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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Inference-only Snowflake Arctic model."""
from collections.abc import Iterable
from typing import Optional, Union
import torch
from torch import nn
from vllm.attention import Attention
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
tensor_model_parallel_all_reduce)
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe import fused_experts, fused_topk
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.deepspeedfp import (
DeepSpeedFPConfig, DeepSpeedFPParameter)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs.arctic import ArcticConfig
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 = init_logger(__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 = isinstance(quant_config, DeepSpeedFPConfig)
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:
self.ws = DeepSpeedFPParameter(
torch.Size((self.num_experts, 2 * self.intermediate_size,
self.hidden_size)),
params_dtype=params_dtype,
quant_config=quant_config,
)
self.w2s = DeepSpeedFPParameter(
torch.Size((self.num_experts, self.hidden_size,
self.intermediate_size)),
params_dtype=params_dtype,
quant_config=quant_config,
)
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.ds_dequantize() if self.is_quant else 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]
if self.is_quant:
param.ds_quantize_(param_data)
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, token_expert_indices = fused_topk(
hidden_states, router_logits, self.top_k, renormalize=do_normalize)
# topk_ids: (num_tokens, k)
if self.is_quant:
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,
ws_dequantized if self.is_quant else self.ws,
w2s_dequantized if self.is_quant else 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,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
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 = Attention(self.num_heads,
self.head_dim,
self.scaling,
num_kv_heads=self.num_kv_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn")
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
) -> 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)
output, _ = self.o_proj(attn_output)
return output
class ArcticDecoderLayer(nn.Module):
def __init__(
self,
config: ArcticConfig,
cache_config: Optional[CacheConfig] = None,
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,
cache_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,
) -> torch.Tensor:
residual_input = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = self.self_attn(
positions=positions,
hidden_states=hidden_states,
)
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
@support_torch_compile
class ArcticModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
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, cache_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,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
for layer in self.layers[self.start_layer:self.end_layer]:
hidden_states = layer(positions, hidden_states)
if not get_pp_group().is_last_rank:
return IntermediateTensors({"hidden_states": hidden_states})
hidden_states = self.norm(hidden_states)
return hidden_states
class ArcticForCausalLM(nn.Module, SupportsPP, SupportsQuant):
packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
self.config = config
self.model = ArcticModel(vllm_config=vllm_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.unpadded_vocab_size,
config.vocab_size)
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)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.model(input_ids, positions, intermediate_tensors,
inputs_embeds)
return hidden_states
def compute_logits(
self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[torch.Tensor]:
logits = self.logits_processor(self.lm_head, hidden_states,
sampling_metadata)
return logits
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 % 2 == 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
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