feat: support grammars in batch

benchmark/src/lib.rs

@@ -45,6 +45,7 @@ pub async fn run(
         repetition_penalty: repetition_penalty.unwrap_or(1.0),
         frequency_penalty: frequency_penalty.unwrap_or(0.0),
         watermark,
+        fsm_grammar_state: 0,
     };
 
     // Initialize terminal properties

proto/generate.proto

@@ -71,7 +71,9 @@ message NextTokenChooserParameters {
     /// token watermarking using "A Watermark for Large Language Models"
     bool watermark = 8;
     /// grammar (applied if not empty)
-    string grammar = 10;
+    repeated string grammar = 10;
+    /// fsm_grammar_state
+    repeated uint32 fsm_grammar_state = 11;
 }
 
 message StoppingCriteriaParameters {

router/client/src/client.rs

@@ -129,6 +129,7 @@ impl Client {
                     frequency_penalty: 0.1,
                     watermark: true,
                     grammar: String::new(),
+                    fsm_grammar_state: 0,
                 }),
                 stopping_parameters: Some(StoppingCriteriaParameters {
                     max_new_tokens: max_total_tokens - truncate,

router/src/health.rs

@@ -46,6 +46,7 @@ impl Health {
                     frequency_penalty: 0.0,
                     watermark: false,
                     grammar: String::new(),
+                    fsm_grammar_state: 0,
                 }),
                 stopping_parameters: Some(StoppingCriteriaParameters {
                     max_new_tokens: 1,

router/src/queue.rs

@@ -369,6 +369,7 @@ mod tests {
                     frequency_penalty: 0.0,
                     watermark: false,
                     grammar: String::new(),
+                    fsm_grammar_state: 0,
                 },
                 stopping_parameters: StoppingCriteriaParameters {
                     ignore_eos_token: false,

router/src/validation.rs

@@ -304,6 +304,7 @@ impl Validation {
             seed,
             watermark,
             grammar,
+            fsm_grammar_state: 0,
         };
         let stopping_parameters = StoppingCriteriaParameters {
             max_new_tokens,

server/text_generation_server/models/flash_causal_lm.py

@@ -99,6 +99,9 @@ class FlashCausalLMBatch(Batch):
     # Maximum number of blocks
     max_blocks: int
 
+    # The states for the grammar FSM
+    fsm_states: Dict[int, int] = None
+
     def to_pb(self) -> generate_pb2.CachedBatch:
         return generate_pb2.CachedBatch(
             id=self.batch_id,
@@ -137,6 +140,7 @@ class FlashCausalLMBatch(Batch):
         read_offsets = []
         all_input_ids = []
         requests_idx_mapping = {}
+        fsm_states = {}
 
         all_prefill_logprobs = True
         no_prefill_logprobs = True
@@ -319,6 +323,7 @@ class FlashCausalLMBatch(Batch):
             blocks=blocks,
             max_blocks=max_blocks,
             speculative_ids=None,
+            fsm_states=fsm_states,
         )
 
     @tracer.start_as_current_span("filter")
@@ -594,7 +599,6 @@ class FlashCausalLMBatch(Batch):
             dtype=batches[0].next_token_chooser.dtype,
             device=batches[0].next_token_chooser.device,
             tokenizer=batches[0].next_token_chooser.tokenizer,
-            grammar=batches[0].requests.parameters.grammar,
         )
 
         speculative_ids = (
@@ -1015,9 +1019,9 @@ class FlashCausalLM(Model):
                 # Copy batch.input_ids to prefill_token_indices
                 if prefill_logprobs:
                     if len(batch) > 1:
-                        prefill_tokens_indices[
+                        prefill_tokens_indices[out_start_index : out_end_index - 1] = (
-                            out_start_index : out_end_index - 1
+                            batch.input_ids[start_index + 1 : start_index + out_length]
-                        ] = batch.input_ids[start_index + 1 : start_index + out_length]
+                        )
                     else:
                         # Set prefill_tokens_indices to the correct slice
                         prefill_tokens_indices = batch.input_ids[
@@ -1168,7 +1172,7 @@ class FlashCausalLM(Model):
 
                 if top_n_tokens > 0:
                     all_top_tokens = []
-                    for (top_token_ids, top_token_logprobs) in zip(
+                    for top_token_ids, top_token_logprobs in zip(
                         top_token_ids, top_token_logprobs
                     ):
                         toptoken_texts = self.tokenizer.batch_decode(

server/text_generation_server/utils/tokens.py

@@ -27,6 +27,7 @@ from functools import lru_cache
 # TODO: remove when done debugging
 import time
 
+
 class NextTokenChooser:
     def __init__(
         self,
@@ -42,6 +43,7 @@ class NextTokenChooser:
         device="cpu",
         tokenizer=None,
         grammar=None,
+        fsm_grammar_state=None,
     ):
         self.watermark_processor = (
             WatermarkLogitsProcessor(device=device) if watermark else None
@@ -73,6 +75,9 @@ class NextTokenChooser:
 
         sampling = do_sample or has_warpers
 
+        self.fsm_grammar_state = fsm_grammar_state
+        self.grammars = grammar
+
         # TODO: is grammar a subset of sampling? If so, we should merge them
         if grammar:
             self.choice = Grammar(tokenizer, device, grammar)
@@ -92,7 +97,9 @@ class NextTokenChooser:
         else:
             scores, next_logprob = self.static_warper(scores)
 
-        next_id = self.choice(scores[-1]).view(1, 1)
+        next_id = self.choice(scores[-1], self.fsm_grammar_state, self.grammars).view(
+            1, 1
+        )
 
         return next_id, next_logprob
 
@@ -116,6 +123,7 @@ class NextTokenChooser:
             device=device,
             tokenizer=tokenizer,
             grammar=pb.grammar,
+            fsm_grammar_state=pb.fsm_grammar_state,
         )
 
 
@@ -222,6 +230,7 @@ class HeterogeneousNextTokenChooser:
         seeds: List[int],
         tokenizer=None,
         grammar=None,
+        fsm_grammar_states=None,
     ):
         warpers = []
 
@@ -275,9 +284,8 @@ class HeterogeneousNextTokenChooser:
 
         self.warpers = warpers
 
-        first_grammar = grammar[0] if grammar else None
+        if grammar is not None:
-        if first_grammar:
+            self.choice = Grammar(tokenizer, device)
-            self.choice = Grammar(tokenizer, device, first_grammar)
         elif any(do_sample):
             self.choice = HeterogeneousSampling(do_sample, seeds, device)
         else:
@@ -288,6 +296,9 @@ class HeterogeneousNextTokenChooser:
         self.do_sample = do_sample
         self.dtype = dtype
         self.device = device
+        self.tokenizer = tokenizer
+        self.fsm_grammar_states = fsm_grammar_states
+        self.grammars = grammar
 
     def __call__(
         self,
@@ -320,7 +331,7 @@ class HeterogeneousNextTokenChooser:
             for warper in self.warpers:
                 _scores = warper(input_ids, _scores)
 
-            _next_ids = self.choice(_scores)
+            _next_ids = self.choice(_scores, self.fsm_grammar_states, self.grammars)
             scores[:, j] = _scores
             next_ids[:, j] = _next_ids
         next_ids = next_ids.view(B * S)
@@ -398,7 +409,7 @@ class HeterogeneousNextTokenChooser:
         self.do_sample = [self.do_sample[i] for i in indices]
 
         if self.use_grammar or any(self.do_sample):
-            self.choice.filter(indices)
+            self.choice.filter(indices, self.fsm_grammar_states, self.grammars)
         else:
             self.choice = Greedy()
 
