Fix TPU generation modifier

aiserver.py

@@ -1001,19 +1001,7 @@ else:
         )
         return soft_tokens
 
-    def tpumtjgenerate_warper_callback(generated, scores, excluded_world_info, n_generated) -> Tuple[List[set], bool, bool]:
-        vars.generated_tkns += 1
-
-        assert len(excluded_world_info) == len(generated)
-        regeneration_required = vars.lua_koboldbridge.regeneration_required
-        halt = not vars.lua_koboldbridge.generating or vars.generated_tkns >= vars.genamt
-        vars.lua_koboldbridge.regeneration_required = False
-
-        global past
-
-        for i in range(vars.numseqs):
-            vars.lua_koboldbridge.generated[i+1][vars.generated_tkns] = int(generated[i, tpu_mtj_backend.params["seq"] + n_generated - 1].item())
-
+    def tpumtjgenerate_warper_callback(scores) -> "np.array":
         scores_shape = scores.shape
         scores_list = scores.tolist()
         vars.lua_koboldbridge.logits = vars.lua_state.table()
@@ -1029,6 +1017,21 @@ else:
         )
         assert scores.shape == scores_shape
 
+        return scores
+    
+    def tpumtjgenerate_stopping_callback(generated, n_generated, excluded_world_info) -> Tuple[List[set], bool, bool]:
+        vars.generated_tkns += 1
+
+        assert len(excluded_world_info) == len(generated)
+        regeneration_required = vars.lua_koboldbridge.regeneration_required
+        halt = not vars.lua_koboldbridge.generating or vars.generated_tkns >= vars.genamt
+        vars.lua_koboldbridge.regeneration_required = False
+
+        global past
+
+        for i in range(vars.numseqs):
+            vars.lua_koboldbridge.generated[i+1][vars.generated_tkns] = int(generated[i, tpu_mtj_backend.params["seq"] + n_generated - 1].item())
+
         if(not vars.dynamicscan or halt):
             return excluded_world_info, regeneration_required, halt
 
@@ -1054,6 +1057,7 @@ else:
         assert vars.model == "TPUMeshTransformerGPTJ" and vars.custmodpth and os.path.isdir(vars.custmodpth)
         import tpu_mtj_backend
         tpu_mtj_backend.warper_callback = tpumtjgenerate_warper_callback
+        tpu_mtj_backend.stopping_callback = tpumtjgenerate_stopping_callback
         tpu_mtj_backend.load_model(vars.custmodpth)
         vars.allowsp = True
         vars.modeldim = int(tpu_mtj_backend.params["d_model"])

tpu_mtj_backend.py

@@ -20,9 +20,12 @@ from mesh_transformer.transformer_shard import CausalTransformer, CausalTransfor
 params: Dict[str, Any] = {}
 
 
-def warper_callback(generated, logits, excluded_world_info, n_generated) -> Tuple[bool, bool]:
+def warper_callback(logits) -> np.array:
     raise NotImplementedError("`tpu_mtj_backend.warper_callback()` needs to be defined")
 
+def stopping_callback(generated, n_generated, excluded_world_info) -> Tuple[List[set], bool, bool]:
+    raise NotImplementedError("`tpu_mtj_backend.stopping_callback()` needs to be defined")
+
 
 def show_spinner():
     bar = progressbar.ProgressBar(max_value=progressbar.UnknownLength, widgets=[progressbar.Timer(), '  ', progressbar.BouncingBar(left='[', right=']', marker='█')])
@@ -340,12 +343,18 @@ class PenalizingCausalTransformer(CausalTransformer):
             generate_data, = self.generate_once_xmap(generate_data, self.state, numseqs_aux, soft_embeddings)
             for i in range(numseqs):
                 sample_data[i][2] = np.array(generate_data[i][0][0, 0], copy=True)
+            if use_callback:
+                logits = np.float32(tuple(d[2] for d in sample_data))
+                logits = warper_callback(logits)
+                for i in range(numseqs):
+                    sample_data[i][2] = logits[i]
             sample_data, sample_key = sample_func(sample_data, sample_key, _numseqs_aux, badwords, repetition_penalty, sampler_options)
+            n_generated += 1
             for i in range(numseqs):
                 generate_data[i][3] = np.tile(sample_data[i][0][sample_data[i][1]-1][np.newaxis, np.newaxis], (params["cores_per_replica"], 1, 1))
-            n_generated += 1
             if use_callback:
-                excluded_world_info, regeneration_required, halt = warper_callback(np.uint32(tuple(d[0] for d in sample_data)), np.float32(tuple(d[2] for d in sample_data)), excluded_world_info, n_generated)
+                generated = np.uint32(tuple(d[0] for d in sample_data))
+                excluded_world_info, regeneration_required, halt = stopping_callback(generated, n_generated, excluded_world_info)
                 if regeneration_required or halt:
                     break
             else: