EngineX-Hygon/sglang
5
0
Fork 0
Code Issues Pull Requests Actions 7 Projects Releases Wiki Activity

Revert "[Model] Support ArcticForCausalLM architecture (Snowflake/snowflake-arctic-instruct)" (#5754)

Browse Source
This commit is contained in:
Lianmin Zheng
2025-04-25 15:50:28 -07:00
committed by GitHub
parent 3dd3538c18
commit 5641a09458
5 changed files with 0 additions and 766 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
python/sglang/srt/configs/__init__.py
View File

@@ -1,4 +1,3 @@
from sglang.srt.configs.arctic import ArcticConfig
from sglang.srt.configs.chatglm import ChatGLMConfig
from sglang.srt.configs.dbrx import DbrxConfig
from sglang.srt.configs.deepseekvl2 import DeepseekVL2Config
@@ -6,7 +5,6 @@ from sglang.srt.configs.exaone import ExaoneConfig
from sglang.srt.configs.janus_pro import MultiModalityConfig
__all__ = [
"ArcticConfig",
"ExaoneConfig",
"ChatGLMConfig",
"DbrxConfig",

127
python/sglang/srt/configs/arctic.py
View File

@@ -1,127 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
"""Arctic model configuration"""
from typing import Any, Dict, Optional
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
ARCTIC_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"arctic": "https://huggingface.co/Snowflake/snowflake-arctic-instruct/tree/main/config.json",
}
class ArcticConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ArcticModel`]. It is used to instantiate an
Arctic model according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Arctic model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ArcticModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 14336):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
pad_token_id (`int`, *optional*):
The id of the padding token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 1000000.0):
The base period of the RoPE embeddings.
sliding_window (`int`, *optional*):
Sliding window attention window size. If not specified, will default to `4096`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
num_experts_per_tok (`int`, *optional*, defaults to 2):
The number of experts to root per-token, can be also interpreted as the `top-p` routing parameter
num_local_experts (`int`, *optional*, defaults to 8):
Number of experts per Sparse MLP layer.
moe_layer_frequency (`int`, *optional*, defaults to 2):
Frequency of MoE layers in the model.
"""
model_type = "arctic"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=14336,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_theta=1e6,
sliding_window=None,
attention_dropout=0.0,
num_experts_per_tok=1,
num_local_experts=8,
moe_layer_frequency=2,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.sliding_window = sliding_window
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
self.num_experts_per_tok = num_experts_per_tok
self.num_local_experts = num_local_experts
self.moe_layer_frequency = moe_layer_frequency
# For backward compatibility
self._attn_implementation = kwargs.pop("_attn_implementation", "eager")
self.use_residual = kwargs.pop("use_residual", True)
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)

2
python/sglang/srt/hf_transformers_utils.py
View File

@@ -31,7 +31,6 @@ from transformers import (
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from sglang.srt.configs import (
ArcticConfig,
ChatGLMConfig,
DbrxConfig,
DeepseekVL2Config,
@@ -42,7 +41,6 @@ from sglang.srt.connector import create_remote_connector
from sglang.srt.utils import is_remote_url
_CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
ArcticConfig.model_type: ArcticConfig,
ChatGLMConfig.model_type: ChatGLMConfig,
DbrxConfig.model_type: DbrxConfig,
ExaoneConfig.model_type: ExaoneConfig,

