332 lines
12 KiB
Python
332 lines
12 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import math
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import os
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import torch
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import torch.distributed as dist
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import bitsandbytes.functional as F
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from bitsandbytes.optim.optimizer import Optimizer2State
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class Adam(Optimizer2State):
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def __init__(
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self,
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params,
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lr=1e-3,
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betas=(0.9, 0.999),
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eps=1e-8,
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weight_decay=0,
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amsgrad=False,
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optim_bits=32,
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args=None,
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min_8bit_size=4096,
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percentile_clipping=100,
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block_wise=True,
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):
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super(Adam, self).__init__(
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"adam",
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params,
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lr,
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betas,
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eps,
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weight_decay,
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optim_bits,
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args,
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min_8bit_size,
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percentile_clipping,
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block_wise,
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)
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class Adam8bit(Optimizer2State):
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def __init__(
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self,
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params,
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lr=1e-3,
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betas=(0.9, 0.999),
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eps=1e-8,
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weight_decay=0,
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amsgrad=False,
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args=None,
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min_8bit_size=4096,
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percentile_clipping=100,
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block_wise=True,
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):
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super(Adam8bit, self).__init__(
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"adam",
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params,
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lr,
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betas,
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eps,
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weight_decay,
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8,
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args,
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min_8bit_size,
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percentile_clipping,
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block_wise,
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)
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class Adam32bit(Optimizer2State):
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def __init__(
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self,
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params,
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lr=1e-3,
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betas=(0.9, 0.999),
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eps=1e-8,
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weight_decay=0,
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amsgrad=False,
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args=None,
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min_8bit_size=4096,
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percentile_clipping=100,
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block_wise=True,
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):
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super(Adam32bit, self).__init__(
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"adam",
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params,
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lr,
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betas,
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eps,
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weight_decay,
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32,
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args,
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min_8bit_size,
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percentile_clipping,
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block_wise,
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)
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class AnalysisAdam(torch.optim.Optimizer):
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"""Adam that performs 8-bit vs 32-bit error analysis.
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This implementation is modified from torch.optim.Adam based on:
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`Fixed Weight Decay Regularization in Adam`
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(see https://arxiv.org/abs/1711.05101)
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It has been proposed in `Adam: A Method for Stochastic Optimization`_.
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Arguments:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 1e-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square (default: (0.9, 0.999))
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-8)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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amsgrad (boolean, optional): whether to use the AMSGrad variant of this
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algorithm from the paper `On the Convergence of Adam and Beyond`_
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.. _Adam: A Method for Stochastic Optimization:
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https://arxiv.org/abs/1412.6980
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.. _On the Convergence of Adam and Beyond:
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https://openreview.net/forum?id=ryQu7f-RZ
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"""
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def __init__(
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self,
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params,
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lr=1e-3,
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betas=(0.9, 0.999),
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eps=1e-8,
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weight_decay=0,
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amsgrad=False,
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bnb_analysis="dynamic-blockwise",
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savedir=None,
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):
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defaults = dict(
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lr=lr,
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betas=betas,
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eps=eps,
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weight_decay=weight_decay,
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amsgrad=amsgrad,
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)
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super(AnalysisAdam, self).__init__(params, defaults)
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self.analysis = bnb_analysis
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self.savedir = savedir
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@property
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def supports_memory_efficient_fp16(self):
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return True
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@property
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def supports_flat_params(self):
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return True
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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loss = closure()
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for group in self.param_groups:
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for p_id, p in enumerate(group["params"]):
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if p.grad is None:
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continue
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grad = p.grad.data
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if grad.dtype in {torch.float16, torch.bfloat16}:
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grad = grad.float()
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if grad.is_sparse:
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raise RuntimeError(
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"Adam does not support sparse gradients, please consider SparseAdam instead"
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)
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amsgrad = group.get("amsgrad", False)
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assert not amsgrad
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p_data_fp32 = p.data
