Refactor system architecture (#82)

cacheflow/master/scheduler.py → cacheflow/core/scheduler.py

@@ -4,8 +4,8 @@ import pickle
 import time
 from typing import Any, Dict, List, Optional, Tuple
 
-from cacheflow.master.block_manager import BlockSpaceManager
-from cacheflow.master.policy import PolicyFactory
+from cacheflow.core.block_manager import BlockSpaceManager
+from cacheflow.core.policy import PolicyFactory
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.sequence import Sequence
 from cacheflow.sequence import SequenceGroup

cacheflow/master/server.py → cacheflow/core/server.py

@@ -8,20 +8,21 @@ try:
 except ImportError:
     ray = None
 
+from cacheflow.core.scheduler import Scheduler
+from cacheflow.frontend.simple_frontend import SimpleFrontend
 from cacheflow.logger import init_logger
-from cacheflow.master.scheduler import Scheduler
-from cacheflow.master.simple_frontend import SimpleFrontend
-from cacheflow.models import get_memory_analyzer
-from cacheflow.worker.controller import Controller, DeviceID
+from cacheflow.model_executor import get_memory_analyzer
 from cacheflow.sequence import SequenceGroup
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.utils import get_gpu_memory, get_cpu_memory
+from cacheflow.worker.controller import Controller, DeviceID
 
 
 logger = init_logger(__name__)
 
 
 class Server:
+
     def __init__(
         self,
         model: str,

cacheflow/http_frontend/fastapi_frontend.py → cacheflow/frontend/fastapi_frontend.py

 import argparse
 import asyncio
+import json
 import time
 from typing import List, Dict, Optional
-import json
 
-import ray
-from transformers import AutoTokenizer
 from fastapi import FastAPI, Request
 from fastapi.responses import StreamingResponse
+import ray
+from transformers import AutoTokenizer
 import uvicorn
 
-from cacheflow.sampling_params import SamplingParams
-from cacheflow.sequence import Sequence, SequenceGroup
-from cacheflow.master.server import (Server, add_server_arguments,
+from cacheflow.core.server import (Server, add_server_arguments,
                                    process_server_arguments,
                                    initialize_cluster)
-from cacheflow.worker.controller import DeviceID
+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.worker.controller import DeviceID
 
 TIMEOUT_TO_PREVENT_DEADLOCK = 1 # seconds
 app = FastAPI()

cacheflow/model_executor/__init__.py

+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
+
+
+__all__ = [
+    "InputMetadata",
+    "get_model",
+    "get_memory_analyzer",
+    "set_random_seed",
+]

cacheflow/models/attention.py → cacheflow/model_executor/layers/attention.py

@@ -7,7 +7,7 @@ from xformers import ops as xops
 from cacheflow import attention_ops
 from cacheflow import cache_ops
 from cacheflow import pos_encoding_ops
-from cacheflow.models import InputMetadata
+from cacheflow.model_executor.input_metadata import InputMetadata
 
 
 class GPTCacheFlowAttention(nn.Module):

cacheflow/models/sample.py → cacheflow/model_executor/layers/sampler.py

@@ -3,10 +3,11 @@ from typing import Dict, List, Tuple
 import torch
 import torch.nn as nn
 
-from cacheflow.models import InputMetadata
+from cacheflow.model_executor.input_metadata import InputMetadata
+from cacheflow.model_executor.parallel_utils.tensor_parallel import (
+    gather_from_tensor_model_parallel_region)
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.sequence import SequenceOutputs
-from cacheflow.parallel_utils.tensor_parallel import gather_from_tensor_model_parallel_region
 
 
 class Sampler(nn.Module):
@@ -27,7 +28,7 @@ class Sampler(nn.Module):
         # Get the logits for the next tokens.
         logits = torch.matmul(hidden_states, embedding.t())
         logits = gather_from_tensor_model_parallel_region(logits)
-        # Remove paddings in vocab.
+        # Remove paddings in vocab (if any).
         logits = logits[:, :self.vocab_size]
 
         # Apply temperature scaling.

cacheflow/models/memory_analyzer.py → cacheflow/model_executor/memory_analyzer.py

@@ -2,7 +2,7 @@ import torch
 from transformers import AutoConfig
 
 from cacheflow.logger import init_logger
-from cacheflow.models.utils import get_dtype_size
+from cacheflow.model_executor.utils import get_dtype_size
 
 
 logger = init_logger(__name__)

cacheflow/models/model_utils.py → cacheflow/model_executor/model_loader.py

@@ -5,16 +5,13 @@ import torch.nn as nn
 from transformers import AutoConfig
 from transformers import PretrainedConfig
 
