Streaming for serving with chat's generate function (#1426)

litgpt/deploy/serve.py

@@ -12,7 +12,8 @@
 from litgpt.model import GPT
 from litgpt.config import Config
 from litgpt.tokenizer import Tokenizer
-from litgpt.generate.base import generate
+from litgpt.generate.base import generate as plain_generate
+from litgpt.chat.base import generate as stream_generate
 from litgpt.prompts import load_prompt_style, has_prompt_style, PromptStyle
 from litgpt.utils import (
     extend_checkpoint_dir,
@@ -28,7 +29,7 @@
     LitAPI, LitServer = object, object
 
 
-class SimpleLitAPI(LitAPI):
+class BaseLitAPI(LitAPI):
     def __init__(self,
                  checkpoint_dir: Path,
                  precision: Optional[str] = None,
@@ -86,12 +87,26 @@ def decode_request(self, request: Dict[str, Any]) -> Any:
         encoded = self.tokenizer.encode(prompt, device=self.device)
         return encoded
 
+
+class SimpleLitAPI(BaseLitAPI):
+    def __init__(self,
+                 checkpoint_dir: Path,
+                 precision: Optional[str] = None,
+                 temperature: float = 0.8,
+                 top_k: int = 50,
+                 top_p: float = 1.0,
+                 max_new_tokens: int = 50):
+        super().__init__(checkpoint_dir, precision, temperature, top_k, top_p, max_new_tokens)   
+
+    def setup(self, device: str):
+        super().setup(device)
+
     def predict(self, inputs: torch.Tensor) -> Any:
         # Run the model on the input and return the output.
         prompt_length = inputs.size(0)
         max_returned_tokens = prompt_length + self.max_new_tokens
 
-        y = generate(
+        y = plain_generate(
             self.model,
             inputs,
             max_returned_tokens,
@@ -111,6 +126,42 @@ def encode_response(self, output: torch.Tensor) -> Dict[str, Any]:
         return {"output": decoded_output}
 
 
+class StreamLitAPI(BaseLitAPI):
+    def __init__(self,
+                 checkpoint_dir: Path,
+                 precision: Optional[str] = None,
+                 temperature: float = 0.8,
+                 top_k: int = 50,
+                 top_p: float = 1.0,
+                 max_new_tokens: int = 50):
+        super().__init__(checkpoint_dir, precision, temperature, top_k, top_p, max_new_tokens)   
+
+    def setup(self, device: str):
+        super().setup(device)
+
+    def predict(self, inputs: torch.Tensor) -> Any:
+        # Run the model on the input and return the output.
+        prompt_length = inputs.size(0)
+        max_returned_tokens = prompt_length + self.max_new_tokens
+
+        for block in self.model.transformer.h:
+            block.attn.kv_cache.reset_parameters()
+
+        yield from stream_generate(
+            self.model,
+            inputs,
+            max_returned_tokens,
+            temperature=self.temperature,
+            top_k=self.top_k,
+            top_p=self.top_p,
+            stop_tokens=([self.tokenizer.eos_id],)
+        )
+
+    def encode_response(self, output):
+        for out in output:
+            yield {"output": self.tokenizer.decode(out)}
+
+
 def run_server(
     checkpoint_dir: Path,
     precision: Optional[str] = None,
@@ -120,7 +171,8 @@ def run_server(
     max_new_tokens: int = 50,
     devices: int = 1,
     accelerator: str = "auto",
-    port: int = 8000
+    port: int = 8000,
+    stream: bool = False
 ) -> None:
     """Serve a LitGPT model using LitServe.
 
@@ -153,22 +205,40 @@ def run_server(
         accelerator: The type of accelerator to use. For example, "auto", "cuda", "cpu", or "mps".
             The "auto" setting (default) chooses a GPU if available, and otherwise uses a CPU.
         port: The network port number on which the model is configured to be served.
+        stream: Whether to stream the responses.
     """
     checkpoint_dir = extend_checkpoint_dir(checkpoint_dir)
     pprint(locals())
 
     check_valid_checkpoint_dir(checkpoint_dir, model_filename="lit_model.pth")
 
-    server = LitServer(
-        SimpleLitAPI(
-            checkpoint_dir=checkpoint_dir,
-            precision=precision,
-            temperature=temperature,
-            top_k=top_k,
-            top_p=top_p,
-            max_new_tokens=max_new_tokens,
-            ),
-        accelerator=accelerator,
-        devices=devices)
+    if not stream:
+        server = LitServer(
+            SimpleLitAPI(
+                checkpoint_dir=checkpoint_dir,
+                precision=precision,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+                max_new_tokens=max_new_tokens,
+                ),
+            accelerator=accelerator,
+            devices=devices
+            )
+
+    else:
+        server = LitServer(
+            StreamLitAPI(
+                checkpoint_dir=checkpoint_dir,
+                precision=precision,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+                max_new_tokens=max_new_tokens,
+                ),
+            accelerator=accelerator,
+            devices=devices,
+            stream=True
+            )
 
     server.run(port=port)

pyproject.toml

@@ -38,7 +38,7 @@ all = [
     "tokenizers>=0.15.2",        # pythia, falcon, redpajama
     "requests>=2.31.0",          # litgpt.data
     "litdata==0.2.6",            # litgpt.data
-    "litserve>=0.1.0",           # litgpt.deploy
+    "litserve==0.1.1dev0",       # litgpt.deploy
     "zstandard>=0.22.0",         # litgpt.data.prepare_slimpajama.py
     "pandas>=1.9.0",             # litgpt.data.prepare_starcoder.py
     "pyarrow>=15.0.2",           # litgpt.data.prepare_starcoder.py