mirror of https://github.com/hpcaitech/ColossalAI
258 lines
10 KiB
Python
258 lines
10 KiB
Python
import math
|
|
from dataclasses import dataclass
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torch.autograd import Function
|
|
|
|
|
|
def check_config(config):
|
|
if config.hidden_size % config.nhead != 0:
|
|
raise Exception("hidden_size % nhead != 0")
|
|
|
|
factor = 8 if config.fp16 else 4
|
|
upbound = factor * 1024 * 4
|
|
if config.hidden_size > upbound:
|
|
# as required by ln backward kernel currently
|
|
raise Exception(f"hidden_size > {upbound}")
|
|
|
|
head_dim = config.hidden_size // config.nhead
|
|
if head_dim % factor != 0:
|
|
# as required by reshape kernel
|
|
raise Exception(f"head_dim({head_dim}) % {factor} != 0")
|
|
|
|
|
|
def calc_offset(sizes):
|
|
offsets = [0]
|
|
tmp = 0
|
|
for x in sizes:
|
|
tmp += x
|
|
offsets.append(tmp)
|
|
return offsets
|
|
|
|
|
|
colossal_multihead_attention = None
|
|
|
|
|
|
@dataclass
|
|
class Config:
|
|
max_batch_tokens: int # max batch token numbers
|
|
max_seq_len: int # max sequence length
|
|
hidden_size: int # size of transformer hidden layers
|
|
nhead: int # number of heads in attention
|
|
attn_prob_dropout_ratio: float # attention score dropout ratio
|
|
hidden_dropout_ratio: float # dropout ration before residual
|
|
norm_first: bool # norm_first
|
|
fp16: bool # fp16 presion
|
|
|
|
|
|
class MultiHeadAttention1DFunc(Function):
|
|
|
|
@staticmethod
|
|
def forward(ctx, input, input_mask, in_proj_weight, in_proj_bias, out_proj_weight, out_proj_bias, norm_weight,
|
|
norm_bias, config):
|
|
cuda_module = colossal_multihead_attention
|
|
forward_func = (cuda_module.multihead_attention_fw_fp16
|
|
if config.fp16 else cuda_module.multihead_attention_fw_fp32)
|
|
if config.fp16:
|
|
input = input.to(torch.half)
|
|
input_mask = input_mask.to(torch.half)
|
|
|
|
(output,) = forward_func(config.layer_id, input, input_mask, in_proj_weight, in_proj_bias, out_proj_weight,
|
|
out_proj_bias, norm_weight, norm_bias, config.training, config.norm_first)
|
|
|
|
if config.is_grad_enabled and config.training:
|
|
ctx.save_for_backward(output, input, input_mask, in_proj_weight, in_proj_bias, out_proj_weight,
|
|
out_proj_bias, norm_weight, norm_bias)
|
|
ctx.config = config
|
|
return output
|
|
|
|
@staticmethod
|
|
def backward(ctx, grad_output):
|
|
assert ctx.config.training
|
|
|
|
cuda_module = colossal_multihead_attention
|
|
backward_func = (cuda_module.multihead_attention_bw_fp16
|
|
if ctx.config.fp16 else cuda_module.multihead_attention_bw_fp32)
|
|
|
|
output, input, input_mask, in_proj_weight, in_proj_bias, out_proj_weight, \
|
|
out_proj_bias, norm_weight, norm_bias = ctx.saved_tensors
|
|
|
|
grad_input = None
|
|
grad_in_proj_weight = None
|
|
grad_in_proj_bias = None
|
|
grad_out_proj_weight = None
|
|
grad_out_proj_bias = None
|
|
grad_norm_weight = None
|
|
grad_norm_bias = None
|
|
|
|
if ctx.config.fp16:
|
|
grad_output = grad_output.to(torch.half)
|
|
output = output.to(torch.half)
|
|
input = input.to(torch.half)
|
|
input_mask = input_mask.to(torch.half)
|
|
grad_input, grad_in_proj_weight, grad_in_proj_bias, grad_out_proj_weight, \
|
|
grad_out_proj_bias, grad_norm_weight, grad_norm_bias = backward_func(
|
|
ctx.config.layer_id, grad_output, output, input, input_mask, in_proj_weight,
|
|
in_proj_bias, out_proj_weight, out_proj_bias, norm_weight, norm_bias)
|
|
|
|
return (grad_input, None, grad_in_proj_weight, grad_in_proj_bias, grad_out_proj_weight, grad_out_proj_bias,
|
|
grad_norm_weight, grad_norm_bias, None)
|
|
|
|
|
|
class MultiHeadAttention(nn.Module):
|
|
"""Initialize the MultiHeadAttention.
