model_runner.py

import time
from typing import Dict, List, Tuple, Union

import numpy as np
import torch
import torch.nn as nn

from vllm.config import ModelConfig, ParallelConfig, SchedulerConfig
from vllm.logger import init_logger
from vllm.model_executor import get_model, InputMetadata, SamplingMetadata
from vllm.sampling_params import SamplingParams, SamplingType
from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata

logger = init_logger(__name__)

KVCache = Tuple[torch.Tensor, torch.Tensor]
_PAD_SLOT_ID = -1
# Capture graphs for batch size 1, 2, 4, 8, 16, 24, 32, 40, ..., 256.
# NOTE: _get_graph_batch_size needs to be updated if this list is changed.
_BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [8 * i for i in range(1, 33)]


class ModelRunner:

    def __init__(
        self,
        model_config: ModelConfig,
        parallel_config: ParallelConfig,
        scheduler_config: SchedulerConfig,
    ):
        self.model_config = model_config
        self.parallel_config = parallel_config
        self.scheduler_config = scheduler_config

        # model_config can be None in tests/samplers/test_sampler.py.
        # FIXME(woosuk): This is a hack to make the tests work. Refactor this.
        self.sliding_window = (model_config.get_sliding_window()
                               if model_config is not None else None)
        self.model = None
        self.block_size = None  # Set after initial profiling.

        self.graph_runners: Dict[int, CUDAGraphRunner] = {}
        self.graph_memory_pool = None  # Set during graph capture.

        self.max_context_len_to_capture = (
            self.model_config.max_context_len_to_capture
            if self.model_config is not None else 0)
        # When using CUDA graph, the input block tables must be padded to
        # max_context_len_to_capture. However, creating the block table in
        # Python can be expensive. To optimize this, we cache the block table
        # in numpy and only copy the actual input content at every iteration.
        # The shape of the cached block table will be
        # (max batch size to capture, max context len to capture / block size).
        self.graph_block_tables = None  # Set after initial profiling.

    def load_model(self) -> None:
        self.model = get_model(self.model_config)

    def set_block_size(self, block_size: int) -> None:
        self.block_size = block_size

        max_num_blocks = (self.max_context_len_to_capture + block_size -
                          1) // block_size
        self.graph_block_tables = np.zeros(
            (max(_BATCH_SIZES_TO_CAPTURE), max_num_blocks), dtype=np.int32)

    def _prepare_prompt(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
    ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata]:
        assert len(seq_group_metadata_list) > 0
        input_tokens: List[List[int]] = []
        input_positions: List[List[int]] = []
        slot_mapping: List[List[int]] = []

        prompt_lens: List[int] = []
        for seq_group_metadata in seq_group_metadata_list:
            assert seq_group_metadata.is_prompt
            seq_ids = list(seq_group_metadata.seq_data.keys())
            assert len(seq_ids) == 1
            seq_id = seq_ids[0]

            seq_data = seq_group_metadata.seq_data[seq_id]
            prompt_tokens = seq_data.get_token_ids()
            prompt_len = len(prompt_tokens)
            prompt_lens.append(prompt_len)

            input_tokens.append(prompt_tokens)
            # NOTE(woosuk): Here we assume that the first token in the prompt
            # is always the first token in the sequence.
            input_positions.append(list(range(prompt_len)))

            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.append([_PAD_SLOT_ID] * prompt_len)
                continue

            # Compute the slot mapping.
            slot_mapping.append([])
            block_table = seq_group_metadata.block_tables[seq_id]
            # Mask the [0, start_idx) tokens of the prompt with _PAD_SLOT_ID,
            # where start_idx is max(0, prompt_len - sliding_window).
            # For example, if the prompt len is 10, sliding window is 8, and
            # block size is 4, the first two tokens are masked and the slot
            # mapping will be [-1, -1, 2, 3, 4, 5, 6, 7, 0, 1].
            start_idx = 0
            if self.sliding_window is not None:
                start_idx = max(0, prompt_len - self.sliding_window)
            for i in range(prompt_len):
                if i < start_idx:
                    slot_mapping[-1].append(_PAD_SLOT_ID)
                    continue

                block_number = block_table[i // self.block_size]
                block_offset = i % self.block_size
                slot = block_number * self.block_size + block_offset
                slot_mapping[-1].append(slot)

        max_prompt_len = max(prompt_lens)
        input_tokens = _make_tensor_with_pad(input_tokens,
                                             max_prompt_len,
                                             pad=0,
                                             dtype=torch.long)
        input_positions = _make_tensor_with_pad(input_positions,
                                                max_prompt_len,
                                                pad=0,
                                                dtype=torch.long)
        slot_mapping = _make_tensor_with_pad(slot_mapping,
                                             max_prompt_len,
                                             pad=_PAD_SLOT_ID,
                                             dtype=torch.long)

