feat: Support stop sequences (#7)

README.md

@@ -15,6 +15,8 @@ to power Bloom, BloomZ and MT0-XXL api-inference widgets.
 - Quantization with [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
 - [Safetensors](https://github.com/huggingface/safetensors) weight loading
 - 45ms per token generation for BLOOM with 8xA100 80GB
+- Logits warpers (temperature scaling, topk ...)
+- Stop sequences
 
 ## Officially supported models
 

proto/generate.proto

@@ -28,12 +28,23 @@ message ClearCacheRequest {}
 message ClearCacheResponse {}
 
 message LogitsWarperParameters {
+    /// exponential scaling output probability distribution
     float temperature = 1;
+    /// restricting to the k highest probability elements
     uint32 top_k = 2;
+    /// restricting to top tokens summing to prob_cut_off <= prob_cut_off
     float top_p = 3;
+    /// apply sampling on the logits
     bool do_sample = 4;
 }
 
+message StoppingCriteriaParameters {
+    /// Maximum number of generated tokens
+    uint32 max_new_tokens = 1;
+    /// Optional stopping sequences
+    repeated string stop_sequences = 2;
+}
+
 message Request {
     /// Request ID
     uint64 id = 1;
@@ -43,8 +54,8 @@ message Request {
     uint32 input_length = 3;
     /// Logits Warper Parameters
     LogitsWarperParameters parameters = 4;
-    /// Stopping criteria
-    uint32 max_new_tokens = 5;
+    /// Stopping Criteria Parameters
+    StoppingCriteriaParameters stopping_parameters = 5;
 }
 
 message Batch {
@@ -63,6 +74,8 @@ message GeneratedText {
     string output = 2;
     /// Number of generated tokens
     uint32 tokens = 3;
+    /// Finish reason
+    string finish_reason = 4;
 }
 
 message GenerateRequest {

router/client/src/lib.rs

@@ -6,7 +6,9 @@ mod pb;
 mod sharded_client;
 
 pub use client::Client;
-pub use pb::generate::v1::{Batch, GeneratedText, LogitsWarperParameters, Request};
+pub use pb::generate::v1::{
+    Batch, GeneratedText, LogitsWarperParameters, Request, StoppingCriteriaParameters,
+};
 pub use sharded_client::ShardedClient;
 use thiserror::Error;
 use tonic::transport;

router/src/batcher.rs

@@ -190,6 +190,7 @@ fn send_generated(finished: Vec<GeneratedText>, db: &Db) {
         let response = InferResponse {
             output: output.output,
             tokens: output.tokens,
+            finish_reason: output.finish_reason,
             queued: entry.time,
             start: entry.batch_time.unwrap(), // unwrap is always valid
             end: Instant::now(),
@@ -203,6 +204,7 @@ fn send_generated(finished: Vec<GeneratedText>, db: &Db) {
 pub(crate) struct InferResponse {
     pub(crate) output: String,
     pub(crate) tokens: u32,
+    pub(crate) finish_reason: String,
     pub(crate) queued: Instant,
     pub(crate) start: Instant,
     pub(crate) end: Instant,

router/src/db.rs

@@ -4,7 +4,9 @@ use crate::{GenerateParameters, GenerateRequest};
 use parking_lot::Mutex;
 use std::collections::BTreeMap;
 use std::sync::Arc;
-use text_generation_client::{Batch, ClientError, LogitsWarperParameters, Request};
+use text_generation_client::{
+    Batch, ClientError, LogitsWarperParameters, Request, StoppingCriteriaParameters,
+};
 use tokio::sync::oneshot::Sender;
 use tokio::time::Instant;
 
@@ -72,7 +74,9 @@ impl State {
                 parameters: Some(LogitsWarperParameters::from(
                     entry.request.parameters.clone(),
                 )),
-                max_new_tokens: entry.request.parameters.max_new_tokens,
+                stopping_parameters: Some(StoppingCriteriaParameters::from(
+                    entry.request.parameters.clone(),
+                )),
             });
 
             ids.push(*id);
@@ -168,3 +172,12 @@ impl From<GenerateParameters> for LogitsWarperParameters {
         }
     }
 }
+
+impl From<GenerateParameters> for StoppingCriteriaParameters {
+    fn from(parameters: GenerateParameters) -> Self {
+        Self {
+            stop_sequences: parameters.stop,
+            max_new_tokens: parameters.max_new_tokens,
+        }
+    }
+}

router/src/lib.rs

@@ -21,6 +21,7 @@ pub(crate) struct GenerateParameters {
     pub do_sample: bool,
     #[serde(default = "default_max_new_tokens")]
     pub max_new_tokens: u32,
+    pub stop: Vec<String>,
 }
 
 fn default_temperature() -> f32 {
@@ -50,6 +51,7 @@ fn default_parameters() -> GenerateParameters {
         top_p: default_top_p(),
         do_sample: default_do_sample(),
         max_new_tokens: default_max_new_tokens(),
+        stop: vec![],
     }
 }
 
@@ -63,6 +65,7 @@ pub(crate) struct GenerateRequest {
 #[derive(Serialize)]
 pub(crate) struct GeneratedText {
     pub generated_text: String,
+    pub finish_reason: String,
 }
 
 #[derive(Serialize)]

router/src/server.rs

@@ -53,6 +53,7 @@ async fn health(state: Extension<ServerState>) -> Result<(), (StatusCode, Json<E
                     top_p: 1.0,
                     do_sample: false,
                     max_new_tokens: 1,
+                    stop: vec![],
                 },
             },
         )
@@ -88,11 +89,8 @@ async fn generate(
     })?;
 
     // Validate request
-    let (input_length, validated_request) = state
-        .validation
-        .validate(req.0)
-        .await
-        .map_err(|err| {
+    let (input_length, validated_request) =
+        state.validation.validate(req.0).await.map_err(|err| {
             tracing::error!("{}", err.to_string());
             err
         })?;
@@ -148,6 +146,7 @@ async fn generate(
     // Send response
     let response = vec![GeneratedText {
         generated_text: response.output,
+        finish_reason: response.finish_reason,
     }];
     Ok((headers, Json(response)))
 }

router/src/validation.rs

@@ -121,6 +121,14 @@ fn validation_worker(
                 .unwrap_or(());
             continue;
         }
+        if request.parameters.stop.len() > 4 {
+            response_tx
+                .send(Err(ValidationError::StopSequence(
+                    request.parameters.stop.len(),
+                )))
+                .unwrap_or(());
+            continue;
+        }
 
         // Get the number of tokens in the input
         match tokenizer.encode(request.inputs.clone(), false) {
@@ -163,6 +171,8 @@ pub enum ValidationError {
     MaxNewTokens,
     #[error("inputs must have less than {1} tokens. Given: {0}")]
     InputLength(usize, usize),
+    #[error("stop supports up to 4 stop sequences. Given: {0}")]
+    StopSequence(usize),
     #[error("tokenizer error {0}")]
     Tokenizer(String),
 }

server/tests/conftest.py

@@ -15,6 +15,11 @@ def default_pb_parameters():
     )
 
 
+@pytest.fixture
+def default_pb_stop_parameters():
+    return generate_pb2.StoppingCriteriaParameters(stop_sequences=[], max_new_tokens=10)
+
+
 @pytest.fixture(scope="session")
 def bloom_560m_tokenizer():
     return AutoTokenizer.from_pretrained("bigscience/bloom-560m", padding_side="left")

server/tests/models/test_bloom.py

@@ -9,13 +9,13 @@ from text_generation.models.bloom import BloomCausalLMBatch, BLOOM
 
 
 @pytest.fixture
-def default_pb_request(default_pb_parameters):
+def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
     return generate_pb2.Request(
         id=0,
         inputs="Test",
         input_length=1,
         parameters=default_pb_parameters,
-        max_new_tokens=10,
+        stopping_parameters=default_pb_stop_parameters,
     )
 
