update scripts

Former-commit-id: 51d087cbc14bf3c7dfa06b8b66052cd80a6081be

scripts/cal_flops.py

@@ -27,7 +27,7 @@ from llamafactory.chat import ChatModel
 def calculate_flops(
     model_name_or_path: str,
     batch_size: int = 1,
-    seq_length: int = 256,
+    seq_length: int = 512,
     flash_attn: str = "auto",
 ):
     r"""

scripts/cal_lr.py

@@ -39,7 +39,7 @@ def calculate_lr(
     model_name_or_path: str,
     batch_size: int,  # total batch size, namely (batch size * gradient accumulation * world size)
     stage: Literal["pt", "sft"] = "sft",
-    dataset: str = "alpaca_en",
+    dataset: str = "alpaca_en_demo",
     dataset_dir: str = "data",
     template: str = "default",
     cutoff_len: int = 1024,  # i.e. maximum input length during training
@@ -48,7 +48,8 @@ def calculate_lr(
 ):
     r"""
     Calculates the optimal learning rate for 7B/13B models using LLaMA's hyper-parameters.
-    Usage: python cal_lr.py --model_name_or_path path_to_model --dataset alpaca_en --cutoff_len 1024 --batch_size 16
+    Usage:
+    python cal_lr.py --model_name_or_path path_to_model --dataset alpaca_en_demo --cutoff_len 1024 --batch_size 16
     """
     model_args, data_args, training_args, _, _ = get_train_args(
         dict(

scripts/cal_mfu.py

@@ -102,8 +102,9 @@ def compute_device_flops() -> float:
 
 def calculate_mfu(
     model_name_or_path: str,
-    batch_size: int,
-    seq_length: int,
+    batch_size: int = 1,
+    seq_length: int = 1024,
+    num_steps: int = 100,
     finetuning_type: str = "lora",
     flash_attn: str = "auto",
     deepspeed_stage: int = 0,
@@ -129,7 +130,7 @@ def calculate_mfu(
         "output_dir": os.path.join("saves", "test_mfu"),
         "overwrite_output_dir": True,
         "per_device_train_batch_size": batch_size,
-        "max_steps": 100,
+        "max_steps": num_steps,
         "bf16": True,
     }
     if deepspeed_stage in [2, 3]:

scripts/cal_ppl.py

@@ -60,7 +60,7 @@ def calculate_ppl(
     save_name: str,
     batch_size: int = 4,
     stage: Literal["pt", "sft", "rm"] = "sft",
-    dataset: str = "alpaca_en",
+    dataset: str = "alpaca_en_demo",
     dataset_dir: str = "data",
     template: str = "default",
     cutoff_len: int = 1024,
@@ -69,7 +69,7 @@ def calculate_ppl(
 ):
     r"""
     Calculates the ppl on the dataset of the pre-trained models.
-    Usage: python cal_ppl.py --model_name_or_path path_to_model --save_name ppl.json
+    Usage: python cal_ppl.py --model_name_or_path path_to_model --dataset alpaca_en_demo --save_name ppl.json
     """
     model_args, data_args, training_args, finetuning_args, _ = get_train_args(
         dict(

scripts/length_cdf.py

@@ -25,14 +25,14 @@ from llamafactory.model import load_tokenizer
 
 def length_cdf(
     model_name_or_path: str,
-    dataset: str = "alpaca_en",
+    dataset: str = "alpaca_en_demo",
     dataset_dir: str = "data",
     template: str = "default",
     interval: int = 1000,
 ):
     r"""
     Calculates the distribution of the input lengths in the dataset.
-    Usage: python length_cdf.py --model_name_or_path path_to_model --dataset alpaca_en --template default
+    Usage: python length_cdf.py --model_name_or_path path_to_model --dataset alpaca_en_demo --template default
     """
     model_args, data_args, training_args, _, _ = get_train_args(
         dict(

src/llamafactory/chat/vllm_engine.py

@@ -21,13 +21,18 @@ from ..data import get_template_and_fix_tokenizer
 from ..extras.constants import IMAGE_PLACEHOLDER
 from ..extras.logging import get_logger
 from ..extras.misc import get_device_count
-from ..extras.packages import is_vllm_available
+from ..extras.packages import is_pillow_available, is_vllm_available
 from ..model import load_config, load_tokenizer
 from ..model.model_utils.quantization import QuantizationMethod
 from ..model.model_utils.visual import LlavaMultiModalProjectorForYiVLForVLLM
 from .base_engine import BaseEngine, Response
 
 
+if is_pillow_available():
+    from PIL import Image
+    from PIL.Image import Image as ImageObject
+
+
 if is_vllm_available():
     from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams
     from vllm.lora.request import LoRARequest
@@ -153,6 +158,12 @@ class VllmEngine(BaseEngine):
         )
 
         if image is not None:  # add image features
+            if not isinstance(image, (str, ImageObject)):
+                raise ValueError("Expected image input is a path or PIL.Image, but got {}.".format(type(image)))
+
+            if isinstance(image, str):
+                image = Image.open(image).convert("RGB")
+
             multi_modal_data = {"image": image}
         else:
             multi_modal_data = None