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0000000000000000000000000000000000000000..881bdbffc06e471924ecea57f962bc5f8e2a9f21 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1-dev-V1/downloads/_hf_raw/ckpt/t5/t5-v1_1-xxl/special_tokens_map.json @@ -0,0 +1 @@ +{"eos_token": "", "unk_token": "", "pad_token": "", "additional_special_tokens": ["", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""]} \ No newline at end of file diff --git a/FlowCache/FlowCache4MAGI-1-dev-V1/downloads/_hf_raw/ckpt/vae/config.json b/FlowCache/FlowCache4MAGI-1-dev-V1/downloads/_hf_raw/ckpt/vae/config.json new file mode 100644 index 0000000000000000000000000000000000000000..441ae16ddd09dc4ea5a5269fce6827bb3ab6b799 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1-dev-V1/downloads/_hf_raw/ckpt/vae/config.json @@ -0,0 +1,22 @@ +{ + "_class_name": "ViTVAE", + "_diffusers_version": "0.28.2", + "ddconfig": { + "conv_last_layer": true, + "depth": 24, + "double_z": true, + "embed_dim": 1024, + "in_chans": 3, + "ln_in_attn": true, + "mlp_ratio": 4, + "norm_code": false, + "num_heads": 16, + "patch_length": 4, + "patch_size": 8, + "qkv_bias": true, + "video_length": 16, + "video_size": 256, + "z_chans": 16 + }, + "model_type": "vit" +} diff --git a/FlowCache/FlowCache4MAGI-1-dev-V1/inference/__init__.py b/FlowCache/FlowCache4MAGI-1-dev-V1/inference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/FlowCache/FlowCache4MAGI-1-dev-V1/logs/flowcache_vbench_20260520_102116.log b/FlowCache/FlowCache4MAGI-1-dev-V1/logs/flowcache_vbench_20260520_102116.log new file mode 100644 index 0000000000000000000000000000000000000000..7b8997a8c4a294f2fafc7b3e7692d8dd2422a868 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1-dev-V1/logs/flowcache_vbench_20260520_102116.log @@ -0,0 +1,68 @@ +🚀 Starting multi-GPU benchmark sampling +🎮 GPUs: 1,3,4,6 +🔢 Total dimensions to process: 3 +📋 Dimensions: overall_consistency subject_consistency scene +🔍 Processing dimension: overall_consistency +Loaded configuration from: yaml_config/sample/flowcache_vbench.yaml.tmp +Traceback (most recent call last): + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 403, in + main() + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 371, in main + setup_save_path(config) + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 357, in setup_save_path + os.makedirs(config["save_path"], exist_ok=True) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + [Previous line repeated 2 more times] + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 225, in makedirs + mkdir(name, mode) +PermissionError: [Errno 13] Permission denied: '/path' +✅ Completed: overall_consistency +--- +🔍 Processing dimension: subject_consistency +Loaded configuration from: yaml_config/sample/flowcache_vbench.yaml.tmp +Traceback (most recent call last): + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 403, in + main() + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 371, in main + setup_save_path(config) + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 357, in setup_save_path + os.makedirs(config["save_path"], exist_ok=True) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + [Previous line repeated 2 more times] + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 225, in makedirs + mkdir(name, mode) +PermissionError: [Errno 13] Permission denied: '/path' +✅ Completed: subject_consistency +--- +🔍 Processing dimension: scene +Loaded configuration from: yaml_config/sample/flowcache_vbench.yaml.tmp +Traceback (most recent call last): + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 403, in + main() + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 371, in main + setup_save_path(config) + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 357, in setup_save_path + os.makedirs(config["save_path"], exist_ok=True) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + [Previous line repeated 2 more times] + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 225, in makedirs + mkdir(name, mode) +PermissionError: [Errno 13] Permission denied: '/path' +✅ Completed: scene +--- +🎉 All sampling tasks completed. diff --git a/FlowCache/FlowCache4MAGI-1-dev-V1/logs/flowcache_vbench_20260520_103047.log b/FlowCache/FlowCache4MAGI-1-dev-V1/logs/flowcache_vbench_20260520_103047.log new file mode 100644 index 0000000000000000000000000000000000000000..7b8997a8c4a294f2fafc7b3e7692d8dd2422a868 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1-dev-V1/logs/flowcache_vbench_20260520_103047.log @@ -0,0 +1,68 @@ +🚀 Starting multi-GPU benchmark sampling +🎮 GPUs: 1,3,4,6 +🔢 Total dimensions to process: 3 +📋 Dimensions: overall_consistency subject_consistency scene +🔍 Processing dimension: overall_consistency +Loaded configuration from: yaml_config/sample/flowcache_vbench.yaml.tmp +Traceback (most recent call last): + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 403, in + main() + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 371, in main + setup_save_path(config) + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 357, in setup_save_path + os.makedirs(config["save_path"], exist_ok=True) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + [Previous line repeated 2 more times] + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 225, in makedirs + mkdir(name, mode) +PermissionError: [Errno 13] Permission denied: '/path' +✅ Completed: overall_consistency +--- +🔍 Processing dimension: subject_consistency +Loaded configuration from: yaml_config/sample/flowcache_vbench.yaml.tmp +Traceback (most recent call last): + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 403, in + main() + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 371, in main + setup_save_path(config) + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 357, in setup_save_path + os.makedirs(config["save_path"], exist_ok=True) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + [Previous line repeated 2 more times] + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 225, in makedirs + mkdir(name, mode) +PermissionError: [Errno 13] Permission denied: '/path' +✅ Completed: subject_consistency +--- +🔍 Processing