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xc-llm-ascend/vllm_ascend/quantization/w8a8.py
LHXuuu bdc66972db [Quantization] Support compressed tensors w8a8 static and w8a8 dynamic weight (#4036) 
### What this PR does / why we need it?

While using the LLM Compressor quantization tool from the VLLM community
to generate quantized weights, the VLLM Ascend engine needs to be
adapted to support the compressed tensors quantization format.

1. Add AscendCompressedTensorsConfig to replace CompressedTensorsConfig
in vllm.
2. Support CompressedTensorsW8A8 static weight.
- weight: per-channel, int8, symmetric; activation: per-tensor, int8,
symmetric.
4. Support CompressedTensorsW8A8Dynamic weight.
- weight: per-channel, int8, symmetric; activation: per-token, int8,
symmetric, dynamic.
5. Modify the override_quantization_method in AscendQuantConfig.

Co-authored-by: taoqun110 taoqun@huawei.com
Co-authored-by: chenxi-hh chen464822955@163.com

- vLLM version: v0.11.2

---------

Signed-off-by: LHXuuu <scut_xlh@163.com>
Signed-off-by: chenxi-hh <chen464822955@163.com>
Signed-off-by: chenxi-hh <32731611+chenxi-hh@users.noreply.github.com>
Co-authored-by: chenxi-hh <chen464822955@163.com>
Co-authored-by: chenxi-hh <32731611+chenxi-hh@users.noreply.github.com>
2025-11-28 14:09:39 +08:00

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#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, Optional
import torch
import torch_npu
from vllm.attention.backends.abstract import AttentionType
from vllm.distributed.parallel_state import get_ep_group
from vllm.forward_context import get_forward_context
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.ops.fused_moe.experts_selector import select_experts
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ,
COMPRESSED_TENSORS_METHOD, AscendDeviceType,
get_ascend_device_type, is_enable_nz)
def quant_per_tensor(in_tensor: torch.Tensor,
input_scale: torch.Tensor,
input_offset: torch.Tensor,
function=False):
return torch_npu.npu_quantize(in_tensor, input_scale, input_offset,
torch.qint8, -1, function)
class AscendW8A8LinearMethod:
"""Linear method for Ascend W8A8.
Args:
w_sym: whether the linear weight is symmetrically quantized.
"""
def __init__(self) -> None:
# aclnn quant matmul requires to transpose matrix B, set to true by default.
self.transpose_weight = get_ascend_device_type(
) != AscendDeviceType._310P
@staticmethod
def get_weight(
input_size: int,
output_size: int,
params_dtype: torch.dtype = torch.bfloat16,
) -> Dict[str, Any]:
params_dict = {
"weight": torch.empty(output_size, input_size, dtype=torch.int8)
}
return params_dict
@staticmethod
def get_pertensor_param(params_dtype: torch.dtype) -> Dict[str, Any]:
params_dict = {}
params_dict["input_scale"] = torch.empty(1, dtype=params_dtype)
params_dict["input_offset"] = torch.empty(1, dtype=torch.int8)
return params_dict
@staticmethod
def get_perchannel_param(
output_size: int,
params_dtype: torch.dtype,
) -> Dict[str, Any]:
params_dict = {}
params_dict["quant_bias"] = torch.empty(output_size, dtype=torch.int32)
if params_dtype == torch.bfloat16:
params_dict["deq_scale"] = torch.empty(output_size,
dtype=torch.float32)
elif params_dtype == torch.float16:
params_dict["deq_scale"] = torch.empty(output_size,
dtype=torch.int64)
params_dict["weight_scale"] = torch.empty(output_size,
