mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-14 06:15:01 +08:00
469 lines
18 KiB
Python
469 lines
18 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import math
|
|
from typing import Iterable, Optional, Set, Tuple, Union
|
|
|
|
import torch
|
|
from torch import nn
|
|
from transformers.configuration_utils import PretrainedConfig
|
|
|
|
from vllm.attention import Attention
|
|
from vllm.config import CacheConfig, VllmConfig
|
|
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
|
|
get_tensor_model_parallel_world_size)
|
|
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
|
|
QKVParallelLinear,
|
|
RowParallelLinear)
|
|
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
|
from vllm.model_executor.layers.quantization import QuantizationConfig
|
|
from vllm.model_executor.layers.rotary_embedding import get_rope
|
|
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
|
|
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
|
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
|
|
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
|
from vllm.model_executor.sampling_metadata import SamplingMetadata
|
|
from vllm.platforms import current_platform
|
|
from vllm.sequence import IntermediateTensors
|
|
|
|
from .interfaces import SupportsPP
|
|
from .utils import (is_pp_missing_parameter,
|
|
make_empty_intermediate_tensors_factory, make_layers,
|
|
maybe_prefix)
|
|
|
|
|
|
def load_column_parallel_weight(param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor):
|
|
tp = get_tensor_model_parallel_world_size()
|
|
rk = get_tensor_model_parallel_rank()
|
|
assert param.size(0) * tp == loaded_weight.size(0)
|
|
s = rk * param.size(0)
|
|
e = (rk + 1) * param.size(0)
|
|
loaded_weight = loaded_weight[s:e]
|
|
assert param.shape == loaded_weight.shape
|
|
param.data.copy_(loaded_weight)
|
|
|
|
|
|
class HeadMajorQKVParallelLinear(QKVParallelLinear):
|
|
|
|
def weight_loader(self, param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor):
|
|
return load_column_parallel_weight(param, loaded_weight)
|
|
|
|
|
|
class HeadMajorColumnParallelLinear(MergedColumnParallelLinear):
|
|
|
|
def weight_loader(self, param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor):
|
|
return load_column_parallel_weight(param, loaded_weight)
|
|
|
|
|
|
@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
|
|
def quick_gelu(x):
|
|
return x * torch.sigmoid(1.702 * x)
|
|
|
|
|
|
@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
|
|
def gegelu(input, limit: Optional[float] = None):
|
|
a_gelu, a_linear = input[..., ::2], input[..., 1::2]
|
|
if limit is not None:
|
|
a_gelu = torch.where(torch.isinf(a_gelu), a_gelu,
|
|
a_gelu.clamp(min=None, max=limit))
|
|
a_linear = torch.where(
|
|
torch.isinf(a_linear),
|
|
a_linear,
|
|
a_linear.clamp(min=-limit, max=limit),
|
|
)
|
|
out_gelu = quick_gelu(a_gelu)
|
|
return out_gelu * (a_linear + 1)
|
|
|
|
|
|
class Phi3SmallMLP(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
assert (self.config.hidden_act == "gegelu"
|
|
), "Only `gegelu` is supported for the 4.7 series of models .."
