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[Frontend] Dynamic RoPE scaling (#4638)

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sasha0552 2024-05-22 05:32:35 +00:00 committed by GitHub
parent 99eff67ba9
commit 9b9a10d6cb
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5 changed files with 89 additions and 12 deletions
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56
tests/test_config.py
View File

@ -36,4 +36,58 @@ def test_get_sliding_window():
assert mistral_model_config.get_sliding_window() is None
mistral_model_config.hf_config.sliding_window = TEST_SLIDING_WINDOW
assert mistral_model_config.get_sliding_window() == TEST_SLIDING_WINDOW
assert mistral_model_config.get_sliding_window() == TEST_SLIDING_WINDOW
def test_rope_scaling():
TEST_ROPE_SCALING = {"type": "dynamic", "factor": 2.0}
LONGCHAT_ROPE_SCALING = {"type": "linear", "factor": 8.0}
llama_model_config = ModelConfig(
"meta-llama/Meta-Llama-3-8B-Instruct",
"meta-llama/Meta-Llama-3-8B-Instruct",
tokenizer_mode="auto",
trust_remote_code=False,
dtype="float16",
seed=0,
)
assert getattr(llama_model_config.hf_config, "rope_scaling", None) is None
assert llama_model_config.max_model_len == 8192
llama_model_config = ModelConfig(
"meta-llama/Meta-Llama-3-8B-Instruct",
"meta-llama/Meta-Llama-3-8B-Instruct",
tokenizer_mode="auto",
trust_remote_code=False,
dtype="float16",
seed=0,
rope_scaling=TEST_ROPE_SCALING,
)
assert getattr(llama_model_config.hf_config, "rope_scaling",
None) == TEST_ROPE_SCALING
assert llama_model_config.max_model_len == 16384
longchat_model_config = ModelConfig(
"lmsys/longchat-13b-16k",
"lmsys/longchat-13b-16k",
tokenizer_mode="auto",
trust_remote_code=False,
dtype="float16",
seed=0,
)
assert getattr(longchat_model_config.hf_config, "rope_scaling",
None) == LONGCHAT_ROPE_SCALING
assert longchat_model_config.max_model_len == 16384
longchat_model_config = ModelConfig(
"lmsys/longchat-13b-16k",
"lmsys/longchat-13b-16k",
tokenizer_mode="auto",
trust_remote_code=False,
dtype="float16",
seed=0,
rope_scaling=TEST_ROPE_SCALING,
)
assert getattr(longchat_model_config.hf_config, "rope_scaling",
None) == TEST_ROPE_SCALING
assert longchat_model_config.max_model_len == 4096

7
vllm/config.py
View File

@ -45,6 +45,9 @@ class ModelConfig:
code_revision: The specific revision to use for the model code on
Hugging Face Hub. It can be a branch name, a tag name, or a
commit id. If unspecified, will use the default version.
rope_scaling: Dictionary containing the scaling configuration for the
RoPE embeddings. When using this flag, don't update
`max_position_embeddings` to the expected new maximum.
tokenizer_revision: The specific tokenizer version to use. It can be a
branch name, a tag name, or a commit id. If unspecified, will use
the default version.
@ -84,6 +87,7 @@ class ModelConfig:
seed: int,
revision: Optional[str] = None,
code_revision: Optional[str] = None,
rope_scaling: Optional[dict] = None,
tokenizer_revision: Optional[str] = None,
max_model_len: Optional[int] = None,
quantization: Optional[str] = None,
@ -102,6 +106,7 @@ class ModelConfig:
self.seed = seed
self.revision = revision
self.code_revision = code_revision
self.rope_scaling = rope_scaling
self.tokenizer_revision = tokenizer_revision
self.quantization = quantization
self.quantization_param_path = quantization_param_path
@ -116,7 +121,7 @@ class ModelConfig:
self.skip_tokenizer_init = skip_tokenizer_init
self.hf_config = get_config(self.model, trust_remote_code, revision,
code_revision)
code_revision, rope_scaling)
self.hf_text_config = get_hf_text_config(self.hf_config)
self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
self.max_model_len = _get_and_verify_max_len(self.hf_text_config,

