Use runtime profiling to replace manual memory analyzers (#81)

cacheflow/core/server.py

@@ -6,15 +6,14 @@ try:
     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 @@ class Server:
         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 @@ class Server:
             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 @@ class Server:
                 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,
@@ -238,6 +243,7 @@ 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')
@@ -245,8 +251,11 @@ def add_server_arguments(parser: argparse.ArgumentParser):
 
 
 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.core.server import (Server, add_server_arguments,
 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
@@ -34,6 +34,7 @@ class FastAPIServer:
         dtype: str,
         seed: int,
         swap_space: int,
+        gpu_memory_utilization: float,
         max_num_batched_tokens: int,
         max_num_sequences: int,
         num_nodes: int,
@@ -41,6 +42,7 @@ class FastAPIServer:
         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 @@ class FastAPIServer:
             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] = {}
@@ -182,6 +184,7 @@ if __name__ == "__main__":
         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,
@@ -189,6 +192,7 @@ if __name__ == "__main__":
         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.model_loader import get_model
-from cacheflow.model_executor.utils import set_random_seed
+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 import pos_encoding_ops
 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 @@ class GPTCacheFlowAttention(nn.Module):
         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,
@@ -100,8 +102,8 @@ class GPTCacheFlowAttention(nn.Module):
         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]
+        key_cache: Optional[torch.Tensor],      # [num_blocks, num_heads, head_size/x, block_size, x]
-        value_cache: torch.Tensor,              # [num_blocks, num_heads, head_size, block_size]
+        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]
@@ -109,11 +111,9 @@ class GPTCacheFlowAttention(nn.Module):
         # tensor of shape [num_tokens, 3 * num_heads * head_size].
 
         # Reshape the query, key, and value tensors.
-        num_heads = value_cache.shape[1]
+        query = query.view(-1, self.num_heads, self.head_size)
-        head_size = value_cache.shape[2]
+        key = key.view(-1, self.num_heads, self.head_size)
-        query = query.view(-1, num_heads, head_size)
+        value = value.view(-1, self.num_heads, self.head_size)
-        key = key.view(-1, num_heads, head_size)
-        value = value.view(-1, num_heads, head_size)
 
         # Pre-allocate the output tensor.
         output = torch.empty_like(query)
@@ -134,8 +134,11 @@ class GPTCacheFlowAttention(nn.Module):
             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 @@ class GPTCacheFlowAttention(nn.Module):
             )
 
         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],
@@ -156,7 +163,7 @@ class GPTCacheFlowAttention(nn.Module):
 
         # 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):
@@ -164,12 +171,14 @@ class GPTNeoXCacheFlowAttention(GPTCacheFlowAttention):
 
     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 @@ class GPTNeoXCacheFlowAttention(GPTCacheFlowAttention):
     ) -> 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 @@ class Sampler(nn.Module):
         # 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.

