Add some docstrings

Author: turboderp

exllamav2/model.py

@@ -244,6 +244,32 @@ def load(
         callback_gen: Callable[[int, int], None] | None = None,
         progress: bool = False
     ):
+        """
+        Load model, regular manual split mode.
+
+        :param gpu_split:
+            List of VRAM allocations for weights and fixed buffers per GPU. Does not account for the size of the cache
+            which must be allocated with reference to the model subsequently and whose split across GPUs will depend
+            on which devices end up receiving which attention layers.
+
+            If None, only the first GPU is used.
+
+        :param lazy:
+            Only set the device map according to the split, but don't actually load any of the modules. Modules can
+            subsequently be loaded and unloaded one by one for layer-streaming mode.
+
+        :param stats:
+            Legacy, unused
+
+        :param callback:
+            Callable function that triggers after each layer has loaded, for progress update etc.
+
+        :param callback_gen:
+            Same as callback, but for use by async functions
+
+        :param progress:
+            If True, create a rich progress bar in the console while loading. Cannot be used with callbacks
+        """
 
         if progress:
             progressbar = get_basic_progress()
@@ -270,7 +296,6 @@ def load_gen(
         callback: Callable[[int, int], None] | None = None,
         callback_gen: Callable[[int, int], None] | None = None
     ):
-
         with torch.inference_mode():
 
             stats_ = self.set_device_map(gpu_split or [99999])
@@ -306,7 +331,34 @@ def load_tp(
         expect_cache_tokens: int = 0,
         expect_cache_base: type = None
     ):
+        """
+        Load model, tensor-parallel mode.
+
+        :param gpu_split:
+            List of VRAM allocations per GPU. The loader attempts to balance tensor splits to stay within these
+            allocations, accounting for an uneven distribution of attention heads and the expected size of the cache.
+
+            If None, the loader attempts to use all available GPUs and creates a split based on the currently available
+            VRAM according to nvidia-smi etc.
+
+        :param callback:
+            Callable function that triggers after each layer has loaded, for progress update etc.
+
+        :param callback_gen:
+            Same as callback, but for use by async functions
 
+        :param progress:
+            If True, create a rich progress bar in the console while loading. Cannot be used with callbacks
+
+        :param expect_cache_tokens:
+            Expected size of the cache, in tokens (i.e. max_seq_len * max_batch_size, or just the cache size for use
+            with the dynamic generator) to inform the automatic tensor split. If not provided, the configured
+            max_seq_len for the model is assumed.
+
+        :param expect_cache_base:
+            Cache type to expect, e.g. ExLlamaV2Cache_Q6. Also informs the tensor split. If not provided, FP16 cache
+            is assumed.
+        """
         if progress:
             progressbar = get_basic_progress()
             progressbar.start()
@@ -400,7 +452,31 @@ def load_autosplit(
         callback_gen: Callable[[int, int], None] | None = None,
         progress: bool = False
     ):
+        """
+        Load model, auto-split mode. This mode loads the model and builds the cache in parallel, using available
+        devices in turn and moving on to the next device whenever the previous one is full.
+
+        :param cache:
+            Cache constructed with lazy = True. Actual tensor allocation for the cache will happen while loading the
+            model.
+
+        :param reserve_vram:
+            Number of bytes to reserve on each device, either for all devices (as an int) or per-device (as a list).
 
+        :param last_id_only:
+            If True, model will be loaded in a mode that does can only output one set of logits (i.e. one token
+            position) per forward pass. This conserves memory if the model is only to be used for generating text and
+            not e.g. perplexity measurement.
+
+        :param callback:
+            Callable function that triggers after each layer has loaded, for progress update etc.
+
+        :param callback_gen:
+            Same as callback, but for use by async functions
+
+        :param progress:
+            If True, create a rich progress bar in the console while loading. Cannot be used with callbacks
+        """
         if progress:
             progressbar = get_basic_progress()
             progressbar.start()
@@ -569,6 +645,9 @@ def load_autosplit_gen(
 
 
     def unload(self):
+        """
+        Unloads the model and frees all unmanaged resources.
+        """
 
         for module in self.modules:
             module.unload()