dimension: scene +Loaded configuration from: yaml_config/sample/flowcache_vbench.yaml.tmp +Traceback (most recent call last): + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 403, in + main() + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 371, in main + setup_save_path(config) + File "/home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1-dev/sample_video.py", line 357, in setup_save_path + os.makedirs(config["save_path"], exist_ok=True) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 215, in makedirs + makedirs(head, exist_ok=exist_ok) + [Previous line repeated 2 more times] + File "/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/os.py", line 225, in makedirs + mkdir(name, mode) +PermissionError: [Errno 13] Permission denied: '/path' +✅ Completed: scene +--- +🎉 All sampling tasks completed. diff --git a/FlowCache/FlowCache4MAGI-1-dev-V1/scripts/metric.sh b/FlowCache/FlowCache4MAGI-1-dev-V1/scripts/metric.sh new file mode 100644 index 0000000000000000000000000000000000000000..c513748df78937d28806c827ef746c56b1f94684 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1-dev-V1/scripts/metric.sh @@ -0,0 +1,5 @@ +export CUDA_VISIBLE_DEVICES=3 + +python tools/video_metrics.py \ +--original_video "/path/to/original_video.mp4" \ +--generated_video "/path/to/generated_video.mp4" \ No newline at end of file diff --git a/FlowCache/FlowCache4MAGI-1-dev-V1/tools/plot_l1_rel.py b/FlowCache/FlowCache4MAGI-1-dev-V1/tools/plot_l1_rel.py new file mode 100644 index 0000000000000000000000000000000000000000..b117dbc6d97323a0ad71303b700f2cc251773257 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1-dev-V1/tools/plot_l1_rel.py @@ -0,0 +1,152 @@ +#!/usr/bin/env python3 + +import argparse +import json +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Set, Tuple + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + + +def parse_int_list(value: Optional[str]) -> Optional[Set[int]]: + if not value: + return None + return {int(item.strip()) for item in value.split(",") if item.strip()} + + +def load_l1_rel_records(json_path: Path) -> List[dict]: + with json_path.open("r") as f: + payload = json.load(f) + if isinstance(payload, list): + return payload + if isinstance(payload, dict) and isinstance(payload.get("records"), list): + return payload["records"] + raise ValueError(f"Cannot find records in {json_path}") + + +def collect_by_chunk(records: List[dict], chunk_ids: Optional[Set[int]], max_chunks: Optional[int]) -> Dict[int, List[dict]]: + chunks = defaultdict(list) + for record in records: + chunk_idx = int(record["chunk_idx"]) + if chunk_ids is not None and chunk_idx not in chunk_ids: + continue + chunks[chunk_idx].append(record) + + chunks = dict(sorted(chunks.items())) + if max_chunks is not None: + chunks = dict(list(chunks.items())[:max_chunks]) + return chunks + + +def plot_l1_rel( + chunks: Dict[int, List[dict]], + output_path: Path, + x_field: str, + y_field: str, + reverse_x: bool, + title: Optional[str], + figsize: Tuple[float, float], + dpi: int, +) -> None: + fig, ax = plt.subplots(figsize=figsize) + + for chunk_idx, records in chunks.items(): + points = [] + for record in records: + if x_field not in record or y_field not in record: + continue + if record[x_field] is None or record[y_field] is None: + continue + points.append((float(record[x_field]), float(record[y_field]))) + if not points: + continue + + points.sort(key=lambda item: item[0]) + xs, ys = zip(*points) + ax.plot(xs, ys, marker="o", linewidth=1.6, markersize=3, label=f"chunk {chunk_idx}") + + ax.set_xlabel(x_field) + ax.set_ylabel(y_field) + ax.set_title(title or f"{y_field} by timestep") + ax.grid(True, alpha=0.3) + if reverse_x: + ax.invert_xaxis() + ax.legend(loc="best", fontsize="small", ncols=2) + fig.tight_layout() + + output_path.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(output_path, dpi=dpi) + plt.close(fig) + + +def parse_arguments(): + parser = argparse.ArgumentParser(description="Plot per-chunk MAGI relative L1 change curves.") + parser.add_argument("json_path", type=Path, help="Path to L1 relative change JSON saved by --l1_rel_stats_path.") + parser.add_argument("-o", "--output", type=Path, help="Output image path. Defaults to _plot.png.") + parser.add_argument("--chunks", type=str, help="Comma-separated chunk_idx list to plot, for example: 0,1,2.") + parser.add_argument("--max-chunks", type=int, help="Plot at most this many chunks after filtering.") + parser.add_argument( + "--x-field", + choices=["timestep", "next_timestep", "cur_denoise_step", "denoise_idx"], + default="next_timestep", + help="Record field used for the x axis. next_timestep is the cleaner MAGI step.", + ) + parser.add_argument( + "--y-field", + choices=[ + "l1_rel", + "l1_rel_ratio", + "delta_l1_norm", + "x_l1_norm", + "x_embedder_l1_rel", + "x_embedder_l1_rel_ratio", + "x_embedder_delta_l1_norm", + "x_embedder_x_l1_norm", + "flowcache_rel_l1", + "flowcache_rel_l1_ratio", + "flowcache_delta_l1_norm", + "flowcache_prev_feat_l1_norm", + "flowcache_accumulated_rel_l1", + "rel_l1_thresh", + ], + default="l1_rel", + help="Record field used for the y axis.", + ) + parser.add_argument("--reverse-x", action="store_true", help="Reverse the x axis.") + parser.add_argument("--title", type=str, help="Figure title.") + parser.add_argument("--figsize", type=str, default="10,6", help="Figure size as width,height.") + parser.add_argument("--dpi", type=int, default=160, help="Output image DPI.") + return parser.parse_args() + + +def main(): + args = parse_arguments() + output_path = args.output or args.json_path.with_name(f"{args.json_path.stem}_plot.png") + figsize = [float(part.strip()) for part in args.figsize.split(",")] + if len(figsize) != 2: + raise ValueError("--figsize must be formatted as width,height") + + records = load_l1_rel_records(args.json_path) + chunks = collect_by_chunk(records, parse_int_list(args.chunks), args.max_chunks) + if not chunks: + raise ValueError("No records matched the requested chunks.") + + plot_l1_rel( + chunks=chunks, + output_path=output_path, + x_field=args.x_field, + y_field=args.y_field, + reverse_x=args.reverse_x, + title=args.title, + figsize=(figsize[0], figsize[1]), + dpi=args.dpi, + ) + print(f"Saved plot to {output_path}") + + +if __name__ == "__main__": + main() diff --git a/FlowCache/FlowCache4MAGI-1-dev-V1/tools/plot_residual_norms.py b/FlowCache/FlowCache4MAGI-1-dev-V1/tools/plot_residual_norms.py new file mode 100644 index 0000000000000000000000000000000000000000..600e0c35144bca5f6cdc48e6c4e2098972e358ff --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1-dev-V1/tools/plot_residual_norms.