Merge branch 'main' into remove-threadsafe

lmdeploy/pytorch/engine/engine.py

@@ -772,8 +772,7 @@ def __update_inputs(next_token_ids):
         logger.debug('<ForwardTask>: '
                      f'batch_size={inputs.seq_length.size(0)} '
                      f'num_tokens={inputs.input_ids.size(-1)}')
-        if self.gpu_count == 1:
-            inputs = inputs.to_device('cuda')
+        inputs = inputs.to_device('cuda')
         is_decoding = inputs.is_decoding
         if all_ids is not None:
             all_ids = all_ids.cuda()

lmdeploy/pytorch/engine/model_agent.py

@@ -135,7 +135,6 @@ def model_forward(
     stream = stream or torch.cuda.current_stream()
     with torch.cuda.stream(stream):
         # forward
-        inputs = inputs.to_device('cuda')
         ctx_mgr = model.ctx_mgr
         context = ctx_mgr.build_context(
             inputs=inputs,
@@ -372,14 +371,26 @@ def _broadcast_config(cache_config):
     return patched_model, cache_engine, cache_config
 
 
-def _broadcast_inputs(rank: int, inputs: Any, stream: torch.cuda.Stream):
+def _broadcast_inputs(rank: int, inputs: Any, group: dist.group,
+                      stream: torch.cuda.Stream):
     """get input tensor parallel."""
     # broadcast meta info
     if rank != 0:
         inputs = [None, None, None]
+    else:
+        device_inputs = inputs[0]
+        meta_inputs = device_inputs.to_device('meta')
+        inputs[0] = meta_inputs
 
     with torch.cuda.stream(stream):
-        dist.broadcast_object_list(inputs)
+        dist.broadcast_object_list(inputs, group=group)
+        if rank == 0:
+            device_inputs.broadcast()
+        else:
+            device_inputs = inputs[0].broadcast()
+
+    inputs[0] = device_inputs
+
     return inputs
 
 
@@ -392,6 +403,7 @@ def _tp_model_loop(
     adapters: Dict[str, str],
     world_size: int,
     barrier: mp.Barrier,
+    cpu_group: dist.group,
 ):
     """Start model loops for tensor parallel model inference.
 
@@ -417,11 +429,12 @@ def _tp_model_loop(
     while True:
         barrier.wait()
         inputs, swap_in_map, swap_out_map = _broadcast_inputs(
-            rank, None, stream)
+            rank, None, cpu_group, stream)
 
         cache_swapping(cache_engine,
                        swap_in_map=swap_in_map,
                        swap_out_map=swap_out_map)
+        inputs = inputs.to_device('cuda')
 
         model_forward(
             patched_model,
@@ -453,10 +466,13 @@ def _start_tp_process(proc_id: int,
     try:
         from lmdeploy.pytorch.check_env import check_env_deeplink
         check_env_deeplink(device_context.device_type)
+        timeout = timedelta(days=35600)
         dist.init_process_group('nccl',
                                 rank=rank,
                                 world_size=world_size,
-                                timeout=timedelta(days=35600))
+                                timeout=timeout)
+        cpu_group = dist.new_group(timeout=timeout, backend='gloo')
+        kwargs['cpu_group'] = cpu_group
         dist_ctx = DistContext(rank=rank, world_size=world_size)
         torch.cuda.set_device(rank)
         with get_dist_manager().context(dist_ctx), get_device_manager(
@@ -626,12 +642,15 @@ def _start_sub_process(self, model_path: str, model_config: ModelConfig,
 
         rank = 0
         try:
+            timeout = timedelta(days=35600)
             dist.init_process_group('nccl',
                                     rank=rank,
                                     world_size=world_size,
-                                    timeout=timedelta(days=35600))
+                                    timeout=timeout)
+            cpu_group = dist.new_group(timeout=timeout, backend='gloo')
             dist_ctx = DistContext(rank=rank, world_size=world_size)
             self._dist_ctx = dist_ctx
+            self._cpu_group = cpu_group
         except Exception as e:
             from traceback import print_exc
             logger.error(f'Rank[{rank}] failed.')
@@ -673,7 +692,8 @@ def _forward_impl(self, inputs: ModelInputs, swap_in_map: SwapMap,
             self.mp_bar.wait()
             rank = 0
             _broadcast_inputs(rank, [inputs, swap_in_map, swap_out_map],
-                              self.stream)
+                              self._cpu_group, self.stream)
+
             cache_swapping(self.cache_engine,
                            swap_in_map=swap_in_map,
                            swap_out_map=swap_out_map)
@@ -699,8 +719,6 @@ async def async_forward(self, inputs: ModelInputs, swap_in_map: SwapMap,
                                     swap_in_map=swap_in_map,
                                     swap_out_map=swap_out_map)
         await asyncio.sleep(0)
-        while not self.stream.query():
-            await asyncio.sleep(0)
         return output
 
     def get_logits(self, hidden_states: torch.Tensor):

lmdeploy/pytorch/model_inputs.py

@@ -4,12 +4,21 @@
 from typing import Any, Dict, List, Literal
 
 import torch
+from torch import distributed as dist
 
 from lmdeploy.pytorch.backends import get_backend
 from lmdeploy.pytorch.config import ModelConfig
 from lmdeploy.pytorch.multimodal.data_type import MultiModalTensor
 
