Fix formatting errors

vllm/hpu/ops.py

@@ -254,15 +254,19 @@ def forward(self, state):
 class StaticFusedMOE(torch.nn.Module):
  def __init__(self, num_total_experts):
  super().__init__()
- self.w13_list = torch.nn.ModuleList([MoeMatmul() for _ in range(num_total_experts)])
- self.w2_list = torch.nn.ModuleList([MoeMatmul() for _ in range(num_total_experts)])
+ self.w13_list = torch.nn.ModuleList(
+ [MoeMatmul() for _ in range(num_total_experts)])
+ self.w2_list = torch.nn.ModuleList(
+ [MoeMatmul() for _ in range(num_total_experts)])
  self.num_total_experts = num_total_experts
 
 
  def forward(self, hidden_states, w1, w2, score, topk):
  B, D = hidden_states.shape
  routing_weights = F.softmax(score, dim=1, dtype=torch.float32)
- routing_weights, selected_experts = torch.topk(routing_weights, topk, dim=-1)
+ routing_weights, selected_experts = torch.topk(routing_weights,
+ topk,
+ dim=-1)
  routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
  routing_weights = routing_weights.to(hidden_states.dtype)
  final_hidden_states = torch.zeros((1, B, D),
@@ -278,7 +282,9 @@ def forward(self, hidden_states, w1, w2, score, topk):
 
  for expert_idx in range(self.num_total_experts):
  padded_weight = padded_weights[expert_idx]
- w_output = self.w13_list[expert_idx].calc(hidden_states, expert_idx, w1)
+ w_output = self.w13_list[expert_idx].calc(hidden_states,
+ expert_idx,
+ w1)
  w_output = silu_and_mul(w_output)
  w_output = self.w2_list[expert_idx].calc(w_output, expert_idx, w2)
  final_hidden_states += w_output * padded_weight

vllm/model_executor/layers/fused_moe/layer.py

@@ -13,7 +13,6 @@
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.utils import is_hpu
 
-
 logger = init_logger(__name__)
 
 
@@ -76,7 +75,8 @@ def apply(
  ) -> torch.Tensor:
  return self.forward(x, layer.w13_weight, layer.w2_weight,
  router_logits, top_k, renormalize,
- use_grouped_topk, num_expert_group, topk_group, layer)
+ use_grouped_topk, num_expert_group, topk_group,
+ layer)
 
  def forward_cuda(
  self,
@@ -106,11 +106,13 @@ def forward_cuda(
  def forward_hpu(self, x: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor,
  router_logits: torch.Tensor, top_k: int, renormalize: bool,
  use_grouped_topk: bool, num_expert_group: Optional[int],
- topk_group: Optional[int], layer: Optional[torch.nn.Module],):
+ topk_group: Optional[int], layer: Optional[torch.nn.Module]
+ ):
  assert not use_grouped_topk, 'use_grouped_topk must be False on HPU'
  assert num_expert_group is None, ('num_expert_group is '
  'not supported on HPU')
  assert topk_group is None, 'topk_group is not supported on HPU'
+ assert layer is not None, 'layer has to be provided on HP'
  return layer.hpu_static_fused_moe(x, w1, w2, router_logits, top_k)
 
  def forward_cpu(self, *args, **kwargs):

vllm/model_executor/layers/quantization/inc.py

@@ -1,14 +1,13 @@
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional
 
 import torch
-from torch.nn import Module
-from torch.nn.parameter import Parameter
 import torch.nn.functional as F
+from torch.nn.parameter import Parameter
 
-from vllm import _custom_ops as ops
 from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.layer import (
+ FusedMoE, UnquantizedFusedMoEMethod)
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
-from vllm.model_executor.layers.fused_moe.layer import FusedMoE, UnquantizedFusedMoEMethod
 from vllm.model_executor.layers.quantization.base_config import (
  QuantizationConfig)
 from vllm.model_executor.utils import set_weight_attrs
@@ -88,7 +87,8 @@ class INCLinearMethod(LinearMethodBase):
  quant_config: The quantization config.
  """
 
- def __init__(self, quant_config: INCConfig, separate_bias_add: bool = False):
+ def __init__(self, quant_config: INCConfig,
+ separate_bias_add: bool = False):
  self.separate_bias_add = separate_bias_add
  self.quant_config = quant_config