Use runtime profiling to replace manual memory analyzers

cacheflow/core/server.py

@@ -6,15 +6,14 @@
     import ray
 except ImportError:
     ray = None
+import numpy as np
 import torch
 
 from cacheflow.core.scheduler import Scheduler
 from cacheflow.frontend.simple_frontend import SimpleFrontend
 from cacheflow.logger import init_logger
-from cacheflow.model_executor import get_memory_analyzer
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.sequence import SequenceGroup
-from cacheflow.utils import get_gpu_memory, get_cpu_memory
 from cacheflow.worker.controller import Controller, DeviceID
 
 logger = init_logger(__name__)
@@ -34,14 +33,13 @@ def __init__(
         dtype: str,
         seed: int,
         swap_space: int,
+        gpu_memory_utilization: float,
         max_num_batched_tokens: int,
         max_num_sequences: int,
         num_nodes: int,
         num_devices_per_node: int,
         distributed_init_method: str,
         all_stage_devices: List[List[DeviceID]],
-        gpu_memory: int,
-        cpu_memory: int,
         use_ray: bool,
         log_stats: bool,
     ):
@@ -63,19 +61,6 @@ def __init__(
             assert self.world_size == 1, (
                 "Only support single GPU without Ray.")
 
-        self.memory_analyzer = get_memory_analyzer(
-            model_name=model,
-            block_size=block_size,
-            dtype=dtype,
-            gpu_memory=gpu_memory,
-            cpu_memory=cpu_memory,
-            tensor_parallel_size=tensor_parallel_size,
-        )
-        self.num_gpu_blocks = self.memory_analyzer.get_max_num_gpu_blocks(
-            max_num_batched_tokens=max_num_batched_tokens)
-        self.num_cpu_blocks = self.memory_analyzer.get_max_num_cpu_blocks(
-            swap_space_gib=swap_space)
-
         # Create a controller for each pipeline stage.
         self.controllers: List[Controller] = []
         for i in range(pipeline_parallel_size):
@@ -87,19 +72,35 @@ def __init__(
                 tensor_parallel_size=tensor_parallel_size,
                 distributed_init_method=distributed_init_method,
                 model_name=model,
-                block_size=block_size,
-                num_gpu_blocks=self.num_gpu_blocks,
-                num_cpu_blocks=self.num_cpu_blocks,
                 dtype=dtype,
                 seed=seed,
                 cache_dir=cache_dir,
                 use_dummy_weights=use_dummy_weights,
                 use_np_cache=use_np_cache,
                 max_num_batched_tokens=max_num_batched_tokens,
+                max_num_sequences=max_num_sequences,
                 use_ray=use_ray,
             )
             self.controllers.append(controller)
 
+        # Initialize cache engine.
+        all_worker_num_available_blocks = []
+        for controller in self.controllers:
+            all_worker_num_available_blocks.extend(
+                controller.get_num_available_blocks(
+                    block_size, swap_space, gpu_memory_utilization)
+            )
+        # Since we use a shared centralized controller, we take the minimum
+        # number of blocks across all workers to make sure all the memory
+        # operators can be applied to all workers.
+        self.num_gpu_blocks = np.min([b[0] for b in all_worker_num_available_blocks])
+        self.num_cpu_blocks = np.min([b[1] for b in all_worker_num_available_blocks])
+        logger.info(f'# GPU blocks: {self.num_gpu_blocks}, '
+                    f'# CPU blocks: {self.num_cpu_blocks}')
+        for controller in self.controllers:
+            controller.init_cache_engine(block_size, self.num_gpu_blocks,
+                                         self.num_cpu_blocks)
+
         # Create a scheduler.
         self.scheduler = Scheduler(
             controllers=self.controllers,
@@ -214,7 +215,11 @@ def initialize_cluster(
             all_stage_devices)
 
 
+_GiB = 1 << 30
+
+
 def add_server_arguments(parser: argparse.ArgumentParser):
+    """Shared arguments for CacheFlow servers."""
     # Model arguments
     parser.add_argument('--model', type=str, default='facebook/opt-125m', help='model name')
     parser.add_argument('--cache-dir', type=str, default=None,
@@ -238,15 +243,19 @@ 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('--gpu-memory-utilization', type=float, default=0.95, help='the percentage of GPU memory to be used for the model executor')
     parser.add_argument('--max-num-batched-tokens', type=int, default=2560, help='maximum number of batched tokens per iteration')
     parser.add_argument('--max-num-sequences', type=int, default=256, help='maximum number of sequences per iteration')
     parser.add_argument('--log-stats', action='store_true', help='log system statistics')
     return parser
 
 
 def process_server_arguments(args: argparse.Namespace):
+    """Post process the parsed arguments."""
     if args.pipeline_parallel_size * args.tensor_parallel_size > 1:
         args.use_ray = True
+    args.swap_space = args.swap_space * _GiB
+    args.max_num_sequences = min(args.max_num_sequences, args.max_num_batched_tokens)
     return args
 
