1e67089bc9
<!-- Thanks for sending a pull request! BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing/overview.html --> ### What this PR does / why we need it? 1. This PR introduces native `all_to_all` communication operator to fix `allgather` bugs when dp_size > 1. Besides, it adds a naive implementation of force-load-balance when doing profile runs. 2. The operator `npu_dequant_swiglu_quant` only supports input hidden_states with dtype `torch.int32`. This tensor occupies space of `global_bs * seq_len * topk * hidden_size`, which might be very large as `ep_size` grows. Therefore we need to disable this operator and use original `swiglu` && `quantize`. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? By performing offline inference:  --------- Signed-off-by: angazenn <zengyanjia@huawei.com> Co-authored-by: angazenn <zengyanjia@huawei.com>
674 lines
26 KiB
Python
674 lines
26 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from typing import Any, Callable, Dict, List, Optional
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import torch
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import torch.distributed as dist
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import torch_npu
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from vllm.distributed import GroupCoordinator
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import vllm_ascend.envs as envs_ascend
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from vllm_ascend.distributed.parallel_state import get_ep_group
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from vllm_ascend.ops.fused_moe import select_experts
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VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
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def apply_mlp(hidden_states_wrapper: List[torch.Tensor],
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w1: torch.Tensor,
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w1_scale: torch.Tensor,
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w2: torch.Tensor,
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w2_scale: torch.Tensor,
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group_list: torch.Tensor,
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dynamic_scale: torch.Tensor = None,
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group_list_type: int = 1) -> torch.Tensor:
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"""
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apply MLP: gate_up_proj -> swiglu -> down_proj
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Args:
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hidden_states_wrapper: wrapper of input hidden states with shape (num_tokens, hidden_size).
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w1: expert weights1 with shape
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(num_experts, hidden_size, intermediate_size * 2)
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w1_scale: weights1 scale with shape (num_experts, intermediate_size * 2)
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w2: expert weights2 with shape
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(num_experts, intermediate_size, hidden_size)
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w2_scale: weights2 scale with shape (num_experts, hidden_size)
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group_list: number of tokens for each expert, follow cumsum mode, and
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with shape (num_experts).
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transpose_weight:
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w1: (num_experts, intermediate_size * 2, hidden_size) ->
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(num_experts, hidden_size, intermediate_size * 2)
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w2: (num_experts, hidden_size, intermediate_size) ->
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(num_experts, intermediate_size, hidden_size)
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Returns:
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hidden_states: output hidden states after MLP.
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"""
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assert len(hidden_states_wrapper) == 1
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hidden_states = hidden_states_wrapper.pop()
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if dynamic_scale is None:
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hidden_states, pertoken_scale = torch_npu.npu_dynamic_quant(
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hidden_states)
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else:
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pertoken_scale = dynamic_scale
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# gmm1: gate_up_proj
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hidden_states = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w1],
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scale=[w1_scale],
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per_token_scale=[pertoken_scale],
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split_item=2,
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group_list_type=group_list_type,
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group_type=0,
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group_list=group_list,
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output_dtype=w2_scale.dtype)[0]
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# act_fn: swiglu
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hidden_states = torch_npu.npu_swiglu(hidden_states)
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hidden_states, swiglu_out_scale = torch_npu.npu_dynamic_quant(
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hidden_states)
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# gmm2: down_proj
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hidden_states = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w2],
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scale=[w2_scale],
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per_token_scale=[swiglu_out_scale],
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split_item=2,
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group_list_type=group_list_type,
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group_type=0,
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group_list=group_list,
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output_dtype=w2_scale.dtype)[0]
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return hidden_states
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def fused_experts_with_mc2(
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hidden_states: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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w1_scale: torch.Tensor,
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w2_scale: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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top_k: int,
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expert_map: torch.Tensor = None,
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moe_all_to_all_group_name: str = "",
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) -> torch.Tensor:
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global_bs = 0
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moe_expert_num = len(expert_map)
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# hidden_states = hidden_states.bfloat16()
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kwargs = {
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"x": hidden_states,
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"expert_ids": topk_ids,
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"expert_shard_type": 0,
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"shared_expert_rank_num": 0,
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"moe_expert_num": moe_expert_num,
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"global_bs": global_bs,
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}
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rank = torch.distributed.get_rank()
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quant_mode = 2
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ep_group = get_ep_group().device_group
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local_rank = torch.distributed.get_rank(group=ep_group)
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all_to_all_group_size = torch.distributed.get_world_size(ep_group)
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world_szie = torch.distributed.get_world_size()
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tp_size = world_szie // all_to_all_group_size
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tp_rank = rank % tp_size
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stage1_kwargs = {
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"scales": None,
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"quant_mode": quant_mode,
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"group_ep": moe_all_to_all_group_name,
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"ep_world_size": all_to_all_group_size,
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"ep_rank_id": local_rank,
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# "group_tp": self.moe_rs_group_name,
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"group_tp": moe_all_to_all_group_name,
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"tp_world_size": tp_size,
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"tp_rank_id": tp_rank,
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}
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kwargs.update(stage1_kwargs)
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output = torch_npu.npu_moe_distribute_dispatch(**kwargs)
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# comm_stream.wait_stream(torch.npu.current_stream())
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expand_x, dynamic_scale, expand_idx, expert_token_nums, ep_recv_counts = output[
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0:5]
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if quant_mode == 0:
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dynamic_scale = None
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# place hidden_states in a list to transfer its ownership into the `apply_mlp` function
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hidden_states_wrapper = [expand_x]
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del expand_x
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down_out_list = apply_mlp(hidden_states_wrapper,
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w1,
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w1_scale,
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w2,
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w2_scale,
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expert_token_nums,
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dynamic_scale=dynamic_scale)
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# moeCombine
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kwargs = {
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"expand_x": down_out_list,
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"expert_ids": topk_ids,
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"expand_idx": expand_idx,
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"expert_scales": topk_weights.to(torch.float32),
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"expert_shard_type": 0,
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"shared_expert_rank_num": 0,
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"moe_expert_num": moe_expert_num,
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"global_bs": 0,
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}
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tp_recv_counts = torch.empty(1,
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dtype=torch.int32,
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device=hidden_states.device)
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stage3_kwargs = {
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"ep_send_counts": ep_recv_counts,
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"group_ep": moe_all_to_all_group_name,
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"ep_world_size": all_to_all_group_size,
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"ep_rank_id": local_rank,
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"tp_send_counts": tp_recv_counts,
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# "group_tp": self.moe_rs_group_name,
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"group_tp": moe_all_to_all_group_name,
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"tp_world_size": tp_size,
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"tp_rank_id": tp_rank,
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}
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kwargs.update(stage3_kwargs)
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hidden_states = torch_npu.npu_moe_distribute_combine(**kwargs)
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return hidden_states
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# currently expert parallelism implemented with all2all
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# is under-optimized.