|
|
|
|
Static variable:
|
|
|
|
layer_id: The layer-index counter starting from 0 and incrementing by 1 every time a layer object is instantiated,
|
|
e.g. if a model has 24 transformer layers, layer_id goes from 0 to 23.
|
|
|
|
Arguments:
|
|
hidden_size: Total dimension of hidden_size.
|
|
nhead: Number of parallel attention heads.
|
|
batch_size: Batch Size for one forward
|
|
max_seq_len: Max length of input sequence
|
|
dropout: Dropout probability
|
|
norm_first: perform LayerNorms before attention
|
|
"""
|
|
|
|
layer_id = 0
|
|
|
|
def __init__(self, hidden_size, nhead, batch_size, max_seq_len, dropout=0.0, norm_first=False, fp16=True, pg=None):
|
|
super(MultiHeadAttention, self).__init__()
|
|
|
|
self.config = Config(batch_size * max_seq_len, max_seq_len, hidden_size, nhead, dropout, dropout, norm_first,
|
|
fp16)
|
|
check_config(self.config)
|
|
self.pg = pg
|
|
self.pg_size = 1
|
|
if self.pg:
|
|
self.pg_size = pg.size()
|
|
self.config.layer_id = MultiHeadAttention.layer_id
|
|
MultiHeadAttention.layer_id = MultiHeadAttention.layer_id + 1
|
|
|
|
# Load cuda modules if needed
|
|
global colossal_multihead_attention
|
|
if colossal_multihead_attention is None:
|
|
from colossalai.kernel.op_builder import MultiHeadAttnBuilder
|
|
multihead_attention = MultiHeadAttnBuilder().load()
|
|
colossal_multihead_attention = multihead_attention
|
|
|
|
# create the layer in cuda kernels.
|
|
cuda_module = colossal_multihead_attention
|
|
create_layer_func = (cuda_module.create_multihead_attention_fp16
|
|
if self.config.fp16 else cuda_module.create_multihead_attention_fp32)
|
|
|
|
create_layer_func(
|
|
self.config.layer_id,
|
|
self.config.max_batch_tokens,
|
|
self.config.max_seq_len,
|
|
self.config.hidden_size,
|
|
self.config.nhead,
|
|
self.config.attn_prob_dropout_ratio,
|
|
self.config.hidden_dropout_ratio,
|
|
self.config.norm_first,
|
|
self.pg,
|
|
)
|
|
|
|
hs = self.config.hidden_size
|
|
|
|
self.precision = torch.float32
|
|
if self.config.fp16:
|
|
self.precision = torch.half
|
|
|
|
self.hs_per_rank = int(hs / self.pg_size)
|
|
|
|
self.in_proj_weight = nn.Parameter(torch.Tensor(3, self.hs_per_rank, hs))
|
|
self.in_proj_bias = nn.Parameter(torch.Tensor(3, self.hs_per_rank))
|
|
self.out_proj_weight = nn.Parameter(torch.Tensor(hs, self.hs_per_rank))
|
|
self.out_proj_bias = nn.Parameter(torch.Tensor(hs))
|
|
self.norm_weight = nn.Parameter(torch.Tensor(hs))
|
|
self.norm_bias = nn.Parameter(torch.Tensor(hs))
|
|
|
|
self.reset_parameters()
|
|
torch.cuda.empty_cache()
|
|
|
|
def calc_bound(self, w):
|
|
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(w)
|
|
bound = 1.0 / math.sqrt(fan_in)
|
|
return bound
|
|
|
|
def reset_parameters(self):
|
|
hs = self.config.hidden_size
|
|
|
|
nn.init.zeros_(self.out_proj_bias)
|
|
|
|
nn.init.ones_(self.norm_weight)
|
|
nn.init.zeros_(self.norm_bias)
|
|
|
|
if self.pg_size > 1:
|
|
rank_in_pg = torch.distributed.get_rank(self.pg)