cacheflow/model_executor/memory_analyzer.py

@@ -1,370 +0,0 @@
-import torch
-from transformers import AutoConfig
-
-from cacheflow.logger import init_logger
-from cacheflow.model_executor.utils import get_dtype_size
-
-logger = init_logger(__name__)
-
-_GiB = 1 << 30
-
-
-class CacheFlowMemoryAnalyzer:
-
-    def get_max_num_gpu_blocks(
-        self,
-        max_num_batched_tokens: int,
-        memory_utilization: float,
-    ) -> int:
-        raise NotImplementedError()
-
-    def get_workspace_size(self) -> int:
-        return 1 * _GiB
-
-    def get_cache_block_size(self) -> int:
-        raise NotImplementedError()
-
-    def get_max_num_cpu_blocks(
-        self,
-        swap_space_gib: int,
-    ) -> int:
-        swap_space = swap_space_gib * _GiB
-        cpu_memory = self.cpu_memory
-        if swap_space > 0.8 * cpu_memory:
-            raise ValueError(f'The swap space ({swap_space_gib:.2f} GiB) '
-                             'takes more than 80% of the available memory '
-                             f'({cpu_memory / _GiB:.2f} GiB).'
-                             'Please check the swap space size.')
-        if swap_space > 0.5 * cpu_memory:
-            logger.info(f'WARNING: The swap space ({swap_space_gib:.2f} GiB) '
-                        'takes more than 50% of the available memory '
-                        f'({cpu_memory / _GiB:.2f} GiB).'
-                        'This may slow the system performance.')
-        max_num_blocks = swap_space // self.get_cache_block_size()
-        return max_num_blocks
-
-    def get_param_size(self) -> int:
-        raise NotImplementedError()
-
-    def get_max_act_size(self, max_num_batched_tokens: int) -> int:
-        raise NotImplementedError()
-
-    def get_cache_block_size(self) -> int:
-        key_cache_block = self.block_size * self.hidden_size // self.tensor_parallel_size
-        value_cache_block = key_cache_block
-        total = self.num_layers * (key_cache_block + value_cache_block)
-        dtype_size = get_dtype_size(self.dtype)
-        return dtype_size * total
-
-    def get_max_num_gpu_blocks(
-        self,
-        max_num_batched_tokens: int,
-        memory_utilization: float = 0.95,
-    ) -> int:
-        # NOTE(woosuk): This assumes that the machine has homogeneous GPUs.
-        usable_memory = int(memory_utilization * self.gpu_memory)
-
-        param_size = self.get_param_size()
-        act_size = self.get_max_act_size(max_num_batched_tokens)
-        workspace_size = self.get_workspace_size()
-
-        max_cache_size = usable_memory - (param_size + act_size + workspace_size)
-        if max_cache_size <= 0:
-            raise RuntimeError('Not enough GPU memory.')
-        max_num_blocks = max_cache_size // self.get_cache_block_size()
-        return max_num_blocks
-
-
-class GPT2MemoryAnalyzer(CacheFlowMemoryAnalyzer):
-
-    def __init__(
-        self,
-        model_name: str,
-        block_size: int,
-        dtype: torch.dtype,
-        gpu_memory: int,
-        cpu_memory: int,
-        tensor_parallel_size: int,
-    ) -> None:
-        self.model_name = model_name
-        self.block_size = block_size
-        self.dtype = dtype
-        self.gpu_memory = gpu_memory
-        self.cpu_memory = cpu_memory
-        self.tensor_parallel_size = tensor_parallel_size
-
-        config = AutoConfig.from_pretrained(model_name)
-        self.num_layers = config.num_hidden_layers
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_size = config.hidden_size // self.num_heads
-        self.ffn_size = config.n_inner if config.n_inner is not None else 4 * self.hidden_size
-        self.vocab_size = config.vocab_size
-        self.max_position = config.max_position_embeddings
-
-    def get_param_size(self) -> int:
-        word_embedding = self.vocab_size * self.hidden_size // self.tensor_parallel_size
-        position_embedding = self.max_position * self.hidden_size
-
-        ln1 = 2 * self.hidden_size
-        q = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        k = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        v = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        out = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        mha = ln1 + q + k + v + out
-
-        ln2 = 2 * self.hidden_size
-        ffn1 = self.hidden_size * self.ffn_size // self.tensor_parallel_size + self.ffn_size
-        ffn2 = self.ffn_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        ffn = ln2 + ffn1 + ffn2
-
-        total = (word_embedding + position_embedding +
-                 self.num_layers * (mha + ffn))
-        dtype_size = get_dtype_size(self.dtype)
-        return dtype_size * total
-
-    def get_max_act_size(
-        self,
-        max_num_batched_tokens: int,
-    ) -> int:
-        # NOTE: We approxmiately calculate the maximum activation size by
-        # estimating
-        # 1) the maximum activation tensor size during inference
-        # 2) the residual tensor size during inference
-        # Here, we assume that FlashAttention is used and
