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OlivierDehaene 2024-02-20 15:43:44 +01:00
parent f4eec092c6
commit 5da4b01cc5
4 changed files with 208 additions and 257 deletions
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12
server/text_generation_server/models/__init__.py
View File

@ -52,8 +52,8 @@ try:
from text_generation_server.models.flash_llama import (
FlashLlama,
)
from text_generation_server.models.flash_golden_gate import (
FlashGoldenGate,
from text_generation_server.models.flash_gemma import (
FlashGemma,
)
from text_generation_server.models.flash_santacoder import (
FlashSantacoderSharded,
@ -315,9 +315,9 @@ def get_model(
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "golden_gate":
if model_type == "gemma":
if FLASH_ATTENTION:
return FlashGoldenGate(
return FlashGemma(
model_id,
revision,
quantize=quantize,
@ -326,7 +326,9 @@ def get_model(
use_medusa=use_medusa,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Golden Gate"))
raise NotImplementedError(
FLASH_ATT_ERROR_MESSAGE.format("Sharded Golden Gate")
)
else:
return CausalLM(
model_id,

202
server/text_generation_server/models/custom_modeling/flash_golden_gate_modeling.py → server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
View File

@ -20,11 +20,16 @@
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 (
@ -37,8 +42,168 @@ from text_generation_server.utils.layers import (
FastRMSNorm,
)
GemmaTokenizer = None
class GoldenGateConfig(PretrainedConfig):
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
# Copied from transformers.models.llama.tokenization_llama.GemmaTokenizer.default_chat_template
def default_chat_template(self):
raise NotImplementedError
# TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
# Copied from transformers.models.llama.tokenization_llama.GemmaTokenizer.build_inputs_with_special_tokens
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,
@ -93,7 +258,8 @@ class GoldenGateConfig(PretrainedConfig):
**kwargs,
)
class GoldenGateFastRMSNorm(FastRMSNorm):
class GemmaFastRMSNorm(FastRMSNorm):
@classmethod
def load(cls, prefix, weights, eps=1e-6):
weight = weights.get_tensor(f"{prefix}.weight") + 1
@ -138,7 +304,7 @@ def _load_gqa(config, prefix: str, weights):
)
class FlashGoldenGateAttention(torch.nn.Module):
class FlashGemmaAttention(torch.nn.Module):
def __init__(
self,
prefix: str,
@ -242,7 +408,7 @@ class FlashGoldenGateAttention(torch.nn.Module):
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
class GoldenGateMLP(nn.Module):
class GemmaMLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
act = config.hidden_act
@ -251,9 +417,9 @@ class GoldenGateMLP(nn.Module):
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",
approximate=(
"tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
),
)
)
# Fuse gate and up proj
@ -280,19 +446,19 @@ class GoldenGateMLP(nn.Module):
return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
class FlashGoldenGateLayer(nn.Module):
class FlashGemmaLayer(nn.Module):
def __init__(self, layer_id, config, weights):
super().__init__()
prefix = f"model.layers.{layer_id}"
self.self_attn = FlashGoldenGateAttention(
self.self_attn = FlashGemmaAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
)
self.mlp = GoldenGateMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.mlp = GemmaMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.input_layernorm = GoldenGateFastRMSNorm.load(
self.input_layernorm = GemmaFastRMSNorm.load(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.post_attention_layernorm = GoldenGateFastRMSNorm.load(
self.post_attention_layernorm = GemmaFastRMSNorm.load(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
@ -336,14 +502,14 @@ class FlashGoldenGateLayer(nn.Module):
return mlp_output, attn_res
class FlashGoldenGateModel(torch.nn.Module):
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
embed_norm = config.hidden_size**0.5
self.embed_tokens = TensorParallelEmbedding(
prefix="model.embed_tokens", weights=weights
)
@ -351,7 +517,7 @@ class FlashGoldenGateModel(torch.nn.Module):
self.layers = nn.ModuleList(
[
FlashGoldenGateLayer(
FlashGemmaLayer(
layer_id,
config,
weights,
@ -359,7 +525,7 @@ class FlashGoldenGateModel(torch.nn.Module):
for layer_id in range(config.num_hidden_layers)
]
)
self.norm = GoldenGateFastRMSNorm.load(
self.norm = GemmaFastRMSNorm.load(
prefix="model.norm", weights=weights, eps=config.rms_norm_eps
)
@ -408,11 +574,11 @@ class FlashGoldenGateModel(torch.nn.Module):
return hidden_states
class FlashGoldenGateForCausalLM(torch.nn.Module):
class FlashGemmaForCausalLM(torch.nn.Module):
def __init__(self, config, weights):
super().__init__()
self.model = FlashGoldenGateModel(config, weights)
self.model = FlashGemmaModel(config, weights)
self.lm_head = TensorParallelHead.load(
config,
prefix="model.embed_tokens" if config.tie_word_embeddings else "lm_head",

