621 lines
20 KiB
Python
621 lines
20 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
|
# and OPT implementations in this library. It has been modified from its
|
|
# original forms to accommodate minor architectural differences compared
|
|
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
|
#
|
|
# 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.
|
|
|
|
import torch
|
|
import torch.distributed
|
|
import os
|
|
from shutil import copyfile
|
|
|
|
from torch import nn
|
|
from transformers.activations import ACT2FN
|
|
from transformers.configuration_utils import PretrainedConfig
|
|
from typing import Optional, List, Tuple
|
|
from tokenizers import processors
|
|
from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
|
|
from transformers.utils import logging
|
|
|
|
from text_generation_server.utils import paged_attention, flash_attn
|
|
from text_generation_server.utils.layers import (
|
|
TensorParallelRowLinear,
|
|
TensorParallelColumnLinear,
|
|
TensorParallelEmbedding,
|
|
PositionRotaryEmbedding,
|
|
SpeculativeHead,
|
|
get_linear,
|
|
FastRMSNorm,
|
|
)
|
|
|
|
GemmaTokenizer = None
|
|
|
|
logger = logging.get_logger(__name__)
|
|
VOCAB_FILES_NAMES = {
|
|
"vocab_file": "tokenizer.model",
|
|
"tokenizer_file": "tokenizer.json",
|
|
}
|
|
|
|
PRETRAINED_VOCAB_FILES_MAP = {
|
|
"vocab_file": {
|
|
"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
|
|
},
|
|
"tokenizer_file": {
|
|
"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
|
|
},
|
|
}
|
|
B_INST, E_INST = "[INST]", "[/INST]"
|
|
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
|
|
|
|
# fmt: off
|
|
DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
|
|
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
|
|
that your responses are socially unbiased and positive in nature.
|
|
|
|
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
|
|
correct. If you don't know the answer to a question, please don't share false information."""
|
|
# fmt: on
|
|
|
|
|
|
class GemmaTokenizerFast(PreTrainedTokenizerFast):
|
|
vocab_files_names = VOCAB_FILES_NAMES
|
|
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
|
slow_tokenizer_class = GemmaTokenizer
|
|
padding_side = "left"
|
|
model_input_names = ["input_ids", "attention_mask"]
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_file=None,
|
|
tokenizer_file=None,
|
|
clean_up_tokenization_spaces=False,
|
|
unk_token="<unk>",
|
|
bos_token="<bos>",
|
|
eos_token="<eos>",
|
|
pad_token="<pad>",
|
|
add_bos_token=True,
|
|
add_eos_token=False,
|
|
use_default_system_prompt=False,
|
|
**kwargs,
|
|
):
|
|
super().__init__(
|
|
vocab_file=vocab_file,
|
|
tokenizer_file=tokenizer_file,
|
|
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
|
unk_token=unk_token,
|
|
bos_token=bos_token,
|
|
eos_token=eos_token,
|
|
pad_token=pad_token,
|
|
add_bos_token=add_bos_token,
|
|
add_eos_token=add_eos_token,
|
|
use_default_system_prompt=use_default_system_prompt,
|
|
**kwargs,
|
|
)
|
|
self._add_bos_token = add_bos_token
|
|
self._add_eos_token = add_eos_token
|
|
self.update_post_processor()
|
|
self.use_default_system_prompt = use_default_system_prompt
|
|
self.vocab_file = vocab_file
|
|
|
|
@property
|
|
def can_save_slow_tokenizer(self) -> bool:
|
|
return os.path.isfile(self.vocab_file) if self.vocab_file else False
|
|
|
|
def update_post_processor(self):
|
|
"""
|
|
Updates the underlying post processor with the current `bos_token` and `eos_token`.
|
|
"""
|
|
bos = self.bos_token
|
|
bos_token_id = self.bos_token_id
|
|
if bos is None and self.add_bos_token:
|
|
raise ValueError("add_bos_token = True but bos_token = None")
|
|
|
|
eos = self.eos_token
|
|
eos_token_id = self.eos_token_id
|
|
if eos is None and self.add_eos_token:
|
|
raise ValueError("add_eos_token = True but eos_token = None")
|
|
|
|
single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
|
|
pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
|
|
|
|
special_tokens = []
|
|
if self.add_bos_token:
|
|
special_tokens.append((bos, bos_token_id))
|
|
if self.add_eos_token:
|
|
special_tokens.append((eos, eos_token_id))
|
|
self._tokenizer.post_processor = processors.TemplateProcessing(
|
|
single=single, pair=pair, special_tokens=special_tokens
|
|
)
|
|
|
|
@property
|
|
def add_eos_token(self):
|
|
return self._add_eos_token
|
|
|
|
@property
|
|
def add_bos_token(self):
|
|
return self._add_bos_token
|
|
|
|
@add_eos_token.setter
|
|
def add_eos_token(self, value):
|
|
self._add_eos_token = value
|
|
self.update_post_processor()
|
|
|
|
@add_bos_token.setter
|
|
def add_bos_token(self, value):
|
|
self._add_bos_token = value
|
|
self.update_post_processor()
|
|
|
|
def save_vocabulary(
|
|
self, save_directory: str, filename_prefix: Optional[str] = None
|
|
) -> Tuple[str]:
|
|
if not self.can_save_slow_tokenizer:
|
|
raise ValueError(
|
|
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
|
|
"tokenizer."