-from cacheflow.models.memory_analyzer import CacheFlowMemoryAnalyzer
-from cacheflow.models.memory_analyzer import GPT2MemoryAnalyzer
-from cacheflow.models.memory_analyzer import GPTNeoXMemoryAnalyzer
-from cacheflow.models.memory_analyzer import LlamaMemoryAnalyzer
-from cacheflow.models.memory_analyzer import OPTMemoryAnalyzer
-from cacheflow.models.gpt2 import GPT2LMHeadModel
-from cacheflow.models.gpt_neox import GPTNeoXForCausalLM
-from cacheflow.models.llama import LlamaForCausalLM
-from cacheflow.models.opt import OPTForCausalLM
-from cacheflow.models.utils import get_torch_dtype
+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
+from cacheflow.model_executor.weight_utils import initialize_dummy_weights
 
 
 _MODELS = {
@@ -77,7 +74,7 @@ def get_model(
                 model = model.cuda()
                 # NOTE(woosuk): For precise performance evaluation, we assign
                 # random values to the weights.
-                model.initialize_dummy_weights()
+                initialize_dummy_weights(model)
             else:
                 # Create a model instance.
                 model = model_class(config)

cacheflow/model_executor/models/__init__.py

+from cacheflow.model_executor.models.gpt_neox import GPTNeoXForCausalLM
+from cacheflow.model_executor.models.gpt2 import GPT2LMHeadModel
+from cacheflow.model_executor.models.llama import LlamaForCausalLM
+from cacheflow.model_executor.models.opt import OPTForCausalLM
+
+
+__all__ = [
+    "GPT2LMHeadModel",
+    "GPTNeoXForCausalLM",
+    "LlamaForCausalLM",
+    "OPTForCausalLM",
+]

cacheflow/models/gpt2.py → cacheflow/model_executor/models/gpt2.py

@@ -5,16 +5,15 @@ import torch
 from torch import nn
 from transformers import GPT2Config
 
-from cacheflow.models import InputMetadata
-from cacheflow.models.attention import GPTCacheFlowAttention
-from cacheflow.models.sample import Sampler
-from cacheflow.models.utils import (hf_model_weights_iterator,
+from cacheflow.model_executor.input_metadata import InputMetadata
+from cacheflow.model_executor.layers.attention import GPTCacheFlowAttention
+from cacheflow.model_executor.layers.sampler import Sampler
+from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
                                                    load_tensor_parallel_weights)
-from cacheflow.parallel_utils.parallel_state import (
+from cacheflow.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
-from cacheflow.parallel_utils.tensor_parallel import (VocabParallelEmbedding,
-                                                      ColumnParallelLinear,
-                                                      RowParallelLinear)
+from cacheflow.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
 from cacheflow.sequence import SequenceOutputs
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -258,8 +257,5 @@ class GPT2LMHeadModel(nn.Module):
                     raise ValueError(f"Unexpected parameter name {name}")
             load_tensor_parallel_weights(param, loaded_weight, name,
                                          self._column_parallel_weights,
-                                         self._row_parallel_weights)
-
-    def initialize_dummy_weights(self) -> None:
-        for param in self.state_dict().values():
-            param.data.uniform_(-1e-3, 1e-3)
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)

cacheflow/models/gpt_neox.py → cacheflow/model_executor/models/gpt_neox.py

@@ -3,17 +3,17 @@ from typing import Dict, List, Optional, Tuple
 
 import torch
 from torch import nn
+from transformers import GPTNeoXConfig
 
-from cacheflow.models import InputMetadata
-from cacheflow.models.attention import GPTNeoXCacheFlowAttention
-from cacheflow.models.sample import Sampler
-from cacheflow.models.utils import (hf_model_weights_iterator,
+from cacheflow.model_executor.input_metadata import InputMetadata
+from cacheflow.model_executor.layers.attention import GPTNeoXCacheFlowAttention
+from cacheflow.model_executor.layers.sampler import Sampler
+from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
                                                    load_tensor_parallel_weights)
-from cacheflow.parallel_utils.parallel_state import (
+from cacheflow.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
-from cacheflow.parallel_utils.tensor_parallel import (VocabParallelEmbedding,
-                                                      ColumnParallelLinear,
-                                                      RowParallelLinear)
+from cacheflow.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
 from cacheflow.sequence import SequenceOutputs
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -21,7 +21,7 @@ KVCache = Tuple[torch.Tensor, torch.Tensor]
 
 class GPTNeoXAttention(nn.Module):
 
