File size: 8,158 Bytes
ee3e701 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from typing import Optional
import torch
import torch.nn.functional as F
from flash_attn.ops.fused_dense import ColumnParallelLinear, RowParallelLinear
from flash_attn.utils.distributed import all_reduce, reduce_scatter
from torch import nn
from internlm.core.context import ParallelMode
from internlm.core.context import global_context as gpc
from internlm.model.utils import fused_dense_func_torch
class ScaleColumnParallelLinear(nn.Linear):
"""
ScaleColumnParallelLinear.
Args:
in_features (int): size of each input sample
out_features (int): size of each output sample
process_group (Optional[torch.distributed.ProcessGroup]): The group of the current device for `parallel_mode`.
bias (bool): Whether the bias is needed for linears. True by default. But it is typically set to False
in the config.
sequence_parallel (bool): If sequence_parallel is True, we're doing Tensor Parallel with sequence parallelism:
we do an all_gather of x before doing the matmul.
If not, then the input is already gathered.
device (Optional[Union[str, torch.device]]): The device will be used.
dtype (Optional[torch.dtype]): The type of data.
weight_scale (int): For training stability. 1 by default.
"""
def __init__(
self,
in_features: int,
out_features: int,
process_group: Optional[torch.distributed.ProcessGroup],
bias: bool = True,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
weight_scale: int = 1,
) -> None:
world_size = torch.distributed.get_world_size(process_group)
if out_features % world_size != 0:
raise ValueError(f"out_features ({out_features}) must be divisible by " f"world_size ({world_size})")
super().__init__(in_features, out_features // world_size, bias=bias, device=device, dtype=dtype)
self.process_group = process_group
self.weight_scale = weight_scale
def forward(self, input): # pylint: disable=W0622
# If self.sequence_parallel is True, we're doing Tensor Parallel with sequence parallelism:
# we do an all_gather of x before doing the matmul.
# If not, then the input is already gathered.
if self.weight_scale != 1:
weight = self.weight * self.weight_scale + (1 - self.weight_scale) * self.weight.detach()
else:
weight = self.weight
return fused_dense_func_torch(
input,
weight,
self.bias,
process_group=self.process_group,
sequence_parallel=gpc.config.parallel.sequence_parallel,
)
class RewardModelLinear(ScaleColumnParallelLinear):
"""
RewardModelLinear.
Args:
in_features (int): size of each input sample
out_features (int): size of each output sample
process_group (Optional[torch.distributed.ProcessGroup]): The group of the current device for `parallel_mode`.
bias (bool): Whether the bias is needed for linears. True by default. But it is typically set to False
in the config.
sequence_parallel (bool): If sequence_parallel is True, we're doing Tensor Parallel with sequence parallelism:
we do an all_gather of x before doing the matmul.
If not, then the input is already gathered.
device (Optional[Union[str, torch.device]]): The device will be used.
dtype (Optional[torch.dtype]): The type of data.
weight_scale (int): For training stability. 1 by default.
"""
def __init__(
self,
in_features: int,
out_features: int,
process_group: Optional[torch.distributed.ProcessGroup],
bias: bool = True,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
weight_scale: int = 1,
) -> None:
super().__init__(in_features, out_features, process_group, bias, device, dtype, weight_scale)
torch.distributed.broadcast(self.weight, gpc.get_ranks_in_group(ParallelMode.TENSOR)[0], process_group)
if bias:
torch.distributed.broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.TENSOR)[0], process_group)
def forward(self, input): # pylint: disable=W0622
# If self.sequence_parallel is True, we're doing Tensor Parallel with sequence parallelism:
# we do an all_gather of x before doing the matmul.
# If not, then the input is already gathered.
if self.weight_scale != 1:
weight = self.weight * self.weight_scale + (1 - self.weight_scale) * self.weight.detach()
else:
weight = self.weight
return fused_dense_func_torch(
input,
weight,
self.bias,
process_group=self.process_group,
sequence_parallel=gpc.config.parallel.sequence_parallel,
)
class ColumnParallelLinearTorch(ColumnParallelLinear):
def forward(self, x):
# If self.sequence_parallel is True, we're doing Tensor Parallel with sequence parallelism:
# we do an all_gather of x before doing the matmul.
# If not, then the input is already gathered.
return fused_dense_func_torch(
x, self.weight, self.bias, process_group=self.process_group, sequence_parallel=self.sequence_parallel
)
class RowParallelLinearTorch(RowParallelLinear):
def forward(self, x):
"""
We're doing Tensor Parallel with sequence parallelism: we do the matmul and then
a reduce_scatter of the result.
"""
out = fused_dense_func_torch(x, self.weight, self.bias)
reduce_fn = reduce_scatter if self.sequence_parallel else all_reduce
return reduce_fn(out, self.process_group)
class FeedForward(nn.Module):
"""
FeedForward.
Args:
in_features (int): size of each input sample
hidden_features (int): size of hidden state of FFN
out_features (int): size of each output sample
process_group (Optional[torch.distributed.ProcessGroup]): The group of the current device for `parallel_mode`.
bias (bool): Whether the bias is needed for linears. True by default. But it is typically set to False
in the config.
device (Optional[Union[str, torch.device]]): The device will be used.
dtype (Optional[torch.dtype]): The type of data.
multiple_of (int): For efficient training. Reset the size of hidden feature. 256 by default.
"""
def __init__(
self,
in_features: int,
hidden_features: int,
out_features: int = None,
process_group: Optional[torch.distributed.ProcessGroup] = None,
bias: bool = True,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
multiple_of: int = 256,
):
super().__init__()
hidden_features = multiple_of * ((hidden_features + multiple_of - 1) // multiple_of)
self.w1 = ColumnParallelLinearTorch(
in_features,
hidden_features,
process_group,
bias,
sequence_parallel=gpc.config.parallel.sequence_parallel,
device=device,
dtype=dtype,
)
self.w2 = ColumnParallelLinearTorch(
in_features,
hidden_features,
process_group,
bias,
sequence_parallel=gpc.config.parallel.sequence_parallel,
device=device,
dtype=dtype,
)
self.w3 = RowParallelLinearTorch(
hidden_features,
out_features,
process_group,
bias=bias,
sequence_parallel=gpc.config.parallel.sequence_parallel,
device=device,
dtype=dtype,
)
def forward(self, x):
out = self.w3(F.silu(self.w1(x)) * self.w2(x))
return out
|