        input_metadata = InputMetadata(
            prompt_lens=prompt_lens,
            slot_mapping=slot_mapping,
            max_context_len=None,
            context_lens=None,
            block_tables=None,
            use_cuda_graph=False,
        )
        return input_tokens, input_positions, input_metadata

    def _prepare_decode(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
    ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata]:
        assert len(seq_group_metadata_list) > 0
        input_tokens: List[List[int]] = []
        input_positions: List[List[int]] = []
        slot_mapping: List[List[int]] = []
        context_lens: List[int] = []
        block_tables: List[List[int]] = []

        for seq_group_metadata in seq_group_metadata_list:
            assert not seq_group_metadata.is_prompt

            seq_ids = list(seq_group_metadata.seq_data.keys())
            for seq_id in seq_ids:
                seq_data = seq_group_metadata.seq_data[seq_id]
                generation_token = seq_data.get_last_token_id()
                input_tokens.append([generation_token])

                seq_len = seq_data.get_len()
                position = seq_len - 1
                input_positions.append([position])

                context_len = seq_len if self.sliding_window is None else min(
                    seq_len, self.sliding_window)
                context_lens.append(context_len)

                block_table = seq_group_metadata.block_tables[seq_id]
                block_number = block_table[position // self.block_size]
                block_offset = position % self.block_size
                slot = block_number * self.block_size + block_offset
                slot_mapping.append([slot])

                if self.sliding_window is not None:
                    sliding_window_blocks = (self.sliding_window //
                                             self.block_size)
                    block_table = block_table[-sliding_window_blocks:]
                block_tables.append(block_table)

        batch_size = len(input_tokens)
        max_context_len = max(context_lens)
        use_captured_graph = (
            not self.model_config.enforce_eager
            and batch_size <= _BATCH_SIZES_TO_CAPTURE[-1]
            and max_context_len <= self.max_context_len_to_capture)
        if use_captured_graph:
            # Pad the input tokens, positions, and slot mapping to match the
            # batch size of the captured graph.
            graph_batch_size = _get_graph_batch_size(batch_size)
            assert graph_batch_size >= batch_size
            for _ in range(graph_batch_size - batch_size):
                input_tokens.append([])
                input_positions.append([])
                slot_mapping.append([])
                context_lens.append(1)
                block_tables.append([])
            batch_size = graph_batch_size

        # When using CUDA graph, we don't need to make the tensors on the GPU
        # because they will be eventually copied to the designated GPU buffer.
        device = "cpu" if use_captured_graph else "cuda"
        input_tokens = _make_tensor_with_pad(input_tokens,
                                             max_len=1,
                                             pad=0,
                                             dtype=torch.long,
                                             device=device)
        input_positions = _make_tensor_with_pad(input_positions,
                                                max_len=1,
                                                pad=0,
                                                dtype=torch.long,
                                                device=device)
        slot_mapping = _make_tensor_with_pad(slot_mapping,
                                             max_len=1,
                                             pad=_PAD_SLOT_ID,
                                             dtype=torch.long,
                                             device=device)
        context_lens = torch.tensor(context_lens,
                                    dtype=torch.int,
                                    device=device)

        if use_captured_graph:
            # The shape of graph_block_tables is
            # [max batch size, max context len // block size].
            input_block_tables = self.graph_block_tables[:batch_size]
            for i, block_table in enumerate(block_tables):
                if block_table:
                    input_block_tables[i, :len(block_table)] = block_table
            block_tables = torch.from_numpy(input_block_tables).to(device)
        else:
            block_tables = _make_tensor_with_pad(
                block_tables,
                max_len=max_context_len,
                pad=0,
                dtype=torch.int,
            )

        input_metadata = InputMetadata(
            prompt_lens=[],
            slot_mapping=slot_mapping,
            max_context_len=max_context_len,
            context_lens=context_lens,
            block_tables=block_tables,
            use_cuda_graph=use_captured_graph,
        )
        return input_tokens, input_positions, input_metadata

    def _prepare_sample(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        prompt_lens: List[int],
    ) -> SamplingMetadata:
        seq_groups: List[Tuple[List[int], SamplingParams]] = []
        selected_token_indices: List[int] = []
        selected_token_start_idx = 0
        categorized_sample_indices = {t: [] for t in SamplingType}
        categorized_sample_indices_start_idx = 0

        max_prompt_len = max(prompt_lens) if prompt_lens else 1
        for i, seq_group_metadata in enumerate(seq_group_metadata_list):
            seq_ids = list(seq_group_metadata.seq_data.keys())
            sampling_params = seq_group_metadata.sampling_params
            seq_groups.append((seq_ids, sampling_params))

