fix: remove unnecessary code, avoid copies and make deser safer

integration-tests/conftest.py

@@ -146,7 +146,6 @@ class ResponseComparator(JSONSnapshotExtension):
                 response.details, other.details
             )
 
-        # print(serialized_data)
         serialized_data = convert_data(serialized_data)
         snapshot_data = convert_data(snapshot_data)
 

router/src/lib.rs

@@ -44,15 +44,13 @@ impl HubTokenizerConfig {
         serde_json::from_str(&content).unwrap_or_default()
     }
 }
+
 mod json_object_or_string_to_string {
-    // This custom deserializer is used to handle the fact that the grammar field can be either a
-    // string or an object. In both cases we handle it as a string, but also provide this convience
-    // to the user to be flexible with the input.
-    use super::*;
-    use serde::de;
-    use serde::Deserializer;
+    use serde::{Deserialize, Deserializer};
     use serde_json::Value;
 
+    // A custom deserializer that treats both strings and objects as strings.
+    // This provides flexibility with input formats for the 'grammar' field.
     pub fn deserialize<'de, D>(deserializer: D) -> Result<String, D::Error>
     where
         D: Deserializer<'de>,
@@ -61,8 +59,13 @@ mod json_object_or_string_to_string {
 
         match value {
             Value::String(s) => Ok(s),
-            Value::Object(o) => Ok(serde_json::to_string(&o).unwrap()),
-            _ => Err(de::Error::custom("expected string or object for grammar")),
+            // Safely handle serialization and return an error if it fails
+            Value::Object(o) => {
+                serde_json::to_string(&o).map_err(|e| serde::de::Error::custom(e.to_string()))
+            }
+            _ => Err(serde::de::Error::custom(
+                "expected string or object for grammar",
+            )),
         }
     }
 }

server/text_generation_server/models/flash_causal_lm.py

@@ -1050,13 +1050,11 @@ class FlashCausalLM(Model):
             prefill_logprobs = prefill_logprobs.view(-1).tolist()
 
         # GPU <-> CPU sync
-        batch.next_token_chooser = batch.next_token_chooser.advance_grammar(
-            next_input_ids.tolist(),
-        )
         next_token_logprobs = next_token_logprobs.tolist()
         next_token_ids = next_input_ids.tolist()
         accepted_ids = accepted_ids.tolist()
         start_decode = time.time_ns()
+        batch.next_token_chooser = batch.next_token_chooser.advance_grammar(next_token_ids)
 
         # Zipped iterator
         iterator = zip(

server/text_generation_server/utils/logits_process.py

@@ -575,16 +575,14 @@ class HeterogeneousGrammarLogitProcessor(LogitsProcessor):
         fsm_grammar_states: List[int],
         mask: torch.Tensor,
     ):
+        mask = torch.full_like(logits, -math.inf)
         for i in range(logits.shape[0]):
             fsm = self.fsms[i]
             if fsm_grammar_states[i] == -1 or fsm is None:
                 continue
             allowed_tokens = fsm.allowed_token_ids(fsm_grammar_states[i])
-            mask[allowed_tokens] = 0
-            biased_scores = logits[i] + mask
-            mask.fill_(-math.inf)
-            logits[i] = biased_scores
-            
+            mask[i, allowed_tokens] = 0
+        logits += mask
         return logits
 
     def advance_batch(self, next_token_ids, fsm_grammar_states, grammars):