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def fused_experts_with_all2all(
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hidden_states: torch.Tensor,
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w1: torch.Tensor,
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w1_scale: torch.Tensor,
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w2: torch.Tensor,
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w2_scale: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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top_k: int,
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expert_map: torch.Tensor = None,
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ep_group: GroupCoordinator = None,
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):
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original_shape = hidden_states.shape
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if len(original_shape) == 3:
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hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
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num_tokens, _ = hidden_states.shape
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num_experts = w1.shape[0]
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device = hidden_states.device
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if expert_map is not None:
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global_num_experts = len(expert_map)
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local_num_experts = global_num_experts // ep_group.world_size
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row_idx_len = num_tokens * top_k
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row_idx = (torch.arange(0,
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row_idx_len,
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dtype=torch.int32,
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device=device).view(top_k, -1).permute(
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1, 0).contiguous())
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hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
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hidden_states,
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row_idx=row_idx,
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expert_idx=topk_ids,
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active_num=num_tokens)
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global_expert_tokens = torch.bincount(expanded_expert_idx,
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minlength=global_num_experts)
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scatter_sizes = global_expert_tokens.view(ep_group.world_size,
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-1).sum(-1)
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gather_sizes = torch.empty_like(scatter_sizes)
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dist.all_to_all_single(gather_sizes,
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scatter_sizes,
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group=ep_group.device_group)
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scatter_size_list = scatter_sizes.cpu().tolist()
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gather_size_list = gather_sizes.cpu().tolist()
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expanded_expert_idx = expanded_expert_idx % local_num_experts
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hidden_states = ep_group.all_to_all(hidden_states, 0, 0,
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scatter_size_list,
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gather_size_list)
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local_expert_idx = ep_group.all_to_all(expanded_expert_idx, 0, 0,
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scatter_size_list,
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gather_size_list)
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sorted_local_expert_idx, sorted_idx = torch.sort(local_expert_idx)
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expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
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sorted_local_expert_idx, local_num_experts).to(torch.int64)
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hidden_states = hidden_states[sorted_idx]
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group_list_type = 0
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else:
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row_idx_len = num_tokens * top_k
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row_idx = torch.arange(0,
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row_idx_len,
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dtype=torch.int32,
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device=topk_weights.device).view(
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top_k, -1).permute(1, 0).contiguous()
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hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
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hidden_states,
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row_idx=row_idx,
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expert_idx=topk_ids,
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active_num=num_tokens)
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expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
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expanded_expert_idx, num_experts)
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expert_tokens = expert_tokens.to(torch.int64)
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group_list_type = 0
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hidden_states_wrapper = [hidden_states]
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del hidden_states
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hidden_states = apply_mlp(hidden_states_wrapper,
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w1,
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w1_scale,
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w2,
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w2_scale,
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expert_tokens,
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group_list_type=group_list_type)
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if expert_map is not None:
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resorted_idx = torch.argsort(sorted_idx)
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hidden_states = hidden_states[resorted_idx]
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hidden_states = ep_group.all_to_all(hidden_states, 0, 0,
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gather_size_list,
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scatter_size_list)
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final_hidden_states = torch_npu.npu_moe_finalize_routing(
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hidden_states,
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skip1=None,
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skip2=None,
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bias=None,
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scales=topk_weights,
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expanded_src_to_dst_row=expanded_row_idx,
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export_for_source_row=topk_ids,
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)
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else:
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# TODO: Reorder device memory 2 times here, replace the current
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# implementation here when suitable operators become available.
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final_hidden_states = torch_npu.npu_moe_finalize_routing(
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hidden_states,
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skip1=None,
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skip2=None,
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bias=None,
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scales=topk_weights,
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expanded_src_to_dst_row=expanded_row_idx,
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export_for_source_row=topk_ids,
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)
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if len(original_shape) == 3:
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final_hidden_states = final_hidden_states.view(original_shape)
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return final_hidden_states
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def fused_experts(hidden_states: torch.Tensor,
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w1: torch.Tensor,
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w1_scale: torch.Tensor,
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w2: torch.Tensor,
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w2_scale: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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top_k: int,
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expert_map: torch.Tensor = None):
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original_shape = hidden_states.shape
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if len(original_shape) == 3:
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hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
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num_tokens, _ = hidden_states.shape
