mirror of https://github.com/hpcaitech/ColossalAI
Yuanheng Zhao
6 months ago
61 changed files with 6978 additions and 278 deletions
@ -1,9 +1,36 @@
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from galore_torch import GaLoreAdafactor, GaLoreAdamW |
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from .came import CAME |
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from .cpu_adam import CPUAdam |
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from .distributed_adafactor import DistributedAdaFactor |
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from .distributed_came import DistributedCAME |
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from .distributed_galore import DistGaloreAwamW |
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from .distributed_lamb import DistributedLamb |
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from .fused_adam import FusedAdam |
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from .fused_lamb import FusedLAMB |
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from .fused_sgd import FusedSGD |
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from .galore import GaLoreAdamW8bit |
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from .hybrid_adam import HybridAdam |
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from .lamb import Lamb |
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from .lars import Lars |
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__all__ = ["FusedLAMB", "FusedAdam", "FusedSGD", "Lamb", "Lars", "CPUAdam", "HybridAdam"] |
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from .adafactor import Adafactor # noqa |
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__all__ = [ |
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"FusedLAMB", |
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"FusedAdam", |
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"FusedSGD", |
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"Lamb", |
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"Lars", |
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"CPUAdam", |
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"HybridAdam", |
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"DistributedLamb", |
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"DistGaloreAwamW", |
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"GaLoreAdamW", |
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"GaLoreAdafactor", |
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"GaLoreAdamW8bit", |
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"CAME", |
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"DistributedCAME", |
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"Adafactor", |
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"DistributedAdaFactor", |
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] |
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@ -0,0 +1,201 @@
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# coding=utf-8 |
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import math |
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import torch |
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from torch.optim import Optimizer |
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__all__ = ["Adafactor"] |
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# Adafactor |
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class Adafactor(Optimizer): |
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def __init__( |
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self, |
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params, |
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lr=None, |
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eps=(1e-30, 1e-3), |
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clip_threshold=1.0, |
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decay_rate=-0.8, |
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beta1=None, |
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weight_decay=0.0, |
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scale_parameter=True, |
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relative_step=True, |
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warmup_init=False, |
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): |
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lr = None |
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if lr is not None and relative_step: |
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raise ValueError("Cannot combine manual `lr` and `relative_step=True` options") |
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if warmup_init and not relative_step: |
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raise ValueError("`warmup_init=True` requires `relative_step=True`") |
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defaults = { |
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"lr": lr, |
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"eps": eps, |
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"clip_threshold": clip_threshold, |
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"decay_rate": decay_rate, |
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"beta1": beta1, |
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"weight_decay": weight_decay, |
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"scale_parameter": scale_parameter, |
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"relative_step": relative_step, |
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"warmup_init": warmup_init, |
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} |
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super().__init__(params, defaults) |
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@staticmethod |
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def _get_lr(param_group, param_state): |
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rel_step_sz = param_group["lr"] |
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if param_group["relative_step"]: |
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min_step = 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2 |
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rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"])) |
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param_scale = 1.0 |
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if param_group["scale_parameter"]: |
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param_scale = max(param_group["eps"][1], param_state["RMS"]) |
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return param_scale * rel_step_sz |
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@staticmethod |
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def _get_options(param_group, param_shape): |
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factored = len(param_shape) >= 2 |
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use_first_moment = param_group["beta1"] is not None |
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return factored, use_first_moment |
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@staticmethod |
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def _rms(tensor): |
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return tensor.norm(2) / (tensor.numel() ** 0.5) |
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@staticmethod |
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def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col): |
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r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1) |
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c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() |
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return torch.mul(r_factor, c_factor) |
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@torch.no_grad() |
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def step(self, closure=None): |
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""" |
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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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""" |
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param_groups: Dict |
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{ |
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"params":[weight, bias] |
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"lr" |
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"eps" |
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"clip_threshold" |
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"decay_rate" |
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"beta1" |
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"weight_decay" |
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"scale_parameter" |
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"relative_step" |
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"warmup_init" |
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} |
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""" |
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for group in self.param_groups: |
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# update weight & bias |
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for p in group["params"]: |
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if p.grad is None: |
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continue |
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""" |
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# grad shape is same as weigh / bias |
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""" |
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grad = p.grad |
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if grad.is_sparse: |
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raise RuntimeError("Adafactor does not support sparse gradients.") |
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""" |
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p is weight |
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state |
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{'step', |
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'exp_avg_sq_row', |
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'exp_avg_sq_col', |
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'RMS' |
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} |
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p is bias |
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state |
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{'step', |
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'exp_avg_sq', |
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'RMS' |
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} |
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""" |
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state = self.state[p] |
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grad_shape = grad.shape |
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factored, use_first_moment = self._get_options(group, grad_shape) |
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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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if use_first_moment: |
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# Exponential moving average of gradient values |
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state["exp_avg"] = torch.zeros_like(grad) |
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if factored: |
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state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1], device=grad.device) |
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state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:], device=grad.device) |
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else: |
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state["exp_avg_sq"] = torch.zeros_like(grad) |
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state["RMS"] = 0 |
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else: |
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if use_first_moment: |
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state["exp_avg"] = state["exp_avg"] |
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if factored: |
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state["exp_avg_sq_row"] = state["exp_avg_sq_row"] |
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state["exp_avg_sq_col"] = state["exp_avg_sq_col"] |
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else: |
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state["exp_avg_sq"] = state["exp_avg_sq"] |
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state["step"] += 1 |
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# state["RMS"] = self._rms(p_data_fp32) |
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lr = self._get_lr(group, state) |
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beta2t = 1.0 - math.pow(state["step"], group["decay_rate"]) |
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update = (grad**2) + group["eps"][0] |
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if factored: |
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exp_avg_sq_row = state["exp_avg_sq_row"] |
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exp_avg_sq_col = state["exp_avg_sq_col"] |
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# Exponential average of row indexes |
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exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=(1.0 - beta2t)) |
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# Exponential average of columns indexes |
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exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=(1.0 - beta2t)) |
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# Approximation of exponential moving average of square of gradient |
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update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
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update.mul_(grad) |
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else: |
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exp_avg_sq = state["exp_avg_sq"] |
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exp_avg_sq.mul_(beta2t).add_(update, alpha=(1.0 - beta2t)) |
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update = exp_avg_sq.rsqrt().mul_(grad) |
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# RMS |
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update.div_((self._rms(update) / group["clip_threshold"]).clamp_(min=1.0)) |
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update.mul_(lr) |
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if use_first_moment: |
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exp_avg = state["exp_avg"] |
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exp_avg.mul_(group["beta1"]).add_(update, alpha=(1 - group["beta1"])) |
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update = exp_avg |
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if group["weight_decay"] != 0: |
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p.add_(p, alpha=(-group["weight_decay"] * lr)) |
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p.add_(-update) |
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return loss |
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@ -0,0 +1,150 @@
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# Copied from https://github.com/yangluo7/CAME/blob/master/came_pytorch/CAME.py |
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import torch |
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import torch.optim |
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class CAME(torch.optim.Optimizer): |
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"""Implements CAME algorithm. |
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This implementation is based on: |
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`CAME: Confidence-guided Adaptive Memory Efficient Optimization` |
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Args: |
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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): external learning rate (default: None) |
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eps (tuple[float, float]): regularization constants for square gradient |
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and instability respectively (default: (1e-30, 1e-16)) |
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clip_threshold (float): threshold of root-mean-square of |
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final gradient update (default: 1.0) |
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betas (tuple[float, float, float]): coefficient used for computing running averages of |
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update, square gradient and instability (default: (0.9, 0.999, 0.9999))) |
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0) |
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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=None, |
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eps=(1e-30, 1e-16), |
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clip_threshold=1.0, |
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betas=(0.9, 0.999, 0.9999), |
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weight_decay=0.0, |
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): |
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assert lr > 0.0 |
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assert all([0.0 <= beta <= 1.0 for beta in betas]) |
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defaults = dict( |
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lr=lr, |
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eps=eps, |
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clip_threshold=clip_threshold, |
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betas=betas, |
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weight_decay=weight_decay, |
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) |
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super(CAME, self).__init__(params, defaults) |
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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 False |
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def _get_options(self, param_shape): |
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factored = len(param_shape) >= 2 |
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return factored |
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def _rms(self, tensor): |
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return tensor.norm(2) / (tensor.numel() ** 0.5) |
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def _approx_sq_grad(self, exp_avg_sq_row, exp_avg_sq_col): |
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r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1) |
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c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() |
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return torch.mul(r_factor, c_factor) |
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def step(self, closure=None): |
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"""Performs a single optimization step. |
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Args: |
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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 in group["params"]: |
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if p.grad is None: |
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continue |
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grad = p.grad |
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if grad.is_sparse: |
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raise RuntimeError("CAME does not support sparse gradients.") |
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state = self.state[p] |
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grad_shape = grad.shape |
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factored = self._get_options(grad_shape) |
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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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state["exp_avg"] = torch.zeros_like(grad) |
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if factored: |
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state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1], dtype=p.dtype, device=p.device) |
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state["exp_avg_sq_col"] = torch.zeros( |
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grad_shape[:-2] + grad_shape[-1:], dtype=p.dtype, device=p.device |
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) |
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state["exp_avg_res_row"] = torch.zeros(grad_shape[:-1], dtype=p.dtype, device=p.device) |
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state["exp_avg_res_col"] = torch.zeros( |
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grad_shape[:-2] + grad_shape[-1:], dtype=p.dtype, device=p.device |
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) |
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else: |
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state["exp_avg_sq"] = torch.zeros_like(p) |
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state["step"] += 1 |
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update = (grad**2) + group["eps"][0] |
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if factored: |
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exp_avg_sq_row = state["exp_avg_sq_row"] |
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exp_avg_sq_col = state["exp_avg_sq_col"] |
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exp_avg_sq_row.mul_(group["betas"][1]).add_(update.mean(dim=-1), alpha=1.0 - group["betas"][1]) |
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exp_avg_sq_col.mul_(group["betas"][1]).add_(update.mean(dim=-2), alpha=1.0 - group["betas"][1]) |
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# Approximation of exponential moving average of square of gradient |
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update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
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update.mul_(grad) |
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else: |
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exp_avg_sq = state["exp_avg_sq"] |
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exp_avg_sq.mul_(group["betas"][1]).add_(update, alpha=1.0 - group["betas"][1]) |
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update = exp_avg_sq.rsqrt().mul_(grad) |
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update.div_((self._rms(update) / group["clip_threshold"]).clamp_(min=1.0)) |
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exp_avg = state["exp_avg"] |
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exp_avg.mul_(group["betas"][0]).add_(update, alpha=1 - group["betas"][0]) |
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# Confidence-guided strategy |
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# Calculation of instability |
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res = (update - exp_avg) ** 2 + group["eps"][1] |
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if factored: |
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exp_avg_res_row = state["exp_avg_res_row"] |
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exp_avg_res_col = state["exp_avg_res_col"] |
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exp_avg_res_row.mul_(group["betas"][2]).add_(res.mean(dim=-1), alpha=1.0 - group["betas"][2]) |
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exp_avg_res_col.mul_(group["betas"][2]).add_(res.mean(dim=-2), alpha=1.0 - group["betas"][2]) |
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# Approximation of exponential moving average of instability |
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res_approx = self._approx_sq_grad(exp_avg_res_row, exp_avg_res_col) |
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update = res_approx.mul_(exp_avg) |
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else: |
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update = exp_avg.clone() |
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if group["weight_decay"] != 0: |
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p.data.add_(p.data, alpha=-group["weight_decay"] * group["lr"]) |
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update.mul_(group["lr"]) |
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p.data.add_(-update) |
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return loss |
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@ -0,0 +1,440 @@
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import math |
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from typing import Dict |
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import torch |
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import torch.distributed as dist |
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from colossalai.interface.optimizer import DistributedOptim |
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from colossalai.shardformer.layer._operation import _gather, _split |
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from colossalai.tensor.d_tensor import get_sharding_spec, is_distributed_tensor |
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# DistributedAdaFactor (with Tensor parallel and Zero stage 2) |
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__all__ = ["DistributedAdaFactor"] |
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class DistributedAdaFactor(DistributedOptim): |
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def __init__( |
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self, |
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params, |
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lr=None, |
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eps=(1e-30, 1e-3), |
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clip_threshold=1.0, |
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decay_rate=-0.8, |
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beta1=None, |
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weight_decay=0.0, |
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scale_parameter=True, |
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relative_step=True, |
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warmup_init=False, |
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): |
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lr = None |
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if lr is not None and relative_step: |
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raise ValueError("Cannot combine manual `lr` and `relative_step=True` options") |
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if warmup_init and not relative_step: |
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raise ValueError("`warmup_init=True` requires `relative_step=True`") |
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|
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defaults = { |
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"lr": lr, |
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"eps": eps, |
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"clip_threshold": clip_threshold, |
