init

vllm_vacc/vllm/v1/worker/gpu_input_batch.py

@@ -0,0 +1,491 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Datastructures defining a GPU input batch
+
+from dataclasses import dataclass
+from typing import Optional, cast
+
+import numpy as np
+import torch
+from typing_extensions import deprecated
+
+from vllm.lora.request import LoRARequest
+from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalKwargsItems
+from vllm.pooling_params import PoolingParams
+from vllm.sampling_params import SamplingParams, SamplingType
+from vllm.utils import length_from_prompt_token_ids_or_embeds, swap_dict_values
+from vllm.v1.outputs import LogprobsTensors
+from vllm.v1.pool.metadata import PoolingMetadata
+from vllm.v1.sample.logits_processor import (BatchUpdateBuilder,
+                                             LogitsProcessors,
+                                             MoveDirectionality)
+from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.spec_decode.utils import is_spec_decode_unsupported
+from vllm.v1.utils import copy_slice
+from vllm.v1.worker.block_table import MultiGroupBlockTable
+
+
+
+@dataclass
+class CachedRequestState:
+
+    req_id: str
+    prompt_token_ids: Optional[list[int]]
+    mm_features: list[MultiModalFeatureSpec]
+    sampling_params: Optional[SamplingParams]
+    pooling_params: Optional[PoolingParams]
+    generator: Optional[torch.Generator]
+
+    block_ids: tuple[list[int], ...]
+    num_computed_tokens: int
+    output_token_ids: list[int]
+
+    mrope_positions: Optional[torch.Tensor] = None
+    mrope_position_delta: Optional[int] = None
+
+    lora_request: Optional[LoRARequest] = None
+    prompt_embeds: Optional[torch.Tensor] = None
+    deepstack_input_embeds: Optional[torch.Tensor] = None
+
+    def __post_init__(self):
+        self.num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
+            self.prompt_token_ids, self.prompt_embeds)
+
+    @property
+    def num_tokens(self) -> int:
+        return self.num_prompt_tokens + len(self.output_token_ids)
+
+    # Temporary back-compatibility for plugins that define model runner
+    @property
+    @deprecated("`mm_inputs` is superseded by `mm_kwargs` and will be "
+                "removed in v0.13. Please use `mm_kwargs` instead.")
+    def mm_inputs(self) -> list[MultiModalKwargsItems]:
+        return [
+            MultiModalKwargsItems.from_seq([f.data]) for f in self.mm_features
+            if f.data is not None
+        ]
+
+    def get_token_id(self, idx: int) -> int:
+        if idx < self.num_prompt_tokens:
+            if self.prompt_token_ids is None:
+                raise ValueError(
+                    f"Tried to access token index {idx}, but that token was "
+                    "provided via prompt_embeds, and its ID is unknown.")
+            return self.prompt_token_ids[idx]
+        elif idx - self.num_prompt_tokens < len(self.output_token_ids):
+            return self.output_token_ids[idx - self.num_prompt_tokens]
+        else:
+            return -1
+class InputBatch:
+    
+    def __init__(
+        self,
+        max_num_reqs: int,
+        max_model_len: int,
+        max_num_batched_tokens: int,
+        device: torch.device,
+        pin_memory: bool,
+        vocab_size: int,
+        block_sizes: list[int],  # The block_size of each kv cache group
+        logitsprocs: Optional[LogitsProcessors] = None,
+        is_spec_decode: bool = False,
+        is_pooling_model: bool = False,
+        num_speculative_tokens: int = 0,
+    ):
+        self.is_pooling_model = is_pooling_model
+        self.is_spec_decode = is_spec_decode
+        self.max_num_reqs = max_num_reqs
+        self.max_model_len = max_model_len
+        self.max_num_batched_tokens = max_num_batched_tokens
+        self.device = device
+        self.pin_memory = pin_memory
+        self.vocab_size = vocab_size
+
+        self._req_ids: list[Optional[str]] = []
+        self.req_id_to_index: dict[str, int] = {}
+
+        # TODO(woosuk): This buffer could be too large if max_model_len is big.
+        # Find a way to reduce the CPU memory usage.
+        # This buffer is not directly transferred to the GPU, so it does not
+        # need to be pinned.