|
|
self.hidden_size = config.hidden_size
|
|
self.gegelu_limit = config.gegelu_limit
|
|
self.intermediate_size = config.intermediate_size
|
|
|
|
self.up_proj = HeadMajorColumnParallelLinear(
|
|
self.hidden_size,
|
|
2 * [self.intermediate_size],
|
|
bias=True,
|
|
quant_config=quant_config,
|
|
)
|
|
self.down_proj = RowParallelLinear(
|
|
self.intermediate_size,
|
|
self.hidden_size,
|
|
bias=True,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
def forward(self, x):
|
|
gate_up, _ = self.up_proj(x)
|
|
x = gegelu(gate_up)
|
|
x, _ = self.down_proj(x)
|
|
return x
|
|
|
|
|
|
class Phi3SmallSelfAttention(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
layer_idx: int,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
self.layer_idx = layer_idx
|
|
self.config = config
|
|
self.sparse_block_size = config.blocksparse_block_size
|
|
self.homo_heads = config.blocksparse_homo_head_pattern
|
|
self.local_blocks = config.blocksparse_num_local_blocks
|
|
self.vert_stride = config.blocksparse_vert_stride
|
|
|
|
assert (config.blocksparse_block_size ==
|
|
config.blocksparse_triton_kernel_block_size)
|
|
|
|
self.hidden_size = config.hidden_size
|
|
# Number of Query Heads
|
|
self.num_heads = config.num_attention_heads
|
|
|
|
self.head_dim = self.hidden_size // self.num_heads
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
# Number of total Key Value Heads before tensor parallel
|
|
self.num_key_value_heads = config.num_key_value_heads
|
|
self.num_q_per_kv = self.num_heads // self.num_key_value_heads
|
|
if self.tp_size > 1:
|
|
assert self.num_key_value_heads % self.tp_size == 0
|
|
self.num_kv_heads_per_partion = max(
|
|
1, self.num_key_value_heads // self.tp_size)
|
|
self.num_heads_per_partition = self.num_heads // self.tp_size
|
|
|
|
self.max_position_embeddings = config.max_position_embeddings
|
|
self.rope_embedding_base = config.rope_embedding_base
|
|
self.rope_position_scale = config.rope_position_scale
|
|
self.is_causal = True
|
|
|
|
norm_factor = None
|
|
if config.mup_use_scaling:
|
|
norm_factor = self.head_dim / config.mup_attn_multiplier
|
|
else:
|
|
norm_factor = math.sqrt(self.head_dim)
|
|
self.scale = 1 / norm_factor
|
|
|
|
self.query_key_value = HeadMajorQKVParallelLinear(
|
|
self.hidden_size,
|
|
self.head_dim,
|
|
self.num_heads,
|
|
self.num_key_value_heads,
|
|
bias=True,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
self.dense = RowParallelLinear(self.hidden_size,
|
|
self.hidden_size,
|
|
bias=True,
|
|
quant_config=quant_config)
|
|
|
|
if getattr(self.config, "rope_scaling", None) is not None:
|
|
rope_scaling = self.config.rope_scaling
|
|
for key in rope_scaling:
|
|
if isinstance(rope_scaling[key], list):
|
|
rope_scaling[key] = tuple(rope_scaling[key])
|
|
|
|
if "factor" not in rope_scaling:
|
|
rope_scaling["factor"] = self.rope_position_scale
|
|
else:
|
|
rope_scaling = {
|
|
"rope_type": "linear",
|
|
"factor": self.rope_position_scale,
|
|
}
|
|
|
|
self.rotary_emb = get_rope(
|
|
self.head_dim,
|
|
rotary_dim=self.head_dim,
|
|
max_position=self.max_position_embeddings,
|
|
base=self.rope_embedding_base,
|
|
rope_scaling=rope_scaling,
|
|
)
|
|
|
|
# blocksparse params
|
|
self.blocksparse_block_size = config.blocksparse_block_size
|
|
self.blocksparse_num_local_blocks = config.blocksparse_num_local_blocks
|
|
self.blocksparse_vert_stride = config.blocksparse_vert_stride
|
|
|
|
use_dense_attn = (getattr(self.config,
|
|
"dense_attention_every_n_layers", None)
|
|
and (self.layer_idx + 1) %
|
|
self.config.dense_attention_every_n_layers == 0)
|
|
|
|
bs_params = None
|
|
if not use_dense_attn:
|
|
bs_params = {
|
|
'max_seqlen': self.max_position_embeddings,
|
|
'num_heads': self.num_heads_per_partition,
|
|
"num_kv_heads": self.num_kv_heads_per_partion,
|
|
"block_size": self.sparse_block_size,
|
|
"local_blocks": self.local_blocks,
|
|
"vert_stride": self.vert_stride,
|
|
"homo_head": self.homo_heads
|
|
}
|
|
|
|
self.attn = Attention(self.num_heads_per_partition,
|
|
self.head_dim,
|
|
self.scale,
|
|
num_kv_heads=self.num_kv_heads_per_partion,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
blocksparse_params=bs_params,
|
|
prefix=f"{prefix}.attn")
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor],
|
|
Optional[Tuple[torch.Tensor]]]:
|
|
qkv, _ = self.query_key_value(hidden_states)
|
|
|
|
qkv = qkv.view(qkv.shape[:-1] +
|
|
(-1, (self.num_q_per_kv + 2), self.head_dim))
|
|
q, k, v = qkv.split([self.num_q_per_kv, 1, 1], dim=-2)
|
|
|
|
# NOTE: this is required by RotaryEmbed, which indeed does not have to
|
|
# TODO: allow 3D QK for rotary forward
|
|