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"decay_rate": decay_rate, |
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"beta1": beta1, |
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"weight_decay": weight_decay, |
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"scale_parameter": scale_parameter, |
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"relative_step": relative_step, |
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"warmup_init": warmup_init, |
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} |
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self.tp_size = 1 |
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self.tp_group = None |
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self.dp_size = 1 |
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self.dp_group = None |
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self.shard_to_working_param = None # Dict{id:shape}, sample {id(param): torch.tensor} |
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self.use_zero = True |
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self.param_is_dtensor_dict = {} # {id(p): True/False} |
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self.grad_shape_dict = {} # {id(p): master param shape} |
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self.factored_dict = {} # {id(p): True/False} |
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self.use_first_moment_dict = {} # {id(p): True/False} |
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self.shard_spec_dict = {} # {id(p): ShardSpec} |
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super().__init__(params, defaults) |
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|
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def setup_distributed( |
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self, |
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tp_group: dist.ProcessGroup = None, |
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dp_group: dist.ProcessGroup = None, |
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shard_to_working_param: Dict = {}, |
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padding_map=None, |
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use_zero: bool = True, |
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) -> None: |
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"""Setup process groups for TP and ZeRO 2. |
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Inject features to the Optimizer |
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|
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Args: |
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tp_group: The devices group for tensor parallel; |
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dp_group: The devices group for data parallel; |
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shard_to_working_param (Dict): ZeRO 2 feeds the optimizer a sharded param view as grads are sharded. |
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This maps from id(view) to working params used in forward & backward. |
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padding_map: An empty interface placeholder; |
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use_zero: Whether or not to use zero; |
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""" |
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self.tp_group = tp_group # "Expected row process group" |
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self.dp_group = dp_group |
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if self.tp_group is not None: |
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self.tp_size = dist.get_world_size(self.tp_group) |
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if self.dp_group is not None: |
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self.dp_size = dist.get_world_size(self.dp_group) |
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self.use_zero = use_zero |
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self.shard_to_working_param = shard_to_working_param if shard_to_working_param is not None else {} |
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# grad is None, cause we dont setup now |
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for group in self.param_groups: |
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for p in group["params"]: |
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self.shard_to_working_param[id(p)] = self.shard_to_working_param.get( |
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id(p), p |
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) # If not ZeRO, working param is master param |
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self.param_is_dtensor_dict[id(p)] = is_distributed_tensor(self.shard_to_working_param[id(p)]) |
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self.grad_shape_dict[id(p)] = self.shard_to_working_param.get(id(p)).shape |
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self.factored_dict[id(p)], self.use_first_moment_dict[id(p)] = self._get_options( |
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group, self.grad_shape_dict[id(p)] |
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) |
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if self.param_is_dtensor_dict[id(p)]: |
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self.shard_spec_dict[id(p)] = get_sharding_spec(self.shard_to_working_param[id(p)]) |
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else: |
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self.shard_spec_dict[id(p)] = None |
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|
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@staticmethod |
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def _get_lr(param_group, param_state): |
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rel_step_sz = param_group["lr"] |
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if param_group["relative_step"]: |
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min_step = 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2 |
||||
rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"])) |
||||
param_scale = 1.0 |
||||
if param_group["scale_parameter"]: |
||||
param_scale = max(param_group["eps"][1], param_state["RMS"]) |
||||
return param_scale * rel_step_sz |
||||
|
||||
@staticmethod |
||||
def _get_options(param_group, param_shape): |
||||
""" |
||||
Determines whether the current param is factored |
||||
Args: |
||||
param_group : param group |
||||
param_shape : Original Shape of param |
||||
|
||||
""" |
||||
factored = len(param_shape) >= 2 |
||||
use_first_moment = param_group["beta1"] is not None |
||||
return factored, use_first_moment |
||||
|
||||
@staticmethod |
||||
def _rms(tensor, param_is_dtensor, use_zero, tp_size, dp_size, tp_group, dp_group): |
||||
tensor_sum = tensor.pow(2).sum() |
||||
num_of_element = tensor.numel() |
||||
|
||||
if param_is_dtensor: |
||||
# reduce tensor_sum from tp_group |
||||
dist.all_reduce(tensor_sum, group=tp_group) |
||||
num_of_element = num_of_element * tp_size |
||||
if use_zero: |
||||
dist.all_reduce(tensor_sum, group=dp_group) |
||||
num_of_element = num_of_element * dp_size |
||||
rms = (tensor_sum / num_of_element).sqrt() |
||||
return rms |
||||
|
||||
@staticmethod |
||||
def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col): |
||||
r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1) |
||||
c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() |
||||
return torch.mul(r_factor, c_factor) |
||||
|
||||
# approx_sq_grad for row parallel weight |
||||
@staticmethod |
||||
def _approx_sq_grad_row_parallel(exp_avg_sq_row, exp_avg_sq_col, sq_row_meam): |
||||
# row_meam = sq_row_meam |
||||
r_factor = (exp_avg_sq_row / sq_row_meam).rsqrt_().unsqueeze(-1) |
||||
c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() |
||||
return torch.mul(r_factor, c_factor) |
||||
|
||||
def _col_parallel_factor(self, update, grad, state, grad_shape, beta2t): |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
# gather update[flatten] along dp group then reshape to [H, W/tp] |
||||
update = _gather(input_=update, dim=-1, process_group=self.dp_group) |
||||
update_reshape = update.view(-1, grad_shape[1]) |
||||
# gather grad[flatten] along dp group then reshape to [H, W/tp] |
||||
grad = _gather(input_=grad, dim=-1, process_group=self.dp_group) |
||||
grad_reshape = grad.view(-1, grad_shape[1]) |
||||
exp_avg_sq_row = state["exp_avg_sq_row"] # [H] |
||||
exp_avg_sq_col = state["exp_avg_sq_col"] # [W/tp] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
update_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update_reshape.mul_(grad_reshape) |
||||
else: |
||||
update_reshape = update.view(-1, grad_shape[1]) |
||||
grad_reshape = grad.view(-1, grad_shape[1]) |
||||
exp_avg_sq_row = state["exp_avg_sq_row"] # [H/dp] |
||||
exp_avg_sq_col = state["exp_avg_sq_col"] # [W/tp] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
dist.all_reduce(exp_avg_sq_row, group=self.tp_group) |
||||
exp_avg_sq_row.div_(self.tp_size) |
||||
update_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update_reshape.mul_(grad_reshape) |
||||
|
||||
if self.use_zero: |
||||
update = update_reshape.view(-1) |
||||
else: |
||||
update = update_reshape |
||||
return update |
||||
|
||||
def _row_parallel_factor(self, update, grad, state, grad_shape, beta2t): |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
# gather update[flatten] along dp group then reshape to [H/tp, W] |
||||
update = _gather(input_=update, dim=-1, process_group=self.dp_group) |
||||
# view update to origin[tp] shape |
||||
update_reshape = update.view(-1, grad_shape[1]) |
||||
# gather grad[flatten] along dp group then reshape to [H/tp, W] |
||||
grad = _gather(input_=grad, dim=-1, process_group=self.dp_group) |
||||
grad_reshape = grad.view(-1, grad_shape[1]) |
||||
exp_avg_sq_row = state["exp_avg_sq_row"] # [H/tp] |
||||
exp_avg_sq_col = state["exp_avg_sq_col"] # [W] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# reduce col |
||||
dist.all_reduce(exp_avg_sq_col, group=self.tp_group) |
||||
exp_avg_sq_col.div_(self.tp_size) |
||||
update_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update_reshape.mul_(grad_reshape) |
||||
if self.use_zero: |
||||
update = _split(input_=update_reshape.view(-1), dim=-1, process_group=self.dp_group) |
||||
else: |
||||
update = update_reshape |
||||
else: |
||||
update_reshape = update.view(-1, grad_shape[1]) |
||||
grad_reshape = grad.view(-1, grad_shape[1]) |
||||
exp_avg_sq_row = state["exp_avg_sq_row"] # [H/dp/tp] |
||||
exp_avg_sq_col = state["exp_avg_sq_col"] # [W] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# reduce col |
||||
dist.all_reduce(exp_avg_sq_col, group=self.tp_group) |
||||
exp_avg_sq_col.div_(self.tp_size) |
||||
# gather row |
||||
exp_avg_sq_row_gather = _gather(input_=exp_avg_sq_row, dim=-1, process_group=self.tp_group) |
||||
sq_row_meam = exp_avg_sq_row_gather.mean(dim=-1, keepdim=True) |
||||
update_reshape = self._approx_sq_grad_row_parallel(exp_avg_sq_row, exp_avg_sq_col, sq_row_meam) |
||||
update_reshape.mul_(grad_reshape) |
||||
if self.use_zero: |
||||
update = update_reshape.view(-1) |
||||
else: |
||||
update = update_reshape |
||||
return update |
||||
|
||||
def _base_factor(self, update, grad, state, grad_shape, beta2t): |
||||
if self.use_zero: |
||||
# only zero |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
# view update to origin shape update.view(grad_shape[0]//self.data_parallel_size , grad_shape[1]) |
||||
# row mean no change |
||||
# col mean need reduce and div |
||||
# gather update[flatten] along dp group then reshape to [H, W] |
||||
update = _gather(input_=update, dim=-1, process_group=self.dp_group) |
||||
# view update to origin[tp] shape |
||||
update_reshape = update.view(-1, grad_shape[1]) |
||||
# gather grad[flatten] along dp group then reshape to [H, W] |
||||
grad = _gather(input_=grad, dim=-1, process_group=self.dp_group) |
||||
grad_reshape = grad.view(-1, grad_shape[1]) |
||||
exp_avg_sq_row = state["exp_avg_sq_row"] # [H/dp] |
||||
exp_avg_sq_col = state["exp_avg_sq_col"] # [W] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# reduce col |
||||
dist.all_reduce(exp_avg_sq_col, group=self.tp_group) |
||||
exp_avg_sq_col.div_(self.tp_size) |
||||
update_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update_reshape.mul_(grad_reshape) |
||||
update = _split(input_=update_reshape.view(-1), dim=-1, process_group=self.dp_group) |
||||
else: |
||||
# no residual row |
||||
# view update to origin[tp] shape |
||||
update_reshape = update.view(-1, grad_shape[1]) # [H/dp, W] |
||||
grad_reshape = grad.view(-1, grad_shape[1]) # [H/dp, W] |
||||
exp_avg_sq_row = state["exp_avg_sq_row"] # [H/tp] |
||||
exp_avg_sq_col = state["exp_avg_sq_col"] # [W] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# reduce col |
||||
dist.all_reduce(exp_avg_sq_col, group=self.tp_group) |
||||
exp_avg_sq_col.div_(self.tp_size) |
||||
update_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update_reshape.mul_(grad_reshape) |
||||
update = update_reshape.view(-1) |
||||
else: |
||||
# base factor; no tp, no dp |
||||
exp_avg_sq_row = state["exp_avg_sq_row"] |
||||
exp_avg_sq_col = state["exp_avg_sq_col"] |
||||
# Exponential average of row indexes |
||||
exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
# Exponential average of columns indexes |
||||
exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# Approximation of exponential moving average of square of gradient |
||||
update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update.mul_(grad) |
||||
return update |
||||
|
||||
@torch.no_grad() |
||||
def step(self, closure=None): |
||||
""" |
||||
Performs a single optimization steps |
||||
Arguments: |
||||
closure (callable, optional): A closure that reevaluates the model |
||||
and returns the loss. |
||||
""" |
||||
loss = None |
||||
if closure is not None: |
||||
loss = closure() |
||||
""" |
||||
param_groups: Dict |
||||
{ |
||||
"params":[weight, bias] |
||||
"lr" |
||||
"eps" |
||||
"clip_threshold" |
||||
"decay_rate" |
||||
"beta1" |
||||
"weight_decay" |
||||
"scale_parameter" |
||||
"relative_step" |
||||
"warmup_init" |
||||
} |
||||
""" |
||||
for group in self.param_groups: |
||||
# update weight & bias |
||||
for p in group["params"]: |
||||
if p.grad is None: |
||||
continue |
||||
grad = p.grad |
||||
if grad.is_sparse: |
||||
raise RuntimeError("Adafactor does not support sparse gradients.") |
||||
|
||||
state = self.state[p] |
||||
grad_shape = self.grad_shape_dict[id(p)] |
||||
param_is_dtensor = self.param_is_dtensor_dict[id(p)] |
||||
if param_is_dtensor: |
||||
grad_shape = self.shard_to_working_param.get(id(p)).shape # tp shape (2 dim) |
||||
factored, use_first_moment = self.factored_dict[id(p)], self.use_first_moment_dict[id(p)] |
||||
|
||||
shard_spec = self.shard_spec_dict[id(p)] |
||||
if len(state) == 0: |
||||
state["step"] = 0 |
||||
if use_first_moment: |
||||
# Exponential moving average of gradient values |
||||
state["exp_avg"] = torch.zeros_like(p) |
||||
if factored: |
||||
if param_is_dtensor: |
||||
if shard_spec.sharding_sequence[0] == "R": # Col Parallel |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0], device=p.device, dtype=p.dtype |
||||
) # [H] |
||||
else: |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0] // self.dp_size, device=p.device, dtype=p.dtype |
||||
) # [H/dp] |
||||
state["exp_avg_sq_col"] = torch.zeros( |
||||
grad_shape[1], device=p.device, dtype=p.dtype |
||||
) # [W/TP] |
||||
|
||||
if shard_spec.sharding_sequence[-1] == "R": # Row Parallel |
||||
# Row indivisible shape situation |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0], device=p.device, dtype=p.dtype |
||||
) # [H/tp] |
||||
else: |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0] // self.dp_size, device=p.device, dtype=p.dtype |
||||
) # [H/dp/tp] |
||||
|
||||
state["exp_avg_sq_col"] = torch.zeros( |
||||
grad_shape[1], device=p.device, dtype=p.dtype |
||||
) # [W] |
||||
else: |
||||
if self.use_zero: |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
# save all exp_avg_sq_row [H] |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0], device=grad.device, dtype=p.dtype |
||||
) |
||||
else: |
||||
# exp_avg_sq_row [H // dp] |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0] // self.dp_size, device=grad.device, dtype=p.dtype |
||||
) |
||||
else: |
||||
# exp_avg_sq_row [H] |
||||
state["exp_avg_sq_row"] = torch.zeros(grad_shape[0], device=grad.device, dtype=p.dtype) |
||||
# exp_avg_sq_col alaways [W] |
||||
state["exp_avg_sq_col"] = torch.zeros(grad_shape[1], device=grad.device, dtype=p.dtype) |
||||
else: |
||||
state["exp_avg_sq"] = torch.zeros_like(p) |
||||
state["RMS"] = 0 |
||||
else: |
||||
if use_first_moment: |
||||
state["exp_avg"] = state["exp_avg"] |
||||
if factored: |
||||
state["exp_avg_sq_row"] = state["exp_avg_sq_row"] |
||||
state["exp_avg_sq_col"] = state["exp_avg_sq_col"] |
||||
else: |
||||
state["exp_avg_sq"] = state["exp_avg_sq"] |
||||
|
||||
state["step"] += 1 |
||||
lr = self._get_lr(group, state) |
||||
beta2t = 1.0 - math.pow(state["step"], group["decay_rate"]) |
||||
update = (grad**2) + group["eps"][0] |
||||
|
||||
if factored: |
||||
if param_is_dtensor: |
||||
# ============================== |
||||
# First Dim is R, Last Dim is S{} means split dim -1 ---> |
||||
# Coloum Parallel ---> sq_row need Do (col) Reduce |
||||
# ============================== |
||||
if shard_spec.sharding_sequence[0] == "R": |
||||
update = self._col_parallel_factor(update, grad, state, grad_shape, beta2t) |
||||
# ============================== |
||||
# Last Dim is R, First Dim is S{} means split dim 0 ---> |
||||
# Row Parallel ---> sq_col need Do (row) Reduce |
||||
# ============================== |
||||
elif shard_spec.sharding_sequence[-1] == "R": |
||||
update = self._row_parallel_factor(update, grad, state, grad_shape, beta2t) |
||||
else: |
||||
update = self._base_factor(update, grad, state, grad_shape, beta2t) |
||||
else: |
||||
exp_avg_sq = state["exp_avg_sq"] |
||||
exp_avg_sq.mul_(beta2t).add_(update, alpha=(1.0 - beta2t)) |
||||
update = exp_avg_sq.rsqrt().mul_(grad) |
||||
|
||||
# # (Line No.8) RMS |
||||
rms = self._rms( |
||||
update, |
||||
param_is_dtensor, |
||||
self.use_zero, |
||||
self.tp_size, |
||||
self.dp_size, |
||||
self.tp_group, |
||||
self.dp_group, |
||||
) |
||||
update.div_((rms / group["clip_threshold"]).clamp_(min=1.0)) |
||||
|
||||
update.mul_(lr) |
||||
if use_first_moment: |
||||
exp_avg = state["exp_avg"] |
||||
exp_avg.mul_(group["beta1"]).add_(update, alpha=(1 - group["beta1"])) |
||||
update = exp_avg |
||||
|
||||
if group["weight_decay"] != 0: |
||||
p.add_(p, alpha=(-group["weight_decay"] * lr)) |
||||
|
||||
p.add_(-update) |
||||
|
||||
return loss |
||||
@ -0,0 +1,557 @@
|
||||
from typing import Dict |
||||
|
||||
import torch |
||||
import torch.distributed as dist |
||||
|
||||
from colossalai.interface.optimizer import DistributedOptim |
||||
from colossalai.shardformer.layer._operation import _gather, _split |
||||
from colossalai.tensor.d_tensor import get_sharding_spec, is_distributed_tensor |
||||
|
||||
|
||||
class DistributedCAME(DistributedOptim): |
||||
"""Implements CAME algorithm. |
||||
This implementation is based on: |
||||
`CAME: Confidence-guided Adaptive Memory Efficient Optimization` |
||||
Args: |
||||
params (iterable): iterable of parameters to optimize or dicts defining |
||||
parameter groups |
||||
lr (float, optional): external learning rate (default: None) |
||||
eps (tuple[float, float]): regularization constants for square gradient |
||||
and instability respectively (default: (1e-30, 1e-16)) |
||||
clip_threshold (float): threshold of root-mean-square of |
||||
final gradient update (default: 1.0) |
||||
betas (tuple[float, float, float]): coefficient used for computing running averages of |
||||
update, square gradient and instability (default: (0.9, 0.999, 0.9999))) |
||||
weight_decay (float, optional): weight decay (L2 penalty) (default: 0) |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
params, |
||||
lr=None, |
||||
eps=(1e-30, 1e-16), |
||||
clip_threshold=1.0, |
||||
betas=(0.9, 0.999, 0.9999), |
||||
weight_decay=0.0, |
||||
): |
||||
assert lr > 0.0 |
||||
assert all([0.0 <= beta <= 1.0 for beta in betas]) |
||||
|
||||
defaults = dict( |
||||
lr=lr, |
||||
eps=eps, |
||||
clip_threshold=clip_threshold, |
||||
betas=betas, |
||||
weight_decay=weight_decay, |
||||
) |
||||
|
||||
self.tp_size = 1 |
||||
self.tp_group = None |
||||
self.dp_size = 1 |
||||
self.dp_group = None |
||||
self.shard_to_working_param = None # Dict{id:shape}, sample {id(param): torch.tensor} |
||||
self.use_zero = True |
||||
|
||||
self.param_is_dtensor_dict = {} # {id(p): True/False} |
||||
self.grad_shape_dict = {} # {id(p): master param shape} |
||||
self.factored_dict = {} # {id(p): True/False} |
||||
self.use_first_moment_dict = {} # {id(p): True/False} |
||||
self.shard_spec_dict = {} # {id(p): ShardSpec} |
||||
|
||||
super(DistributedCAME, self).__init__(params, defaults) |
||||
|
||||
@property |
||||
def supports_memory_efficient_fp16(self): |
||||
return True |
||||
|
||||
@property |
||||
def supports_flat_params(self): |
||||
return False |
||||
|
||||
def setup_distributed( |
||||
self, |
||||
tp_group: dist.ProcessGroup = None, |
||||
dp_group: dist.ProcessGroup = None, |
||||
shard_to_working_param: Dict = {}, |
||||
padding_map=None, |
||||
use_zero: bool = True, |
||||
) -> None: |
||||
""" |
||||
Inject features to the Optimizer |
||||
|
||||
Args: |
||||
tp_group: The devices group for tensor parallel; |
||||
dp_group: The devices group for data parallel; |
||||
shard_to_working_param (Dict): ZeRO 2 feeds the optimizer a sharded param view as grads are sharded. |
||||
This maps from id(view) to working params used in forward & backward. |
||||
padding_map: Interface placeholder |
||||
use_zero: Whether or not to use zero; |
||||
|
||||
""" |
||||
self.tp_group = tp_group # "Expected row process group" |
||||
self.dp_group = dp_group |
||||
if self.tp_group is not None: |
||||
self.tp_size = dist.get_world_size(self.tp_group) |
||||
if self.dp_group is not None: |
||||
self.dp_size = dist.get_world_size(self.dp_group) |
||||
self.use_zero = use_zero |
||||
|
||||
self.shard_to_working_param = shard_to_working_param if shard_to_working_param is not None else {} |
||||
# grad is None, cause we dont setup now |
||||
for group in self.param_groups: |
||||
for p in group["params"]: |
||||
# w/o ZeRO: master param = working param |
||||
self.shard_to_working_param[id(p)] = self.shard_to_working_param.get(id(p), p) |
||||
self.param_is_dtensor_dict[id(p)] = is_distributed_tensor(self.shard_to_working_param[id(p)]) |
||||
self.grad_shape_dict[id(p)] = self.shard_to_working_param[id(p)].shape |
||||
# Avoid row parallel lead H=1, then factored param is determined as not factored; |
||||
if self.param_is_dtensor_dict[id(p)]: |
||||
self.shard_spec_dict[id(p)] = get_sharding_spec(self.shard_to_working_param[id(p)]) |
||||
if self.shard_spec_dict[id(p)].sharding_sequence[0] == "R": |
||||
self.factored_dict[id(p)] = True |
||||
elif self.shard_spec_dict[id(p)].sharding_sequence[-1] == "R": |
||||
self.factored_dict[id(p)] = True |
||||
else: |
||||
self.factored_dict[id(p)] = self._get_options(self.grad_shape_dict[id(p)]) |
||||
|
||||
else: |
||||
self.shard_spec_dict[id(p)] = None |
||||
self.factored_dict[id(p)] = self._get_options(self.grad_shape_dict[id(p)]) |
||||
|
||||
@staticmethod |
||||
def _get_options(param_shape): |
||||
factored = len(param_shape) >= 2 |
||||
return factored |
||||
|
||||
@staticmethod |
||||
def _rms(tensor, param_is_dtensor, use_zero, tp_size, dp_size, tp_group, dp_group): |
||||
tensor_sum = tensor.pow(2).sum() |
||||
num_of_element = tensor.numel() |
||||
|
||||
if param_is_dtensor: |
||||
# reduce tensor_sum from tp_group |
||||
dist.all_reduce(tensor_sum, group=tp_group) |
||||
num_of_element = num_of_element * tp_size |
||||
if use_zero: |
||||
dist.all_reduce(tensor_sum, group=dp_group) |
||||
num_of_element = num_of_element * dp_size |
||||
rms = (tensor_sum / num_of_element).sqrt() |
||||
return rms |
||||
|
||||
@staticmethod |
||||
def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col): |
||||
r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1) |
||||
c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() |
||||
return torch.mul(r_factor, c_factor) |
||||
|
||||
# approx_sq_grad for row parallel weight |
||||
@staticmethod |
||||
def _approx_sq_grad_row_parallel(exp_avg_sq_row, exp_avg_sq_col, sq_row_meam): |
||||
r_factor = (exp_avg_sq_row / sq_row_meam).rsqrt_().unsqueeze(-1) |
||||
c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() |
||||
return torch.mul(r_factor, c_factor) |
||||
|
||||
def _col_parallel_factor(self, update, grad, state_row, state_col, grad_shape, beta2t): |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
# gather update[flatten] along dp group then reshape to [H, W/tp] |
||||
update = _gather(input_=update, dim=-1, process_group=self.dp_group) |
||||
update_reshape = update.view(-1, grad_shape[1]) |
||||
# gather grad[flatten] along dp group then reshape to [H, W/tp] |
||||
grad = _gather(input_=grad, dim=-1, process_group=self.dp_group) |
||||
grad_reshape = grad.view(-1, grad_shape[1]) |
||||
exp_avg_sq_row = state_row # [H] |
||||
exp_avg_sq_col = state_col # [W/tp] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
update_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update_reshape.mul_(grad_reshape) |
||||
else: |
||||
update_reshape = update.view(-1, grad_shape[1]) |
||||
grad_reshape = grad.view(-1, grad_shape[1]) |
||||
exp_avg_sq_row = state_row # [H] |
||||
exp_avg_sq_col = state_col # [W/tp] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
dist.all_reduce(exp_avg_sq_row, group=self.tp_group) |
||||
exp_avg_sq_row.div_(self.tp_size) |
||||
update_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update_reshape.mul_(grad_reshape) |
||||
|
||||
if self.use_zero: |
||||
update = update_reshape.view(-1) |
||||
else: |
||||
update = update_reshape |
||||
return update |
||||
|
||||
def _row_parallel_factor(self, update, grad, state_row, state_col, grad_shape, beta2t): |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
# gather update[flatten] along dp group then reshape to [H/tp, W] |
||||
update = _gather(input_=update, dim=-1, process_group=self.dp_group) |
||||
# view update to origin[tp] shape |
||||
update_reshape = update.view(-1, grad_shape[1]) |
||||
# gather grad[flatten] along dp group then reshape to [H/tp, W] |
||||
grad = _gather(input_=grad, dim=-1, process_group=self.dp_group) |
||||
grad_reshape = grad.view(-1, grad_shape[1]) |
||||
exp_avg_sq_row = state_row # [H] |
||||
exp_avg_sq_col = state_col # [W/tp] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# reduce col |
||||
dist.all_reduce(exp_avg_sq_col, group=self.tp_group) |
||||
exp_avg_sq_col.div_(self.tp_size) |
||||
update_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update_reshape.mul_(grad_reshape) |
||||
if self.use_zero: |
||||
update = _split(input_=update_reshape.view(-1), dim=-1, process_group=self.dp_group) |
||||
else: |
||||
update = update_reshape |
||||
else: |
||||
update_reshape = update.view(-1, grad_shape[1]) |
||||
grad_reshape = grad.view(-1, grad_shape[1]) |
||||
exp_avg_sq_row = state_row # [H] |
||||
exp_avg_sq_col = state_col # [W/tp] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# reduce col |
||||
dist.all_reduce(exp_avg_sq_col, group=self.tp_group) |
||||
exp_avg_sq_col.div_(self.tp_size) |
||||
# gather row |
||||
exp_avg_sq_row_gather = _gather(input_=exp_avg_sq_row, dim=-1, process_group=self.tp_group) |
||||
sq_row_meam = exp_avg_sq_row_gather.mean(dim=-1, keepdim=True) |
||||
update_reshape = self._approx_sq_grad_row_parallel(exp_avg_sq_row, exp_avg_sq_col, sq_row_meam) |
||||
update_reshape.mul_(grad_reshape) |
||||
if self.use_zero: |
||||