@@ -426,6 +437,7 @@ class HeterogeneousNextTokenChooser:
             dtype=dtype,
             tokenizer=tokenizer,
             grammar=[pb_.grammar for pb_ in pb],
+            fsm_grammar_states=[pb_.fsm_grammar_state for pb_ in pb],
         )
 
 
@@ -444,39 +456,51 @@ class Sampling:
 
 
 class Greedy:
-    def __call__(self, logits):
+    def __call__(self, logits, *args):
         return logits.argmax(dim=-1)
 
+    def filter(self, indices, *args):
+        return self
 
 class Grammar:
     fsm_state: DefaultDict[int, int]
     fsm: RegexFSM
 
-    def __init__(self, tokenizer, device, grammar):
+    def __init__(self, tokenizer, device):
-        fsm = self.compile_fsm(grammar, tokenizer)
-        self.fsm = fsm
-        self.fsm_state = defaultdict(int)
         self.device = device
+        self.tokenizer = tokenizer
 
-    def __call__(self, logits):
+    def __call__(self, logits, fsm_grammar_states, grammars):
-        seq_id = 0
+        empty = torch.ones(logits.shape[0], dtype=torch.int64, device=logits.device)
+        try:
+            for i in range(len(fsm_grammar_states)):
+                if fsm_grammar_states[i] == -1:
+                    continue
 
-        if self.fsm_state[seq_id] == -1:
+                # this is cached and should be fast after the first time
-            return self.fsm_state[seq_id].eos_token_id
+                fsm = self.compile_fsm(grammars[i], self.tokenizer)
-
+                allowed_tokens = fsm.allowed_token_ids(fsm_grammar_states[i])
-        allowed_tokens = self.fsm.allowed_token_ids(self.fsm_state[seq_id])
                 mask = torch.full((logits.shape[-1],), -math.inf, device=self.device)
                 mask[allowed_tokens] = 0
-        biased_scores = logits + mask
+                biased_scores = logits[i : i + 1] + mask
-
-        # greedly pick the token with the highest score
                 greedy = biased_scores.argmax(dim=-1)
 
-        # now update the fsm state
+                # if greedy is empty, return the eos token
-        self.fsm_state[seq_id] = self.fsm.next_state(
+                if greedy.shape[0] == 0:
-            self.fsm_state[seq_id], greedy.item()
+                    continue
+
+                # import ipdb; ipdb.set_trace()
+                fsm_grammar_states[i] = fsm.next_state(
+                    fsm_grammar_states[i], greedy.item()
                 )
-        return greedy
+
+                empty[i] = greedy.item()
+        except Exception as e:
+            print(f"Exception: {e}")
+            import ipdb
+
+            ipdb.set_trace()
+        return empty
 
     @lru_cache(maxsize=32, typed=True)
     def compile_fsm(self, schema, tokenizer):
@@ -488,7 +512,6 @@ class Grammar:
         print(f"Compile FSM: {time.time() - start_time}")
         return fsm
 
-
     def adapt_tokenizer(self, tokenizer):
         """Adapt tokenizer to work with the FSM.
 
@@ -515,6 +538,18 @@ class Grammar:
 
         return tokenizer
 
+    def filter(self, indices, fsm_grammar_states, grammars):
+        new_fsm_grammar_states = []
+        new_grammars = []
+
+        for i in indices:
+            new_fsm_grammar_states.append(fsm_grammar_states[i])
+            new_grammars.append(grammars[i])
+
+        self.fsm_state = new_fsm_grammar_states
+        self.fsm = new_grammars
+        return self
+
 
 class HeterogeneousSampling:
     r"""
@@ -534,7 +569,7 @@ class HeterogeneousSampling:
 
         self.greedy = Greedy()
 
-    def __call__(self, logits):
+    def __call__(self, logits, fsm_grammar_states, grammars):
         out = torch.empty(logits.shape[0], dtype=torch.int64, device=logits.device)
         if self.greedy_indices:
             # Computing for all indices is faster than slicing