|
|
)
|
|
|
|
if not os.path.isdir(save_directory):
|
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
|
return
|
|
out_vocab_file = os.path.join(
|
|
save_directory,
|
|
(filename_prefix + "-" if filename_prefix else "")
|
|
+ VOCAB_FILES_NAMES["vocab_file"],
|
|
)
|
|
|
|
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
|
|
copyfile(self.vocab_file, out_vocab_file)
|
|
|
|
return (out_vocab_file,)
|
|
|
|
@property
|
|
def default_chat_template(self):
|
|
raise NotImplementedError
|
|
|
|
# TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
|
|
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
|
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
|
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
|
|
|
output = bos_token_id + token_ids_0 + eos_token_id
|
|
|
|
if token_ids_1 is not None:
|
|
output = output + bos_token_id + token_ids_1 + eos_token_id
|
|
|
|
return output
|
|
|
|
|
|
class GemmaConfig(PretrainedConfig):
|
|
def __init__(
|
|
self,
|
|
vocab_size=256128,
|
|
hidden_size=3072,
|
|
intermediate_size=24576,
|
|
num_hidden_layers=28,
|
|
num_attention_heads=16,
|
|
num_key_value_heads=16,
|
|
head_dim=256,
|
|
hidden_act="gelu_pytorch_tanh",
|
|
max_position_embeddings=8192,
|
|
initializer_range=0.02,
|
|
rms_norm_eps=1e-6,
|
|
use_cache=True,
|
|
pad_token_id=None,
|
|
bos_token_id=1,
|
|
eos_token_id=2,
|
|
tie_word_embeddings=True,
|
|
rope_theta=10000.0,
|
|
rope_scaling=None,
|
|
attention_bias=False,
|
|
attention_dropout=0.0,
|
|
**kwargs,
|
|
):
|
|
self.vocab_size = vocab_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.hidden_size = hidden_size
|
|
self.head_dim = head_dim
|
|
self.intermediate_size = intermediate_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
|
|
# for backward compatibility
|
|
if num_key_value_heads is None:
|
|
num_key_value_heads = num_attention_heads
|
|
|
|
self.num_key_value_heads = num_key_value_heads
|
|
self.hidden_act = hidden_act
|
|
self.initializer_range = initializer_range
|
|
self.rms_norm_eps = rms_norm_eps
|
|
self.use_cache = use_cache
|
|
self.rope_theta = rope_theta
|
|
self.rope_scaling = rope_scaling
|
|
self.attention_bias = attention_bias
|
|
self.attention_dropout = attention_dropout
|
|
|
|
super().__init__(
|
|
pad_token_id=pad_token_id,
|
|
bos_token_id=bos_token_id,
|
|
eos_token_id=eos_token_id,
|
|
tie_word_embeddings=tie_word_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
class GemmaFastRMSNorm(FastRMSNorm):
|
|
@classmethod
|
|
def load(cls, prefix, weights, eps=1e-6):
|
|
weight = weights.get_tensor(f"{prefix}.weight") + 1
|
|
return cls(weight, eps)
|
|
|
|
# perform the multiplication in full precision and downcast after
|
|
def forward(self, hidden_states, residual=None):
|
|
if residual is not None:
|
|
hidden_states += residual
|
|
residual = hidden_states
|
|
hidden_states = hidden_states.to(torch.float32)
|
|
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
|
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
|
hidden_states = hidden_states * self.weight
|
|
return hidden_states.to(self.weight.dtype), residual
|
|
|
|
|
|
def load_attention(config, prefix, weights):
|
|
if config.num_attention_heads != config.num_key_value_heads:
|
|
return _load_gqa(config, prefix, weights)
|
|
else:
|
|
return TensorParallelColumnLinear.load_multi(
|
|
config,
|
|
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
|
|
dim=0,
|
|
weights=weights,
|
|
bias=False,
|
|
)
|
|
|
|
|
|
def _load_gqa(config, prefix: str, weights):
|
|
assert config.num_attention_heads % weights.process_group.size() == 0
|
|
|
|
weight = weights.get_multi_weights_col(
|
|
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
|
|
quantize=config.quantize,
|
|
dim=0,
|
|
)
|
|
|
|
if config.quantize not in ["gptq", "awq"]:
|
|
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
|
|
|
|
head_size = config.head_dim
|
|
num_heads = config.num_attention_heads // weights.process_group.size()
|
|
