[Frontend] Dynamic RoPE scaling (vllm-project#4638)

Author: sasha0552

tests/test_config.py

@@ -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

vllm/config.py

@@ -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 @@ def __init__(
         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 @@ def __init__(
         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 @@ def __init__(
         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,

vllm/engine/arg_utils.py

@@ -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 @@ def add_cli_args(
                             '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 @@ def create_engine_config(self, ) -> EngineConfig:
         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,

vllm/engine/llm_engine.py

@@ -104,10 +104,11 @@ def __init__(
             "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 @@ def __init__(
             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,

vllm/transformers_utils/config.py

@@ -2,9 +2,12 @@
 
 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 @@
 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