Add CUDA graph-based all reduce launcher (#26)

benchmark/benchmark_latency.py

@@ -35,7 +35,7 @@ def main(args: argparse.Namespace):
         dtype=args.dtype,
         seed=args.seed,
         swap_space=args.swap_space,
-        max_batch_size=args.max_batch_size,
+        max_num_batched_tokens=args.max_num_batched_tokens,
         num_nodes=num_nodes,
         num_devices_per_node=num_devices_per_node,
         distributed_init_method=distributed_init_method,
@@ -94,6 +94,7 @@ if __name__ == '__main__':
     parser.add_argument('--output-len', type=int, default=128)
     parser.add_argument('--batch-size', type=int, default=8)
     args = parser.parse_args()
-    args.max_batch_size = max(args.max_batch_size, args.batch_size * args.input_len)
+    args.max_num_batched_tokens = max(
+        args.max_num_batched_tokens, args.batch_size * args.input_len)
     print(args)
     main(args)

cacheflow/master/server.py

@@ -22,7 +22,7 @@ class Server:
         dtype: str,
         seed: int,
         swap_space: int,
-        max_batch_size: int,
+        max_num_batched_tokens: int,
         num_nodes: int,
         num_devices_per_node: int,
         distributed_init_method: str,
@@ -43,7 +43,7 @@ class Server:
             tensor_parallel_size=tensor_parallel_size,
         )
         self.num_gpu_blocks = self.memory_analyzer.get_max_num_gpu_blocks(
-            max_num_batched_tokens=max_batch_size)
+            max_num_batched_tokens=max_num_batched_tokens)
         self.num_cpu_blocks = self.memory_analyzer.get_max_num_cpu_blocks(
             swap_space=swap_space)
         print(f'# GPU blocks: {self.num_gpu_blocks}, '
@@ -66,6 +66,7 @@ class Server:
                 dtype=dtype,
                 seed=seed,
                 model_path=model_path,
+                max_num_batched_tokens=max_num_batched_tokens,
             )
             self.controllers.append(controller)
 
@@ -75,7 +76,7 @@ class Server:
             block_size=block_size,
             num_gpu_blocks=self.num_gpu_blocks,
             num_cpu_blocks=self.num_cpu_blocks,
-            max_num_batched_tokens=max_batch_size,
+            max_num_batched_tokens=max_num_batched_tokens,
         )
         # Connect the controllers.
         for i in range(len(self.controllers) - 1):
@@ -168,8 +169,8 @@ def add_server_arguments(parser: argparse.ArgumentParser):
     parser.add_argument('--model-path', type=str, default='~/.cacheflow/model_weights',
                         help='model path to download and load the weights')
     # Parallel arguments
-    parser.add_argument('--pipeline-parallel-size', type=int, default=1, help='number of pipeline stages')
-    parser.add_argument('--tensor-parallel-size', type=int, default=1, help='number of tensor parallel replicas')
+    parser.add_argument('--pipeline-parallel-size', '-pp', type=int, default=1, help='number of pipeline stages')
+    parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1, help='number of tensor parallel replicas')
     # KV cache arguments
     parser.add_argument('--block-size', type=int, default=8, choices=[8, 16], help='token block size')
     # NOTE(woosuk): If FlashAttention is used, the float data type is not supported.
@@ -177,5 +178,5 @@ def add_server_arguments(parser: argparse.ArgumentParser):
     # TODO(woosuk): Support fine-grained seeds (e.g., seed per request).
     parser.add_argument('--seed', type=int, default=0, help='random seed')
     parser.add_argument('--swap-space', type=int, default=20, help='CPU swap space size (GiB) per GPU')
-    parser.add_argument('--max-batch-size', type=int, default=2560, help='maximum number of batched tokens')
+    parser.add_argument('--max-num-batched-tokens', type=int, default=2560, help='maximum number of batched tokens')
     return parser

cacheflow/parallel_utils/parallel_state.py

@@ -47,6 +47,7 @@ _DATA_PARALLEL_GLOBAL_RANKS = None
 # Memory buffers to avoid dynamic memory allocation
 _GLOBAL_MEMORY_BUFFER = None
 