num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
|
|
assert list(weight.shape) == [
|
|
(num_heads + 2 * num_key_value_heads) * head_size,
|
|
config.hidden_size,
|
|
], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
|
|
|
|
return TensorParallelColumnLinear(
|
|
get_linear(weight, bias=None, quantize=config.quantize)
|
|
)
|
|
|
|
|
|
class FlashGemmaAttention(torch.nn.Module):
|
|
def __init__(
|
|
self,
|
|
prefix: str,
|
|
config,
|
|
weights,
|
|
):
|
|
super().__init__()
|
|
self.num_heads = config.num_attention_heads
|
|
self.head_size = config.head_dim
|
|
|
|
self.rotary_emb = PositionRotaryEmbedding.static(
|
|
config=config,
|
|
dim=self.head_size,
|
|
base=config.rope_theta,
|
|
device=weights.device,
|
|
)
|
|
|
|
self.softmax_scale = self.head_size**-0.5
|
|
|
|
if self.num_heads % weights.process_group.size() != 0:
|
|
raise ValueError(
|
|
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
|
|
f"and `num_shards`: {weights.process_group.size()}"
|
|
)
|
|
self.num_heads = self.num_heads // weights.process_group.size()
|
|
self.num_key_value_heads = (
|
|
config.num_key_value_heads // weights.process_group.size()
|
|
)
|
|
|
|
self.query_key_value = load_attention(config, prefix, weights)
|
|
|
|
self.o_proj = TensorParallelRowLinear.load(
|
|
config,
|
|
prefix=f"{prefix}.o_proj",
|
|
weights=weights,
|
|
bias=False,
|
|
)
|
|
self.num_groups = self.num_heads // self.num_key_value_heads
|
|
self.kv_head_mapping = torch.arange(
|
|
0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
|
|
).repeat_interleave(self.num_groups)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
cos,
|
|
sin,
|
|
cu_seqlen_prefill,
|
|
kv_cache,
|
|
block_tables,
|
|
slots,
|
|
input_lengths,
|
|
max_s,
|
|
):
|
|
qkv = self.query_key_value(hidden_states)
|
|
query, kv = qkv.split(
|
|
[
|
|
self.head_size * self.num_heads,
|
|
2 * self.head_size * self.num_key_value_heads,
|
|
],
|
|
dim=1,
|
|
)
|
|
query = query.view(-1, self.num_heads, self.head_size)
|
|
kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
|
|
|
|
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
|
|
|
|
paged_attention.reshape_and_cache(
|
|
kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
|
|
)
|
|
|
|
# output tensor
|
|
attn_output = torch.empty_like(query)
|
|
|
|
# Prefill
|
|
if cu_seqlen_prefill is not None:
|
|
# flash attention
|
|
flash_attn.attention(
|
|
query,
|
|
torch.select(kv, dim=1, index=0),
|
|
torch.select(kv, dim=1, index=1),
|
|
attn_output,
|
|
cu_seqlen_prefill,
|
|
max_s,
|
|
self.softmax_scale,
|
|
)
|
|
# Decode
|
|
else:
|
|
paged_attention.attention(
|
|
attn_output,
|
|
query,
|
|
kv_cache[0],
|
|
kv_cache[1],
|
|
self.kv_head_mapping,
|
|
self.softmax_scale,
|
|
block_tables,
|
|
input_lengths,
|
|
max_s,
|
|
)
|
|
|
|
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
|
|
|
|
|
|
class GemmaMLP(nn.Module):
|
|
def __init__(self, prefix, config, weights):
|
|
super().__init__()
|
|
act = config.hidden_act
|
|
self.act = (
|
|
ACT2FN[act]
|
|
if "gelu" not in act
|
|
else lambda x: torch.nn.functional.gelu(
|
|
x,
|
|
approximate=(
|
|
"tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
|
|
),
|
|
)
|
|
)
|
|
# Fuse gate and up proj
|
|
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
|
|
config,
|
|
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
|
|
weights=weights,
|
|
dim=0,
|
|
bias=False,
|
|
)
|
|
self.down_proj = TensorParallelRowLinear.load(
|
|
config,
|
|
prefix=f"{prefix}.down_proj",
|
|
weights=weights,
|
|
bias=False,
|
|
)
|
|
self.intermediate_size = (
|
|
config.intermediate_size // weights.process_group.size()
|
|
)
|
|
|
|
def forward(self, hidden_states):
|
|
gate_up_states = self.gate_up_proj(hidden_states)
|
|
gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
|
|
return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
|
|
|
|
|
|
class FlashGemmaLayer(nn.Module):
|
|
def __init__(self, layer_id, config, weights):
|
|
super().__init__()
|
|