update = update_reshape.view(-1) |
||||
else: |
||||
update = update_reshape |
||||
return update |
||||
|
||||
def _base_factor(self, update, grad, state_row, state_col, grad_shape, beta2t): |
||||
if self.use_zero: |
||||
# only zero |
||||
# [30522, 128], [2, 128] |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
# view update to origin shape update.view(grad_shape[0]//self.data_parallel_size , grad_shape[1]) |
||||
# row mean no change |
||||
# col mean need reduce and div |
||||
# gather update[flatten] along dp group then reshape to [H, W] |
||||
update = _gather(input_=update, dim=-1, process_group=self.dp_group) |
||||
# view update to origin[tp] shape |
||||
update_reshape = update.view(-1, grad_shape[1]) |
||||
# gather grad[flatten] along dp group then reshape to [H, W] |
||||
grad = _gather(input_=grad, dim=-1, process_group=self.dp_group) |
||||
grad_reshape = grad.view(-1, grad_shape[1]) |
||||
exp_avg_sq_row = state_row # [H/dp] |
||||
exp_avg_sq_col = state_col # [W] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# reduce col |
||||
dist.all_reduce(exp_avg_sq_col, group=self.tp_group) |
||||
exp_avg_sq_col.div_(self.tp_size) |
||||
update_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update_reshape.mul_(grad_reshape) |
||||
update = _split(input_=update_reshape.view(-1), dim=-1, process_group=self.dp_group) |
||||
else: |
||||
# no residual row |
||||
# view update to origin[tp] shape |
||||
update_reshape = update.view(-1, grad_shape[1]) # [H/dp, W] |
||||
grad_reshape = grad.view(-1, grad_shape[1]) # [H/dp, W] |
||||
exp_avg_sq_row = state_row # [H/dp] |
||||
exp_avg_sq_col = state_col # [W] |
||||
exp_avg_sq_row.mul_(beta2t).add_(update_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(update_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# reduce col |
||||
dist.all_reduce(exp_avg_sq_col, group=self.tp_group) |
||||
exp_avg_sq_col.div_(self.tp_size) |
||||
update_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update_reshape.mul_(grad_reshape) |
||||
update = update_reshape.view(-1) |
||||
else: |
||||
# # base factor; no tp, no dp |
||||
exp_avg_sq_row = state_row # [H/dp] |
||||
exp_avg_sq_col = state_col # [W] |
||||
# Exponential average of row indexes |
||||
exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
# Exponential average of columns indexes |
||||
exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# Approximation of exponential moving average of square of gradient |
||||
update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
update.mul_(grad) |
||||
return update |
||||
|
||||
# factor |
||||
def _base_res_factor(self, res, exp_avg, state_row, state_col, grad_shape, beta2t): |
||||
if self.use_zero: |
||||
# only zero |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
# view res to origin shape res.view(grad_shape[0]//self.data_parallel_size , grad_shape[1]) |
||||
# row mean no change |
||||
# col mean need reduce and div |
||||
# gather res[flatten] along dp group then reshape to [H, W] |
||||
res = _gather(input_=res, dim=-1, process_group=self.dp_group) |
||||
# view res to origin[tp] shape |
||||
res_reshape = res.view(-1, grad_shape[1]) |
||||
# gather exp_avg[flatten] along dp group then reshape to [H, W] |
||||
exp_avg = _gather(input_=exp_avg, dim=-1, process_group=self.dp_group) |
||||
exp_avg_reshape = exp_avg.view(-1, grad_shape[1]) |
||||
exp_avg_sq_row = state_row # [H/dp] |
||||
exp_avg_sq_col = state_col # [W] |
||||
exp_avg_sq_row.mul_(beta2t).add_(res_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(res_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# reduce col |
||||
dist.all_reduce(exp_avg_sq_col, group=self.tp_group) |
||||
exp_avg_sq_col.div_(self.tp_size) |
||||
res_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
res_reshape.mul_(exp_avg_reshape) |
||||
res = _split(input_=res_reshape.view(-1), dim=-1, process_group=self.dp_group) |
||||
else: |
||||
# no residual row |
||||
# view res to origin[tp] shape |
||||
res_reshape = res.view(-1, grad_shape[1]) # [H/dp, W] |
||||
exp_avg_reshape = exp_avg.view(-1, grad_shape[1]) # [H/dp, W] |
||||
exp_avg_sq_row = state_row # [H/dp] |
||||
exp_avg_sq_col = state_col # [W] |
||||
exp_avg_sq_row.mul_(beta2t).add_(res_reshape.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
exp_avg_sq_col.mul_(beta2t).add_(res_reshape.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# reduce col |
||||
dist.all_reduce(exp_avg_sq_col, group=self.tp_group) |
||||
exp_avg_sq_col.div_(self.tp_size) |
||||
res_reshape = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
res_reshape.mul_(exp_avg_reshape) |
||||
res = res_reshape.view(-1) |
||||
else: |
||||
# # base factor; no tp, no dp |
||||
exp_avg_sq_row = state_row # [H/dp] |
||||
exp_avg_sq_col = state_col # [W] |
||||
# Exponential average of row indexes |
||||
exp_avg_sq_row.mul_(beta2t).add_(res.mean(dim=-1), alpha=(1.0 - beta2t)) |
||||
# Exponential average of columns indexes |
||||
exp_avg_sq_col.mul_(beta2t).add_(res.mean(dim=-2), alpha=(1.0 - beta2t)) |
||||
# Approximation of exponential moving average of square of gradient |
||||
res = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) |
||||
res.mul_(exp_avg) |
||||
return res |
||||
|
||||
@torch.no_grad() |
||||
def step(self, closure=None): |
||||
"""Performs a single optimization step. |
||||
Args: |
||||
closure (callable, optional): A closure that reevaluates the model |
||||
and returns the loss. |
||||
""" |
||||
loss = None |
||||
if closure is not None: |
||||
loss = closure() |
||||
|
||||
for group in self.param_groups: |
||||
for p in group["params"]: |
||||
if p.grad is None: |
||||
continue |
||||
grad = p.grad |
||||
if grad.is_sparse: |
||||
raise RuntimeError("CAME does not support sparse gradients.") |
||||
|
||||
state = self.state[p] |
||||
# Under zero the grad_shape is the original grad that is flattened and then cut (only one dimension) |
||||
grad_shape = grad.shape |
||||
grad_shape = self.grad_shape_dict[id(p)] |
||||
param_is_dtensor = self.param_is_dtensor_dict[id(p)] |
||||
if param_is_dtensor: |
||||
grad_shape = self.shard_to_working_param.get(id(p)).shape # tp shape (2 dim) |
||||
factored = self.factored_dict[id(p)] |
||||
shard_spec = self.shard_spec_dict[id(p)] |
||||
|
||||
# State Initialization |
||||
if len(state) == 0: |
||||
state["step"] = 0 |
||||
state["exp_avg"] = torch.zeros_like(p) |
||||
if factored: |
||||
if param_is_dtensor: |
||||
if shard_spec.sharding_sequence[0] == "R": # Col Parallel |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0], device=p.device, dtype=p.dtype |
||||
) # [H] |
||||
state["exp_avg_res_row"] = torch.zeros( |
||||
grad_shape[0], device=p.device, dtype=p.dtype |
||||
) # [H] |
||||
else: |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0] // self.dp_size, device=p.device, dtype=p.dtype |
||||
) # [H/dp] |
||||
state["exp_avg_res_row"] = torch.zeros( |
||||
grad_shape[0] // self.dp_size, device=p.device, dtype=p.dtype |
||||
) # [H/dp] |
||||
state["exp_avg_sq_col"] = torch.zeros( |
||||
grad_shape[1], device=p.device, dtype=p.dtype |
||||
) # [W/TP] |
||||
state["exp_avg_res_col"] = torch.zeros( |
||||
grad_shape[1], device=p.device, dtype=p.dtype |
||||
) # [W/TP] |
||||
|
||||
if shard_spec.sharding_sequence[-1] == "R": # Row Parallel |
||||
# Row indivisible shape situation |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0], device=p.device, dtype=p.dtype |
||||
) # [H/tp] |
||||
state["exp_avg_res_row"] = torch.zeros( |
||||
grad_shape[0], device=p.device, dtype=p.dtype |
||||
) # [H/tp] |
||||
else: |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0] // self.dp_size, device=p.device, dtype=p.dtype |
||||
) # [H/dp/tp] |
||||
state["exp_avg_res_row"] = torch.zeros( |
||||
grad_shape[0] // self.dp_size, device=p.device, dtype=p.dtype |
||||
) # [H/dp/tp] |
||||
|
||||
state["exp_avg_sq_col"] = torch.zeros( |
||||
grad_shape[1], device=p.device, dtype=p.dtype |
||||
) # [W] |
||||
state["exp_avg_res_col"] = torch.zeros( |
||||
grad_shape[1], device=p.device, dtype=p.dtype |
||||
) # [W] |
||||
else: |
||||
if self.use_zero: |
||||
if grad_shape[0] % self.dp_size != 0: |
||||
# save all exp_avg_sq_row [H] |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0], device=grad.device, dtype=p.dtype |
||||
) |
||||
state["exp_avg_res_row"] = torch.zeros( |
||||
grad_shape[0], device=grad.device, dtype=p.dtype |
||||
) |
||||
else: |
||||
# exp_avg_sq_row [H // dp] |
||||
state["exp_avg_sq_row"] = torch.zeros( |
||||
grad_shape[0] // self.dp_size, device=grad.device, dtype=p.dtype |
||||
) |
||||
state["exp_avg_res_row"] = torch.zeros( |
||||
grad_shape[0] // self.dp_size, device=grad.device, dtype=p.dtype |
||||
) |
||||
else: |
||||
# exp_avg_sq_row [H] |
||||
state["exp_avg_sq_row"] = torch.zeros(grad_shape[0], device=grad.device, dtype=p.dtype) |
||||
state["exp_avg_res_row"] = torch.zeros(grad_shape[0], device=grad.device, dtype=p.dtype) |
||||
# exp_avg_sq_col alaways [W] |
||||
state["exp_avg_sq_col"] = torch.zeros(grad_shape[1], device=grad.device, dtype=p.dtype) |
||||
state["exp_avg_res_col"] = torch.zeros(grad_shape[1], device=grad.device, dtype=p.dtype) |
||||
else: |
||||
state["exp_avg_sq"] = torch.zeros_like(p) |
||||
state["RMS"] = 0 |
||||
else: |
||||
if factored: |
||||
state["exp_avg_sq_row"] = state["exp_avg_sq_row"] |
||||
state["exp_avg_sq_col"] = state["exp_avg_sq_col"] |
||||
state["exp_avg_res_row"] = state["exp_avg_sq_row"] |
||||
state["exp_avg_res_col"] = state["exp_avg_sq_col"] |
||||
else: |
||||
state["exp_avg_sq"] = state["exp_avg_sq"] |
||||
|
||||
state["step"] += 1 |
||||
|
||||
update = (grad**2) + group["eps"][0] |
||||
if factored: |
||||
if param_is_dtensor: |
||||
# ============================== |
||||
# First Dim is R, Last Dim is S{} means split dim -1 ---> |
||||
# Coloum Parallel ---> sq_row need Do (col) Reduce |
||||
# ============================== |
||||
if shard_spec.sharding_sequence[0] == "R": |
||||
update = self._col_parallel_factor( |
||||
update, |
||||
grad, |
||||
state["exp_avg_sq_row"], |
||||
state["exp_avg_sq_col"], |
||||
grad_shape, |
||||
group["betas"][1], |
||||
) |
||||
# ============================== |
||||
# Last Dim is R, First Dim is S{} means split dim 0 ---> |
||||
# Row Parallel ---> sq_col need Do (row) Reduce |
||||
# ============================== |
||||
elif shard_spec.sharding_sequence[-1] == "R": |
||||
update = self._row_parallel_factor( |
||||
update, |
||||
grad, |
||||
state["exp_avg_sq_row"], |
||||
state["exp_avg_sq_col"], |
||||
grad_shape, |
||||
group["betas"][1], |
||||
) |
||||
else: |
||||
update = self._base_factor( |
||||
update, |
||||
grad, |
||||
state["exp_avg_sq_row"], |
||||
state["exp_avg_sq_col"], |
||||
grad_shape, |
||||
group["betas"][1], |
||||
) |
||||
else: |
||||
exp_avg_sq = state["exp_avg_sq"] |
||||
exp_avg_sq.mul_(group["betas"][1]).add_(update, alpha=(1.0 - group["betas"][1])) |
||||
update = exp_avg_sq.rsqrt().mul_(grad) |
||||
rms = self._rms( |
||||
update, |
||||
param_is_dtensor, |
||||
self.use_zero, |
||||
self.tp_size, |
||||
self.dp_size, |
||||
self.tp_group, |
||||
self.dp_group, |
||||
) |
||||
|
||||
update.div_((rms / group["clip_threshold"]).clamp_(min=1.0)) |
||||
|
||||
exp_avg = state["exp_avg"] |
||||
exp_avg.mul_(group["betas"][0]).add_(update, alpha=1 - group["betas"][0]) |
||||
# Confidence-guided strategy |
||||
# Calculation of instability |
||||
res = (update - exp_avg) ** 2 + group["eps"][1] |
||||
if factored: |
||||
if param_is_dtensor: |
||||
# ============================== |
||||
# First Dim is R, Last Dim is S{} means split dim -1 ---> |
||||
# Coloum Parallel ---> sq_row need Do (col) Reduce |
||||
# ============================== |
||||
if shard_spec.sharding_sequence[0] == "R": |
||||
update = self._col_parallel_factor( |
||||
res, |
||||
exp_avg, |
||||
state["exp_avg_res_row"], |
||||
state["exp_avg_res_col"], |
||||
grad_shape, |
||||
group["betas"][2], |
||||
) |
||||
# ============================== |
||||
# Last Dim is R, First Dim is S{} means split dim 0 ---> |
||||
# Row Parallel ---> sq_col need Do (row) Reduce |
||||
# ============================== |
||||
elif shard_spec.sharding_sequence[-1] == "R": |
||||
update = self._row_parallel_factor( |
||||
res, |
||||
exp_avg, |
||||
state["exp_avg_res_row"], |
||||
state["exp_avg_res_col"], |
||||
grad_shape, |
||||
group["betas"][2], |
||||
) |
||||
else: |
||||
update = self._base_res_factor( |
||||
res, |
||||
exp_avg, |
||||
state["exp_avg_res_row"], |
||||
state["exp_avg_res_col"], |
||||
grad_shape, |
||||
group["betas"][2], |
||||
) |
||||
else: |
||||
update = exp_avg |
||||
|
||||
if group["weight_decay"] != 0: |
||||
p.add_(p, alpha=-group["weight_decay"] * group["lr"]) |
||||
update.mul_(group["lr"]) |
||||
p.add_(-update) |
||||
return loss |
||||
@ -0,0 +1,279 @@
|
||||
""" adapted from https://github.com/jiaweizzhao/GaLore/blob/master/galore_torch/adamw8bit.py""" |
||||
|
||||
import warnings |
||||
from collections import defaultdict |
||||
from typing import Dict, Optional |
||||
|
||||
import torch |
||||
import torch.distributed as dist |
||||
import torch.nn.functional as F |
||||
from bitsandbytes.optim.optimizer import Optimizer2State |
||||
|
||||
from colossalai.interface.optimizer import DistributedOptim |
||||
from colossalai.tensor.d_tensor import get_shard_dim_1d, is_distributed_tensor |
||||
|
||||
from .galore import GaLoreProjector, make_low_rank_buffer |
||||
|
||||
__all__ = ["DistributedGalore"] |
||||
# Mark sharded dimension |
||||
|
||||
|
||||
class DistGaloreAwamW(DistributedOptim, Optimizer2State): |
||||
r"""Implements Galore, a optimizer-agonistic gradient compression technique on 8-bit AdamW. |
||||
It largely compresses gradient via low-rank projection and is claimed to be insensitive to hyperparams like lr. |
||||
Supports Tensor Parallel and ZeRO stage 1 and 2 via booster and plugin. |
||||
Proposed in `GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection` |
||||
https://arxiv.org/abs/2403.03507 |
||||
|
||||
Arguments: |
||||
params (iterable): iterable of parameters to optimize or dicts defining |
||||
parameter groups. |
||||
lr (float, optional): learning rate. (default: 1e-3) |
||||
betas (Tuple[float, float], optional): coefficients used for computing |
||||
running averages of gradient and its norm. (default: (0.9, 0.999)) |
||||
eps (float, optional): term added to the denominator to improve |
||||
numerical stability. (default: 1e-6) |
||||
weight_decay (float, optional): weight decay (L2 penalty) (default: 0.01) |
||||
nbits: Number of bits for quantization optim states. Only 32 and 8 are supported. |
||||
min_8bit_size (`int`, defaults to 4096): |
||||
The minimum number of elements of the parameter tensors for 8-bit optimization. |
||||
percentile_clipping (`int`, defaults to 100): |
||||
Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. |
||||
block_wise (`bool`, defaults to `True`): |
||||
Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. |
||||
is_paged (`bool`, defaults to `False`): |
||||
Whether the optimizer is a paged optimizer (handle memory spike via CPU-GPU transfer) or not. |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
params, |
||||
lr=1e-3, |
||||
betas=(0.9, 0.999), |
||||
eps=1e-8, |
||||
weight_decay=1e-2, |
||||
nbits=8, |
||||
min_8bit_size=4096, |
||||
percentile_clipping=100, |
||||
block_wise=True, |
||||
is_paged=False, |
||||
): |
||||
super().__init__( |
||||
"adam", |
||||
params, |
||||
lr, |
||||
betas, |
||||
eps, |
||||
weight_decay, |
||||
nbits, |
||||
None, |
||||
min_8bit_size, |
||||
percentile_clipping, |
||||
block_wise, |
||||
is_paged=is_paged, |
||||
) |
||||
self.tp_size = 1 |
||||
self.dp_size = 1 |
||||
self.is_dist = {} |
||||
proj_none = all(["rank" not in group for group in self.param_groups]) |
||||
if proj_none: |
||||
warnings.warn( |
||||
"Will not apply GaLore as rank isn't in any param group. If you forgot to, try get_galore_param_groups" |
||||
) |
||||
|
||||
# Default from the paper |
||||
for group in self.param_groups: |
||||
if "rank" in group: |
||||
group["update_proj_gap"] = group.get("update_proj_gap", 200) |
||||
group["proj_type"] = group.get("proj_type", "std") |
||||
group["scale"] = group.get("scale", 0.25) |
||||
|
||||
def setup_distributed( |
||||
self, |
||||
tp_group: Optional[dist.ProcessGroup] = None, |
||||
dp_group: Optional[dist.ProcessGroup] = None, |
||||
shard_to_working_param: Optional[Dict] = {}, |
||||
padding_map: Optional[Dict] = defaultdict(int), |
||||
is_zero: Optional[bool] = False, |
||||
): |
||||
"""Setup process groups for TP and ZeRO 2. |
||||
Arguments: |
||||
tp_group (dist.ProcessGroup): Tensor Parallel process group |
||||
dp_group (dist.ProcessGroup): ZeRO 2 process group |
||||
shard_to_working_param (Dict): ZeRO 2 feeds the optimizer a sharded param view as grads are sharded. |
||||
This maps from id(view) to working params used in forward & backward. |
||||
padding_map (Dict): Padding size of each param from ZeRO's param store. Required if ZeRO is used. |
||||
is_zero (bool): Whether to use ZeRO 2. |
||||
""" |
||||
assert dist.is_initialized(), "You forgot to initialized distributed backend..." |
||||
|
||||
self.tp_group = tp_group |
||||
self.dp_group = dp_group |
||||
if tp_group is not None: |
||||
self.tp_size = dist.get_world_size(tp_group) |
||||
if dp_group is not None: |
||||
self.dp_size = dist.get_world_size(dp_group) |
||||
|
||||
self.shard_to_working_param = shard_to_working_param if shard_to_working_param is not None else {} |
||||
self.is_zero = is_zero and self.dp_size > 1 |
||||
self.padding_map = padding_map if padding_map is not None else defaultdict(int) |
||||
if is_zero: |
||||
assert self.padding_map is not defaultdict( |
||||
int |
||||
), "We can't do SVD without knowing ZeRO's per-param padding size" |
||||
self.distributed_on = self.tp_size > 0 or self.dp_size > 0 |
||||
|
||||
# Cache working param layout |
||||
self.shard_dim = {} |
||||
for group in self.param_groups: |
||||
for p in group["params"]: |
||||
# w/o ZeRO: master param = working param |
||||
self.shard_to_working_param[id(p)] = self.shard_to_working_param.get(id(p), p) |
||||
if id(p) not in self.padding_map: |
||||
self.padding_map[id(p)] = 0 |
||||
|
||||
self.is_dist[id(p)] = is_distributed_tensor(self.shard_to_working_param[id(p)]) |
||||
if is_distributed_tensor(self.shard_to_working_param[id(p)]): |
||||
self.shard_dim[id(p)] = get_shard_dim_1d(self.shard_to_working_param[id(p)]) |
||||
|
||||
@torch.no_grad() |
||||
def step(self, closure=None): |
||||
"""Performs a single optimization step. |
||||
|
||||
Arguments: |
||||
closure (callable, optional): A closure that reevaluates the model |
||||
and returns the loss. |
||||
""" |
||||
loss = None |
||||
if closure is not None: |
||||
with torch.enable_grad(): |
||||
loss = closure() |
||||
|
||||
if not self.initialized: |
||||
self.check_overrides() |
||||
self.to_gpu() |
||||
self.initialized = True |
||||
|
||||
for gindex, group in enumerate(self.param_groups): |
||||
for pindex, p in enumerate(group["params"]): |
||||
if p.grad is None: |
||||
continue |
||||
state = self.state[p] |
||||
|
||||
if "step" not in state: |
||||
state["step"] = 0 |
||||
|
||||
# GaLore Projection |
||||
if "rank" in group: |
||||
if "projector" not in state: |
||||
state["projector"] = GaLoreProjector( |
||||
group["rank"], |
||||
scale=group["scale"], |
||||
update_proj_gap=group["update_proj_gap"], |
||||
proj_type=group["proj_type"], |
||||
) |
||||
# decoupled weight decay |
||||
if "weight_decay" in group and group["weight_decay"] > 0: |
||||
group["weight_decay_saved"] = group["weight_decay"] |
||||
group["weight_decay"] = 0 |
||||
|
||||
grad = p.grad |
||||
working_shape = list(self.shard_to_working_param[id(p)].shape) |
||||
padding = self.padding_map[id(p)] |
||||
|
||||
# All-gather grads for projection step |
||||
if self.distributed_on: |
||||
# Gather for ZeRO 1 & 2 implementation don't retain full grads |
||||
if self.is_zero: |
||||
# (m, n).flatten().chunk(dp_size) equals to (m / dp_size, n).flatten() |
||||
working_shape[0] //= self.dp_size |
||||
# Gather grads for projection |
||||
if state["step"] % group["update_proj_gap"] == 0: |
||||
all_grads = [ |
||||
torch.empty_like(grad, dtype=p.grad.dtype, device=p.grad.device) |
||||
for _ in range(self.dp_size) |
||||
] |
||||
dist.all_gather(all_grads, grad, self.dp_group) |
||||
grad = torch.cat(all_grads) |
||||
# To working param shape |
||||
if padding > 0: |
||||
grad = grad[:-padding] |
||||
working_shape[0] *= self.dp_size |
||||
grad = grad.reshape(working_shape) # unflatten |
||||
|
||||
# Gather TP grads |
||||
if self.is_dist[id(p)] and state["step"] % group["update_proj_gap"] == 0: |
||||
all_grads = [ |
||||
torch.empty_like(grad, dtype=p.grad.dtype, device=p.grad.device) |
||||
for _ in range(self.tp_size) |
||||
] |
||||
dist.all_gather(all_grads, grad.contiguous(), self.tp_group) |
||||
grad = torch.cat(all_grads, dim=self.shard_dim[id(p)]) |
||||
|
||||
# Compute SVD. Will use a subset of singular vectors when grads are sharded. |
||||
grad = state["projector"].project(grad, state["step"]) |
||||
|
||||
# Re-shard gathered grads after SVD |
||||
if self.distributed_on and state["step"] % group["update_proj_gap"] == 0: |
||||
# TP |
||||
if self.is_dist[id(p)]: |
||||
grad = grad.chunk(self.tp_size, dim=self.shard_dim[id(p)])[dist.get_rank(self.tp_group)] |
||||
# ZeRO |
||||
# TODO: this might not work with padding, e.g. (3, 3) with dp size 2 |
||||
# Need extra logic in ZeRO to pad nRows/nCols to be divisible by dp_size |
||||
if self.is_zero: |
||||
grad = grad.chunk(self.dp_size)[dist.get_rank(self.dp_group)] |
||||
grad = grad.contiguous() # avoid bitsandbytes update error |
||||
|
||||
working_shape = grad.shape |
||||
# To flattended master param shape |
||||
grad = self.to_master_shape(grad, padding) |
||||
make_low_rank_buffer(p, grad) |
||||
|
||||
if "state1" not in state: |
||||
self.init_state(group, p, gindex, pindex) |
||||
|
||||
self.prefetch_state(p) |
||||
self.update_step(group, p, gindex, pindex) |
||||
torch.cuda.synchronize() |
||||
|
||||
# Project Back to working param shape |
||||
if "rank" in group: |
||||
# Unpad |
||||
if self.is_zero: |
||||
if padding > 0: |
||||
p.data = p.data[:-padding] |
||||
p.data = p.data.reshape(working_shape) |
||||
|
||||
p.data = state["projector"].project_back(p.data) |
||||
# Re-flatten grads for ZeRO |
||||
p.data = self.to_master_shape(p.data, padding) |
||||
p.data = p.saved_data.add_(p.data) |
||||
|
||||
# apply decoupled weight decay |
||||
if "weight_decay_saved" in group: |
||||
p.data.add_(p.data, alpha=-group["lr"] * group["weight_decay_saved"]) |
||||
group["weight_decay"] = group["weight_decay_saved"] |
||||
del group["weight_decay_saved"] |
||||
|
||||
if self.is_paged: |
||||
# all paged operation are asynchronous, we need |
||||
# to sync to make sure all tensors are in the right state |
||||
torch.cuda.synchronize() |
||||
return loss |
||||
|
||||
def to_master_shape(self, data, padding): |
||||
"""Pad to master (optimizer) param shape""" |
||||
if not self.is_zero: |
||||
return data |
||||
data = data.view(-1) |
||||
if padding > 0: |
||||
data = F.pad(data, [0, padding]) |
||||
return data |
||||
|
||||
def __del__(self): |
||||
"""Avoid buffer memory leak""" |
||||
for group in self.param_groups: |
||||
for p in group["params"]: |
||||
if hasattr(p, "saved_data"): |
||||
del p.saved_data |
||||
@ -0,0 +1,181 @@
|
||||
# Disclaimer: Modified from https://github.com/NUS-HPC-AI-Lab/pytorch-lamb/blob/master/optim/lamb.py |
||||
|
||||
|
||||
from typing import Dict, Optional |
||||
|
||||
import torch |
||||
import torch.distributed as dist |
||||
|
||||
from colossalai.interface.optimizer import DistributedOptim |
||||
from colossalai.tensor.d_tensor import is_distributed_tensor |
||||
|
||||
__all__ = ["DistributedLamb"] |
||||
|
||||
|
||||
class DistributedLamb(DistributedOptim): |
||||
r"""Implements the Lamb algorithm, with extra support for ZeRO 2 and Tensor Parallel. |
||||
Proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_. |
||||
It's recommended to use this with HybridParallelPlugin/ZeRO plugin and booster, |
||||
which will take care of setup_distributed. |
||||
Example with 4 devices: |
||||
>>> optim = DistributedLamb(model.parameters(), lr=1e-3) |
||||
>>> proc_mesh = ProcessGroupMesh(tp_size, zero_size) |
||||
>>> tp_group = proc_mesh.get_group_along_axis(0) |
||||
>>> dp_group = proc_mesh.get_group_along_axis(1) |
||||
>>> optim.setup_distributed(tp_group, dp_group) |
||||
|
||||
Arguments: |
||||
params (iterable): iterable of parameters to optimize or dicts defining |
||||
parameter groups |
||||
lr (float, optional): learning rate (default: 1e-3) |
||||
betas (Tuple[float, float], optional): coefficients used for computing |
||||
running averages of gradient and its square (default: (0.9, 0.999)) |
||||
eps (float, optional): term added to the denominator to improve |
||||
numerical stability (default: 1e-8) |
||||
weight_decay (float, optional): weight decay (L2 penalty) (default: 0) |
||||
.. _Large Batch Optimization for Deep Learning: Training BERT in 76 minutes: |
||||
https://arxiv.org/abs/1904.00962 |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
params, |
||||
lr=1e-3, |
||||
betas=(0.9, 0.999), |
||||
eps=1e-6, |
||||
weight_decay=0, |
||||
bias_correction=True, |
||||
): |
||||
if not 0.0 <= lr: |
||||
raise ValueError("Invalid learning rate: {}".format(lr)) |
||||
if not 0.0 <= eps: |
||||
raise ValueError("Invalid epsilon value: {}".format(eps)) |
||||
if not 0.0 <= betas[0] < 1.0: |
||||
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) |
||||
if not 0.0 <= betas[1] < 1.0: |
||||
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) |
||||
|
||||
# self.setup_distributed(tp_group, dp_group) |
||||
self.shard_to_working_param = {} |
||||
self.tp_size = self.dp_size = 1 |
||||
self.is_zero = False |
||||
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, bias_correction=bias_correction) |
||||
super().__init__(params, defaults) |
||||
|
||||
def setup_distributed( |
||||
self, |
||||
tp_group: Optional[dist.ProcessGroup] = None, |
||||
dp_group: Optional[dist.ProcessGroup] = None, |
||||
shard_to_working_param: Optional[Dict] = {}, |
||||
padding_map=None, |
||||
is_zero: Optional[bool] = False, |
||||
): |
||||
"""Assign process groups for TP and ZeRO 2. |
||||
Arguments: |
||||
tp_group (dist.ProcessGroup): Tensor Parallel process group |
||||
dp_group (dist.ProcessGroup): ZeRO 2 process group |
||||
shard_to_working_param (Dict): ZeRO 2 feeds the optimizer a sharded param view as grads are sharded. |
||||
This maps from id(view) to working params used in forward & backward. |
||||
padding_map: An empty interface placeholder |
||||
is_zero (bool): Whether to use ZeRO 2. |
||||
""" |
||||
self.tp_group = tp_group |
||||
self.dp_group = dp_group |
||||
if tp_group is not None: |
||||
self.tp_size = dist.get_world_size(tp_group) |
||||
if dp_group is not None: |
||||
self.dp_size = dist.get_world_size(dp_group) |
||||
|
||||
self.shard_to_working_param = shard_to_working_param if shard_to_working_param is not None else {} |
||||
self.is_zero = is_zero |
||||
self.is_dist = {} |
||||
# Cache parameter layout |
||||
for group in self.param_groups: |
||||
for p in group["params"]: |
||||
# w/o ZeRO: master param = working param |
||||
self.shard_to_working_param[id(p)] = self.shard_to_working_param.get(id(p), p) |
||||
self.is_dist[p] = ( |
||||
is_distributed_tensor(p) |
||||
if self.dp_size <= 1 |
||||
else is_distributed_tensor(self.shard_to_working_param.get(id(p), None)) |
||||
) |
||||
|
||||
@torch.no_grad() |
||||
def step(self, closure=None): |
||||
"""Performs a single optimization step. |
||||
Arguments: |
||||
closure (callable, optional): A closure that reevaluates the model |
||||
and returns the loss. |
||||
""" |
||||
loss = None |
||||
if closure is not None: |
||||
loss = closure() |
||||
|
||||
for group in self.param_groups: |
||||
for p in group["params"]: |
||||
if p.grad is None: |
||||
continue |
||||
grad = p.grad.data |
||||
if grad.is_sparse: |
||||
raise RuntimeError("Lamb does not support sparse gradients, consider SparseAdam instad.") |
||||
|
||||
state = self.state[p] |
||||
# State initialization |
||||
if len(state) == 0: |
||||
state["step"] = 0 |
||||
# Exponential moving average of gradient values |
||||
state["exp_avg"] = torch.zeros_like(p.data) |
||||
# Exponential moving average of squared gradient values |
||||
state["exp_avg_sq"] = torch.zeros_like(p.data) |
||||
|
||||
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] |
||||
beta1, beta2 = group["betas"] |
||||
|
||||
state["step"] += 1 |
||||
|
||||
# Decay the first and second moment running average coefficient |
||||
# m_t |
||||
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) |
||||
# v_t |
||||
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) |
||||
|
||||
scaled_lr = group["lr"] |
||||
if group["bias_correction"]: |
||||
bias_correction1 = 1 - beta1 ** state["step"] |
||||
bias_correction2 = 1 - beta2 ** state["step"] |
||||
# Apply debiasing to lr to avoid broadcast |
||||
scaled_lr *= (bias_correction2**0.5) / bias_correction1 |
||||
# exp_avg.div_(bias_correction1) |
||||
# exp_avg_sq.div_(bias_correction2) |
||||
|
||||
update = exp_avg / exp_avg_sq.sqrt().add(group["eps"]) |
||||
if group["weight_decay"] != 0: |
||||
update.add_(p.data, alpha=group["weight_decay"]) |
||||
|
||||
# Compute global layer-wise trust ratio |
||||
if self.is_dist[p] or self.is_zero: |
||||
p_local = p |
||||
g_sum = (update**2).sum() |
||||
if self.dp_size > 1 and self.is_zero: |
||||
# ZeRO 2 doesn't shard param. Compute full param norm w/o communication. |
||||
dist.all_reduce(g_sum, group=self.dp_group) |
||||
p_local = self.shard_to_working_param[id(p)] |
||||
|
||||
w_sum = (p_local**2).sum() |
||||
sums = torch.stack([w_sum, g_sum]) |
||||
|
||||
# Get global l2 norms |
||||
if self.tp_size > 1: |
||||
dist.all_reduce(sums, group=self.tp_group) |
||||
w_norm, g_norm = sums.sqrt().chunk(2) |
||||
else: |
||||
# Fall back to vanilla Lamb |
||||
w_norm = torch.norm(p) |
||||
g_norm = torch.norm(update) |
||||
|
||||
trust_ratio = torch.where(w_norm > 0 and g_norm > 0, (w_norm / g_norm), 1.0).item() |
||||
|
||||
scaled_lr *= trust_ratio |
||||
p.data.add_(update, alpha=-scaled_lr) |
||||
|
||||
return loss |
||||
@ -0,0 +1,315 @@
|
||||
""" adapted from https://github.com/jiaweizzhao/GaLore/blob/master/galore_torch/adamw8bit.py""" |
||||
|
||||
import warnings |
||||
from typing import List |
||||
|
||||
import torch |
||||
from bitsandbytes.optim.optimizer import Optimizer2State |
||||
from torch._C import _LinAlgError |
||||
|
||||
|
||||
def get_galore_param_groups( |
||||
model, weight_decay, rank=256, update_proj_gap=200, scale=0.25, proj_type="std" |
||||
) -> List[dict]: |
||||
""" |
||||
It's advised to use this instead of manually specifying which param groups |
||||
to apply GaLore on. |
||||
""" |
||||
galore_params = [] |
||||
non_galore = [] |
||||
no_decay_params = [] |
||||
no_decay = ["bias", "LayerNorm.weight"] |
||||
|
||||
for name, param in model.named_parameters(): |
||||
# Only make sense to do SVD on 2d gradient matrices |
||||
# e.g. nn.Linear, VocabEmbedding, etc. |
||||
if any(nd in name for nd in no_decay): |
||||
no_decay_params.append(param) |
||||
elif param.dim() == 2: |
||||
galore_params.append(param) |
||||
else: |
||||
non_galore.append(param) |
||||
|
||||
param_groups = [ |
||||
{ |
||||
"params": galore_params, |
||||
"rank": rank, |
||||
"update_proj_gap": update_proj_gap, |
||||
"scale": scale, |
||||
"proj_type": proj_type, |
||||
"weight_decay": weight_decay, |
||||
}, |
||||
{"params": non_galore, "weight_decay": weight_decay}, |
||||
{"params": no_decay_params, "weight_decay": 0.0}, |
||||
] |
||||
|
||||
return param_groups |
||||
|
||||
|
||||
def make_low_rank_buffer(p, grad): |
||||
"""For compatibility with bitsandbytes's update_step, we need an empty low-rank |
||||
param update buffer to avoid mutating original params. |
||||
TODO: optimize by reusing the memory for p.grad? Need to modify bitsandbytes? |
||||
""" |
||||
p.saved_data = p.data.clone() |
||||
# p.data = grad.clone().to(p.data.dtype).to(p.data.device) |
||||
p.data = torch.zeros_like(grad, device=grad.device, dtype=grad.dtype) |
||||
# p.data.zero_() |
||||
p.grad = grad |
||||
|
||||
|
||||
class GaLoreProjector: |
||||
def __init__(self, rank, verbose=False, update_proj_gap=200, scale=1.0, proj_type="std"): |
||||
self.rank = rank |
||||
self.verbose = verbose |
||||
self.update_proj_gap = update_proj_gap |
||||
self.scale = scale |
||||
self.ortho_matrix = None |
||||
self.proj_type = proj_type |
||||
self.svd_type = None |
||||
|
||||
def project(self, full_rank_grad, iter): |
||||
dim = full_rank_grad.dim() |
||||
if dim != 2: |
||||
warnings.warn( |
||||
f"Warning: You shouldn't specify projection rank for {dim}D params in param_groups. Skipping SVD." |
||||
) |
||||
return full_rank_grad |
||||
|
||||
m, n = full_rank_grad.shape # For ZeRO sharded grads |
||||
if self.proj_type == "std": |
||||
# Project the lower dim to minimize information loss |
||||
if self.svd_type is None: |
||||
self.svd_type = "right" if m >= n else "left" |
||||
# SVD step |
||||
if self.ortho_matrix is None or iter % self.update_proj_gap == 0: |
||||
self.ortho_matrix = self.get_orthogonal_matrix(full_rank_grad, self.rank, type=self.svd_type) |
||||
if self.svd_type == "right": |
||||
low_rank_grad = torch.matmul(full_rank_grad, self.ortho_matrix.t()[:n]) |
||||
else: |
||||
low_rank_grad = torch.matmul(self.ortho_matrix.t()[:, :m], full_rank_grad) |
||||
|
||||
elif self.proj_type == "reverse_std": |
||||
if self.svd_type is None: |
||||
self.svd_type = "left" if m >= n else "right" |
||||
# SVD step |
||||
if self.ortho_matrix is None or iter % self.update_proj_gap == 0: |
||||
self.ortho_matrix = self.get_orthogonal_matrix(full_rank_grad, self.rank, type=self.svd_type) |
||||
|
||||
if self.svd_type == "left": |
||||
low_rank_grad = torch.matmul(self.ortho_matrix.t()[:, :m], full_rank_grad) |
||||
else: |
||||
low_rank_grad = torch.matmul(full_rank_grad, self.ortho_matrix.t()[:n]) |
||||
return low_rank_grad |
||||
|
||||
def project_back(self, low_rank_grad): |
||||
if low_rank_grad.dim() != 2: |
||||
return |
||||
|
||||
m, n = low_rank_grad.shape |
||||
if self.svd_type == "right": |
||||
full_rank_grad = torch.matmul(low_rank_grad, self.ortho_matrix[:n]) |
||||
else: |
||||
full_rank_grad = torch.matmul(self.ortho_matrix[:, :m], low_rank_grad) |
||||
|
||||
return full_rank_grad * self.scale |
||||
|
||||
# svd decomposition |
||||
def get_orthogonal_matrix(self, weights, rank, type): |
||||
module_params = weights |
||||
|
||||
if module_params.data.dtype != torch.float: |
||||
float_data = False |
||||
original_type = module_params.data.dtype |
||||
original_device = module_params.data.device |
||||
matrix = module_params.data.float() |
||||
else: |
||||
float_data = True |
||||
matrix = module_params.data |
||||
|
||||
# TODO: redo SVD in the next step. |
||||
if matrix.isnan().any(): |
||||
print(f"{__file__}: skipping SVD due to NaN matrix") |
||||
return self.ortho_matrix |
||||
try: |
||||
U, s, Vh = torch.linalg.svd(matrix, full_matrices=False) |
||||
except _LinAlgError as e: |
||||
print(f"{__file__}: skipping SVD due to {e}") |
||||
return self.ortho_matrix |
||||
|
||||
# make the smaller matrix always to be orthogonal matrix |
||||
if type == "right": |
||||
B = Vh[:rank, :] |
||||
|
||||
if not float_data: |
||||
B = B.to(original_device).type(original_type) |
||||
return B |
||||
elif type == "left": |
||||
A = U[:, :rank] |
||||
if not float_data: |
||||
A = A.to(original_device).type(original_type) |
||||
return A |
||||
elif type == "full": |
||||
A = U[:, :rank] |
||||
B = Vh[:rank, :] |
||||
if not float_data: |
||||
A = A.to(original_device).type(original_type) |
||||
B = B.to(original_device).type(original_type) |
||||
return [A, B] |
||||
else: |
||||
raise ValueError("type should be left, right or full") |
||||
|
||||
|
||||
class GaLoreAdamW8bit(Optimizer2State): |
||||
r"""Implements Galore, a optimizer-agonistic gradient compression technique on 8-bit AdamW. |
||||
Proposed in `GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection`. It compresses |
||||
gradient via low-rank projection and is claimed to be insensitive to hyperparams like lr. |
||||
https://arxiv.org/abs/2403.03507 |
||||
|
||||
Arguments: |
||||
params (iterable): iterable of parameters to optimize or dicts defining |
||||
parameter groups. |
||||
lr (float, optional): learning rate. (default: 1e-3) |
||||
betas (Tuple[float, float], optional): coefficients used for computing |
||||
running averages of gradient and its norm. (default: (0.9, 0.999)) |
||||
eps (float, optional): term added to the denominator to improve |
||||
numerical stability. (default: 1e-6) |
||||
weight_decay (float, optional): weight decay (L2 penalty) (default: 0.01) |
||||
nbits (int): The number of bits of optim states. Only 32 and 8 are supported. |
||||
min_8bit_size (`int`, defaults to 4096): |
||||
The minimum number of elements of the parameter tensors for 8-bit optimization. |
||||
percentile_clipping (`int`, defaults to 100): |
||||
Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. |
||||
block_wise (`bool`, defaults to `True`): |
||||
Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. |
||||
is_paged (`bool`, defaults to `False`): |
||||
Whether the optimizer is a paged optimizer (handle memory spike via CPU-GPU transfer) or not. |
||||
Example: |
||||
|
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
params, |
||||
lr=1e-2, |
||||
betas=(0.9, 0.999), |
||||
eps=1e-8, |
||||
weight_decay=1e-2, |
||||
nbits=8, |
||||
min_8bit_size=4096, |
||||
percentile_clipping=100, |
||||
block_wise=True, |
||||
is_paged=False, |
||||
): |
||||
super().__init__( |
||||
"adam", |
||||
params, |
||||
lr, |
||||
betas, |
||||
eps, |
||||
weight_decay, |
||||
nbits, |
||||
None, |
||||
min_8bit_size, |
||||
percentile_clipping, |
||||
block_wise, |
||||
is_paged=is_paged, |
||||
) |
||||
|
||||
proj_none = all(["rank" not in group for group in self.param_groups]) |
||||
if proj_none: |
||||
warnings.warn( |
||||
"Will not apply GaLore as no rank is specified. Or did you forget to? Try get_galore_param_groups" |
||||
) |
||||
|
||||
# Defaults from the paper |
||||
for group in self.param_groups: |
||||
if "rank" in group: |
||||
group["update_proj_gap"] = group.get("update_proj_gap", 200) |
||||
group["proj_type"] = group.get("proj_type", "std") |
||||
group["scale"] = group.get("scale", 0.25) |
||||
|
||||
@torch.no_grad() |
||||
def step(self, closure=None): |
||||
"""Performs a single optimization step. |
||||
|
||||
Arguments: |
||||
closure (callable, optional): A closure that reevaluates the model |
||||
and returns the loss. |
||||
""" |
||||
|
||||
loss = None |
||||
if closure is not None: |
||||
with torch.enable_grad(): |
||||
loss = closure() |
||||
|
||||
if not self.initialized: |
||||
self.check_overrides() |
||||
self.to_gpu() # needed for fairseq pure fp16 training |
||||
self.initialized = True |
||||
|
||||
for gindex, group in enumerate(self.param_groups): |
||||
for pindex, p in enumerate(group["params"]): |
||||
if p.grad is None: |
||||
continue |
||||
if p is self.param_groups[0]["params"][0]: |
||||
torch.save(p.grad, "grad.pt") |
||||
state = self.state[p] |
||||
|
||||
if "step" not in state: |
||||
state["step"] = 0 |
||||
|
||||
# GaLore Projection |
||||
if "rank" in group: |
||||
if "projector" not in state: |
||||
state["projector"] = GaLoreProjector( |
||||
group["rank"], |
||||
scale=group["scale"], |
||||
update_proj_gap=group["update_proj_gap"], |
||||
proj_type=group["proj_type"], |
||||
) |
||||
|
||||
if "weight_decay" in group and group["weight_decay"] > 0: |
||||
# ensure that the weight decay is not applied to the norm grad |
||||
group["weight_decay_saved"] = group["weight_decay"] |
||||
group["weight_decay"] = 0 |
||||
|
||||
grad = state["projector"].project(p.grad, state["step"]) |
||||
make_low_rank_buffer(p, grad) |
||||
|
||||
if "state1" not in state: |
||||
self.init_state(group, p, gindex, pindex) |
||||
|
||||
# p.grad = p.grad.contiguous() # avoid bitsandbytes update error |
||||
# Prefetch if paged |
||||
self.prefetch_state(p) |
||||
# Adam update step using the buffer |
||||
self.update_step(group, p, gindex, pindex) |
||||
torch.cuda.synchronize() |
||||
|
||||
# GaLore Projection Back |
||||
if "rank" in group: |
||||
if p is self.param_groups[0]["params"][1]: |
||||
pass |
||||
update = state["projector"].project_back(p.data) |
||||
p.data = p.saved_data.add_(update) |
||||
|
||||
# apply weight decay |
||||
if "weight_decay_saved" in group: |
||||
p.data.add_(p.data, alpha=-group["lr"] * group["weight_decay_saved"]) |
||||
group["weight_decay"] = group["weight_decay_saved"] |
||||
del group["weight_decay_saved"] |
||||
|
||||
if self.is_paged: |
||||
# all paged operation are asynchronous, we need |
||||
# to sync to make sure all tensors are in the right state |
||||
torch.cuda.synchronize() |
||||
|
||||
return loss |
||||
|
||||
def __del__(self): |
||||
"""Avoid buffer memory leak""" |
||||
for group in self.param_groups: |
||||
for p in group["params"]: |
||||
if hasattr(p, "saved_data"): |
||||
del p.saved_data |
||||
@ -0,0 +1,758 @@
|
||||
from typing import List, Optional, Tuple, Union |
||||
|
||||
import torch |
||||
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
||||
from transformers.modeling_outputs import ( |
||||
BaseModelOutputWithPast, |
||||
CausalLMOutputWithPast, |
||||
SequenceClassifierOutputWithPast, |
||||
) |
||||
|
||||
try: |
||||
from transformers.models.qwen2.modeling_qwen2 import ( |
||||
Qwen2Attention, |
||||
Qwen2ForCausalLM, |
||||
Qwen2ForSequenceClassification, |
||||
Qwen2Model, |
||||
_prepare_4d_causal_attention_mask, |
||||
_prepare_4d_causal_attention_mask_for_sdpa, |
||||
apply_rotary_pos_emb, |
||||
repeat_kv, |
||||
) |
||||
except ImportError: |
||||
Qwen2Model = "Qwen2Model" |
||||
Qwen2ForCausalLM = "Qwen2ForCausalLM" |
||||
Qwen2Attention = "Qwen2Attention" |
||||
Qwen2ForSequenceClassification = "Qwen2ForSequenceClassification" |
||||
|
||||
from transformers.utils import logging |
||||
|
||||
from colossalai.pipeline.stage_manager import PipelineStageManager |
||||
from colossalai.shardformer.shard import ShardConfig |
||||
|
||||
from ..layer import ColoAttention, cross_entropy_1d |
||||
|
||||
|
||||
class Qwen2PipelineForwards: |
||||
""" |
||||
This class serves as a micro library for forward function substitution of Qwen2 models |
||||
under pipeline setting. |
||||
""" |
||||
|
||||
@staticmethod |
||||
def qwen2_model_forward( |
||||
self: Qwen2Model, |
||||
input_ids: torch.LongTensor = None, |
||||
attention_mask: Optional[torch.Tensor] = None, |
||||
position_ids: Optional[torch.LongTensor] = None, |
||||
past_key_values: Optional[List[torch.FloatTensor]] = None, |
||||
inputs_embeds: Optional[torch.FloatTensor] = None, |
||||
use_cache: Optional[bool] = None, |
||||
output_attentions: Optional[bool] = None, |
||||
output_hidden_states: Optional[bool] = None, |
||||
return_dict: Optional[bool] = None, |
||||
stage_manager: Optional[PipelineStageManager] = None, |
||||
hidden_states: Optional[torch.FloatTensor] = None, |
||||
stage_index: Optional[List[int]] = None, |
||||
shard_config: ShardConfig = None, |
||||
) -> Union[Tuple, BaseModelOutputWithPast]: |
||||
logger = logging.get_logger(__name__) |
||||
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
||||
output_hidden_states = ( |
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
||||
) |
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache |
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
||||
|
||||
# retrieve input_ids and inputs_embeds |
||||
if stage_manager.is_first_stage(): |
||||
if input_ids is not None and inputs_embeds is not None: |
||||
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") |
||||
elif input_ids is not None: |
||||
batch_size, seq_length = input_ids.shape |
||||
elif inputs_embeds is not None: |
||||
batch_size, seq_length, _ = inputs_embeds.shape |
||||
else: |
||||
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") |
||||
|
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device |
||||
if inputs_embeds is None: |
||||
inputs_embeds = self.embed_tokens(input_ids) |
||||
hidden_states = inputs_embeds |
||||
else: |
||||
input_shape = hidden_states.shape[:-1] |
||||
batch_size, seq_length = input_shape |
||||
device = hidden_states.device |
||||
|
||||
seq_length_with_past = seq_length |
||||
past_key_values_length = 0 |
||||
|
||||
# TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. |
||||
if output_attentions: |
||||
logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.") |
||||
output_attentions = False |
||||
if output_hidden_states: |
||||
logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.") |
||||
output_hidden_states = False |
||||
if use_cache: |
||||
logger.warning_once("use_cache=True is not supported for pipeline models at the moment.") |
||||
use_cache = False |
||||
|
||||
# assert past_key_values is None, "past_key_values is not supported for Qwen2 models at the moment." |
||||
|
||||
if past_key_values is not None: |
||||
past_key_values_length = past_key_values[0][0].shape[2] |
||||
seq_length_with_past = seq_length_with_past + past_key_values_length |
||||
|
||||
if position_ids is None: |
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device |
||||
position_ids = torch.arange( |
||||
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device |
||||
) |
||||
position_ids = position_ids.unsqueeze(0).view(-1, seq_length) |
||||
else: |
||||
position_ids = position_ids.view(-1, seq_length).long() |
||||
|
||||
if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: |
||||
is_padding_right = attention_mask[:, -1].sum().item() != batch_size |
||||
if is_padding_right: |
||||
raise ValueError( |
||||
"You are attempting to perform batched generation with padding_side='right'" |
||||
" this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to " |
||||
" call `tokenizer.padding_side = 'left'` before tokenizing the input. " |
||||
) |
||||
# embed positions, for the first stage, hidden_states is the input embeddings, |
||||
# for the other stages, hidden_states is the output of the previous stage |
||||
if shard_config.enable_flash_attention: |
||||
# in this case, attention_mask is a dict rather than a tensor |
||||
mask_shape = (batch_size, 1, seq_length_with_past, seq_length_with_past) |
||||
attention_mask = ColoAttention.prepare_attn_kwargs( |
||||
mask_shape, |
||||
hidden_states.dtype, |
||||
hidden_states.device, |
||||
q_padding_mask=attention_mask, |
||||
is_causal=True, |
||||
) |
||||
else: |
||||
if self._attn_implementation == "flash_attention_2": |
||||
# 2d mask is passed through the layers |
||||
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None |
||||
elif self._attn_implementation == "sdpa" and not output_attentions: |
||||
# output_attentions=True can not be supported when using SDPA, and we fall back on |
||||
# the manual implementation that requires a 4D causal mask in all cases. |
||||
|
||||
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( |
||||
attention_mask, |
||||
(batch_size, seq_length), |
||||
inputs_embeds, |
||||
past_key_values_length, |
||||
) |
||||
else: |
||||
# 4d mask is passed through the layers |
||||
|
||||
attention_mask = _prepare_4d_causal_attention_mask( |
||||
attention_mask, |
||||
(batch_size, seq_length), |
||||
hidden_states, |
||||
past_key_values_length, |
||||
sliding_window=self.config.sliding_window, |
||||
) |
||||
|
||||
# decoder layers |
||||
all_hidden_states = () if output_hidden_states else None |
||||
all_self_attns = () if output_attentions else None |
||||
next_decoder_cache = None |
||||
|
||||
start_idx, end_idx = stage_index[0], stage_index[1] |
||||
for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_idx): |
||||
if output_hidden_states: |
||||
all_hidden_states += (hidden_states,) |
||||
|
||||
past_key_value = past_key_values[idx] if past_key_values is not None else None |
||||
|
||||
if self.gradient_checkpointing and self.training: |
||||
layer_outputs = self._gradient_checkpointing_func( |
||||
decoder_layer.__call__, |
||||
hidden_states, |
||||
attention_mask, |
||||
position_ids, |
||||
past_key_values, |
||||
output_attentions, |
||||
use_cache, |
||||
) |
||||
else: |
||||
layer_outputs = decoder_layer( |
||||
hidden_states, |
||||
attention_mask=attention_mask, |
||||
position_ids=position_ids, |
||||
past_key_value=past_key_value, |
||||
output_attentions=output_attentions, |
||||
use_cache=use_cache, |
||||
) |
||||
|
||||
hidden_states = layer_outputs[0] |
||||
|
||||
if use_cache: |
||||
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) |
||||
|
||||
if output_attentions: |
||||
all_self_attns += (layer_outputs[1],) |
||||
|
||||
if stage_manager.is_last_stage(): |
||||
hidden_states = self.norm(hidden_states) |
||||
|
||||
# add hidden states from the last decoder layer |
||||
if output_hidden_states: |
||||
all_hidden_states += (hidden_states,) |
||||
|
||||
next_cache = next_decoder_cache if use_cache else None |
||||
|
||||
if stage_manager.is_last_stage(): |
||||
if not return_dict: |
||||
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) |
||||
return BaseModelOutputWithPast( |
||||
last_hidden_state=hidden_states, |
||||
past_key_values=next_cache, |
||||
hidden_states=all_hidden_states, |
||||
attentions=all_self_attns, |
||||
) |
||||
# always return dict for imediate stage |
||||
return {"hidden_states": hidden_states} |
||||
|
||||
@staticmethod |
||||
def qwen2_for_causal_lm_forward( |
||||
self: Qwen2ForCausalLM, |
||||
input_ids: torch.LongTensor = None, |
||||
attention_mask: Optional[torch.Tensor] = None, |
||||
position_ids: Optional[torch.LongTensor] = None, |
||||
past_key_values: Optional[List[torch.FloatTensor]] = None, |
||||
inputs_embeds: Optional[torch.FloatTensor] = None, |
||||
labels: Optional[torch.LongTensor] = None, |
||||
use_cache: Optional[bool] = None, |
||||
output_attentions: Optional[bool] = None, |
||||
output_hidden_states: Optional[bool] = None, |
||||
return_dict: Optional[bool] = None, |
||||
stage_manager: Optional[PipelineStageManager] = None, |
||||
hidden_states: Optional[torch.FloatTensor] = None, |
||||
stage_index: Optional[List[int]] = None, |
||||
shard_config: ShardConfig = None, |
||||
): |
||||
r""" |
||||
Args: |
||||
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
||||
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., |
||||
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored |
||||
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. |
||||
|
||||
Returns: |
||||
|
||||
Example: |
||||
|
||||
```python |
||||
>>> from transformers import AutoTokenizer, Qwen2ForCausalLM |
||||
|
||||
>>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) |
||||
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) |
||||
|
||||
>>> prompt = "Hey, are you consciours? Can you talk to me?" |
||||
>>> inputs = tokenizer(prompt, return_tensors="pt") |
||||
|
||||
>>> # Generate |
||||
>>> generate_ids = model.generate(inputs.input_ids, max_length=30) |
||||
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
||||
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." |
||||
```""" |
||||
logger = logging.get_logger(__name__) |
||||
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
||||
output_hidden_states = ( |
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
||||
) |
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
||||
|
||||
# TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. |
||||
if output_attentions: |
||||
logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.") |
||||
output_attentions = False |
||||
if output_hidden_states: |
||||
logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.") |
||||
output_hidden_states = False |
||||
|
||||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) |
||||
outputs = Qwen2PipelineForwards.qwen2_model_forward( |
||||
self.model, |
||||
input_ids=input_ids, |
||||
attention_mask=attention_mask, |
||||
position_ids=position_ids, |
||||
past_key_values=past_key_values, |
||||
inputs_embeds=inputs_embeds, |
||||
use_cache=use_cache, |
||||
output_attentions=output_attentions, |
||||
output_hidden_states=output_hidden_states, |
||||
return_dict=return_dict, |
||||
stage_manager=stage_manager, |
||||
hidden_states=hidden_states, |
||||
stage_index=stage_index, |
||||
shard_config=shard_config, |
||||
) |
||||
past_key_values = None |
||||
|
||||
if stage_manager.is_last_stage(): |
||||
hidden_states = outputs[0] |
||||
logits = self.lm_head(hidden_states) |
||||
loss = None |
||||
if labels is not None: |
||||
# Shift so that tokens < n predict n |
||||
shift_logits = logits[..., :-1, :].contiguous() |
||||
shift_labels = labels[..., 1:].contiguous() |
||||
# Flatten the tokens |
||||
loss_fct = CrossEntropyLoss() |
||||
shift_labels = shift_labels.view(-1) |
||||
# Enable model parallelism |
||||
shift_labels = shift_labels.to(shift_logits.device) |
||||
if shard_config.enable_tensor_parallelism: |
||||
new_vocab_size = logits.shape[-1] |
||||
shift_logits = shift_logits.view(-1, new_vocab_size) |
||||
loss = cross_entropy_1d( |
||||
shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group |
||||
) |
||||
else: |
||||
shift_logits = shift_logits.view(-1, self.config.vocab_size) |
||||
loss = loss_fct(shift_logits, shift_labels) |
||||
|
||||
if not return_dict: |
||||
output = (logits,) + outputs[1:] |
||||
return (loss,) + output if loss is not None else output |
||||
|
||||
return CausalLMOutputWithPast( |
||||
loss=loss, |
||||
logits=logits, |
||||
past_key_values=outputs.past_key_values, |
||||
hidden_states=outputs.hidden_states, |
||||
attentions=outputs.attentions, |
||||
) |
||||
else: |
||||
hidden_states = outputs.get("hidden_states") |
||||
return {"hidden_states": hidden_states} |
||||
|
||||
@staticmethod |
||||
def qwen2_for_sequence_classification_forward( |
||||
self: Qwen2ForSequenceClassification, |
||||
input_ids: torch.LongTensor = None, |
||||
attention_mask: Optional[torch.Tensor] = None, |
||||
position_ids: Optional[torch.LongTensor] = None, |
||||
past_key_values: Optional[List[torch.FloatTensor]] = None, |
||||
inputs_embeds: Optional[torch.FloatTensor] = None, |
||||
labels: Optional[torch.LongTensor] = None, |
||||
use_cache: Optional[bool] = None, |
||||
output_attentions: Optional[bool] = None, |
||||
output_hidden_states: Optional[bool] = None, |
||||
return_dict: Optional[bool] = None, |
||||
stage_manager: Optional[PipelineStageManager] = None, |
||||
hidden_states: Optional[torch.FloatTensor] = None, |
||||
stage_index: Optional[List[int]] = None, |
||||
shard_config: ShardConfig = None, |
||||
): |
||||
r""" |
||||
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
||||
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., |
||||
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If |
||||
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
||||
""" |
||||
logger = logging.get_logger(__name__) |
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
||||
|
||||
# TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. |
||||
if output_attentions: |
||||
logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.") |
||||
output_attentions = False |
||||
if output_hidden_states: |
||||
logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.") |
||||
output_hidden_states = False |
||||
|
||||
transformer_outputs = Qwen2PipelineForwards.qwen2_model_forward( |
||||
self.model, |
||||
input_ids, |
||||
attention_mask=attention_mask, |
||||
position_ids=position_ids, |
||||
past_key_values=past_key_values, |
||||
inputs_embeds=inputs_embeds, |
||||
use_cache=use_cache, |
||||
output_attentions=output_attentions, |
||||
output_hidden_states=output_hidden_states, |
||||
return_dict=return_dict, |
||||
stage_manager=stage_manager, |
||||
hidden_states=hidden_states, |
||||
stage_index=stage_index, |
||||
shard_config=shard_config, |
||||
) |
||||
|
||||
if input_ids is not None: |
||||
batch_size = input_ids.shape[0] |