            if seq_group_metadata.is_prompt:
                assert len(seq_ids) == 1
                prompt_len = prompt_lens[i]
                if sampling_params.prompt_logprobs is not None:
                    # NOTE: prompt token positions do not need sample, skip
                    categorized_sample_indices_start_idx += prompt_len - 1

                categorized_sample_indices[
                    sampling_params.sampling_type].append(
                        categorized_sample_indices_start_idx)
                categorized_sample_indices_start_idx += 1

                if sampling_params.prompt_logprobs is not None:
                    selected_token_indices.extend(
                        range(selected_token_start_idx,
                              selected_token_start_idx + prompt_len - 1))
                selected_token_indices.append(selected_token_start_idx +
                                              prompt_len - 1)
                selected_token_start_idx += max_prompt_len
            else:
                num_seqs = len(seq_ids)
                selected_token_indices.extend(
                    range(selected_token_start_idx,
                          selected_token_start_idx + num_seqs))
                selected_token_start_idx += num_seqs

                categorized_sample_indices[
                    sampling_params.sampling_type].extend(
                        range(categorized_sample_indices_start_idx,
                              categorized_sample_indices_start_idx + num_seqs))
                categorized_sample_indices_start_idx += num_seqs

        selected_token_indices = torch.tensor(selected_token_indices,
                                              dtype=torch.long,
                                              device="cuda")
        categorized_sample_indices = {
            t: torch.tensor(seq_ids, dtype=torch.int, device="cuda")
            for t, seq_ids in categorized_sample_indices.items()
        }

        seq_data: Dict[int, SequenceData] = {}
        for seq_group_metadata in seq_group_metadata_list:
            seq_data.update(seq_group_metadata.seq_data)

        sampling_metadata = SamplingMetadata(
            seq_groups=seq_groups,
            seq_data=seq_data,
            prompt_lens=prompt_lens,
            selected_token_indices=selected_token_indices,
            categorized_sample_indices=categorized_sample_indices,
        )
        return sampling_metadata

    @torch.inference_mode()
    def execute_model(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        kv_caches: List[Tuple[torch.Tensor, torch.Tensor]],
    ) -> SamplerOutput:
        # NOTE: We assume that all sequences in the group are all prompts or
        # all decodes.
        is_prompt = seq_group_metadata_list[0].is_prompt
        # Prepare input tensors.
        if is_prompt:
            inputs = self._prepare_prompt(seq_group_metadata_list)
            input_tokens, input_positions, input_metadata = inputs
        else:
            inputs = self._prepare_decode(seq_group_metadata_list)
            input_tokens, input_positions, input_metadata = inputs
        sampling_metadata = self._prepare_sample(seq_group_metadata_list,
                                                 input_metadata.prompt_lens)

        # Execute the model.
        if input_metadata.use_cuda_graph:
            graph_batch_size = input_tokens.shape[0]
            model_executable = self.graph_runners[graph_batch_size]
        else:
            model_executable = self.model
        hidden_states = model_executable(
            input_ids=input_tokens,
            positions=input_positions,
            kv_caches=kv_caches,
            input_metadata=input_metadata,
        )

        # Sample the next token.
        output = self.model.sample(
            hidden_states=hidden_states,
            sampling_metadata=sampling_metadata,
        )
        return output

    @torch.inference_mode()
    def profile_run(self) -> None:
        # Enable top-k sampling to reflect the accurate memory usage.
        vocab_size = self.model_config.get_vocab_size()
        sampling_params = SamplingParams(top_p=0.99, top_k=vocab_size - 1)
        max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
        max_num_seqs = self.scheduler_config.max_num_seqs

        # Profile memory usage with max_num_sequences sequences and the total
        # number of tokens equal to max_num_batched_tokens.
        seqs: List[SequenceGroupMetadata] = []
        for group_id in range(max_num_seqs):
            seq_len = (max_num_batched_tokens // max_num_seqs +
                       (group_id < max_num_batched_tokens % max_num_seqs))
            seq_data = SequenceData([0] * seq_len)
            seq = SequenceGroupMetadata(
                request_id=str(group_id),
                is_prompt=True,
                seq_data={group_id: seq_data},
                sampling_params=sampling_params,
                block_tables=None,
            )
            seqs.append(seq)

        # Run the model with the dummy inputs.
        num_layers = self.model_config.get_num_layers(self.parallel_config)
        kv_caches = [(None, None)] * num_layers
        self.execute_model(seqs, kv_caches)
        torch.cuda.synchronize()
        return

    @torch.inference_mode()
    def capture_model(self, kv_caches: List[KVCache]) -> None:
        assert not self.model_config.enforce_eager
        logger.info("Capturing the model for CUDA graphs. This may lead to "
                    "unexpected consequences if the model is not static. To "
                    "run the model in eager mode, set 'enforce_eager=True' or "
                    "use '--enforce-eager' in the CLI.")
        logger.info("CUDA graphs can take additional 1~3 GiB memory per GPU. "
                    "If you are running out of memory, consider decreasing "
                    "`gpu_memory_utilization` or enforcing eager mode.")
        start_time = time.perf_counter()

