Merge branch 'UI2' of https://github.com/ebolam/KoboldAI into UI2

aiserver.py

@@ -5178,6 +5178,9 @@ def raw_generate(
     if koboldai_vars.model == "ReadOnly":
         raise NotImplementedError("No loaded model")
 
+    result: GenerationResult
+    time_start = time.time()
+
     if koboldai_vars.use_colab_tpu or koboldai_vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"):
         batch_encoded = tpu_raw_generate(
             prompt_tokens=prompt_tokens,
@@ -5185,7 +5188,7 @@ def raw_generate(
             batch_count=batch_count,
             gen_settings=gen_settings
         )
-        return GenerationResult(
+        result = GenerationResult(
             out_batches=batch_encoded, prompt=prompt_tokens, is_whole_generation=True
         )
     elif koboldai_vars.model in model_functions:
@@ -5195,7 +5198,7 @@ def raw_generate(
             batch_count=batch_count,
             gen_settings=gen_settings
         )
-        return GenerationResult(
+        result = GenerationResult(
             out_batches=batch_encoded, prompt=prompt_tokens, is_whole_generation=True
         )
     elif koboldai_vars.model.startswith("RWKV"):
@@ -5205,25 +5208,33 @@ def raw_generate(
             batch_count=batch_count,
             gen_settings=gen_settings
         )
-        return GenerationResult(
+        result = GenerationResult(
             out_batches=batch_encoded, prompt=prompt_tokens, is_whole_generation=True, output_includes_prompt=True
         )
+    else:
+        # Torch HF
+        batch_encoded = torch_raw_generate(
+            prompt_tokens=prompt_tokens,
+            max_new=max_new if not bypass_hf_maxlength else int(2e9),
+            do_streaming=do_streaming,
+            do_dynamic_wi=do_dynamic_wi,
+            batch_count=batch_count,
+            gen_settings=gen_settings
+        )
+        result = GenerationResult(
+            out_batches=batch_encoded,
+            prompt=prompt_tokens,
+            is_whole_generation=False,
+            output_includes_prompt=True,
+        )
+    
+    time_end = round(time.time() - time_start, 2)
+    tokens_per_second = round(len(result.encoded[0]) / time_end, 2)
 
-    # Torch HF
-    batch_encoded = torch_raw_generate(
-        prompt_tokens=prompt_tokens,
-        max_new=max_new if not bypass_hf_maxlength else int(2e9),
-        do_streaming=do_streaming,
-        do_dynamic_wi=do_dynamic_wi,
-        batch_count=batch_count,
-        gen_settings=gen_settings
-    )
-    return GenerationResult(
-        out_batches=batch_encoded,
-        prompt=prompt_tokens,
-        is_whole_generation=False,
-        output_includes_prompt=True,
-    )
+    if not koboldai_vars.quiet:
+        logger.info(f"Generated {len(result.encoded[0])} tokens in {time_end} seconds, for an average rate of {tokens_per_second} tokens per second.")
+    
+    return result
 
 def tpu_raw_generate(
     prompt_tokens: List[int],