278 lines
9.4 KiB
Python
278 lines
9.4 KiB
Python
# coding=utf-8
|
|
# Adapted from https://huggingface.co/mosaicml/mpt-7b/tree/main
|
|
import math
|
|
from typing import List, Optional, Tuple
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from vllm.model_executor.input_metadata import InputMetadata
|
|
from vllm.model_executor.layers.activation import get_act_fn
|
|
from vllm.model_executor.layers.attention import PagedAttentionWithALiBi
|
|
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
|
|
LinearMethodBase,
|
|
QKVParallelLinear,
|
|
RowParallelLinear)
|
|
from vllm.model_executor.layers.sampler import Sampler
|
|
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
|
VocabParallelEmbedding)
|
|
from vllm.model_executor.parallel_utils.parallel_state import (
|
|
get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
|
|
from vllm.model_executor.weight_utils import (default_weight_loader,
|
|
hf_model_weights_iterator)
|
|
from vllm.sequence import SamplerOutput
|
|
from vllm.transformers_utils.configs.mpt import MPTConfig
|
|
|
|
KVCache = Tuple[torch.Tensor, torch.Tensor]
|
|
|
|
|
|
def _get_alibi_slopes(
|
|
total_num_heads: int,
|
|
alibi_bias_max: int,
|
|
) -> torch.Tensor:
|
|
next_power_of_2 = 2**math.ceil(math.log2(total_num_heads))
|
|
m = torch.arange(1, next_power_of_2 + 1, dtype=torch.float32)
|
|
m = m.mul(alibi_bias_max / next_power_of_2)
|
|
slopes = 1.0 / torch.pow(2, m)
|
|
if next_power_of_2 != total_num_heads:
|
|
slopes = torch.concat([slopes[1::2], slopes[::2]])[:total_num_heads]
|
|
return slopes
|
|
|
|
|
|
class MPTAttention(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: MPTConfig,
|
|
linear_method: Optional[LinearMethodBase] = None,
|
|
):
|
|
super().__init__()
|
|
self.d_model = config.d_model
|
|
self.total_num_heads = config.n_heads
|
|
self.clip_qkv = config.attn_config["clip_qkv"]
|
|
self.qk_ln = config.attn_config["qk_ln"]
|
|
self.alibi_bias_max = config.attn_config["alibi_bias_max"]
|
|
assert not config.attn_config["prefix_lm"]
|
|
assert config.attn_config["alibi"]
|
|
|
|
# pylint: disable=invalid-name
|
|
self.Wqkv = QKVParallelLinear(
|
|
self.d_model,
|
|
self.d_model // self.total_num_heads,
|
|
self.total_num_heads,
|
|
bias=not config.no_bias,
|
|
linear_method=linear_method,
|
|
)
|
|
if self.qk_ln:
|
|
self.q_ln = nn.LayerNorm(self.d_model)
|
|
self.k_ln = nn.LayerNorm(self.d_model)
|
|
self.out_proj = RowParallelLinear(
|
|
self.d_model,
|
|
self.d_model,
|
|
bias=not config.no_bias,
|
|
linear_method=linear_method,
|
|
)
|
|
|
|
tp_world_size = get_tensor_model_parallel_world_size()
|
|
assert self.total_num_heads % tp_world_size == 0
|
|
self.num_heads = self.total_num_heads // tp_world_size
|
|
|
|
# Create the alibi slopes and slice them.
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
head_start = tp_rank * self.num_heads
|
|
head_end = (tp_rank + 1) * self.num_heads
|
|
alibi_slopes = _get_alibi_slopes(self.total_num_heads,
|
|
self.alibi_bias_max)
|
|
alibi_slopes = alibi_slopes[head_start:head_end].tolist()
|
|
|
|
self.head_dim = self.d_model // self.total_num_heads
|
|
scaling = self.head_dim**-0.5
|
|
self.attn = PagedAttentionWithALiBi(self.num_heads, self.head_dim,
|
|
scaling, alibi_slopes)
|
|
|
|
def forward(
|
|
self,
|
|
position_ids: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: KVCache,
|
|
input_metadata: InputMetadata,
|
|
cache_event: Optional[torch.cuda.Event],
|
|
) -> torch.Tensor:
|
|
del position_ids # unused.