 
@@ -36,7 +36,7 @@ def default_multi_requests_bloom_batch(default_pb_request, bloom_560m_tokenizer)
     req_0 = copy(default_pb_request)
     req_1 = default_pb_request
     req_1.id = 1
-    req_1.max_new_tokens = 5
+    req_1.stopping_parameters.max_new_tokens = 5
 
     batch_pb = generate_pb2.Batch(id=0, requests=[req_0, req_1], size=2)
     return BloomCausalLMBatch.from_pb(
@@ -56,7 +56,6 @@ def test_batch_from_pb(default_pb_batch, default_bloom_batch):
     assert batch.requests == default_pb_batch.requests
 
     assert len(batch.input_ids) == default_pb_batch.size
-    assert len(batch.input_ids[0]) == 8
     assert batch.input_ids[0][-1] == 10264
     assert torch.all(batch.input_ids[0][:-1] == 3)
 
@@ -85,6 +84,7 @@ def test_causal_lm_batch_type(default_bloom):
 
 
 def test_causal_lm_generate_token(default_bloom, default_bloom_batch):
+    sequence_length = len(default_bloom_batch.all_input_ids[0])
     generated_texts, next_batch = default_bloom.generate_token(default_bloom_batch)
 
     assert generated_texts == []
@@ -92,7 +92,11 @@ def test_causal_lm_generate_token(default_bloom, default_bloom_batch):
     assert not next_batch.keys_head_dim_last
 
     assert len(next_batch.all_input_ids) == next_batch.size
-    assert len(next_batch.all_input_ids[0]) == len(next_batch.attention_mask[0]) == 9
+    assert (
+        len(next_batch.all_input_ids[0])
+        == len(next_batch.attention_mask[0])
+        == sequence_length + 1
+    )
     assert torch.all(next_batch.all_input_ids[0][-2:] == 10264)
     assert torch.all(next_batch.all_input_ids[0][:-2] == 3)
 
@@ -106,8 +110,12 @@ def test_causal_lm_generate_token(default_bloom, default_bloom_batch):
     assert next_batch.max_sequence_length == next_batch.input_lengths[0]
 
     assert next_batch.past_key_values is not None
-    assert all([p[0].shape == (16, 64, 8) for p in next_batch.past_key_values])
-    assert all([p[1].shape == (16, 8, 64) for p in next_batch.past_key_values])
+    assert all(
+        [p[0].shape == (16, 64, sequence_length) for p in next_batch.past_key_values]
+    )
+    assert all(
+        [p[1].shape == (16, sequence_length, 64) for p in next_batch.past_key_values]
+    )
 
 
 def test_causal_lm_generate_token_completion(default_bloom, default_bloom_batch):

server/tests/models/test_causal_lm.py

@@ -8,13 +8,13 @@ from text_generation.models.causal_lm import CausalLM, CausalLMBatch
 
 
 @pytest.fixture
-def default_pb_request(default_pb_parameters):
+def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
     return generate_pb2.Request(
         id=0,
         inputs="Test",
         input_length=1,
         parameters=default_pb_parameters,
-        max_new_tokens=10,
+        stopping_parameters=default_pb_stop_parameters,
     )
 
 
@@ -33,7 +33,7 @@ def default_multi_requests_causal_lm_batch(default_pb_request, gpt2_tokenizer):
     req_0 = copy(default_pb_request)
     req_1 = default_pb_request
     req_1.id = 1
-    req_1.max_new_tokens = 5
+    req_1.stopping_parameters.max_new_tokens = 5
 
     batch_pb = generate_pb2.Batch(id=0, requests=[req_0, req_1], size=2)
     return CausalLMBatch.from_pb(batch_pb, gpt2_tokenizer, torch.device("cpu"))
@@ -51,7 +51,6 @@ def test_batch_from_pb(default_pb_batch, default_causal_lm_batch):
     assert batch.requests == default_pb_batch.requests
 
     assert len(batch.input_ids) == default_pb_batch.size
-    assert len(batch.input_ids[0]) == 8
     assert batch.input_ids[0][-1] == 14402
     assert torch.all(batch.input_ids[0][:-1] == 50256)
 
@@ -80,6 +79,7 @@ def test_causal_lm_batch_type(default_causal_lm):
 
 
 def test_causal_lm_generate_token(default_causal_lm, default_causal_lm_batch):
+    sequence_length = len(default_causal_lm_batch.all_input_ids[0])
     generated_texts, next_batch = default_causal_lm.generate_token(
         default_causal_lm_batch
     )
@@ -88,8 +88,12 @@ def test_causal_lm_generate_token(default_causal_lm, default_causal_lm_batch):
     assert isinstance(next_batch, CausalLMBatch)
 
     assert len(next_batch.all_input_ids) == next_batch.size
-    assert len(next_batch.all_input_ids[0]) == len(next_batch.attention_mask[0]) == 9
-    assert next_batch.all_input_ids[0][-1] == 6208
+    assert (
+        len(next_batch.all_input_ids[0])
+        == len(next_batch.attention_mask[0])
+        == sequence_length + 1
+    )
+    assert next_batch.all_input_ids[0][-1] == 13
     assert next_batch.all_input_ids[0][-2] == 14402
     assert torch.all(next_batch.all_input_ids[0][:-2] == 50256)
 
@@ -97,14 +101,18 @@ def test_causal_lm_generate_token(default_causal_lm, default_causal_lm_batch):
     assert torch.all(next_batch.attention_mask[0][:-2] == 0)
 
     assert next_batch.input_ids.shape == (next_batch.size, 1)
-    assert next_batch.input_ids[0, 0] == 6208
+    assert next_batch.input_ids[0, 0] == 13
 
     assert next_batch.input_lengths == [2]
     assert next_batch.max_sequence_length == next_batch.input_lengths[0]
 
     assert next_batch.past_key_values is not None
-    assert all([p[0].shape == (1, 12, 8, 64) for p in next_batch.past_key_values])
-    assert all([p[1].shape == (1, 12, 8, 64) for p in next_batch.past_key_values])
+    assert all(
+        [p[0].shape == (1, 12, sequence_length, 64) for p in next_batch.past_key_values]
+    )
+    assert all(
+        [p[1].shape == (1, 12, sequence_length, 64) for p in next_batch.past_key_values]
+    )
 
 
 def test_causal_lm_generate_token_completion(
@@ -119,10 +127,7 @@ def test_causal_lm_generate_token_completion(
     assert next_batch is None
 
     assert len(generated_texts) == 1
-    assert (
-        generated_texts[0].output
-        == "Test Test Test Test Test Test Test Test Test Test Test"
-    )
+    assert generated_texts[0].output == "Test.java:784) at net.minecraft."
     assert generated_texts[0].request == default_causal_lm_batch.requests[0]
     assert (
         generated_texts[0].tokens
@@ -145,7 +150,7 @@ def test_causal_lm_generate_token_completion_multi(
     assert next_batch is not None
 
     assert len(generated_texts) == 1
-    assert generated_texts[0].output == "Test Test Test Test Test Test"
+    assert generated_texts[0].output == "Test.java:784)"
     assert (
         generated_texts[0].request == default_multi_requests_causal_lm_batch.requests[1]
     )
@@ -166,10 +171,7 @@ def test_causal_lm_generate_token_completion_multi(
     assert next_batch is None
 
     assert len(generated_texts) == 1
-    assert (
-        generated_texts[0].output
-        == "Test Test Test Test Test Test Test Test Test Test Test"
-    )
+    assert generated_texts[0].output == "Test.java:784) at net.minecraft."
     assert (
         generated_texts[0].request == default_multi_requests_causal_lm_batch.requests[0]
     )
@@ -200,7 +202,8 @@ def test_batch_concatenate(
     assert torch.all(next_batch.attention_mask[1:, :-2] == 0)
 
     assert next_batch.batch_id == 0
-    assert torch.all(next_batch.input_ids == 6208)
+    assert next_batch.input_ids[0, 0] == 12355
+    assert torch.all(next_batch.input_ids[1:] == 13)
 
     assert next_batch.input_lengths == [3, 2, 2]
     assert next_batch.max_sequence_length == 3
@@ -239,7 +242,7 @@ def test_batch_concatenate(
     assert next_batch is not None
 
