Merge pull request #82 from VE-FORBRYDERNE/tpu-config

Allow TPU models to specify settings/config in config.json

aiserver.py

@@ -134,6 +134,7 @@ class vars:
     wifolders_d = {}     # Dictionary of World Info folder UID-info pairs
     wifolders_l = []     # List of World Info folder UIDs
     wifolders_u = {}     # Dictionary of pairs of folder UID - list of WI UID
+    modelconfig = {}     # Raw contents of the model's config.json, or empty dictionary if none found
     lua_state   = None   # Lua state of the Lua scripting system
     lua_koboldbridge = None  # `koboldbridge` from bridge.lua
     lua_kobold  = None   # `kobold` from` bridge.lua
@@ -416,11 +417,17 @@ def loadmodelsettings():
         model_js_config = str(model_config).partition(' ')[2]
         js   = json.loads(model_js_config)
     except Exception as e:
+        try:
             try:
                 model_js_config = open(vars.custmodpth + "/config.json", "r")
             except Exception as e:
                 model_js_config = open(vars.custmodpth.replace('/', '_') + "/config.json", "r")
             js   = json.load(model_js_config)
+        except Exception as e:
+            js   = {}
+    if vars.model_type == "xglm" or js.get("modelcompat", "j") == "fairseq_lm":
+        vars.newlinemode = "s"  # Default to </s> newline mode if using XGLM
+    vars.modelconfig = js
     if("badwordsids" in js):
         vars.badwordsids = js["badwordsids"]
     if("nobreakmodel" in js):
@@ -1192,7 +1199,8 @@ else:
     # Load the TPU backend if requested
     elif(vars.model == "TPUMeshTransformerGPTJ"):
         print("{0}Initializing Mesh Transformer JAX, please wait...{1}".format(colors.PURPLE, colors.END))
-        assert vars.model == "TPUMeshTransformerGPTJ" and vars.custmodpth and os.path.isdir(vars.custmodpth)
+        if not vars.custmodpth or not os.path.isdir(vars.custmodpth):
+            raise FileNotFoundError(f"The specified model path {repr(vars.custmodpth)} is not the path to a valid folder")
         import tpu_mtj_backend
         tpu_mtj_backend.vars = vars
         tpu_mtj_backend.warper_callback = tpumtjgenerate_warper_callback
@@ -1200,7 +1208,8 @@ else:
         tpu_mtj_backend.compiling_callback = tpumtjgenerate_compiling_callback
         tpu_mtj_backend.stopped_compiling_callback = tpumtjgenerate_stopped_compiling_callback
         tpu_mtj_backend.settings_callback = tpumtjgenerate_settings_callback
-        tpu_mtj_backend.load_model(vars.custmodpth)
+        loadmodelsettings()
+        tpu_mtj_backend.load_model(vars.custmodpth, **vars.modelconfig)
         vars.allowsp = True
         vars.modeldim = int(tpu_mtj_backend.params["d_model"])
         tokenizer = tpu_mtj_backend.tokenizer
@@ -4921,7 +4930,7 @@ if(vars.model in ("TPUMeshTransformerGPTJ",)):
 def send_debug():
     if vars.debug:
         debug_info = ""
-        for variable in [["Action Length", len(vars.actions)], ["Actions Metadata Length", len(vars.actions_metadata)], ["Actions Metadata", vars.actions_metadata], ["Newline Mode", vars.newlinemode]]:
+        for variable in [["Newline Mode", vars.newlinemode], ["Action Length", len(vars.actions)], ["Actions Metadata Length", len(vars.actions_metadata)], ["Actions Metadata", vars.actions_metadata]]:
             debug_info = "{}{}: {}\n".format(debug_info, variable[0], variable[1])
         emit('from_server', {'cmd': 'debug_info', 'data': debug_info}, broadcast=True)
     

tpu_mtj_backend.py

@@ -791,12 +791,24 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs)
         "pe_rotary_dims": 64,
         "seq": 2048,
         "cores_per_replica": 8,
+        "tokenizer_class": "GPT2TokenizerFast",
+        "tokenizer": "gpt2",
     }
     params = kwargs
+    if "compat" in params:
+        default_params["compat"] = params["compat"]
+    if default_params["compat"] == "fairseq_lm":
+        default_params["tokenizer"] = "KoboldAI/fairseq-dense-125M"
     for param in default_params:
         if param not in params:
             params[param] = default_params[param]
 
+    # Load tokenizer
+    if not isinstance(params["tokenizer_class"], str) or not any(params["tokenizer_class"].endswith(s) for s in ("Tokenizer", "TokenizerFast")):
+        raise ValueError("`tokenizer_class` must be a string ending in 'Tokenizer' or 'TokenizerFast'")
+    tokenizer_class = getattr(__import__("transformers"), params["tokenizer_class"])
+    tokenizer = tokenizer_class.from_pretrained(params["tokenizer"])
+
     # Disable JAX warnings about these two functions having been renamed
     jax.host_count = jax.process_count
     jax.host_id = jax.process_index
@@ -819,7 +831,6 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs)
     devices = np.array(jax.devices()[:cores_per_replica]).reshape(mesh_shape)
     thread_resources_env = maps.ResourceEnv(maps.Mesh(devices, ('dp', 'mp')), ())
     maps.thread_resources.env = thread_resources_env
-    tokenizer = transformers.GPT2TokenizerFast.from_pretrained('gpt2')
 
     global shard_xmap, batch_xmap
     shard_xmap = __shard_xmap()