Use the generation config. (#1808)

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Fixes # (issue)


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router/src/lib.rs

@@ -589,7 +589,9 @@ pub(crate) struct ChatCompletionChoice {
 #[derive(Clone, Debug, Deserialize, Serialize, ToSchema)]
 pub(crate) struct ChatCompletionDelta {
     #[schema(example = "user")]
-    pub role: String,
+    // TODO Modify this to a true enum.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub role: Option<String>,
     #[serde(default, skip_serializing_if = "Option::is_none")]
     #[schema(example = "What is Deep Learning?")]
     pub content: Option<String>,
@@ -623,6 +625,31 @@ impl ChatCompletionChunk {
         logprobs: Option<ChatCompletionLogprobs>,
         finish_reason: Option<String>,
     ) -> Self {
+        let delta = match (delta, tool_calls) {
+            (Some(delta), _) => ChatCompletionDelta {
+                role: Some("assistant".to_string()),
+                content: Some(delta),
+                tool_calls: None,
+            },
+            (None, Some(tool_calls)) => ChatCompletionDelta {
+                role: Some("assistant".to_string()),
+                content: None,
+                tool_calls: Some(DeltaToolCall {
+                    index: 0,
+                    id: String::new(),
+                    r#type: "function".to_string(),
+                    function: Function {
+                        name: None,
+                        arguments: tool_calls[0].to_string(),
+                    },
+                }),
+            },
+            (None, None) => ChatCompletionDelta {
+                role: None,
+                content: None,
+                tool_calls: None,
+            },
+        };
         Self {
             id: String::new(),
             object: "text_completion".to_string(),
@@ -631,19 +658,7 @@ impl ChatCompletionChunk {
             system_fingerprint,
             choices: vec![ChatCompletionChoice {
                 index: 0,
-                delta: ChatCompletionDelta {
+                delta,
-                    role: "assistant".to_string(),
-                    content: delta,
-                    tool_calls: tool_calls.map(|tc| DeltaToolCall {
-                        index: 0,
-                        id: String::new(),
-                        r#type: "function".to_string(),
-                        function: Function {
-                            name: None,
-                            arguments: tc[0].to_string(),
-                        },
-                    }),
-                },
                 logprobs,
                 finish_reason,
             }],

router/src/server.rs

@@ -1103,7 +1103,13 @@ async fn chat_completions(
             let (content, tool_calls) = if tool_grammar.is_some() {
                 (None, Some(vec![stream_token.token.text]))
             } else {
-                (Some(stream_token.token.text), None)
+                let content = if !stream_token.token.special {
+                    Some(stream_token.token.text)
+                } else {
+                    None
+                };
+
+                (content, None)
             };
 
             event

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -38,58 +38,6 @@ from text_generation_server.utils.layers import (
 )
 
 
-class LlamaConfig(PretrainedConfig):
-    def __init__(
-        self,
-        vocab_size=32000,
-        hidden_size=4096,
-        intermediate_size=11008,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=None,
-        hidden_act="silu",
-        max_position_embeddings=2048,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        pad_token_id=0,
-        bos_token_id=1,
-        eos_token_id=2,
-        pretraining_tp=1,
-        tie_word_embeddings=False,
-        rope_scaling=None,
-        rope_theta=10000.0,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        # for backward compatibility
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.pretraining_tp = pretraining_tp
-        self.use_cache = use_cache
-        self.rope_scaling = rope_scaling
-        self.rope_theta = rope_theta
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-
 def load_attention(config, prefix, weights):
     if config.num_attention_heads != config.num_key_value_heads:
         return _load_gqa(config, prefix, weights)

server/text_generation_server/models/flash_llama.py

@@ -2,14 +2,13 @@ import torch
 import torch.distributed
 
 from opentelemetry import trace
-from transformers import AutoConfig, AutoTokenizer
+from transformers import AutoConfig, AutoTokenizer, GenerationConfig
 from transformers.models.llama import LlamaTokenizer
 from typing import Optional
 