     assert len(generated_texts) == 1
-    assert generated_texts[0].output == "Test Test Test Test Test Test"
+    assert generated_texts[0].output == "Test.java:784)"
     assert (
         generated_texts[0].request == default_multi_requests_causal_lm_batch.requests[1]
     )
@@ -260,10 +263,7 @@ def test_batch_concatenate(
     assert next_batch is not None
 
     assert len(generated_texts) == 1
-    assert (
-        generated_texts[0].output
-        == "Test Test Test Test Test Test Test Test Test Test Test"
-    )
+    assert generated_texts[0].output == "Test.java:784) at net.minecraft."
     assert generated_texts[0].request == default_causal_lm_batch.requests[0]
     assert (
         generated_texts[0].tokens
@@ -283,10 +283,7 @@ def test_batch_concatenate(
     assert next_batch is None
 
     assert len(generated_texts) == 1
-    assert (
-        generated_texts[0].output
-        == "Test Test Test Test Test Test Test Test Test Test Test"
-    )
+    assert generated_texts[0].output == "Test.java:784) at net.minecraft."
     assert (
         generated_texts[0].request == default_multi_requests_causal_lm_batch.requests[0]
     )

server/tests/models/test_seq2seq_lm.py

@@ -8,13 +8,13 @@ from text_generation.models.seq2seq_lm import Seq2SeqLM, Seq2SeqLMBatch
 
 
 @pytest.fixture
-def default_pb_request(default_pb_parameters):
+def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
     return generate_pb2.Request(
         id=0,
         inputs="Test",
         input_length=2,
         parameters=default_pb_parameters,
-        max_new_tokens=10,
+        stopping_parameters=default_pb_stop_parameters,
     )
 
 
@@ -35,7 +35,7 @@ def default_multi_requests_seq2seq_lm_batch(default_pb_request, mt0_small_tokeni
     req_0 = copy(default_pb_request)
     req_1 = default_pb_request
     req_1.id = 1
-    req_1.max_new_tokens = 5
+    req_1.stopping_parameters.max_new_tokens = 5
 
     batch_pb = generate_pb2.Batch(id=0, requests=[req_0, req_1], size=2)
     return Seq2SeqLMBatch.from_pb(batch_pb, mt0_small_tokenizer, torch.device("cpu"))
@@ -48,11 +48,12 @@ def default_seq2seq_lm():
 
 def test_batch_from_pb(default_pb_batch, default_seq2seq_lm_batch):
     batch = default_seq2seq_lm_batch
+    sequence_length = len(default_seq2seq_lm_batch.input_ids[0])
 
     assert batch.batch_id == default_pb_batch.id
     assert batch.requests == default_pb_batch.requests
 
-    assert batch.input_ids.shape == (default_pb_batch.size, 8)
+    assert batch.input_ids.shape == (default_pb_batch.size, sequence_length)
     assert batch.input_ids[0][-2] == 4268
     assert batch.input_ids[0][-1] == 1
     assert torch.all(batch.input_ids[0][:-2] == 0)
@@ -86,6 +87,7 @@ def test_seq2seq_lm_batch_type(default_seq2seq_lm):
 
 
 def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch):
+    sequence_length = len(default_seq2seq_lm_batch.input_ids[0])
     generated_texts, next_batch = default_seq2seq_lm.generate_token(
         default_seq2seq_lm_batch
     )
@@ -108,7 +110,7 @@ def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch)
     assert next_batch.decoder_input_ids[0, 0] == 0
     assert next_batch.decoder_input_ids[0, 1] == 259
     assert next_batch.decoder_attention_mask is None
-    assert next_batch.encoder_last_hidden_state.shape == (1, 8, 512)
+    assert next_batch.encoder_last_hidden_state.shape == (1, sequence_length, 512)
 
     assert next_batch.decoder_input_lengths == [2]
     assert next_batch.max_decoder_input_length == 2
@@ -121,10 +123,16 @@ def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch)
         [p[1].shape == (next_batch.size, 6, 1, 64) for p in next_batch.past_key_values]
     )
     assert all(
-        [p[2].shape == (next_batch.size, 6, 8, 64) for p in next_batch.past_key_values]
+        [
+            p[2].shape == (next_batch.size, 6, sequence_length, 64)
+            for p in next_batch.past_key_values
+        ]
     )
     assert all(
-        [p[3].shape == (next_batch.size, 6, 8, 64) for p in next_batch.past_key_values]
+        [
+            p[3].shape == (next_batch.size, 6, sequence_length, 64)
+            for p in next_batch.past_key_values
+        ]
     )
 