-        # thus the attention maps are never materialized in GPU DRAM.
-        residual = max_num_batched_tokens * self.hidden_size
-        qkv = 3 * (max_num_batched_tokens * self.hidden_size) // self.tensor_parallel_size
-        ffn = max_num_batched_tokens * self.ffn_size // self.tensor_parallel_size
-        # Double the activation size for input and output.
-        max_act = 2 * (max(qkv, ffn) + residual)
-        # Size of output logits.
-        output_logits = 2 * (max_num_batched_tokens * self.vocab_size)
-        max_act = max(max_act, output_logits)
-        dtype_size = get_dtype_size(self.dtype)
-        return dtype_size * max_act
-
-
-class OPTMemoryAnalyzer(CacheFlowMemoryAnalyzer):
-
-    def __init__(
-        self,
-        model_name: str,
-        block_size: int,
-        dtype: torch.dtype,
-        gpu_memory: int,
-        cpu_memory: int,
-        tensor_parallel_size: int,
-    ) -> None:
-        self.model_name = model_name
-        self.block_size = block_size
-        self.dtype = dtype
-        self.gpu_memory = gpu_memory
-        self.cpu_memory = cpu_memory
-        self.tensor_parallel_size = tensor_parallel_size
-
-        config = AutoConfig.from_pretrained(model_name)
-        self.num_layers = config.num_hidden_layers
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_size = config.hidden_size // self.num_heads
-        self.ffn_size = config.ffn_dim
-        self.embedding_size = config.word_embed_proj_dim
-        self.vocab_size = config.vocab_size
-        self.max_position = config.max_position_embeddings
-
-    def get_param_size(self) -> int:
-        word_embedding = self.vocab_size * self.embedding_size // self.tensor_parallel_size
-        if self.embedding_size != self.hidden_size:
-            # Project in/out.
-            word_embedding += 2 * self.embedding_size * self.hidden_size
-        position_embedding = self.max_position * self.hidden_size
-
-        ln1 = 2 * self.hidden_size
-        q = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        k = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        v = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        out = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        mha = ln1 + q + k + v + out
-
-        ln2 = 2 * self.hidden_size
-        ffn1 = self.hidden_size * self.ffn_size // self.tensor_parallel_size + self.ffn_size
-        ffn2 = self.ffn_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        ffn = ln2 + ffn1 + ffn2
-
-        total = (word_embedding + position_embedding +
-                 self.num_layers * (mha + ffn))
-        dtype_size = get_dtype_size(self.dtype)
-        return dtype_size * total
-
-    def get_max_act_size(
-        self,
-        max_num_batched_tokens: int,
-    ) -> int:
-        # NOTE: We approxmiately calculate the maximum activation size by
-        # estimating
-        # 1) the maximum activation tensor size during inference
-        # 2) the residual tensor size during inference
-        # Here, we assume that we use memory-efficient attention which
-        # does not materialize the attention maps in GPU DRAM.
-        residual = max_num_batched_tokens * self.hidden_size
-        qkv = 3 * (max_num_batched_tokens * self.hidden_size) // self.tensor_parallel_size
-        ffn = max_num_batched_tokens * self.ffn_size // self.tensor_parallel_size
-        # Double the activation size for input and output.
-        max_act = 2 * (max(qkv, ffn) + residual)
-        # Size of output logits.
-        output_logits = 2 * (max_num_batched_tokens * self.vocab_size)
-        max_act = max(max_act, output_logits)
-        dtype_size = get_dtype_size(self.dtype)
-        return dtype_size * max_act
-
-
-class LlamaMemoryAnalyzer(CacheFlowMemoryAnalyzer):
-
-    def __init__(
-        self,
-        model_name: str,
-        block_size: int,
-        dtype: torch.dtype,
-        gpu_memory: int,
-        cpu_memory: int,
-        tensor_parallel_size: int,
-    ) -> None:
-        self.model_name = model_name
-        self.block_size = block_size
-        self.dtype = dtype
-        self.gpu_memory = gpu_memory
-        self.cpu_memory = cpu_memory
-        self.tensor_parallel_size = tensor_parallel_size
-
-        config = AutoConfig.from_pretrained(model_name)
-        self.num_layers = config.num_hidden_layers
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_size = config.hidden_size // self.num_heads
-        self.ffn_size = config.intermediate_size
-        self.vocab_size = config.vocab_size
-        self.max_position = 8192
-
-    def get_param_size(self) -> int:
-        # NOTE: LLaMA does not tie the two embeddings.
-        word_embedding = self.vocab_size * self.hidden_size // self.tensor_parallel_size
-        lm_head = self.vocab_size * self.hidden_size // self.tensor_parallel_size
-
-        # NOTE: LLaMA does not have bias terms.
-        ln1 = self.hidden_size
-        q = self.hidden_size * self.hidden_size // self.tensor_parallel_size
-        k = self.hidden_size * self.hidden_size // self.tensor_parallel_size
-        v = self.hidden_size * self.hidden_size // self.tensor_parallel_size
-        out = self.hidden_size * self.hidden_size // self.tensor_parallel_size
-        # Rotary embedding.
-        # TODO(woosuk): Share the rotary embedding between layers.
-        rot = self.max_position * self.head_size
-        mha = ln1 + q + k + v + out + rot
-
-        ln2 = self.hidden_size
-        gate = self.hidden_size * self.ffn_size // self.tensor_parallel_size
-        down = self.ffn_size * self.hidden_size // self.tensor_parallel_size
-        up = self.hidden_size * self.ffn_size // self.tensor_parallel_size
-        ffn = ln2 + gate + down + up
-
-        total = word_embedding + self.num_layers * (mha + ffn) + lm_head
-        dtype_size = get_dtype_size(self.dtype)
-        return dtype_size * total
-
-    def get_max_act_size(
-        self,
-        max_num_batched_tokens: int,
-    ) -> int:
-        # NOTE: We approxmiately calculate the maximum activation size by
-        # estimating
-        # 1) the maximum activation tensor size during inference
-        # 2) the residual tensor size during inference
-        # Here, we assume that we use memory-efficient attention which
-        # does not materialize the attention maps in GPU DRAM.
-        residual = max_num_batched_tokens * self.hidden_size
-        qkv = 3 * (max_num_batched_tokens * self.hidden_size) // self.tensor_parallel_size
-        ffn = 2 * (max_num_batched_tokens * self.ffn_size) // self.tensor_parallel_size
-        # Double the activation size for input and output.
-        max_act = 2 * (max(qkv, ffn) + residual)
-        # Size of output logits.
-        output_logits = 2 * (max_num_batched_tokens * self.vocab_size)
-        max_act = max(max_act, output_logits)
-        dtype_size = get_dtype_size(self.dtype)
-        return dtype_size * max_act
-
-
-class GPTNeoXMemoryAnalyzer(CacheFlowMemoryAnalyzer):
-
-    def __init__(
-        self,
-        model_name: str,
-        block_size: int,
-        dtype: torch.dtype,
-        gpu_memory: int,
-        cpu_memory: int,
-        tensor_parallel_size: int,
-    ) -> None:
-        self.model_name = model_name
-        self.block_size = block_size
-        self.dtype = dtype
-        self.gpu_memory = gpu_memory
-        self.cpu_memory = cpu_memory
-        self.tensor_parallel_size = tensor_parallel_size
-
-        config = AutoConfig.from_pretrained(model_name)
-        self.num_layers = config.num_hidden_layers
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_size = config.hidden_size // self.num_heads
-        self.ffn_size = config.intermediate_size
-        self.vocab_size = config.vocab_size
-        self.max_position = 8192
-        self.tie_word_embeddings = config.tie_word_embeddings
-
-    def get_param_size(self) -> int:
-        word_embedding = self.vocab_size * self.hidden_size // self.tensor_parallel_size
-        if self.tie_word_embeddings:
-            lm_head = 0
-        else:
-            lm_head = self.vocab_size * self.hidden_size // self.tensor_parallel_size
-
-        ln1 = 2 * self.hidden_size
-        q = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        k = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        v = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        out = self.hidden_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        # Rotary embedding.
-        # TODO(woosuk): Share the rotary embedding between layers.
-        rot = self.max_position * self.head_size
-        mha = ln1 + q + k + v + out + rot
-
-        ln2 = 2 * self.hidden_size
-        ffn1 = self.hidden_size * self.ffn_size // self.tensor_parallel_size + self.ffn_size
-        ffn2 = self.ffn_size * self.hidden_size // self.tensor_parallel_size + self.hidden_size
-        ffn = ln2 + ffn1 + ffn2
-
-        total = word_embedding + self.num_layers * (mha + ffn) + lm_head
-        dtype_size = get_dtype_size(self.dtype)
-        return dtype_size * total
-
-    def get_max_act_size(
-        self,
-        max_num_batched_tokens: int,
-    ) -> int:
-        # NOTE: We approxmiately calculate the maximum activation size by
-        # estimating
-        # 1) the maximum activation tensor size during inference
-        # 2) the residual tensor size during inference
-        # Here, we assume that we use memory-efficient attention which
-        # does not materialize the attention maps in GPU DRAM.
-        residual = max_num_batched_tokens * self.hidden_size
-        qkv = 3 * (max_num_batched_tokens * self.hidden_size) // self.tensor_parallel_size
-        ffn = 2 * (max_num_batched_tokens * self.ffn_size) // self.tensor_parallel_size
-        # Double the activation size for input and output.
-        max_act = 2 * (max(qkv, ffn) + residual)
-        # Size of output logits.
-        output_logits = 2 * (max_num_batched_tokens * self.vocab_size)
-        max_act = max(max_act, output_logits)
-        dtype_size = get_dtype_size(self.dtype)
-        return dtype_size * max_act

cacheflow/model_executor/model_loader.py

@@ -5,9 +5,6 @@ import torch
 import torch.nn as nn
 from transformers import AutoConfig, PretrainedConfig
 
-from cacheflow.model_executor.memory_analyzer import (
-    CacheFlowMemoryAnalyzer, GPT2MemoryAnalyzer, GPTNeoXMemoryAnalyzer,
-    LlamaMemoryAnalyzer, OPTMemoryAnalyzer)
 from cacheflow.model_executor.models import (
     GPT2LMHeadModel, GPTNeoXForCausalLM, LlamaForCausalLM, OPTForCausalLM)
 from cacheflow.model_executor.utils import get_torch_dtype
@@ -22,14 +19,6 @@ _MODEL_REGISTRY = {
     "OPTForCausalLM": OPTForCausalLM,
 }
 
-_MEMORY_ANALYZERS = {
-    "GPT2LMHeadModel": GPT2MemoryAnalyzer,
-    "GPTNeoXForCausalLM": GPTNeoXMemoryAnalyzer,
-    "LlamaForCausalLM": LlamaMemoryAnalyzer,
-    "OPTForCausalLM": OPTMemoryAnalyzer,
-}
-
-
 def _get_model_architecture(config: PretrainedConfig) -> nn.Module:
     architectures = getattr(config, "architectures", [])
     for arch in architectures:
@@ -41,17 +30,6 @@ def _get_model_architecture(config: PretrainedConfig) -> nn.Module:
     )
 
 
-def _get_memory_analyzer(config: PretrainedConfig) -> CacheFlowMemoryAnalyzer:
-    architectures = getattr(config, "architectures", [])
-    for arch in architectures:
-        if arch in _MEMORY_ANALYZERS:
-            return _MEMORY_ANALYZERS[arch]
-    raise ValueError(
-        f"Model architectures {architectures} are not supported for now. "
-        f"Supported architectures: {list(_MEMORY_ANALYZERS.keys())}"
-    )
-
-
 def _get_dtype(config: PretrainedConfig, dtype: str) -> torch.dtype:
     # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
     # because config.torch_dtype can be None.
@@ -100,18 +78,3 @@ def get_model(
         model = model.cuda()
     return model.eval(), torch_dtype
 
-
-def get_memory_analyzer(
-    model_name: str,
-    block_size: int,
-    dtype: str,
-    gpu_memory: int,
-    cpu_memory: int,
-    tensor_parallel_size: int = 1,
-) -> CacheFlowMemoryAnalyzer:
-    config = AutoConfig.from_pretrained(model_name)
-    torch_dtype = _get_dtype(config, dtype)
-    memory_analyzer = _get_memory_analyzer(config)
-    return memory_analyzer(
-        model_name, block_size, torch_dtype, gpu_memory, cpu_memory,
-        tensor_parallel_size)

cacheflow/model_executor/models/gpt2.py

@@ -58,7 +58,8 @@ class GPT2Attention(nn.Module):
         self.c_proj = RowParallelLinear(self.hidden_size, self.hidden_size, bias=True,
                                         input_is_parallel=True,
                                         perform_initialization=False)
-        self.attn = GPTCacheFlowAttention(scale=self.scale)
+        self.attn = GPTCacheFlowAttention(self.num_heads, self.head_dim,
+                                          scale=self.scale)
 
     def forward(
         self,

cacheflow/model_executor/models/gpt_neox.py

@@ -62,7 +62,8 @@ class GPTNeoXAttention(nn.Module):
         scaling = self.head_size ** -0.5
         rotary_dim = int(self.head_size * config.rotary_pct)
         assert rotary_dim % 2 == 0
-        self.attn = GPTNeoXCacheFlowAttention(scaling, rotary_dim)
+        self.attn = GPTNeoXCacheFlowAttention(self.num_heads, self.head_size,
+                                              scaling, rotary_dim)
 
     def forward(
         self,

cacheflow/model_executor/models/llama.py

@@ -104,7 +104,8 @@ class LlamaAttention(nn.Module):
             input_is_parallel=True,
             perform_initialization=False,
         )
-        self.attn = GPTNeoXCacheFlowAttention(self.scaling, self.head_dim)
+        self.attn = GPTNeoXCacheFlowAttention(self.num_heads, self.head_dim,
+                                              self.scaling, rotary_dim=self.head_dim)
 
     def forward(
         self,

cacheflow/model_executor/models/opt.py

@@ -74,7 +74,8 @@ class OPTAttention(nn.Module):
         self.out_proj = RowParallelLinear(embed_dim, embed_dim, bias=bias,
                                           input_is_parallel=True,
                                           perform_initialization=False)
-        self.attn = GPTCacheFlowAttention(scale=self.scaling)
+        self.attn = GPTCacheFlowAttention(self.num_heads, self.head_dim,
+                                          scale=self.scaling)
 
     def forward(
         self,

cacheflow/model_executor/utils.py

@@ -40,3 +40,15 @@ def set_random_seed(seed: int) -> None:
 
     if model_parallel_is_initialized():
         model_parallel_cuda_manual_seed(seed)
+
+
+def get_cache_block_size(block_size: int,
+                         num_heads: int,
+                         head_size: int,
+                         num_layers: int,
+                         dtype: str) -> int:
+    key_cache_block = block_size * num_heads * head_size
+    value_cache_block = key_cache_block
+    total = num_layers * (key_cache_block + value_cache_block)
+    dtype_size = get_dtype_size(dtype)
+    return dtype_size * total

cacheflow/worker/controller.py

@@ -23,23 +23,18 @@ class Controller:
         pipeline_parallel_size: int,
         distributed_init_method: str,
         model_name: str,
-        block_size: int,
-        num_gpu_blocks: int,
-        num_cpu_blocks: int,
         dtype: str,
         seed: int,
         cache_dir: Optional[str],
         use_dummy_weights: bool,
         use_np_cache: bool,
         max_num_batched_tokens: int,
+        max_num_sequences: int,
         use_ray: bool,
     ) -> None:
         self.stage_id = stage_id
         self.stage_devices = stage_devices
         self.model_name = model_name
-        self.block_size = block_size
-        self.num_gpu_blocks = num_gpu_blocks
-        self.num_cpu_blocks = num_cpu_blocks
         self.use_ray = use_ray
 
