mirror of
https://github.com/vllm-project/vllm-ascend.git
synced 2025-10-20 13:43:53 +08:00
46e62efd44
### What this PR does / why we need it? Currently, MTP Model in deepseek can not be capture in ACLGraph. This PR is use to allow MTP to be captured in ACLGraph mode. ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
662 lines
31 KiB
Python
662 lines
31 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
from typing import Optional
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
from vllm.attention.layer import Attention
|
|
from vllm.config import (CompilationLevel, CUDAGraphMode, VllmConfig,
|
|
get_layers_from_vllm_config)
|
|
from vllm.distributed.parallel_state import get_pp_group
|
|
from vllm.logger import logger
|
|
from vllm.model_executor.model_loader import get_model
|
|
from vllm.model_executor.models import supports_multimodal
|
|
from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
|
|
from vllm.v1.core.sched.output import SchedulerOutput
|
|
from vllm.v1.sample.metadata import SamplingMetadata
|
|
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
|
|
|
|
from vllm_ascend.ascend_forward_context import set_ascend_forward_context
|
|
from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
|
|
from vllm_ascend.attention.attention_v1 import AscendAttentionState
|
|
from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
|
|
from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType
|
|
|
|
PADDING_SLOT_ID = -1
|
|
|
|
|
|
class EagleProposer(Proposer):
|
|
|
|
def __init__(self,
|
|
vllm_config: VllmConfig,
|
|
device: torch.device,
|
|
runner=None):
|
|
self.name = SpecDcodeType.EAGLE if vllm_config.speculative_config.method == "eagle" else SpecDcodeType.EAGLE3
|
|
self.vllm_config = vllm_config
|
|
self.device = device
|
|
self.runner = runner
|
|
|
|
self.block_size = vllm_config.cache_config.block_size
|
|
# We need to get the hidden size from the draft model config because
|
|
# the draft model's hidden size can be different from the target model's
|
|
# hidden size (e.g., Llama 3.3 70B).
|
|
self.hidden_size = vllm_config.speculative_config.draft_model_config.get_hidden_size(
|
|
)
|
|
|
|
self.use_cuda_graph = (self.vllm_config.compilation_config.level
|
|
== CompilationLevel.PIECEWISE and
|
|
not self.vllm_config.model_config.enforce_eager)
|
|
self.cudagraph_batch_sizes = list(
|
|
reversed(
|
|
self.vllm_config.compilation_config.cudagraph_capture_sizes))
|
|
|
|
# persistent buffers for cuda graph
|
|
self.input_ids = torch.zeros(
|
|
self.vllm_config.scheduler_config.max_num_batched_tokens,
|
|
dtype=torch.int32,
|
|
device=device)
|
|
self.positions = torch.zeros(
|
|
self.vllm_config.scheduler_config.max_num_batched_tokens,
|
|
dtype=torch.int64,
|
|
device=device)
|
|
self.hidden_states = torch.zeros(
|
|
(self.vllm_config.scheduler_config.max_num_batched_tokens,
|
|
self.hidden_size),
|
|
dtype=self.vllm_config.model_config.dtype,
|
|
device=device)
|
|
# We need +1 here because the arange is used to set query_start_loc,
|
|
# which has one more element than batch_size.
|
|
self.arange = torch.arange(vllm_config.scheduler_config.max_num_seqs +
|
|
1,
|
|
device=device,
|
|
dtype=torch.int32)
|
|
attn_mask_len = self.vllm_config.model_config.max_model_len
|
|
self.attn_mask_builder = AttentionMaskBuilder(
|
|
attn_mask_len, self.vllm_config.model_config.dtype)
|
|
|
|
def load_model(self, model: nn.Module) -> None:
|
|
target_attn_layer_names = set(
|
|
get_layers_from_vllm_config(self.vllm_config, Attention).keys())
|
|
self.model = get_model(vllm_config=self.vllm_config,
|
|
model_config=self.vllm_config.
|
|
speculative_config.draft_model_config)
|
|
draft_attn_layer_names = (
|
|
get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
|
|
target_attn_layer_names)
|
|
self.attn_layer_name = next(iter(draft_attn_layer_names))
|
|
|
|
# share embed_tokens with the target model if needed
|
|
if get_pp_group().world_size == 1:
|
|
logger.info(
|
|
"The EAGLE head shares the same vocab embedding" \
|
|
" with the target model."
|
|
)
|
|
self.model.model.embed_tokens = model.model.embed_tokens
|
|
else:
|
|
logger.info(
|
|
"Since PP > 1, the EAGLE head loaded its own vocab embedding" \
|
|
" weights instead of sharing them with the target model."
|
|
)
|
|
|
|
# share lm_head with the target model if needed
|
|
# some model definition do not define lm_head explicitly
|
|
# and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
|
|
if self.name == SpecDcodeType.EAGLE and hasattr(model, "lm_head"):
|
|
logger.info("Loading EAGLE LM head weights from the target model.")
|
|
if supports_multimodal(model):
|
|
self.model.lm_head = model.get_language_model().lm_head
|
|
else:
|
|
self.model.lm_head = model.lm_head
|
|
|
|
@torch.inference_mode()
|
|
def dummy_run(self,
|
|
num_tokens: int,
|
|
with_prefill: bool = False,
|
|
skip_attn: bool = False,
|
|
num_reqs: int = 0,
|
|
num_tokens_across_dp: Optional[torch.Tensor] = None,
|
|
aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
|
|
batch_descriptor=None):
|
|
moe_comm_type = self.runner._select_moe_comm_method(
|
|
num_tokens, with_prefill)
|
|
with set_ascend_forward_context(None,
|
|
self.vllm_config,
|
|
moe_comm_type=moe_comm_type,
|
|
num_tokens=num_tokens):
|
|
self.model(
|
|
input_ids=self.input_ids[:num_tokens],
|
|
positions=self.positions[:num_tokens],
|
|
hidden_states=self.hidden_states[:num_tokens],
|
|
)
|
|
|
|
def generate_token_ids(self,
|
|
valid_sampled_token_ids: list[list[int]],
|
|
sampling_metadata: SamplingMetadata = None,
|
|
scheduler_output: SchedulerOutput = None,
|
|
spec_decode_metadata: SpecDecodeMetadata = None,
|
|
positions: torch.Tensor = None,
|
|
num_scheduled_tokens: int = 0,
|
|
hidden_states: torch.Tensor = None,
|
|
attn_metadata=None,
|
|
aux_hidden_states: torch.Tensor = None):
|
|
|
|
attn_metadata = self._get_eagle_atten_dict(scheduler_output)
|
|
next_token_ids: list[int] = []
|
|
for i, token_ids in enumerate(valid_sampled_token_ids):
|
|
if token_ids:
|
|
# Common case.
|
|
next_token_id = token_ids[-1]
|
|
else:
|
|
# Partial prefill (rare case).
|
|
# Get the next token id from the request state.
|
|
req_id = self.runner.input_batch.req_ids[i]
|
|
req_state = self.runner.requests[req_id]
|
|
seq_len = (req_state.num_computed_tokens +
|
|
scheduler_output.num_scheduled_tokens[req_id])
|
|
|
|
next_token_id = req_state.get_token_id(seq_len)
|
|
next_token_ids.append(next_token_id)
|
|
next_token_ids = torch.tensor(next_token_ids,
|
|
dtype=torch.int32,
|
|
device=self.device)
|
|
eagle_attn_metadata = attn_metadata[self.attn_layer_name]
|
|
if spec_decode_metadata is None:
|
|
# input_ids can be None for multimodal models.
|
|
target_token_ids = self.runner.input_ids[:num_scheduled_tokens]
|
|
target_positions = positions[:num_scheduled_tokens]
|
|
if self.name == SpecDcodeType.EAGLE3:
|
|
target_hidden_states = torch.cat(
|
|
[h[:num_scheduled_tokens] for h in aux_hidden_states],
|
|
dim=-1)
|
|
else:
|
|
target_hidden_states = hidden_states[:num_scheduled_tokens]
|
|
target_slot_mapping = eagle_attn_metadata.slot_mapping
|
|
cu_num_tokens = eagle_attn_metadata.query_start_loc
|
|
else:
|
|
num_draft_tokens = spec_decode_metadata.num_draft_tokens
|
|
num_rejected_tokens = [
|
|
n + 1 - len(valid_sampled_token_ids[i]) if n > 0 else 0
|
|
for i, n in enumerate(num_draft_tokens)
|
|
]
|
|
num_rejected_tokens = torch.tensor(
|
|
num_rejected_tokens,
|
|
dtype=torch.int32,
|
|
device=self.device,
|
|
)
|
|
num_tokens = num_scheduled_tokens - sum(num_rejected_tokens)
|
|
cu_num_tokens, token_indices = self._prepare_inputs(
|
|
eagle_attn_metadata.query_start_loc, num_rejected_tokens,
|
|
num_tokens)
|
|
target_token_ids = self.runner.input_ids[token_indices]
|
|
target_positions = positions[token_indices]
|
|
if self.name == SpecDcodeType.EAGLE3:
|
|
target_hidden_states = torch.cat(
|
|
[h[token_indices] for h in aux_hidden_states], dim=-1)
|
|
else:
|
|
target_hidden_states = hidden_states[token_indices]
|
|
target_slot_mapping = eagle_attn_metadata.slot_mapping[
|
|
token_indices]
|
|
|
|
draft_token_ids = self._propose(
|
|
target_token_ids=target_token_ids,
|
|
target_positions=target_positions,
|
|
target_hidden_states=target_hidden_states,
|
|
target_slot_mapping=target_slot_mapping,
|
|
next_token_ids=next_token_ids,
|
|
cu_num_tokens=cu_num_tokens,
|
|
block_table=eagle_attn_metadata.block_tables,
|
|
sampling_metadata=sampling_metadata,
|
|
)
|
|
spec_token_ids = draft_token_ids.tolist()
|
|
return spec_token_ids
|
|
|
|
def _get_eagle_atten_dict(
|
|
self,
|
|
scheduler_output: "SchedulerOutput",
|
|
):
|
|
total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
|
|
assert total_num_scheduled_tokens > 0
|
|
num_reqs = self.runner.input_batch.num_reqs
|
|
assert num_reqs > 0
|
|
|
|
# OPTIMIZATION: Start copying the block table first.
|
|
# This way, we can overlap the copy with the following CPU operations.
|
|
self.runner.input_batch.block_table.commit_block_table(num_reqs)
|
|
|
|
# Get the number of scheduled tokens for each request.
|
|
req_ids = self.runner.input_batch.req_ids
|
|
tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
|
|
num_scheduled_tokens = np.array(tokens, dtype=np.int32)
|
|
max_num_scheduled_tokens = max(tokens)
|
|
self.runner.query_lens = torch.from_numpy(num_scheduled_tokens)
|
|
# Get request indices.
|
|
# E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
|
|
req_indices = np.repeat(self.runner.arange_np[:num_reqs],
|
|
num_scheduled_tokens)
|
|
|
|
# cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
|
|
# arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
|
|
cu_num_tokens, arange = self._get_cumsum_and_arange(
|
|
num_scheduled_tokens)
|
|
|
|
# Get positions.
|
|
positions_np = self.runner.positions_np[:total_num_scheduled_tokens]
|
|
np.add(self.runner.input_batch.num_computed_tokens_cpu[req_indices],
|
|
arange,
|
|
out=positions_np)
|
|
|
|
# Calculate M-RoPE positions.
|
|
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
|
|
if self.runner.uses_mrope:
|
|
self.runner._calc_mrope_positions(scheduler_output)
|
|
|
|
# Get token indices.
|
|
# E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
|
|
# -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
|
|
# where M is the max_model_len.
|
|
token_indices = (
|
|
positions_np +
|
|
req_indices * self.runner.input_batch.token_ids_cpu.shape[1])
|
|
|
|
# NOTE(woosuk): We use torch.index_select instead of np.take here
|
|
# because torch.index_select is much faster than np.take for large
|
|
# tensors.
|
|
torch.index_select(
|
|
self.runner.input_batch.token_ids_cpu_tensor.flatten(),
|
|
0,
|
|
torch.from_numpy(token_indices),
|
|
out=self.runner.input_ids_cpu[:total_num_scheduled_tokens])
|
|
|
|
# Prepare the attention metadata for each KV cache group and make layers
|
|
# in the same group share the same metadata.
|
|
# NOTE(Chen): there is exactly one KV cache group that contains all
|
|
# attetnion layers in the model for now, so the current logic for
|
|
# getting attn_metadata is not related to kv_cache_group information.
|
|
# Will extend this part to support multiple KV cache groups later.
|
|
for kv_cache_group_id, kv_cache_group_spec in enumerate(
|
|
self.runner.kv_cache_config.kv_cache_groups):
|
|
block_size = kv_cache_group_spec.kv_cache_spec.block_size
|
|
block_table = self.runner.input_batch.block_table[
|
|
kv_cache_group_id]
|
|
# E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
|
|
# -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
|
|
# where K is the max_num_blocks_per_req and the block size is 2.
|
|
# NOTE(woosuk): We can't simply use `token_indices // block_size`
|
|
# here because M (max_model_len) is not necessarily divisible by
|
|
# block_size.
|
|
block_table_indices = (
|
|
req_indices * block_table.max_num_blocks_per_req +
|
|
positions_np // block_size)
|
|
block_table_cpu = block_table.get_cpu_tensor()
|
|
block_numbers = block_table_cpu.flatten(
|
|
)[block_table_indices].numpy()
|
|
block_offsets = positions_np % block_size
|
|
np.add(
|
|
block_numbers * block_size,
|
|
block_offsets,
|
|
out=block_table.slot_mapping_np[:total_num_scheduled_tokens])
|
|
|
|
# Prepare the attention metadata.
|
|
self.runner.query_start_loc_np[0] = 0
|
|
self.runner.query_start_loc_np[1:num_reqs + 1] = cu_num_tokens
|
|
|
|
self.runner.seq_lens_np[:num_reqs] = (
|
|
self.runner.input_batch.num_computed_tokens_cpu[:num_reqs] +
|
|
num_scheduled_tokens)
|
|
|
|
# Copy the tensors to the NPU.
|
|
self.runner.input_ids[:total_num_scheduled_tokens].copy_(
|
|
self.runner.input_ids_cpu[:total_num_scheduled_tokens],
|
|
non_blocking=True)
|
|
if self.runner.uses_mrope:
|
|
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
|
|
self.runner.mrope_positions[:, :total_num_scheduled_tokens].copy_(
|
|
self.runner.
|
|
mrope_positions_cpu[:, :total_num_scheduled_tokens],
|
|
non_blocking=True)
|
|
else:
|
|
# Common case (1D positions)
|
|
self.runner.positions[:total_num_scheduled_tokens].copy_(
|
|
self.runner.positions_cpu[:total_num_scheduled_tokens],
|
|
non_blocking=True)
|
|
|
|
self.runner.query_start_loc[:num_reqs + 1].copy_(
|
|
self.runner.query_start_loc_cpu[:num_reqs + 1], non_blocking=True)
|
|
self.runner.seq_lens[:num_reqs].copy_(
|
|
self.runner.seq_lens_cpu[:num_reqs], non_blocking=True)
|
|
|
|
# Fill unused with -1. Needed for reshape_and_cache
|
|
self.runner.seq_lens[num_reqs:].fill_(0)
|
|
self.runner.query_start_loc[num_reqs + 1:].fill_(-1)