 

server/tests/test_utils.py

@@ -4,10 +4,55 @@ from text_generation.utils import (
     weight_hub_files,
     download_weights,
     weight_files,
+    StopSequenceCriteria,
+    StoppingCriteria,
     LocalEntryNotFoundError,
 )
 
 
+def test_stop_sequence_criteria():
+    criteria = StopSequenceCriteria([1, 2, 3])
+
+    assert not criteria(1)
+    assert criteria.current_token_idx == 1
+    assert not criteria(2)
+    assert criteria.current_token_idx == 2
+    assert criteria(3)
+    assert criteria.current_token_idx == 3
+
+
+def test_stop_sequence_criteria_reset():
+    criteria = StopSequenceCriteria([1, 2, 3])
+
+    assert not criteria(1)
+    assert criteria.current_token_idx == 1
+    assert not criteria(2)
+    assert criteria.current_token_idx == 2
+    assert not criteria(4)
+    assert criteria.current_token_idx == 0
+
+
+def test_stop_sequence_criteria_empty():
+    with pytest.raises(ValueError):
+        StopSequenceCriteria([])
+
+
+def test_stopping_criteria():
+    criteria = StoppingCriteria([StopSequenceCriteria([1, 2, 3])], max_new_tokens=5)
+    assert criteria([1]) == (False, None)
+    assert criteria([1, 2]) == (False, None)
+    assert criteria([1, 2, 3]) == (True, "stop_sequence")
+
+
+def test_stopping_criteria_max():
+    criteria = StoppingCriteria([StopSequenceCriteria([1, 2, 3])], max_new_tokens=5)
+    assert criteria([1]) == (False, None)
+    assert criteria([1, 1]) == (False, None)
+    assert criteria([1, 1, 1]) == (False, None)
+    assert criteria([1, 1, 1, 1]) == (False, None)
+    assert criteria([1, 1, 1, 1, 1]) == (True, "length")
+
+
 def test_weight_hub_files():
     filenames = weight_hub_files("bigscience/bloom-560m")
     assert filenames == ["model.safetensors"]

server/text_generation/models/causal_lm.py

@@ -57,23 +57,17 @@ class CausalLMBatch:
         for r in pb.requests:
             inputs.append(r.inputs)
             input_lengths.append(r.input_length)
-            next_token_choosers.append(
-                NextTokenChooser(
-                    temperature=r.parameters.temperature,
-                    top_k=r.parameters.top_k,
-                    top_p=r.parameters.top_p,
-                    do_sample=r.parameters.do_sample,
-                )
-            )
+            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters))
             stopping_criterias.append(
-                StoppingCriteria(
-                    eos_token_id=tokenizer.eos_token_id, max_new_tokens=r.max_new_tokens
-                )
+                StoppingCriteria.from_pb(r.stopping_parameters, tokenizer)
             )
 
         pad_to_multiple_of = 8 if "gpu" in str(device) else None
         tokenized_inputs = tokenizer(
-            inputs, return_tensors="pt", padding=True, pad_to_multiple_of=pad_to_multiple_of
+            inputs,
+            return_tensors="pt",
+            padding=True,
+            pad_to_multiple_of=pad_to_multiple_of,
         ).to(device)
         all_input_ids = tokenized_inputs["input_ids"].unsqueeze(-1)
 
@@ -123,8 +117,8 @@ class CausalLMBatch:
             end_index = start_index + batch.size
 
             # We only concatenate batches that did at least one step
-            if batch.input_ids.shape[1] > 1:
-                raise ValueError("Batch input_ids should be of shape (batch_size, 1)")
+            if batch.past_key_values is None:
+                raise ValueError("only concatenate prefilled batches")
 
             # Create empty tensor
             # input_ids is always of shape [batch_size, 1]
@@ -331,14 +325,17 @@ class CausalLM(Model):
             all_tokens = torch.cat([all_tokens, next_token])
 