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num_experts = w1.shape[0]
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dtype = hidden_states.dtype
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device = hidden_states.device
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if expert_map is not None:
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# Generate token indices and flatten
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token_indices = (torch.arange(num_tokens,
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device=device,
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dtype=torch.int64).unsqueeze(1).expand(
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-1, top_k).reshape(-1))
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# Flatten token-to-expert mappings and map to local experts
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weights_flat = topk_weights.view(-1)
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experts_flat = topk_ids.view(-1)
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local_experts_flat = expert_map[experts_flat]
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# Filter valid token-expert pairs
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mask = local_experts_flat != -1
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filtered_weights = torch.where(
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mask, weights_flat, torch.zeros_like(weights_flat)).to(dtype)
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filtered_experts = torch.where(
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mask, local_experts_flat,
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torch.full_like(local_experts_flat,
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num_experts)).to(topk_ids.dtype)
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# Sort by local expert IDs
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sort_indices = torch.argsort(filtered_experts)
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sorted_token_indices = token_indices[sort_indices]
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sorted_weights = filtered_weights[sort_indices]
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# Compute token counts with minlength of num_experts
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# This is equivalent to but faster than:
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# >>> token_counts = torch.bincount(filtered_experts, minlength=num_experts)[:-1]
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token_counts = torch.zeros(num_experts + 1,
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device=device,
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dtype=torch.int64)
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ones = torch.ones_like(filtered_experts, dtype=torch.int64)
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token_counts.scatter_add_(0, filtered_experts.to(torch.int64), ones)
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expert_tokens = token_counts[:num_experts]
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# Rearrange hidden_states
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hidden_states = hidden_states[sorted_token_indices]
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group_list_type = 1
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else:
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row_idx_len = num_tokens * top_k
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row_idx = torch.arange(0,
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row_idx_len,
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dtype=torch.int32,
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device=topk_weights.device).view(
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top_k, -1).permute(1, 0).contiguous()
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hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
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hidden_states,
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row_idx=row_idx,
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expert_idx=topk_ids,
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active_num=num_tokens)
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expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
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expanded_expert_idx, num_experts)
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expert_tokens = expert_tokens.to(torch.int64)
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group_list_type = 0
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# place hidden_states in a list to transfer its ownership into the `apply_mlp` function
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hidden_states_wrapper = [hidden_states]
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del hidden_states
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hidden_states = apply_mlp(hidden_states_wrapper,
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w1,
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w1_scale,
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w2,
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w2_scale,
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expert_tokens,
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group_list_type=group_list_type)
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if expert_map is not None:
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hidden_states.mul_(sorted_weights.unsqueeze(1))
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final_hidden_states = torch.zeros(*original_shape,
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device=device,
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dtype=dtype)
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num_valid_tokens = mask.sum()
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valid_token_mask = torch.arange(
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0, sorted_token_indices.shape[0],
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device=device).unsqueeze(1) < num_valid_tokens
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hidden_states = hidden_states.masked_fill_(~valid_token_mask,
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0).to(dtype)
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final_hidden_states.index_add_(0, sorted_token_indices, hidden_states)
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else:
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# TODO: Reorder device memory 2 times here, replace the current
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# implementation here when suitable operators become available.
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final_hidden_states = torch_npu.npu_moe_finalize_routing(
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hidden_states,
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skip1=None,
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skip2=None,
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bias=None,
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scales=topk_weights,
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expanded_src_to_dst_row=expanded_row_idx,
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export_for_source_row=topk_ids,
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)
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if len(original_shape) == 3:
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final_hidden_states = final_hidden_states.view(original_shape)
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return final_hidden_states
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class AscendW8A8DynamicLinearMethod:
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"""Linear method for Ascend W8A8_DYNAMIC.
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"""
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def __init__(self):
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self.transpose_weight = True
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@staticmethod
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def get_weight(input_size: int, output_size: int,
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params_dtype: torch.dtype) -> Dict[str, Any]:
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params_dict = {
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"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]:
|
|
return {}
|
|
|
|
@staticmethod
|
|
def get_perchannel_param(
|
|
output_size: int,
|
|
params_dtype: torch.dtype,
|
|
) -> Dict[str, Any]:
|
|
params_dict = {}
|
|
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
|
|
|
|
@staticmethod
|
|
def apply(
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: Optional[torch.Tensor] = None,
|
|
tp_rank: Optional[int] = 0,
|
|
) -> torch.Tensor:
|
|
original_dtype = x.dtype
|
|
# use ATB quantize
|
|
quant_out, dynamic_scale = torch_npu.npu_dynamic_quant(x)
|
|
return torch_npu.npu_quant_matmul(
|
|
quant_out,
|
|
layer.weight,
|
|
layer.weight_scale,
|
|
pertoken_scale=dynamic_scale,
|
|
bias=bias,
|
|
output_dtype=original_dtype,
|
|
)
|
|
|
|
def process_weights_after_loading(self, layer):
|
|
if self.transpose_weight:
|
|
layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
|
|
# cast quantized weight tensors in NZ format (29) for higher inference speed
|
|
layer.weight.data = torch_npu.npu_format_cast(layer.weight.data, 29)
|
|
layer.weight_scale.data = layer.weight_scale.data.flatten()
|
|
layer.weight_scale_fp32 = layer.weight_scale.data.to(torch.float32)
|
|
layer.weight_offset.data = layer.weight_offset.data.flatten()
|
|
|
|
|
|
class AscendW8A8DynamicFusedMoEMethod:
|
|
"""FusedMoe method for Ascend W8A8_DYNAMIC.