|
|
qkv, _ = self.Wqkv(hidden_states)
|
|
if self.clip_qkv is not None:
|
|
qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
|
|
q, k, v = qkv.chunk(chunks=3, dim=-1)
|
|
if self.qk_ln:
|
|
q = self.q_ln(q)
|
|
k = self.k_ln(k)
|
|
k_cache, v_cache = kv_cache
|
|
attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata,
|
|
cache_event)
|
|
output, _ = self.out_proj(attn_output)
|
|
return output
|
|
|
|
|
|
class MPTMLP(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: MPTConfig,
|
|
linear_method: Optional[LinearMethodBase] = None,
|
|
):
|
|
super().__init__()
|
|
hidden_size = config.d_model
|
|
expansion_ratio = config.expansion_ratio
|
|
intermediate_size = expansion_ratio * hidden_size
|
|
self.up_proj = ColumnParallelLinear(
|
|
hidden_size,
|
|
intermediate_size,
|
|
bias=not config.no_bias,
|
|
linear_method=linear_method,
|
|
)
|
|
self.act = get_act_fn("gelu")
|
|
self.down_proj = RowParallelLinear(
|
|
intermediate_size,
|
|
hidden_size,
|
|
bias=not config.no_bias,
|
|
linear_method=linear_method,
|
|
)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
x, _ = self.up_proj(x)
|
|
x = self.act(x)
|
|
x, _ = self.down_proj(x)
|
|
return x
|
|
|
|
|
|
class MPTBlock(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: MPTConfig,
|
|
linear_method: Optional[LinearMethodBase] = None,
|
|
):
|
|
super().__init__()
|
|
hidden_size = config.d_model
|
|
self.norm_1 = nn.LayerNorm(hidden_size)
|
|
self.attn = MPTAttention(config, linear_method)
|
|
self.norm_2 = nn.LayerNorm(hidden_size)
|
|
self.ffn = MPTMLP(config, linear_method)
|
|
|
|
def forward(
|
|
self,
|
|
position_ids: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: KVCache,
|
|
input_metadata: InputMetadata,
|
|
cache_event: Optional[torch.cuda.Event],
|
|
) -> torch.Tensor:
|
|
x = self.norm_1(hidden_states)
|
|
x = self.attn(
|
|
position_ids=position_ids,
|
|
hidden_states=x,
|
|
kv_cache=kv_cache,
|
|
input_metadata=input_metadata,
|
|
cache_event=cache_event,
|
|
)
|
|
hidden_states = hidden_states + x
|
|
x = self.norm_2(hidden_states)
|
|
x = self.ffn(x)
|
|
hidden_states = hidden_states + x
|
|
return hidden_states
|
|
|
|
|
|
class MPTModel(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: MPTConfig,
|
|
linear_method: Optional[LinearMethodBase] = None,
|
|
):
|
|
super().__init__()
|
|
assert config.embedding_fraction == 1.0
|
|
assert config.norm_type == "low_precision_layernorm"
|
|
|
|
self.wte = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.d_model,
|
|
)
|
|
self.blocks = nn.ModuleList(
|
|
[MPTBlock(config, linear_method) for _ in range(config.n_layers)])
|
|
self.norm_f = nn.LayerNorm(config.d_model)
|
|
if config.no_bias:
|
|
for module in self.modules():
|
|
if hasattr(module, "bias"):
|
|
if isinstance(module.bias, nn.Parameter):
|
|
# Remove the bias term in Linear and LayerNorm.
|
|
module.register_parameter("bias", None)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
position_ids: torch.Tensor,
|
|
kv_caches: List[KVCache],
|
|
input_metadata: InputMetadata,
|
|
cache_events: Optional[List[torch.cuda.Event]],
|
|
) -> torch.Tensor:
|
|
hidden_states = self.wte(input_ids)
|
|
for i in range(len(self.blocks)):
|
|
if cache_events is None:
|
|
cache_event = None
|
|
else:
|
|
cache_event = cache_events[i]
|
|
block = self.blocks[i]
|
|
hidden_states = block(
|
|
position_ids,
|
|
hidden_states,
|
|
kv_caches[i],
|
|
input_metadata,
|
|
cache_event,
|
|
)
|
|
hidden_states = self.norm_f(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class MPTForCausalLM(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: MPTConfig,
|
|
linear_method: Optional[LinearMethodBase] = None,
|
|
):
|
|
super().__init__()
|
|
self.config = config
|
|
assert config.tie_word_embeddings
|
|
self.linear_method = linear_method
|
|
|
|
self.transformer = MPTModel(config, linear_method)
|
|
self.lm_head_weight = self.transformer.wte.weight
|
|
self.sampler = Sampler(config.vocab_size)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[KVCache],
|
|
input_metadata: InputMetadata,
|
|
cache_events: Optional[List[torch.cuda.Event]],
|
|
) -> SamplerOutput:
|
|
hidden_states = self.transformer(input_ids, positions, kv_caches,
|
|
input_metadata, cache_events)
|
|
next_tokens = self.sampler(self.lm_head_weight, hidden_states,
|
|
input_metadata)
|
|
return next_tokens
|
|
|
|
def load_weights(self,
|
|
model_name_or_path: str,
|
|
cache_dir: Optional[str] = None,
|
|
load_format: str = "auto",
|
|
revision: Optional[str] = None):
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
for name, loaded_weight in hf_model_weights_iterator(
|
|
model_name_or_path, cache_dir, load_format, revision):
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|