|
|
attn_qkvw_global = torch.empty(hs * 3, hs)
|
|
attn_qkvb_global = torch.empty(hs * 3)
|
|
nn.init.xavier_uniform_(attn_qkvw_global, 1.0 / math.sqrt(2.0))
|
|
bound = self.calc_bound(attn_qkvw_global)
|
|
nn.init.uniform_(attn_qkvb_global, -bound, bound)
|
|
|
|
attn_qkvw_global = attn_qkvw_global.cuda()
|
|
attn_qkvb_global = attn_qkvb_global.cuda()
|
|
torch.distributed.broadcast(attn_qkvw_global, src=0, group=self.pg)
|
|
torch.distributed.broadcast(attn_qkvb_global, src=0, group=self.pg)
|
|
attn_qkvw_global = attn_qkvw_global.cpu()
|
|
attn_qkvb_global = attn_qkvb_global.cpu()
|
|
|
|
with torch.no_grad():
|
|
self.in_proj_weight.copy_(
|
|
attn_qkvw_global.view(3, hs, hs)[:,
|
|
int(hs * rank_in_pg / self.pg_size):int(hs * (rank_in_pg + 1) /
|
|
self.pg_size), :])
|
|
self.in_proj_bias.copy_(
|
|
attn_qkvb_global.view(3, hs)[:,
|
|
int(hs * rank_in_pg / self.pg_size):int(hs * (rank_in_pg + 1) /
|
|
self.pg_size)])
|
|
|
|
attn_ow_global = torch.empty(hs, hs)
|
|
nn.init.xavier_uniform_(attn_ow_global, 1.0)
|
|
attn_ow_global = attn_ow_global.cuda()
|
|
torch.distributed.broadcast(attn_ow_global, src=0, group=self.pg)
|
|
attn_ow_global = attn_ow_global.cpu()
|
|
with torch.no_grad():
|
|
self.out_proj_weight.copy_(attn_ow_global[:,
|
|
int(hs * rank_in_pg /
|
|
self.pg_size):int(hs * (rank_in_pg + 1) / self.pg_size)])
|
|
|
|
else:
|
|
attn_qkvw = self.in_proj_weight.view(-1, hs)
|
|
nn.init.xavier_uniform_(attn_qkvw, 1.0 / math.sqrt(2.0))
|
|
bound = self.calc_bound(attn_qkvw)
|
|
nn.init.uniform_(self.in_proj_bias, -bound, bound)
|
|
|
|
nn.init.xavier_uniform_(self.out_proj_weight, 1.0)
|
|
|
|
def state_dict(self, destination=None, prefix="", keep_vars=False):
|
|
destination = torch.nn.Module.state_dict(self, destination=destination, prefix=prefix, keep_vars=keep_vars)
|
|
return destination
|
|
|
|
def forward(self, hidden_states, encoder_padding_mask):
|
|
self.config.training = self.training
|
|
self.config.is_grad_enabled = torch.is_grad_enabled()
|
|
hidden_states = hidden_states.contiguous()
|
|
encoder_padding_mask = ((encoder_padding_mask * -1e8).type_as(hidden_states).contiguous())
|
|
|
|
bs, sl, dim = hidden_states.size()
|
|
if bs * sl > self.config.max_batch_tokens:
|
|
raise ValueError(f"Batch token numbers {bs * sl} exceeds the limit {self.config.max_batch_tokens}.")
|
|
if sl > self.config.max_seq_len:
|
|
raise ValueError(f"Sequence length {sl} exceeds the limit {self.config.max_seq_len}.")
|
|
if len(encoder_padding_mask.size()) == 1:
|
|
assert bs == 1 and sl == encoder_padding_mask.size(0)
|
|
else:
|
|
assert bs == encoder_padding_mask.size(0) and sl == encoder_padding_mask.size(1)
|
|
|
|
output = MultiHeadAttention1DFunc.apply(hidden_states, encoder_padding_mask, self.in_proj_weight,
|
|
self.in_proj_bias, self.out_proj_weight, self.out_proj_bias,
|
|
self.norm_weight, self.norm_bias, self.config)
|
|
|
|
return output.to(self.precision)
|