||||
elif inputs_embeds is not None: |
||||
batch_size = inputs_embeds.shape[0] |
||||
else: |
||||
batch_size = hidden_states.shape[0] |
||||
|
||||
if stage_manager.is_last_stage(): |
||||
hidden_states = transformer_outputs[0] |
||||
logits = self.score(hidden_states) |
||||
|
||||
if self.config.pad_token_id is None and batch_size != 1: |
||||
print(self.config.pad_token_id) |
||||
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") |
||||
if self.config.pad_token_id is None: |
||||
sequence_lengths = -1 |
||||
else: |
||||
if input_ids is not None: |
||||
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) |
||||
else: |
||||
sequence_lengths = -1 |
||||
|
||||
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] |
||||
|
||||
loss = None |
||||
if labels is not None: |
||||
labels = labels.to(logits.device) |
||||
if self.config.problem_type is None: |
||||
if self.num_labels == 1: |
||||
self.config.problem_type = "regression" |
||||
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): |
||||
self.config.problem_type = "single_label_classification" |
||||
else: |
||||
self.config.problem_type = "multi_label_classification" |
||||
|
||||
if self.config.problem_type == "regression": |
||||
loss_fct = MSELoss() |
||||
if self.num_labels == 1: |
||||
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) |
||||
else: |
||||
loss = loss_fct(pooled_logits, labels) |
||||
elif self.config.problem_type == "single_label_classification": |
||||
loss_fct = CrossEntropyLoss() |
||||
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) |
||||
elif self.config.problem_type == "multi_label_classification": |
||||
loss_fct = BCEWithLogitsLoss() |
||||
loss = loss_fct(pooled_logits, labels) |
||||
if not return_dict: |
||||
output = (pooled_logits,) + transformer_outputs[1:] |
||||
return ((loss,) + output) if loss is not None else output |
||||
|
||||
return SequenceClassifierOutputWithPast( |
||||
loss=loss, |
||||
logits=pooled_logits, |
||||
past_key_values=transformer_outputs.past_key_values, |
||||
hidden_states=transformer_outputs.hidden_states, |
||||
attentions=transformer_outputs.attentions, |
||||
) |
||||
|
||||
else: |
||||
hidden_states = transformer_outputs.get("hidden_states") |
||||
return {"hidden_states": hidden_states} |
||||
|
||||
|
||||
def get_qwen2_flash_attention_forward(shard_config: ShardConfig): |
||||
def forward( |
||||
self: Qwen2Attention, |
||||
hidden_states: torch.Tensor, |
||||
attention_mask: Optional[torch.Tensor] = None, |
||||
position_ids: Optional[torch.LongTensor] = None, |
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
||||
output_attentions: bool = False, |
||||
use_cache: bool = False, |
||||
**kwargs, |
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
||||
bsz, q_len, _ = hidden_states.size() |
||||
|
||||
query_states = self.q_proj(hidden_states) |
||||
key_states = self.k_proj(hidden_states) |
||||
value_states = self.v_proj(hidden_states) |
||||
|
||||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
||||
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
||||
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
||||
|
||||
kv_seq_len = key_states.shape[-2] |
||||
if past_key_value is not None: |
||||
if self.layer_idx is None: |
||||
raise ValueError( |
||||
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " |
||||
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " |
||||
"with a layer index." |
||||
) |
||||
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) |
||||
# Because the input can be padded, the absolute sequence length depends on the max position id. |
||||
rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1 |
||||
cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len) |
||||
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) |
||||
|
||||
if past_key_value is not None: |
||||
# Activate slicing cache only if the config has a value `sliding_windows` attribute |
||||
cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0 |
||||
if ( |
||||
getattr(self.config, "sliding_window", None) is not None |
||||
and kv_seq_len > self.config.sliding_window |
||||
and cache_has_contents |
||||
): |
||||
slicing_tokens = 1 - self.config.sliding_window |
||||
|
||||
past_key = past_key_value[self.layer_idx][0] |
||||
past_value = past_key_value[self.layer_idx][1] |
||||
|
||||
past_key = past_key[:, :, slicing_tokens:, :].contiguous() |
||||
past_value = past_value[:, :, slicing_tokens:, :].contiguous() |
||||
|
||||
if past_key.shape[-2] != self.config.sliding_window - 1: |
||||
raise ValueError( |
||||
f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got" |
||||
f" {past_key.shape}" |
||||
) |
||||
|
||||
if attention_mask is not None: |
||||
attention_mask = attention_mask[:, slicing_tokens:] |
||||
attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1) |
||||
|
||||
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models |
||||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) |
||||
|
||||
# repeat k/v heads if n_kv_heads < n_heads |
||||
key_states = repeat_kv(key_states, self.num_key_value_groups) |
||||
value_states = repeat_kv(value_states, self.num_key_value_groups) |
||||
|
||||
assert isinstance(attention_mask, dict), "Flash Attention Error: attention_mask should be a dict." |
||||
attn_output = ColoAttention.attention(query_states, key_states, value_states, **attention_mask) |
||||
attn_output = attn_output.transpose(1, 2).contiguous() |
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) |
||||
attn_output = self.o_proj(attn_output) |
||||
|
||||
return attn_output, None, past_key_value |
||||
|
||||
return forward |
||||
|
||||
|
||||
def get_qwen2_model_forward_for_flash_attn(shard_config: ShardConfig): |
||||
logger = logging.get_logger(__name__) |
||||
assert shard_config.enable_flash_attention, "Flash Attention is not enabled." |
||||
|
||||
def forward( |
||||
self, |
||||
input_ids: torch.LongTensor = None, |
||||
attention_mask: Optional[torch.Tensor] = None, |
||||
position_ids: Optional[torch.LongTensor] = None, |
||||
past_key_values: Optional[List[torch.FloatTensor]] = None, |
||||
inputs_embeds: Optional[torch.FloatTensor] = None, |
||||
use_cache: Optional[bool] = None, |
||||
output_attentions: Optional[bool] = None, |
||||
output_hidden_states: Optional[bool] = None, |
||||
return_dict: Optional[bool] = None, |
||||
) -> Union[Tuple, BaseModelOutputWithPast]: |
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
||||
output_hidden_states = ( |
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
||||
) |
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache |
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
||||
|
||||
# retrieve input_ids and inputs_embeds |
||||
if input_ids is not None and inputs_embeds is not None: |
||||
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") |
||||
elif input_ids is not None: |
||||
batch_size, seq_length = input_ids.shape |
||||
elif inputs_embeds is not None: |
||||
batch_size, seq_length, _ = inputs_embeds.shape |
||||
else: |
||||
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") |
||||
|
||||
seq_length_with_past = seq_length |
||||
past_key_values_length = 0 |
||||
|
||||
if position_ids is None: |
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device |
||||
position_ids = torch.arange( |
||||
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device |
||||
) |
||||
position_ids = position_ids.unsqueeze(0).view(-1, seq_length) |
||||
else: |
||||
position_ids = position_ids.view(-1, seq_length).long() |
||||
|
||||
if inputs_embeds is None: |
||||
inputs_embeds = self.embed_tokens(input_ids) |
||||
|
||||
# embed positions |
||||
hidden_states = inputs_embeds |
||||
|
||||
# in this case, attention_mask is a dict rather than a tensor |
||||
mask_shape = (batch_size, 1, seq_length_with_past, seq_length_with_past) |
||||
attention_mask = ColoAttention.prepare_attn_kwargs( |
||||
mask_shape, |
||||
hidden_states.dtype, |
||||
hidden_states.device, |
||||
q_padding_mask=attention_mask, |
||||
is_causal=True, |
||||
) |
||||
|
||||
if self.gradient_checkpointing and self.training: |
||||
if use_cache: |
||||
logger.warning_once( |
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." |
||||
) |
||||
use_cache = False |
||||
|
||||
# decoder layers |
||||
all_hidden_states = () if output_hidden_states else None |
||||
all_self_attns = () if output_attentions else None |
||||
next_decoder_cache = None |
||||
|
||||
for decoder_layer in self.layers: |
||||
if output_hidden_states: |
||||
all_hidden_states += (hidden_states,) |
||||
|
||||
if self.gradient_checkpointing and self.training: |
||||
layer_outputs = self._gradient_checkpointing_func( |
||||
decoder_layer.__call__, |
||||
hidden_states, |
||||
attention_mask, |
||||
position_ids, |
||||
past_key_values, |
||||
output_attentions, |
||||
use_cache, |
||||
) |
||||
else: |
||||
layer_outputs = decoder_layer( |
||||
hidden_states, |
||||
attention_mask=attention_mask, |
||||
position_ids=position_ids, |
||||
past_key_value=past_key_values, |
||||
output_attentions=output_attentions, |
||||
use_cache=use_cache, |
||||
) |
||||
|
||||
hidden_states = layer_outputs[0] |
||||
|
||||
if use_cache: |
||||
next_decoder_cache = layer_outputs[2 if output_attentions else 1] |
||||
|
||||
if output_attentions: |
||||
all_self_attns += (layer_outputs[1],) |
||||
|
||||
hidden_states = self.norm(hidden_states) |
||||
|
||||
# add hidden states from the last decoder layer |
||||
if output_hidden_states: |
||||
all_hidden_states += (hidden_states,) |
||||
|
||||
next_cache = next_decoder_cache if use_cache else None |
||||
|
||||
if not return_dict: |
||||
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) |
||||
return BaseModelOutputWithPast( |
||||
last_hidden_state=hidden_states, |
||||
past_key_values=next_cache, |
||||
hidden_states=all_hidden_states, |
||||
attentions=all_self_attns, |
||||
) |
||||
|
||||
return forward |
||||
|
||||
|
||||
def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig): |
||||
def forward( |
||||
self: Qwen2ForCausalLM, |
||||
input_ids: torch.LongTensor = None, |
||||
attention_mask: Optional[torch.Tensor] = None, |
||||
position_ids: Optional[torch.LongTensor] = None, |
||||
past_key_values: Optional[List[torch.FloatTensor]] = None, |
||||
inputs_embeds: Optional[torch.FloatTensor] = None, |
||||
labels: Optional[torch.LongTensor] = None, |
||||
use_cache: Optional[bool] = None, |
||||
output_attentions: Optional[bool] = None, |
||||
output_hidden_states: Optional[bool] = None, |
||||
return_dict: Optional[bool] = None, |
||||
) -> Union[Tuple, CausalLMOutputWithPast]: |
||||
r""" |
||||
Args: |
||||
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
||||
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., |
||||
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored |
||||
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. |
||||
|
||||
Returns: |
||||
|
||||
Example: |
||||
|
||||
```python |
||||
>>> from transformers import AutoTokenizer, Qwen2ForCausalLM |
||||
|
||||
>>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) |
||||
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) |
||||
|
||||
>>> prompt = "Hey, are you conscious? Can you talk to me?" |
||||
>>> inputs = tokenizer(prompt, return_tensors="pt") |
||||
|
||||
>>> # Generate |
||||
>>> generate_ids = model.generate(inputs.input_ids, max_length=30) |
||||
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
||||
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." |
||||
```""" |
||||
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
||||
output_hidden_states = ( |
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
||||
) |
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
||||
|
||||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) |
||||
outputs = self.model( |
||||
input_ids=input_ids, |
||||
attention_mask=attention_mask, |
||||
position_ids=position_ids, |
||||
past_key_values=past_key_values, |
||||
inputs_embeds=inputs_embeds, |
||||
use_cache=use_cache, |
||||
output_attentions=output_attentions, |
||||
output_hidden_states=output_hidden_states, |
||||
return_dict=return_dict, |
||||
) |
||||
|
||||
hidden_states = outputs[0] |
||||
logits = self.lm_head(hidden_states) |
||||
logits = logits.float() |
||||
|
||||
loss = None |
||||
if labels is not None: |
||||
# Shift so that tokens < n predict n |
||||
shift_logits = logits[..., :-1, :].contiguous() |
||||
shift_labels = labels[..., 1:].contiguous() |
||||
# Flatten the tokens |
||||
loss_fct = CrossEntropyLoss() |
||||
shift_labels = shift_labels.view(-1) |
||||
# Enable model parallelism |
||||
shift_labels = shift_labels.to(shift_logits.device) |
||||
if shard_config.enable_tensor_parallelism: |
||||
new_vocab_size = logits.shape[-1] |
||||
shift_logits = shift_logits.view(-1, new_vocab_size) |
||||
loss = cross_entropy_1d( |
||||
shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group |
||||
) |
||||
else: |
||||
shift_logits = shift_logits.view(-1, self.config.vocab_size) |
||||
loss = loss_fct(shift_logits, shift_labels) |
||||
|
||||
if not return_dict: |
||||
output = (logits,) + outputs[1:] |
||||
return (loss,) + output if loss is not None else output |
||||
|
||||
return CausalLMOutputWithPast( |
||||
loss=loss, |
||||
logits=logits, |
||||
past_key_values=outputs.past_key_values, |
||||
hidden_states=outputs.hidden_states, |
||||
attentions=outputs.attentions, |
||||
) |
||||
|
||||
return forward |
||||
@ -0,0 +1,374 @@
|
||||
import warnings |
||||
from functools import partial |
||||
from typing import Callable, Dict, List, Union |
||||
|
||||
import torch.nn as nn |
||||
from torch import Tensor |
||||
from torch.nn import Module |
||||
|
||||
from colossalai.shardformer.layer import ( |
||||
FusedRMSNorm, |
||||
Linear1D_Col, |
||||
Linear1D_Row, |
||||
PaddingEmbedding, |
||||
RMSNorm, |
||||
VocabParallelEmbedding1D, |
||||
) |
||||
|
||||
from ..modeling.qwen2 import ( |
||||
Qwen2PipelineForwards, |
||||
get_lm_forward_with_dist_cross_entropy, |
||||
get_qwen2_flash_attention_forward, |
||||
get_qwen2_model_forward_for_flash_attn, |
||||
) |
||||
|
||||
try: |
||||
from transformers.models.qwen2.modeling_qwen2 import ( |
||||
Qwen2Attention, |
||||
Qwen2DecoderLayer, |
||||
Qwen2FlashAttention2, |
||||
Qwen2ForCausalLM, |
||||
Qwen2ForSequenceClassification, |
||||
Qwen2Model, |
||||
Qwen2SdpaAttention, |
||||
) |
||||
except ImportError: |
||||
Qwen2ForCausalLM = "Qwen2ForCausalLM" |
||||
Qwen2ForSequenceClassification = "Qwen2ForSequenceClassification" |
||||
Qwen2Attention = "Qwen2Attention" |
||||
Qwen2FlashAttention2 = "Qwen2FlashAttention2" |
||||
Qwen2SdpaAttention = "Qwen2SdpaAttention" |
||||
Qwen2DecoderLayer = "Qwen2DecoderLayer" |
||||
Qwen2Model = "Qwen2Model" |
||||
|
||||
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription |
||||
|
||||
__all__ = ["Qwen2Policy", "Qwen2ForCausalLMPolicy", "Qwen2ForSequenceClassificationPolicy"] |
||||
|
||||
|
||||
class Qwen2Policy(Policy): |
||||
def __init__(self) -> None: |
||||
super().__init__() |
||||
import transformers |
||||
from packaging.version import Version |
||||
|
||||
assert Version(transformers.__version__) >= Version( |
||||
"4.39.1" |
||||
), "The Qwen2 model should run on a transformers version of 4.39.1." |
||||
|
||||
def config_sanity_check(self): |
||||
pass |
||||
|
||||
def preprocess(self): |
||||
self.tie_weight = self.tie_weight_check() |
||||
self.origin_attn_implement = self.model.config._attn_implementation |
||||
return self.model |
||||
|
||||
def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: |
||||
ATTN_IMPLEMENTATION = { |
||||
"eager": Qwen2Attention, |
||||
"flash_attention_2": Qwen2FlashAttention2, |
||||
"sdpa": Qwen2SdpaAttention, |
||||
} |
||||
|
||||
policy = {} |
||||
|
||||
attn_cls = ATTN_IMPLEMENTATION[self.origin_attn_implement] |
||||
embedding_cls = None |
||||
if self.shard_config.enable_tensor_parallelism: |
||||
embedding_cls = VocabParallelEmbedding1D |
||||
else: |
||||
if self.tie_weight: |
||||
embedding_cls = PaddingEmbedding |
||||
norm_cls = FusedRMSNorm if self.shard_config.enable_fused_normalization else RMSNorm |
||||
|
||||
if self.shard_config.enable_sequence_parallelism: |
||||
self.shard_config.enable_sequence_parallelism = False |
||||
warnings.warn("Qwen2 doesn't support sequence parallelism now, will ignore the sequence parallelism flag.") |
||||
|
||||
if self.shard_config.enable_tensor_parallelism: |
||||
assert ( |
||||
self.model.config.num_attention_heads % self.shard_config.tensor_parallel_size == 0 |
||||
), f"The number of attention heads must be divisible by tensor parallel size." |
||||
if hasattr(self.model.config, "num_key_value_heads"): |
||||
assert ( |
||||
self.model.config.num_key_value_heads % self.shard_config.tensor_parallel_size == 0 |
||||
), f"The number of key_value heads must be divisible by tensor parallel size." |
||||
decoder_attribute_replacement = { |
||||
"self_attn.hidden_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, |
||||
"self_attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, |
||||
} |
||||
if getattr(self.model.config, "num_key_value_heads", False): |
||||
decoder_attribute_replacement["self_attn.num_key_value_heads"] = ( |
||||
self.model.config.num_key_value_heads // self.shard_config.tensor_parallel_size |
||||
) |
||||
|
||||
policy[Qwen2DecoderLayer] = ModulePolicyDescription( |
||||
attribute_replacement=decoder_attribute_replacement, |
||||
sub_module_replacement=[ |
||||
SubModuleReplacementDescription( |
||||
suffix="self_attn.q_proj", |
||||
target_module=Linear1D_Col, |
||||
), |
||||
SubModuleReplacementDescription( |
||||
suffix="self_attn.k_proj", |
||||
target_module=Linear1D_Col, |
||||
), |
||||
SubModuleReplacementDescription( |
||||
suffix="self_attn.v_proj", |
||||
target_module=Linear1D_Col, |
||||
), |
||||
SubModuleReplacementDescription( |
||||
suffix="self_attn.o_proj", |
||||
target_module=Linear1D_Row, |
||||
), |
||||
SubModuleReplacementDescription( |
||||
suffix="mlp.gate_proj", |
||||
target_module=Linear1D_Col, |
||||
), |
||||
SubModuleReplacementDescription( |
||||
suffix="mlp.up_proj", |
||||
target_module=Linear1D_Col, |
||||
), |
||||
SubModuleReplacementDescription( |
||||
suffix="mlp.down_proj", |
||||
target_module=Linear1D_Row, |
||||
), |
||||
], |
||||
) |
||||
|
||||
if embedding_cls is not None: |
||||
self.append_or_create_submodule_replacement( |
||||
description=SubModuleReplacementDescription( |
||||
suffix="embed_tokens", |
||||
target_module=embedding_cls, |
||||
kwargs={"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by}, |
||||
), |
||||
policy=policy, |
||||
target_key=Qwen2Model, |
||||
) |
||||
|
||||
# optimization configuration |
||||
self.append_or_create_submodule_replacement( |
||||
description=[ |
||||
SubModuleReplacementDescription( |
||||
suffix="input_layernorm", |
||||
target_module=norm_cls, |
||||
), |
||||
SubModuleReplacementDescription( |
||||
suffix="post_attention_layernorm", |
||||
target_module=norm_cls, |
||||
), |
||||
], |
||||
policy=policy, |
||||
target_key=Qwen2DecoderLayer, |
||||
) |
||||
|
||||
self.append_or_create_submodule_replacement( |
||||
description=SubModuleReplacementDescription( |
||||
suffix="norm", |
||||
target_module=norm_cls, |
||||
), |
||||
policy=policy, |
||||
target_key=Qwen2Model, |
||||
) |
||||
|
||||
# use flash attention |
||||
if self.shard_config.enable_flash_attention: |
||||
self.append_or_create_method_replacement( |
||||
description={ |
||||
"forward": get_qwen2_flash_attention_forward(self.shard_config), |
||||
}, |
||||
policy=policy, |
||||
target_key=attn_cls, |
||||
) |
||||
if self.pipeline_stage_manager is None: |
||||
# replace qwen2 model forward method |
||||
self.append_or_create_method_replacement( |
||||
description={ |
||||
"forward": get_qwen2_model_forward_for_flash_attn(self.shard_config), |
||||
}, |
||||
policy=policy, |
||||
target_key=Qwen2Model, |
||||
) |
||||
|
||||
return policy |
||||
|
||||
def postprocess(self): |
||||
return self.model |
||||
|
||||
def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None: |
||||
"""If under pipeline parallel setting, replacing the original forward method of huggingface |
||||
to customized forward method, and add this changing to policy.""" |
||||
if self.pipeline_stage_manager is None: |
||||
return |
||||
|
||||
stage_manager = self.pipeline_stage_manager |
||||
if self.model.__class__.__name__ == "Qwen2Model": |
||||
module = self.model |
||||
else: |
||||
module = self.model.model |
||||
|
||||
if stage_manager.is_interleave: |
||||
layers_per_stage = stage_manager.distribute_layers(len(module.layers)) |
||||
stage_manager.stage_indices = stage_manager.get_stage_index(layers_per_stage) |
||||
method_replacement = { |
||||
"forward": partial(new_forward, stage_manager=stage_manager, shard_config=self.shard_config) |
||||
} |
||||
|
||||
else: |
||||
layers_per_stage = stage_manager.distribute_layers(len(module.layers)) |
||||
stage_index = stage_manager.get_stage_index(layers_per_stage) |
||||
method_replacement = { |
||||
"forward": partial( |
||||
new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config |
||||
) |
||||
} |
||||
self.append_or_create_method_replacement( |
||||
description=method_replacement, policy=policy, target_key=model_cls |
||||
) |
||||
|
||||
self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls) |
||||
|
||||
def get_held_layers(self) -> List[Module]: |
||||
"""Get pipeline layers for current stage.""" |
||||
assert self.pipeline_stage_manager is not None |
||||
|
||||
if self.model.__class__.__name__ == "Qwen2Model": |
||||
module = self.model |
||||
else: |
||||
module = self.model.model |
||||
|
||||
stage_manager = self.pipeline_stage_manager |
||||
|
||||
held_layers = [] |
||||
if stage_manager.is_interleave: |
||||
assert stage_manager.num_model_chunks is not None |
||||
layers_per_stage = stage_manager.distribute_layers(len(module.layers)) |
||||
stage_indices = stage_manager.get_stage_index(layers_per_stage) |
||||
if stage_manager.is_first_stage(ignore_chunk=True): |
||||
held_layers.append(module.embed_tokens) |
||||
for start_idx, end_idx in stage_indices: |
||||
held_layers.extend(module.layers[start_idx:end_idx]) |
||||
if stage_manager.is_last_stage(ignore_chunk=True): |
||||
held_layers.append(module.norm) |
||||
|
||||
else: |
||||
layers_per_stage = stage_manager.distribute_layers(len(module.layers)) |
||||
if stage_manager.is_first_stage(): |
||||
held_layers.append(module.embed_tokens) |
||||
start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage) |
||||
held_layers.extend(module.layers[start_idx:end_idx]) |
||||
if stage_manager.is_last_stage(): |
||||
held_layers.append(module.norm) |
||||
|
||||
return held_layers |
||||
|
||||
|
||||
class Qwen2ModelPolicy(Qwen2Policy): |
||||
def module_policy(self): |
||||
policy = super().module_policy() |
||||
|
||||
if self.pipeline_stage_manager: |
||||
# set None as default |
||||
self.set_pipeline_forward( |
||||
model_cls=Qwen2Model, new_forward=Qwen2PipelineForwards.qwen2_model_forward, policy=policy |
||||
) |
||||
return policy |
||||
|
||||
def get_held_layers(self) -> List[Module]: |
||||
"""Get pipeline layers for current stage.""" |
||||
held_layers = super().get_held_layers() |
||||
return held_layers |
||||
|
||||
def get_shared_params(self) -> List[Dict[int, Tensor]]: |
||||
"""No shared params in Qwen2 model""" |
||||
return [] |
||||
|
||||
|
||||
class Qwen2ForCausalLMPolicy(Qwen2Policy): |
||||
def module_policy(self): |
||||
policy = super().module_policy() |
||||
setattr(self.shard_config, "causal_lm", True) |
||||
|
||||
if self.shard_config.enable_tensor_parallelism: |
||||
# add a new item for casual lm |
||||
new_item = { |
||||
Qwen2ForCausalLM: ModulePolicyDescription( |
||||
sub_module_replacement=[ |
||||
SubModuleReplacementDescription(suffix="lm_head", target_module=Linear1D_Col) |
||||
], |
||||
method_replacement={"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)}, |
||||
) |
||||
} |
||||
policy.update(new_item) |
||||
|
||||
if self.pipeline_stage_manager: |
||||
# set None as default |
||||
self.set_pipeline_forward( |
||||
model_cls=Qwen2ForCausalLM, new_forward=Qwen2PipelineForwards.qwen2_for_causal_lm_forward, policy=policy |
||||
) |
||||
|
||||
return policy |
||||
|
||||
def get_held_layers(self) -> List[Module]: |
||||
"""Get pipeline layers for current stage.""" |
||||
stage_manager = self.pipeline_stage_manager |
||||
held_layers = super().get_held_layers() |
||||
if stage_manager.is_last_stage(ignore_chunk=True): |
||||
held_layers.append(self.model.lm_head) |
||||
return held_layers |
||||
|
||||
def get_shared_params(self) -> List[Dict[int, Tensor]]: |
||||
qwen2_model = self.model.model |
||||
if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: |
||||
if ( |
||||
id(qwen2_model.embed_tokens.weight) == id(self.model.lm_head.weight) |
||||
and self.pipeline_stage_manager.num_stages > 1 |
||||
): |
||||
# tie weights |
||||
return [ |
||||
{ |
||||
0: qwen2_model.embed_tokens.weight, |
||||
self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight, |
||||
} |
||||
] |
||||
return [] |
||||
|
||||
|
||||
class Qwen2ForSequenceClassificationPolicy(Qwen2Policy): |
||||
def module_policy(self): |
||||
policy = super().module_policy() |
||||
if self.shard_config.enable_tensor_parallelism: |
||||
# add a new item for sequence classification |
||||
new_item = { |
||||
Qwen2ForSequenceClassification: ModulePolicyDescription( |
||||
sub_module_replacement=[ |
||||
SubModuleReplacementDescription( |
||||
suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True) |
||||
) |
||||
] |
||||
) |
||||
} |
||||
policy.update(new_item) |
||||
# to be confirmed |
||||
if self.pipeline_stage_manager: |
||||
# set None as default |
||||
self.set_pipeline_forward( |
||||
model_cls=Qwen2ForSequenceClassification, |
||||
new_forward=Qwen2PipelineForwards.qwen2_for_sequence_classification_forward, |
||||
policy=policy, |
||||
) |
||||
return policy |
||||
|
||||
def get_held_layers(self) -> List[Module]: |
||||
"""Get pipeline layers for current stage.""" |
||||
stage_manager = self.pipeline_stage_manager |
||||
held_layers = super().get_held_layers() |
||||
if stage_manager.is_last_stage(ignore_chunk=True): |
||||
held_layers.append(self.model.score) |
||||
return held_layers |
||||
|
||||
def get_shared_params(self) -> List[Dict[int, Tensor]]: |
||||
"""No shared params in Qwen2 for sequence classification model""" |
||||
return [] |
||||
@ -1,4 +1,5 @@
|
||||
from .hanging_param_model import * |
||||
from .nested_model import * |
||||
from .repeated_computed_layers import * |
||||
from .simple_mlp import * |
||||
from .simple_net import * |
||||
|
||||
@ -0,0 +1,61 @@
|
||||
from copy import deepcopy |
||||
|
||||
import torch |
||||
import torch.nn as nn |
||||
|
||||
from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row |
||||
|
||||
from ..registry import model_zoo |
||||
|
||||
_BS = 16 |
||||
_IN_DIM = 32 |
||||
_HID_DIM = 128 |
||||
|
||||
|
||||
class Net(nn.Module): |
||||
def __init__(self, in_dim=_IN_DIM, hid_dim=_HID_DIM, identity=False, dtype=torch.float32): |
||||
super().__init__() |
||||
if identity: |
||||
self.fc0 = nn.Identity() |
||||
else: |
||||
self.fc0 = nn.Linear(in_dim, in_dim).to(dtype=dtype) |
||||
|
||||
self.fc1 = nn.Linear(in_dim, hid_dim).to(dtype=dtype) |
||||
self.fc2 = nn.Linear(hid_dim, in_dim).to(dtype=dtype) |
||||
|
||||
def forward(self, x): |
||||
return self.fc2(self.fc1(self.fc0(x))) |
||||
|
||||
|
||||
class TPNet(nn.Module): |
||||
def __init__( |
||||
self, |
||||
fc0=nn.Linear(_IN_DIM, _IN_DIM), |
||||
fc1=nn.Linear(_IN_DIM, _HID_DIM), |
||||
fc2=nn.Linear(_HID_DIM, _IN_DIM), |
||||
tp_group=None, |
||||
dtype=torch.float32, |
||||
): |
||||
super().__init__() |
||||
self.fc0 = deepcopy(fc0) |
||||
self.fc1 = Linear1D_Col.from_native_module( |
||||
deepcopy(fc1), process_group=tp_group, gather_output=False, overlap=True, dtype=dtype |
||||
) |
||||
self.fc2 = Linear1D_Row.from_native_module( |
||||
deepcopy(fc2), process_group=tp_group, parallel_input=True, dtype=dtype |
||||
) |
||||
|
||||