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 + +import argparse +import json +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Set, Tuple + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + + +def parse_int_list(value: Optional[str]) -> Optional[Set[int]]: + if not value: + return None + return {int(item.strip()) for item in value.split(",") if item.strip()} + + +def load_records(json_path: Path) -> List[dict]: + with json_path.open("r") as f: + payload = json.load(f) + if isinstance(payload, list): + return payload + if isinstance(payload, dict) and isinstance(payload.get("records"), list): + return payload["records"] + raise ValueError(f"Cannot find records in {json_path}") + + +def group_records(records: List[dict], chunk_ids: Optional[Set[int]], max_chunks: Optional[int]) -> Dict[int, List[dict]]: + grouped = defaultdict(list) + for record in records: + chunk_idx = int(record["chunk_idx"]) + if chunk_ids is not None and chunk_idx not in chunk_ids: + continue + grouped[chunk_idx].append(record) + + grouped = dict(sorted(grouped.items())) + if max_chunks is not None: + grouped = dict(list(grouped.items())[:max_chunks]) + return grouped + + +def build_plot( + grouped_records: Dict[int, List[dict]], + output_path: Path, + x_field: str, + y_field: str, + title: Optional[str], + reverse_x: bool, + figsize: Tuple[float, float], + dpi: int, +) -> None: + fig, ax = plt.subplots(figsize=figsize) + + for chunk_idx, records in grouped_records.items(): + points = [] + for record in records: + if x_field not in record or y_field not in record: + continue + if record[x_field] is None or record[y_field] is None: + continue + points.append((float(record[x_field]), float(record[y_field]))) + if not points: + continue + + points.sort(key=lambda item: item[0]) + xs, ys = zip(*points) + ax.plot(xs, ys, marker="o", linewidth=1.6, markersize=3, label=f"chunk {chunk_idx}") + + ax.set_xlabel(x_field) + ax.set_ylabel(y_field) + ax.set_title(title or f"{y_field} by timestep") + ax.grid(True, alpha=0.3) + if reverse_x: + ax.invert_xaxis() + ax.legend(loc="best", fontsize="small", ncols=2) + fig.tight_layout() + + output_path.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(output_path, dpi=dpi) + plt.close(fig) + + +def parse_arguments(): + parser = argparse.ArgumentParser(description="Plot per-chunk residual norm curves from MAGI residual stats JSON.") + parser.add_argument("json_path", type=Path, help="Path to residual stats JSON saved by --residual_stats_path.") + parser.add_argument( + "-o", + "--output", + type=Path, + help="Output image path. Defaults to _residual_norms.png.", + ) + parser.add_argument("--chunks", type=str, help="Comma-separated chunk_idx list to plot, for example: 0,1,2.") + parser.add_argument("--max-chunks", type=int, help="Plot at most this many chunks after filtering.") + parser.add_argument( + "--x-field", + choices=["timestep", "cur_denoise_step", "denoise_idx"], + default="timestep", + help="Record field used for the x axis.", + ) + parser.add_argument( + "--y-field", + choices=["residual_norm", "residual_diff_norm"], + default="residual_norm", + help="Record field used for the y axis.", + ) + parser.add_argument("--reverse-x", action="store_true", help="Reverse the x axis.") + parser.add_argument("--title", type=str, help="Figure title.") + parser.add_argument("--figsize", type=str, default="10,6", help="Figure size as width,height.") + parser.add_argument("--dpi", type=int, default=160, help="Output image DPI.") + return parser.parse_args() + + +def main(): + args = parse_arguments() + output_path = args.output or args.json_path.with_name(f"{args.json_path.stem}_residual_norms.png") + figsize_parts = [float(part.strip()) for part in args.figsize.split(",")] + if len(figsize_parts) != 2: + raise ValueError("--figsize must be formatted as width,height") + + records = load_records(args.json_path) + grouped_records = group_records(records, parse_int_list(args.chunks), args.max_chunks) + if not grouped_records: + raise ValueError("No records matched the requested chunks.") + + build_plot( + grouped_records=grouped_records, + output_path=output_path, + x_field=args.x_field, + y_field=args.y_field, + title=args.title, + reverse_x=args.reverse_x, + figsize=(figsize_parts[0], figsize_parts[1]), + dpi=args.dpi, + ) + print(f"Saved plot to {output_path}") + + +if __name__ == "__main__": + main() diff --git a/FlowCache/FlowCache4MAGI-1-dev-V1/tools/video_metrics.py b/FlowCache/FlowCache4MAGI-1-dev-V1/tools/video_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..3873573d52ac923c98523b5ebff648f00c9287c6 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1-dev-V1/tools/video_metrics.py @@ -0,0 +1,108 @@ +import os +import cv2 +import argparse +import torch +import lpips +import numpy as np +from tqdm import tqdm +from torchmetrics.image import StructuralSimilarityIndexMeasure + +def load_video_frames(path, resize_to=None): + """ + Load all frames from a video file as a list of HxWx3 uint8 arrays. + Optionally resize each frame to `resize_to` (w, h). + """ + + cap = cv2.VideoCapture(path) + frames = [] + while True: + ret, img = cap.read() + if not ret: + break + if resize_to is not None: + img = cv2.resize(img, resize_to) + frames.append(np.expand_dims(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), axis=0)) + cap.release() + return np.concatenate(frames) + + +def compute_video_metrics(frames_gt, frames_gen, + device, ssim_metric, lpips_fn): + """ + Compute PSNR, SSIM, LPIPS for two lists of frames (uint8 BGR). + All computations on `device`. + Returns (psnr, ssim, lpips) scalars. + """ + # ensure same frame count + # convert to tensors [N,3,H,W], normalize to [0,1] + gt_t = torch.from_numpy(frames_gt).float().to(device).permute(0, 3, 1, 2).div_(255).contiguous() + + gen_t = torch.from_numpy(frames_gen).float().to(device).permute(0, 3, 1, 2).div_(255).contiguous() + + # PSNR (data_range=1.0): -10 * log10(mse) + mse = torch.mean((gt_t - gen_t) ** 2) + psnr = -10.0 * torch.log10(mse) + + # SSIM: returns average over batch + ssim_val = ssim_metric(gen_t, gt_t) + + # LPIPS: expects [-1,1] + with torch.no_grad(): + lpips_val = lpips_fn(gt_t * 2.0 - 1.0, gen_t * 2.0 - 1.0).mean() + + return psnr.item(), ssim_val.item(), lpips_val.item() + + +def main(): + parser = argparse.ArgumentParser( + description="Compute PSNR/SSIM/LPIPS on GPU for two folders of .mp4 videos" + ) + parser.add_argument("--original_video", required=True, + help="ground-truth .mp4 videos") + parser.add_argument("--generated_video", required=True, + help="generated .mp4 videos") + parser.add_argument("--device", default="cuda", + help="Torch device, e.g. 'cuda' or 'cpu'") + parser.add_argument("--lpips_net", default="alex", choices=["alex", "vgg"], + help="Backbone for LPIPS") + args = parser.parse_args() + + device = torch.device(args.device if torch.cuda.is_available() or args.device == "cpu" else "cpu") + # instantiate metrics on device + ssim_metric = StructuralSimilarityIndexMeasure(data_range=1.0).to(device) + lpips_fn = lpips.LPIPS(net=args.lpips_net, spatial=True).to(device) + + # gather .mp4 filenames + gt_files = args.original_video + gen_set = args.generated_video + + psnrs, ssims, lpips_vals = [], [], [] + for fname in tqdm([gt_files], desc="Videos"): + path_gt = gt_files + path_gen = gen_set + + # load frames; resize generated to match GT dimensions + frames_gt = load_video_frames(path_gt) + frames_gen = load_video_frames(path_gen) + + res = compute_video_metrics(frames_gt, frames_gen, + device, ssim_metric, lpips_fn) + if res is None: + continue + p, s, l = res + psnrs.append(p) + ssims.append(s) + lpips_vals.append(l) + + if not psnrs: + print("No valid videos processed.") + return + + print("\n=== Overall Averages ===") + print(f"Average PSNR : {np.mean(psnrs):.2f} dB") + print(f"Average SSIM : {np.mean(ssims):.4f}") + print(f"Average LPIPS: {np.mean(lpips_vals):.4f}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/context_parallel.cpython-312.pyc b/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/context_parallel.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d4559a33221db2b8e2f3e4cbe7b900d6429a27c4 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/context_parallel.cpython-312.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/pipeline_parallel.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/pipeline_parallel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a33790dc4d7458c30b092ec0820c3bb849df9061 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/pipeline_parallel.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/pipeline_parallel.cpython-312.pyc b/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/pipeline_parallel.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6219368b896808f8791c766c8068dc787fb36814 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/pipeline_parallel.cpython-312.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/tile_parallel.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/tile_parallel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cd9bd747fb6f92fe264bc26bb17050142926456e Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/tile_parallel.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/tile_parallel.cpython-312.pyc b/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/tile_parallel.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0498a929061e89bbfdcf5bf2bb536ec647c9dfe8 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/infra/parallelism/__pycache__/tile_parallel.cpython-312.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/dit/__init__.py b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..518895680251c2b1e91bd1a67426e1bd12ba9e08 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__init__.py @@ -0,0 +1,18 @@ +# 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 .dit_model import get_dit, VideoDiTModel +from .dit_module import FullyParallelAttention + +__all__ = ["get_dit", "VideoDiTModel", "FullyParallelAttention"] diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/__init__.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..302f828fc9bf55c8de87cb646e916792b5672235 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/__init__.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/__init__.cpython-312.pyc b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1b546f93c997fcca08967575205a0bdb10cb8073 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/__init__.cpython-312.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_model.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_model.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4a4caa39a725a8e7ab3bc02bc3f806ac7c5b26a1 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_model.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_model.cpython-312.pyc b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_model.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5dd2433d40a15e77238be45722d08d066b30cffe Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_model.cpython-312.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_module.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_module.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d66eb2621a5dfb467832fb79bbe2cba4e7aafa7c Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_module.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_module.cpython-312.pyc b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_module.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..64f16e15bd7360ef558fef9d4fded7721d6a4305 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/model/dit/__pycache__/dit_module.cpython-312.