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.transpose_weight = True
|
|
|
|
self.ep_group = get_ep_group()
|
|
|
|
try:
|
|
device_group = self.ep_group.device_group
|
|
# TODO: Try local_rank = ep_group.rank_in_group
|
|
local_rank = torch.distributed.get_rank(group=device_group)
|
|
backend = device_group._get_backend(torch.device("npu"))
|
|
self.moe_all_to_all_group_name = backend.get_hccl_comm_name(
|
|
local_rank)
|
|
except AttributeError:
|
|
self.moe_all_to_all_group_name = ""
|
|
|
|
@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)
|
|
param_dict["w2_weight"] = torch.empty(num_experts,
|
|
hidden_sizes,
|
|
intermediate_size_per_partition,
|
|
dtype=torch.int8)
|
|
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=params_dtype)
|
|
param_dict["w13_weight_offset"] = torch.empty(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
1,
|
|
dtype=params_dtype)
|
|
param_dict["w2_weight_scale"] = torch.empty(num_experts,
|
|
hidden_sizes,
|
|
1,
|
|
dtype=params_dtype)
|
|
param_dict["w2_weight_offset"] = torch.empty(num_experts,
|
|
hidden_sizes,
|
|
1,
|
|
dtype=params_dtype)
|
|
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 = True,
|
|
dp_size: int = 1,
|
|
**kwargs,
|
|
) -> torch.Tensor:
|
|
assert router_logits.shape[
|
|
1] == global_num_experts, "Number of global experts mismatch"
|
|
|
|
# NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
|
|
if global_num_experts == 256:
|
|
topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
|
|
router_logits,
|
|
k=top_k, # topk当前写8
|
|
bias=e_score_correction_bias,
|
|
k_group=topk_group, # fix: 4
|
|
group_count=num_expert_group, # fix 8
|
|
group_select_mode=1, # 0: group中的最大; 1: topk2.sum(fix)
|
|
renorm=0, # 0: softmax->topk(fix); 1: topk->softmax
|
|
norm_type=1, # 0: softmax; 1: sigmoid(fix)
|
|
# out_flag=False, # todo new api; 第三个输出是否输出
|
|
# y2_flag=False, # old api; 第三个输出是否输出
|
|
routed_scaling_factor=1,
|
|
eps=float(1e-20))
|
|
else:
|
|
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,
|
|
)
|
|
|
|
# this is a naive implementation for experts load balance so as
|
|
# to avoid accumulating too much tokens on a single rank.
|
|
# currently it is only activated when doing profile runs.
|
|
if enable_force_load_balance:
|
|
topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)
|
|
|
|
if VLLM_ENABLE_MC2 and not is_prefill:
|
|
return fused_experts_with_mc2(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
top_k=top_k,
|
|
expert_map=expert_map,
|
|
moe_all_to_all_group_name=self.moe_all_to_all_group_name)
|
|
elif dp_size == 1:
|
|
return fused_experts(hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2=layer.w2_weight,
|
|
w2_scale=layer.w2_weight_scale,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
top_k=top_k,
|
|
expert_map=expert_map)
|
|
else:
|
|
return fused_experts_with_all2all(hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2=layer.w2_weight,
|
|
w2_scale=layer.w2_weight_scale,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
top_k=top_k,
|
|
expert_map=expert_map,
|
|
ep_group=self.ep_group)
|
|
|
|
def process_weights_after_loading(self, layer):
|
|
if self.transpose_weight:
|
|
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)
|