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# Copy and modify https://github.com/huggingface/text-generation-inference/blob/main/server/text_generation_server/utils/layers.py
from typing import List
import torch
import torch.distributed
from accelerate import init_empty_weights
from torch import nn
from torch.nn import functional as F
# Monkey patching
@classmethod
def load_layer_norm(cls, prefix, weights, eps):
weight = weights.get_tensor(f"{prefix}.weight")
bias = weights.get_tensor(f"{prefix}.bias")
with init_empty_weights():
ln = cls(weight.shape, eps=eps)
ln.weight = nn.Parameter(weight)
ln.bias = nn.Parameter(bias)
return ln
@classmethod
def load_layer_norm_no_bias(cls, prefix, weights, eps):
weight = weights.get_tensor(f"{prefix}.weight")
with init_empty_weights():
ln = cls(weight.shape, eps=eps)
ln.weight = nn.Parameter(weight)
ln.bias = None
return ln
torch.nn.LayerNorm.load = load_layer_norm
torch.nn.LayerNorm.load_no_bias = load_layer_norm_no_bias
class FastLinear(nn.Module):
def __init__(
self,
weight,
bias,
) -> None:
super().__init__()
self.weight = nn.Parameter(weight)
if bias is not None:
self.bias = nn.Parameter(bias)
else:
self.bias = None
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_tensor(f"{prefix}.weight")
if bias:
bias = weights.get_tensor(f"{prefix}.bias")
else:
bias = None
return cls(weight, bias)
def forward(self, input: torch.Tensor) -> torch.Tensor:
return F.linear(input, self.weight, self.bias)
def get_linear(weight, bias):
linear = FastLinear(weight, bias)
return linear
class SuperLayer(nn.Module):
def __init__(self, linear):
super().__init__()
self.linear = linear
def forward(self, x):
return self.linear.forward(x)
class TensorParallelHead(SuperLayer):
def __init__(self, linear, process_group, should_gather: bool):
super().__init__(linear)
self.process_group = process_group
self.should_gather = should_gather
@staticmethod
def load(config, prefix: str, weights):
if weights.process_group.size() > 1:
try:
weight = weights.get_sharded(f"{prefix}.weight", dim=0)
should_gather = True
except AssertionError:
# If the vocab size is not divisible by number of shards
# just load the entire thing.
weight = weights.get_tensor(f"{prefix}.weight")
should_gather = False
else:
weight = weights.get_tensor(f"{prefix}.weight")
should_gather = False
return TensorParallelHead(
get_linear(weight, bias=None),
process_group=weights.process_group,
should_gather=should_gather,
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
if not self.should_gather:
return super().forward(input)
world_size = self.process_group.size()
if len(input.shape) == 2 and isinstance(self.linear, FastLinear):
out_dim = self.linear.weight.shape[0]
if input.shape[0] == 1:
world_out = input.new_empty(1, out_dim * world_size)
local_out = input.new_empty(1, out_dim)
gather_input = local_out
else:
world_out = input.new_empty(out_dim * world_size, input.shape[0])
gather_input = input.new_empty(out_dim, input.shape[0])
local_out = gather_input.T
torch.mm(input, self.linear.weight.T, out=local_out)
torch.distributed.all_gather_into_tensor(world_out, gather_input, group=self.process_group)
if input.shape[0] == 1:
return world_out
return world_out.T
output = super().forward(input)
world_output = [torch.empty_like(output) for _ in range(self.process_group.size())]
torch.distributed.all_gather(world_output, output, group=self.process_group)
world_output = torch.cat(world_output, dim=-1)
return world_output
class TensorParallelColumnLinear(SuperLayer):
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
return cls.load_multi(config, [prefix], weights, bias, dim=0)
@classmethod
def load_multi(cls, config, prefixes: List[str], weights, bias: bool, dim: int):
weight = weights.get_multi_weights_col(prefixes, dim=dim, quantize=config.quantize)
if bias:
b = [weights.get_sharded(f"{p}.bias", dim=0) for p in prefixes]
bias = torch.cat(b, dim=dim)
else:
bias = None
linear = get_linear(weight, bias)
return cls(linear)
class TensorParallelRowLinear(SuperLayer):
def __init__(self, linear, process_group):
super().__init__(linear)
self.process_group = process_group
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process
bias = weights.get_tensor(f"{prefix}.bias")
else:
bias = None
return cls(
get_linear(weight, bias),
process_group=weights.process_group,
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
out = super().forward(input)
if self.process_group.size() > 1:
torch.distributed.all_reduce(out, group=self.process_group)
return out
class TensorParallelEmbedding(nn.Module):
def __init__(self, prefix: str, weights, reduce=True):
super().__init__()
weight = weights.get_partial_sharded(f"{prefix}.weight", dim=0)
num_embeddings = weights.get_shape(f"{prefix}.weight")[0]
process_group = weights.process_group
world_size = process_group.size()
rank = process_group.rank()
block_size = num_embeddings // world_size
self.min_id = rank * block_size
self.max_id = min(num_embeddings, (rank + 1) * block_size)
self.null_idx = block_size
self.process_group = weights.process_group
self.reduce = reduce
"""Additional 0 entry used for masking"""
self.weight = nn.Parameter(F.pad(weight, (0, 0, 0, 1)))
def forward(self, input: torch.Tensor) -> torch.Tensor:
# default all out of bounds values to `self.null_idx` that will then be mapped to 0
# translate for [0, self.max_id - self.min_id[
input = torch.where(
(self.min_id > input) | (input >= self.max_id),
self.null_idx,
input - self.min_id,
)
out = torch.nn.functional.embedding(input, self.weight)
if self.reduce and self.process_group.size() > 1:
torch.distributed.all_reduce(out, group=self.process_group)
return out
try:
import dropout_layer_norm
class FastLayerNorm(nn.LayerNorm):
def forward(self, hidden_states, residual=None):
if hidden_states.shape[-1] > 8192:
if residual is not None:
hidden_states += residual
residual = hidden_states
return super(FastLayerNorm, self).forward(hidden_states), residual
else:
(
normed_hidden_states,
residual,
*rest,
) = dropout_layer_norm.dropout_add_ln_fwd(
hidden_states,
residual,
self.weight,
self.bias,
None,
None,
None,
None,
0.0,
self.eps,
1.0,
0,
None,
False,
False,
)
if residual is None:
residual = hidden_states
return normed_hidden_states, residual
except ImportError:
pass
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