634
python/sglang/srt/models/arctic.py
View File

@@ -1,634 +0,0 @@
# Copyright 2023-2025 SGLang Team
# 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.
# ==============================================================================
# Copyright 2024 Cohere and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
# ==============================================================================
# Adapted from
# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/arctic.py
"""Inference-only Snowflake Arctic model."""
import logging
from typing import Iterable, List, Optional, Set, Tuple, Union
import torch
from torch import nn
from sglang.srt.configs.arctic import ArcticConfig
from sglang.srt.distributed import (
get_pp_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
tensor_model_parallel_all_reduce,
)
from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.fused_moe import fused_experts, fused_topk
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
MergedColumnParallelLinear,
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
from sglang.srt.layers.quantization import QuantizationConfig
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.rotary_embedding import get_rope
from sglang.srt.layers.vocab_parallel_embedding import (
ParallelLMHead,
VocabParallelEmbedding,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_executor.utils import set_weight_attrs
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.platforms import current_platform
from .interfaces import SupportsPP, SupportsQuant
from .utils import (
extract_layer_index,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory,
make_layers,
maybe_prefix,
)
logger = logging.getLogger(__name__)
class ArcticMLP(nn.Module):
def __init__(
self,
config: ArcticConfig,
expert_id: int = -1,
is_residual_mlp: bool = False,
quant_config: Optional[QuantizationConfig] = None,
reduce_results: bool = True,
prefix: str = "",
):
super().__init__()
self.hidden_size = config.hidden_size
self.expert_id = expert_id
self.ffn_dim = (
config.intermediate_size if not is_residual_mlp else self.hidden_size
)
self.w13 = MergedColumnParallelLinear(
self.hidden_size, [self.ffn_dim] * 2, bias=False, quant_config=quant_config
)
self.w2 = RowParallelLinear(
self.ffn_dim,
self.hidden_size,
bias=False,
reduce_results=reduce_results,
quant_config=quant_config,
)
if config.hidden_act != "silu":
raise ValueError(
f"Unsupported activation: {config.hidden_act}. "
"Only silu is supported for now."
)
self.act_fn = SiluAndMul()
def forward(self, hidden_states):
gate_up, _ = self.w13(hidden_states)
hidden_states = self.act_fn(gate_up)
hidden_states, _ = self.w2(hidden_states)
return hidden_states
class ArcticMoE(nn.Module):
"""
Model-parallel implementation of Arctic MoE Layer.
"""
def __init__(
self,
config: ArcticConfig,
tp_size: Optional[int] = None,
params_dtype: Optional[torch.dtype] = None,
quant_config: Optional[QuantizationConfig] = None,
reduce_results: bool = True,
prefix: str = "",
):
super().__init__()
layer_id = extract_layer_index(prefix)
self.tp_size = tp_size or get_tensor_model_parallel_world_size()
self.hidden_size = config.hidden_size
self.num_experts = config.num_local_experts
self.layer_id = layer_id
self.top_k = config.num_experts_per_tok
self.intermediate_size = config.intermediate_size // self.tp_size
self.is_moe_layer = (layer_id + 1) % config.moe_layer_frequency == 0
self.is_quant = quant_config is not None
self.reduce_results = reduce_results
# Some other parameters
if params_dtype is None:
params_dtype = torch.get_default_dtype()
self.params_dtype = params_dtype
if not self.is_moe_layer:
self.mlp = ArcticMLP(
config,
quant_config=quant_config,
reduce_results=reduce_results,
prefix=f"{prefix}.mlp",
)
else:
self.gate = ReplicatedLinear(
self.hidden_size,
self.num_experts,
bias=False,
params_dtype=self.params_dtype,
quant_config=quant_config,
prefix=f"{prefix}.gate",
)
if self.is_quant:
raise NotImplementedError("Quantization is not supported yet.")
else:
self.ws = nn.Parameter(
torch.empty(
self.num_experts,
2 * self.intermediate_size,
self.hidden_size,
device=current_platform.device_type,
dtype=self.params_dtype,
)
)
self.w2s = nn.Parameter(
torch.empty(
self.num_experts,
self.hidden_size,
self.intermediate_size,
device=current_platform.device_type,
dtype=self.params_dtype,
)
)
set_weight_attrs(
self.ws,
{
"weight_loader": self.weight_loader,
},
)
set_weight_attrs(
self.w2s,
{
"weight_loader": self.weight_loader,
},
)
def weight_loader(
self,
param: nn.Parameter,
loaded_weight: torch.Tensor,
weight_name: str,
expert_id: int,
):
tp_rank = get_tensor_model_parallel_rank()
param_data = param.data
shard_size = self.intermediate_size
shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
if weight_name.endswith("w1.weight"):
param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :]
if weight_name.endswith("w3.weight"):
param_data[expert_id, shard_size : 2 * shard_size, :] = loaded_weight[
shard, :
]
if weight_name.endswith("w2.weight"):
param_data[expert_id, :, :] = loaded_weight[:, shard]
def local_moe_fused(self, hidden_states: torch.Tensor) -> torch.Tensor:
num_tokens, hidden_size = hidden_states.shape
hidden_states = hidden_states.view(-1, self.hidden_size)
# router_logits: (num_tokens, n_experts)
router_logits, _ = self.gate(hidden_states)
do_normalize = self.top_k > 1
topk_weights, topk_ids = fused_topk(
hidden_states, router_logits, self.top_k, renormalize=do_normalize
)
# topk_ids: (num_tokens, k)
if self.is_quant:
raise NotImplementedError("Quantization is not supported yet.")
# if 2 * num_tokens <= self.num_experts:
# # If much fewer tokens than experts, use selective dequantize.
# ws_dequantized = self.ws.ds_selective_dequantize(topk_ids.flatten())
# w2s_dequantized = self.w2s.ds_selective_dequantize(topk_ids.flatten())
# # We gathered the experts to the tokens so update the mapping.
# topk_ids = torch.arange(
# 0,
# topk_ids.numel(),
# device=topk_ids.device,
# ).reshape(topk_ids.shape)
# else:
# ws_dequantized = self.ws.ds_dequantize()
# w2s_dequantized = self.w2s.ds_dequantize()
final_hidden_states = fused_experts(
hidden_states,
self.ws,
self.w2s,
topk_weights,
topk_ids,
inplace=True,
)
if self.reduce_results and self.tp_size > 1:
final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
return final_hidden_states.view(num_tokens, hidden_size)
def forward(self, hidden_states: torch.Tensor):
if self.is_moe_layer:
final_hidden_states = self.local_moe_fused(hidden_states)
else:
final_hidden_states = self.mlp(hidden_states)
return final_hidden_states
class ArcticAttention(nn.Module):
def __init__(
self,
config: ArcticConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
layer_idx = extract_layer_index(prefix)
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = config.num_attention_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = config.num_key_value_heads
if self.total_num_kv_heads >= tp_size:
assert self.total_num_kv_heads % tp_size == 0
else:
assert tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
self.head_dim = self.hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.scaling = self.head_dim**-0.5
self.qkv_proj = QKVParallelLinear(
self.hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_kv_heads,
bias=False,
quant_config=quant_config,
)
self.o_proj = RowParallelLinear(
self.total_num_heads * self.head_dim,
self.hidden_size,
bias=False,
reduce_results=True,
quant_config=quant_config,
)
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=self.max_position_embeddings,
base=int(self.rope_theta),
is_neox_style=True,
)
self.attn = RadixAttention(
self.num_heads,
self.head_dim,
self.scaling,
num_kv_heads=self.num_kv_heads,
layer_id=layer_idx,
prefix=f"{prefix}.attn",
)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
forward_batch: ForwardBatch,
) -> torch.Tensor:
qkv, _ = self.qkv_proj(hidden_states)
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = self.rotary_emb(positions, q, k)
attn_output = self.attn(q, k, v, forward_batch)
output, _ = self.o_proj(attn_output)
return output
class ArcticDecoderLayer(nn.Module):
def __init__(
self,
config: ArcticConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.hidden_size = config.hidden_size
layer_idx = extract_layer_index(prefix)
is_moe_layer = (layer_idx + 1) % config.moe_layer_frequency == 0
self.use_residual = config.use_residual and is_moe_layer
self.self_attn = ArcticAttention(
config, quant_config=quant_config, prefix=f"{prefix}.self_attn"
)
self.block_sparse_moe = ArcticMoE(
config,
quant_config=quant_config,
reduce_results=(not self.use_residual),
prefix=f"{prefix}.block_sparse_moe",
)
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
if self.use_residual:
self.residual_layernorm = RMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
self.residual_mlp = ArcticMLP(
config,
is_residual_mlp=True,
reduce_results=False,
prefix=f"{prefix}.residual_mlp",
)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
forward_batch: ForwardBatch,
) -> torch.Tensor:
residual_input = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = self.self_attn(
positions=positions,
hidden_states=hidden_states,
forward_batch=forward_batch,
)
hidden_states = residual_input + hidden_states