q = q.reshape(-1, self.head_dim * self.num_heads_per_partition)
|
|
k = k.reshape(-1, self.head_dim * self.num_kv_heads_per_partion)
|
|
v = v.reshape(-1, self.head_dim * self.num_kv_heads_per_partion)
|
|
|
|
q, k = self.rotary_emb(positions, q, k)
|
|
attn_output = self.attn(q, k, v)
|
|
output, _ = self.dense(attn_output)
|
|
|
|
return output
|
|
|
|
|
|
class Phi3SmallDecoderLayer(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
layer_idx: int,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.self_attn = Phi3SmallSelfAttention(config,
|
|
layer_idx,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.self_attn")
|
|
self.mlp = Phi3SmallMLP(config, quant_config)
|
|
|
|
self.input_layernorm = nn.LayerNorm(config.hidden_size,
|
|
eps=config.layer_norm_epsilon)
|
|
self.post_attention_layernorm = nn.LayerNorm(
|
|
config.hidden_size, eps=config.layer_norm_epsilon)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
hidden_states = self.self_attn(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
return hidden_states
|
|
|
|
|
|
class Phi3SmallModel(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.config = config
|
|
self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
|
|
config.hidden_size)
|
|
self.mup_embedding_multiplier = config.mup_embedding_multiplier
|
|
self.start_layer, self.end_layer, self.layers = make_layers(
|
|
config.num_hidden_layers,
|
|
lambda prefix: Phi3SmallDecoderLayer(config,
|
|
int(prefix.split('.')[-1]),
|
|
cache_config,
|
|
quant_config,
|
|
prefix=prefix),
|
|
prefix=f"{prefix}.layers")
|
|
|
|
self.final_layernorm = nn.LayerNorm(config.hidden_size,
|
|
eps=config.layer_norm_epsilon)
|
|
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.LongTensor,
|
|
positions: Optional[torch.LongTensor],
|
|
intermediate_tensors: Optional[IntermediateTensors],
|
|
inputs_embeds: Optional[torch.Tensor],
|
|
) -> 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)
|
|
if (self.mup_embedding_multiplier is not None
|
|
and self.mup_embedding_multiplier > 0.0):
|
|
hidden_states = hidden_states * self.mup_embedding_multiplier
|
|
else:
|
|
assert intermediate_tensors
|
|
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.final_layernorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class Phi3SmallForCausalLM(nn.Module, SupportsPP):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
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.quant_config = quant_config
|
|
self.model = Phi3SmallModel(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
self.vocab_size = config.vocab_size
|
|
self.mup_width_multiplier = config.mup_width_multiplier
|
|
self.lm_head = ParallelLMHead(
|
|
self.vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
padding_size=DEFAULT_VOCAB_PADDING_SIZE,
|
|
quant_config=quant_config,
|
|
)
|
|
if self.config.tie_word_embeddings:
|
|
self.lm_head.weight = self.model.embed_tokens.weight
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.sampler = get_sampler()
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
# tokens in tiktoken but not used
|
|
if hasattr(config, 'dummy_token_indices'):
|
|
device = self.lm_head.weight.device
|
|
self.register_buffer('dummy_token_indices',
|
|
torch.LongTensor(
|
|
config.dummy_token_indices).to(device),
|
|
persistent=False)
|
|
else:
|
|
self.dummy_token_indices = None
|
|
|
|
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.get_input_embeddings(input_ids)
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.embed_tokens = value
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, value):
|
|
self.lm_head = value
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
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)
|
|
if self.dummy_token_indices is not None and logits is not None:
|
|
logits.index_fill_(-1, self.dummy_token_indices, -torch.inf)
|
|
return logits
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor,
|
|
positions: Optional[torch.LongTensor],
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
output_hidden_states = self.model(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
intermediate_tensors=intermediate_tensors,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
output_hidden_states = output_hidden_states
|
|
return output_hidden_states
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
|
|
next_tokens = self.sampler(logits / self.mup_width_multiplier,
|
|
sampling_metadata)
|
|
return next_tokens
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: Set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
if "lm_head.weight" in name and self.config.tie_word_embeddings:
|
|
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
|