         # Which pipeline stage is this node assigned to?
@@ -56,9 +51,6 @@ class Controller:
                 worker_cls = Worker
             worker = worker_cls(
                 model_name=model_name,
-                block_size=block_size,
-                num_gpu_blocks=num_gpu_blocks,
-                num_cpu_blocks=num_cpu_blocks,
                 dtype=dtype,
                 seed=seed,
                 distributed_init_method=distributed_init_method,
@@ -70,9 +62,44 @@ class Controller:
                 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,
             )
             self.workers.append(worker)
 
+    def get_num_available_blocks(self, block_size: int, cpu_swap_space: int,
+                                 gpu_memory_utilization: float) -> List[Tuple[int, int]]:
+        all_worker_results = []
+        for worker in self.workers:
+            executor = worker.get_num_available_blocks
+            if self.use_ray:
+                executor = executor.remote
+
+            result = executor(
+                block_size,
+                cpu_swap_space,
+                gpu_memory_utilization,
+            )
+            all_worker_results.append(result)
+        if self.use_ray:
+            all_worker_results = ray.get(all_worker_results)
+        return all_worker_results
+
+    def init_cache_engine(self, block_size: int, num_gpu_blocks: int,
+                          num_cpu_blocks: int):
+        all_worker_futures = []
+        for worker in self.workers:
+            executor = worker.init_cache_engine
+            if self.use_ray:
+                executor = executor.remote
+            future = executor(
+                block_size,
+                num_gpu_blocks,
+                num_cpu_blocks,
+            )
+            all_worker_futures.append(future)
+        if self.use_ray:
+            ray.get(all_worker_futures)
+
     def set_next(
         self,
         next_node: Union['Controller', 'Scheduler'],

cacheflow/worker/worker.py

@@ -3,7 +3,8 @@ from typing import Dict, List, Optional, Tuple
 
 import torch
 
-from cacheflow.model_executor import get_model, InputMetadata, set_random_seed
+from cacheflow.model_executor import (get_model, get_cache_block_size,
+                                      InputMetadata, set_random_seed)
 from cacheflow.model_executor.parallel_utils.parallel_state import (
     initialize_model_parallel,
     initialize_all_reduce_launcher,
@@ -12,6 +13,7 @@ from cacheflow.sampling_params import SamplingParams
 from cacheflow.sequence import (SequenceData, SequenceGroupMetadata,
                                 SequenceOutputs)
 from cacheflow.worker.cache_engine import CacheEngine
+from cacheflow.utils import get_gpu_memory
 
 
 class Worker:
@@ -25,9 +27,6 @@ class Worker:
     def __init__(
         self,
         model_name: str,
-        block_size: int,
-        num_gpu_blocks: int,
-        num_cpu_blocks: int,
         dtype: str,
         seed: int,
         distributed_init_method: str,
@@ -37,6 +36,7 @@ class Worker:
         use_dummy_weights: bool,
         use_np_cache: bool,
         max_num_batched_tokens: int,
+        max_num_sequences: int,
         tensor_parallel_size: int = 1,
         pipeline_parallel_size: int = 1,
     ) -> None:
@@ -46,8 +46,8 @@ class Worker:
                                           tensor_parallel_size,
                                           pipeline_parallel_size)
         self.worker_id = rank
-        self.block_size = block_size
+        self.seed = seed
-        set_random_seed(seed)
+        set_random_seed(self.seed)
 
         # Initialize the model.
         self.model, self.dtype = get_model(
@@ -55,8 +55,10 @@ class Worker:
             use_dummy_weights=use_dummy_weights, use_np_cache=use_np_cache)
         tensor_model_parallel_world_size = (
             get_tensor_model_parallel_world_size())
+        self.max_num_batched_tokens = max_num_batched_tokens
         initialize_all_reduce_launcher(
-            max_num_batched_tokens, self.model.config.hidden_size, self.dtype)
+            self.max_num_batched_tokens, self.model.config.hidden_size, self.dtype)
+        self.max_num_sequences = max_num_sequences
         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
@@ -66,12 +68,80 @@ class Worker:
         # the random state is not affected by the model initialization.
         set_random_seed(seed)
 