1,
dtype=params_dtype)
params_dict["weight_offset"] = torch.empty(output_size,
1,
dtype=params_dtype)
return params_dict
def get_pergroup_param(self,
input_size: int,
output_size: int,
params_dtype: torch.dtype,
layer_type: Optional[str] = None) -> Dict[str, Any]:
return {}
@staticmethod
def apply(
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
tp_rank: Optional[int] = 0,
) -> torch.Tensor:
if x.dtype != torch.int8:
layer_cls_name = layer.__class__.__name__
try:
weight_prefetch_method = get_forward_context(
).weight_prefetch_method
except AssertionError:
weight_prefetch_method = None
# prefetch qkvo_proj.weight preprocess
if weight_prefetch_method:
weight_prefetch_method.maybe_prefetch_attn_weight_preprocess(
layer_cls_name=layer_cls_name,
weight=layer.weight,
start_flag=x,
)
quant_comm_config = getattr(layer, "_quant_comm_config", {})
comm_fn = quant_comm_config.get("communication_fn")
enable_flashcomm2_quant_comm = comm_fn is not None and (
"o_proj" in layer.prefix or "out_proj" in layer.prefix)
if enable_flashcomm2_quant_comm:
quant_input_x = x.contiguous().view(
-1, layer.aclnn_input_scale_reciprocal.size(0))
quant_x = quant_per_tensor(
quant_input_x,
layer.aclnn_input_scale_reciprocal,
layer.aclnn_input_offset,
)
comm_input = quant_x.view(x.size(0), -1)
assert comm_fn is not None
x = comm_fn(comm_input)
else:
# quant
x = quant_per_tensor(
x,
layer.aclnn_input_scale_reciprocal,
layer.aclnn_input_offset,
)
# prefetch qkvo_proj.weight postprocess
if weight_prefetch_method:
weight_prefetch_method.maybe_prefetch_attn_weight_postprocess(
layer_cls_name=layer_cls_name,
stop_flag=x,
)
quant_bias = layer.quant_bias if tp_rank == 0 else None
if getattr(layer, "ascend_quant_method",
"") == COMPRESSED_TENSORS_METHOD:
quant_bias = bias
if get_ascend_device_type() == AscendDeviceType._310P:
# On 300I Duo platform, we need transpose again if
# using nz. This transpose can be skipped in torchair.
output = torch_npu.npu_quant_matmul(
x,
layer.weight.data.transpose(1, 0),
layer.deq_scale,
bias=quant_bias,
output_dtype=layer.params_dtype,
)
else:
output = torch_npu.npu_quant_matmul(
x,
layer.weight,
layer.deq_scale,
bias=quant_bias,
output_dtype=layer.params_dtype,
)
return output
def process_weights_after_loading(self, layer):
expanding_factor = layer.weight.data.shape[1]
layer.aclnn_input_scale = torch.nn.Parameter(
layer.input_scale.data.repeat(expanding_factor),
requires_grad=False)
layer.aclnn_input_scale_reciprocal = 1 / torch.nn.Parameter(
layer.input_scale.data.repeat(expanding_factor),
requires_grad=False)
layer.aclnn_input_offset = torch.nn.Parameter(
layer.input_offset.data.repeat(expanding_factor),
requires_grad=False).to(layer.aclnn_input_scale.dtype)
if self.transpose_weight:
layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
if is_enable_nz():
layer.weight.data = torch_npu.npu_format_cast(
layer.weight.data, ACL_FORMAT_FRACTAL_NZ)
layer.weight_scale.data = torch.flatten(layer.weight_scale.data)
layer.weight_offset.data = torch.flatten(layer.weight_offset.data)
if getattr(layer, "ascend_quant_method",
"") == COMPRESSED_TENSORS_METHOD:
deq_scale = layer.input_scale.data * layer.weight_scale.data
layer.deq_scale = torch.nn.Parameter(deq_scale,
requires_grad=False)
class AscendW8A8FusedMoEMethod:
"""FusedMoe method for Ascend W8A8.
