feat(server): improve flash attention import errors (#465)

@lewtun, is this enough?

Closes #458
Closes #456

server/text_generation_server/models/__init__.py

@@ -18,11 +18,43 @@ from text_generation_server.models.santacoder import SantaCoder
 from text_generation_server.models.t5 import T5Sharded
 from text_generation_server.models.gpt_neox import GPTNeoxSharded
 
+# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
+# in PyTorch 1.12 and later.
+torch.backends.cuda.matmul.allow_tf32 = True
+
+# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
+torch.backends.cudnn.allow_tf32 = True
+
+# Disable gradients
+torch.set_grad_enabled(False)
+
+__all__ = [
+    "Model",
+    "BLOOMSharded",
+    "CausalLM",
+    "FlashCausalLM",
+    "GalacticaSharded",
+    "Seq2SeqLM",
+    "SantaCoder",
+    "OPTSharded",
+    "T5Sharded",
+    "get_model",
+]
+
+FLASH_ATT_ERROR_MESSAGE = (
+    "{} requires CUDA and Flash Attention kernels to be installed.\n"
+    "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) "
+    "or install flash attention with `cd server && make install install-flash-attention`"
+)
+
 try:
-    if (
-        torch.cuda.is_available()
-        and not os.getenv("USE_FLASH_ATTENTION", "").lower() == "false"
-    ):
+    if not os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
+        if not torch.cuda.is_available():
+            FLASH_ATT_ERROR_MESSAGE = (
+                "{} requires CUDA. No compatible CUDA devices found."
+            )
+            raise ImportError("CUDA is not available")
+
         major, minor = torch.cuda.get_device_capability()
         is_sm75 = major == 7 and minor == 5
         is_sm8x = major == 8 and minor >= 0
@@ -30,6 +62,10 @@ try:
 
         supported = is_sm75 or is_sm8x or is_sm90
         if not supported:
+            FLASH_ATT_ERROR_MESSAGE = (
+                "{} requires a CUDA device with capability 7.5, > 8.0 or 9.0. "
+                "No compatible CUDA device found."
+            )
             raise ImportError(
                 f"GPU with CUDA capability {major} {minor} is not supported"
             )
@@ -52,41 +88,12 @@ except ImportError:
     )
     FLASH_ATTENTION = False
 
-__all__ = [
-    "Model",
-    "BLOOMSharded",
-    "CausalLM",
-    "FlashCausalLM",
-    "GalacticaSharded",
-    "Seq2SeqLM",
-    "SantaCoder",
-    "OPTSharded",
-    "T5Sharded",
-    "get_model",
-]
-
 if FLASH_ATTENTION:
     __all__.append(FlashNeoXSharded)
     __all__.append(FlashRWSharded)
     __all__.append(FlashSantacoderSharded)
     __all__.append(FlashLlama)
 
-FLASH_ATT_ERROR_MESSAGE = (
-    "{} requires Flash Attention CUDA kernels to be installed.\n"
-    "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) "
-    "or install flash attention with `cd server && make install install-flash-attention`"
-)
-
-# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
-# in PyTorch 1.12 and later.
-torch.backends.cuda.matmul.allow_tf32 = True
-
-# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
-torch.backends.cudnn.allow_tf32 = True
-
-# Disable gradients
-torch.set_grad_enabled(False)
-
 
 def get_model(
     model_id: str,

server/text_generation_server/utils/convert.py

@@ -16,9 +16,9 @@ def check_file_size(source_file: Path, target_file: Path):
     source_file_size = source_file.stat().st_size
     target_file_size = target_file.stat().st_size
 
-    if (source_file_size - target_file_size) / source_file_size > 0.01:
+    if (source_file_size - target_file_size) / source_file_size > 0.05:
         raise RuntimeError(
-            f"""The file size different is more than 1%:
+            f"""The file size different is more than 5%:
          - {source_file}: {source_file_size}
          - {target_file}: {target_file_size}
          """

server/text_generation_server/utils/hub.py

@@ -26,7 +26,10 @@ def weight_hub_files(
     filenames = [
         s.rfilename
         for s in info.siblings
-        if s.rfilename.endswith(extension) and len(s.rfilename.split("/")) == 1
+        if s.rfilename.endswith(extension)
+        and len(s.rfilename.split("/")) == 1
+        and "arguments" not in s.rfilename
+        and "args" not in s.rfilename
     ]
 
     if not filenames: