353 lines
12 KiB
Python
353 lines
12 KiB
Python
"""
|
|
Copyright 2023-2024 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.
|
|
"""
|
|
|
|
# Adapted from
|
|
# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/olmo.py#L1
|
|
"""Inference-only OLMo model compatible with HuggingFace weights."""
|
|
from typing import Iterable, List, Optional, Tuple
|
|
|
|
import torch
|
|
from torch import nn
|
|
from transformers import OlmoConfig
|
|
from vllm.distributed import get_tensor_model_parallel_world_size
|
|
from vllm.model_executor.layers.rotary_embedding import get_rope
|
|
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
|
|
|
from sglang.srt.layers.activation import SiluAndMul
|
|
from sglang.srt.layers.linear import (
|
|
MergedColumnParallelLinear,
|
|
QKVParallelLinear,
|
|
RowParallelLinear,
|
|
)
|
|
from sglang.srt.layers.logits_processor import LogitsProcessor
|
|
from sglang.srt.layers.quantization.base_config import QuantizationConfig
|
|
from sglang.srt.layers.radix_attention import RadixAttention
|
|
from sglang.srt.layers.vocab_parallel_embedding import (
|
|
ParallelLMHead,
|
|
VocabParallelEmbedding,
|
|
)
|
|
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
|
|
|
|
|
|
class OlmoAttention(nn.Module):
|
|
"""
|
|
This is the attention block where the output is computed as
|
|
``Attention(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
|
|
(plus another skip connection).
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: OlmoConfig,
|
|
layer_id: int = 0,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
):
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
|
|
self.total_num_heads = config.num_attention_heads
|
|
|
|
assert self.hidden_size % self.total_num_heads == 0
|
|
assert self.total_num_heads % tensor_model_parallel_world_size == 0
|
|
|
|
self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
|
|
self.head_dim = self.hidden_size // self.total_num_heads
|
|
self.max_position_embeddings = config.max_position_embeddings
|
|
self.rope_theta = config.rope_theta
|
|
self.clip_qkv = config.clip_qkv
|
|
|
|
# Attention input projection. Projects x -> (q, k, v)
|
|
self.qkv_proj = QKVParallelLinear(
|
|
self.hidden_size,
|
|
self.head_dim,
|
|
self.total_num_heads,
|
|
bias=config.attention_bias,
|
|
)
|
|
|
|
# Rotary embeddings.
|
|
self.rotary_emb = get_rope(
|
|
self.head_dim,
|
|
rotary_dim=self.head_dim,
|
|
max_position=self.max_position_embeddings,
|
|
base=self.rope_theta,
|
|
)
|
|
self.scaling = self.head_dim**-0.5
|
|
self.attn = RadixAttention(
|
|
self.num_heads,
|
|
self.head_dim,
|
|
self.scaling,
|
|
num_kv_heads=self.num_heads,
|
|
layer_id=layer_id,
|
|
)
|
|
|
|
# Attention output projection.
|
|
self.o_proj = RowParallelLinear(
|
|
self.hidden_size,
|
|
self.hidden_size,
|
|
bias=config.attention_bias,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
qkv, _ = self.qkv_proj(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)
|
|
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 OlmoMLP(nn.Module):
|
|
"""
|
|
This is the MLP block where the output is computed as
|
|
``MLP(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
|
|
(plus another skip connection).
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: OlmoConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
):
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
self.intermediate_size = config.intermediate_size
|
|
|
|
# Feed-forward input projection.
|
|
self.gate_up_proj = MergedColumnParallelLinear(
|
|
self.hidden_size,
|
|
[self.intermediate_size] * 2,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
# Activation function.
|
|
self.act_fn = SiluAndMul()
|
|
|
|
# Feed-forward output projection.
|
|
self.down_proj = RowParallelLinear(
|
|
self.intermediate_size,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
x: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
gate_up, _ = self.gate_up_proj(x)
|
|
x = self.act_fn(gate_up)
|
|
x, _ = self.down_proj(x)
|
|
return x
|
|
|
|
|
|
class OlmoDecoderLayer(nn.Module):
|
|
"""
|
|
This is a typical transformer block where the output is
|
|
computed as ``MLP(LN(x + Attention(LN(x))))``
|
|
(plus another skip connection).