+_ALL_REDUCE_LAUNCHER: Optional['GraphAllReduce'] = None
 
 def initialize_model_parallel(
     tensor_model_parallel_size: int = 1,
@@ -205,6 +206,20 @@ def initialize_model_parallel(
     _set_global_memory_buffer()
 
 
+def initialize_all_reduce_launcher(
+    max_num_tokens: int,
+    hidden_size: int,
+    dtype: torch.dtype,
+    disable_graph: bool = False,
+) -> None:
+    global _ALL_REDUCE_LAUNCHER
+    _ALL_REDUCE_LAUNCHER = GraphAllReduce(
+        max_num_tokens=max_num_tokens,
+        hidden_size=hidden_size,
+        dtype=dtype,
+        disable_graph=disable_graph,
+    )
+
 def model_parallel_is_initialized():
     """Check if model and data parallel groups are initialized."""
     if _TENSOR_MODEL_PARALLEL_GROUP is None or \
@@ -491,6 +506,9 @@ def get_global_memory_buffer():
     assert _GLOBAL_MEMORY_BUFFER is not None, 'global memory buffer is not initialized'
     return _GLOBAL_MEMORY_BUFFER
 
+def get_all_reduce_launcher() -> 'GraphAllReduce':
+    assert _ALL_REDUCE_LAUNCHER is not None, 'all reduce launcher is not initialized'
+    return _ALL_REDUCE_LAUNCHER
 
 def destroy_model_parallel():
     """Set the groups to none."""
@@ -520,3 +538,56 @@ def destroy_model_parallel():
     _MPU_PIPELINE_MODEL_PARALLEL_RANK = None
     global _GLOBAL_MEMORY_BUFFER
     _GLOBAL_MEMORY_BUFFER = None
+
+
+class GraphAllReduce:
+
+    def __init__(
+        self,
+        max_num_tokens: int,
+        hidden_size: int,
+        dtype: torch.dtype,
+        disable_graph: bool = False,
+    ) -> None:
+        self.max_num_tokens = max_num_tokens
+        self.hidden_size = hidden_size
+        self.disable_graph = disable_graph
+
+        tp_world_size = get_tensor_model_parallel_world_size()
+        if tp_world_size == 1:
+            return
+
+        self.group = get_tensor_model_parallel_group()
+        self.buffer = torch.empty(
+            size=(max_num_tokens, hidden_size),
+            dtype=dtype,
+            device='cuda',
+        )
+
+        # Build graphs for different number of tokens.
+        if not self.disable_graph:
+            self.graphs = {}
+            for num_tokens in range(8, max_num_tokens + 1, 8):
+                self.graphs[num_tokens] = self._build_graph(num_tokens)
+
+    def _build_graph(self, num_tokens: int) -> torch.cuda.CUDAGraph:
+        # Warm up.
+        torch.distributed.all_reduce(self.buffer[:num_tokens], group=self.group)
+        torch.cuda.synchronize()
+
+        # Build graph.
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph):
+            torch.distributed.all_reduce(
+                self.buffer[:num_tokens], group=self.group)
+        torch.cuda.synchronize()
+        return graph
+
+    def launch(self, x: torch.Tensor) -> torch.Tensor:
+        # NOTE: x must be a slice of self.buffer.
+        num_tokens = x.shape[0]
+        if self.disable_graph:
+            torch.distributed.all_reduce(x, group=self.group)
+        else:
+            self.graphs[num_tokens].replay()
+        return x

