153 lines
4.2 KiB
Python
153 lines
4.2 KiB
Python
from typing import Optional
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from text_generation_server.utils.import_utils import SYSTEM
|
|
from text_generation_server.utils.weights import UnquantizedWeight, Weights
|
|
|
|
if SYSTEM == "ipex":
|
|
from intel_extension_for_pytorch.llm.modules import GatedMLPMOE
|
|
else:
|
|
from moe_kernels.fused_moe import fused_moe
|
|
|
|
|
|
class UnquantizedSparseMoELayer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
n_expert_group: Optional[int],
|
|
n_experts: int,
|
|
prefix: str,
|
|
renormalize: bool,
|
|
topk: int,
|
|
topk_group: Optional[int],
|
|
weights: Weights,
|
|
gate_proj_name: str = "gate_proj",
|
|
up_proj_name: str = "up_proj",
|
|
down_proj_name: str = "down_proj",
|
|
):
|
|
super().__init__()
|
|
|
|
assert (n_expert_group is None) == (
|
|
topk_group is None
|
|
), "n_expert_group and topk_group must both be None or have some value"
|
|
|
|
self.n_expert_group = n_expert_group
|
|
self.topk = topk
|
|
self.topk_group = topk_group
|
|
self.renormalize = renormalize
|
|
|
|
self.gate_up_proj = _load_expert_multi_weights_col(
|
|
prefix=prefix,
|
|
n_experts=n_experts,
|
|
gate_proj_name=gate_proj_name,
|
|
up_proj_name=up_proj_name,
|
|
weights=weights,
|
|
)
|
|
|
|
self.down_proj = _load_expert_weights_row(
|
|
prefix=prefix,
|
|
n_experts=n_experts,
|
|
name=down_proj_name,
|
|
weights=weights,
|
|
)
|
|
if SYSTEM == "ipex":
|
|
self.ipex_fused_moe = GatedMLPMOE(
|
|
W13=self.gate_up_proj, W2=self.down_proj, use_prepack=True
|
|
)
|
|
|
|
def forward(self, x: torch.Tensor, *, gating_output: torch.Tensor) -> torch.Tensor:
|
|
if SYSTEM == "rocm":
|
|
return fused_moe(
|
|
x,
|
|
self.gate_up_proj,
|
|
self.down_proj,
|
|
gating_output,
|
|
self.topk,
|
|
renormalize=self.renormalize,
|
|
inplace=True,
|
|
)
|
|
elif SYSTEM == "ipex":
|
|
return self.ipex_fused_moe(
|
|
hidden_states=x,
|
|
router_logits=gating_output,
|
|
top_k=self.topk,
|
|
renormalize=self.renormalize,
|
|
use_grouped_topk=self.n_expert_group is not None,
|
|
num_expert_group=self.n_expert_group,
|
|
topk_group=self.topk_group,
|
|
)
|
|
|
|
return fused_moe(
|
|
x,
|
|
w1=self.gate_up_proj,
|
|
w2=self.down_proj,
|
|
gating_output=gating_output,
|
|
topk=self.topk,
|
|
renormalize=self.renormalize,
|
|
inplace=True,
|
|
use_grouped_topk=self.n_expert_group is not None,
|
|
num_expert_group=self.n_expert_group,
|
|
topk_group=self.topk_group,
|
|
)
|
|
|
|
|
|
def _load_expert_multi_weights_col(
|
|
*,
|
|
prefix: str,
|
|
n_experts: int,
|
|
gate_proj_name: str,
|
|
up_proj_name: str,
|
|
weights: Weights,
|
|
) -> torch.Tensor:
|
|
all_weight = None
|
|
for i in range(n_experts):
|
|
weight = weights.get_multi_weights_col(
|
|
[f"{prefix}.{i}.{gate_proj_name}", f"{prefix}.{i}.{up_proj_name}"], 0
|
|
)
|
|
|
|
assert isinstance(weight, UnquantizedWeight)
|
|
|
|
if all_weight is None:
|
|
all_weight = torch.empty(
|
|
(n_experts,) + weight.weight.shape,
|
|
dtype=weight.weight.dtype,
|
|
device=weight.weight.device,
|
|
)
|
|
|
|
all_weight[i] = weight.weight
|
|
|
|
assert all_weight is not None
|
|
|
|
return all_weight
|
|
|
|
|
|
def _load_expert_weights_row(
|
|
*,
|
|
prefix: str,
|
|
n_experts: int,
|
|
name: str,
|
|
weights: Weights,
|
|
) -> torch.Tensor:
|
|
all_weight = None
|
|
for i in range(n_experts):
|
|
weight = weights.get_weights_row(
|
|
f"{prefix}.{i}.{name}",
|
|
)
|
|
|
|
assert isinstance(weight, UnquantizedWeight)
|
|
|
|
if all_weight is None:
|
|
all_weight = torch.empty(
|
|
(n_experts,) + weight.weight.shape,
|
|
dtype=weight.weight.dtype,
|
|
device=weight.weight.device,
|
|
)
|
|
|
|
all_weight[i] = weight.weight
|
|
|
|
assert all_weight is not None
|
|
|
|
return all_weight
|