+        self.token_ids_cpu_tensor = torch.zeros(
+            (max_num_reqs, max_model_len),
+            device="cpu",
+            dtype=torch.int32,
+            pin_memory=False,
+        )
+        self.token_ids_cpu = self.token_ids_cpu_tensor.numpy()
+        self.is_token_ids = torch.zeros((max_num_reqs, max_model_len),
+                                        device="cpu",
+                                        dtype=bool,
+                                        pin_memory=False)
+        # Store prompt embeddings per request to avoid OOM from large upfront
+        # allocation if max_model_len is big.
+        # Maps req_index -> tensor of shape (num_prompt_tokens, hidden_size)
+        self.req_prompt_embeds: dict[int, torch.Tensor] = {}
+        #patch to add req_deepstack_input_embeds
+        self.req_deepstack_input_embeds: dict[int, torch.Tensor] = {}
+        self.num_tokens = np.zeros(max_num_reqs, dtype=np.int32)
+        self.num_tokens_no_spec = np.zeros(max_num_reqs, dtype=np.int32)
+        self.num_prompt_tokens = np.zeros(max_num_reqs, dtype=np.int32)
+        self.num_computed_tokens_cpu_tensor = torch.zeros(
+            (max_num_reqs, ),
+            device="cpu",
+            dtype=torch.int32,
+            pin_memory=pin_memory,
+        )
+        self.num_computed_tokens_cpu = \
+            self.num_computed_tokens_cpu_tensor.numpy()
+
+        # Block table.
+        self.block_table = MultiGroupBlockTable(
+            max_num_reqs=max_num_reqs,
+            max_model_len=max_model_len,
+            max_num_batched_tokens=max_num_batched_tokens,
+            pin_memory=pin_memory,
+            device=device,
+            block_sizes=block_sizes,
+            num_speculative_tokens=num_speculative_tokens,
+        )
+
+        # Sampling-related.
+        self.temperature = torch.empty((max_num_reqs, ),
+                                       dtype=torch.float32,
+                                       device=device)
+        self.temperature_cpu_tensor = torch.empty((max_num_reqs, ),
+                                                  dtype=torch.float32,
+                                                  device="cpu",
+                                                  pin_memory=pin_memory)
+        self.temperature_cpu = self.temperature_cpu_tensor.numpy()
+        self.greedy_reqs: set[str] = set()
+        self.random_reqs: set[str] = set()
+
+        self.top_p = torch.empty((max_num_reqs, ),
+                                 dtype=torch.float32,
+                                 device=device)
+        self.top_p_cpu_tensor = torch.empty((max_num_reqs, ),
+                                            dtype=torch.float32,
+                                            device="cpu",
+                                            pin_memory=pin_memory)
+        self.top_p_cpu = self.top_p_cpu_tensor.numpy()
+        self.top_p_reqs: set[str] = set()
+
+        self.top_k = torch.empty((max_num_reqs, ),
+                                 dtype=torch.int32,
+                                 device=device)
+        self.top_k_cpu_tensor = torch.empty((max_num_reqs, ),
+                                            dtype=torch.int32,
+                                            device="cpu",
+                                            pin_memory=pin_memory)
+        self.top_k_cpu = self.top_k_cpu_tensor.numpy()
+        self.top_k_reqs: set[str] = set()
+
+        # IDs of requests which do not support spec decoding
+        self.spec_decode_unsupported_reqs: set[str] = set()
+
+        # Frequency penalty related data structures
+        # self.frequency_penalties = torch.empty((max_num_reqs, ),
+        #                                        dtype=torch.float,
+        #                                        device=device)
+        self.frequency_penalties_cpu_tensor = torch.empty(
+            (max_num_reqs, ),
+            dtype=torch.float,
+            device="cpu",
+            pin_memory=pin_memory)
+        self.frequency_penalties_cpu = \
+            self.frequency_penalties_cpu_tensor.numpy()
+        self.frequency_penalties_reqs: set[str] = set()
+
+        # Presence penalty related data structures
+        # self.presence_penalties = torch.empty((max_num_reqs, ),
+        #                                       dtype=torch.float,
+        #                                       device=device)
+        self.presence_penalties_cpu_tensor = torch.empty((max_num_reqs, ),
+                                                         dtype=torch.float,
+                                                         device="cpu",
+                                                         pin_memory=pin_memory)
+        self.presence_penalties_cpu = self.presence_penalties_cpu_tensor.numpy(
+        )
+        self.presence_penalties_reqs: set[str] = set()
+
+        # Repetition penalty related data structures
+        # self.repetition_penalties = torch.empty((max_num_reqs, ),
+        #                                         dtype=torch.float,
+        #                                         device=device)
+        self.repetition_penalties_cpu_tensor = torch.empty(
+            (max_num_reqs, ),
+            dtype=torch.float,
+            device="cpu",
+            pin_memory=pin_memory)
+        self.repetition_penalties_cpu = \
+            self.repetition_penalties_cpu_tensor.numpy()
+        self.repetition_penalties_reqs: set[str] = set()
+
+        # Speculative decoding
+        self.num_accepted_tokens_cpu_tensor = torch.ones((max_num_reqs, ),
+                                                         dtype=torch.int64,
+                                                         device="cpu",
+                                                         pin_memory=pin_memory)
+        self.num_accepted_tokens_cpu = \
+            self.num_accepted_tokens_cpu_tensor.numpy()
+
+        # lora related
+        self.request_lora_mapping = np.zeros((self.max_num_reqs, ),
+                                             dtype=np.int32)
+        self.lora_id_to_request_ids: dict[int, set[str]] = {}
+        self.lora_id_to_lora_request: dict[int, LoRARequest] = {}
+
+        # req_index -> generator
+        # NOTE(woosuk): The indices of the requests that do not have their own
+        # generator should not be included in the dictionary.
+        self.generators: dict[int, torch.Generator] = {}
+
+        self.num_logprobs: dict[str, int] = {}
+        # NOTE(rob): num_prompt_logprobs only includes reqs
+        # that are currently in the prefill phase.
+        self.num_prompt_logprobs: dict[str, int] = {}
+
+        # To accumulate prompt logprobs tensor chunks across prefill steps.
+        self.in_progress_prompt_logprobs_cpu: dict[str, LogprobsTensors] = {}
+
+        # Internal representation of per-step batch state changes, used for
+        # reordering persistent batch and generating logitsprocs batch state
+        # updates. Should reset each step.
+        self.batch_update_builder = BatchUpdateBuilder()
+
+        # TODO convert this to LogitsProcessor
+        self.has_allowed_token_ids: set[str] = set()
+        # NOTE(lufang): In the mask tensor, if the corresponding token allowed,
+        # the value is False. Since we use masked_fill_ to set -inf.
+        self.allowed_token_ids_mask: Optional[torch.Tensor] = None
+        self.allowed_token_ids_mask_cpu_tensor: Optional[torch.Tensor] = None
+
+        # req_index -> bad_words_token_ids
+        self.bad_words_token_ids: dict[int, list[list[int]]] = {}
+
+        self.logits_processing_needs_token_ids = np.zeros(max_num_reqs,
+                                                          dtype=bool)
+
+        self.req_output_token_ids: list[Optional[list[int]]] = []
+
+        # Store provided logitsprocs. If none are provided, initialize empty
+        # data structure
+        self.logitsprocs = logitsprocs or LogitsProcessors()
+
+        # This is updated each time the batch constituents change.
+        self.sampling_metadata = self._make_sampling_metadata()
+
+        self.pooling_params: dict[str, PoolingParams] = {}
+
+        # Cached reference to the GPU tensor of previously sampled tokens
+        self.prev_sampled_token_ids: Optional[torch.Tensor] = None
+        self.prev_sampled_token_ids_invalid_indices: Optional[set[int]] = None
+        self.prev_req_id_to_index: Optional[dict[str, int]] = None
+
+    def add_request(
+        self,
+        request: "CachedRequestState",
+    ) -> int:
+        req_index = self._register_add_request(request)
+
+        req_id = request.req_id
+        if req_index == len(self._req_ids):
+            self._req_ids.append(req_id)
+            self.req_output_token_ids.append(request.output_token_ids)
+        else:
+            self._req_ids[req_index] = req_id
+            self.req_output_token_ids[req_index] = request.output_token_ids
+
+        self.req_id_to_index[req_id] = req_index
+
+        # Copy the prompt token ids and output token ids.
+        num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
+            request.prompt_token_ids, request.prompt_embeds)
+        self.num_prompt_tokens[req_index] = num_prompt_tokens
+        start_idx = num_prompt_tokens
+        end_idx = start_idx + len(request.output_token_ids)
+        if request.prompt_token_ids is not None:
+            self.token_ids_cpu[
+                req_index, :num_prompt_tokens] = request.prompt_token_ids
+            self.is_token_ids[req_index, :num_prompt_tokens] = True
+        else:
+            self.is_token_ids[req_index, :num_prompt_tokens] = False
+        if request.prompt_embeds is not None:
+            self.req_prompt_embeds[req_index] = request.prompt_embeds
+        #patch to add req_deepstack_input_embeds
+        if request.deepstack_input_embeds is not None:
+            self.req_deepstack_input_embeds[req_index] = request.deepstack_input_embeds    
+        self.token_ids_cpu[req_index,
+                           start_idx:end_idx] = request.output_token_ids
+        self.is_token_ids[req_index, start_idx:end_idx] = True
+        # Number of token ids in prompt (token_ids_cpu or prompt_embeds).