def forward(self, x): |
||||
return self.fc2(self.fc1(self.fc0(x))) |
||||
|
||||
|
||||
def data_gen(): |
||||
return torch.randn(_BS, _IN_DIM) |
||||
|
||||
|
||||
def output_transform(x: torch.Tensor): |
||||
return x |
||||
|
||||
|
||||
model_zoo.register(name="simple_mlp", model_fn=Net, data_gen_fn=data_gen, output_transform_fn=output_transform) |
||||
model_zoo.register(name="simple_tp_mlp", model_fn=TPNet, data_gen_fn=data_gen, output_transform_fn=output_transform) |
||||
@ -0,0 +1,89 @@
|
||||
import torch |
||||
import transformers |
||||
|
||||
from ..registry import ModelAttribute, model_zoo |
||||
|
||||
try: |
||||
from transformers import Qwen2Config |
||||
|
||||
HAS_QWEN2 = True |
||||
except ImportError: |
||||
HAS_QWEN2 = False |
||||
|
||||
if HAS_QWEN2: |
||||
# =============================== |
||||
# Register Qwen2 |
||||
# =============================== |
||||
|
||||
def data_gen(): |
||||
# the input ids are corresponding to the sentence |
||||
# 'Hello, my dog is cute' |
||||
# |
||||
# the code is give below: |
||||
# ----------------------------------- |
||||
# from transformers import Qwen2TokenizerFast |
||||
# tokenizer = Qwen2TokenizerFast.from_pretrained("Qwen/Qwen1.5-7B-Chat") |
||||
# input = 'Hello, my dog is cute' |
||||
# tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') |
||||
# ----------------------------------- |
||||
|
||||
input_ids = torch.Tensor( |
||||
[[9707, 11, 847, 5562, 374, 13, 123, 18838], [9707, 11, 847, 5562, 374, 17, 89, 18838]] |
||||
).long() |
||||
attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]]).long() |
||||
return dict(input_ids=input_ids, attention_mask=attention_mask) |
||||
|
||||
# label is needed for casual lm |
||||
def data_gen_for_casual_lm(): |
||||
data = data_gen() |
||||
labels = data["input_ids"].clone() |
||||
data["labels"] = labels |
||||
return data |
||||
|
||||
# transform the output to a dict |
||||
output_transform_fn = lambda x: x |
||||
|
||||
# function to get the loss |
||||
loss_fn = lambda output: output["last_hidden_state"].mean() |
||||
loss_fn_for_casual_lm = lambda output: output["loss"] |
||||
loss_fn_for_seq_classification = lambda output: output["logits"].mean() |
||||
|
||||
config = Qwen2Config( |
||||
hidden_size=128, |
||||
intermediate_size=256, |
||||
max_window_layers=4, |
||||
num_attention_heads=16, |
||||
num_hidden_layers=4, |
||||
num_key_value_heads=16, |
||||
) |
||||
|
||||
config.pad_token_id = 0 |
||||
|
||||
# register the following models |
||||
# transformers.Qwen2Model, |
||||
# transformers.Qwen2ForCausalLM, |
||||
# transformers.Qwen2ForSequenceClassification, |
||||
model_zoo.register( |
||||
name="transformers_qwen2", |
||||
model_fn=lambda: transformers.Qwen2Model(config), |
||||
data_gen_fn=data_gen, |
||||
output_transform_fn=output_transform_fn, |
||||
loss_fn=loss_fn, |
||||
model_attribute=ModelAttribute(has_control_flow=True), |
||||
) |
||||
model_zoo.register( |
||||
name="transformers_qwen2_for_casual_lm", |
||||
model_fn=lambda: transformers.Qwen2ForCausalLM(config), |
||||
data_gen_fn=data_gen_for_casual_lm, |
||||
output_transform_fn=output_transform_fn, |
||||
loss_fn=loss_fn_for_casual_lm, |
||||
model_attribute=ModelAttribute(has_control_flow=True), |
||||
) |
||||
model_zoo.register( |
||||
name="transformers_qwen2_for_sequence_classification", |
||||
model_fn=lambda: transformers.Qwen2ForSequenceClassification(config), |
||||
data_gen_fn=data_gen, |
||||
output_transform_fn=output_transform_fn, |
||||
loss_fn=loss_fn_for_seq_classification, |
||||
model_attribute=ModelAttribute(has_control_flow=True), |
||||
) |
||||
@ -0,0 +1,272 @@
|
||||
import torch |
||||
import torch.distributed as dist |
||||
from torch.testing import assert_close |
||||
|
||||
import colossalai |
||||
from colossalai.shardformer.layer._operation import _gather |
||||
from colossalai.shardformer.layer.utils import Randomizer |
||||
from colossalai.tensor.d_tensor.api import clear_layout_converter |
||||
from colossalai.testing import parameterize, spawn |
||||
from tests.kit.model_zoo import model_zoo |
||||
from tests.test_shardformer.test_model._utils import ( |
||||
build_model_from_hybrid_plugin, |
||||
check_weight, |
||||
run_forward_backward_with_hybrid_plugin, |
||||
unwrap_model, |
||||
) |
||||
|
||||
|
||||
def check_optim_states(org_optim, sharded_optim): |
||||
for group in org_optim.param_groups: |
||||
for p in group["params"]: |
||||
sharded_state = sharded_optim.state[p] |
||||
state = org_optim.state[p] |
||||
for key in sharded_state: |
||||
assert_close(state[key], sharded_state[key], rtol=1e-5, atol=1e-5) |
||||
|
||||
|
||||
def check_bert_fwd_bwd( |
||||
model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config, optim_class, sharded_optim_class |
||||
): |
||||
org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin( |
||||
model_fn, loss_fn, test_config, optim_class, sharded_optim_class |
||||
) |
||||
|
||||
org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin( |
||||
org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster |
||||
) |
||||
|
||||
stage_manager = booster.plugin.stage_manager |
||||
tp_group = booster.plugin.tp_group |
||||
|
||||
bert = unwrap_model(org_model, "BertModel", "bert") |
||||
sharded_bert = unwrap_model(sharded_model, "BertModel", "bert") |
||||
weight_layer_for_check = ["encoder.layer[0].output.dense", "encoder.layer[1].output.dense"] |
||||
|
||||
# optimizer executes step |
||||
org_optimizer.step() |
||||
sharded_optimizer.step() |
||||
|
||||
# check weights |
||||
if test_config["precision"] == "bf16": |
||||
atol, rtol = 5e-4, 1e-4 |
||||
else: |
||||
atol, rtol = 5e-4, 5e-4 |
||||
if stage_manager is None or stage_manager.is_first_stage(ignore_chunk=True): |
||||
check_weight(bert, sharded_bert, weight_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1) |
||||
|
||||
# check optim states |
||||
check_optim_states(org_optimizer, sharded_optimizer.optim) |
||||
torch.cuda.empty_cache() |
||||
|
||||
|
||||
@parameterize( |
||||
"test_config", |
||||
[ |
||||
{ |
||||
"tp_size": 1, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 2, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 4, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 1, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "fp16", |
||||
}, |
||||
{ |
||||
"tp_size": 2, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "fp16", |
||||
}, |
||||
{ |
||||
"tp_size": 4, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "fp16", |
||||
}, |
||||
{ |
||||
"tp_size": 2, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 1, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 2, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 0, |
||||
"precision": "bf16", |
||||
}, |
||||
], |
||||
) |
||||
def run_bert_test(test_config, optim_class, sharded_optim_class): |
||||
"""Only call this if you've initialized distributed backend and spawned processes""" |
||||
sub_model_zoo = model_zoo.get_sub_registry("transformers_bert") |
||||
test_config["use_lazy_init"] = False |
||||
test_config["pp_size"] = 1 # Do NOT test Pipeline Parallel |
||||
test_config["initial_scale"] = 2**15 # avoid overflow |
||||
|
||||
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): |
||||
check_bert_fwd_bwd( |
||||
model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config, optim_class, sharded_optim_class |
||||
) |
||||
|
||||
clear_layout_converter() |
||||
Randomizer.reset_index() |
||||
torch.cuda.empty_cache() |
||||
|
||||
|
||||
def _run_bert_test(rank, world_size, port, optim_class, sharded_optim_class): |
||||
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") |
||||
run_bert_test(optim_class, sharded_optim_class) |
||||
|
||||
|
||||
def check_optim_on_bert(optim_class, sharded_optim_class): |
||||
spawn(_run_bert_test, 4, optim_class, sharded_optim_class) |
||||
|
||||
|
||||
def check_dist_optim_state(org_optimizer, sharded_optimizer): |
||||
torch.set_default_dtype(torch.bfloat16) |
||||
for group, tp_group in zip(org_optimizer.param_groups, sharded_optimizer.param_groups): |
||||
for p, tp in zip(group["params"], tp_group["params"]): |
||||
p_state = org_optimizer.state[p] |
||||
tp_state = sharded_optimizer.state[tp] |
||||
# TODO "exp_avg_sq_col", "exp_avg_sq_row", "exp_avg_sq" |
||||
for key in ["exp_avg_sq_row"]: |
||||
if key in tp_state.keys() and type(tp_state[key]) is torch.Tensor: |
||||
tp_is_dtensor = sharded_optimizer.param_is_dtensor_dict[id(tp)] |
||||
shard_spec = sharded_optimizer.shard_spec_dict[id(tp)] |
||||
use_zero = sharded_optimizer.use_zero |
||||
tp_optim_state = tp_state[key] |
||||
p_state_shape, tp_state_shape = p_state[key].shape, tp_state[key].shape |
||||
dp_size, tp_size = ( |
||||
sharded_optimizer.dp_size, |
||||
sharded_optimizer.tp_size, |
||||
) |
||||
# we start init model with first tensor parallel then zero; |
||||
# So, we gather model with first zero then tensor parallel |
||||
|
||||
if tp_is_dtensor: |
||||
# col parallel |
||||
if shard_spec.sharding_sequence[0] == "R": |
||||
if use_zero: |
||||
# sq_row need gather alone dp group |
||||
if key == "exp_avg_sq_row": |
||||
tp_optim_state = _gather( |
||||
input_=tp_optim_state, |
||||
dim=-1, |
||||
process_group=sharded_optimizer.dp_group, |
||||
) |
||||
tp_optim_state.shape |
||||
# sq_col don't need gather alone dp group |
||||
if key == "exp_avg_sq_col": |
||||
pass |
||||
else: |
||||
pass |
||||
# gather from tp group |
||||
# sq_row don need gather alone tp group |
||||
if key == "exp_avg_sq_row": |
||||
pass |
||||
# sq_col need gather alone dp group |
||||
if key == "exp_avg_sq_col": |
||||
tp_optim_state = _gather( |
||||
input_=tp_optim_state, dim=-1, process_group=sharded_optimizer.tp_group |
||||
) |
||||
tp_optim_state.shape |
||||
|
||||
# row parallel |
||||
if shard_spec.sharding_sequence[-1] == "R": |
||||
if use_zero: |
||||
# sq_row need gather alone dp group |
||||
if key == "exp_avg_sq_row": |
||||
if p_state[key].shape[0] // tp_size % dp_size != 0: |
||||
pass |
||||
else: |
||||
tp_optim_state = _gather( |
||||
input_=tp_optim_state, |
||||
dim=-1, |
||||
process_group=sharded_optimizer.dp_group, |
||||
) |
||||
tp_optim_state.shape |
||||
# sq_col don't need gather alone dp group |
||||
if key == "exp_avg_sq_col": |
||||
pass |
||||
else: |
||||
pass |
||||
# gather from tp group |
||||
# sq_row need gather alone tp group |
||||
if key == "exp_avg_sq_row": |
||||
tp_optim_state = _gather( |
||||
input_=tp_optim_state, dim=-1, process_group=sharded_optimizer.tp_group |
||||
) |
||||
tp_optim_state.shape |
||||
# sq_col don't need gather alone dp group |
||||
if key == "exp_avg_sq_col": |
||||
pass |
||||
else: |
||||
if use_zero: |
||||
# sq_row need gather alone dp group |
||||
if key == "exp_avg_sq_row": |
||||
# row residule; no gather |
||||
if p_state[key].shape[0] % dp_size != 0: |
||||
pass |
||||
else: |
||||
tp_optim_state = _gather( |
||||
input_=tp_optim_state, |
||||
dim=-1, |
||||
process_group=sharded_optimizer.dp_group, |
||||
) |
||||
tp_optim_state.shape |
||||
# sq_col don't need gather alone dp group |
||||
if key == "exp_avg_sq_col": |
||||
tp_optim_state = tp_optim_state.div_(dp_size) |
||||
# need a div; |
||||
else: |
||||
pass |
||||
# Sovled a New issus: different dtype; |
||||
# So far, only happen in H100 env; |
||||
# Seem torch.set_default_dtype(torch.bfloat16) not act on booster.percision; |
||||
# Or assert_close just update to check dtype; |
||||
if p_state[key].dtype != tp_optim_state.dtype: |
||||
tp_optim_state = tp_optim_state.type(p_state[key].dtype) |
||||
try: |
||||
assert_close(p_state[key], tp_optim_state, atol=5e-4, rtol=1.6e-2) |
||||
except: |
||||
pass |
||||
|
||||
|
||||
def check_dist_param(org_model, sharded_model, weight_layer_for_check, atol, rtol): |
||||
for (org_name, org_param), (sharded_name, sharded_param) in zip( |
||||
org_model.named_parameters(), sharded_model.named_parameters() |
||||
): |
||||
if org_name in weight_layer_for_check: |
||||
assert_close(org_param, sharded_param, atol=atol, rtol=rtol) |
||||
|
||||
|
||||
def check_dist_grad(sharded_optimizer, org_model, sharded_model, weight_layer_for_check, atol, rtol): |
||||
for (org_name, org_param), (sharded_name, sharded_param) in zip( |
||||
org_model.named_parameters(), sharded_model.named_parameters() |
||||
): |
||||
if org_name in weight_layer_for_check: |
||||
org_grad = org_param.grad |
||||
group_id = dist.get_rank(sharded_optimizer.optim.dp_group) |
||||
dist_grad = sharded_optimizer._grad_store.get_partitioned_gradients_by_param_id(group_id, id(sharded_param)) |
||||
|
||||
# dist_grad concat then reshape to org_grad shape |
||||
if dist_grad: |
||||
dist_grad = torch.cat([t for t in dist_grad], 0).view(org_grad.shape) |
||||
assert_close(org_grad, dist_grad, atol=atol, rtol=rtol) |
||||
@ -0,0 +1,698 @@
|
||||
import copy |
||||
|
||||
import pytest |
||||
import torch |
||||
import torch.distributed as dist |
||||
from torch import nn |
||||
from torch.testing import assert_close |
||||
|
||||
import colossalai |
||||
from colossalai.booster import Booster |
||||
from colossalai.booster.plugin import LowLevelZeroPlugin |
||||
from colossalai.cluster import ProcessGroupMesh |
||||
from colossalai.logging import disable_existing_loggers |
||||
from colossalai.nn.optimizer.adafactor import Adafactor |
||||
from colossalai.nn.optimizer.distributed_adafactor import DistributedAdaFactor |
||||
from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row |
||||
from colossalai.shardformer.layer._operation import _gather |
||||
from colossalai.shardformer.layer.utils import Randomizer |
||||
from colossalai.tensor.d_tensor import ( |
||||
distribute_tensor, |
||||
get_device_mesh, |
||||
get_layout, |
||||
get_sharding_spec, |
||||
is_distributed_tensor, |
||||
shard_colwise, |
||||
shard_rowwise, |
||||
) |
||||
from colossalai.tensor.d_tensor.api import clear_layout_converter |
||||
from colossalai.tensor.d_tensor.sharding_spec import DimSpec |
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn |
||||
from colossalai.utils import set_seed |
||||
from colossalai.zero import LowLevelZeroOptimizer |
||||
from tests.kit.model_zoo import model_zoo |
||||
from tests.test_optimizer._utils import check_dist_optim_state, check_dist_param, check_optim_states |
||||
from tests.test_shardformer.test_model._utils import ( |
||||
build_model_from_hybrid_plugin, |
||||
build_model_from_low_level_zero_plugin, |
||||
check_weight, |
||||
run_forward_backward_with_hybrid_plugin, |
||||
run_forward_backward_with_low_level_zero_plugin, |
||||
unwrap_model, |
||||
) |
||||
|
||||
HEIGHT = 4 |
||||
WIDTH = 4 |
||||
_TP_SPEC = DimSpec([0]) |
||||
|
||||
|
||||
def correctness_verify(tensor1: torch.Tensor, tensor2: torch.Tensor, dtype: torch.dtype = torch.float32): |
||||
rtol = None |
||||
atol = None |
||||
if dtype is torch.float32: |
||||
rtol = 5e-04 |
||||
atol = 5e-04 |
||||
elif dtype is torch.float16: |
||||
rtol = 5e-2 |
||||
atol = 5e-4 |
||||
elif dtype is torch.bfloat16: |
||||
rtol = 4e-3 |
||||
atol = 4e-3 |
||||
|
||||
# return torch.all(tensor1.isclose(tensor2, rtol=rtol, atol=atol)) |
||||
assert_close(tensor1, tensor2, rtol=rtol, atol=atol) |
||||
|
||||
|
||||
# setup param groups; (For zero test optim) |
||||
def setup_param_groups_zero(model: nn.Module) -> list: |
||||
no_decay = ["bias", "LayerNorm.weight"] |
||||
optimizer_grouped_parameters = [ |
||||
{ |
||||
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], |
||||
"weight_decay": 0.1, |
||||
}, |
||||
{ |
||||
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], |
||||
"weight_decay": 0.0, |
||||
}, |
||||
] |
||||
return optimizer_grouped_parameters |
||||
|
||||
|
||||
# setup param groups; (For base optim) |
||||
def setup_param_groups(model: nn.Module) -> list: |
||||
optimizer_grouped_parameters = [p for n, p in model.named_parameters()] |
||||
return optimizer_grouped_parameters |
||||
|
||||
|
||||
# setup flatten param groups, sharding spec and shape; (For dist optim) |
||||
def setup_flatten_param_groups_sharding_spec_shape(model: nn.Module) -> dict: |
||||
flatten_optimizer_grouped_parameters = [] |
||||
sharding_spec = {} # {id(flatten param): get_layout(p).global_shape} |
||||
param_shape = {} # {id(flatten param): get_sharding_spec(p)} |
||||
for n, p in model.named_parameters(): |
||||
# flatten_p = copy.deepcopy(p).flatten() |
||||
flatten_p = nn.Parameter(p.clone().flatten().requires_grad_(True)) |
||||
flatten_optimizer_grouped_parameters.append(flatten_p) |
||||
if is_distributed_tensor(p): |
||||
sharding_spec[id(flatten_p)] = get_sharding_spec(p) |
||||
param_shape[id(flatten_p)] = get_layout(p).global_shape |
||||
else: |
||||
sharding_spec[id(flatten_p)] = None |
||||
param_shape[id(flatten_p)] = p.shape |
||||
return flatten_optimizer_grouped_parameters, sharding_spec, param_shape |
||||
|
||||
|
||||
def set_dist_grad( |
||||
dist_module: nn.Module, torch_model: nn.Module, g_dtype: torch.dtype, group: dist.ProcessGroup |
||||
) -> None: |
||||
""" |
||||
Set split grads for Tensor Parallel or ZeRO DP. |
||||
We do not need a separate treatment for ZeRO, |
||||
as the wrapper takes care of reduce-scattering grads. |
||||
""" |
||||
rank = dist.get_rank(group) |
||||
world_size = dist.get_world_size(group) |
||||
|
||||
for p, torch_p in zip(dist_module.parameters(), torch_model.parameters()): |
||||
if torch_p.grad is None: |
||||
torch_p.grad = torch.zeros_like(torch_p) |
||||
|
||||
is_distributed = hasattr(p, "dist_layout") |
||||
if is_distributed: |
||||
sharding = p.dist_layout.sharding_spec.sharding_sequence |
||||
split_dim = sharding.index(_TP_SPEC) |
||||
shape = torch_p.split(world_size, dim=split_dim)[rank].shape |
||||
|
||||
indices = torch.arange(shape[split_dim] * rank, shape[split_dim] * (rank + 1)) |
||||
# Generate grads only for the correctly split chunk |
||||
torch_p.grad.index_add_(split_dim, indices, torch.randn(shape, device=torch_p.device, dtype=g_dtype)) |
||||
|
||||
else: |
||||
shape = torch_p.shape |
||||
torch_p.grad += torch.randn(shape, device=torch_p.device, dtype=g_dtype) |
||||
|
||||
# avoid inconsistent grad and param dtype error |
||||
orig_p = p.data |
||||
p.data = torch_p.grad.clone().to(g_dtype) |
||||
p.grad = p.data |
||||
p.data = orig_p |
||||
|
||||
|
||||
def set_master_param_to_shard_param(master_param_list) -> dict: |
||||
master_param_to_shard_param = {id(p): p for p in master_param_list} |
||||
return master_param_to_shard_param |
||||
|
||||
|
||||
class MlpModel(nn.Module): |
||||
def __init__(self): |
||||
super(MlpModel, self).__init__() |
||||
self.linear1 = nn.Linear(HEIGHT, WIDTH) |
||||
self.linear2 = nn.Linear(WIDTH, HEIGHT) |
||||
|
||||
def forward(self, x): |
||||
x = self.linear1(x) |
||||
x = self.linear2(x) |
||||
return x |
||||
|
||||
|
||||
class TPModel(nn.Module): |
||||
def __init__(self, linear1, linear2, tp_group=None): |
||||
super().__init__() |
||||
self.linear1 = Linear1D_Col.from_native_module( |
||||
linear1, process_group=tp_group, gather_output=False, overlap=True |
||||
) |
||||
self.linear2 = Linear1D_Row.from_native_module(linear2, process_group=tp_group, parallel_input=True) |
||||
|
||||
def forward(self, x): |
||||
x = self.linear1(x) |
||||
x = self.linear2(x) |
||||
return x |
||||
|
||||
|
||||
@parameterize("dtype", [torch.float32, torch.float16, torch.bfloat16]) # torch.float32, torch.float16, torch.bfloat16 |
||||
@parameterize("tp_zero_size", [(4, 1)]) |
||||
def exam_dist_adafactor_base(dtype: torch.dtype, tp_zero_size: tuple[int, int]): |
||||
tp_size, zero_size = tp_zero_size |
||||
local_rank = dist.get_rank() |
||||
use_zero = True if zero_size > 1 else False |
||||
|
||||
proc_mesh = ProcessGroupMesh(tp_size, zero_size) |
||||
tp_group, dp_group = proc_mesh.get_group_along_axis(0), proc_mesh.get_group_along_axis(1) |
||||
|
||||
torch.set_default_dtype(dtype) |
||||
set_seed(42) |
||||
|
||||
# ============================== |
||||
# Base Case |
||||
# ============================== |
||||
H, W = HEIGHT, WIDTH |
||||
model_col = nn.Linear(H, W).to(local_rank) # Col parallel weight |
||||
weight, bias = model_col.weight, model_col.bias |
||||
|
||||
# ============================== |
||||
# Col Parallel |
||||
# ============================== |
||||
weight_col_shard = shard_colwise(weight.clone(), tp_group) |
||||
weight_col_shard_layout = get_layout(weight_col_shard) # Layout info weight_col_shard_layout.global_shape |
||||
weight_col_shard_shard_spec = get_sharding_spec(weight_col_shard) # Shard spec |
||||
weight_col_shard_flatten = nn.Parameter(weight_col_shard.clone().flatten().requires_grad_(True)) |
||||
bias_col_flatten = nn.Parameter(bias.clone().flatten().requires_grad_(True)) |
||||
|
||||
# ============================== |
||||
# Row Parallel |
||||
# ============================== |
||||
weight_row_shard = shard_rowwise(weight.clone(), tp_group) |
||||
weight_row_shard_layout = get_layout(weight_row_shard) # Layout info weight_row_shard_layout.global_shape |
||||
weight_row_shard_shard_spec = get_sharding_spec(weight_row_shard) # Shard spec |
||||
weight_row_shard_flatten = nn.Parameter( |
||||
weight_row_shard.clone().flatten().requires_grad_(True) |
||||
) # flatten input(not dtensor) to optimizer |
||||
bias_row_flatten = nn.Parameter(bias.clone().flatten().requires_grad_(True)) |
||||
|
||||
# base_param_group = setup_param_groups([weight, bias]) |
||||
# cp_param_group = setup_param_groups([weight_col_shard_flatten, bias_col_flatten]) |
||||
# rp_param_group = setup_param_groups([weight_row_shard_flatten, bias_row_flatten]) |
||||
|
||||
# ============================== |
||||
# Init Optimizer |
||||
# ============================== |
||||
|
||||
# base |
||||
optimizer_base = Adafactor([weight, bias]) |
||||
cp_dist_optim = DistributedAdaFactor([weight_col_shard_flatten, bias_col_flatten]) |
||||
rp_dist_optim = DistributedAdaFactor([weight_row_shard_flatten, bias_row_flatten]) |
||||
|
||||
shard_to_param_cp = set_master_param_to_shard_param([weight_col_shard_flatten, bias_col_flatten]) |
||||
cp_dist_optim.setup_distributed( |
||||
tp_group=tp_group, |
||||
dp_group=dp_group, |
||||
shard_to_working_param=shard_to_param_cp, |
||||
use_zero=use_zero, |
||||
) |
||||
|
||||
shard_to_param_rp = set_master_param_to_shard_param([weight_row_shard_flatten, bias_row_flatten]) |
||||
rp_dist_optim.setup_distributed( |
||||
tp_group=tp_group, |
||||
dp_group=dp_group, |
||||
shard_to_working_param=shard_to_param_rp, |
||||
use_zero=use_zero, |
||||
) |
||||
|
||||
N_STEPS = 1 |
||||
for _ in range(N_STEPS): |
||||
# base step |
||||
optimizer_base.zero_grad() |
||||
weight.grad = torch.rand_like(weight) |
||||
bias.grad = torch.rand_like(bias) |
||||
optimizer_base.step() |
||||
|
||||
# col parallel step |
||||
cp_dist_optim.zero_grad() |
||||
weight_col_shard_flatten.grad = ( |
||||
distribute_tensor(weight.grad, get_device_mesh(weight_col_shard), weight_col_shard_shard_spec) |
||||
.clone() |
||||
.flatten() |
||||
) |
||||
bias_col_flatten.grad = bias.grad.clone().flatten() |
||||
cp_dist_optim.step() |
||||
|
||||
# row parallel step |
||||
rp_dist_optim.zero_grad() |
||||
weight_row_shard_flatten.grad = ( |
||||
distribute_tensor(weight.grad, get_device_mesh(weight_row_shard), weight_row_shard_shard_spec) |
||||
.clone() |
||||
.flatten() |
||||
) |
||||
bias_row_flatten.grad = bias.grad.clone().flatten() |
||||
rp_dist_optim.step() |
||||
|
||||
# gather result |
||||
weight_col_gather = _gather( |
||||
input_=weight_col_shard_flatten.data.view(-1, H // tp_size), |
||||
dim=-1, |
||||
process_group=tp_group, |
||||
) # gather |
||||
weight_row_gather = _gather(input_=weight_row_shard_flatten.data, dim=-1, process_group=tp_group).view( |
||||
-1, W |
||||
) # gather |
||||
|
||||
# verify |
||||
correctness_verify(weight.data, weight_col_gather.data, dtype) |
||||
correctness_verify(weight.data, weight_row_gather.data, dtype) |
||||
|
||||
print(f"Base Test Passed") |
||||
|
||||
|
||||
@parameterize("dtype", [torch.float16]) # torch.float32, torch.float16, torch.bfloat16 |
||||
@parameterize("tp_zero_size", [(1, 4)]) # (2, 2), (4, 1), (1, 4) |
||||
def exam_dist_adafactor_zero(dtype: torch.dtype, tp_zero_size: tuple[int, int]): |
||||
tp_size, zero_size = tp_zero_size |
||||
use_zero = True if zero_size > 1 else False |
||||
local_rank = dist.get_rank() |
||||
|
||||
clear_layout_converter() |
||||
|
||||
proc_mesh = ProcessGroupMesh(tp_size, zero_size) |
||||
tp_group, dp_group = proc_mesh.get_group_along_axis(0), proc_mesh.get_group_along_axis(1) |
||||
|
||||
torch.set_default_dtype(dtype) |
||||
set_seed(42) |
||||
|
||||
# ============================== |
||||
# Model Init |
||||
# ============================== |
||||
base_model = MlpModel().to(local_rank) |
||||
tp_model = TPModel(copy.deepcopy(base_model.linear1), copy.deepcopy(base_model.linear2), tp_group).to(local_rank) |
||||
|
||||
base_param_group = setup_param_groups(base_model) |
||||
tp_param_group = setup_param_groups(tp_model) |
||||
tp_param_group_, tp_shard_spec, tp_param_shape = setup_flatten_param_groups_sharding_spec_shape(tp_model) |
||||
|
||||
# ============================== |
||||
# Optimizer Init |
||||
# ============================== |
||||
base_optim = Adafactor(base_param_group) |
||||
dist_optim = DistributedAdaFactor(tp_param_group) |
||||
|
||||
# Setup distributed optimizer |
||||
if zero_size > 1: |
||||
base_optim = LowLevelZeroOptimizer( |
||||
base_optim, |
||||
overlap_communication=True, |
||||
initial_scale=128, |
||||
partition_grad=True, |
||||
dp_process_group=dp_group, |
||||
verbose=True, |
||||
) |
||||
|
||||
dist_optim = LowLevelZeroOptimizer( |
||||
dist_optim, |
||||
overlap_communication=True, |
||||
initial_scale=128, |
||||
partition_grad=True, |
||||
dp_process_group=dp_group, |
||||
verbose=True, |
||||
) |
||||
shard_to_param = dist_optim._param_store.master_to_working_param # {id(): param tensor} but flattened |
||||
dist_optim.optim.setup_distributed( |
||||
tp_group=tp_group, |
||||
dp_group=dp_group, |
||||
shard_to_working_param=shard_to_param, |
||||
use_zero=use_zero, |
||||
) |
||||
else: |
||||
shard_to_param = set_master_param_to_shard_param(tp_param_group) |
||||
dist_optim.setup_distributed( |
||||
tp_group=tp_group, |
||||
dp_group=dp_group, |
||||
shard_to_working_param=shard_to_param, |
||||
use_zero=use_zero, |
||||
) |
||||
|
||||
# ============================== |
||||
# Correctness Verify |
||||
# ============================== |
||||
x = torch.randn(HEIGHT, WIDTH, device=local_rank) |
||||
|
||||
out = base_model(x) |
||||
out_tp = tp_model(x) |
||||
|
||||
if zero_size > 1: |
||||
dist_optim.backward(out_tp.sum()) |
||||
base_optim.backward(out.sum()) |
||||
else: |
||||
out_tp.sum().backward() |
||||
out.sum().backward() |
||||
|
||||
base_optim.step() |
||||
dist_optim.step() |
||||
|
||||
base_optim.zero_grad() |
||||
dist_optim.zero_grad() |
||||
|
||||
for p, tp_p in zip(base_param_group, tp_param_group): |
||||
param_is_distributed = is_distributed_tensor(tp_p) |
||||
if param_is_distributed: |
||||
shard_spec = get_sharding_spec(tp_p) |
||||
if len(shard_spec.sharding_sequence) >= 2: |
||||
# Col Parallel |
||||
if shard_spec.sharding_sequence[0] == "R": |
||||
tp_p = _gather(input_=tp_p, dim=-1, process_group=tp_group) # gather |
||||
# ROW Parallel |
||||
if shard_spec.sharding_sequence[-1] == "R": |
||||
tp_p = _gather(input_=tp_p, dim=0, process_group=tp_group) # gather |
||||
else: |
||||
# TP bias |
||||
tp_p = _gather(input_=tp_p, dim=-1, process_group=tp_group) # gather |
||||
else: |
||||
# No TP bias |
||||
pass |
||||
correctness_verify(p.data, tp_p.data, dtype) |
||||
clear_layout_converter() |
||||
Randomizer.reset_index() |
||||
torch.cuda.empty_cache() |
||||
print(f"Zero Test Passed") |
||||
|
||||
|
||||
@parameterize("dtype", [torch.float16]) |
||||
@parameterize("tp_zero_size", [(1, 4)]) |
||||
def exam_dist_adafactor_booster(dtype: torch.dtype, tp_zero_size: tuple[int, int]): |
||||
tp_size, zero_size = tp_zero_size |
||||
use_zero = True if zero_size > 1 else False |
||||
local_rank = dist.get_rank() |
||||
|
||||
clear_layout_converter() |
||||
|
||||
proc_mesh = ProcessGroupMesh(tp_size, zero_size) |