             # Evaluate stopping criteria
-            if stopping_criteria(all_tokens):
+            stop, reason = stopping_criteria(all_tokens)
+            if stop:
                 # Decode all tokens
                 output = self.tokenizer.decode(
                     all_tokens.squeeze(-1), skip_special_tokens=True
                 )
                 # Add to the list of finished generations with the original request
                 generated_texts.append(
-                    GeneratedText(request, output, stopping_criteria.current_tokens)
+                    GeneratedText(
+                        request, output, stopping_criteria.current_tokens, reason
+                    )
                 )
             # add to the next batch
             else:

server/text_generation/models/galactica.py

@@ -94,18 +94,9 @@ class GalacticaCausalLMBatch(CausalLMBatch):
             # Add escape_custom_split_sequence to the CausalLMBatch logic
             inputs.append(escape_custom_split_sequence(r.inputs))
             input_lengths.append(r.input_length)
-            next_token_choosers.append(
-                NextTokenChooser(
-                    temperature=r.parameters.temperature,
-                    top_k=r.parameters.top_k,
-                    top_p=r.parameters.top_p,
-                    do_sample=r.parameters.do_sample,
-                )
-            )
+            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters))
             stopping_criterias.append(
-                StoppingCriteria(
-                    eos_token_id=tokenizer.eos_token_id, max_new_tokens=r.max_new_tokens
-                )
+                StoppingCriteria.from_pb(r.stopping_parameters, tokenizer)
             )
 
         tokenized_inputs = tokenizer(
@@ -207,11 +198,7 @@ class GalacticaSharded(Galactica):
                         continue
 
                     module_name, param_name = name.rsplit(".", 1)
-                    try:
                     module = model.get_submodule(module_name)
-                    except Exception as e:
-                        print(type(model), name, module_name, param_name)
-                        raise e
                     current_tensor = parameters[name]
 
                     slice_ = f.get_slice(name)

server/text_generation/models/seq2seq_lm.py

@@ -68,24 +68,18 @@ class Seq2SeqLMBatch:
             # Decoder sequence only contains the bos_token
             decoder_input_ids.append(tokenizer.bos_token_id)
             decoder_input_lengths.append(1)
-            next_token_choosers.append(
-                NextTokenChooser(
-                    temperature=r.parameters.temperature,
-                    top_k=r.parameters.top_k,
-                    top_p=r.parameters.top_p,
-                    do_sample=r.parameters.do_sample,
-                )
-            )
+            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters))
             stopping_criterias.append(
-                StoppingCriteria(
-                    eos_token_id=tokenizer.eos_token_id, max_new_tokens=r.max_new_tokens
-                )
+                StoppingCriteria.from_pb(r.stopping_parameters, tokenizer)
             )
 
         # Tokenize batch
         pad_to_multiple_of = 8 if "gpu" in str(device) else None
         tokenized_inputs = tokenizer(
-            inputs, return_tensors="pt", padding=True, pad_to_multiple_of=pad_to_multiple_of
+            inputs,
+            return_tensors="pt",
+            padding=True,
+            pad_to_multiple_of=pad_to_multiple_of,
         ).to(device)
         # Convert decoder_input_ids to torch tensor of size [batch_size, 1]
         decoder_input_ids = torch.tensor(decoder_input_ids, device=device).unsqueeze(-1)
@@ -431,12 +425,15 @@ class Seq2SeqLM(Model):
             decoder_tokens = torch.cat([decoder_tokens, next_token.squeeze(1)])
 