 
@@ -274,14 +283,13 @@ def init_local_server_and_frontend_with_arguments(args: argparse.Namespace):
         dtype=args.dtype,
         seed=args.seed,
         swap_space=args.swap_space,
+        gpu_memory_utilization=args.gpu_memory_utilization,
         max_num_batched_tokens=args.max_num_batched_tokens,
         max_num_sequences=args.max_num_sequences,
         num_nodes=num_nodes,
         num_devices_per_node=num_devices_per_node,
         distributed_init_method=distributed_init_method,
         all_stage_devices=all_stage_devices,
-        gpu_memory=get_gpu_memory(),
-        cpu_memory=get_cpu_memory(),
         use_ray=args.use_ray,
         log_stats=args.log_stats,
     )

cacheflow/frontend/fastapi_frontend.py

@@ -15,7 +15,7 @@
 from cacheflow.frontend.utils import get_tokenizer
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.sequence import Sequence, SequenceGroup
-from cacheflow.utils import Counter, get_gpu_memory, get_cpu_memory
+from cacheflow.utils import Counter
 from cacheflow.worker.controller import DeviceID
 
 TIMEOUT_TO_PREVENT_DEADLOCK = 1 # seconds
@@ -34,13 +34,15 @@ def __init__(
         dtype: str,
         seed: int,
         swap_space: int,
+        gpu_memory_utilization: float,
         max_num_batched_tokens: int,
         max_num_sequences: int,
         num_nodes: int,
         num_devices_per_node: int,
         distributed_init_method: str,
         all_stage_devices: List[List[DeviceID]],
         server_use_ray: bool,
+        log_stats: bool,
     ):
         self.block_size = block_size
 
@@ -62,15 +64,15 @@ def __init__(
             dtype=dtype,
             seed=seed,
             swap_space=swap_space,
+            gpu_memory_utilization=gpu_memory_utilization,
             max_num_batched_tokens=max_num_batched_tokens,
             max_num_sequences=max_num_sequences,
             num_nodes=num_nodes,
             num_devices_per_node=num_devices_per_node,
             distributed_init_method=distributed_init_method,
             all_stage_devices=all_stage_devices,
-            gpu_memory=get_gpu_memory(),
-            cpu_memory=get_cpu_memory(),
             use_ray=server_use_ray,
+            log_stats=log_stats,
         )
 
         self.running_seq_groups: Dict[int, SequenceGroup] = {}
@@ -182,13 +184,15 @@ async def generate_stream(request: Request):
         dtype=args.dtype,
         seed=args.seed,
         swap_space=args.swap_space,
+        gpu_memory_utilization=args.gpu_memory_utilization,
         max_num_batched_tokens=args.max_num_batched_tokens,
         max_num_sequences=args.max_num_sequences,
         num_nodes=num_nodes,
         num_devices_per_node=num_devices_per_node,
         distributed_init_method=distributed_init_method,
         all_stage_devices=all_stage_devices,
         server_use_ray=args.use_ray,
+        log_stats=args.log_stats,
     )
 
     uvicorn.run(app, host=args.host, port=args.port, log_level="info")

cacheflow/model_executor/__init__.py

@@ -1,11 +1,12 @@
 from cacheflow.model_executor.input_metadata import InputMetadata
-from cacheflow.model_executor.model_loader import get_model, get_memory_analyzer
-from cacheflow.model_executor.utils import set_random_seed
+from cacheflow.model_executor.model_loader import get_model
+from cacheflow.model_executor.utils import (set_random_seed,
+                                            get_cache_block_size)
 
 
 __all__ = [
     "InputMetadata",
+    "get_cache_block_size",
     "get_model",
-    "get_memory_analyzer",
     "set_random_seed",
 ]

cacheflow/model_executor/layers/attention.py

@@ -11,6 +11,8 @@
 from cacheflow.model_executor.input_metadata import InputMetadata
 
 
+_SUPPORTED_HEAD_SIZES = [32, 64, 80, 96, 128, 160, 192, 256]
+
 class GPTCacheFlowAttention(nn.Module):
     """GPT-style multi-head attention.
 
@@ -39,11 +41,19 @@ class GPTCacheFlowAttention(nn.Module):
     5. Output a flattened 1D tensor.
     """
 
-    def __init__(self, scale: float) -> None:
+    def __init__(self, num_heads: int, head_size: int, scale: float) -> None:
         super().__init__()
+        self.num_heads = num_heads
+        self.head_size = head_size
         self.scale = float(scale)
         self.attn_op = xops.fmha.cutlass.FwOp()
 