 from text_generation_server.models import FlashCausalLM
 from text_generation_server.models.custom_modeling.flash_llama_modeling import (
     FlashLlamaForCausalLM,
-    LlamaConfig,
 )
 from text_generation_server.utils import (
     initialize_torch_distributed,
@@ -53,8 +52,17 @@ class FlashLlama(FlashCausalLM):
                 truncation_side="left",
                 trust_remote_code=trust_remote_code,
             )
+        try:
+            generation_config = GenerationConfig.from_pretrained(
+                model_id, revision=revision, trust_remote_code=trust_remote_code
+            )
+            if isinstance(generation_config.eos_token_id, (list, set)):
+                # TODO Huge hack
+                tokenizer._eos_token_ids = set(generation_config.eos_token_id)
+        except Exception:
+            pass
 
-        config = LlamaConfig.from_pretrained(
+        config = AutoConfig.from_pretrained(
             model_id, revision=revision, trust_remote_code=trust_remote_code
         )
         config.quantize = quantize

server/text_generation_server/models/model.py

@@ -27,7 +27,14 @@ class Model(ABC):
     ):
         self.model = model.eval()
         self.tokenizer = tokenizer
+
+        # all_special_ids is not set correctly if the rust tokenizer is unpacked
+        # TODO report this to transformers.
+        other_special_ids = {
+            id for id, token in tokenizer.added_tokens_decoder.items() if token.special
+        }
         self.all_special_ids = set(tokenizer.all_special_ids)
+        self.all_special_ids.update(other_special_ids)
         self.requires_padding = requires_padding
         self.dtype = dtype
         self.device = device

server/text_generation_server/utils/tokens.py

@@ -1,5 +1,5 @@
 import re
-from typing import List, Optional, Tuple
+from typing import List, Optional, Tuple, Set, Union
 
 import math
 import torch
@@ -143,12 +143,22 @@ class StopSequenceCriteria:
 class StoppingCriteria:
     def __init__(
         self,
-        eos_token_id: int,
+        eos_token_ids: Optional[Union[Set[int], int]],
         stop_sequence_criterias: List[StopSequenceCriteria],
         max_new_tokens: int = 20,
         ignore_eos_token: bool = False,
     ):
-        self.eos_token_id = eos_token_id
+        if eos_token_ids is None:
+            eos_token_ids = set()
+        elif isinstance(eos_token_ids, int):
+            eos_token_ids = set([eos_token_ids])
+        elif isinstance(eos_token_ids, set):
+            eos_token_ids = eos_token_ids
+        else:
+            raise RuntimeError(
+                f"eos_token_ids is of invalid type {type(eos_token_ids)}, expected int, None or set[int]"
+            )
+        self.eos_token_ids = eos_token_ids
         self.stop_sequence_criterias = stop_sequence_criterias
         self.max_new_tokens = max_new_tokens
         self.current_tokens = 0
@@ -160,7 +170,10 @@ class StoppingCriteria:
         if self.current_tokens >= self.max_new_tokens:
             return True, FinishReason.FINISH_REASON_LENGTH
 
-        if not self.ignore_eos_token and last_token == self.eos_token_id:
+        if isinstance(last_token, torch.Tensor):
+            last_token = last_token.item()
+
+        if not self.ignore_eos_token and last_token in self.eos_token_ids:
             return True, FinishReason.FINISH_REASON_EOS_TOKEN
 
         if self.stop_sequence_criterias:
@@ -184,8 +197,10 @@ class StoppingCriteria:
         stop_sequence_criterias = [
             StopSequenceCriteria(sequence) for sequence in pb.stop_sequences
         ]
+        # TODO Hack because eos_token_id cannot be what we want.
+        eos_token_id = getattr(tokenizer, "_eos_token_ids", tokenizer.eos_token_id)
         return StoppingCriteria(
-            tokenizer.eos_token_id,
+            eos_token_id,
             stop_sequence_criterias,
             pb.max_new_tokens,
             pb.ignore_eos_token,