File size: 3,451 Bytes
2bfd19c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | # Copyright (c) 2025 SandAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import List, Optional
import numpy as np
import torch
@dataclass(frozen=True)
class PackedCoreAttnParams:
# Packed sequence parameters for core_attn
q_range: torch.Tensor
k_range: torch.Tensor
np_q_range: np.ndarray
np_k_range: np.ndarray
max_seqlen_q: int
max_seqlen_k: int
@dataclass(frozen=True)
class PackedCrossAttnParams:
# Packed sequence parameters for cross_attn
q_ranges: torch.Tensor = None
kv_ranges: torch.Tensor = None
cu_seqlens_q: torch.Tensor = None
cu_seqlens_kv: torch.Tensor = None
max_seqlen_q: int = None
max_seqlen_kv: int = None
@dataclass(frozen=True)
class ModelMetaArgs:
H: int
W: int
cp_pad_size: int
cp_split_sizes: List[int]
slice_point: int
denoising_range_num: int
range_num: int
extract_prefix_video_feature: bool
fwd_extra_1st_chunk: bool
distill_nearly_clean_chunk: bool
clip_token_nums: int
enable_cuda_graph: bool
core_attn_params: PackedCoreAttnParams
cross_attn_params: PackedCrossAttnParams
timestep: torch.Tensor
get_attn_weights_layer_num: int
save_kvcache_every_forward: bool
cur_denoise_step: int
# Includes all chunks of the current sequence
start_chunk_id: int
end_chunk_id: int
compress_kv: bool # use kv cache compression or not
total_cache_len: int
budget_cache_len: int
chunk_num: int
debug: bool
near_clean_chunk_idx: int
# MotionCache sparse forward (Phase 2): gather active tokens only
sparse_active_indices: Optional[torch.Tensor] = None
sparse_total_tokens: int = 0
class InferenceParams:
"""Inference parameters that are passed to the main model in order
to efficienly calculate and store the context during inference."""
def __init__(self, max_batch_size, max_sequence_length):
self.max_sequence_length = max_sequence_length
self.max_batch_size = max_batch_size
self.sequence_len_offset = 0
self.key_value_memory_dict = {}
self.update_kv_cache = False
self.kv_compressed = False
def swap_key_value_dict(self, batch_idx):
"swap between batches"
if len(self.key_value_memory_dict) == 0:
raise ValueError("should not swap when dict in empty")
for layer_number in self.key_value_memory_dict.keys():
inference_key_memory, inference_value_memory = self.key_value_memory_dict[layer_number]
assert len(batch_idx) == inference_key_memory.shape[1] # make sure batch size is the same
new_inference_key_memory = inference_key_memory[:, batch_idx]
new_inference_value_memory = inference_value_memory[:, batch_idx]
self.key_value_memory_dict[layer_number] = (new_inference_key_memory, new_inference_value_memory)
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