Add update_weights_from_tensor (#2631)

.gitignore

@@ -220,3 +220,5 @@ work_dirs/
 *.app
 
 compile_commands.json
+
+*.iml

python/sglang/srt/managers/io_struct.py

@@ -21,6 +21,8 @@ from dataclasses import dataclass
 from enum import Enum
 from typing import Dict, List, Optional, Tuple, Union
 
+import torch
+
 from sglang.srt.managers.schedule_batch import BaseFinishReason
 from sglang.srt.sampling.sampling_params import SamplingParams
 
@@ -407,6 +409,18 @@ class UpdateWeightsFromDistributedReqOutput:
     message: str
 
 
+@dataclass
+class UpdateWeightsFromTensorReqInput:
+    name: str
+    tensor: torch.Tensor
+
+
+@dataclass
+class UpdateWeightsFromTensorReqOutput:
+    success: bool
+    message: str
+
+
 @dataclass
 class InitWeightsUpdateGroupReqInput:
     # The master address

python/sglang/srt/managers/scheduler.py

@@ -22,7 +22,7 @@ import warnings
 from collections import deque
 from concurrent import futures
 from types import SimpleNamespace
-from typing import Callable, Dict, List, Optional, Tuple
+from typing import Dict, List, Optional
 
 import psutil
 import setproctitle
@@ -52,6 +52,8 @@ from sglang.srt.managers.io_struct import (
     UpdateWeightFromDiskReqOutput,
     UpdateWeightsFromDistributedReqInput,
     UpdateWeightsFromDistributedReqOutput,
+    UpdateWeightsFromTensorReqInput,
+    UpdateWeightsFromTensorReqOutput,
 )
 from sglang.srt.managers.schedule_batch import (
     FINISH_ABORT,
@@ -478,6 +480,11 @@ class Scheduler:
                 self.send_to_tokenizer.send_pyobj(
                     UpdateWeightsFromDistributedReqOutput(success, message)
                 )
+            elif isinstance(recv_req, UpdateWeightsFromTensorReqInput):
+                success, message = self.update_weights_from_tensor(recv_req)
+                self.send_to_tokenizer.send_pyobj(
+                    UpdateWeightsFromTensorReqOutput(success, message)
+                )
             elif isinstance(recv_req, GetWeightsByNameReqInput):
                 parameter = self.get_weights_by_name(recv_req)
                 self.send_to_tokenizer.send_pyobj(GetWeightsByNameReqOutput(parameter))
@@ -1458,6 +1465,17 @@ class Scheduler:
             logger.error(message)
         return success, message
 
+    def update_weights_from_tensor(self, recv_req: UpdateWeightsFromTensorReqInput):
+        """Update the online model parameter from tensors."""
+        success, message = self.tp_worker.update_weights_from_tensor(recv_req)
+        # TODO extract common code b/t update_weights_from_distributed and update_weights_from_tensor later
+        if success:
+            flash_cache_success = self.flush_cache()
+            assert flash_cache_success, "Cache flush failed after updating weights"
+        else:
+            logger.error(message)
+        return success, message
+
     def get_weights_by_name(self, recv_req: GetWeightsByNameReqInput):
         parameter = self.tp_worker.get_weights_by_name(recv_req)
         return parameter

python/sglang/srt/managers/tokenizer_manager.py

@@ -59,6 +59,8 @@ from sglang.srt.managers.io_struct import (
     UpdateWeightFromDiskReqOutput,
     UpdateWeightsFromDistributedReqInput,
     UpdateWeightsFromDistributedReqOutput,
+    UpdateWeightsFromTensorReqInput,
+    UpdateWeightsFromTensorReqOutput,
 )
 from sglang.srt.metrics.collector import TokenizerMetricsCollector
 from sglang.srt.sampling.sampling_params import SamplingParams
@@ -179,6 +181,9 @@ class TokenizerManager:
         self.update_weights_from_distributed_communicator = _Communicator(
             self.send_to_scheduler, server_args.dp_size
         )
+        self.update_weights_from_tensor_communicator = _Communicator(
+            self.send_to_scheduler, server_args.dp_size
+        )
         self.get_weights_by_name_communicator = _Communicator(
             self.send_to_scheduler, server_args.dp_size
         )
@@ -515,6 +520,22 @@ class TokenizerManager:
             result = (await self.update_weights_from_distributed_communicator(obj))[0]
             return result.success, result.message
 
+    async def update_weights_from_tensor(
+        self,
+        obj: UpdateWeightsFromTensorReqInput,
+        request: Optional[fastapi.Request] = None,
+    ) -> Tuple[bool, str]:
+        self.auto_create_handle_loop()
+        assert (
+            self.server_args.dp_size == 1
+        ), "dp_size must be for update weights from distributed"
+
+        # This means that weight sync
+        # cannot run while requests are in progress.
+        async with self.model_update_lock.writer_lock:
+            result = (await self.update_weights_from_tensor_communicator(obj))[0]
+            return result.success, result.message
+
     async def get_weights_by_name(
         self, obj: GetWeightsByNameReqInput, request: Optional[fastapi.Request] = None
     ):
@@ -708,6 +729,11 @@ class TokenizerManager:
                     self.server_args.dp_size == 1
                 ), "dp_size must be 1 for update weights from distributed"
                 self.update_weights_from_distributed_communicator.handle_recv(recv_obj)
+            elif isinstance(recv_obj, UpdateWeightsFromTensorReqOutput):
+                assert (
+                    self.server_args.dp_size == 1
+                ), "dp_size must be 1 for update weights from distributed"
+                self.update_weights_from_tensor_communicator.handle_recv(recv_obj)
             elif isinstance(recv_obj, GetWeightsByNameReqOutput):
                 self.get_weights_by_name_communicator.handle_recv(recv_obj)
             else:

python/sglang/srt/managers/tp_worker.py

@@ -24,6 +24,7 @@ from sglang.srt.managers.io_struct import (
     InitWeightsUpdateGroupReqInput,
     UpdateWeightFromDiskReqInput,
     UpdateWeightsFromDistributedReqInput,
+    UpdateWeightsFromTensorReqInput,
 )
 from sglang.srt.managers.schedule_batch import ModelWorkerBatch, global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
@@ -188,6 +189,12 @@ class TpModelWorker:
         )
         return success, message
 
