefficient
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exp_avg.mul_(self.beta1).add_(grad, alpha=1 - self.beta1)
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exp_avg_sq.mul_(self.beta2).addcmul_(grad, grad, value=1 - self.beta2)
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denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(self.eps)
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step_size = self.lr / bias_correction1
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param.addcdiv_(exp_avg, denom, value=-step_size)
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class FusedAdamKernel(AdamKernel):
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def __init__(self, lr: float, beta1: float, beta2: float, eps: float, weight_decay: float, use_adamw: bool) -> None:
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super().__init__(lr, beta1, beta2, eps, weight_decay, use_adamw)
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from colossalai.kernel.op_builder import FusedOptimBuilder
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fused_optim = FusedOptimBuilder().load()
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self.fused_adam = fused_optim.multi_tensor_adam
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self.dummy_overflow_buf = torch.cuda.IntTensor([0])
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def update(self, step: int, param: Tensor, grad: Tensor, exp_avg: Tensor, exp_avg_sq: Tensor):
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multi_tensor_applier(self.fused_adam, self.dummy_overflow_buf, [[grad], [param], [exp_avg], [exp_avg_sq]],
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self.lr, self.beta1, self.beta2, self.eps, step, self.use_adamw, True, self.weight_decay,
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-1)
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class CPUAdamKernel(AdamKernel):
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def __init__(self, lr: float, beta1: float, beta2: float, eps: float, weight_decay: float, use_adamw: bool) -> None:
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super().__init__(lr, beta1, beta2, eps, weight_decay, use_adamw)
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from colossalai.kernel.op_builder import CPUAdamBuilder
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cpu_optim = CPUAdamBuilder().load()
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self.cpu_adam_op = cpu_optim.CPUAdamOptimizer(lr, beta1, beta2, eps, weight_decay, use_adamw)
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def update(self, step: int, param: Tensor, grad: Tensor, exp_avg: Tensor, exp_avg_sq: Tensor):
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self.cpu_adam_op.step(step, self.lr, self.beta1, self.beta2, self.eps, self.weight_decay, True, param.view(-1),
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grad.view(-1), exp_avg.view(-1), exp_avg_sq.view(-1), -1)
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def check_adam_kernel(kernel: Type[AdamKernel], adamw: bool, weight_decay: float, p_dtype: torch.dtype,
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g_dtype: torch.dtype, device: torch.device, n_steps: int, rtol: float, atol: float):
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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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torch_adam = TorchAdamKernel(lr, beta1, beta2, eps, weight_decay, adamw)
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adam_kernel = kernel(lr, beta1, beta2, eps, weight_decay, adamw)
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master_p = torch.rand(64, device=device)
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master_g = torch.rand_like(master_p)
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master_exp_avg = torch.zeros_like(master_p)
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master_exp_avg_sq = torch.zeros_like(master_p)
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p = master_p.clone().to(p_dtype)
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g = master_g.clone().to(g_dtype)
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exp_avg = master_exp_avg.clone()
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exp_avg_sq = master_exp_avg_sq.clone()
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for step in range(1, 1 + n_steps):
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torch_adam.update(step, master_p, master_g, master_exp_avg, master_exp_avg_sq)
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adam_kernel.update(step, p, g, exp_avg, exp_avg_sq)
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# if overflow, the weight won't be updated. so there will be no nan in p
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assert not torch.isnan(p).any()
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assert torch.allclose(master_p, p.float(), rtol=rtol, atol=atol)
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@pytest.mark.parametrize('adamw', [False, True])
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@pytest.mark.parametrize('weight_decay', [0.0, 0.1])
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@pytest.mark.parametrize('p_dtype, g_dtype', _FUSED_ALLOWED_P_G_TYPES)
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def test_fused_adam_kernel(adamw, weight_decay, p_dtype, g_dtype):
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rtol, atol = 1e-5, 1e-8
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if p_dtype is torch.float16 or g_dtype is torch.float16:
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rtol, atol = 1e-3, 1e-3
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if p_dtype is torch.bfloat16 or g_dtype is torch.bfloat16:
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rtol, atol = 4e-3, 4e-3
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check_adam_kernel(FusedAdamKernel, adamw, weight_decay, p_dtype, g_dtype, get_current_device(), 3, rtol, atol)
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@pytest.mark.parametrize('adamw', [False, True])
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@pytest.mark.parametrize('weight_decay', [0.0, 0.1])
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@pytest.mark.parametrize('p_dtype, g_dtype', _CPU_ALLOWED_P_G_TYPES)
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def test_cpu_adam_kernel(adamw, weight_decay, p_dtype, g_dtype):
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rtol, atol = 1e-5, 1e-8
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if p_dtype is torch.float16 or g_dtype is torch.float16:
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rtol, atol = 1e-3, 1e-3
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check_adam_kernel(CPUAdamKernel, adamw, weight_decay, p_dtype, g_dtype, torch.device('cpu'), 3, rtol, atol)
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