||||
tp_group, dp_group = proc_mesh.get_group_along_axis(0), proc_mesh.get_group_along_axis(1) |
||||
|
||||
torch.set_default_dtype(dtype) |
||||
set_seed(42) |
||||
|
||||
# ============================== |
||||
# Model Init |
||||
# ============================== |
||||
base_model = MlpModel().to(local_rank) |
||||
# tp_model = TPModel(copy.deepcopy(base_model.linear1), copy.deepcopy(base_model.linear2), tp_group).to(local_rank) |
||||
tp_model = copy.deepcopy(base_model).to(local_rank) |
||||
|
||||
base_param_group = setup_param_groups(base_model) |
||||
tp_param_group = setup_param_groups(tp_model) |
||||
tp_param_group_, tp_shard_spec, tp_param_shape = setup_flatten_param_groups_sharding_spec_shape(tp_model) |
||||
|
||||
# ============================== |
||||
# Optimizer Init |
||||
# ============================== |
||||
base_optim = Adafactor(base_param_group) |
||||
dist_optim = DistributedAdaFactor(tp_param_group) |
||||
|
||||
# Setup distributed optimizer |
||||
if zero_size > 1: |
||||
base_optim = LowLevelZeroOptimizer( |
||||
base_optim, |
||||
overlap_communication=True, |
||||
initial_scale=128, |
||||
partition_grad=True, |
||||
dp_process_group=dp_group, |
||||
verbose=True, |
||||
) |
||||
|
||||
dist_optim = LowLevelZeroOptimizer( |
||||
dist_optim, |
||||
overlap_communication=True, |
||||
initial_scale=128, |
||||
partition_grad=True, |
||||
dp_process_group=dp_group, |
||||
verbose=True, |
||||
) |
||||
shard_to_param = dist_optim._param_store.master_to_working_param # {id(): param tensor} but flattened |
||||
dist_optim.optim.setup_distributed( |
||||
tp_group=tp_group, |
||||
dp_group=dp_group, |
||||
shard_to_working_param=shard_to_param, |
||||
use_zero=use_zero, |
||||
) |
||||
else: |
||||
shard_to_param = set_master_param_to_shard_param(tp_param_group) |
||||
dist_optim.setup_distributed( |
||||
tp_group=tp_group, |
||||
dp_group=dp_group, |
||||
shard_to_working_param=shard_to_param, |
||||
use_zero=use_zero, |
||||
) |
||||
|
||||
# ============================== |
||||
# Booster Init |
||||
# ============================== |
||||
plugin = LowLevelZeroPlugin() |
||||
booster = Booster(plugin=plugin) |
||||
criterion = lambda x: x.mean() |
||||
|
||||
tp_model, dist_optim, criterion, _, _ = booster.boost(tp_model, dist_optim, criterion) |
||||
|
||||
# ============================== |
||||
# Correctness Verify |
||||
# ============================== |
||||
x = torch.randn(HEIGHT, WIDTH, device=local_rank) |
||||
|
||||
out = base_model(x) |
||||
out_tp = tp_model(x) |
||||
|
||||
if zero_size > 1: |
||||
dist_optim.backward(out_tp.sum()) |
||||
base_optim.backward(out.sum()) |
||||
else: |
||||
out_tp.sum().backward() |
||||
out.sum().backward() |
||||
|
||||
base_optim.step() |
||||
dist_optim.step() |
||||
|
||||
base_optim.zero_grad() |
||||
dist_optim.zero_grad() |
||||
|
||||
for p, tp_p in zip(base_param_group, tp_param_group): |
||||
param_is_distributed = is_distributed_tensor(tp_p) |
||||
if param_is_distributed: |
||||
shard_spec = get_sharding_spec(tp_p) |
||||
if len(shard_spec.sharding_sequence) >= 2: |
||||
# Col Parallel |
||||
if shard_spec.sharding_sequence[0] == "R": |
||||
tp_p = _gather(input_=tp_p, dim=-1, process_group=tp_group) # gather |
||||
# ROW Parallel |
||||
if shard_spec.sharding_sequence[-1] == "R": |
||||
tp_p = _gather(input_=tp_p, dim=0, process_group=tp_group) # gather |
||||
else: |
||||
# TP bias |
||||
tp_p = _gather(input_=tp_p, dim=-1, process_group=tp_group) # gather |
||||
else: |
||||
# No TP bias |
||||
pass |
||||
correctness_verify(p.data, tp_p.data, dtype) |
||||
Randomizer.reset_index() |
||||
torch.cuda.empty_cache() |
||||
print(f"Booster Test Passed") |
||||
|
||||
|
||||
@parameterize( |
||||
"test_config", |
||||
[ |
||||
{ |
||||
"stage": 1, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"stage": 2, |
||||
"precision": "bf16", |
||||
}, |
||||
], |
||||
) |
||||
def exam_bert_test_on_lowlevelzero_plugin(test_config): |
||||
sub_model_zoo = model_zoo.get_sub_registry("transformers_bert") |
||||
model_list = [ |
||||
"transformers_bert", |
||||
"transformers_bert_for_pretraining", |
||||
"transformers_bert_lm_head_model", |
||||
"transformers_bert_for_masked_lm", |
||||
"transformers_bert_for_sequence_classification", |
||||
"transformers_bert_for_token_classification", |
||||
"transformers_bert_for_next_sentence", |
||||
"transformers_bert_for_mcq", |
||||
"transformers_bert_for_question_answering", |
||||
] |
||||
clear_layout_converter() |
||||
torch.set_default_dtype(torch.bfloat16) |
||||
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): |
||||
if name in model_list: |
||||
( |
||||
org_model, |
||||
org_optimizer, |
||||
sharded_model, |
||||
sharded_optimizer, |
||||
criterion, |
||||
booster, |
||||
) = build_model_from_low_level_zero_plugin(model_fn, loss_fn, test_config, Adafactor, DistributedAdaFactor) |
||||
|
||||
org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_low_level_zero_plugin( |
||||
org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster |
||||
) |
||||
|
||||
# LowLevelZero not need warp |
||||
# bert = unwrap_model(org_model, "BertModel", "bert") |
||||
# sharded_bert = unwrap_model(sharded_model, "BertModel", "bert") |
||||
weight_layer_for_check = [ |
||||
"bert.encoder.layer.0.output.dense.weight", |
||||
"bert.encoder.layer.0.output.dense.weight", |
||||
] |
||||
|
||||
org_optimizer.step() |
||||
sharded_optimizer.step() |
||||
|
||||
# check weights |
||||
if test_config["precision"] == "bf16": |
||||
atol, rtol = 5e-4, 5e-4 |
||||
else: |
||||
atol, rtol = 5e-4, 5e-4 |
||||
|
||||
check_dist_param(org_model, sharded_model, weight_layer_for_check, atol, rtol) |
||||
check_optim_states(org_optimizer, sharded_optimizer.optim) |
||||
|
||||
Randomizer.reset_index() |
||||
torch.cuda.empty_cache() |
||||
print(f"Bert Model Zoo Test Passed") |
||||
|
||||
|
||||
@parameterize( |
||||
"test_config", |
||||
[ |
||||
{ |
||||
"tp_size": 1, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 2, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 4, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 2, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 1, |
||||
"precision": "bf16", |
||||
}, |
||||
# @duanjunwen TODO: fix this test case. Currently params are sharded but are not dtensor here, throwing an error. |
||||
# Probably due to HybridParallelAMPOptimizer replacing some master params ? |
||||
# { |
||||
# "tp_size": 4, |
||||
# "num_microbatches": 4, |
||||
# "zero_stage": 0, |
||||
# "precision": "bf16", |
||||
# }, |
||||
], |
||||
) |
||||
def exam_bert_test_on_hybrid_plugin(test_config): |
||||
sub_model_zoo = model_zoo.get_sub_registry("transformers_bert") |
||||
test_config["use_lazy_init"] = False |
||||
test_config["pp_size"] = 1 # Do NOT test Pipeline Parallel |
||||
test_config["initial_scale"] = 2**16 # avoid overflow |
||||
model_list = [ |
||||
"transformers_bert", |
||||
"transformers_bert_for_pretraining", |
||||
"transformers_bert_lm_head_model", |
||||
"transformers_bert_for_masked_lm", |
||||
"transformers_bert_for_sequence_classification", |
||||
"transformers_bert_for_token_classification", |
||||
"transformers_bert_for_next_sentence", |
||||
"transformers_bert_for_mcq", |
||||
"transformers_bert_for_question_answering", |
||||
] |
||||
clear_layout_converter() |
||||
torch.set_default_dtype(torch.bfloat16) |
||||
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): |
||||
if name in model_list: |
||||
( |
||||
org_model, |
||||
org_optimizer, |
||||
sharded_model, |
||||
sharded_optimizer, |
||||
criterion, |
||||
booster, |
||||
) = build_model_from_hybrid_plugin(model_fn, loss_fn, test_config, Adafactor, DistributedAdaFactor) |
||||
|
||||
org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin( |
||||
org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster |
||||
) |
||||
|
||||
stage_manager = booster.plugin.stage_manager |
||||
tp_group = booster.plugin.tp_group |
||||
|
||||
bert = unwrap_model(org_model, "BertModel", "bert") |
||||
sharded_bert = unwrap_model(sharded_model, "BertModel", "bert") |
||||
weight_layer_for_check = ["encoder.layer[0].output.dense", "encoder.layer[1].output.dense"] |
||||
|
||||
org_optimizer.step() |
||||
sharded_optimizer.step() |
||||
|
||||
# check weights |
||||
if test_config["precision"] == "bf16": |
||||
atol, rtol = 5e-4, 5e-4 |
||||
else: |
||||
atol, rtol = 5e-4, 5e-4 |
||||
if stage_manager is None or stage_manager.is_first_stage(ignore_chunk=True): |
||||
check_weight(bert, sharded_bert, weight_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1) |
||||
# check optim states |
||||
check_dist_optim_state(org_optimizer, sharded_optimizer.optim) |
||||
|
||||
Randomizer.reset_index() |
||||
torch.cuda.empty_cache() |
||||
print(f"Bert Model Zoo Test Passed") |
||||
|
||||
|
||||
def run_dist(rank, world_size, port): |
||||
disable_existing_loggers() |
||||
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") |
||||
exam_bert_test_on_lowlevelzero_plugin() |
||||
exam_bert_test_on_hybrid_plugin() |
||||
exam_dist_adafactor_base() |
||||
exam_dist_adafactor_zero() |
||||
exam_dist_adafactor_booster() |
||||
|
||||
|
||||
@pytest.mark.dist |
||||
@rerun_if_address_is_in_use() |
||||
def test_dist_adafactor(): |
||||
spawn(run_dist, nprocs=4) |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
test_dist_adafactor() |
||||
@ -0,0 +1,475 @@
|
||||
import copy |
||||
|
||||
import pytest |
||||
import torch |
||||
import torch.distributed as dist |
||||
from torch import nn |
||||
from torch.testing import assert_close |
||||
|
||||
import colossalai |
||||
from colossalai.cluster import ProcessGroupMesh |
||||
from colossalai.logging import disable_existing_loggers |
||||
from colossalai.nn.optimizer.came import CAME |
||||
from colossalai.nn.optimizer.distributed_came import DistributedCAME |
||||
from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row |
||||
from colossalai.shardformer.layer._operation import _gather |
||||
from colossalai.shardformer.layer.utils import Randomizer |
||||
from colossalai.tensor.d_tensor import get_layout, get_sharding_spec, is_distributed_tensor |
||||
from colossalai.tensor.d_tensor.api import clear_layout_converter |
||||
from colossalai.tensor.d_tensor.sharding_spec import DimSpec |
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn |
||||
from colossalai.testing.random import seed_all |
||||
from colossalai.zero import LowLevelZeroOptimizer |
||||
from tests.kit.model_zoo import model_zoo |
||||
from tests.test_optimizer._utils import check_dist_grad, check_dist_optim_state, check_dist_param, check_optim_states |
||||
from tests.test_shardformer.test_model._utils import ( |
||||
build_model_from_hybrid_plugin, |
||||
build_model_from_low_level_zero_plugin, |
||||
run_forward_backward_with_hybrid_plugin, |
||||
run_forward_backward_with_low_level_zero_plugin, |
||||
unwrap_model, |
||||
) |
||||
|
||||
HEIGHT = 128 |
||||
WIDTH = 128 |
||||
_TP_SPEC = DimSpec([0]) |
||||
_SEED = 0 |
||||
|
||||
|
||||
def correctness_verify(tensor1: torch.Tensor, tensor2: torch.Tensor, dtype: torch.dtype = torch.float32): |
||||
rtol = None |
||||
atol = None |
||||
if dtype is torch.float32: |
||||
rtol = 5e-04 |
||||
atol = 5e-04 |
||||
elif dtype is torch.float16: |
||||
rtol = 5e-2 |
||||
atol = 5e-4 |
||||
elif dtype is torch.bfloat16: |
||||
rtol = 4e-3 |
||||
atol = 4e-3 |
||||
|
||||
# return torch.all(tensor1.isclose(tensor2, rtol=rtol, atol=atol)) |
||||
assert_close(tensor1, tensor2, rtol=rtol, atol=atol) |
||||
|
||||
|
||||
# setup param groups; (For zero test optim) |
||||
def setup_param_groups_zero(model: nn.Module) -> list: |
||||
no_decay = ["bias", "LayerNorm.weight"] |
||||
optimizer_grouped_parameters = [ |
||||
{ |
||||
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], |
||||
"weight_decay": 0.1, |
||||
}, |
||||
{ |
||||
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], |
||||
"weight_decay": 0.0, |
||||
}, |
||||
] |
||||
return optimizer_grouped_parameters |
||||
|
||||
|
||||
# setup param groups; (For base optim) |
||||
def setup_param_groups(model: nn.Module) -> list: |
||||
optimizer_grouped_parameters = [p for n, p in model.named_parameters()] |
||||
return optimizer_grouped_parameters |
||||
|
||||
|
||||
# setup flatten param groups, sharding spec and shape; (For dist optim) |
||||
def setup_flatten_param_groups_sharding_spec_shape(model: nn.Module) -> dict: |
||||
flatten_optimizer_grouped_parameters = [] |
||||
sharding_spec = {} # {id(flatten param): get_layout(p).global_shape} |
||||
param_shape = {} # {id(flatten param): get_sharding_spec(p)} |
||||
for n, p in model.named_parameters(): |
||||
flatten_p = nn.Parameter(p.clone().flatten().requires_grad_(True)) |
||||
flatten_optimizer_grouped_parameters.append(flatten_p) |
||||
if is_distributed_tensor(p): |
||||
sharding_spec[id(flatten_p)] = get_sharding_spec(p) |
||||
param_shape[id(flatten_p)] = get_layout(p).global_shape |
||||
else: |
||||
sharding_spec[id(flatten_p)] = None |
||||
param_shape[id(flatten_p)] = p.shape |
||||
return flatten_optimizer_grouped_parameters, sharding_spec, param_shape |
||||
|
||||
|
||||
def set_dist_grad( |
||||
dist_module: nn.Module, torch_model: nn.Module, g_dtype: torch.dtype, group: dist.ProcessGroup |
||||
) -> None: |
||||
""" |
||||
Set split grads for Tensor Parallel or ZeRO DP. |
||||
We do not need a separate treatment for ZeRO, |
||||
as the wrapper takes care of reduce-scattering grads. |
||||
""" |
||||
rank = dist.get_rank(group) |
||||
world_size = dist.get_world_size(group) |
||||
|
||||
for p, torch_p in zip(dist_module.parameters(), torch_model.parameters()): |
||||
if torch_p.grad is None: |
||||
torch_p.grad = torch.zeros_like(torch_p) |
||||
|
||||
is_distributed = hasattr(p, "dist_layout") |
||||
if is_distributed: |
||||
sharding = p.dist_layout.sharding_spec.sharding_sequence |
||||
split_dim = sharding.index(_TP_SPEC) |
||||
shape = torch_p.split(world_size, dim=split_dim)[rank].shape |
||||
|
||||
indices = torch.arange(shape[split_dim] * rank, shape[split_dim] * (rank + 1)) |
||||
# Generate grads only for the correctly split chunk |
||||
torch_p.grad.index_add_(split_dim, indices, torch.randn(shape, device=torch_p.device, dtype=g_dtype)) |
||||
|
||||
else: |
||||
shape = torch_p.shape |
||||
torch_p.grad += torch.randn(shape, device=torch_p.device, dtype=g_dtype) |
||||
|
||||
# avoid inconsistent grad and param dtype error |
||||
orig_p = p.data |
||||
p.data = torch_p.grad.clone().to(g_dtype) |
||||
p.grad = p.data |
||||
p.data = orig_p |
||||
|
||||
|
||||
def set_master_param_to_shard_param(master_param_list) -> dict: |
||||
master_param_to_shard_param = {id(p): p for p in master_param_list} |
||||
return master_param_to_shard_param |
||||
|
||||
|
||||
class MlpModel(nn.Module): |
||||
def __init__(self): |
||||
super(MlpModel, self).__init__() |
||||
self.linear1 = nn.Linear(HEIGHT, WIDTH) |
||||
self.linear2 = nn.Linear(WIDTH, HEIGHT) |
||||
|
||||
def forward(self, x): |
||||
x = self.linear1(x) |
||||
x = self.linear2(x) |
||||
return x |
||||
|
||||
|
||||
class TPModel(nn.Module): |
||||
def __init__(self, linear1, linear2, tp_group=None): |
||||
super().__init__() |
||||
self.linear1 = Linear1D_Col.from_native_module( |
||||
linear1, process_group=tp_group, gather_output=False, overlap=True |
||||
) |
||||
self.linear2 = Linear1D_Row.from_native_module(linear2, process_group=tp_group, parallel_input=True) |
||||
|
||||
def forward(self, x): |
||||
x = self.linear1(x) |
||||
x = self.linear2(x) |
||||
return x |
||||
|
||||
|
||||
@parameterize("dtype", [torch.float32]) # torch.float32, torch.float16, torch.bfloat16 |
||||
@parameterize("tp_zero_size", [(2, 2), (4, 1), (1, 4)]) # (4, 1), (1, 4) |
||||
def exam_dist_came_base(dtype: torch.dtype, tp_zero_size: tuple[int, int]): |
||||
tp_size, zero_size = tp_zero_size |
||||
use_zero = True if zero_size > 1 else False |
||||
local_rank = dist.get_rank() |
||||
|
||||
clear_layout_converter() |
||||
|
||||
proc_mesh = ProcessGroupMesh(tp_size, zero_size) |
||||
tp_group, dp_group = proc_mesh.get_group_along_axis(0), proc_mesh.get_group_along_axis(1) |
||||
|
||||
torch.set_default_dtype(dtype) |
||||
# set_seed(42) |
||||
|
||||
# ============================== |
||||
# Model Init |
||||
# ============================== |
||||
base_model = MlpModel().to(local_rank) |
||||
tp_model = TPModel(copy.deepcopy(base_model.linear1), copy.deepcopy(base_model.linear2), tp_group).to(local_rank) |
||||
|
||||
base_param_group = setup_param_groups(base_model) |
||||
tp_param_group = setup_param_groups(tp_model) |
||||
tp_param_group_, tp_shard_spec, tp_param_shape = setup_flatten_param_groups_sharding_spec_shape(tp_model) |
||||
|
||||
# ============================== |
||||
# Optimizer Init |
||||
# ============================== |
||||
base_optim = CAME(base_param_group, lr=1e-3) |
||||
dist_optim = DistributedCAME(tp_param_group, lr=1e-3) |
||||
|
||||
# Setup distributed optimizer |
||||
if zero_size > 1: |
||||
dist_optim = LowLevelZeroOptimizer( |
||||
dist_optim, |
||||
overlap_communication=True, |
||||
initial_scale=128, |
||||
partition_grad=True, |
||||
dp_process_group=dp_group, |
||||
verbose=True, |
||||
) |
||||
shard_to_param = dist_optim._param_store.master_to_working_param # {id(): param tensor} but flattened |
||||
dist_optim.optim.setup_distributed( |
||||
tp_group=tp_group, |
||||
dp_group=dp_group, |
||||
shard_to_working_param=shard_to_param, |
||||
use_zero=use_zero, |
||||
) |
||||
else: |
||||
shard_to_param = set_master_param_to_shard_param(tp_param_group) |
||||
dist_optim.setup_distributed( |
||||
tp_group=tp_group, |
||||
dp_group=dp_group, |
||||
shard_to_working_param=shard_to_param, |
||||
use_zero=use_zero, |
||||
) |
||||
|
||||
# ============================== |
||||
# Correctness Verify |
||||
# ============================== |
||||
seed_all(1024) |
||||
x = torch.randn(WIDTH, HEIGHT, device=local_rank) |
||||
|
||||
out = base_model(x) |
||||
out_tp = tp_model(x) |
||||
|
||||
if zero_size > 1: |
||||
dist_optim.backward(out_tp.sum()) |
||||
out.sum().backward() |
||||
else: |
||||
out_tp.sum().backward() |
||||
out.sum().backward() |
||||
|
||||
base_optim.step() |
||||
dist_optim.step() |
||||
|
||||
base_optim.zero_grad() |
||||
dist_optim.zero_grad() |
||||
|
||||
for p, tp_p in zip(base_param_group, tp_param_group): |
||||
param_is_distributed = is_distributed_tensor(tp_p) |
||||
if param_is_distributed: |
||||
shard_spec = get_sharding_spec(tp_p) |
||||
if len(shard_spec.sharding_sequence) >= 2: |
||||
# Col Parallel |
||||
if shard_spec.sharding_sequence[0] == "R": |
||||
tp_p = _gather(input_=tp_p, dim=-1, process_group=tp_group) # gather |
||||
# ROW Parallel |
||||
if shard_spec.sharding_sequence[-1] == "R": |
||||
tp_p = _gather(input_=tp_p, dim=0, process_group=tp_group) # gather |
||||
else: |
||||
# TP bias |
||||
tp_p = _gather(input_=tp_p, dim=-1, process_group=tp_group) # gather |
||||
else: |
||||
# No TP bias |
||||
pass |
||||
correctness_verify(p.data, tp_p.data, dtype) |
||||
clear_layout_converter() |
||||
Randomizer.reset_index() |
||||
torch.cuda.empty_cache() |
||||
print(f"Fwd/Bwd Test Passed") |
||||
|
||||
|
||||
@parameterize( |
||||
"test_config", |
||||
[ |
||||
{ |
||||
"stage": 1, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"stage": 2, |
||||
"precision": "bf16", |
||||
}, |
||||
], |
||||
) |
||||
def exam_bert_test_on_lowlevelzero_plugin(test_config): |
||||
sub_model_zoo = model_zoo.get_sub_registry("transformers_bert") |
||||
test_config["use_lazy_init"] = False |
||||
test_config["initial_scale"] = 2**10 |
||||
# check weights |
||||
if test_config["precision"] == "bf16": |
||||
atol, rtol = 5e-4, 5e-4 |
||||
else: |
||||
atol, rtol = 5e-4, 5e-4 |
||||
# test_config["initial_scale"] = 1 |
||||
model_list = [ |
||||
"transformers_bert", |
||||
"transformers_bert_for_pretraining", |
||||
"transformers_bert_lm_head_model", |
||||
"transformers_bert_for_masked_lm", |
||||
"transformers_bert_for_sequence_classification", |
||||
"transformers_bert_for_token_classification", |
||||
"transformers_bert_for_next_sentence", |
||||
"transformers_bert_for_mcq", |
||||
"transformers_bert_for_question_answering", |
||||
"simple_mlp", |
||||
] |
||||
clear_layout_converter() |
||||
torch.set_default_dtype(torch.bfloat16) |
||||
seed_all(_SEED) |
||||
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): |
||||
if name in model_list: |
||||
( |
||||
org_model, |
||||
org_optimizer, |
||||
sharded_model, |
||||
sharded_optimizer, |
||||
criterion, |
||||
booster, |
||||
) = build_model_from_low_level_zero_plugin(model_fn, loss_fn, test_config, CAME, DistributedCAME) |
||||
|
||||
org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_low_level_zero_plugin( |
||||
org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster |
||||
) |
||||
|
||||
# assert same output |
||||
# assert_close(org_output, org_output, atol=atol, rtol=rtol) |
||||
|
||||
weight_layer_for_check = [ |
||||
"bert.encoder.layer.1.intermediate.dense", |
||||
# TODO: error in layer: |
||||
# "bert.encoder.layer.0.output.dense", |
||||
# "bert.encoder.layer.1.output.dense", |
||||
] |
||||
|
||||
# assert same weight before step; pass |
||||
check_dist_param(org_model, sharded_model, weight_layer_for_check, atol, rtol) |
||||
|
||||
# asserr loss; pass |
||||
assert_close(org_loss, sharded_loss) |
||||
|
||||
# assert same grad before step |
||||
# TODO: err here; backward diff gard; Only transformers_bert pass; |
||||
check_dist_grad(sharded_optimizer, org_model, sharded_model, weight_layer_for_check, atol, rtol) |
||||
|
||||
org_optimizer.step() |
||||
sharded_optimizer.step() |
||||
|
||||
# assert same weight after step |
||||
check_dist_param(org_model, sharded_model, weight_layer_for_check, atol, rtol) |
||||
check_optim_states(org_optimizer, sharded_optimizer.optim) |
||||
|
||||
Randomizer.reset_index() |
||||
torch.cuda.empty_cache() |
||||
print(f"LowLevelZeroPlugin + Bert Model Zoo Test Passed") |
||||
|
||||
|
||||
@parameterize( |
||||
"test_config", |
||||
[ |
||||
{ |
||||
"tp_size": 1, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 2, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 4, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 2, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 2, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 1, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 4, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 0, |
||||
"precision": "bf16", |
||||
}, |
||||
], |
||||
) |
||||
def exam_bert_test_on_hybrid_plugin(test_config): |
||||
sub_model_zoo = model_zoo.get_sub_registry("transformers_bert") |
||||
test_config["use_lazy_init"] = False |
||||
test_config["pp_size"] = 1 # Do NOT test Pipeline Parallel |
||||
test_config["initial_scale"] = 2**16 # avoid overflow |
||||
model_list = [ |
||||
"transformers_bert", |
||||
"transformers_bert_for_pretraining", |
||||
"transformers_bert_lm_head_model", |
||||
"transformers_bert_for_masked_lm", |
||||
"transformers_bert_for_sequence_classification", |
||||
"transformers_bert_for_token_classification", |
||||
"transformers_bert_for_next_sentence", |
||||
"transformers_bert_for_mcq", |
||||
"transformers_bert_for_question_answering", |
||||
] |
||||
|
||||
# pass "transformers_bert", |
||||
clear_layout_converter() |
||||
torch.set_default_dtype(torch.bfloat16) |
||||
# check weights |
||||
if test_config["precision"] == "bf16": |
||||
atol, rtol = 5e-3, 5e-3 |
||||
else: |
||||
atol, rtol = 5e-3, 5e-3 |
||||
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): |
||||
if name in model_list: |
||||
( |
||||
org_model, |
||||
org_optimizer, |
||||
sharded_model, |
||||
sharded_optimizer, |
||||
criterion, |
||||
booster, |
||||
) = build_model_from_hybrid_plugin(model_fn, loss_fn, test_config, CAME, DistributedCAME) |
||||
|
||||
org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin( |
||||
org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster |
||||
) |
||||
|
||||
stage_manager = booster.plugin.stage_manager |
||||
booster.plugin.tp_group |
||||
|
||||
bert = unwrap_model(org_model, "BertModel", "bert") |
||||
sharded_bert = unwrap_model(sharded_model, "BertModel", "bert") |
||||
|
||||
# TODO: model |
||||
# "encoder.layer.0.output.dense.weight", "encoder.layer.1.output.dense.weight" not match |
||||
# "encoder.layer[0].output.dense", "encoder.layer[1].output.dense" not match |
||||
weight_layer_for_check = ["embeddings.word_embeddings"] # [30522, 128] |
||||
|
||||
# # assert same weight before step; all pass |
||||
# check_dist_param(org_model, sharded_model, weight_layer_for_check, atol, rtol) |
||||
|
||||
# # assert loss; all pass |
||||
# assert_close(org_loss, sharded_loss) |
||||
|
||||
# # assert same grad before step; all pass |
||||
# check_dist_grad(org_model, sharded_model, weight_layer_for_check, atol, rtol) |
||||
|
||||
org_optimizer.step() |
||||
sharded_optimizer.step() |
||||
|
||||
if stage_manager is None or stage_manager.is_first_stage(ignore_chunk=True): |
||||
check_dist_param(bert, sharded_bert, weight_layer_for_check, atol, rtol) |
||||
# check_weight(bert, sharded_bert, weight_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1) |
||||
|
||||
# check optim states |
||||
check_dist_optim_state(org_optimizer, sharded_optimizer.optim) |
||||
|
||||
Randomizer.reset_index() |
||||
torch.cuda.empty_cache() |
||||
print(f"HybridParallelPlugin + Bert Model Zoo Test Passed") |
||||
|
||||
|
||||
def run_dist(rank, world_size, port): |
||||
disable_existing_loggers() |
||||
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") |
||||
exam_bert_test_on_lowlevelzero_plugin() # err in TODO layer |
||||
exam_bert_test_on_hybrid_plugin() # pass |
||||
exam_dist_came_base() # pass |
||||
|
||||
|
||||
@pytest.mark.dist |
||||
@rerun_if_address_is_in_use() |
||||
def test_dist_came(): |
||||
spawn(run_dist, nprocs=4) |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
test_dist_came() |
||||
@ -0,0 +1,336 @@
|
||||
"""Usage(requires 4 GPUs): python test_dist_galore.py""" |
||||
|
||||
import pytest |
||||
import torch |
||||
import torch.distributed as dist |
||||
import torch.nn as nn |
||||
from torch.testing import assert_close |
||||
|
||||
import colossalai |
||||
from colossalai.cluster import DistCoordinator, ProcessGroupMesh |
||||
from colossalai.logging import disable_existing_loggers |
||||
from colossalai.nn.optimizer import DistGaloreAwamW, GaLoreAdamW8bit |
||||
from colossalai.nn.optimizer.galore import get_galore_param_groups |
||||
from colossalai.tensor.d_tensor import get_shard_dim_1d, is_distributed_tensor |
||||
from colossalai.tensor.d_tensor.api import clear_layout_converter |
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn |
||||
from colossalai.testing.random import seed_all |
||||
from colossalai.zero import LowLevelZeroOptimizer |
||||
from tests.kit.model_zoo import model_zoo |
||||
from tests.test_optimizer._utils import check_optim_states, run_bert_test |
||||
|
||||
_ALLOWED_P_G_TYPES = [ |
||||
(torch.float, torch.float), # pure fp32 |
||||
(torch.half, torch.half), # fp16 amp |
||||
(torch.bfloat16, torch.bfloat16), # bfloat16 amp |
||||
] |
||||
|
||||
# Identifiers for Tensor Parallel linear layers |
||||
_IN_DIM = 32 |
||||
_HID_DIM = 128 |
||||
_N_STEP = 3 |
||||
_SEED = 0 |
||||
coordinator = None |
||||
lr = 1e-2 |
||||
beta1, beta2 = 0.9, 0.999 |
||||
eps = 1e-8 |
||||
decay = 1e-3 |
||||
|
||||
Net, data_gen, *_ = next(iter(model_zoo.get_sub_registry("simple_mlp").values())) |
||||
TPNet, *_ = next(iter(model_zoo.get_sub_registry("simple_tp_mlp").values())) |
||||
|
||||
# Doesn't support ZeRO for now |
||||
test_config = [ |
||||
{ |
||||
"tp_size": 1, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 0, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 2, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 0, |
||||
"precision": "bf16", |
||||
}, |
||||
{ |
||||
"tp_size": 4, |
||||
"num_microbatches": 4, |
||||
"zero_stage": 0, |
||||
"precision": "bf16", |
||||
}, |
||||
] |
||||
|
||||
|
||||
def assert_grad_close(tp_model, torch_model, tp_group): |
