227 lines
7.4 KiB
Python
227 lines
7.4 KiB
Python
"""
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Helpers to train with 16-bit precision.
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"""
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import numpy as np
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import torch as th
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import torch.nn as nn
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from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
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INITIAL_LOG_LOSS_SCALE = 20.0
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def convert_module_to_f16(l):
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"""
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Convert primitive modules to float16.
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"""
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if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
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l.weight.data = l.weight.data.half()
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if l.bias is not None:
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l.bias.data = l.bias.data.half()
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def convert_module_to_f32(l):
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"""
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Convert primitive modules to float32, undoing convert_module_to_f16().
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"""
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if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
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l.weight.data = l.weight.data.float()
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if l.bias is not None:
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l.bias.data = l.bias.data.float()
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def make_master_params(param_groups_and_shapes):
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"""
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Copy model parameters into a (differently-shaped) list of full-precision
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parameters.
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"""
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master_params = []
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for param_group, shape in param_groups_and_shapes:
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master_param = nn.Parameter(
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_flatten_dense_tensors(
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[param.detach().float() for (_, param) in param_group]
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).view(shape)
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)
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master_param.requires_grad = True
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master_params.append(master_param)
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return master_params
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def model_grads_to_master_grads(param_groups_and_shapes, master_params):
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"""
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Copy the gradients from the model parameters into the master parameters
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from make_master_params().
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"""
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for master_param, (param_group, shape) in zip(
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master_params, param_groups_and_shapes
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):
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master_param.grad = _flatten_dense_tensors(
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[param_grad_or_zeros(param) for (_, param) in param_group]
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).view(shape)
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def master_params_to_model_params(param_groups_and_shapes, master_params):
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"""
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Copy the master parameter data back into the model parameters.
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"""
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# Without copying to a list, if a generator is passed, this will
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# silently not copy any parameters.
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for master_param, (param_group, _) in zip(master_params, param_groups_and_shapes):
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for (_, param), unflat_master_param in zip(
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param_group, unflatten_master_params(param_group, master_param.view(-1))
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):
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param.detach().copy_(unflat_master_param)
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def unflatten_master_params(param_group, master_param):
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return _unflatten_dense_tensors(master_param, [param for (_, param) in param_group])
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def get_param_groups_and_shapes(named_model_params):
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named_model_params = list(named_model_params)
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scalar_vector_named_params = (
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[(n, p) for (n, p) in named_model_params if p.ndim <= 1],
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(-1),
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)
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matrix_named_params = (
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[(n, p) for (n, p) in named_model_params if p.ndim > 1],
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(1, -1),
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)
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return [scalar_vector_named_params, matrix_named_params]
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def master_params_to_state_dict(
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model, param_groups_and_shapes, master_params, use_fp16
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):
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if use_fp16:
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state_dict = model.state_dict()
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for master_param, (param_group, _) in zip(
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master_params, param_groups_and_shapes
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):
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for (name, _), unflat_master_param in zip(
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param_group, unflatten_master_params(param_group, master_param.view(-1))
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):
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assert name in state_dict
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state_dict[name] = unflat_master_param
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else:
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state_dict = model.state_dict()
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for i, (name, _value) in enumerate(model.named_parameters()):
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assert name in state_dict
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state_dict[name] = master_params[i]
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return state_dict
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def state_dict_to_master_params(model, state_dict, use_fp16):
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if use_fp16:
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named_model_params = [
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(name, state_dict[name]) for name, _ in model.named_parameters()
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]
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param_groups_and_shapes = get_param_groups_and_shapes(named_model_params)
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master_params = make_master_params(param_groups_and_shapes)
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else:
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master_params = [state_dict[name] for name, _ in model.named_parameters()]
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return master_params
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def zero_master_grads(master_params):
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for param in master_params:
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param.grad = None
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def zero_grad(model_params):
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for param in model_params:
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# Taken from https://pytorch.org/docs/stable/_modules/torch/optim/optimizer.html#Optimizer.add_param_group
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if param.grad is not None:
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param.grad.detach_()
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param.grad.zero_()
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def param_grad_or_zeros(param):
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if param.grad is not None:
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return param.grad.data.detach()
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else:
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return th.zeros_like(param)
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class MixedPrecisionTrainer:
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def __init__(
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self,
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*,
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model,
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use_fp16=False,
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fp16_scale_growth=1e-3,
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initial_lg_loss_scale=INITIAL_LOG_LOSS_SCALE,
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):
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self.model = model
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self.use_fp16 = use_fp16
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self.fp16_scale_growth = fp16_scale_growth
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self.model_params = list(self.model.parameters())
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self.master_params = self.model_params
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self.param_groups_and_shapes = None
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self.lg_loss_scale = initial_lg_loss_scale
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if self.use_fp16:
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self.param_groups_and_shapes = get_param_groups_and_shapes(
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self.model.named_parameters()
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)
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self.master_params = make_master_params(self.param_groups_and_shapes)
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self.model.convert_to_fp16()
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def zero_grad(self):
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zero_grad(self.model_params)
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def backward(self, loss: th.Tensor):
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if self.use_fp16:
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loss_scale = 2 ** self.lg_loss_scale
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(loss * loss_scale).backward()
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else:
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loss.backward()
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def optimize(self, opt: th.optim.Optimizer):
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if self.use_fp16:
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return self._optimize_fp16(opt)
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else:
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return self._optimize_normal(opt)
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def _optimize_fp16(self, opt: th.optim.Optimizer):
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model_grads_to_master_grads(self.param_groups_and_shapes, self.master_params)
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grad_norm, param_norm = self._compute_norms(grad_scale=2 ** self.lg_loss_scale)
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if check_overflow(grad_norm):
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self.lg_loss_scale -= 1
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zero_master_grads(self.master_params)
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return False
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opt.step(grad_scale=2.0 ** self.lg_loss_scale)
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zero_master_grads(self.master_params)
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master_params_to_model_params(self.param_groups_and_shapes, self.master_params)
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self.lg_loss_scale += self.fp16_scale_growth
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return True
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def _optimize_normal(self, opt: th.optim.Optimizer):
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grad_norm, param_norm = self._compute_norms()
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opt.step()
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return True
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def _compute_norms(self, grad_scale=1.0):
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grad_norm = 0.0
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param_norm = 0.0
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for p in self.master_params:
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with th.no_grad():
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param_norm += th.norm(p, p=2, dtype=th.float32).item() ** 2
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if p.grad is not None:
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grad_norm += th.norm(p.grad, p=2, dtype=th.float32).item() ** 2
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return np.sqrt(grad_norm) / grad_scale, np.sqrt(param_norm)
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def master_params_to_state_dict(self, master_params):
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return master_params_to_state_dict(
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self.model, self.param_groups_and_shapes, master_params, self.use_fp16
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)
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def state_dict_to_master_params(self, state_dict):
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return state_dict_to_master_params(self.model, state_dict, self.use_fp16)
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def check_overflow(value):
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return (value == float("inf")) or (value == -float("inf")) or (value != value)
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