tiny fix

src/llamafactory/api/chat.py

@@ -107,15 +107,14 @@ def _process_request(
                     input_messages.append({"role": ROLE_MAPPING[message.role], "content": input_item.text})
                 else:
                     image_url = input_item.image_url.url
-                    if re.match("^data:image\/(png|jpg|jpeg|gif|bmp);base64,(.+)$", image_url):
-                        image_data = base64.b64decode(image_url.split(",", maxsplit=1)[1])
-                        image_path = io.BytesIO(image_data)
+                    if re.match(r"^data:image\/(png|jpg|jpeg|gif|bmp);base64,(.+)$", image_url):  # base64 image
+                        image_stream = io.BytesIO(base64.b64decode(image_url.split(",", maxsplit=1)[1]))
                     elif os.path.isfile(image_url):  # local file
-                        image_path = open(image_url, "rb")
+                        image_stream = open(image_url, "rb")
                     else:  # web uri
-                        image_path = requests.get(image_url, stream=True).raw
+                        image_stream = requests.get(image_url, stream=True).raw
 
-                    image = np.array(Image.open(image_path).convert("RGB"))
+                    image = np.array(Image.open(image_stream).convert("RGB"))
         else:
             input_messages.append({"role": ROLE_MAPPING[message.role], "content": message.content})
 

src/llamafactory/extras/misc.py

@@ -156,6 +156,18 @@ def get_logits_processor() -> "LogitsProcessorList":
     return logits_processor
 
 
+def get_peak_memory() -> Tuple[int, int]:
+    r"""
+    Gets the peak memory usage for the current device (in Bytes).
+    """
+    if is_torch_npu_available():
+        return torch.npu.max_memory_allocated(), torch.npu.max_memory_reserved()
+    elif is_torch_cuda_available():
+        return torch.cuda.max_memory_allocated(), torch.cuda.max_memory_reserved()
+    else:
+        return 0, 0
+
+
 def has_tokenized_data(path: "os.PathLike") -> bool:
     r"""
     Checks if the path has a tokenized dataset.

src/llamafactory/train/callbacks.py

@@ -35,6 +35,7 @@ from transformers.utils import (
 
 from ..extras.constants import TRAINER_LOG, V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
 from ..extras.logging import LoggerHandler, get_logger
+from ..extras.misc import get_peak_memory
 
 
 if is_safetensors_available():
@@ -304,14 +305,21 @@ class LogCallback(TrainerCallback):
             percentage=round(self.cur_steps / self.max_steps * 100, 2) if self.max_steps != 0 else 100,
             elapsed_time=self.elapsed_time,
             remaining_time=self.remaining_time,
-            throughput="{:.2f}".format(state.num_input_tokens_seen / (time.time() - self.start_time)),
-            total_tokens=state.num_input_tokens_seen,
         )
+        if state.num_input_tokens_seen:
+            logs["throughput"] = round(state.num_input_tokens_seen / (time.time() - self.start_time), 2)
+            logs["total_tokens"] = state.num_input_tokens_seen
+
+        if os.environ.get("RECORD_VRAM", "0").lower() in ["true", "1"]:
+            vram_allocated, vram_reserved = get_peak_memory()
+            logs["vram_allocated"] = round(vram_allocated / 1024 / 1024 / 1024, 2)
+            logs["vram_reserved"] = round(vram_reserved / 1024 / 1024 / 1024, 2)
+
         logs = {k: v for k, v in logs.items() if v is not None}
         if self.webui_mode and all(key in logs for key in ["loss", "learning_rate", "epoch"]):
             logger.info(
                 "{{'loss': {:.4f}, 'learning_rate': {:2.4e}, 'epoch': {:.2f}, 'throughput': {}}}".format(
-                    logs["loss"], logs["learning_rate"], logs["epoch"], logs["throughput"]
+                    logs["loss"], logs["learning_rate"], logs["epoch"], logs.get("throughput")
                 )
             )