"""
def __init__(self):
self.transpose_weight = True
@staticmethod
def get_weight(num_experts: int, intermediate_size_per_partition: int,
hidden_sizes: int,
params_dtype: torch.dtype) -> Dict[str, Any]:
param_dict = {}
param_dict["w13_weight"] = torch.empty(num_experts,
2 *
intermediate_size_per_partition,
hidden_sizes,
dtype=torch.int8,
requires_grad=False)
param_dict["w2_weight"] = torch.empty(num_experts,
hidden_sizes,
intermediate_size_per_partition,
dtype=torch.int8,
requires_grad=False)
return param_dict
@staticmethod
def get_dynamic_quant_param(num_experts: int,
intermediate_size_per_partition: int,
hidden_sizes: int,
params_dtype: torch.dtype) -> Dict[str, Any]:
param_dict = {}
param_dict["w13_weight_scale"] = torch.empty(
num_experts,
2 * intermediate_size_per_partition,
1,
dtype=torch.float32)
param_dict["w13_weight_offset"] = torch.empty(
num_experts,
2 * intermediate_size_per_partition,
1,
dtype=torch.float16)
param_dict["w2_weight_scale"] = torch.empty(num_experts,
hidden_sizes,
1,
dtype=torch.float32)
param_dict["w2_weight_offset"] = torch.empty(num_experts,
hidden_sizes,
1,
dtype=torch.float16)
param_dict["w2_deq_scale"] = torch.empty(num_experts,
hidden_sizes,
dtype=torch.float32)
param_dict["w13_deq_scale"] = torch.empty(
num_experts,
2 * intermediate_size_per_partition,
dtype=torch.float32)
param_dict["w2_input_scale"] = torch.empty(num_experts,
1,
dtype=torch.float32)
param_dict["w13_input_scale"] = torch.empty(num_experts,
1,
dtype=torch.float32)
param_dict["w2_input_offset"] = torch.empty(num_experts,
1,
dtype=torch.int8)
param_dict["w13_input_offset"] = torch.empty(num_experts,
1,
dtype=torch.int8)
param_dict["quant_bias"] = torch.empty(num_experts,
hidden_sizes,
dtype=torch.int32)
return param_dict
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
router_logits: torch.Tensor,
top_k: int,
renormalize: bool,
use_grouped_topk: bool = False,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None,
is_prefill: bool = True,
enable_force_load_balance: bool = False,
log2phy: torch.Tensor = None,
global_redundant_expert_num: int = 0,
shared_experts: Optional[Any] = None,
**kwargs,
) -> torch.Tensor:
assert router_logits.shape[
1] == global_num_experts - global_redundant_expert_num, "Number of global experts mismatch (excluding redundancy)"
topk_weights, topk_ids = select_experts(
hidden_states=x,
router_logits=router_logits,
top_k=top_k,
use_grouped_topk=use_grouped_topk,
renormalize=renormalize,
topk_group=topk_group,
num_expert_group=num_expert_group,
custom_routing_function=custom_routing_function,
scoring_func=scoring_func,
e_score_correction_bias=e_score_correction_bias,
global_num_experts=global_num_experts)
if get_ascend_device_type() == AscendDeviceType._310P:
return fused_experts_310p(hidden_states=x,
w1=layer.w13_weight,
w1_scale=layer.w13_weight_scale,
w1_input_scale=layer.w13_input_scale,
w2=layer.w2_weight,
w2_scale=layer.w2_weight_scale,
w2_input_scale=layer.w2_input_scale,
topk_weights=topk_weights,
topk_ids=topk_ids,
top_k=top_k,
global_num_experts=global_num_experts,
expert_map=expert_map)
return fused_experts(hidden_states=x,
w1=layer.w13_weight,
w1_scale=layer.w13_weight_scale,
w1_input_scale=layer.w13_input_scale,
w1_input_offset=layer.w13_input_offset,
w2=layer.w2_weight,
w2_scale=layer.w2_weight_scale,
w2_input_scale=layer.w2_input_scale,
w2_input_offset=layer.w2_input_offset,
topk_weights=topk_weights,
topk_ids=topk_ids,
top_k=top_k,
global_num_experts=global_num_experts,
expert_map=expert_map)
def process_weights_after_loading(self, layer):
if get_ascend_device_type() != AscendDeviceType._310P:
layer.w13_weight.data = layer.w13_weight.data.transpose(
1, 2).contiguous()
layer.w2_weight.data = layer.w2_weight.data.transpose(
1, 2).contiguous()
layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(
layer.w13_weight_scale.data.shape[0], -1)
layer.w13_weight_offset.data = layer.w13_weight_offset.data.view(
layer.w13_weight_offset.data.shape[0], -1)
layer.w2_weight_scale.data = layer.w2_weight_scale.data.view(
layer.w2_weight_scale.data.shape[0], -1)
layer.w2_weight_offset.data = layer.w2_weight_offset.data.view(
layer.w2_weight_offset.data.shape[0], -1)
expanding_factor_w13 = layer.w13_weight.data.shape[1]
expanding_factor_w2 = layer.w2_weight.data.shape[1]
if get_ascend_device_type() == AscendDeviceType._310P:
layer.w13_input_scale.data = torch.nn.Parameter(
layer.w13_input_scale.data.max())
layer.w2_input_scale.data = torch.nn.Parameter(
layer.w2_input_scale.data.max())
else:
layer.w13_input_scale.data = torch.nn.Parameter(
layer.w13_input_scale.data.repeat(1,
expanding_factor_w13)[0:1])
layer.w2_input_scale.data = torch.nn.Parameter(
layer.w2_input_scale.data.repeat(1, expanding_factor_w2)[0:1])
layer.w13_input_offset.data = torch.nn.Parameter(
layer.w13_input_scale.data.repeat(1, expanding_factor_w13)[0:1])
layer.w2_input_offset.data = torch.nn.Parameter(
layer.w2_input_scale.data.repeat(1, expanding_factor_w2)[0:1])
# converting ACL_FORMAT_FRACTAL_NZ.
# npu_quant_grouped_matmul_dequant in eager mode does not accept
# ACL_FORMAT_FRACTAL_NZ.
if get_ascend_device_type() != AscendDeviceType._310P and is_enable_nz(
):
layer.w13_weight.data = torch_npu.npu_format_cast(
layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ).contiguous()
layer.w2_weight.data = torch_npu.npu_format_cast(
layer.w2_weight.data, ACL_FORMAT_FRACTAL_NZ).contiguous()
class AscendC8KVCacheMethod:
def __init__(self) -> None:
self.antiquant_scale_comb = None
@staticmethod
def create_weights(layer) -> None:
param_dict = {} # num_kv_heads * head_size
param_dict["key_antiquant_scale"] = torch.empty(layer.num_kv_heads *
layer.head_size,
dtype=torch.float16,
requires_grad=False)
param_dict["value_antiquant_scale"] = torch.empty(layer.num_kv_heads *
layer.head_size,
dtype=torch.float16,
requires_grad=False)
for weight_name, weight_param in param_dict.items():
param = torch.nn.Parameter(weight_param, requires_grad=False)
layer.register_parameter(weight_name, param)
def process_weights_after_loading(self, layer):
self.antiquant_scale_comb = torch.cat(
(layer.key_antiquant_scale.data.unsqueeze(0),
layer.value_antiquant_scale.data.unsqueeze(0)),
dim=0).to(torch.float16).contiguous()
def apply(self, layer, query, key, value, kv_cache, attn_metadata,
attn_type, scale, output) -> torch.Tensor:
num_tokens = query.shape[0]
if attn_metadata is None:
return output.view(num_tokens, layer.num_heads * layer.head_size)
assert layer._k_scale_float == 1.0 and layer._v_scale_float == 1.0
if attn_type != AttentionType.DECODER:
raise NotImplementedError("Encoder self-attention and "
"encoder/decoder cross-attention "
"are not implemented for "
"PallasAttentionBackendImpl")
# C8
quant_key = quant_per_tensor(
key.view(-1, layer.num_kv_heads * layer.head_size),
layer.key_antiquant_scale.data.view(-1), None, True)
quant_value = quant_per_tensor(
value.view(-1, layer.num_kv_heads * layer.head_size),
layer.value_antiquant_scale.data.view(-1), None, True)
# View q k v to BSH.
query = query.view(-1, layer.num_heads, layer.head_size)
key = key.view(-1, layer.num_kv_heads, layer.head_size)
value = value.view(-1, layer.num_kv_heads, layer.head_size)
