chore: import upstream snapshot with attribution

2026-07-13 13:37:14 +08:00
commit 2aaeece67c
5662 changed files with 1035705 additions and 0 deletions
@@ -0,0 +1,137 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import random
+import string
+import time
+
+import paddle
+import uvicorn
+from fastapi import FastAPI, Response, status
+from pydantic import BaseModel
+from sse_starlette.sse import EventSourceResponse
+
+from paddlenlp.transformers import CodeGenForCausalLM, CodeGenTokenizer
+from paddlenlp.utils.log import logger
+
+
+class DefaultConfig:
+    model_name_or_path = "Salesforce/codegen-350M-mono"
+    device = "gpu"
+    temperature = 0.5
+    top_k = 10
+    top_p = 1.0
+    repetition_penalty = 1.0
+    min_length = 0
+    max_length = 16
+    decode_strategy = "greedy_search"
+    use_faster = True
+    use_fp16_decoding = True
+    default_dtype = "float16" if use_faster and use_fp16_decoding else "float32"
+
+
+class Input(BaseModel):
+    prompt: str
+    stream: bool = False
+
+
+class Output(BaseModel):
+    id: str
+    model: str = "codegen"
+    object: str = "text_completion"
+    created: int = int(time.time())
+    choices: list = None
+    usage = {
+        "completion_tokens": None,
+        "prompt_tokens": None,
+        "total_tokens": None,
+    }
+
+
+generate_config = DefaultConfig()
+paddle.set_device(generate_config.device)
+paddle.set_default_dtype(generate_config.default_dtype)
+
+tokenizer = CodeGenTokenizer.from_pretrained(generate_config.model_name_or_path)
+model = CodeGenForCausalLM.from_pretrained(generate_config.model_name_or_path)
+
+app = FastAPI()
+
+
+def random_completion_id():
+    return "cmpl-" + "".join(random.choice(string.ascii_letters + string.digits) for _ in range(29))
+
+
+@app.post("/v1/engines/codegen/completions", status_code=200)
+async def gen(item: Input):
+    item = item.dict()
+    logger.info(f"Request: {item}")
+    temperature = item.get("temperature", generate_config.temperature)
+    top_k = item.get("top_k", generate_config.top_k)
+    if temperature == 0.0:
+        temperature = 1.0
+        top_k = 1
+    repetition_penalty = item.get("frequency_penalty", generate_config.repetition_penalty)
+
+    start_time = time.time()
+    logger.info("Start generating code")
+    tokenized = tokenizer([item["prompt"]], truncation=True, return_tensors="pd")
+    output, _ = model.generate(
+        tokenized["input_ids"],
+        max_length=16,
+        min_length=generate_config.min_length,
+        decode_strategy=generate_config.decode_strategy,
+        top_k=top_k,
+        repetition_penalty=repetition_penalty,
+        temperature=temperature,
+        use_fast=generate_config.use_faster,
+        use_fp16_decoding=generate_config.use_fp16_decoding,
+    )
+    logger.info("Finish generating code")
+    end_time = time.time()
+    logger.info(f"Time cost: {end_time - start_time}")
+    output = tokenizer.decode(output[0], skip_special_tokens=True)
+    logger.info(f"Generated code: {output}")
+    output_json = Output(
+        id=random_completion_id(),
+        choices=[
+            {
+                "text": output,
+                "index": 0,
+                "finish_reason": "stop",
+                "logprobs": None,
+            }
+        ],
+        usage={
+            "completion_tokens": None,
+            "prompt_tokens": None,
+            "total_tokens": None,
+        },
+    ).json()
+
+    def stream_response(response):
+        yield f"{response}\n\n"
+        yield "data: [DONE]\n\n"
+
+    if item.get("stream", False):
+        return EventSourceResponse(stream_response(output_json))
+    else:
+        return Response(
+            status_code=status.HTTP_200_OK,
+            content=output_json,
+            media_type="application/json",
+        )
+
+
+if __name__ == "__main__":
+    uvicorn.run("codegen_server:app", host="0.0.0.0", port=8978)