+        # NOTE(woosuk): This may include spec decode tokens.
+        self.num_tokens[req_index] = request.num_tokens
+        # Number of tokens without spec decode tokens.
+        self.num_tokens_no_spec[req_index] = request.num_tokens
+
+        self.num_computed_tokens_cpu[req_index] = request.num_computed_tokens
+        self.block_table.add_row(request.block_ids, req_index)
+
+        if sampling_params := request.sampling_params:
+            if (self.is_spec_decode
+                    and is_spec_decode_unsupported(sampling_params)):
+                self.spec_decode_unsupported_reqs.add(req_id)
+            if sampling_params.sampling_type == SamplingType.GREEDY:
+                # Should avoid division by zero later when apply_temperature.
+                self.temperature_cpu[req_index] = 0.0
+                self.greedy_reqs.add(req_id)
+            else:
+                self.temperature_cpu[req_index] = sampling_params.temperature
+                self.random_reqs.add(req_id)
+
+            self.top_p_cpu[req_index] = sampling_params.top_p
+            if sampling_params.top_p < 1:
+                self.top_p_reqs.add(req_id)
+            top_k = sampling_params.top_k
+            if 0 < top_k < self.vocab_size:
+                self.top_k_reqs.add(req_id)
+            else:
+                top_k = self.vocab_size
+            self.top_k_cpu[req_index] = top_k
+            self.frequency_penalties_cpu[
+                req_index] = sampling_params.frequency_penalty
+            if sampling_params.frequency_penalty != 0.0:
+                self.frequency_penalties_reqs.add(req_id)
+            self.presence_penalties_cpu[
+                req_index] = sampling_params.presence_penalty
+            if sampling_params.presence_penalty != 0.0:
+                self.presence_penalties_reqs.add(req_id)
+            self.repetition_penalties_cpu[
+                req_index] = sampling_params.repetition_penalty
+            if sampling_params.repetition_penalty != 1.0:
+                self.repetition_penalties_reqs.add(req_id)
+
+            # NOTE(woosuk): self.generators should not include the requests that
+            # do not have their own generator.
+            if request.generator is not None:
+                self.generators[req_index] = request.generator
+
+            if sampling_params.logprobs is not None:
+                self.num_logprobs[req_id] = (self.vocab_size
+                                             if sampling_params.logprobs == -1
+                                             else sampling_params.logprobs)
+            if sampling_params.prompt_logprobs is not None:
+                self.num_prompt_logprobs[req_id] = (
+                    self.vocab_size if sampling_params.prompt_logprobs == -1
+                    else sampling_params.prompt_logprobs)
+
+            if sampling_params.allowed_token_ids:
+                self.has_allowed_token_ids.add(req_id)
+                if self.allowed_token_ids_mask_cpu_tensor is None:
+                    # Lazy allocation for this tensor, which can be large.
+                    # False means we don't fill with -inf.
+                    self.allowed_token_ids_mask = torch.zeros(
+                        self.max_num_reqs,
+                        self.vocab_size,
+                        dtype=torch.bool,
+                        device=self.device)
+                    self.allowed_token_ids_mask_cpu_tensor = torch.zeros(
+                        self.max_num_reqs,
+                        self.vocab_size,
+                        dtype=torch.bool,
+                        device="cpu")
+                self.allowed_token_ids_mask_cpu_tensor[req_index] = True
+                # False means we don't fill with -inf.
+                self.allowed_token_ids_mask_cpu_tensor[req_index][
+                    sampling_params.allowed_token_ids] = False
+
+            if sampling_params.bad_words_token_ids:
+                self.bad_words_token_ids[
+                    req_index] = sampling_params.bad_words_token_ids
+        elif pooling_params := request.pooling_params:
+            self.pooling_params[req_id] = pooling_params
+            self.logits_processing_needs_token_ids[req_index] = (
+                pooling_params.requires_token_ids)
+        else:
+            raise NotImplementedError("Unrecognized request type")
+
+        # Speculative decoding: by default 1 token is generated.