cacheflow/parallel_utils/tensor_parallel/layers.py

@@ -12,6 +12,7 @@ from torch.nn.parameter import Parameter
 from cacheflow.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
+    get_all_reduce_launcher,
 )
 from .mappings import (
     copy_to_tensor_model_parallel_region,
@@ -407,8 +408,7 @@ class RowParallelLinear(torch.nn.Module):
                 self.bias.zero_()
         else:
             self.register_parameter('bias', None)
-
-
+        self.weight_t = self.weight.t()
 
     def forward(self, input_):
         """Forward of RowParallelLinear
@@ -425,11 +425,18 @@ class RowParallelLinear(torch.nn.Module):
             input_parallel = input_
         else:
             input_parallel = scatter_to_tensor_model_parallel_region(input_)
-        # Matrix multiply.
-        output_parallel = F.linear(input_parallel, self.weight)
+        if get_tensor_model_parallel_world_size() == 1:
+            # Matrix multiply.
+            output_ = F.linear(input_parallel, self.weight)
+        else:
+            # Matrix multiply.
+            all_reduce_launcher = get_all_reduce_launcher()
+            num_tokens = input_parallel.shape[0]
+            output_buffer = all_reduce_launcher.buffer[:num_tokens]
+            torch.matmul(input_parallel, self.weight_t, out=output_buffer)
+            # All-reduce across all the partitions.
+            output_ = all_reduce_launcher.launch(output_buffer)
 
-        # All-reduce across all the partitions.
-        output_ = reduce_from_tensor_model_parallel_region(output_parallel)
         if not self.skip_bias_add:
             output = output_ + self.bias if self.bias is not None else output_
             output_bias = None

cacheflow/worker/controller.py

@@ -27,6 +27,7 @@ class Controller:
         dtype: str,
         seed: int,
         model_path: str,
+        max_num_batched_tokens: int,
     ) -> None:
         self.stage_id = stage_id
         self.stage_devices = stage_devices
@@ -57,6 +58,7 @@ class Controller:
                 tensor_parallel_size=tensor_parallel_size,
                 pipeline_parallel_size=pipeline_parallel_size,
                 model_path=model_path,
+                max_num_batched_tokens=max_num_batched_tokens,
             )
             self.workers.append(worker)
 

cacheflow/worker/worker.py

@@ -9,7 +9,9 @@ from cacheflow.sequence import SequenceGroupInputs
 from cacheflow.sequence import SequenceOutputs
 from cacheflow.worker.cache_engine import CacheEngine
 from cacheflow.parallel_utils.parallel_state import (
-    initialize_model_parallel, get_tensor_model_parallel_world_size)
+    initialize_model_parallel,
+    initialize_all_reduce_launcher,
+    get_tensor_model_parallel_world_size)
 from cacheflow.utils import set_random_seed
 
 
@@ -27,6 +29,7 @@ class Worker:
         rank: int,
         world_size: int,
         model_path: str,
+        max_num_batched_tokens: int,
         tensor_parallel_size: int = 1,
         pipeline_parallel_size: int = 1,
     ) -> None:
@@ -44,6 +47,8 @@ class Worker:
         self.model = self.model.cuda()
         tensor_model_parallel_world_size = (
             get_tensor_model_parallel_world_size())
+        initialize_all_reduce_launcher(
+            max_num_batched_tokens, self.model.config.hidden_size, self.dtype)
         self.num_layers = self.model.config.num_hidden_layers
         assert self.model.config.num_attention_heads % tensor_model_parallel_world_size == 0
         self.num_heads = self.model.config.num_attention_heads // tensor_model_parallel_world_size

simple_server.py

@@ -28,7 +28,7 @@ def main(args: argparse.Namespace):
         dtype=args.dtype,
         seed=args.seed,
         swap_space=args.swap_space,
-        max_batch_size=args.max_batch_size,
+        max_num_batched_tokens=args.max_num_batched_tokens,
         num_nodes=num_nodes,
         num_devices_per_node=num_devices_per_node,
         distributed_init_method=distributed_init_method,