Add Worker class

cacheflow/worker/worker.py

+from typing import Dict, List, Tuple
+
+import torch
+
+from cacheflow.models import get_model
+from cacheflow.models import InputMetadata
+from cacheflow.worker.cache_engine import CacheEngine
+
+
+class Worker:
+
+    def __init__(
+        self,
+        worker_id: int,
+        gpu_id: int,
+        model_name: str,
+        block_size: int,
+        num_gpu_blocks: int,
+        num_cpu_blocks: int,
+    ) -> None:
+        self.worker_id = worker_id
+        self.gpu_id = gpu_id
+        self.block_size = block_size
+
+        self.device = torch.device('cuda', index=gpu_id)
+
+        # Initialize the model.
+        # FIXME(woosuk): This is a hack.
+        self.model = get_model(model_name).to(device=gpu_id)
+        self.num_layers = self.model.config.num_hidden_layers
+        self.num_heads = self.model.config.num_attention_heads
+        self.head_size = self.model.config.hidden_size // self.num_heads
+        self.dtype = self.model.dtype
+
+        self.cache_engine = CacheEngine(
+            worker_id=worker_id,
+            gpu_id=gpu_id,
+            num_layers=self.num_layers,
+            num_heads=self.num_heads,
+            head_size=self.head_size,
+            block_size=block_size,
+            num_gpu_blocks=num_gpu_blocks,
+            num_cpu_blocks=num_cpu_blocks,
+            dtype=self.dtype,
+        )
+        self.cache_events = self.cache_engine.events
+        self.gpu_cache = self.cache_engine.gpu_cache
+
+    def prepare_inputs(
+        self,
+        prompt_tokens: Dict[int, List[int]],    # Seq id -> List of input token ids.
+        generation_tokens: Dict[int, int],      # Seq id -> Input token id.
+        context_lens: Dict[int, int],           # Seq id -> Number of tokens participating in attention.
+        block_tables: Dict[int, List[int]],     # Seq id -> List of physical block numbers.
+    ) -> Tuple[torch.LongTensor, torch.LongTensor, InputMetadata]:
+        # TODO(woosuk): Support interactive generation.
+        # Add the prompt tokens.
+        prompt_lens: List[int] = []
+        input_tokens: List[int] = []
+        input_positions: List[int] = []
+        slot_mapping: List[int] = []
+
+        prompt_seq_ids = sorted(prompt_tokens.keys())
+        for seq_id in prompt_seq_ids:
+            prompt_len = len(prompt_tokens[seq_id])
+            prompt_lens.append(prompt_len)
+
+            input_tokens.extend(prompt_tokens[seq_id])
+            input_positions.extend(range(len(prompt_tokens[seq_id])))
+
+            block_table = block_tables[seq_id]
+            for i in range(prompt_len):
+                block_number = block_table[i // self.block_size]
+                block_offset = i % self.block_size
+                slot = block_number * self.block_size + block_offset
+                slot_mapping.append(slot)
+
+        # Add the generation tokens.
+        max_context_len = 0
+        max_num_blocks_per_seq = 0
+        generation_block_tables: List[List[int]] = []
+
+        generation_seq_ids = sorted(generation_tokens.keys())
+        for seq_id in generation_seq_ids:
+            input_tokens.append(generation_tokens[seq_id])
+            input_positions.append(context_lens[seq_id] - 1)
+            generation_block_tables.append(block_tables[seq_id])
+
+            max_context_len = max(max_context_len, context_lens[seq_id])
+            max_num_blocks_per_seq = max(
+                max_num_blocks_per_seq, len(block_tables[seq_id]))
+
+        # Optimization: Pad the input length to be a multiple of 8.
+        # This is required for utilizing the Tensor Cores in NVIDIA GPUs.
+        input_tokens = _pad_to_alignment(input_tokens, multiple_of=8)
+        input_positions = _pad_to_alignment(input_positions, multiple_of=8)
+
+        # Convert to tensors.
+        tokens_tensor = torch.tensor(
+            input_tokens, dtype=torch.long, device=self.device)
+        positions_tensor = torch.tensor(
+            input_positions, dtype=torch.long, device=self.device)
+        slot_mapping_tensor = torch.tensor(
+            slot_mapping, dtype=torch.int, device=self.device)
+        context_lens_tensor = torch.tensor(
+            [context_lens[seq_id] for seq_id in generation_seq_ids],
+            dtype=torch.int, device=self.device)
+        block_tables_tensor = torch.tensor(
+            [_pad_to_max(block_table) for block_table in generation_block_tables],
+            dtype=int, device=self.device)
+
+        input_metadata = InputMetadata(
+            prompt_lens=prompt_lens,
+            slot_mapping=slot_mapping_tensor,
+            context_lens=context_lens_tensor,
+            max_context_len=max_context_len,
+            block_tables=block_tables_tensor,
+        )
+        return tokens_tensor, positions_tensor, input_metadata
+
+
+    @torch.inference_mode()
+    def execute_stage(
+        self,
+        prompt_tokens: Dict[int, List[int]],    # Seq id -> List of input token ids.
+        generation_tokens: Dict[int, int],      # Seq id -> Input token id.
+        context_lens: Dict[int, int],           # Seq id -> Number of tokens participating in attention.
+        block_tables: Dict[int, List[int]],     # Seq id -> List of physical block numbers.
+        blocks_to_swap_in: Dict[int, int],
+        blocks_to_swap_out: Dict[int, int],
+        blocks_to_copy: Dict[int, int],
+    ) -> torch.Tensor:
+        # Issue cache operations.
+        command_issued = False
+        if blocks_to_swap_in:
+            self.cache_engine.swap_in(blocks_to_swap_in)
+            command_issued = True
+        if blocks_to_swap_out:
+            self.cache_engine.swap_out(blocks_to_swap_out)
+            command_issued = True
+        if blocks_to_copy:
+            self.cache_engine.copy(blocks_to_copy)
+            command_issued = True
+
+        if command_issued:
+            cache_events = self.cache_events
+        else:
+            cache_events = None
+
+        # Prepare input tensors.
+        input_tokens, input_positions, input_metadata = self.prepare_inputs(
+            prompt_tokens, generation_tokens, context_lens, block_tables)
+
+        # Execute the model.
+        output = self.model(
+            input_ids=input_tokens,
+            positions=input_positions,
+            input_metadata=input_metadata,
+            cache_events=cache_events,
+        )
+        return output
+
+
+def _pad_to_alignment(x: List[int], multiple_of: int) -> List[int]:
+    return x + [0] * ((-len(x)) % multiple_of)
+
+
+def _pad_to_max(x: List[int], max_len: int) -> List[int]:
+    return x + [0] * (max_len - len(x))