||||
tp_size = dist.get_world_size(tp_group) |
||||
|
||||
# Check equal grads |
||||
for p, torch_p in zip(tp_model.parameters(), torch_model.parameters()): |
||||
grads = p.grad |
||||
if is_distributed_tensor(p): |
||||
split_dim = get_shard_dim_1d(p) |
||||
all_grads = [torch.empty_like(grads) for _ in range(tp_size)] |
||||
dist.all_gather(all_grads, grads.contiguous(), group=tp_group) |
||||
all_grads = torch.cat(all_grads, dim=split_dim) |
||||
else: |
||||
all_grads = grads |
||||
try: |
||||
assert (all_grads != 0).any() |
||||
assert_close(all_grads, torch_p.grad) |
||||
except Exception as e: |
||||
print(f"Before gather: {grads.shape}, after: {all_grads.shape}") |
||||
raise e |
||||
|
||||
|
||||
def assert_distributed_close(tp_model, torch_model, rtol, atol, tp_group): |
||||
rank = dist.get_rank(tp_group) |
||||
tp_size = dist.get_world_size(tp_group) |
||||
|
||||
for (name, p), torch_p in zip(tp_model.named_parameters(), torch_model.parameters()): |
||||
# if overflow, the weight won't be updated. so there will be no nan in p |
||||
assert not torch.isnan(p).any() |
||||
try: |
||||
if is_distributed_tensor(p): |
||||
split_dim = get_shard_dim_1d(p) |
||||
torch_p = torch_p.chunk(tp_size, dim=split_dim)[rank] |
||||
|
||||
assert_close(p, torch_p, rtol=rtol, atol=atol) |
||||
except AssertionError as e: |
||||
print(f"grad mismatch in {name}") |
||||
raise e |
||||
|
||||
|
||||
def force_assign_grad(p, g_dtype, grad=None): |
||||
"""avoid inconsistent grad and param dtype error""" |
||||
orig_p = p.data |
||||
p.data = torch.randn_like(p, device=orig_p.device, dtype=g_dtype) if grad == None else grad |
||||
p.grad = p.data |
||||
p.data = orig_p |
||||
|
||||
|
||||
def set_dist_grad( |
||||
dist_module: nn.Module, |
||||
torch_model: nn.Module, |
||||
g_dtype: torch.dtype, |
||||
group: dist.ProcessGroup, |
||||
) -> None: |
||||
""" |
||||
Set grads chunks for Tensor Parallel or ZeRO DP. |
||||
We do not need a separate treatment for ZeRO, |
||||
as the LowLevelOptimizer takes care of reduce-scattering grads. |
||||
""" |
||||
rank = dist.get_rank(group) |
||||
world_size = dist.get_world_size(group) |
||||
|
||||
for p, torch_p in zip(dist_module.parameters(), torch_model.parameters()): |
||||
if torch_p.grad is None: |
||||
# avoid inconsistent grad and param dtype error |
||||
force_assign_grad(torch_p, g_dtype) |
||||
else: |
||||
torch_p.grad += torch.randn_like(torch_p, device=torch_p.device, dtype=g_dtype) |
||||
|
||||
if p.grad is None: |
||||
force_assign_grad(p, g_dtype) |
||||
|
||||
if is_distributed_tensor(p): |
||||
split_dim = get_shard_dim_1d(p) |
||||
# Add grads only to the correctly split chunk |
||||
force_assign_grad(p, g_dtype, torch_p.grad.chunk(world_size, dim=split_dim)[rank].contiguous()) |
||||
# assert_close(p.grad, torch_p.grad.chunk(world_size, dim=split_dim)[rank]) |
||||
else: |
||||
force_assign_grad(p, g_dtype, torch_p.grad) |
||||
|
||||
|
||||
@parameterize("p_g_dtype", _ALLOWED_P_G_TYPES) |
||||
@parameterize("tp_zero_size", [(4, 1), (1, 4), (2, 2)]) |
||||
def run_dist_galore_basic(p_g_dtype: tuple[torch.dtype, torch.dtype], tp_zero_size: tuple[int, int]) -> None: |
||||
"""Test without forward""" |
||||
p_dtype, g_dtype = p_g_dtype |
||||
tp_size, zero_size = tp_zero_size |
||||
|
||||
# Set distributed groups |
||||
rank = dist.get_rank() |
||||
clear_layout_converter() # Ensure correct sharding |
||||
proc_mesh = ProcessGroupMesh(tp_size, zero_size) |
||||
tp_group = proc_mesh.get_group_along_axis(0) |
||||
dp_group = proc_mesh.get_group_along_axis(1) |
||||
|
||||
dist.get_rank(tp_group) |
||||
seed_all(_SEED) # Fix model init |
||||
torch_model = Net(in_dim=_IN_DIM, hid_dim=_HID_DIM, identity=True, dtype=p_dtype).to(rank) |
||||
tp_model = TPNet(torch_model.fc0, torch_model.fc1, torch_model.fc2, tp_group, dtype=p_dtype).to(rank) |
||||
assert_distributed_close(tp_model, torch_model, rtol=0, atol=0, tp_group=tp_group) |
||||
|
||||
# Set up optimizers |
||||
torch_optim = GaLoreAdamW8bit( |
||||
get_galore_param_groups(torch_model, decay, rank=8), |
||||
lr=lr, |
||||
betas=(beta1, beta2), |
||||
eps=eps, |
||||
percentile_clipping=101, |
||||
block_wise=False, |
||||
min_8bit_size=1e10, # Disable quantization |
||||
) |
||||
optim = DistGaloreAwamW( |
||||
get_galore_param_groups(tp_model, decay, rank=8), |
||||
lr=lr, |
||||
betas=(beta1, beta2), |
||||
eps=eps, |
||||
percentile_clipping=101, |
||||
block_wise=False, |
||||
min_8bit_size=1e10, |
||||
) |
||||
optim.setup_distributed(tp_group, dp_group) |
||||
|
||||
rtol, atol = 8e-7, 8e-7 |
||||
if p_dtype is torch.float16 or g_dtype is torch.float16: |
||||
rtol, atol = 1e-6, 1e-6 |
||||
if p_dtype is torch.bfloat16 or g_dtype is torch.bfloat16: |
||||
rtol, atol = 2e-6, 2e-6 |
||||
|
||||
for i in range(_N_STEP): |
||||
seed_all(_SEED + i) # NOTE: having only one manual_seed above doesn't work? |
||||
set_dist_grad(tp_model, torch_model, g_dtype, tp_group) |
||||
try: |
||||
torch_optim.step() |
||||
optim.step() |
||||
assert_grad_close(tp_model, torch_model, tp_group) |
||||
|
||||
torch_optim.zero_grad() |
||||
optim.zero_grad() |
||||
assert_distributed_close(tp_model, torch_model, rtol, atol, tp_group) |
||||
check_optim_states(torch_optim, optim) |
||||
|
||||
except Exception as e: |
||||
coordinator.print_on_master(f"step {i}: p_g_dtype: {p_g_dtype}, tp_zero_size: {tp_zero_size}") |
||||
raise e |
||||
|
||||
|
||||
@parameterize("p_g_dtype", _ALLOWED_P_G_TYPES) |
||||
@parameterize("tp_zero_size", [(4, 1), (2, 2), (1, 4)]) |
||||
def run_dist_galore_fwd_bwd(p_g_dtype: tuple[torch.dtype, torch.dtype], tp_zero_size: tuple[int, int]) -> None: |
||||
p_dtype, g_dtype = p_g_dtype |
||||
tp_size, zero_size = tp_zero_size |
||||
|
||||
# Set distributed groups |
||||
rank = dist.get_rank() |
||||
proc_mesh = ProcessGroupMesh(tp_size, zero_size) |
||||
tp_group = proc_mesh.get_group_along_axis(0) |
||||
dp_group = proc_mesh.get_group_along_axis(1) |
||||
dist.get_rank(tp_group) |
||||
|
||||
seed_all(_SEED) |
||||
clear_layout_converter() # Ensure correct sharding |
||||
torch_model = Net(_IN_DIM, _HID_DIM, identity=True, dtype=p_dtype).to(rank) |
||||
tp_model = TPNet(torch_model.fc0, torch_model.fc1, torch_model.fc2, tp_group, dtype=p_dtype).to(rank) |
||||
assert_distributed_close(tp_model, torch_model, rtol=0, atol=0, tp_group=tp_group) |
||||
|
||||
# Set up optimizers |
||||
torch_optim = GaLoreAdamW8bit( |
||||
get_galore_param_groups(torch_model, decay, rank=8), |
||||
lr=lr, |
||||
betas=(beta1, beta2), |
||||
eps=eps, |
||||
percentile_clipping=101, |
||||
block_wise=False, |
||||
min_8bit_size=1e10, |
||||
) |
||||
optim = DistGaloreAwamW( |
||||
get_galore_param_groups(tp_model, decay, rank=8), |
||||
lr=lr, |
||||
betas=(beta1, beta2), |
||||
eps=eps, |
||||
percentile_clipping=101, |
||||
block_wise=False, |
||||
min_8bit_size=1e10, |
||||
) |
||||
|
||||
# Setup distributed optimizer |
||||
if zero_size > 1: |
||||
optim = LowLevelZeroOptimizer( |
||||
optim, |
||||
overlap_communication=True, |
||||
initial_scale=128, |
||||
partition_grad=True, |
||||
dp_process_group=dp_group, |
||||
verbose=True, |
||||
) |
||||
shard_to_param = optim.get_master_to_working_map() |
||||
optim.optim.setup_distributed( |
||||
tp_group, dp_group, shard_to_param, padding_map=optim.get_param_padding_map(), is_zero=True |
||||
) |
||||
else: |
||||
optim.setup_distributed(tp_group) |
||||
|
||||
rtol, atol = 8e-7, 8e-7 |
||||
if p_dtype is torch.float16 or g_dtype is torch.float16: |
||||
rtol, atol = 1e-6, 1e-6 |
||||
if p_dtype is torch.bfloat16 or g_dtype is torch.bfloat16: |
||||
rtol, atol = 2e-6, 2e-6 |
||||
|
||||
seed_all(_SEED) # NOTE: having only one manual_seed above doesn't work? |
||||
x = data_gen().cuda().to(dtype=p_dtype) |
||||
|
||||
out_tp = tp_model(x) |
||||
out = torch_model(x) |
||||
try: |
||||
assert_close(out, out_tp, rtol=rtol, atol=atol) |
||||
except Exception as e: |
||||
coordinator.print_on_master(f"p_g_dtype: {p_g_dtype}, tp_zero_size: {tp_zero_size}") |
||||
raise e |
||||
|
||||
if zero_size > 1: |
||||
optim.backward(out_tp.sum()) |
||||
out.sum().backward() |
||||
else: |
||||
out_tp.sum().backward() |
||||
out.sum().backward() |
||||
|
||||
torch_optim.step() |
||||
optim.step() |
||||
|
||||
torch_optim.zero_grad() |
||||
optim.zero_grad() |
||||
try: |
||||
assert_distributed_close(tp_model, torch_model, rtol, atol, tp_group) |
||||
check_optim_states(getattr(torch_optim, "optim", torch_optim), getattr(optim, "optim", optim)) |
||||
except Exception as e: |
||||
coordinator.print_on_master(f"p_g_dtype: {p_g_dtype}, tp_zero_size: {tp_zero_size}") |
||||
raise e |
||||
|
||||
|
||||
def check_dist_galore(rank, world_size, port): |
||||
disable_existing_loggers() |
||||
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") |
||||
global coordinator |
||||
coordinator = DistCoordinator() |
||||
|
||||
run_dist_galore_basic() |
||||
coordinator.print_on_master("Basic backward tests passed") |
||||
|
||||
coordinator.print_on_master("Skipping forward-backward tests due to SVD instability") |
||||
# run_dist_galore_fwd_bwd() |
||||
# _COORDINATOR.print_on_master("Forward-backward tests passed") |
||||
|
||||
coordinator.print_on_master( |
||||
"Running bert tests, which are expected to produce minor errors due to instability in SVD convergence. \ |
||||
For example, a 1e-9 grad diff causes drastic difference in SVD output." |
||||
) |
||||
for config in test_config: |
||||
try: |
||||
run_bert_test(test_config=config, optim_class=GaLoreAdamW8bit, sharded_optim_class=DistGaloreAwamW) |
||||
except Exception as e: |
||||
print(e) |
||||
dist.barrier() |
||||
print(f"rank {rank} tests passed :)") |
||||
|
||||
|
||||
@pytest.mark.dist |
||||
@rerun_if_address_is_in_use() |
||||
def test_dist_galore(): |
||||
spawn(check_dist_galore, nprocs=4) |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
test_dist_galore() |
||||
@ -0,0 +1,303 @@
|
||||
import pytest |
||||
import torch |
||||
import torch.distributed as dist |
||||
import torch.nn as nn |
||||
from torch.testing import assert_close |
||||
|
||||
import colossalai |
||||
from colossalai.cluster import DistCoordinator, ProcessGroupMesh |
||||
from colossalai.logging import disable_existing_loggers |
||||
from colossalai.nn.optimizer import DistributedLamb, Lamb |
||||
from colossalai.tensor.d_tensor import get_shard_dim_1d, is_distributed_tensor |
||||
from colossalai.tensor.d_tensor.api import clear_layout_converter |
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn |
||||
from colossalai.testing.random import seed_all |
||||
from colossalai.zero import LowLevelZeroOptimizer |
||||
from tests.kit.model_zoo import model_zoo |
||||
from tests.test_optimizer._utils import check_optim_states, run_bert_test |
||||
|
||||
_ALLOWED_P_G_TYPES = [ |
||||
(torch.float, torch.float), # pure fp32 |
||||
(torch.float, torch.half), # fp16 amp |
||||
(torch.float, torch.bfloat16), # bfloat16 amp |
||||
] |
||||
|
||||
_IN_DIM = 32 |
||||
_HID_DIM = 128 |
||||
_N_STEP = 3 |
||||
_SEED = 1024 |
||||
coordinator = None |
||||
|
||||
Net, data_gen, *_ = next(iter(model_zoo.get_sub_registry("simple_mlp").values())) |
||||
TPNet, *_ = next(iter(model_zoo.get_sub_registry("simple_tp_mlp").values())) |
||||
|
||||
|
||||
def assert_distributed_close(tp_model, torch_model, rtol, atol, tp_group): |
||||
rank = dist.get_rank(tp_group) |
||||
tp_size = dist.get_world_size(tp_group) |
||||
|
||||
for (name, p), torch_p in zip(tp_model.named_parameters(), torch_model.parameters()): |
||||
# if overflow, the weight won't be updated. so there will be no nan in p |
||||
assert not torch.isnan(p).any() |
||||
try: |
||||
if is_distributed_tensor(p): |
||||
split_dim = get_shard_dim_1d(p) |
||||
torch_p = torch_p.chunk(tp_size, dim=split_dim)[rank] |
||||
|
||||
assert_close(p.float(), torch_p, rtol=rtol, atol=atol) |
||||
except AssertionError as e: |
||||
print(f"grad mismatch in {name}") |
||||
raise e |
||||
|
||||
|
||||
def setup_param_groups(bert_model: nn.Module) -> list: |
||||
no_decay = ["bias", "LayerNorm.weight"] |
||||
optimizer_grouped_parameters = [ |
||||
{ |
||||
"params": [p for n, p in bert_model.named_parameters() if not any(nd in n for nd in no_decay)], |
||||
"weight_decay": 0.1, |
||||
}, |
||||
{ |
||||
"params": [p for n, p in bert_model.named_parameters() if any(nd in n for nd in no_decay)], |
||||
"weight_decay": 0.0, |
||||
}, |
||||
] |
||||
return optimizer_grouped_parameters |
||||
|
||||
|
||||
def force_assign_grad(p, g_dtype, grad=None): |
||||
"""avoid inconsistent grad and param dtype error""" |
||||
orig_p = p.data |
||||
p.data = torch.randn_like(p, device=orig_p.device, dtype=g_dtype) if grad == None else grad |
||||
p.grad = p.data |
||||
p.data = orig_p |
||||
|
||||
|
||||
def set_dist_grad( |
||||
dist_module: nn.Module, |
||||
torch_model: nn.Module, |
||||
g_dtype: torch.dtype, |
||||
group: dist.ProcessGroup, |
||||
) -> None: |
||||
""" |
||||
Set grads chunks for Tensor Parallel or ZeRO DP. |
||||
We do not need a separate treatment for ZeRO, |
||||
as the LowLevelOptimizer takes care of reduce-scattering grads. |
||||
""" |
||||
rank = dist.get_rank(group) |
||||
world_size = dist.get_world_size(group) |
||||
|
||||
for p, torch_p in zip(dist_module.parameters(), torch_model.parameters()): |
||||
if torch_p.grad is None: |
||||
# avoid inconsistent grad and param dtype error |
||||
force_assign_grad(torch_p, g_dtype) |
||||
else: |
||||
torch_p.grad += torch.randn_like(torch_p, device=torch_p.device, dtype=g_dtype) |
||||
|
||||
if p.grad is None: |
||||
force_assign_grad(p, g_dtype) |
||||
|
||||
if is_distributed_tensor(p): |
||||
split_dim = get_shard_dim_1d(p) |
||||
# Add grads only to the correctly split chunk |
||||
force_assign_grad(p, g_dtype, torch_p.grad.chunk(world_size, dim=split_dim)[rank]) |
||||
# assert_close(p.grad, torch_p.grad.chunk(world_size, dim=split_dim)[rank]) |
||||
else: |
||||
force_assign_grad(p, g_dtype, torch_p.grad) |
||||
|
||||
|
||||
@parameterize("p_g_dtype", _ALLOWED_P_G_TYPES) |
||||
@parameterize("bias_correction", [False, True]) |
||||
@parameterize("tp_zero_size", [(1, 4), (4, 1), (2, 2)]) |
||||
def run_dist_lamb_basic( |
||||
bias_correction: bool, p_g_dtype: tuple[torch.dtype, torch.dtype], tp_zero_size: tuple[int, int] |
||||
) -> None: |
||||
"""Test without forward""" |
||||
p_dtype, g_dtype = p_g_dtype |
||||
tp_size, zero_size = tp_zero_size |
||||
|
||||
# Set distributed groups |
||||
rank = dist.get_rank() |
||||
clear_layout_converter() # Ensure correct sharding |
||||
proc_mesh = ProcessGroupMesh(tp_size, zero_size) |
||||
tp_group = proc_mesh.get_group_along_axis(0) |
||||
|
||||
tp_rank = dist.get_rank(tp_group) |
||||
seed_all(_SEED) # Fix model init |
||||
torch_model = Net(in_dim=_IN_DIM, hid_dim=_HID_DIM, identity=True).to(rank) |
||||
tp_model = TPNet(torch_model.fc0, torch_model.fc1, torch_model.fc2, tp_group).to(rank) |
||||
# Ensure equal weight init |
||||
assert_close( |
||||
torch_model.fc1.weight[tp_rank * _HID_DIM // tp_size : (tp_rank + 1) * _HID_DIM // tp_size], |
||||
tp_model.fc1.weight, |
||||
) |
||||
assert_close( |
||||
torch_model.fc2.weight[:, tp_rank * _HID_DIM // tp_size : (tp_rank + 1) * _HID_DIM // tp_size], |
||||
tp_model.fc2.weight, |
||||
) |
||||
|
||||
# Set up optimizers |
||||
lr = 1e-3 |
||||
beta1, beta2 = 0.9, 0.999 |
||||
eps = 1e-8 |
||||
torch_optim = Lamb( |
||||
setup_param_groups(torch_model), lr=lr, betas=(beta1, beta2), eps=eps, bias_correction=bias_correction |
||||
) |
||||
optim = DistributedLamb( |
||||
setup_param_groups(tp_model), |
||||
lr=lr, |
||||
betas=(beta1, beta2), |
||||
eps=eps, |
||||
bias_correction=bias_correction, |
||||
) |
||||
optim.setup_distributed(tp_group) |
||||
|
||||
rtol, atol = 8e-7, 8e-7 |
||||
if p_dtype is torch.float16 or g_dtype is torch.float16: |
||||
rtol, atol = 1e-6, 1e-6 |
||||
if p_dtype is torch.bfloat16 or g_dtype is torch.bfloat16: |
||||
rtol, atol = 2e-6, 2e-6 |
||||
|
||||
for i in range(_N_STEP): |
||||
seed_all(_SEED + i) # NOTE: having only one manual_seed above doesn't work? |
||||
set_dist_grad(tp_model, torch_model, g_dtype, tp_group) |
||||
|
||||
torch_optim.step() |
||||
optim.step() |
||||
torch_optim.zero_grad() |
||||
optim.zero_grad() |
||||
try: |
||||
assert_distributed_close(tp_model, torch_model, rtol, atol, tp_group) |
||||
except Exception as e: |
||||
coordinator.print_on_master( |
||||
f"step {i + 1}: bias_correction: {bias_correction}, p_g_dtype: {p_g_dtype}, tp_zero_size: {tp_zero_size}" |
||||
) |
||||
raise e |
||||
|
||||
|
||||
@parameterize("p_g_dtype", _ALLOWED_P_G_TYPES) |
||||
@parameterize("bias_correction", [False, True]) |
||||
@parameterize("tp_zero_size", [(2, 2), (4, 1), (1, 4)]) |
||||
def run_dist_lamb_fwd_bwd( |
||||
bias_correction: bool, p_g_dtype: tuple[torch.dtype, torch.dtype], tp_zero_size: tuple[int, int] |
||||
) -> None: |
||||
p_dtype, g_dtype = p_g_dtype |
||||
tp_size, zero_size = tp_zero_size |
||||
|
||||
# Set distributed groups |
||||
rank = dist.get_rank() |
||||
proc_mesh = ProcessGroupMesh(tp_size, zero_size) |
||||
tp_group = proc_mesh.get_group_along_axis(0) |
||||
dp_group = proc_mesh.get_group_along_axis(1) |
||||
tp_rank = dist.get_rank(tp_group) |
||||
|
||||
seed_all(_SEED) |
||||
clear_layout_converter() # Ensure correct sharding |
||||
torch_model = Net(_IN_DIM, _HID_DIM).to(rank) |
||||
tp_model = TPNet(torch_model.fc0, torch_model.fc1, torch_model.fc2, tp_group).to(rank) |
||||
|
||||
assert_close( |
||||
torch_model.fc1.weight[tp_rank * _HID_DIM // tp_size : (tp_rank + 1) * _HID_DIM // tp_size], |
||||
tp_model.fc1.weight, |
||||
) |
||||
assert_close( |
||||
torch_model.fc2.weight[:, tp_rank * _HID_DIM // tp_size : (tp_rank + 1) * _HID_DIM // tp_size], |
||||
tp_model.fc2.weight, |
||||
) |
||||
|
||||
# Set up optimizers |
||||
lr = 1e-3 |
||||
beta1, beta2 = 0.9, 0.999 |
||||
eps = 1e-8 |
||||
torch_optim = Lamb( |
||||
setup_param_groups(torch_model), lr=lr, betas=(beta1, beta2), eps=eps, bias_correction=bias_correction |
||||
) |
||||
optim = DistributedLamb( |
||||
setup_param_groups(tp_model), |
||||
lr=lr, |
||||
betas=(beta1, beta2), |
||||
eps=eps, |
||||
bias_correction=bias_correction, |
||||
) |
||||
|
||||
# Setup distributed optimizer |
||||
if zero_size > 1: |
||||
optim = LowLevelZeroOptimizer( |
||||
optim, |
||||
overlap_communication=True, |
||||
initial_scale=128, |
||||
partition_grad=True, |
||||
dp_process_group=dp_group, |
||||
verbose=True, |
||||
) |
||||
shard_to_param = optim._param_store.master_to_working_param |
||||
optim.optim.setup_distributed(tp_group, dp_group, shard_to_param, is_zero=True) |
||||
else: |
||||
optim.setup_distributed(tp_group) |
||||
|
||||
rtol, atol = 8e-7, 8e-7 |
||||
if p_dtype is torch.float16 or g_dtype is torch.float16: |
||||
rtol, atol = 1e-6, 1e-6 |
||||
if p_dtype is torch.bfloat16 or g_dtype is torch.bfloat16: |
||||
rtol, atol = 2e-6, 2e-6 |
||||
|
||||
seed_all(_SEED) # NOTE: having only one manual_seed above doesn't work? |
||||
x = data_gen() |
||||
x = x.cuda().to(dtype=p_dtype) |
||||
|
||||
out_tp = tp_model(x) |
||||
out = torch_model(x) |
||||
try: |
||||
assert_close(out, out_tp, rtol=rtol, atol=atol) |
||||
except Exception as e: |
||||
coordinator.print_on_master( |
||||
f"bias_correction: {bias_correction}, p_g_dtype: {p_g_dtype}, tp_zero_size: {tp_zero_size}" |
||||
) |
||||
raise e |
||||
|
||||
if zero_size > 1: |
||||
optim.backward(out_tp.sum()) |
||||
out.sum().backward() |
||||
else: |
||||
out_tp.sum().backward() |
||||
out.sum().backward() |
||||
|
||||
torch_optim.step() |
||||
optim.step() |
||||
dist.barrier() |
||||
torch_optim.zero_grad() |
||||
optim.zero_grad() |
||||
try: |
||||
assert_distributed_close(tp_model, torch_model, rtol, atol, tp_group) |
||||
check_optim_states(getattr(torch_optim, "optim", torch_optim), getattr(optim, "optim", optim)) |
||||
except Exception as e: |
||||
coordinator.print_on_master( |
||||
f"bias_correction: {bias_correction}, p_g_dtype: {p_g_dtype}, tp_zero_size: {tp_zero_size}" |
||||
) |
||||
raise e |
||||
|
||||
|
||||
def check_dist_lamb(rank, world_size, port): |
||||
disable_existing_loggers() |
||||
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") |
||||
global coordinator |
||||
coordinator = DistCoordinator() |
||||
|
||||
run_dist_lamb_basic() |
||||
coordinator.print_on_master("Basic tests passed") |
||||
|
||||
run_dist_lamb_fwd_bwd() |
||||
coordinator.print_on_master("Forward-backward tests passed") |
||||
|
||||
run_bert_test(optim_class=Lamb, sharded_optim_class=DistributedLamb) |
||||
print(f"rank {rank} tests passed :)") |
||||
|
||||
|
||||
@pytest.mark.dist |
||||
@rerun_if_address_is_in_use() |
||||
def test_dist_lamb(): |
||||
spawn(check_dist_lamb, nprocs=4) |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
test_dist_lamb() |
||||
@ -0,0 +1,235 @@
|
||||
import os |
||||
|
||||
import pytest |
||||
import torch |
||||
import transformers |
||||
|
||||
import colossalai |
||||
from colossalai.logging import disable_existing_loggers |
||||
from colossalai.shardformer.layer.utils import Randomizer |
||||
from colossalai.tensor.d_tensor.api import clear_layout_converter |
||||
from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn |
||||
from tests.kit.model_zoo import model_zoo |
||||
from tests.test_shardformer.test_model._utils import ( |
||||
build_model_from_hybrid_plugin, |
||||
check_all_grad_tensors, |
||||
check_loss, |
||||
check_output_hidden_state, |
||||
check_weight, |
||||
get_grad_tensors_for_check, |
||||
run_forward_backward_with_hybrid_plugin, |
||||
unwrap_model, |
||||
) |
||||
|
||||
os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true" |
||||
|
||||
|
||||
def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config): |
||||
org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin( |
||||
model_fn, loss_fn, test_config |
||||
) |
||||
|
||||
org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin( |
||||
org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster |
||||
) |
||||
|
||||
stage_manager = booster.plugin.stage_manager |
||||
tp_group = booster.plugin.tp_group |
||||
|
||||
# unwrap model |
||||
qwen2_model = unwrap_model(org_model, "Qwen2Model", "model") |
||||
shard_qwen2_model = unwrap_model(sharded_model, "Qwen2Model", "model") |
||||
|
||||
row_layer_for_check = ["layers[0].self_attn.q_proj", "embed_tokens"] |
||||
col_layer_for_check = ["layers[0].self_attn.o_proj"] |
||||
|
||||
# Save gradient tensors for comparison between the original model and the sharded model before optimizer step. |
||||
grads_to_check = {} |
||||
if (stage_manager is None or stage_manager.is_first_stage(ignore_chunk=True)) and booster.plugin.zero_stage == 0: |
||||
if test_config["precision"] == "fp32": |
||||
atol, rtol = 1e-6, 1e-4 |
||||
else: |
||||
atol, rtol = 5e-3, 5e-3 |
||||
row_layer_grads = get_grad_tensors_for_check( |
||||
qwen2_model, shard_qwen2_model, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False |
||||
) |
||||
col_layer_grads = get_grad_tensors_for_check( |
||||
qwen2_model, shard_qwen2_model, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False |
||||
) |
||||
grads_to_check.update(col_layer_grads) |
||||
grads_to_check.update(row_layer_grads) |
||||
|
||||
# optimizer executes step |
||||
org_optimizer.step() |
||||
sharded_optimizer.step() |
||||
|
||||
# check last hidden state & loss |
||||
if stage_manager is None or stage_manager.is_last_stage(ignore_chunk=True): |
||||
if test_config["precision"] == "fp32": |
||||
atol, rtol = 1e-5, 1e-3 |
||||
else: |
||||
atol, rtol = 5e-3, 5e-3 |
||||
|
||||
if org_model.__class__.__name__ == "Qwen2Model": |
||||
check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol) |
||||
|
||||
check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol) |
||||
|
||||
# check weights |
||||
if stage_manager is None or stage_manager.is_first_stage(ignore_chunk=True): |
||||
if test_config["precision"] == "fp32": |
||||
atol, rtol = 1e-4, 1e-3 |
||||
else: |
||||
atol, rtol = 5e-3, 5e-3 |
||||
check_weight( |
||||
qwen2_model, shard_qwen2_model, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False |
||||
) |
||||
|
||||
# check grads |
||||
check_all_grad_tensors(grads_to_check) |
||||
|
||||
torch.cuda.empty_cache() |
||||
|
||||
|
||||
@parameterize( |
||||
"test_config", |
||||
[ |
||||
{ |
||||
"tp_size": 2, |
||||
"pp_size": 2, |
||||
"num_microbatches": 2, |
||||
"enable_all_optimization": True, |
||||
"use_lazy_init": True, |
||||
"precision": "fp16", |
||||
"initial_scale": 1, |
||||
}, |
||||
{ |
||||
"tp_size": 1, |
||||
"pp_size": 2, |
||||
"num_microbatches": 4, |
||||
"use_lazy_init": False, |
||||
"precision": "fp32", |
||||
}, |
||||
{ |
||||
"tp_size": 4, |
||||
"pp_size": 1, |
||||
"enable_all_optimization": True, |
||||
"use_lazy_init": False, |
||||
"precision": "fp32", |
||||
}, |
||||
{ |
||||
"tp_size": 1, |
||||
"pp_size": 4, |
||||
"num_microbatches": 4, |
||||
"enable_all_optimization": False, |
||||
"use_lazy_init": False, |
||||
"precision": "fp32", |
||||
}, |
||||
{"tp_size": 2, "pp_size": 1, "enable_all_optimization": True, "use_lazy_init": False, "precision": "fp32"}, |
||||
{ |
||||
"tp_size": 2, |
||||
"pp_size": 1, |
||||
"enable_all_optimization": True, |
||||
"use_lazy_init": True, |
||||
"zero_stage": 2, |
||||
"precision": "fp16", |
||||
"initial_scale": 1, |
||||
}, |
||||
{ |
||||
"tp_size": 1, |
||||
"pp_size": 2, |
||||
"num_microbatches": 2, |
||||
"enable_all_optimization": True, |
||||
"use_lazy_init": True, |
||||
"zero_stage": 1, |
||||
"precision": "fp16", |
||||
"initial_scale": 1, |
||||
}, |
||||
], |
||||
) |
||||
def run_qwen2_test(test_config): |
||||
sub_model_zoo = model_zoo.get_sub_registry("transformers_qwen2") |
||||
|
||||
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): |
||||
check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) |
||||
|
||||
clear_layout_converter() |
||||
Randomizer.reset_index() |
||||
torch.cuda.empty_cache() |
||||
|
||||
|
||||
@parameterize( |
||||
"test_config", |
||||
[ |
||||
{ |
||||
"tp_size": 2, |
||||
"pp_size": 2, |
||||
"num_microbatches": 4, |
||||
"enable_all_optimization": False, |
||||
"use_lazy_init": False, |
||||
"precision": "fp32", |
||||
"initial_scale": 1, |
||||
}, |
||||
{ |
||||
"tp_size": 2, |
||||
"pp_size": 2, |
||||
"num_microbatches": 4, |
||||
"enable_all_optimization": False, |
||||
"use_lazy_init": False, |
||||
"precision": "fp16", |
||||
"zero_stage": 1, |
||||
"initial_scale": 1, |
||||
}, |
||||
{ |
||||
"tp_size": 2, |
||||
"pp_size": 2, |
||||
"pp_style": "interleaved", |
||||
"num_model_chunks": 2, |
||||
"num_microbatches": 4, |
||||
"enable_all_optimization": False, |
||||
"precision": "fp16", |
||||
"zero_stage": 1, |
||||
"initial_scale": 1, |
||||
}, |
||||
], |
||||
) |
||||
def run_qwen2_3d_test(test_config): |
||||
sub_model_zoo = model_zoo.get_sub_registry("transformers_qwen2") |
||||
|
||||
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): |
||||
check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) |
||||
|
||||
clear_layout_converter() |
||||
Randomizer.reset_index() |
||||
torch.cuda.empty_cache() |
||||
|
||||
|
||||
def check_qwen2(rank, world_size, port): |
||||
disable_existing_loggers() |
||||
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") |
||||
run_qwen2_test() |
||||
|
||||
|
||||
def check_qwen2_3d(rank, world_size, port): |
||||
disable_existing_loggers() |
||||
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") |
||||
run_qwen2_3d_test() |
||||
|
||||
|
||||
@pytest.mark.skipif(transformers.__version__ < "4.39.1", reason="Requires transformers version 4.39.1 or later") |
||||
@rerun_if_address_is_in_use() |
||||
@clear_cache_before_run() |
||||
def test_qwen2(): |
||||
spawn(check_qwen2, 4) |
||||
|
||||
|
||||
@pytest.mark.skipif(transformers.__version__ < "4.39.1", reason="Requires transformers version 4.39.1 or later") |
||||
@rerun_if_address_is_in_use() |
||||
@clear_cache_before_run() |
||||
def test_qwen2_3d(): |
||||
spawn(check_qwen2_3d, 8) |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
test_qwen2() |
||||
test_qwen2_3d() |
||||
Loading…
Reference in new issue