+    def update_weights_from_tensor(self, recv_req: UpdateWeightsFromTensorReqInput):
+        success, message = self.model_runner.update_weights_from_tensor(
+            recv_req.name, recv_req.tensor
+        )
+        return success, message
+
     def get_weights_by_name(self, recv_req: GetWeightsByNameReqInput):
         parameter = self.model_runner.get_weights_by_name(
             recv_req.name, recv_req.truncate_size

python/sglang/srt/managers/tp_worker_overlap_thread.py

@@ -28,6 +28,7 @@ from sglang.srt.managers.io_struct import (
     InitWeightsUpdateGroupReqInput,
     UpdateWeightFromDiskReqInput,
     UpdateWeightsFromDistributedReqInput,
+    UpdateWeightsFromTensorReqInput,
 )
 from sglang.srt.managers.schedule_batch import ModelWorkerBatch
 from sglang.srt.managers.tp_worker import TpModelWorker
@@ -225,6 +226,10 @@ class TpModelWorkerClient:
         success, message = self.worker.update_weights_from_distributed(recv_req)
         return success, message
 
+    def update_weights_from_tensor(self, recv_req: UpdateWeightsFromTensorReqInput):
+        success, message = self.worker.update_weights_from_tensor(recv_req)
+        return success, message
+
     def get_weights_by_name(self, recv_req: GetWeightsByNameReqInput):
         return self.worker.get_weights_by_name(recv_req)
 

python/sglang/srt/model_executor/model_runner.py

@@ -429,6 +429,10 @@ class ModelRunner:
             logger.error(error_msg)
             return False, error_msg
 
+    def update_weights_from_tensor(self, name, tensor: torch.Tensor):
+        self.model.load_weights([(name, tensor)])
+        return True, "Success"  # TODO error handling
+
     def get_weights_by_name(
         self, name: str, truncate_size: int = 100
     ) -> Optional[torch.Tensor]:

python/sglang/srt/server.py

@@ -57,6 +57,7 @@ from sglang.srt.managers.io_struct import (
     OpenSessionReqInput,
     UpdateWeightFromDiskReqInput,
     UpdateWeightsFromDistributedReqInput,
+    UpdateWeightsFromTensorReqInput,
 )
 from sglang.srt.managers.scheduler import run_scheduler_process
 from sglang.srt.managers.tokenizer_manager import TokenizerManager
@@ -109,6 +110,7 @@ app.add_middleware(
 tokenizer_manager: TokenizerManager = None
 scheduler_info: Dict = None
 
+
 ##### Native API endpoints #####
 
 
@@ -866,6 +868,14 @@ class Engine:
             tokenizer_manager.update_weights_from_distributed(obj, None)
         )
 
+    def update_weights_from_tensor(self, name, tensor):
+        """Update weights from distributed source."""
+        obj = UpdateWeightsFromTensorReqInput(name=name, tensor=tensor)
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(
+            tokenizer_manager.update_weights_from_tensor(obj, None)
+        )
+
     def get_weights_by_name(self, name, truncate_size=100):
         """Get weights by parameter name."""
         obj = GetWeightsByNameReqInput(name=name, truncate_size=truncate_size)

test/srt/run_suite.py

@@ -40,6 +40,7 @@ suites = {
         "test_triton_attention_kernels.py",
         "test_triton_attention_backend.py",
         "test_update_weights_from_disk.py",
+        "test_update_weights_from_tensor.py",
         "test_vision_chunked_prefill.py",
         "test_vision_openai_server.py",
         "test_session_control.py",

test/srt/test_update_weights_from_tensor.py

@@ -0,0 +1,32 @@
+import unittest
+
+import torch
+
+import sglang as sgl
+from sglang.test.test_utils import DEFAULT_SMALL_MODEL_NAME_FOR_TEST
+
+
+class TestReleaseGPUOccupation(unittest.TestCase):
+    def test_release_and_resume_occupation(self):
+        engine = sgl.Engine(model_path=DEFAULT_SMALL_MODEL_NAME_FOR_TEST)
+
+        param_name = "model.layers.2.self_attn.k_proj.weight"
+
+        def _check_param(expect_values):
+            actual_values = torch.tensor(engine.get_weights_by_name(param_name))[0, :5]
+            assert torch.allclose(
+                actual_values, torch.tensor(expect_values), atol=0.001
+            ), f"{actual_values=}"
+
+        _check_param([0.0571, -0.0114, 0.0444, 0.0215, -0.0149])
+
+        new_tensor = torch.full((3072, 2048), 1.5)
+        engine.update_weights_from_tensor(param_name, new_tensor)
+
+        _check_param([1.5] * 5)
+
+        engine.shutdown()
+
+
+if __name__ == "__main__":
+    unittest.main()