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Use the generation config. (#1808) 

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This commit is contained in:
Nicolas Patry 2024-04-25 19:41:50 +02:00 committed by GitHub
parent eb08b9faef
commit ee47973a2f
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
6 changed files with 74 additions and 75 deletions
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43
router/src/lib.rs
View File

@ -589,7 +589,9 @@ pub(crate) struct ChatCompletionChoice {
#[derive(Clone, Debug, Deserialize, Serialize, ToSchema)]
pub(crate) struct ChatCompletionDelta {
#[schema(example = "user")]
pub role: String,
// TODO Modify this to a true enum.
#[serde(default, skip_serializing_if = "Option::is_none")]
pub role: Option<String>,
#[serde(default, skip_serializing_if = "Option::is_none")]
#[schema(example = "What is Deep Learning?")]
pub content: Option<String>,
@ -623,6 +625,31 @@ impl ChatCompletionChunk {
logprobs: Option<ChatCompletionLogprobs>,
finish_reason: Option<String>,
) -> Self {
let delta = match (delta, tool_calls) {
(Some(delta), _) => ChatCompletionDelta {
role: Some("assistant".to_string()),
content: Some(delta),
tool_calls: None,
},
(None, Some(tool_calls)) => ChatCompletionDelta {
role: Some("assistant".to_string()),
content: None,
tool_calls: Some(DeltaToolCall {
index: 0,
id: String::new(),
r#type: "function".to_string(),
function: Function {
name: None,
arguments: tool_calls[0].to_string(),
},
}),
},
(None, None) => ChatCompletionDelta {
role: None,
content: None,
tool_calls: None,
},
};
Self {
id: String::new(),
object: "text_completion".to_string(),
@ -631,19 +658,7 @@ impl ChatCompletionChunk {
system_fingerprint,
choices: vec![ChatCompletionChoice {
index: 0,
delta: ChatCompletionDelta {
role: "assistant".to_string(),
content: delta,
tool_calls: tool_calls.map(|tc| DeltaToolCall {
index: 0,
id: String::new(),
r#type: "function".to_string(),
function: Function {
name: None,
arguments: tc[0].to_string(),
},
}),
},
delta,
logprobs,
finish_reason,
}],

8
router/src/server.rs
View File

@ -1103,7 +1103,13 @@ async fn chat_completions(
let (content, tool_calls) = if tool_grammar.is_some() {
(None, Some(vec![stream_token.token.text]))
} else {
(Some(stream_token.token.text), None)
let content = if !stream_token.token.special {
Some(stream_token.token.text)
} else {
None
};
(content, None)
};
event

52
server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
View File

@ -38,58 +38,6 @@ from text_generation_server.utils.layers import (
)
class LlamaConfig(PretrainedConfig):
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
rope_scaling=None,
rope_theta=10000.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
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.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_scaling = rope_scaling
self.rope_theta = rope_theta
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,
)
def load_attention(config, prefix, weights):
if config.num_attention_heads != config.num_key_value_heads:
return _load_gqa(config, prefix, weights)

14
server/text_generation_server/models/flash_llama.py
View File

@ -2,14 +2,13 @@ import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoConfig, AutoTokenizer
from transformers import AutoConfig, AutoTokenizer, GenerationConfig
from transformers.models.llama import LlamaTokenizer
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_llama_modeling import (
FlashLlamaForCausalLM,
LlamaConfig,
)
from text_generation_server.utils import (
initialize_torch_distributed,
@ -53,8 +52,17 @@ class FlashLlama(FlashCausalLM):
truncation_side="left",
trust_remote_code=trust_remote_code,
)
try:
generation_config = GenerationConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
if isinstance(generation_config.eos_token_id, (list, set)):
# TODO Huge hack
tokenizer._eos_token_ids = set(generation_config.eos_token_id)
except Exception:
pass
config = LlamaConfig.from_pretrained(
config = AutoConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize

7
server/text_generation_server/models/model.py
View File

@ -27,7 +27,14 @@ class Model(ABC):
):
self.model = model.eval()
self.tokenizer = tokenizer
# all_special_ids is not set correctly if the rust tokenizer is unpacked
# TODO report this to transformers.
other_special_ids = {
id for id, token in tokenizer.added_tokens_decoder.items() if token.special
}
self.all_special_ids = set(tokenizer.all_special_ids)
self.all_special_ids.update(other_special_ids)
self.requires_padding = requires_padding
self.dtype = dtype
self.device = device

25
server/text_generation_server/utils/tokens.py
View File

@ -1,5 +1,5 @@
import re
from typing import List, Optional, Tuple
from typing import List, Optional, Tuple, Set, Union
import math
import torch
@ -143,12 +143,22 @@ class StopSequenceCriteria:
class StoppingCriteria:
def __init__(
self,
eos_token_id: int,
eos_token_ids: Optional[Union[Set[int], int]],
stop_sequence_criterias: List[StopSequenceCriteria],
max_new_tokens: int = 20,
ignore_eos_token: bool = False,
):
self.eos_token_id = eos_token_id
if eos_token_ids is None:
eos_token_ids = set()
elif isinstance(eos_token_ids, int):
eos_token_ids = set([eos_token_ids])
elif isinstance(eos_token_ids, set):
eos_token_ids = eos_token_ids
else:
raise RuntimeError(
f"eos_token_ids is of invalid type {type(eos_token_ids)}, expected int, None or set[int]"
)
self.eos_token_ids = eos_token_ids
self.stop_sequence_criterias = stop_sequence_criterias
self.max_new_tokens = max_new_tokens
self.current_tokens = 0
@ -160,7 +170,10 @@ class StoppingCriteria:
if self.current_tokens >= self.max_new_tokens:
return True, FinishReason.FINISH_REASON_LENGTH
if not self.ignore_eos_token and last_token == self.eos_token_id:
if isinstance(last_token, torch.Tensor):
last_token = last_token.item()
if not self.ignore_eos_token and last_token in self.eos_token_ids:
return True, FinishReason.FINISH_REASON_EOS_TOKEN
if self.stop_sequence_criterias:
@ -184,8 +197,10 @@ class StoppingCriteria:
stop_sequence_criterias = [
StopSequenceCriteria(sequence) for sequence in pb.stop_sequences
]
# TODO Hack because eos_token_id cannot be what we want.
eos_token_id = getattr(tokenizer, "_eos_token_ids", tokenizer.eos_token_id)
return StoppingCriteria(
tokenizer.eos_token_id,
eos_token_id,
stop_sequence_criterias,
pb.max_new_tokens,
pb.ignore_eos_token,