        # Prepare dummy inputs. These will be reused for all batch sizes.
        max_batch_size = max(_BATCH_SIZES_TO_CAPTURE)
        input_tokens = torch.zeros(max_batch_size, 1, dtype=torch.long).cuda()
        input_positions = torch.zeros(max_batch_size, 1,
                                      dtype=torch.long).cuda()
        slot_mapping = torch.empty(max_batch_size, 1, dtype=torch.long).cuda()
        slot_mapping.fill_(_PAD_SLOT_ID)
        context_lens = torch.ones(max_batch_size, dtype=torch.int32).cuda()
        block_tables = torch.from_numpy(self.graph_block_tables).cuda()

        # NOTE: Capturing the largest batch size first may help reduce the
        # memory usage of CUDA graph.
        for batch_size in reversed(_BATCH_SIZES_TO_CAPTURE):
            # Create dummy input_metadata.
            input_metadata = InputMetadata(
                prompt_lens=[],
                slot_mapping=slot_mapping[:batch_size],
                max_context_len=self.max_context_len_to_capture,
                context_lens=context_lens[:batch_size],
                block_tables=block_tables[:batch_size],
                use_cuda_graph=True,
            )

            graph_runner = CUDAGraphRunner(self.model)
            graph_runner.capture(
                input_tokens[:batch_size],
                input_positions[:batch_size],
                kv_caches,
                input_metadata,
                memory_pool=self.graph_memory_pool,
            )
            self.graph_memory_pool = graph_runner.graph.pool()
            self.graph_runners[batch_size] = graph_runner

        end_time = time.perf_counter()
        elapsed_time = end_time - start_time
        # This usually takes < 10 seconds.
        logger.info(f"Graph capturing finished in {elapsed_time:.0f} secs.")


class CUDAGraphRunner:

    def __init__(self, model: nn.Module):
        self.model = model
        self.graph = None
        self.input_buffers: Dict[str, torch.Tensor] = {}
        self.output_buffers: Dict[str, torch.Tensor] = {}

    def capture(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        memory_pool,
    ) -> None:
        assert self.graph is None
        # Run the model once without capturing the graph.
        # This is to make sure that the captured graph does not include the
        # kernel launches for initial benchmarking (e.g., Triton autotune).
        self.model(
            input_ids,
            positions,
            kv_caches,
            input_metadata,
        )
        torch.cuda.synchronize()

        # Capture the graph.
        self.graph = torch.cuda.CUDAGraph()
        with torch.cuda.graph(self.graph, pool=memory_pool):
            hidden_states = self.model(
                input_ids,
                positions,
                kv_caches,
                input_metadata,
            )
        torch.cuda.synchronize()

        # Save the input and output buffers.
        self.input_buffers = {
            "input_ids": input_ids,
            "positions": positions,
            "kv_caches": kv_caches,
            "slot_mapping": input_metadata.slot_mapping,
            "context_lens": input_metadata.context_lens,
            "block_tables": input_metadata.block_tables,
        }
        self.output_buffers = {"hidden_states": hidden_states}
        return

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[Tuple[torch.Tensor, torch.Tensor]],
        input_metadata: InputMetadata,
    ) -> torch.Tensor:
        # KV caches are fixed tensors, so we don't need to copy them.
        del kv_caches

        # Copy the input tensors to the input buffers.
        self.input_buffers["input_ids"].copy_(input_ids)
        self.input_buffers["positions"].copy_(positions)
        self.input_buffers["slot_mapping"].copy_(input_metadata.slot_mapping)
        self.input_buffers["context_lens"].copy_(input_metadata.context_lens)
        self.input_buffers["block_tables"].copy_(input_metadata.block_tables)

        # Run the graph.
        self.graph.replay()

        # Return the output tensor.
        return self.output_buffers["hidden_states"]

    def __call__(self, *args, **kwargs):
        return self.forward(*args, **kwargs)


def _pad_to_max(x: List[int], max_len: int, pad: int) -> List[int]:
    assert len(x) <= max_len
    return x + [pad] * (max_len - len(x))


def _make_tensor_with_pad(
    x: List[List[int]],
    max_len: int,
    pad: int,
    dtype: torch.dtype,
    device: Union[str, torch.device] = "cuda",
) -> torch.Tensor:
    padded_x = [_pad_to_max(x_i, max_len, pad) for x_i in x]
    return torch.tensor(padded_x, dtype=dtype, device=device)


def _get_graph_batch_size(batch_size: int) -> int:
    if batch_size <= 2:
        return batch_size
    elif batch_size <= 4:
        return 4
    else:
        return (batch_size + 7) // 8 * 8