-    def __init__(self, config):
+    def __init__(self, config: GPTNeoXConfig):
         super().__init__()
         self.total_num_heads = config.num_attention_heads
         self.hidden_size = config.hidden_size
@@ -63,7 +63,7 @@ class GPTNeoXAttention(nn.Module):
 
 
 class GPTNeoXMLP(nn.Module):
-    def __init__(self, config):
+    def __init__(self, config: GPTNeoXConfig):
         super().__init__()
         self.dense_h_to_4h = ColumnParallelLinear(config.hidden_size,
                                                   config.intermediate_size,
@@ -86,7 +86,7 @@ class GPTNeoXMLP(nn.Module):
 
 class GPTNeoXLayer(nn.Module):
 
-    def __init__(self, config):
+    def __init__(self, config: GPTNeoXConfig):
         super().__init__()
         self.use_parallel_residual = config.use_parallel_residual
         self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
@@ -129,7 +129,7 @@ class GPTNeoXLayer(nn.Module):
 
 
 class GPTNeoXModel(nn.Module):
-    def __init__(self, config):
+    def __init__(self, config: GPTNeoXConfig):
         super().__init__()
         self.config = config
 
@@ -227,8 +227,5 @@ class GPTNeoXForCausalLM(nn.Module):
                     raise ValueError(f"Unexpected weight name: {name}")
             load_tensor_parallel_weights(param, loaded_weight, name,
                                          self._column_parallel_weights,
-                                         self._row_parallel_weights)
-
-    def initialize_dummy_weights(self) -> None:
-        for param in self.state_dict().values():
-            param.data.uniform_(-1e-3, 1e-3)
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)

cacheflow/models/llama.py → cacheflow/model_executor/models/llama.py

@@ -5,18 +5,18 @@ import torch
 from torch import nn
 from transformers import LlamaConfig
 
-from cacheflow.models import InputMetadata
-from cacheflow.models.activation import SiluAndMul
-from cacheflow.models.attention import GPTNeoXCacheFlowAttention
-from cacheflow.models.layernorm import RMSNorm
-from cacheflow.models.sample import Sampler
-from cacheflow.models.utils import (hf_model_weights_iterator,
+from cacheflow.sequence import SequenceOutputs
+from cacheflow.model_executor.input_metadata import InputMetadata
+from cacheflow.model_executor.layers.activation import SiluAndMul
+from cacheflow.model_executor.layers.layernorm import RMSNorm
+from cacheflow.model_executor.layers.attention import GPTNeoXCacheFlowAttention
+from cacheflow.model_executor.layers.sampler import Sampler
+from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
                                                    load_tensor_parallel_weights)
-from cacheflow.parallel_utils.parallel_state import (
+from cacheflow.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
-from cacheflow.parallel_utils.tensor_parallel import (VocabParallelEmbedding,
-                                                      ColumnParallelLinear,
-                                                      RowParallelLinear)
+from cacheflow.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
 from cacheflow.sequence import SequenceOutputs
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -263,8 +263,5 @@ class LlamaForCausalLM(nn.Module):
             param = state_dict[name]
             load_tensor_parallel_weights(param, loaded_weight, name,
                                          self._column_parallel_weights,
-                                         self._row_parallel_weights)
-
-    def initialize_dummy_weights(self) -> None:
-        for param in self.state_dict().values():
-            param.data.uniform_(-1e-3, 1e-3)
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)

cacheflow/models/opt.py → cacheflow/model_executor/models/opt.py

@@ -5,16 +5,15 @@ import torch
 from torch import nn
 from transformers import OPTConfig
 
-from cacheflow.models import InputMetadata
-from cacheflow.models.attention import GPTCacheFlowAttention
-from cacheflow.models.sample import Sampler
-from cacheflow.models.utils import (hf_model_weights_iterator,
+from cacheflow.model_executor.input_metadata import InputMetadata
+from cacheflow.model_executor.layers.attention import GPTCacheFlowAttention
+from cacheflow.model_executor.layers.sampler import Sampler
+from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
                                                    load_tensor_parallel_weights)
-from cacheflow.parallel_utils.parallel_state import (
+from cacheflow.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
-from cacheflow.parallel_utils.tensor_parallel import (VocabParallelEmbedding,
-                                                      ColumnParallelLinear,
-                                                      RowParallelLinear)
+from cacheflow.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
 from cacheflow.sequence import SequenceOutputs
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -288,8 +287,5 @@ class OPTForCausalLM(nn.Module):
             param = state_dict[name]
             load_tensor_parallel_weights(param, loaded_weight, name,
                                          self._column_parallel_weights,
-                                         self._row_parallel_weights)
-
-    def initialize_dummy_weights(self) -> None:
-        for param in self.state_dict().values():
-            param.data.uniform_(-1e-3, 1e-3)
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)