+        self.num_accepted_tokens_cpu[req_index] = 1
+
+        # Add request lora ID
+        if request.lora_request:
+            lora_id = request.lora_request.lora_int_id
+            if lora_id not in self.lora_id_to_request_ids:
+                self.lora_id_to_request_ids[lora_id] = set()
+
+            self.request_lora_mapping[req_index] = lora_id
+            self.lora_id_to_request_ids[lora_id].add(request.req_id)
+            self.lora_id_to_lora_request[lora_id] = request.lora_request
+        else:
+            # No LoRA
+            self.request_lora_mapping[req_index] = 0
+
+        return req_index
+
+
+    def _make_sampling_metadata(self) -> SamplingMetadata:
+        num_reqs = self.num_reqs
+        # if not self.all_greedy:
+        #     temperature = copy_slice(self.temperature_cpu_tensor,
+        #                              self.temperature, num_reqs)
+        # else:
+        #     temperature = None
+        # if not self.no_top_p:
+        #     copy_slice(self.top_p_cpu_tensor, self.top_p, num_reqs)
+        # if not self.no_top_k:
+        #     copy_slice(self.top_k_cpu_tensor, self.top_k, num_reqs)
+
+        # if not self.no_penalties:
+        #     # Since syncing these tensors is expensive only copy them
+        #     # if necessary i.e. if there are requests which require
+        #     # penalties to be applied during sampling.
+        #     copy_slice(self.frequency_penalties_cpu_tensor,
+        #                self.frequency_penalties, num_reqs)
+        #     copy_slice(self.presence_penalties_cpu_tensor,
+        #                self.presence_penalties, num_reqs)
+        #     copy_slice(self.repetition_penalties_cpu_tensor,
+        #                self.repetition_penalties, num_reqs)
+
+        needs_prompt_token_ids = (
+            not self.no_penalties
+            or self.logits_processing_needs_token_ids[:num_reqs].any())
+        if needs_prompt_token_ids:
+            # The prompt tokens are used only for applying penalties or
+            # step pooling during the sampling/pooling process.
+            # Hence copy these tensors only when there are requests which
+            # need penalties/step_pooler to be applied.
+            prompt_token_ids = self._make_prompt_token_ids_tensor()
+        else:
+            prompt_token_ids = None
+
+        allowed_token_ids_mask: Optional[torch.Tensor] = None
+        if not self.no_allowed_token_ids:
+            assert self.allowed_token_ids_mask is not None
+            copy_slice(self.allowed_token_ids_mask_cpu_tensor,
+                       self.allowed_token_ids_mask, num_reqs)
+            allowed_token_ids_mask = self.allowed_token_ids_mask[:num_reqs]
+
+        return SamplingMetadata(
+            temperature=self.temperature_cpu_tensor[:num_reqs].to(self.device),
+            all_greedy=self.all_greedy,
+            all_random=self.all_random,
+            # top_p=None if self.no_top_p else self.top_p_cpu_tensor[:num_reqs].to(self.device),
+            # top_k=None if self.no_top_k else torch.tensor([40 for _ in range(num_reqs)]).to(torch.int32).to(self.device),
+            top_p=torch.tensor([1 for _ in range(num_reqs)]).to(torch.float32).to(self.device) if self.no_top_p else self.top_p_cpu_tensor[:num_reqs].to(self.device),
+            top_k=torch.tensor([40 for _ in range(num_reqs)]).to(torch.int32).to(self.device),
+            generators=self.generators,
+            max_num_logprobs=self.max_num_logprobs,
+            prompt_token_ids=prompt_token_ids,
+            frequency_penalties=self.frequency_penalties_cpu_tensor[:num_reqs].tolist(),
+            presence_penalties=self.presence_penalties_cpu_tensor[:num_reqs].tolist(),
+            repetition_penalties=self.repetition_penalties_cpu_tensor[:num_reqs].tolist(),
+            output_token_ids=cast(list[list[int]], self.req_output_token_ids),
+            no_penalties=self.no_penalties,
+            allowed_token_ids_mask=allowed_token_ids_mask,
+            bad_words_token_ids=self.bad_words_token_ids,
+            logitsprocs=self.logitsprocs,
+            temperature_cpu = self.temperature_cpu_tensor[:num_reqs],
+            top_p_cpu = self.top_p_cpu_tensor[:num_reqs],
+            top_k_cpu = torch.tensor([40 for _ in range(num_reqs)]).to(torch.int32),
+        )