18
vllm/engine/arg_utils.py
View File

@ -1,5 +1,6 @@
import argparse
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
@ -49,6 +50,7 @@ class EngineArgs:
disable_log_stats: bool = False
revision: Optional[str] = None
code_revision: Optional[str] = None
rope_scaling: Optional[dict] = None
tokenizer_revision: Optional[str] = None
quantization: Optional[str] = None
enforce_eager: bool = False
@ -330,6 +332,11 @@ class EngineArgs:
'None, we assume the model weights are not '
'quantized and use `dtype` to determine the data '
'type of the weights.')
parser.add_argument('--rope-scaling',
default=None,
type=json.loads,
help='RoPE scaling configuration in JSON format. '
'For example, {"type":"dynamic","factor":2.0}')
parser.add_argument('--enforce-eager',
action='store_true',
help='Always use eager-mode PyTorch. If False, '
@ -548,11 +555,12 @@ class EngineArgs:
model_config = ModelConfig(
self.model, self.tokenizer, self.tokenizer_mode,
self.trust_remote_code, self.dtype, self.seed, self.revision,
self.code_revision, self.tokenizer_revision, self.max_model_len,
self.quantization, self.quantization_param_path,
self.enforce_eager, self.max_context_len_to_capture,
self.max_seq_len_to_capture, self.max_logprobs,
self.skip_tokenizer_init, self.served_model_name)
self.code_revision, self.rope_scaling, self.tokenizer_revision,
self.max_model_len, self.quantization,
self.quantization_param_path, self.enforce_eager,
self.max_context_len_to_capture, self.max_seq_len_to_capture,
self.max_logprobs, self.skip_tokenizer_init,
self.served_model_name)
cache_config = CacheConfig(self.block_size,
self.gpu_memory_utilization,
self.swap_space, self.kv_cache_dtype,

10
vllm/engine/llm_engine.py
View File

@ -104,10 +104,11 @@ class LLMEngine:
"Initializing an LLM engine (v%s) with config: "
"model=%r, speculative_config=%r, tokenizer=%r, "
"skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, "
"tokenizer_revision=%s, trust_remote_code=%s, dtype=%s, "
"max_seq_len=%d, download_dir=%r, load_format=%s, "
"tensor_parallel_size=%d, disable_custom_all_reduce=%s, "
"quantization=%s, enforce_eager=%s, kv_cache_dtype=%s, "
"rope_scaling=%r, tokenizer_revision=%s, "
"trust_remote_code=%s, dtype=%s, max_seq_len=%d, "
"download_dir=%r, load_format=%s, tensor_parallel_size=%d, "
"disable_custom_all_reduce=%s, quantization=%s, "
"enforce_eager=%s, kv_cache_dtype=%s, "
"quantization_param_path=%s, device_config=%s, "
"decoding_config=%r, seed=%d, served_model_name=%s)",
vllm.__version__,
@ -117,6 +118,7 @@ class LLMEngine:
model_config.skip_tokenizer_init,
model_config.tokenizer_mode,
model_config.revision,
model_config.rope_scaling,
model_config.tokenizer_revision,
model_config.trust_remote_code,
model_config.dtype,

10
vllm/transformers_utils/config.py
View File

@ -2,9 +2,12 @@ from typing import Dict, Optional
from transformers import AutoConfig, PretrainedConfig
from vllm.logger import init_logger
from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
JAISConfig, MPTConfig, RWConfig)
logger = init_logger(__name__)
_CONFIG_REGISTRY: Dict[str, PretrainedConfig] = {
"chatglm": ChatGLMConfig,
"dbrx": DbrxConfig,
@ -18,7 +21,8 @@ _CONFIG_REGISTRY: Dict[str, PretrainedConfig] = {
def get_config(model: str,
trust_remote_code: bool,
revision: Optional[str] = None,
code_revision: Optional[str] = None) -> PretrainedConfig:
code_revision: Optional[str] = None,
rope_scaling: Optional[dict] = None) -> PretrainedConfig:
try:
config = AutoConfig.from_pretrained(
model,
@ -41,6 +45,10 @@ def get_config(model: str,
config = config_class.from_pretrained(model,
revision=revision,
code_revision=code_revision)
if rope_scaling is not None:
logger.info("Updating rope_scaling from %r to %r",
getattr(config, "rope_scaling", None), rope_scaling)
config.update({"rope_scaling": rope_scaling})
return config