+        if self.head_size not in _SUPPORTED_HEAD_SIZES:
+            raise ValueError(f'head_size ({self.head_size}) is not supported by '
+                             'the single_query_cached_kv_attention kernel. '
+                             'Use one of the following head sizes: '
+                             f'{_SUPPORTED_HEAD_SIZES}.')
+
     def multi_query_kv_attention(
         self,
         output: torch.Tensor,                   # [num_prompt_tokens, num_heads, head_size]
@@ -74,14 +84,6 @@ def single_query_cached_kv_attention(
         value_cache: torch.Tensor,      # [num_blocks, num_heads, head_size, block_size]
         input_metadata: InputMetadata,
     ) -> None:
-        head_size = value_cache.shape[2]
-        supported_head_sizes = [32, 64, 80, 96, 128, 160, 192, 256]
-        if head_size not in supported_head_sizes:
-            raise ValueError(f'head_size ({head_size}) is not supported by '
-                             'the single_query_cached_kv_attention kernel. '
-                             'Use one of the following head sizes: '
-                             f'{supported_head_sizes}.')
-
         block_size = value_cache.shape[3]
         attention_ops.single_query_cached_kv_attention(
             output,
@@ -100,20 +102,18 @@ def forward(
         query: torch.Tensor,                    # [num_tokens, num_heads * head_size]
         key: torch.Tensor,                      # [num_tokens, num_heads * head_size]
         value: torch.Tensor,                    # [num_tokens, num_heads * head_size]
-        key_cache: torch.Tensor,                # [num_blocks, num_heads, head_size/x, block_size, x]
-        value_cache: torch.Tensor,              # [num_blocks, num_heads, head_size, block_size]
+        key_cache: Optional[torch.Tensor],      # [num_blocks, num_heads, head_size/x, block_size, x]
+        value_cache: Optional[torch.Tensor],    # [num_blocks, num_heads, head_size, block_size]
         input_metadata: InputMetadata,
         cache_event: Optional[torch.cuda.Event],
     ) -> torch.Tensor:                          # [num_tokens, num_heads * head_size]
         # NOTE: The query, key, and value tensors must be sliced from a qkv
         # tensor of shape [num_tokens, 3 * num_heads * head_size].
 
         # Reshape the query, key, and value tensors.
-        num_heads = value_cache.shape[1]
-        head_size = value_cache.shape[2]
-        query = query.view(-1, num_heads, head_size)
-        key = key.view(-1, num_heads, head_size)
-        value = value.view(-1, num_heads, head_size)
+        query = query.view(-1, self.num_heads, self.head_size)
+        key = key.view(-1, self.num_heads, self.head_size)
+        value = value.view(-1, self.num_heads, self.head_size)
 
         # Pre-allocate the output tensor.
         output = torch.empty_like(query)
@@ -134,8 +134,11 @@ def forward(
             cache_event.wait()
 
         # Reshape the keys and values and store them in the cache.
+        # When key_cache and value_cache are not provided, the new key
+        # and value vectors will not be cached.
         num_valid_tokens = input_metadata.num_valid_tokens
-        if num_valid_tokens > 0:
+        if (num_valid_tokens > 0 and key_cache is not None
+            and value_cache is not None):
             # The stride is 3 because the key and value are sliced from qkv.
             cache_ops.reshape_and_cache(
                 key[:num_valid_tokens],
@@ -146,6 +149,10 @@ def forward(
             )
 
         if input_metadata.num_generation_tokens > 0:
+            assert key_cache is not None and value_cache is not None, (
+                "key_cache and value_cache must be provided when "
+                "generating tokens."
+            )
             # Compute the attention op for generation tokens.
             self.single_query_cached_kv_attention(
                 output[num_prompt_tokens:num_valid_tokens],
@@ -156,20 +163,22 @@ def forward(
 
         # Reshape the output tensor.
         # NOTE(woosuk): The output tensor may include paddings.
-        return output.view(-1, num_heads * head_size)
+        return output.view(-1, self.num_heads * self.head_size)
 
 
 class GPTNeoXCacheFlowAttention(GPTCacheFlowAttention):
     """Attention with GPT-NeoX style rotary embedding."""
 
     def __init__(
         self,
+        num_heads: int,
+        head_size: int,
         scale: float,
         rotary_dim: int,
         max_position: int = 8192,
         base: int = 10000,
     ) -> None:
-        super().__init__(scale)
+        super().__init__(num_heads, head_size, scale)
 
         # Create the cos and sin cache.
         inv_freq = 1.0 / (base ** (torch.arange(0, rotary_dim, 2) / rotary_dim))
@@ -199,12 +208,11 @@ def forward(
     ) -> torch.Tensor:                          # [num_tokens, num_heads * head_size]
         # Apply rotary embedding to the query and key before passing them
         # to the attention op.
-        head_size = value_cache.shape[2]
         pos_encoding_ops.rotary_embedding_neox(
             positions,
             query,
             key,
-            head_size,
+            self.head_size,
             self.cos_sin_cache,
         )
         return super().forward(

cacheflow/model_executor/layers/sampler.py

@@ -74,7 +74,7 @@ def forward(
         # Apply top-p and top-k truncation.
         top_ps, top_ks = _get_top_p_top_k(input_metadata, self.vocab_size)
         assert len(top_ps) == len(top_ks) == probs.shape[0]
-        if any(p < 1.0 for p in top_ps) or any(k != -1 for k in top_ks):
+        if any(p < 1.0 for p in top_ps) or any(k != self.vocab_size for k in top_ks):
             probs = _apply_top_p_top_k(probs, top_ps, top_ks)
 
         # Sample the next tokens.