Fixing some legacy behavior (big swapout of serverless on legacy stuff). (#1937)

# What does this PR do?

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Fixes # (issue)

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---------

Co-authored-by: Daniël de Kok <me@github.danieldk.eu>

launcher/src/main.rs

@@ -23,10 +23,28 @@ use tracing_subscriber::EnvFilter;
 
 mod env_runtime;
 
+#[derive(Deserialize)]
+struct RawConfig {
+    max_position_embeddings: Option<usize>,
+    n_positions: Option<usize>,
+    max_seq_len: Option<usize>,
+}
+
 #[derive(Deserialize)]
 struct Config {
     max_position_embeddings: Option<usize>,
-    max_seq_len: Option<usize>,
+}
+
+impl From<RawConfig> for Config {
+    fn from(other: RawConfig) -> Self {
+        let max_position_embeddings = other
+            .max_position_embeddings
+            .or(other.max_seq_len)
+            .or(other.n_positions);
+        Config {
+            max_position_embeddings,
+        }
+    }
 }
 
 #[derive(Clone, Copy, Debug, ValueEnum)]
@@ -1324,13 +1342,13 @@ fn main() -> Result<(), LauncherError> {
         };
 
         let content = std::fs::read_to_string(filename)?;
-        let config: Config = serde_json::from_str(&content)?;
+        let config: RawConfig = serde_json::from_str(&content)?;
+        let config: Config = config.into();
 
         // Quantization usually means you're even more RAM constrained.
         let max_default = 4096;
 
-        let max_position_embeddings = match (config.max_position_embeddings, config.max_seq_len) {
+        if let Some(max_position_embeddings) = config.max_position_embeddings {
-            (Some(max_position_embeddings), _) | (None, Some(max_position_embeddings)) => {
             if max_position_embeddings > max_default {
                 let max = max_position_embeddings;
                 if args.max_input_tokens.is_none()
@@ -1339,18 +1357,15 @@ fn main() -> Result<(), LauncherError> {
                 {
                     tracing::info!("Model supports up to {max} but tgi will now set its default to {max_default} instead. This is to save VRAM by refusing large prompts in order to allow more users on the same hardware. You can increase that size using `--max-batch-prefill-tokens={} --max-total-tokens={max} --max-input-tokens={}`.", max + 50, max - 1);
                 }
-                    max_default
+                Ok(max_default)
             } else {
-                    max_position_embeddings
-                }
-            }
-            _ => {
-                return Err(Box::new(LauncherError::ArgumentValidation(
-                    "no max defined".to_string(),
-                )));
-            }
-        };
                 Ok(max_position_embeddings)
+            }
+        } else {
+            Err(Box::new(LauncherError::ArgumentValidation(
+                "no max defined".to_string(),
+            )))
+        }
     };
     let max_position_embeddings: usize = get_max_position_embeddings().unwrap_or(4096);
 

server/text_generation_server/server.py

@@ -21,6 +21,18 @@ from text_generation_server.pb import generate_pb2_grpc, generate_pb2
 from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
 from text_generation_server.models.globals import set_model_id
 
+try:
+    from text_generation_server.models.pali_gemma import PaliGemmaBatch
+    from text_generation_server.models.vlm_causal_lm import (
+        VlmCausalLMBatch,
+    )
+    from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
+
+    VLM_BATCH_TYPES = {PaliGemmaBatch, VlmCausalLMBatch, IdeficsCausalLMBatch}
+except (ImportError, NotImplementedError):
+    # These imports can fail on CPU/Non flash.
+    VLM_BATCH_TYPES = set()
+
 
 class SignalHandler:
     KEEP_PROCESSING = True
@@ -91,9 +103,22 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
 
     async def Prefill(self, request, context):
         start = time.time_ns()
+        if (
+            self.model.batch_type in VLM_BATCH_TYPES
+        ):  # Hack, i would rather use kwargs in the `from_pb` call
+            batch = self.model.batch_type.from_pb_processor(
+                request.batch,
+                self.model.tokenizer,
+                self.model.processor,
+                self.model.model.config,
+                self.model.dtype,
+                self.model.device,
+            )
+        else:
             batch = self.model.batch_type.from_pb(
                 request.batch, self.model.tokenizer, self.model.dtype, self.model.device
             )
+     
         generations, next_batch, timings = self.model.generate_token([batch])
         self.cache.set(next_batch)