+        # Uninitialized cache engine. Will be initialized with
+        # self.init_cache_engine().
+        self.block_size = None
+        self.cache_engine = None
+        self.cache_events = None
+        self.gpu_cache = None
+
+    @torch.inference_mode()
+    def get_num_available_blocks(
+        self, block_size: int, cpu_swap_space: int,
+        gpu_memory_utilization: float) -> Tuple[int, int]:
+        # Profile the memory usage of the model and get the maximum number of
+        # cache blocks that can be allocated with the remaining free memory.
+        torch.cuda.empty_cache()
+        torch.cuda.reset_peak_memory_stats()
+
+        # Profile memory usage with max_num_sequences sequences and the total
+        # number of tokens equal to max_num_batched_tokens.
+
+        # Enable top-k sampling to reflect the accurate memory usage.
+        sampling_params = SamplingParams(top_p=0.99,
+                                         top_k=self.model.config.vocab_size - 1)
+        seqs = []
+        for group_id in range(self.max_num_sequences):
+            seq_len = (self.max_num_batched_tokens // self.max_num_sequences +
+                       (group_id < self.max_num_batched_tokens %
+                        self.max_num_sequences))
+            seq_data = SequenceData([0] * seq_len)
+            seq = SequenceGroupMetadata(
+                group_id=group_id,
+                is_prompt=True,
+                seq_data={group_id: seq_data},
+                sampling_params=sampling_params,
+                block_tables=None,
+            )
+            seqs.append(seq)
+
+        input_tokens, input_positions, input_metadata = self.prepare_inputs(seqs)
+
+        # Execute the model.
+        self.model(
+            input_ids=input_tokens,
+            positions=input_positions,
+            kv_caches=[(None, None)] * self.num_layers,
+            input_metadata=input_metadata,
+            cache_events=None,
+        )
+
+        # Calculate the number of blocks that can be allocated with the
+        # profiled peak memory.
+        torch.cuda.synchronize()
+        peak_memory = torch.cuda.max_memory_allocated()
+        total_gpu_memory = get_gpu_memory()
+        cache_block_size = get_cache_block_size(block_size, self.num_heads,
+                                                self.head_size, self.num_layers,
+                                                self.dtype)
+        num_gpu_blocks = int((total_gpu_memory * gpu_memory_utilization
+                              - peak_memory) // cache_block_size)
+        num_cpu_blocks = int(cpu_swap_space // cache_block_size)
+        torch.cuda.empty_cache()
+        # Reset the seed to ensure that the model output is not affected by
+        # the profiling.
+        set_random_seed(self.seed)
+        return num_gpu_blocks, num_cpu_blocks
+
+    def init_cache_engine(self, block_size: int, num_gpu_blocks: int,
+                          num_cpu_blocks: int):
+        self.block_size = block_size
         self.cache_engine = CacheEngine(
             worker_id=self.worker_id,
             num_layers=self.num_layers,
             num_heads=self.num_heads,
             head_size=self.head_size,
-            block_size=block_size,
+            block_size=self.block_size,
             num_gpu_blocks=num_gpu_blocks,
             num_cpu_blocks=num_cpu_blocks,
             dtype=self.dtype,
@@ -129,6 +199,12 @@ class Worker:
             # is always the first token in the sequence.
             input_positions.extend(range(len(prompt_tokens)))
 
+            if seq_group_metadata.block_tables is None:
+                # During memory profiling, the block tables are not initialized
+                # yet. In this case, we just use a dummy slot mapping.
+                slot_mapping.extend([0] * prompt_len)
+                continue
+
             # Compute the slot mapping.
             block_table = seq_group_metadata.block_tables[seq_id]
             for i in range(prompt_len):