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from torch import nn
import torch.nn.functional as F
import torch.distributed as dist
def divide(numerator, denominator):
assert numerator % denominator == 0
return numerator // denominator
class LinearBase(nn.Module):
def __init__(
self,
input_size: int,
output_size: int,
tp_dim: int | None = None,
):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.tp_dim = tp_dim
self.tp_rank = dist.get_rank()
self.tp_size = dist.get_world_size()
def forward(self, x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError
class ReplicatedLinear(LinearBase):
def __init__(
self,
input_size: int,
output_size: int,
bias: bool = False,
):
super().__init__(input_size, output_size)
self.weight = nn.Parameter(torch.empty(self.output_size, self.input_size))
self.weight.weight_loader = self.weight_loader
if bias:
self.bias = nn.Parameter(torch.empty(self.output_size))
self.bias.weight_loader = self.weight_loader
else:
self.register_parameter("bias", None)
def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
param.data.copy_(loaded_weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return F.linear(x, self.weight, self.bias)
class ColumnParallelLinear(LinearBase):
def __init__(
self,
input_size: int,
output_size: int,
bias: bool = False,
):
super().__init__(input_size, output_size, 0)
self.input_size_per_partition = input_size
self.output_size_per_partition = divide(output_size, self.tp_size)
self.weight = nn.Parameter(torch.empty(self.output_size_per_partition, self.input_size))
self.weight.weight_loader = self.weight_loader
if bias:
self.bias = nn.Parameter(torch.empty(self.output_size_per_partition))
self.bias.weight_loader = self.weight_loader
else:
self.register_parameter("bias", None)
def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
param_data = param.data
shard_size = param_data.size(self.tp_dim)
start_idx = self.tp_rank * shard_size
loaded_weight = loaded_weight.narrow(self.tp_dim, start_idx, shard_size)
param_data.copy_(loaded_weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return F.linear(x, self.weight, self.bias)
class MergedColumnParallelLinear(ColumnParallelLinear):
def __init__(
self,
input_size: int,
output_sizes: list[int],
bias: bool = False,
):
self.output_sizes = output_sizes
super().__init__(input_size, sum(output_sizes), bias=bias)
def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor, loaded_shard_id: int):
param_data = param.data
shard_offset = sum(self.output_sizes[:loaded_shard_id]) // self.tp_size
shard_size = self.output_sizes[loaded_shard_id] // self.tp_size
param_data = param_data.narrow(self.tp_dim, shard_offset, shard_size)
loaded_weight = loaded_weight.chunk(self.tp_size, self.tp_dim)[self.tp_rank]
param_data.copy_(loaded_weight)
class QKVParallelLinear(ColumnParallelLinear):
def __init__(
self,
hidden_size: int,
head_size: int,
total_num_heads: int,
total_num_kv_heads: int | None = None,
bias: bool = False,
):
self.head_size = head_size
self.total_num_heads = total_num_heads
self.total_num_kv_heads = total_num_kv_heads or total_num_heads
tp_size = dist.get_world_size()
self.num_heads = divide(self.total_num_heads, tp_size)
self.num_kv_heads = divide(self.total_num_kv_heads, tp_size)
input_size = hidden_size
output_size = (self.total_num_heads + 2 * self.total_num_kv_heads) * self.head_size
super().__init__(input_size, output_size, bias)
def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor, loaded_shard_id: str):
param_data = param.data
assert loaded_shard_id in ["q", "k", "v"]
if loaded_shard_id == "q":
shard_size = self.num_heads * self.head_size
shard_offset = 0
elif loaded_shard_id == "k":
shard_size = self.num_kv_heads * self.head_size
shard_offset = self.num_heads * self.head_size
else:
shard_size = self.num_kv_heads * self.head_size
shard_offset = self.num_heads * self.head_size + self.num_kv_heads * self.head_size
param_data = param_data.narrow(self.tp_dim, shard_offset, shard_size)
loaded_weight = loaded_weight.chunk(self.tp_size, self.tp_dim)[self.tp_rank]
param_data.copy_(loaded_weight)
class RowParallelLinear(LinearBase):
def __init__(
self,
input_size: int,
output_size: int,
bias: bool = False,
):
super().__init__(input_size, output_size, 1)
self.input_size_per_partition = divide(input_size, self.tp_size)
self.output_size_per_partition = output_size
self.weight = nn.Parameter(torch.empty(self.output_size, self.input_size_per_partition))
self.weight.weight_loader = self.weight_loader
if bias:
self.bias = nn.Parameter(torch.empty(self.output_size))
self.bias.weight_loader = self.weight_loader
else:
self.register_parameter("bias", None)
def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
param_data = param.data
shard_size = param_data.size(self.tp_dim)
start_idx = self.tp_rank * shard_size
loaded_weight = loaded_weight.narrow(self.tp_dim, start_idx, shard_size)
param_data.copy_(loaded_weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
y = F.linear(x, self.weight, self.bias if self.tp_rank == 0 else None)
if self.tp_size > 1:
dist.all_reduce(y)
return y
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