fix(batching): Avoid theoretical hang in batcher loop (#5)

- Avoid theoretical hang in batcher loop
- Avoid a couple of clones in the router generate method
- Keep attention mask tensors as integers
- Remove num_heads attribute

Co-authored-by: OlivierDehaene <Olivier.dehaene@gmail.com>

router/src/batcher.rs

@@ -104,10 +104,9 @@ async fn batching_task(
         // Get the next batch from the DB
         // This batch might be smaller than the maximum batch size if there are not enough requests
         // waiting in the DB
-        let mut waiting_tokens = 0;
+        while let Some((request_ids, batch)) = db.next_batch(None, max_batch_size) {
-        if let Some((request_ids, batch)) = db.next_batch(None, max_batch_size) {
             let mut cached_batch = wrap_future(client.generate(batch), request_ids, &db).await;
-            waiting_tokens += 1;
+            let mut waiting_tokens = 1;
 
             // We loop until we do not receive any cached batch from the inference server (== until
             // all requests have met their stopping criteria)
@@ -131,11 +130,11 @@ async fn batching_task(
                     if let Some((new_request_ids, new_batch)) =
                         db.next_batch(min_size, max_batch_size)
                     {
-                        // Reset waiting counter
-                        waiting_tokens = 0;
                         // Generate one token for this new batch to have the attention past in cache
                         let new_cached_batch =
                             wrap_future(client.generate(new_batch), new_request_ids, &db).await;
+                        // Reset waiting counter
+                        waiting_tokens = 1;
                         // Extend current batch with the new batch
                         if let Some(new_cached_batch) = new_cached_batch {
                             request_ids.extend(new_cached_batch.requests.iter().map(|req| req.id));

router/src/server.rs

@@ -90,11 +90,7 @@ async fn generate(
     // Validate request
     let (input_length, validated_request) = state
         .validation
-        // FIXME: can't we get rid of the cloning here??
+        .validate(req.0)
-        .validate(GenerateRequest {
-            inputs: req.inputs.clone(),
-            parameters: req.parameters.clone(),
-        })
         .await
         .map_err(|err| {
             tracing::error!("{}", err.to_string());

router/src/validation.rs

@@ -155,7 +155,7 @@ type ValidationRequest = (
 pub enum ValidationError {
     #[error("temperature must be strictly positive")]
     Temperature,
-    #[error("top_p must be >= 0.0 or < 1.0")]
+    #[error("top_p must be > 0.0 and <= 1.0")]
     TopP,
     #[error("top_k must be strictly positive")]
     TopK,

server/text_generation/models/bloom.py

@@ -82,7 +82,6 @@ class BLOOMSharded(CausalLM):
         torch.distributed.barrier(group=self.process_group)
         super(CausalLM, self).__init__(
             tokenizer=tokenizer,
-            num_heads=config.n_head // self.process_group.size(),
             device=device,
         )
 

server/text_generation/models/causal_lm.py

@@ -251,7 +251,6 @@ class CausalLM(Model):
 
         super(CausalLM, self).__init__(
             tokenizer=tokenizer,
-            num_heads=self.model.config.num_attention_heads,
             device=device,
         )
 
@@ -358,7 +357,7 @@ class CausalLM(Model):
             # Force past to be of dim [batch_size, num_heads, ...] for easy indexing
             next_batch_past_key_values = [
                 [
-                    t.view(-1, self.num_heads, *t.shape[-2:])[next_batch_keep_indices]
+                    t.view(batch.size, -1, *t.shape[-2:])[next_batch_keep_indices]
                     for t in layer
                 ]
                 for layer in past
@@ -381,7 +380,7 @@ class CausalLM(Model):
         next_batch_attention_mask = torch.cat(
             [
                 next_batch_attention_mask,
-                torch.ones((next_batch_size, 1)).to(self.device),
+                next_batch_attention_mask.new_ones(next_batch_size, 1),
             ],
             dim=1,
         )

server/text_generation/models/galactica.py

@@ -185,7 +185,6 @@ class GalacticaSharded(Galactica):
         torch.distributed.barrier(group=self.process_group)
         super(CausalLM, self).__init__(
             tokenizer=tokenizer,
-            num_heads=config.num_attention_heads // self.process_group.size(),
             device=device,
         )
 

server/text_generation/models/model.py

@@ -10,9 +10,8 @@ B = TypeVar("B", bound=Batch)
 
 
 class Model(ABC):
-    def __init__(self, tokenizer: Tokenizer, num_heads: int, device: torch.device):
+    def __init__(self, tokenizer: Tokenizer, device: torch.device):
         self.tokenizer = tokenizer
-        self.num_heads = num_heads
         self.device = device
 
     @property

server/text_generation/models/seq2seq_lm.py

@@ -87,7 +87,7 @@ class Seq2SeqLMBatch:
             inputs, return_tensors="pt", padding=True, pad_to_multiple_of=8
         ).to(device)
         # Convert decoder_input_ids to torch tensor of size [batch_size, 1]
-        decoder_input_ids = torch.tensor(decoder_input_ids).to(device).unsqueeze(-1)
+        decoder_input_ids = torch.tensor(decoder_input_ids, device=device).unsqueeze(-1)
 
         return cls(
             batch_id=pb.id,
@@ -319,7 +319,6 @@ class Seq2SeqLM(Model):
 
         super(Seq2SeqLM, self).__init__(
             tokenizer=tokenizer,
-            num_heads=self.model.config.num_attention_heads,
             device=device,
         )
 
@@ -499,7 +498,7 @@ class Seq2SeqLM(Model):
             next_batch_decoder_attention_mask = torch.cat(
                 [
                     next_batch_decoder_attention_mask,
-                    torch.ones((next_batch_size, 1)).to(self.device),
+                    next_batch_decoder_attention_mask.new_ones(next_batch_size, 1),
                 ],
                 dim=1,
             )