fixup: autosplit, start work on metrics

backends/exllamav3/model.py

@@ -1,8 +1,6 @@
 import asyncio
 import gc
-import math
 import pathlib
-from loguru import logger
 from typing import (
     Any,
     AsyncIterator,
@@ -12,9 +10,14 @@ from typing import (
 )
 
 import torch
+from exllamav3 import AsyncGenerator, AsyncJob, Cache, Config, Model, Tokenizer
+from loguru import logger
 
 from backends.base_model_container import BaseModelContainer
 from common.concurrency import iterate_in_threadpool
+from common.gen_logging import (
+    log_metrics,
+)
 from common.multimodal import MultimodalEmbeddingWrapper
 from common.sampling import BaseSamplerRequest
 from common.templating import PromptTemplate, find_prompt_template
@@ -22,8 +25,6 @@ from common.transformers_utils import GenerationConfig
 from common.utils import unwrap
 from endpoints.core.types.model import ModelCard
 
-from exllamav3 import AsyncGenerator, AsyncJob, Config, Model, Cache, Tokenizer
-
 
 class ExllamaV3Container(BaseModelContainer):
     """Abstract base class for model containers."""
@@ -112,7 +113,7 @@ class ExllamaV3Container(BaseModelContainer):
 
                 # Reserve VRAM for each GPU
                 self.autosplit_reserve = [
-                    int(math.ceil(value/1024))
+                    value/1024
                     for value in autosplit_reserve_megabytes
                 ]
         # TODO: speculative decoding
@@ -504,15 +505,17 @@ class ExllamaV3Container(BaseModelContainer):
         )
 
         generation = {}
-        print(max_tokens)
         job = AsyncJob(
             self.generator,
             input_ids=self.tokenizer.encode(prompt, add_bos=False),
             max_new_tokens=max_tokens,
             stop_conditions=stop_conditions,
         )
+
         generated_tokens = 0
         full_response = ""
+        metrics_result = {}
+
         async for result in job:
             chunk = unwrap(result.get("text"), "")
             if chunk:
@@ -530,6 +533,25 @@ class ExllamaV3Container(BaseModelContainer):
 
             if result.get("eos"):
                 generation = self.handle_finish_chunk(result, generation)
+
+                # Save the final result for metrics logging
+                metrics_result = result
+
                 yield generation
+                break
         # Assign the active job to the request ID
         self.active_job_ids[request_id] = job
+
+        # Log the metrics if present
+        if metrics_result:
+            log_metrics(
+                request_id,
+                metrics_result.get("time_enqueued"),
+                metrics_result.get("prompt_tokens"),
+                metrics_result.get("cached_tokens"),
+                metrics_result.get("time_prefill"),
+                metrics_result.get("new_tokens"),
+                metrics_result.get("time_generate"),
+                context_len,
+                self.max_seq_len,
+            )