|
|
|
|
attn_metadata = {}
|
|
# Prepare the attention metadata for each KV cache group and make layers
|
|
# in the same group share the same metadata.
|
|
for kv_cache_group_id, kv_cache_group_spec in enumerate(
|
|
self.runner.kv_cache_config.kv_cache_groups):
|
|
common_attn_metadata = AscendCommonAttentionMetadata(
|
|
query_start_loc=self.runner.query_start_loc[:num_reqs + 1],
|
|
query_start_loc_cpu=self.runner.query_start_loc_cpu[:num_reqs +
|
|
1],
|
|
seq_lens_cpu=self.runner.seq_lens_cpu,
|
|
num_reqs=num_reqs,
|
|
max_query_len=max_num_scheduled_tokens,
|
|
num_actual_tokens=total_num_scheduled_tokens,
|
|
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
|
|
block_table_tensor=self.runner.input_batch.block_table[0].
|
|
get_device_tensor(),
|
|
slot_mapping=self.runner.slot_mapping,
|
|
positions=self.runner.positions,
|
|
attn_mask=self.runner.attn_mask,
|
|
spec_attn_mask=self.runner.spec_attn_mask,
|
|
attn_state=self.runner.attn_state,
|
|
decode_token_per_req=self.runner.decode_token_per_req,
|
|
num_computed_tokens_cpu=None,
|
|
seq_lens=None)
|
|
builder = self.runner.attn_groups[0][0].get_metadata_builder()
|
|
attn_metadata_i = builder.build(0, common_attn_metadata,
|
|
self.runner.get_model())
|
|
for layer_name in kv_cache_group_spec.layer_names:
|
|
attn_metadata[layer_name] = attn_metadata_i
|
|
|
|
return attn_metadata
|
|
|
|
def _get_cumsum_and_arange(
|
|
self,
|
|
num_tokens: np.ndarray,
|
|
cumsum_dtype: Optional[np.dtype] = None,
|
|
) -> tuple[np.ndarray, np.ndarray]:
|
|
"""Get the cumulative sum and batched arange of the given array.
|
|
# E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2])
|
|
# Equivalent to but faster than:
|
|
# np.concatenate([np.arange(n) for n in num_tokens])
|
|
"""
|
|
# Step 1. [2, 5, 3] -> [2, 7, 10]
|
|
cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype)
|
|
total_num_tokens = cu_num_tokens[-1]
|
|
# Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7]
|
|
cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens)
|
|
# Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
|
|
arange = self.runner.arange_np[:total_num_tokens] - cumsums_offsets
|
|
|
|
return cu_num_tokens, arange
|
|
|
|
def _propose(
|
|
self,
|
|
# [num_tokens]
|
|
target_token_ids: torch.Tensor,
|
|
# [num_tokens]
|
|
target_positions: torch.Tensor,
|
|
# [num_tokens, hidden_size]
|
|
target_hidden_states: torch.Tensor,
|
|
# [num_tokens]
|
|
target_slot_mapping: torch.Tensor,
|
|
# [batch_size]
|
|
next_token_ids: torch.Tensor,
|
|
# [batch_size + 1] starting with 0
|
|
cu_num_tokens: torch.Tensor,
|
|
# [batch_size, max_num_blocks_per_req]
|
|
block_table: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> torch.Tensor:
|
|
device = cu_num_tokens.device
|
|
cu_num_tokens = cu_num_tokens.cpu()
|
|
block_table = block_table.cpu()
|
|
num_tokens = target_token_ids.shape[0]
|
|
batch_size = next_token_ids.shape[0]
|
|
last_token_indices = cu_num_tokens[1:] - 1
|
|
target_positions = target_positions.cpu()
|
|
if self.name == SpecDcodeType.EAGLE3:
|
|
assert isinstance(self.model, Eagle3LlamaForCausalLM)
|
|
target_hidden_states = self.model.combine_hidden_states(
|
|
target_hidden_states)
|
|
assert target_hidden_states.shape[-1] == self.hidden_size
|
|
|
|
# Shift the input ids by one token.
|
|
# E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
|
|
self.input_ids[:num_tokens - 1] = target_token_ids[1:]