 
+def _broadcast_tensor(value: torch.Tensor, src: int = 0, device: str = 'cuda'):
+    """broadcast tensor."""
+    if value.device.type == 'meta':
+        value = torch.empty_like(value, device=device)
+    dist.broadcast(value, src)
+    return value
+
+
 @dataclass
 class VisionModelInputs:
     """Vision model inputs."""
@@ -36,10 +45,45 @@ def to_device(self, device: str):
             elif k == 'input_embeddings':
                 v = [[e.to(device) for e in li] for li in v]
             elif k == 'input_multimodals':
+                new_v = []
                 for mm_datas in v:
+                    new_mm_datas = dict()
                     for modal_type, data in mm_datas.items():
                         data = [d.to_device(device) for d in data]
-                        mm_datas[modal_type] = data
+                        new_mm_datas[modal_type] = data
+                    new_v.append(new_mm_datas)
+                v = new_v
+            out_dict[k] = v
+
+        return VisionModelInputs(**out_dict)
+
+    def broadcast(self):
+        """broadcast inputs.
+
+        Do `dist.broadcast_object_list(inputs.to_device('meta'))`
+        before broadcast tensors.
+        """
+        out_dict = dict()
+        for f in fields(self):
+            k = f.name
+            v = getattr(self, k)
+            if v is None:
+                continue
+            if isinstance(v, torch.Tensor):
+                v = _broadcast_tensor(v)
+            elif k == 'input_embedding_ranges':
+                v = [_broadcast_tensor(e) for e in v]
+            elif k == 'input_embeddings':
+                v = [[_broadcast_tensor(e) for e in li] for li in v]
+            elif k == 'input_multimodals':
+                new_v = []
+                for mm_datas in v:
+                    new_mm_datas = dict()
+                    for modal_type, data in mm_datas.items():
+                        data = [d.broadcast() for d in data]
+                        new_mm_datas[modal_type] = data
+                    new_v.append(new_mm_datas)
+                v = new_v
             out_dict[k] = v
 
         return VisionModelInputs(**out_dict)
@@ -202,6 +246,24 @@ def to_device(self, device: str):
 
         return ModelInputs(**out_dict)
 
+    def broadcast(self):
+        """broadcast inputs.
+
+        Do `dist.broadcast_object_list(inputs.to_device('meta'))`
+        before broadcast tensors.
+        """
+        out_dict = dict()
+        for f in fields(self):
+            k = f.name
+            v = getattr(self, k)
+            if isinstance(v, torch.Tensor):
+                v = _broadcast_tensor(v)
+            elif isinstance(v, VisionModelInputs):
+                v = v.broadcast()
+            out_dict[k] = v
+
+        return ModelInputs(**out_dict)
+
 
 @dataclass
 class StepContext:

lmdeploy/pytorch/multimodal/data_type.py

@@ -1,8 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from dataclasses import dataclass
+from dataclasses import dataclass, fields
 from typing import Any, Dict, List, Union
 
+import torch
 from torch import Tensor
+from torch import distributed as dist
 
 
 class MultiModalData:
@@ -14,6 +16,14 @@ class MultiModalData:
 NestedTensor = Union[Tensor, List[Tensor]]
 
 
+def _broadcast_tensor(value: torch.Tensor, src: int = 0, device: str = 'cuda'):
+    """broadcast tensor."""
+    if value.device.type == 'meta':
+        value = torch.empty_like(value, device=device)
+    dist.broadcast(value, src)
+    return value
+
+
 @dataclass
 class MultiModalTensor:
     data: NestedTensor
@@ -28,24 +38,67 @@ def __post_init__(self):
 
     def to_device(self, device: str, non_blocking: bool = False):
         """to device."""
+        out_dict = dict()
+        for f in fields(self):
+            k = f.name
+            if k in ('data', 'meta'):
+                continue
+            v = getattr(self, k)
+            out_dict[k] = v
+
         if isinstance(self.data, Tensor):
-            self.data = self.data.to(device=device, non_blocking=non_blocking)
+            data = self.data.to(device=device, non_blocking=non_blocking)
         else:
             data = [
                 d.to(device=device, non_blocking=non_blocking)
                 for d in self.data
             ]
-            self.data = data
+        out_dict['data'] = data
 
+        new_meta = None
         if self.meta is not None:
+            new_meta = dict()
             for k, v in self.meta.items():
                 if isinstance(v, Tensor):
                     v = v.to(device=device, non_blocking=non_blocking)
-                    self.meta[k] = v
                 elif hasattr(v, 'to_device'):
                     v = v.to_device(device=device, non_blocking=non_blocking)
+                new_meta[k] = v
+
+        out_dict['meta'] = new_meta
+        return MultiModalTensor(**out_dict)
+
+    def broadcast(self):
+        """broadcast inputs tensors."""
+        out_dict = dict()
+        for f in fields(self):
+            k = f.name
+            if k in ('data', 'meta'):
+                continue
+            v = getattr(self, k)
+            out_dict[k] = v
+
+        if isinstance(self.data, Tensor):
+            data = _broadcast_tensor(self.data)
+        else:
+            data = [_broadcast_tensor(d) for d in self.data]
+        out_dict['data'] = data
+
+        new_meta = None
+        if self.meta is not None:
+            new_meta = dict()
+            for k, v in self.meta.items():
+                if isinstance(v, Tensor):
+                    v = _broadcast_tensor(v)
+                    self.meta[k] = v
+                elif hasattr(v, 'to_device'):
+                    assert hasattr(v, 'broadcast')
+                    v = v.broadcast()
                     self.meta[k] = v
-        return self
+                new_meta[k] = v
+
+        out_dict['meta'] = new_meta
+        return MultiModalTensor(**out_dict)
 
 
 MultiModalInputs = Dict[str, List[MultiModalTensor]]