# TODO: Remove this contiguous in the future.
value = value.contiguous()
if kv_cache[0].numel() > 0:
# if key_cache is None:
key_cache, value_cache = kv_cache[0], kv_cache[1]
slots = attn_metadata.slot_mapping
block_size = key_cache.shape[1]
slots_indices = slots.reshape(-1, 1)
block_indices = slots_indices // block_size
slots_indices = slots_indices % block_size
indices = torch.cat((block_indices, slots_indices), dim=1)
# C8
torch_npu.npu_scatter_nd_update_(key_cache, indices, quant_key)
torch_npu.npu_scatter_nd_update_(value_cache, indices, quant_value)
# V0-Style scheduler situation.
if attn_metadata.attn_state == AscendAttentionState.PrefillNoCache:
assert attn_metadata is not None
assert attn_metadata.attn_mask is not None
mask = attn_metadata.attn_mask
torch_npu._npu_flash_attention(query=query,
key=key,
value=value,
mask=mask,
seq_len=attn_metadata.seq_lens,
scale_value=scale,
num_heads=layer.num_heads,
num_kv_heads=layer.num_kv_heads,
out=output.reshape(query.shape))
elif attn_metadata.attn_state == AscendAttentionState.PrefillCacheHit:
raise NotImplementedError("kv cache int8 are not "
"implemented for "
"PrefillCacheHit")
elif attn_metadata.attn_state == AscendAttentionState.DecodeOnly: # changed attn_metadata.attn_state == AscendAttentionState.DecodeOnly
if hasattr(attn_metadata, "decode"):
# torch_air
decode_meta = attn_metadata.decode
seq_lens = decode_meta.seq_lens_list
else:
seq_lens = attn_metadata.seq_lens
block_size = key_cache.shape[1]
query = query.view(num_tokens, 1, layer.num_heads *
layer.head_size).contiguous() # changed
# [num_blocks, block_size, N, D] --> [num_blocks, N, block_size, D]
key = key_cache
value = value_cache
output = torch_npu.npu_incre_flash_attention(
query,
key,
value,
num_key_value_heads=layer.num_kv_heads,
num_heads=layer.num_heads,
actual_seq_lengths=seq_lens,
scale_value=scale,
input_layout='BSH',
block_size=block_size,
block_table=attn_metadata.block_tables,
antiquant_scale=self.antiquant_scale_comb,
)
# Normal V1 situation.
else:
raise NotImplementedError("kv cache int8 are not "
"implemented for "
"other case")
return output
def fused_experts_310p(
hidden_states: torch.Tensor,
w1: torch.Tensor,
w1_scale: torch.Tensor,
w1_input_scale: torch.Tensor,
w2: torch.Tensor,
w2_scale: torch.Tensor,
w2_input_scale: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
top_k: int,
global_num_experts: int,
expert_map: torch.Tensor = None,
) -> torch.Tensor:
ep_size = get_ep_group().world_size
local_num_experts = global_num_experts // ep_size
local_num_group = top_k // ep_size
bsz, _ = hidden_states.shape
flatten_topk_ids = topk_ids.view(-1)
sorted_topk_ids = torch.argsort(flatten_topk_ids.float())
sorted_topk_ids = sorted_topk_ids.to(torch.int32)
sorted_hidden_states = hidden_states.index_select(
0, sorted_topk_ids // local_num_group)
experts_id = torch.arange(0,
local_num_experts,
dtype=topk_ids.dtype,
device=topk_ids.device)
num_tokens_per_expert = (flatten_topk_ids.unsqueeze(-1) == experts_id).to(
torch.float32).sum(0)
topk_scales = topk_weights.view(-1).index_select(
0, sorted_topk_ids).unsqueeze(-1)
group_list = num_tokens_per_expert.cumsum(dim=0).to(torch.int64)
gate_up_out = torch_npu.npu_quant_grouped_matmul_dequant(
x=sorted_hidden_states,
quantized_weight=w1,
weight_scale=w1_scale,
group_list=group_list,
x_scale=w1_input_scale,
quant_mode="pertensor")
gate_up_out = torch_npu.npu_swiglu(gate_up_out.to(torch.float32)).to(
torch.float16)
gate_up_out *= topk_scales
down_out = torch_npu.npu_quant_grouped_matmul_dequant(
x=gate_up_out,
quantized_weight=w2,
weight_scale=w2_scale,
group_list=group_list,
x_scale=w2_input_scale,
quant_mode="pertensor")
unsorted_topk_ids = torch.argsort(sorted_topk_ids.float()).to(torch.int32)
unsorted_hidden_states = down_out.index_select(0, unsorted_topk_ids)
final_hidden_states = unsorted_hidden_states.reshape(
bsz, top_k // ep_size, -1).sum(1)
return final_hidden_states
def fused_experts(
hidden_states: torch.Tensor,
w1: torch.Tensor,
w1_scale: torch.Tensor,
w1_input_scale: torch.Tensor,
w1_input_offset: torch.Tensor,
w2: torch.Tensor,
w2_scale: torch.Tensor,
w2_input_scale: torch.Tensor,
w2_input_offset: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
top_k: int,
global_num_experts: int,
expert_map: torch.Tensor = None,
) -> torch.Tensor:
"""
Fused experts with top-k routing.
