Add vLLM quantization option for colocate

trl/experimental/gold/gold_config.py

@@ -304,6 +304,10 @@ class GOLDConfig(SFTConfig):
         default=None,
         metadata={"help": "Regex pattern used for vLLM guided decoding (optional)."},
     )
+    vllm_quantization: str | None = field(
+        default=None,
+        metadata={"help": "Quantization method to use for vLLM. If `None` (default), no quantization is applied."},
+    )
     vllm_sync_frequency: int = field(
         default=1,
         metadata={

trl/experimental/gold/gold_trainer.py

@@ -952,6 +952,7 @@ def __init__(
                     # Feed identical seed for tp groups to ensure sampling results are the same across workers
                     seed=self.accelerator.process_index // self.vllm_tensor_parallel_size,
                     enable_sleep_mode=self.vllm_enable_sleep_mode,
+                    quantization=self.args.vllm_quantization,
                 )
 
                 if self.vllm_enable_sleep_mode:

trl/scripts/grpo.py

@@ -27,6 +27,7 @@
 import sys
 from dataclasses import dataclass, field
 
+import torch
 from accelerate import logging
 from datasets import load_dataset
 
@@ -38,7 +39,9 @@
     ScriptArguments,
     TrlParser,
     get_dataset,
+    get_kbit_device_map,
     get_peft_config,
+    get_quantization_config,
 )
 from trl.rewards import accuracy_reward, get_soft_overlong_punishment, think_format_reward
 
@@ -112,6 +115,21 @@ def main(script_args, training_args, model_args, dataset_args):
                     f"Could not load reward function '{func_name}'. Expected one of "
                     f"{list(reward_funcs_registry.keys())} or a valid import path."
                 )
+    dtype = model_args.dtype if model_args.dtype in ["auto", None] else getattr(torch, model_args.dtype)
+
+    model_kwargs = dict(
+        revision=model_args.model_revision,
+        attn_implementation=model_args.attn_implementation,
+        dtype=dtype,
+    )
+    quantization_config = get_quantization_config(model_args)
+
+    if quantization_config is not None:
+        # Passing None would not be treated the same as omitting the argument, so we include it only when valid.
+        model_kwargs["device_map"] = get_kbit_device_map()
+        model_kwargs["quantization_config"] = quantization_config
+
+    training_args.model_init_kwargs = model_kwargs
 
     # Load the dataset
     if dataset_args.datasets and script_args.dataset_name:

trl/trainer/grpo_config.py

@@ -489,6 +489,10 @@ class GRPOConfig(TrainingArguments):
         default=None,
         metadata={"help": "Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled."},
     )
+    vllm_quantization: str | None = field(
+        default=None,
+        metadata={"help": "Quantization method to use for vLLM. If `None` (default), no quantization is applied."},
+    )
 
     # Parameters that control the vLLM server (only used when `vllm_mode` is `"server"`)
     vllm_server_base_url: str | None = field(

trl/trainer/grpo_trainer.py

@@ -613,6 +613,7 @@ def cast_outputs_to_original_dtype(module, args, output):
                     enable_sleep_mode=self.args.vllm_enable_sleep_mode,
                     # Important so temperature scaling/logit tweaking affects the TIS log probs
                     logprobs_mode="processed_logprobs",
+                    quantization=self.args.vllm_quantization,
                 )
                 if self.args.vllm_enable_sleep_mode:
                     self.llm.sleep(level=2)

trl/trainer/model_config.py

@@ -176,6 +176,10 @@ class ModelConfig:
         default=False,
         metadata={"help": "Whether to use nested quantization."},
     )
+    bnb_4bit_quant_storage: str | None = field(
+        default=None,
+        metadata={"help": "Quantization storage dtype"},
+    )
     # Deprecated params
     torch_dtype: str | None = field(
         default=None,

trl/trainer/online_dpo_config.py

@@ -307,6 +307,10 @@ class may differ from those in [`~transformers.TrainingArguments`].
         default=None,
         metadata={"help": "Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled."},
     )
+    vllm_quantization: str | None = field(
+        default=None,
+        metadata={"help": "Quantization method to use for vLLM. If `None` (default), no quantization is applied."},
+    )
     vllm_gpu_memory_utilization: float | None = field(
         default=0.55,
         metadata={

trl/trainer/online_dpo_trainer.py

@@ -489,6 +489,7 @@ def __init__(
                     "seed": self.accelerator.process_index // self.vllm_tensor_parallel_size,
                     # Latest vLLM v1 memory profiler is misled by the high default value (i.e., 32768)
                     "max_num_batched_tokens": 4096,
+                    "quantization": self.args.vllm_quantization,
                 }
 
                 # vLLM requires the environment variables to be set for distributed training.

trl/trainer/rloo_config.py

@@ -415,6 +415,10 @@ class RLOOConfig(TrainingArguments):
         default=None,
         metadata={"help": "Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled."},
     )
+    vllm_quantization: str | None = field(
+        default=None,
+        metadata={"help": "Quantization method to use for vLLM. If `None` (default), no quantization is applied."},
+    )
 
     # Parameters that control the vLLM server (only used when `vllm_mode` is `"server"`)
     vllm_server_base_url: str | None = field(

trl/trainer/rloo_trainer.py

@@ -528,6 +528,7 @@ def __init__(
                     max_num_batched_tokens=4096,
                     model_impl=self.args.vllm_model_impl,
                     enable_sleep_mode=self.args.vllm_enable_sleep_mode,
+                    quantization=self.args.vllm_quantization,
                 )
                 if self.args.vllm_enable_sleep_mode:
                     self.llm.sleep(level=2)

trl/trainer/utils.py

@@ -662,7 +662,7 @@ def get_quantization_config(model_args: ModelConfig) -> BitsAndBytesConfig | Non
             bnb_4bit_compute_dtype=model_args.dtype,  # For consistency with model weights, we use the same value as `dtype`
             bnb_4bit_quant_type=model_args.bnb_4bit_quant_type,
             bnb_4bit_use_double_quant=model_args.use_bnb_nested_quant,
-            bnb_4bit_quant_storage=model_args.dtype,
+            bnb_4bit_quant_storage=model_args.bnb_4bit_quant_storage,
         )
     elif model_args.load_in_8bit:
         quantization_config = BitsAndBytesConfig(