FlowCache/FlowCache4MAGI-1-dev3-motion/inference/common/dataclass.py

# 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)