Args:
hidden_states: Hidden states of shape (num_tokens, hidden_size).
w1: Expert weights1 of shape (num_experts, intermediate_size * 2, hidden_size).
w2: Expert weights2 of shape (num_experts, hidden_size, intermediate_size).
topk_weights: Routing weights of shape (num_tokens, top_k).
topk_ids: Selected expert IDs of shape (num_tokens, top_k).
top_k: Number of experts to select.
expert_map: Expert mapping of shape (num_experts,).
Returns:
hidden_states: Hidden states after routing.
"""
"""
# Check constraints.
assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
assert w1.is_contiguous(), "Expert weights1 must be contiguous"
assert w2.is_contiguous(), "Expert weights2 must be contiguous"
"""
original_dtype = hidden_states.dtype
ep_size = get_ep_group().world_size
local_num_experts = global_num_experts // ep_size
w1_input_scale, _ = w1_input_scale.max(0)
quant_sorted_hidden_states = quant_per_tensor(
hidden_states,
w1_input_scale,
None,
True,
)
if expert_map is not None:
expanded_x, expanded_row_idx, expert_token_count, expanded_scale = torch_npu.npu_moe_init_routing_v2(
quant_sorted_hidden_states,
topk_ids,
scale=None,
active_num=topk_ids.numel(),
expert_capacity=-1,
expert_num=local_num_experts,
drop_pad_mode=0,
expert_tokens_num_type=1,
expert_tokens_num_flag=True,
quant_mode=-1,
active_expert_range=[0, local_num_experts],
row_idx_type=0,
)
else:
raise NotImplementedError(
"The quantified version of MOE class models "
"currently does not support tensor parallelism")
if expanded_x.dtype != w1.dtype:
w1_input_scale, _ = w1_input_scale.max(0)
quant_sorted_hidden_states = quant_per_tensor(
expanded_x,
w1_input_scale,
None,
True,
)
else:
quant_sorted_hidden_states = expanded_x
gate_up_out = torch_npu.npu_grouped_matmul(
x=[quant_sorted_hidden_states],
weight=[w1],
scale=[w1_scale * w1_input_scale[0]],
split_item=2,
group_list_type=1,
group_type=0,
group_list=expert_token_count,
output_dtype=original_dtype,
)[0]
gate_up_out = torch_npu.npu_swiglu(gate_up_out)
if gate_up_out.dtype != w2.dtype:
w2_input_scale, _ = w2_input_scale.max(0)
quant_gate_up_out = quant_per_tensor(
gate_up_out,
w2_input_scale,
None,
True,
)
else:
quant_gate_up_out = gate_up_out
down_out = torch_npu.npu_grouped_matmul(
x=[quant_gate_up_out],
weight=[w2],
scale=[w2_scale * w2_input_scale[0]],
split_item=2,
group_list_type=1,
group_type=0,
group_list=expert_token_count,
output_dtype=original_dtype,
)[0]
if expert_map is not None:
final_hidden_states = torch_npu.npu_moe_finalize_routing(
down_out,
skip1=None,
skip2=None,
bias=None,
scales=topk_weights.to(down_out.dtype),
expanded_src_to_dst_row=expanded_row_idx,
export_for_source_row=topk_ids,
drop_pad_mode=2,
)
else:
raise NotImplementedError(
"The quantified version of MOE class models "
"currently does not support tensor parallelism")
return final_hidden_states
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