216
server/text_generation_server/models/custom_modeling/temp_tok.py
View File

@ -1,216 +0,0 @@
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. 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.
import os
from shutil import copyfile
from typing import Optional, Tuple
from tokenizers import processors
from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
from transformers.utils import logging
from transformers.utils.versions import require_version
require_version("tokenizers>=0.13.3")
GoldenGateTokenizer = 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 GoldenGateTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a GoldenGate tokenizer. Based on byte-level Byte-Pair-Encoding.
This uses notably ByteFallback and no normalization.
```python
>>> from transformers import GoldenGateTokenizerFast
>>> tokenizer = GoldenGateTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
>>> tokenizer.encode("Hello this is a test")
[1, 15043, 445, 338, 263, 1243]
```
If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
[post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`, *optional*):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
contains the vocabulary necessary to instantiate a tokenizer.
tokenizer_file (`str`, *optional*):
[tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
contains everything needed to load the tokenizer.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
extra spaces.
unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
The end of sequence token.
add_bos_token (`bool`, *optional*, defaults to `True`):
Whether or not to add an `bos_token` at the start of sequences.
add_eos_token (`bool`, *optional*, defaults to `False`):
Whether or not to add an `eos_token` at the end of sequences.
use_default_system_prompt (`bool`, *optional*, defaults to `False`):
Whether or not the default system prompt for GoldenGate should be used.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
slow_tokenizer_class = GoldenGateTokenizer
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
# Copied from transformers.models.llama.tokenization_llama.GoldenGateTokenizer.default_chat_template
def default_chat_template(self):
raise NotImplementedError
# TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
# Copied from transformers.models.llama.tokenization_llama.GoldenGateTokenizer.build_inputs_with_special_tokens
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

35
server/text_generation_server/models/flash_golden_gate.py → server/text_generation_server/models/flash_gemma.py
View File

@ -3,12 +3,12 @@ import torch.distributed
from opentelemetry import trace
from typing import Optional
from transformers import AutoTokenizer
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_golden_gate_modeling import (
FlashGoldenGateForCausalLM,
GoldenGateConfig,
from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
GemmaTokenizerFast,
FlashGemmaForCausalLM,
GemmaConfig,
)
from text_generation_server.utils import (
initialize_torch_distributed,
@ -19,7 +19,7 @@ from text_generation_server.utils import (
tracer = trace.get_tracer(__name__)
class FlashGoldenGate(FlashCausalLM):
class FlashGemma(FlashCausalLM):
def __init__(
self,
model_id: str,
@ -32,12 +32,11 @@ class FlashGoldenGate(FlashCausalLM):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashGoldenGate is only available on GPU")
raise NotImplementedError("FlashGemma is only available on GPU")
from text_generation_server.models.custom_modeling.temp_tok import GoldenGateTokenizerFast
tokenizer = GoldenGateTokenizerFast.from_pretrained(
tokenizer = GemmaTokenizerFast.from_pretrained(
model_id,
revision=revision,
padding_side="left",
@ -47,7 +46,7 @@ class FlashGoldenGate(FlashCausalLM):
from_slow=False,
)
config = GoldenGateConfig.from_pretrained(
config = GemmaConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
@ -59,18 +58,18 @@ class FlashGoldenGate(FlashCausalLM):
if config.quantize in ["gptq", "awq"]:
weights._set_gptq_params(model_id, revision)
model = FlashGoldenGateForCausalLM(config, weights)
model = FlashGemmaForCausalLM(config, weights)
if use_medusa:
from text_generation_server.utils.medusa import MedusaModel
from huggingface_hub import hf_hub_download
import json
import os
from pathlib import Path
is_local_model = (Path(use_medusa).exists() and Path(use_medusa).is_dir()) or os.getenv(
"WEIGHTS_CACHE_OVERRIDE", None
) is not None
is_local_model = (
Path(use_medusa).exists() and Path(use_medusa).is_dir()
) or os.getenv("WEIGHTS_CACHE_OVERRIDE", None) is not None
if not is_local_model:
medusa_config = hf_hub_download(
use_medusa, revision=revision, filename="config.json"
@ -81,7 +80,7 @@ class FlashGoldenGate(FlashCausalLM):
else:
medusa_config = str(Path(use_medusa) / "config.json")
medusa_head = str(Path(use_medusa) / "medusa_lm_head.pt")
with open(medusa_config, "r") as f:
config = json.load(f)
medusa_sf = medusa_head[: -len(".pt")] + ".safetensors"
@ -92,7 +91,7 @@ class FlashGoldenGate(FlashCausalLM):
model.lm_head = MedusaModel(config, weights, lm_head)
torch.distributed.barrier(group=self.process_group)
super(FlashGoldenGate, self).__init__(
super(FlashGemma, self).__init__(
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),