|
|
# Replace the last token with the next token.
|
|
# E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
|
|
self.input_ids[last_token_indices] = next_token_ids
|
|
seq_lens = (target_positions[last_token_indices] + 1).int()
|
|
|
|
query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1]
|
|
max_query_len = query_lens.max().item()
|
|
attn_mask = self.attn_mask_builder.get_splitfuse_attn_mask(
|
|
seq_lens, target_positions, self.vllm_config.model_config.dtype,
|
|
self.device)
|
|
|
|
common_attn_metadata = AscendCommonAttentionMetadata(
|
|
query_start_loc=cu_num_tokens.to(device),
|
|
query_start_loc_cpu=cu_num_tokens,
|
|
seq_lens_cpu=seq_lens.cpu(),
|
|
max_query_len=max_query_len,
|
|
num_reqs=batch_size,
|
|
num_actual_tokens=num_tokens,
|
|
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
|
|
block_table_tensor=self.runner.input_batch.block_table[0].
|
|
get_device_tensor(),
|
|
slot_mapping=target_slot_mapping,
|
|
positions=target_positions,
|
|
attn_mask=attn_mask,
|
|
spec_attn_mask=self.runner.spec_attn_mask,
|
|
attn_state=self.runner.attn_state,
|
|
decode_token_per_req=self.runner.decode_token_per_req,
|
|
num_computed_tokens_cpu=None,
|
|
seq_lens=None)
|
|
# FIXME(woosuk): The below two ops cause synchronization. Optimize.
|
|
builder = self.runner.attn_groups[0][0].get_metadata_builder()
|
|
attn_metadata = builder.build(0, common_attn_metadata,
|
|
self.runner.get_model())
|
|
if self.use_cuda_graph and \
|
|
num_tokens <= self.cudagraph_batch_sizes[-1]:
|
|
num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
|
|
else:
|
|
num_input_tokens = num_tokens
|
|
|
|
with_prefill = attn_metadata.attn_state not in [
|
|
AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding
|
|
]
|
|
moe_comm_type = self.runner._select_moe_comm_method(
|
|
num_input_tokens, with_prefill)
|
|
|
|
# copy inputs to buffer for cudagraph
|
|
self.positions[:num_tokens] = target_positions.to(device)
|
|
self.hidden_states[:num_tokens] = target_hidden_states
|
|
attn_metadata.block_tables = block_table.to(device)
|
|
with set_ascend_forward_context(attn_metadata,
|
|
self.vllm_config,
|
|
moe_comm_type=moe_comm_type,
|
|
num_tokens=num_input_tokens):
|
|
last_hidden_states, hidden_states = self.model(
|
|
input_ids=self.input_ids[:num_input_tokens],
|
|
positions=self.positions[:num_input_tokens],
|
|
hidden_states=self.hidden_states[:num_input_tokens],
|
|
)
|
|
sample_hidden_states = last_hidden_states[last_token_indices]
|
|
logits = self.model.compute_logits(sample_hidden_states)
|
|
draft_token_ids = logits.argmax(dim=-1)
|
|
|
|
# Early exit if there is only one draft token to be generated.
|
|
if self.vllm_config.speculative_config.num_speculative_tokens == 1:
|
|
# [batch_size, 1]
|
|
return draft_token_ids.view(-1, 1)
|
|
|
|
# Generate the remaining draft tokens.
|
|
draft_token_ids_tensor = torch.zeros(
|
|
(self.vllm_config.speculative_config.num_speculative_tokens,
|
|
*draft_token_ids.shape),
|
|
dtype=draft_token_ids.dtype)
|
|
draft_token_ids_tensor[0] = draft_token_ids
|
|
|
|
positions_cpu = target_positions[last_token_indices].cpu().to(
|
|
torch.int64)
|
|
hidden_states = hidden_states[last_token_indices]
|
|
if self.use_cuda_graph and \
|
|
batch_size <= self.cudagraph_batch_sizes[-1]:
|
|
input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
|
|
else:
|
|
input_batch_size = batch_size
|
|
|
|
moe_comm_type = self.runner._select_moe_comm_method(
|
|
input_batch_size, False)
|
|
|
|
attn_metadata.num_actual_tokens = batch_size
|
|
attn_metadata.max_query_len = 1
|
|
attn_metadata.query_start_loc = self.arange[:batch_size + 1]
|
|
query_lens.fill_(1)
|
|
attn_metadata.query_lens = query_lens
|
|
|
|
attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
|
|
for now_speculative in range(
|
|
self.vllm_config.speculative_config.num_speculative_tokens -
|
|
1):
|
|
# Update the inputs.
|
|
# cast to int32 is crucial when eagle model is compiled.
|
|
# tensor.argmax() returns int64 by default.
|
|
input_ids = draft_token_ids_tensor[now_speculative].to(device)
|
|
positions_cpu += 1
|
|
|
|
# NOTE(woosuk): We should handle the case where the draft model
|
|
# generates tokens beyond the max model length. Since it is complex
|
|
# to remove such requests from the batch, we keep them in the batch
|
|
# but adjust the position ids and slot mappings to avoid the
|
|
# out-of-range access during the model execution. The draft tokens
|
|
# generated with this adjustment should be ignored.
|
|
exceeds_max_model_len = positions_cpu >= self.vllm_config.model_config.max_model_len
|
|
# Mask out the position ids that exceed the max model length.
|
|
# Otherwise, we may get out-of-range error in RoPE.
|
|
clamped_positions_cpu = torch.where(exceeds_max_model_len, 0,
|
|
positions_cpu)
|
|
clamped_positions = clamped_positions_cpu.to(device)
|
|
|
|
# TODO: Increment the sequence lengths.
|
|
|
|
attn_metadata.seq_lens += 1
|
|
# TODO: Consider max model length.
|
|
# attn_metadata.max_seq_len = min(attn_metadata.max_seq_len,
|
|
# self.max_model_len)
|
|
# For the requests that exceed the max model length, we set the
|
|
# TODO: sequence length to 1 to minimize their overheads in attention.
|
|
|
|
# Compute the slot mapping.
|
|
block_numbers = (clamped_positions_cpu // self.block_size)
|
|
block_ids = block_table.gather(dim=1,
|
|
index=block_numbers.view(-1, 1))
|
|
block_ids = block_ids.view(-1)
|
|
slot_mapping_cpu = (
|
|
block_ids * self.vllm_config.cache_config.block_size +
|
|
clamped_positions_cpu % self.block_size)
|
|
|
|
# Mask out the slot mappings that exceed the max model length.
|
|
# Otherwise, the KV cache will be inadvertently updated with the
|
|
# padding tokens.
|
|
slot_mapping_cpu.masked_fill_(exceeds_max_model_len,
|
|
PADDING_SLOT_ID)
|
|
# NOTE: ASCEND slot_mapping must on cpu
|
|
attn_metadata.slot_mapping = slot_mapping_cpu.to(
|
|
torch.int32).to(device)
|
|
# copy inputs to buffer for cudagraph
|
|
self.input_ids[:batch_size] = input_ids
|
|
self.positions[:batch_size] = clamped_positions
|
|
self.hidden_states[:batch_size] = hidden_states
|
|
attn_mask = self.attn_mask_builder.get_splitfuse_attn_mask(
|
|
attn_metadata.seq_lens, positions_cpu,
|
|
self.vllm_config.model_config.dtype, self.device)
|
|
|
|
attn_metadata.attn_mask = attn_mask
|
|
attn_metadata.block_tables = block_table.to(device)
|
|
# Run the model.
|
|
with set_ascend_forward_context(attn_metadata,
|
|
self.vllm_config,
|
|
moe_comm_type=moe_comm_type,
|
|
num_tokens=input_batch_size):
|
|
|
|
last_hidden_states, hidden_states = self.model(
|
|
input_ids=self.input_ids[:input_batch_size],
|
|
positions=self.positions[:input_batch_size],
|
|
hidden_states=self.hidden_states[:input_batch_size],
|
|
)
|
|
hidden_states = hidden_states[:batch_size]
|
|
logits = self.model.compute_logits(last_hidden_states[:batch_size])
|
|
|
|
# TODO(wenlong): get more than one token for tree attention
|
|
draft_token_ids = logits.argmax(dim=-1)
|
|
draft_token_ids_tensor[now_speculative + 1] = draft_token_ids.cpu()
|
|
|
|
# [batch_size, num_speculative_tokens]
|
|
draft_token_ids = draft_token_ids_tensor.swapaxes(0, 1)
|
|
return draft_token_ids
|
|
|
|
def _prepare_inputs(
|
|
self,
|
|
# [batch_size + 1]
|
|
cu_target_query_lens: torch.Tensor,
|
|
# [batch_size]
|
|
num_rejected_tokens: torch.Tensor,
|
|
num_tokens: int,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
# cu_target_query_lens: [0, a, a + b, a + b + c]
|
|
# num_rejected_tokens: [n1, n2, n3]
|
|
# num_tokens_per_req: [a - n1, b - n2, c - n3]
|
|
# cu_num_tokens: [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
|
|
# token_indices: [0, 1, ..., a - n1 - 1,
|
|
# a, a + 1, ..., a + b - n2 - 1,
|
|
# a + b, a + b + 1, ..., a + b + c - n3 - 1]
|
|
|
|
# [0, a, a + b, a + b + c] -> [a, b, c]
|
|
query_len_per_req = (cu_target_query_lens[1:] -
|
|
cu_target_query_lens[:-1])
|
|
# [a, b, c] -> [a - n1, b - n2, c - n3]
|
|
num_tokens_per_req = query_len_per_req - num_rejected_tokens
|
|
|
|
# [a - n1, b - n2, c - n3] ->
|
|
# [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
|
|
cu_num_tokens = torch.zeros_like(cu_target_query_lens)
|
|
torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
|
|
token_indices = torch.empty(
|
|
num_tokens,
|
|
dtype=torch.int32,
|
|
device=cu_target_query_lens.device,
|
|
)
|
|
BLOCK_SIZE = 1024
|
|
self._prepare_eagle_input_sequential(
|
|
token_indices,
|
|
cu_target_query_lens,
|
|
cu_num_tokens,
|
|
block_size=BLOCK_SIZE,
|
|
)
|
|
return cu_num_tokens, token_indices
|
|
|
|
def _prepare_eagle_input_sequential(self, out_tensor: torch.Tensor,
|
|
cu_query_lens: torch.Tensor,
|
|
cu_num_tokens: torch.Tensor,
|
|
block_size: int):
|
|
num_programs = len(cu_num_tokens) - 1
|
|
for pid in range(num_programs):
|
|
start_pos = cu_num_tokens[pid].item()
|
|
end_pos = cu_num_tokens[pid + 1].item()
|
|
num_tokens = end_pos - start_pos
|
|
index_start = cu_query_lens[pid].item()
|
|
num_blocks = int(
|
|
torch.ceil(torch.tensor(num_tokens / block_size)).item())
|
|
|
|
for i in range(num_blocks):
|
|
offset_tensor = torch.arange(0,
|
|
block_size,
|
|
dtype=torch.int32,
|
|
device=out_tensor.device)
|
|
global_start_offset = i * block_size
|
|
target_indices = torch.tensor(
|
|
start_pos + global_start_offset,
|
|
dtype=torch.int32,
|
|
device=out_tensor.device) + offset_tensor
|
|
values_to_store = torch.tensor(
|
|
index_start + global_start_offset,
|
|
dtype=torch.int32,
|
|
device=out_tensor.device) + offset_tensor
|
|
mask = (target_indices >= start_pos) & \
|
|
(target_indices < end_pos) & \
|
|
(offset_tensor < num_tokens)
|
|
out_tensor[target_indices[mask]] = values_to_store[mask]
|