prefix = f"model.layers.{layer_id}"
|
|
self.self_attn = FlashGemmaAttention(
|
|
prefix=f"{prefix}.self_attn", config=config, weights=weights
|
|
)
|
|
self.mlp = GemmaMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
|
|
|
|
self.input_layernorm = GemmaFastRMSNorm.load(
|
|
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
|
|
)
|
|
self.post_attention_layernorm = GemmaFastRMSNorm.load(
|
|
prefix=f"{prefix}.post_attention_layernorm",
|
|
weights=weights,
|
|
eps=config.rms_norm_eps,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
residual,
|
|
cos,
|
|
sin,
|
|
cu_seqlen_prefill,
|
|
kv_cache,
|
|
block_tables,
|
|
slots,
|
|
input_lengths,
|
|
max_s,
|
|
):
|
|
normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
|
|
|
|
# Self Attention
|
|
attn_output = self.self_attn(
|
|
normed_hidden_states,
|
|
cos,
|
|
sin,
|
|
cu_seqlen_prefill,
|
|
kv_cache,
|
|
block_tables,
|
|
slots,
|
|
input_lengths,
|
|
max_s,
|
|
)
|
|
|
|
# faster post attention rms norm
|
|
normed_attn_res_output, attn_res = self.post_attention_layernorm(
|
|
attn_output, res
|
|
)
|
|
|
|
mlp_output = self.mlp(normed_attn_res_output)
|
|
|
|
return mlp_output, attn_res
|
|
|
|
|
|
class FlashGemmaModel(torch.nn.Module):
|
|
def __init__(self, config, weights):
|
|
super().__init__()
|
|
|
|
process_group = weights.process_group
|
|
self.tp_rank = process_group.rank()
|
|
self.tp_world_size = process_group.size()
|
|
embed_norm = config.hidden_size**0.5
|
|
self.embed_tokens = TensorParallelEmbedding(
|
|
prefix="model.embed_tokens", weights=weights
|
|
)
|
|
self.embed_tokens.weight *= embed_norm
|
|
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
FlashGemmaLayer(
|
|
layer_id,
|
|
config,
|
|
weights,
|
|
)
|
|
for layer_id in range(config.num_hidden_layers)
|
|
]
|
|
)
|
|
self.norm = GemmaFastRMSNorm.load(
|
|
prefix="model.norm", weights=weights, eps=config.rms_norm_eps
|
|
)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
self.head_size = self.layers[0].self_attn.head_size
|
|
self.num_heads = self.layers[0].self_attn.num_heads
|
|
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
position_ids: torch.Tensor,
|
|
cu_seqlen_prefill: Optional[torch.Tensor],
|
|
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
|
block_tables: torch.Tensor,
|
|
slots: torch.Tensor,
|
|
input_lengths: torch.Tensor,
|
|
max_s: int,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
|
|
# Get rotary cos and sin for this forward
|
|
# Avoid to index in each layer
|
|
cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
|
|
position_ids, max_s, hidden_states.dtype
|
|
)
|
|
|
|
residual = None
|
|
for i, layer in enumerate(self.layers):
|
|
hidden_states, residual = layer(
|
|
hidden_states,
|
|
residual,
|
|
cos,
|
|
sin,
|
|
cu_seqlen_prefill,
|
|
kv_cache[i],
|
|
block_tables,
|
|
slots,
|
|
input_lengths,
|
|
max_s,
|
|
)
|
|
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class FlashGemmaForCausalLM(torch.nn.Module):
|
|
def __init__(self, config, weights):
|
|
super().__init__()
|
|
|
|
self.model = FlashGemmaModel(config, weights)
|
|
self.lm_head = SpeculativeHead.load(
|
|
config,
|
|
prefix="model.embed_tokens" if config.tie_word_embeddings else "lm_head",
|
|
weights=weights,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
position_ids: torch.Tensor,
|
|
cu_seqlen_prefill: Optional[torch.Tensor],
|
|
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
|
block_tables: torch.Tensor,
|
|
slots: torch.Tensor,
|
|
input_lengths: torch.Tensor,
|
|
max_s: int,
|
|
lm_head_indices: Optional[torch.Tensor] = None,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
|
|
hidden_states = self.model(
|
|
input_ids,
|
|
position_ids,
|
|
cu_seqlen_prefill,
|
|
kv_cache,
|
|
block_tables,
|
|
slots,
|
|
input_lengths,
|
|
max_s,
|
|
)
|
|
if lm_head_indices is not None:
|
|
hidden_states = hidden_states[lm_head_indices]
|
|
logits, speculative_logits = self.lm_head(hidden_states)
|
|
return logits, speculative_logits
|