residual_attn = hidden_states
if self.use_residual:
hidden_states = self.residual_layernorm(hidden_states)
hidden_states = self.residual_mlp(hidden_states)
residual_mlp = hidden_states
hidden_states = self.post_attention_layernorm(residual_input)
hidden_states = self.block_sparse_moe(hidden_states)
hidden_states = residual_mlp + hidden_states
hidden_states = tensor_model_parallel_all_reduce(hidden_states)
hidden_states = residual_attn + hidden_states
else:
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.block_sparse_moe(hidden_states)
hidden_states = residual_attn + hidden_states
return hidden_states
class ArcticModel(nn.Module):
def __init__(
self,
*,
config: ArcticConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.vocab_size = config.vocab_size
self.embed_tokens = VocabParallelEmbedding(
self.vocab_size, config.hidden_size, org_num_embeddings=self.vocab_size
)
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: ArcticDecoderLayer(config, quant_config, prefix=prefix),
prefix=f"{prefix}.layers",
)
self._attn_implementation = config._attn_implementation
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
["hidden_states"], config.hidden_size
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
forward_batch: ForwardBatch,
input_embeds: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if input_embeds is not None:
hidden_states = input_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
for layer in self.layers[self.start_layer : self.end_layer]:
hidden_states = layer(positions, hidden_states, forward_batch)
hidden_states = self.norm(hidden_states)
return hidden_states
class ArcticForCausalLM(nn.Module):
packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
def __init__(
self,
*,
config: ArcticConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.config = config
self.supports_torch_tp = True
self.model = ArcticModel(
config=config,
quant_config=quant_config,
prefix=maybe_prefix(prefix, "model"),
)
self.vocab_size = config.vocab_size
self.lm_head = ParallelLMHead(
self.vocab_size,
config.hidden_size,
quant_config=quant_config,
)
if self.config.tie_word_embeddings:
self.lm_head.weight = self.model.embed_tokens.weight
self.num_experts = config.num_local_experts
self.num_experts_per_tok = config.num_experts_per_tok
self.unpadded_vocab_size = config.vocab_size
self.logits_processor = LogitsProcessor(self.config)
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)
@torch.no_grad()
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
forward_batch: ForwardBatch,
input_embeds: Optional[torch.Tensor] = None,
) -> LogitsProcessorOutput:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("qkv_proj", "q_proj", "q"),
("qkv_proj", "k_proj", "k"),
("qkv_proj", "v_proj", "v"),
]
mlp_params_mapping: List[Tuple[str, str, int]] = []
expert_params_mapping: List[Tuple[str, str, int]] = []
num_layers = self.config.num_hidden_layers
for layer in range(num_layers):
mlp_params_mapping.append(
(
f"layers.{layer}.residual_mlp.w13.weight",
f"layers.{layer}.residual_mlp.w1.weight",
0,
)
)
mlp_params_mapping.append(
(
f"layers.{layer}.residual_mlp.w13.weight",
f"layers.{layer}.residual_mlp.w3.weight",
1,
)
)
if (layer + 1) % self.config.moe_layer_frequency != 0:
# MLP layers
mlp_params_mapping.append(
(
f"layers.{layer}.block_sparse_moe.mlp.w13.weight",
f"layers.{layer}.block_sparse_moe.mlp.w1.weight",
0,
)
)
mlp_params_mapping.append(
(
f"layers.{layer}.block_sparse_moe.mlp.w13.weight",
f"layers.{layer}.block_sparse_moe.mlp.w3.weight",
1,
)
)
else:
# MoE layers
for expert_id in range(self.config.num_local_experts):
expert_params_mapping.append(
("ws", f"experts.{expert_id}.w1.weight", expert_id)
)
expert_params_mapping.append(
("w2s", f"experts.{expert_id}.w2.weight", expert_id)
)
expert_params_mapping.append(
("ws", f"experts.{expert_id}.w3.weight", expert_id)
)
params_dict = dict(self.named_parameters())
loaded_params: Set[str] = set()
logger.info(
"It will take ~10 minutes loading from the 16-bit weights. "
"Alternatively, use the prequantized 8-bit weights of arctic "
"and set load-format to `sharded_state` will accelerate loading."
)
for name, loaded_weight in weights:
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
for param_name, weight_name, shard_id in mlp_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
for param_name, weight_name, shard_id in expert_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(
param, loaded_weight, weight_name, expert_id=shard_id
)
break
else:
if name.endswith(".bias") and name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params