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: OlmoConfig,
|
|
layer_id: int = 0,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
):
|
|
super().__init__()
|
|
# Attention block.
|
|
self.self_attn = OlmoAttention(config, layer_id, quant_config)
|
|
|
|
# MLP block.
|
|
self.mlp = OlmoMLP(config, quant_config)
|
|
|
|
# LayerNorm
|
|
self.input_layernorm = nn.LayerNorm(
|
|
config.hidden_size, elementwise_affine=False, bias=False
|
|
)
|
|
self.post_attention_layernorm = nn.LayerNorm(
|
|
config.hidden_size, elementwise_affine=False, bias=False
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
|
|
# Attention block.
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
hidden_states = self.self_attn(positions, hidden_states, forward_batch)
|
|
hidden_states = hidden_states + residual
|
|
|
|
# MLP block.
|
|
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 OlmoModel(nn.Module):
|
|
|
|
def __init__(
|
|
self, config: OlmoConfig, quant_config: Optional[QuantizationConfig] = None
|
|
):
|
|
super().__init__()
|
|
self.config = config
|
|
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size, config.hidden_size
|
|
)
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
OlmoDecoderLayer(config, layer_id, quant_config)
|
|
for layer_id in range(config.num_hidden_layers)
|
|
]
|
|
)
|
|
self.norm = nn.LayerNorm(
|
|
config.hidden_size, elementwise_affine=False, bias=False
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
) -> torch.Tensor:
|
|
"""
|
|
:param input_ids: A tensor of shape `(batch_size, seq_len)`.
|
|
"""
|
|
# Get embeddings of input.
|
|
# shape: (batch_size, seq_len, d_model)
|
|
|
|
if input_embeds is None:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
else:
|
|
hidden_states = input_embeds
|
|
|
|
# Apply blocks one-by-one.
|
|
for layer_id, decoder_layer in enumerate(self.layers):
|
|
# shape: (batch_size, seq_len, d_model)
|
|
hidden_states = decoder_layer(
|
|
positions,
|
|
hidden_states,
|
|
forward_batch,
|
|
)
|
|
|
|
# Apply final layer norm.
|
|
# shape: (batch_size, seq_len or 1, d_model)
|
|
hidden_states = self.norm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class OlmoForCausalLM(nn.Module):
|
|
"""
|
|
Extremely barebones HF model wrapper.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: OlmoConfig,
|
|
cache_config=None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
):
|
|
super().__init__()
|
|
self.config = config
|
|
self.model = OlmoModel(config, quant_config)
|
|
if config.tie_word_embeddings:
|
|
self.lm_head = self.model.embed_tokens
|
|
else:
|
|
self.unpadded_vocab_size = config.vocab_size
|
|
self.lm_head = ParallelLMHead(
|
|
self.unpadded_vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
quant_config=quant_config,
|
|
)
|
|
self.logits_processor = LogitsProcessor(config)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.model(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
forward_batch=forward_batch,
|
|
input_embeds=input_embeds,
|
|
)
|
|
return self.logits_processor(
|
|
input_ids, hidden_states, self.lm_head.weight, forward_batch
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
|
|
# Models trained using ColossalAI may include these tensors in
|
|
# the checkpoint. Skip them.
|
|
continue
|
|
# With tie_word_embeddings, we can skip lm_head.weight
|
|
# The weight might appear unnecessarily in the files if the model is
|
|
# processed with quantization, LoRA, fine-tuning, etc.
|
|
if self.config.tie_word_embeddings and "lm_head.weight" in name:
|
|
continue
|
|
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
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
# 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)
|
|
|
|
|
|
EntryClass = OlmoForCausalLM
|