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# 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 PagedAttention
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.sampling_metadata import SamplingMetadata
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.head_dim = self.d_model // self.total_num_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"]
if "kv_n_heads" in config.attn_config:
self.total_num_kv_heads = config.attn_config['kv_n_heads']
else:
self.total_num_kv_heads = self.total_num_heads
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,
self.total_num_kv_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
if self.total_num_kv_heads >= tp_world_size:
# Number of KV heads is greater than TP size, so we partition
# the KV heads across multiple tensor parallel GPUs.
assert self.total_num_kv_heads % tp_world_size == 0
else:
# Number of KV heads is less than TP size, so we replicate
# the KV heads across multiple tensor parallel GPUs.
assert tp_world_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_world_size)
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
# 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 = PagedAttention(self.num_heads,
self.head_dim,
scaling,
alibi_slopes=alibi_slopes,
num_kv_heads=self.num_kv_heads)
def forward(
self,
position_ids: torch.Tensor,
hidden_states: torch.Tensor,
kv_cache: KVCache,
input_metadata: InputMetadata,
) -> 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.split([self.q_size, self.kv_size, self.kv_size], 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)
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,
)
quant_config = getattr(linear_method, "quant_config", None)
self.act = get_act_fn("gelu", quant_config, intermediate_size)
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,
) -> 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,
)
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") and 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,
) -> torch.Tensor:
hidden_states = self.wte(input_ids)
for i in range(len(self.blocks)):
block = self.blocks[i]
hidden_states = block(
position_ids,
hidden_states,
kv_caches[i],
input_metadata,
)
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,
) -> torch.Tensor:
hidden_states = self.transformer(input_ids, positions, kv_caches,
input_metadata)
return hidden_states
def sample(
self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
next_tokens = self.sampler(self.lm_head_weight, hidden_states,
sampling_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):
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
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