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if p.data.dtype in {torch.float16, torch.bfloat16}:
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p_data_fp32 = p_data_fp32.float()
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state["step"] = 0
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# Exponential moving average of gradient values
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state["exp_avg"] = torch.zeros_like(p_data_fp32)
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# Exponential moving average of squared gradient values
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state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
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state["abserrors"] = torch.zeros(
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(256, 256), device=p_data_fp32.device
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)
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state["relerrors"] = torch.zeros(
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(256, 256), device=p_data_fp32.device
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)
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state["counts"] = torch.zeros(
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(256, 256), device=p_data_fp32.device
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)
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if amsgrad:
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# Maintains max of all exp. moving avg. of sq. grad. values
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state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32)
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else:
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state["exp_avg"] = state["exp_avg"].to(p_data_fp32)
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state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32)
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if amsgrad:
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state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to(
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p_data_fp32
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)
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state["step"] += 1
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beta1, beta2 = group["betas"]
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bias_correction1 = 1 - beta1 ** state["step"]
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bias_correction2 = 1 - beta2 ** state["step"]
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step_size = (
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group["lr"] * math.sqrt(bias_correction2) / bias_correction1
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)
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e = state["abserrors"]
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rele = state["relerrors"]
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counts = state["counts"]
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if group["weight_decay"] != 0:
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p_data_fp32.add_(
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p_data_fp32, alpha=-group["weight_decay"] * group["lr"]
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)
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exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
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if amsgrad:
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max_exp_avg_sq = state["max_exp_avg_sq"]
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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().add_(group["eps"])
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update_fp32 = exp_avg / denom
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if (
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p_data_fp32.numel() <= 8192
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or p_data_fp32.numel() > 50000 * 1000
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):
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# embedding layer or too small
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p_data_fp32 += -step_size * update_fp32
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else:
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if self.analysis == "dynamic-blockwise":
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code1 = F.create_dynamic_map(signed=True).to(p.device)
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code2 = F.create_dynamic_map(signed=False).to(p.device)
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C1, S1 = F.quantize_blockwise(exp_avg, code=code1)
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state1 = F.dequantize_blockwise(C1, S1)
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C2, S2 = F.quantize_blockwise(exp_avg_sq, code=code2)
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state2 = F.dequantize_blockwise(C2, S2)
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elif self.analysis == "dynamic":
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code1 = F.create_dynamic_map(signed=True).to(p.device)
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code2 = F.create_dynamic_map(signed=False).to(p.device)
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C1, S1 = F.quantize(exp_avg, code=code1)
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state1 = F.dequantize(C1, S1)
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C2, S2 = F.quantize(exp_avg_sq, code=code2)
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state2 = F.dequantize(C2, S2)
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elif self.analysis == "linear":
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code1 = F.create_linear_map(signed=True).to(p.device)
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code2 = F.create_linear_map(signed=False).to(p.device)
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C1, S1 = F.quantize(exp_avg, code=code1)
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state1 = F.dequantize(C1, S1)
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C2, S2 = F.quantize(exp_avg_sq, code=code2)
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state2 = F.dequantize(C2, S2)
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elif self.analysis == "quantile":
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code1 = F.estimate_quantiles(exp_avg)
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code2 = F.estimate_quantiles(exp_avg_sq)
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C1 = F.quantize_no_absmax(exp_avg, code=code1)
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state1 = F.dequantize_no_absmax(C1, code1)
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C2 = F.quantize_no_absmax(exp_avg_sq, code=code2)
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state2 = F.dequantize_no_absmax(C2, code2)
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elif self.analysis == "my-quantization-routine":
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pass
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# 1. get code
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# 2. quantize
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# 3. dequantize
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# Error will be calculated automatically!
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else:
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raise ValueError(
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f"Invalid analysis value: {self.analysis}!"
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)
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denom = state2.sqrt().add_(group["eps"])
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update_8bit = state1 / denom
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abserr = torch.abs(update_8bit - update_fp32)
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relerr = abserr / torch.abs(update_fp32 + 1e-6)
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C1, C2 = C1.int(), C2.int()
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F.histogram_scatter_add_2d(e, C1.int(), C2.int(), abserr)
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F.histogram_scatter_add_2d(rele, C1.int(), C2.int(), relerr)
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F.histogram_scatter_add_2d(
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counts, C1.int(), C2.int(), torch.ones_like(abserr)
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)
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p_data_fp32 += -step_size * update_fp32
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if not dist.is_initialized() or dist.get_rank() == 0:
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if self.savedir != "" and state["step"] % 100 == 0:
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if not os.path.exists(self.savedir):
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os.makedirs(self.savedir)
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shapestr = "_".join(
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[str(dim) for dim in p_data_fp32.shape]
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)
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pathe = os.path.join(
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self.savedir, f"{p_id}_{shapestr}_abserr.pkl"
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)
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pathrele = os.path.join(
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self.savedir, f"{p_id}_{shapestr}_relerr.pkl"
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)
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pathcounts = os.path.join(
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self.savedir, f"{p_id}_{shapestr}_counts.pkl"
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)
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torch.save(e, pathe)
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torch.save(rele, pathrele)
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torch.save(counts, pathcounts)
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if p.data.dtype in {torch.float16, torch.bfloat16}:
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p.data.copy_(p_data_fp32)
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return loss
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