             # Evaluate stopping criteria
-            if stopping_criteria(decoder_tokens):
+            stop, reason = stopping_criteria(decoder_tokens)
+            if stop:
                 # Decode tokens
                 output = self.tokenizer.decode(decoder_tokens, skip_special_tokens=True)
                 # Add to the list of finished generations with the original request
                 generated_texts.append(
-                    GeneratedText(request, output, stopping_criteria.current_tokens)
+                    GeneratedText(
+                        request, output, stopping_criteria.current_tokens, reason
+                    )
                 )
             # add to the next batch
             else:

server/text_generation/models/types.py

@@ -32,8 +32,12 @@ class GeneratedText:
     request: generate_pb2.Request
     output: str
     tokens: int
+    reason: str
 
     def to_pb(self) -> generate_pb2.GeneratedText:
         return generate_pb2.GeneratedText(
-            request=self.request, output=self.output, tokens=self.tokens
+            request=self.request,
+            output=self.output,
+            tokens=self.tokens,
+            finish_reason=self.reason,
         )

server/text_generation/utils.py

@@ -1,6 +1,5 @@
 import concurrent
 import os
-import signal
 import torch
 import torch.distributed
 
@@ -11,6 +10,8 @@ from functools import partial
 from huggingface_hub import HfApi, hf_hub_download, try_to_load_from_cache
 from huggingface_hub.utils import LocalEntryNotFoundError
 from tqdm import tqdm
+from typing import List, Optional, Tuple
+from transformers import AutoTokenizer
 from transformers.generation.logits_process import (
     LogitsProcessorList,
     TemperatureLogitsWarper,
@@ -18,6 +19,8 @@ from transformers.generation.logits_process import (
     TopKLogitsWarper,
 )
 
+from text_generation.pb import generate_pb2
+
 
 class Sampling:
     def __call__(self, logits):
@@ -56,20 +59,72 @@ class NextTokenChooser:
         next_ids = self.choice(scores)
         return next_ids.unsqueeze(-1)
 
+    @classmethod
+    def from_pb(cls, pb: generate_pb2.LogitsWarperParameters) -> "NextTokenChooser":
+        return NextTokenChooser(
+            temperature=pb.temperature,
+            top_k=pb.top_k,
+            top_p=pb.top_p,
+            do_sample=pb.do_sample,
+        )
+
+
+class StopSequenceCriteria:
+    def __init__(self, tokens: List[int]):
+        if not tokens:
+            raise ValueError("tokens cannot be empty")
+
+        self.tokens = tokens
+        self.current_token_idx = 0
+
+    def __call__(self, last_token: int) -> bool:
+        if last_token == self.tokens[self.current_token_idx]:
+            # Increase idx to go to next token
+            self.current_token_idx += 1
+        else:
+            # Reset to first token of the stopping sequence
+            self.current_token_idx = 0
+
+        if self.current_token_idx == len(self.tokens):
+            # We matched the entire sequence without resetting
+            return True
+        return False
+
 
 class StoppingCriteria:
-    def __init__(self, eos_token_id, max_new_tokens=20):
-        self.eos_token_id = eos_token_id
+    def __init__(
+        self, stop_sequence_criterias: List[StopSequenceCriteria], max_new_tokens=20
+    ):
+        self.stop_sequence_criterias = stop_sequence_criterias
         self.max_new_tokens = max_new_tokens
         self.current_tokens = 0
 
-    def __call__(self, all_ids):
+    def __call__(self, all_ids) -> Tuple[bool, Optional[str]]:
         self.current_tokens += 1
         if self.current_tokens >= self.max_new_tokens:
-            return True
-        if self.eos_token_id is not None and all_ids[-1] == self.eos_token_id:
-            return True
-        return False
+            return True, "length"
+
+        last_token = all_ids[-1]
+        for stop_sequence_criteria in self.stop_sequence_criterias:
+            if stop_sequence_criteria(last_token):
+                return True, "stop_sequence"
+
+        return False, None
+
+    @classmethod
+    def from_pb(
+        cls, pb: generate_pb2.StoppingCriteriaParameters, tokenizer: AutoTokenizer
+    ) -> "StoppingCriteria":
+        stop_sequence_criterias = []
+        for stop_sequence in pb.stop_sequences:
+            tokens = tokenizer(
+                stop_sequence, padding=False, return_attention_mask=False
+            ).input_ids
+            if tokens:
+                stop_sequence_criterias.append(StopSequenceCriteria(tokens))
+        stop_sequence_criterias.append(StopSequenceCriteria([tokenizer.eos_token_id]))
+
+        return StoppingCriteria(stop_sequence_criterias, pb.max_new_tokens)
 
 
 def initialize_torch_distributed():