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/dit/dit_model.py b/FlowCache/FlowCache4MAGI-1/inference/model/dit/dit_model.py new file mode 100644 index 0000000000000000000000000000000000000000..b6cd2d98c52322baa2bf6519705e4a4cc65dd0c5 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/model/dit/dit_model.py @@ -0,0 +1,733 @@ +# 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. + +import gc +import math +import os +from typing import Tuple + +import torch +import torch.distributed +import torch.nn as nn +from einops import rearrange + +from inference.common import ( + InferenceParams, + MagiConfig, + ModelMetaArgs, + PackedCoreAttnParams, + PackedCrossAttnParams, + env_is_true, + print_per_rank, + print_rank_0, +) +from inference.infra.checkpoint import load_checkpoint +from inference.infra.distributed import parallel_state as mpu +from inference.infra.parallelism import cp_post_process, cp_pre_process, pp_scheduler + +from .dit_module import CaptionEmbedder, FinalLinear, LearnableRotaryEmbeddingCat, TimestepEmbedder, TransformerBlock + + +class VideoDiTModel(torch.nn.Module): + """VideoDiT model for video diffusion. + + Args: + config (MagiConfig): Transformer config + pre_process (bool, optional): Include embedding layer (used with pipeline parallelism). Defaults to True. + post_process (bool, optional): Include an output layer (used with pipeline parallelism). Defaults to True. + """ + + def __init__(self, config: MagiConfig, pre_process: bool = True, post_process: bool = True) -> None: + super().__init__() + + self.model_config = config.model_config + self.runtime_config = config.runtime_config + self.engine_config = config.engine_config + + self.pre_process = pre_process + self.post_process = post_process + self.in_channels = self.model_config.in_channels + self.out_channels = self.model_config.out_channels + self.patch_size = self.model_config.patch_size + self.t_patch_size = self.model_config.t_patch_size + self.caption_max_length = self.model_config.caption_max_length + self.num_heads = self.model_config.num_attention_heads + + self.x_embedder = nn.Conv3d( + self.model_config.in_channels, + self.model_config.hidden_size, + kernel_size=(self.model_config.t_patch_size, self.model_config.patch_size, self.model_config.patch_size), + stride=(self.model_config.t_patch_size, self.model_config.patch_size, self.model_config.patch_size), + bias=False, + ) + self.t_embedder = TimestepEmbedder(model_config=self.model_config) + self.y_embedder = CaptionEmbedder(model_config=self.model_config) + self.rope = LearnableRotaryEmbeddingCat( + self.model_config.hidden_size // self.model_config.num_attention_heads, in_pixels=False + ) + + # trm block + self.videodit_blocks = TransformerBlock( + model_config=self.model_config, + engine_config=self.engine_config, + pre_process=pre_process, + post_process=post_process, + ) + + self.final_linear = FinalLinear( + self.model_config.hidden_size, self.model_config.patch_size, self.model_config.t_patch_size, self.out_channels + ) + + def generate_kv_range_for_uncondition(self, uncond_x) -> torch.Tensor: + device = f"cuda:{torch.cuda.current_device()}" + B, C, T, H, W = uncond_x.shape + chunk_token_nums = ( + (T // self.model_config.t_patch_size) * (H // self.model_config.patch_size) * (W // self.model_config.patch_size) + ) + + k_chunk_start = torch.linspace(0, (B - 1) * chunk_token_nums, steps=B).reshape((B, 1)) + k_chunk_end = torch.linspace(chunk_token_nums, B * chunk_token_nums, steps=B).reshape((B, 1)) + return torch.concat([k_chunk_start, k_chunk_end], dim=1).to(torch.int32).to(device) + + def unpatchify(self, x, H, W): + return rearrange( + x, + "(T H W) N (pT pH pW C) -> N C (T pT) (H pH) (W pW)", + H=H, + W=W, + pT=self.t_patch_size, + pH=self.patch_size, + pW=self.patch_size, + ).contiguous() + + @torch.no_grad() + def get_embedding_and_meta(self, x, t, y, caption_dropout_mask, xattn_mask, kv_range, **kwargs): + """ + Forward embedding and meta for VideoDiT. + NOTE: This function should only handle single card behavior. + + Input: + x: (N, C, T, H, W). torch.Tensor of spatial inputs (images or latent representations of images) + t: (N, denoising_range_num). torch.Tensor of diffusion timesteps + y: (N * denoising_range_num, 1, L, C). torch.Tensor of class labels + caption_dropout_mask: (N). torch.Tensor of whether to drop caption + xattn_mask: (N * denoising_range_num, 1, L). torch.Tensor of xattn mask + kv_range: (N * denoising_range_num, 2). torch.Tensor of kv range + + Output: + x: (S, N, D). torch.Tensor of inputs embedding (images or latent representations of images) + condition: (N, denoising_range_num, D). torch.Tensor of condition embedding + condition_map: (S, N). torch.Tensor determine which condition to use for each token + rope: (S, 96). torch.Tensor of rope + y_xattn_flat: (total_token, D). torch.Tensor of y_xattn_flat + cuda_graph_inputs: (y_xattn_flat, xattn_mask) or None. None means no cuda graph + NOTE: y_xattn_flat and xattn_mask with static shape + H: int. Height of the input + W: int. Width of the input + ardf_meta: dict. Meta information for ardf + cross_attn_params: PackedCrossAttnParams. Packed sequence parameters for cross_atten + """ + + ################################### + # Part1: Embed x # + ################################### + x = self.x_embedder(x) # [N, C, T, H, W] + batch_size, _, T, H, W = x.shape + + # Prepare necessary variables + range_num = kwargs["range_num"] + denoising_range_num = kwargs["denoising_range_num"] + slice_point = kwargs.get("slice_point", 0) + frame_in_range = T // denoising_range_num + prev_clean_T = frame_in_range * slice_point + T_total = T + prev_clean_T + + ################################### + # Part2: rope # + ################################### + # caculate rescale_factor for multi-resolution & multi aspect-ratio training + # the base_size [16*16] is A predefined size based on data:(256x256) vae: (8,8,4) patch size: (1,1,2) + # This definition do not have any relationship with the actual input/model/setting. + # ref_feat_shape is used to calculate innner rescale factor, so it can be float. + rescale_factor = math.sqrt((H * W) / (16 * 16)) + rope = self.rope.get_embed(shape=[T_total, H, W], ref_feat_shape=[T_total, H / rescale_factor, W / rescale_factor]) + # the shape of rope is (T*H*W, -1) aka (seq_length, head_dim), as T is the first dimension, we can directly cut it. + rope = rope[-(T * H * W) :] + + + ################################### + # Part3: Embed t # + ################################### + assert t.shape[0] == batch_size, f"Invalid t shape, got {t.shape[0]} != {batch_size}" # nolint + assert t.shape[1] == denoising_range_num, f"Invalid t shape, got {t.shape[1]} != {denoising_range_num}" # nolint + t_flat = t.flatten() # (N * denoising_range_num,) + t = self.t_embedder(t_flat) # (N, D) + + if self.engine_config.distill: + distill_dt_scalar = 2 + if kwargs["num_steps"] == 12: + base_chunk_step = 4 + distill_dt_factor = base_chunk_step / kwargs["distill_interval"] * distill_dt_scalar + else: + distill_dt_factor = kwargs["num_steps"] / 4 * distill_dt_scalar + distill_dt = torch.ones_like(t_flat) * distill_dt_factor + distill_dt_embed = self.t_embedder(distill_dt) + t = t + distill_dt_embed + t = t.reshape(batch_size, denoising_range_num, -1) # (N, range_num, D) + + ###################################################### + # Part4: Embed y, prepare condition and y_xattn_flat # + ###################################################### + # (N * denoising_range_num, 1, L, D) + y_xattn, y_adaln = self.y_embedder(y, self.training, caption_dropout_mask) + + assert xattn_mask is not None + xattn_mask = xattn_mask.squeeze(1).squeeze(1) + + # condition: (N, range_num, D) + y_adaln = y_adaln.squeeze(1) # (N, D) + condition = t + y_adaln.unsqueeze(1) + + assert condition.shape[0] == batch_size + assert condition.shape[1] == denoising_range_num + seqlen_per_chunk = (T * H * W) // denoising_range_num + condition_map = torch.arange(batch_size * denoising_range_num, device=x.device) + condition_map = torch.repeat_interleave(condition_map, seqlen_per_chunk) + condition_map = condition_map.reshape(batch_size, -1).transpose(0, 1).contiguous() + + # y_xattn_flat: (total_token, D) + y_xattn_flat = torch.masked_select(y_xattn.squeeze(1), xattn_mask.unsqueeze(-1).bool()).reshape(-1, y_xattn.shape[-1]) + xattn_mask_for_cuda_graph = None + + ###################################################### + # Part5: Prepare cross_attn_params for cross_atten # + ###################################################### + # (N * denoising_range_num, L) + xattn_mask = xattn_mask.reshape(xattn_mask.shape[0], -1) + y_index = torch.sum(xattn_mask, dim=-1) + clip_token_nums = H * W * frame_in_range + + cu_seqlens_q = torch.Tensor([0] + ([clip_token_nums] * denoising_range_num * batch_size)).to(torch.int64).to(x.device) + cu_seqlens_k = torch.cat([y_index.new_tensor([0]), y_index]).to(torch.int64).to(x.device) + cu_seqlens_q = cu_seqlens_q.cumsum(-1).to(torch.int32) + cu_seqlens_k = cu_seqlens_k.cumsum(-1).to(torch.int32) + + assert ( + cu_seqlens_q.shape == cu_seqlens_k.shape + ), f"cu_seqlens_q.shape: {cu_seqlens_q.shape}, cu_seqlens_k.shape: {cu_seqlens_k.shape}" + + xattn_q_ranges = torch.cat([cu_seqlens_q[:-1].unsqueeze(1), cu_seqlens_q[1:].unsqueeze(1)], dim=1) + xattn_k_ranges = torch.cat([cu_seqlens_k[:-1].unsqueeze(1), cu_seqlens_k[1:].unsqueeze(1)], dim=1) + assert ( + xattn_q_ranges.shape == xattn_k_ranges.shape + ), f"xattn_q_ranges.shape: {xattn_q_ranges.shape}, xattn_k_ranges.shape: {xattn_k_ranges.shape}" + + cross_attn_params = PackedCrossAttnParams( + q_ranges=xattn_q_ranges, + kv_ranges=xattn_k_ranges, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_kv=cu_seqlens_k, + max_seqlen_q=clip_token_nums, + max_seqlen_kv=self.caption_max_length, + ) + + ################################################## + # Part6: Prepare core_atten related q/kv range # + ################################################## + q_range = torch.cat([cu_seqlens_q[:-1].unsqueeze(1), cu_seqlens_q[1:].unsqueeze(1)], dim=1) + flat_kv = torch.unique(kv_range, sorted=True) + max_seqlen_k = (flat_kv[-1] - flat_kv[0]).cpu().item() + + ardf_meta = dict( + clip_token_nums=clip_token_nums, + slice_point=slice_point, + range_num=range_num, + denoising_range_num=denoising_range_num, + q_range=q_range, + k_range=kv_range, + max_seqlen_q=clip_token_nums, + max_seqlen_k=max_seqlen_k, + ) + + return (x, condition, condition_map, rope, y_xattn_flat, xattn_mask_for_cuda_graph, H, W, ardf_meta, cross_attn_params) + + @torch.no_grad() + def forward_pre_process( + self, x, t, y, caption_dropout_mask=None, xattn_mask=None, kv_range=None, **kwargs + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, ModelMetaArgs]: + assert kv_range is not None, "Please ensure kv_range is provided" + + x = x * self.model_config.x_rescale_factor + + if self.model_config.half_channel_vae: + assert x.shape[1] == 16 + x = torch.cat([x, x], dim=1) + + x = x.float() + t = t.float() + y = y.float() + # embedder context will ensure that the processing is in high precision even if the embedder params is in bfloat16 mode + with torch.autocast(device_type="cuda", dtype=torch.float32): + ( + x, + condition, + condition_map, + rope, + y_xattn_flat, + xattn_mask_for_cuda_graph, + H, + W, + ardf_meta, + cross_attn_params, + ) = self.get_embedding_and_meta(x, t, y, caption_dropout_mask, xattn_mask, kv_range, **kwargs) + + # Downcast x and rearrange x + x = x.to(self.model_config.params_dtype) + x = rearrange(x, "N C T H W -> (T H W) N C").contiguous() # (thw, N, D) + + # condition and y_xattn_flat will be downcast to bfloat16 in transformer block. + condition = condition.to(self.model_config.params_dtype) + y_xattn_flat = y_xattn_flat.to(self.model_config.params_dtype) + + core_attn_params = PackedCoreAttnParams( + q_range=ardf_meta["q_range"], + k_range=ardf_meta["k_range"], + np_q_range=ardf_meta["q_range"].cpu().numpy(), + np_k_range=ardf_meta["k_range"].cpu().numpy(), + max_seqlen_q=ardf_meta["max_seqlen_q"], + max_seqlen_k=ardf_meta["max_seqlen_k"], + ) + + (x, condition_map, rope, cp_pad_size, cp_split_sizes, core_attn_params, cross_attn_params) = cp_pre_process( + self.engine_config.cp_size, + self.engine_config.cp_strategy, + x, + condition_map, + rope, + xattn_mask_for_cuda_graph, + ardf_meta, + core_attn_params, + cross_attn_params, + ) + + meta_args = ModelMetaArgs( + H=H, + W=W, + cp_pad_size=cp_pad_size, + cp_split_sizes=cp_split_sizes, + slice_point=ardf_meta["slice_point"], + denoising_range_num=ardf_meta["denoising_range_num"], + range_num=ardf_meta["range_num"], + extract_prefix_video_feature=kwargs.get("extract_prefix_video_feature", False), + fwd_extra_1st_chunk=kwargs["fwd_extra_1st_chunk"], + distill_nearly_clean_chunk=kwargs.get("distill_nearly_clean_chunk", False), + clip_token_nums=ardf_meta["clip_token_nums"], + enable_cuda_graph=xattn_mask_for_cuda_graph is not None, + core_attn_params=core_attn_params, + cross_attn_params=cross_attn_params, + timestep=t, # add to get attention weights for each timestep + get_attn_weights_layer_num=-1, + save_kvcache_every_forward=kwargs.get("save_kvcache_every_forward", False), + cur_denoise_step=kwargs.get("cur_denoise_step", 0), + start_chunk_id=kwargs["start_chunk_id"], + end_chunk_id=kwargs["end_chunk_id"], + compress_kv=kwargs.get("compress_kv", False), + total_cache_len=kwargs.get("total_cache_len", 0), + budget_cache_len=kwargs.get("budget_cache_len", 0), + chunk_num=kwargs["chunk_num"], + debug=kwargs.get("debug", False), + near_clean_chunk_idx=kwargs.get("near_clean_chunk_idx", -1), + ) + + return (x, condition, condition_map, y_xattn_flat, rope, meta_args) + + @torch.no_grad() + def forward_post_process(self, x, meta_args: ModelMetaArgs) -> torch.Tensor: + x = x.float() + # embedder context will ensure that the processing is in high precision even if the embedder params is in bfloat16 mode + with torch.autocast(device_type="cuda", dtype=torch.float32): + x = self.final_linear(x) # (thw/cp, N, patch_size ** 2 * out_channels) + + # leave context parallel region + x = cp_post_process(self.engine_config.cp_size, self.engine_config.cp_strategy, x, meta_args) + + # N C T H W + x = self.unpatchify(x, meta_args.H, meta_args.W) + + if self.model_config.half_channel_vae: + assert x.shape[1] == 32 + x = x[:, :16] + + x = x / self.model_config.x_rescale_factor + + return x + + @torch.no_grad() + def forward( + self, + x, + t, + y, + caption_dropout_mask=None, + xattn_mask=None, + kv_range=None, + inference_params: InferenceParams = None, + **kwargs, + ) -> torch.Tensor: + (x, condition, condition_map, y_xattn_flat, rope, meta_args) = self.forward_pre_process( + x, t, y, caption_dropout_mask, xattn_mask, kv_range, **kwargs + ) + + if not self.pre_process: + x = pp_scheduler().recv_prev_data(x.shape, x.dtype) + self.videodit_blocks.set_input_tensor(x) + else: + # clone a new tensor to ensure x is not a view of other tensor + x = x.clone() + + x = self.videodit_blocks.forward( + hidden_states=x, + condition=condition, + condition_map=condition_map, + y_xattn_flat=y_xattn_flat, + rotary_pos_emb=rope, + inference_params=inference_params, + meta_args=meta_args, + ) + + if not self.post_process: + pp_scheduler().isend_next(x) + + return self.forward_post_process(x, meta_args) + + def forward_3cfg( + self, x, timestep, y, mask, kv_range, inference_params, **kwargs + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int]: + """ + Forward pass of PixArt, but also batches the unconditional forward pass for classifier-free guidance. + """ + # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb + + assert x.shape[0] == 2 + assert mask.shape[0] % 2 == 0 # mask should be a multiple of 2 + x = torch.cat([x[0:1], x[0:1]], dim=0) + caption_dropout_mask = torch.tensor([False, True], dtype=torch.bool, device=x.device) + + inference_params.update_kv_cache = False + out_cond_pre_and_text = self.forward( + x[0:1], + timestep[0:1], + y[0 : y.shape[0] // 2], + caption_dropout_mask=caption_dropout_mask[0:1], + xattn_mask=mask[0 : y.shape[0] // 2], + kv_range=kv_range, + inference_params=inference_params, + **kwargs, + ) + + inference_params.update_kv_cache = True + out_cond_pre = self.forward( + x[1:2], + timestep[1:2], + y[y.shape[0] // 2 : y.shape[0]], + caption_dropout_mask=caption_dropout_mask[1:2], + xattn_mask=mask[y.shape[0] // 2 : y.shape[0]], + kv_range=kv_range, + inference_params=inference_params, + **kwargs, + ) + + def chunk_to_batch(input, denoising_range_num): + input = input.squeeze(0) + input = input.reshape(-1, denoising_range_num, kwargs["chunk_width"], *input.shape[2:]) + return input.transpose(0, 1) # (denoising_range_num, chn, chunk_width, h, w) + + def batch_to_chunk(input, denoising_range_num): + input = input.transpose(0, 1) + input = input.reshape(1, -1, denoising_range_num * kwargs["chunk_width"], *input.shape[3:]) + return input + + class UnconditionGuard: + def __init__(self, kwargs): + self.kwargs = kwargs + self.prev_state = { + "range_num": kwargs["range_num"], + "denoising_range_num": kwargs["denoising_range_num"], + "slice_point": kwargs["slice_point"], + "fwd_extra_1st_chunk": kwargs["fwd_extra_1st_chunk"], + } + + def __enter__(self): + if self.kwargs.get("fwd_extra_1st_chunk", False): + self.kwargs["denoising_range_num"] -= 1 + self.kwargs["slice_point"] += 1 + self.kwargs["fwd_extra_1st_chunk"] = False + + def __exit__(self, exc_type, exc_val, exc_tb): + self.kwargs["range_num"] = self.prev_state["range_num"] + self.kwargs["denoising_range_num"] = self.prev_state["denoising_range_num"] + self.kwargs["slice_point"] = self.prev_state["slice_point"] + self.kwargs["fwd_extra_1st_chunk"] = self.prev_state["fwd_extra_1st_chunk"] + + with UnconditionGuard(kwargs): + denoising_range_num = kwargs["denoising_range_num"] + denoise_width = kwargs["chunk_width"] * denoising_range_num + uncond_x = chunk_to_batch(x[0:1, :, -denoise_width:], denoising_range_num) + timestep = timestep[0:1, -denoising_range_num:].transpose(0, 1) + uncond_y = y[y.shape[0] // 2 : y.shape[0]][-denoising_range_num:] + caption_dropout_mask = torch.tensor([True], dtype=torch.bool, device=x.device) + uncond_mask = mask[y.shape[0] // 2 : y.shape[0]][-denoising_range_num:] + uncond_kv_range = self.generate_kv_range_for_uncondition(uncond_x) + + kwargs["range_num"] = 1 + kwargs["denoising_range_num"] = 1 + kwargs["slice_point"] = 0 + out_uncond = self.forward( + uncond_x, + timestep, + uncond_y, + caption_dropout_mask=caption_dropout_mask, + xattn_mask=uncond_mask, + kv_range=uncond_kv_range, + inference_params=None, + **kwargs, + ) + out_uncond = batch_to_chunk(out_uncond, denoising_range_num) + + return out_cond_pre_and_text, out_cond_pre, out_uncond, denoise_width + + def get_cfg_scale(self, t, cfg_t_range, prev_chunk_scale_s, text_scale_s): + indices = torch.searchsorted(cfg_t_range - 1e-7, t) - 1 + assert indices.min() >= 0 and indices.max() < len(prev_chunk_scale_s) + return prev_chunk_scale_s[indices], text_scale_s[indices] + + def forward_dispatcher(self, x, timestep, y, mask, kv_range, inference_params, **kwargs): + if self.runtime_config.cfg_number == 3: + (out_cond_pre_and_text, out_cond_pre, out_uncond, denoise_width) = self.forward_3cfg( + x, timestep, y, mask, kv_range, inference_params, **kwargs + ) + + prev_chunk_scale_s = torch.tensor(self.runtime_config.prev_chunk_scales).cuda() + text_scale_s = torch.tensor(self.runtime_config.text_scales).cuda() + cfg_t_range = torch.tensor(self.runtime_config.cfg_t_range).cuda() + applied_cfg_range_num, chunk_width = (kwargs["denoising_range_num"], kwargs["chunk_width"]) + if kwargs["fwd_extra_1st_chunk"]: + applied_cfg_range_num -= 1 + cfg_timestep = timestep[0, -applied_cfg_range_num:] + + assert len(prev_chunk_scale_s) == len(cfg_t_range), "prev_chunks_scale and t_range should have the same length" + assert len(text_scale_s) == len(cfg_t_range), "text_scale and t_range should have the same length" + + cfg_output_list = [] + + for chunk_idx in range(applied_cfg_range_num): + prev_chunk_scale, text_scale = self.get_cfg_scale( + cfg_timestep[chunk_idx], cfg_t_range, prev_chunk_scale_s, text_scale_s + ) + l = chunk_idx * chunk_width + r = (chunk_idx + 1) * chunk_width + cfg_output = ( + (1 - prev_chunk_scale) * out_uncond[:, :, l:r] + + (prev_chunk_scale - text_scale) * out_cond_pre[:, :, -denoise_width:][:, :, l:r] + + text_scale * out_cond_pre_and_text[:, :, -denoise_width:][:, :, l:r] + ) + cfg_output_list.append(cfg_output) + + cfg_output = torch.cat(cfg_output_list, dim=2) + + # Reconstruct input x for the next diffusion step + x = torch.cat([x[0:1, :, :-denoise_width], cfg_output], dim=2) + x = torch.cat([x, x], dim=0) + return x + elif self.runtime_config.cfg_number == 1: + assert x.shape[0] == 2 + x = torch.cat([x[0:1], x[0:1]], dim=0) + + kwargs["caption_dropout_mask"] = torch.tensor([False], dtype=torch.bool, device=x.device) + inference_params.update_kv_cache = True + if kwargs.get("distill_nearly_clean_chunk", False): + prev_chunks_scale = float(os.getenv("prev_chunks_scale", 0.7)) + slice_start = 1 if kwargs["fwd_extra_1st_chunk"] else 0 + cond_pre_and_text_channel = x.shape[2] + new_x_chunk = x[0:1, :, slice_start * kwargs["chunk_width"] : (slice_start + 1) * kwargs["chunk_width"]] + new_kvrange = self.generate_kv_range_for_uncondition(new_x_chunk) + kwargs["denoising_range_num"] += 1 + cat_x_chunk = torch.cat([x[0:1], new_x_chunk], dim=2) + new_kvrange = new_kvrange + kv_range.max() + cat_kvrange = torch.cat([kv_range, new_kvrange], dim=0) + cat_t = torch.cat([timestep[0:1], timestep[0:1, slice_start : slice_start + 1]], dim=1) + cat_y = torch.cat([y[0 : y.shape[0] // 2], y[slice_start : slice_start + 1]], dim=0) + cat_xattn_mask = torch.cat([mask[0 : y.shape[0] // 2], mask[slice_start : slice_start + 1]], dim=0) + + cat_out = self.forward( + cat_x_chunk, + cat_t, + cat_y, + xattn_mask=cat_xattn_mask, + kv_range=cat_kvrange, + inference_params=inference_params, + **kwargs, + ) + # flowcache processes one chunk at a time and returns all chunks in a dictionary after processing is complete + if type(cat_out) == dict: + # No artifact chunk in 3 cases: + # 1. Discard artifact chunk is set + # 2. No recomputed output part + # 3. Although there is artifact chunk, the corresponding nearly clean chunk can be reused directly, so no need to compute artifact chunk separately + if self.discard_nearly_clean_chunk or (not cat_out.keys()) or max(cat_out) != self.near_clean_chunk_idx: + out_cond_pre_and_text = cat_out + else: + near_clean_out_cond_text = cat_out[max(cat_out)] + near_clean_out_cond_pre_and_text = cat_out[min(cat_out)] + cat_out[min(cat_out)] = ( + near_clean_out_cond_pre_and_text * prev_chunks_scale + near_clean_out_cond_text * (1 - prev_chunks_scale) + ) + # Remove the output corresponding to nearly clean chunk + cat_out.pop(max(cat_out)) + out_cond_pre_and_text = cat_out + elif type(cat_out) == torch.Tensor: + # Adapt to teacache + if hasattr(self, "discard_nearly_clean_chunk") and self.discard_nearly_clean_chunk: + # No need to do extra forward for nearly clean chunk, so no need to add proportionally + out_cond_pre_and_text = cat_out + # Reset + self.discard_nearly_clean_chunk = False + else: + near_clean_out_cond_pre_and_text = cat_out[ + :, :, slice_start * kwargs["chunk_width"] : (slice_start + 1) * kwargs["chunk_width"] + ] + near_clean_out_cond_text = cat_out[:, :, cond_pre_and_text_channel:] + + near_out_cond_pre_and_text = ( + near_clean_out_cond_pre_and_text * prev_chunks_scale + near_clean_out_cond_text * (1 - prev_chunks_scale) + ) + + cat_out[ + :, :, slice_start * kwargs["chunk_width"] : (slice_start + 1) * kwargs["chunk_width"] + ] = near_out_cond_pre_and_text + out_cond_pre_and_text = cat_out[:, :, :cond_pre_and_text_channel] + else: + raise RuntimeError + else: + out_cond_pre_and_text = self.forward( + x[0:1], + timestep[0:1], + y[0 : y.shape[0] // 2], + xattn_mask=mask[0 : y.shape[0] // 2], + kv_range=kv_range, + inference_params=inference_params, + **kwargs, + ) + + if type(out_cond_pre_and_text) == dict: + return_velocity = {} + for key, value in out_cond_pre_and_text.items(): + return_velocity[key] = torch.cat([value[0:1], value[0:1]], dim=0) + return return_velocity + else: + # Adapt to teacache + # "denoising_range_num" will be modified inside forward, note that kwargs here is still before modification + if hasattr(self, "denoising_range_num"): + kwargs["denoising_range_num"] = self.denoising_range_num + del self.denoising_range_num + + denoise_width = kwargs["chunk_width"] * kwargs["denoising_range_num"] + if kwargs["fwd_extra_1st_chunk"]: + denoise_width -= kwargs["chunk_width"] + + if hasattr(self, "single_chunk_inference") and self.single_chunk_inference: + x = torch.cat([out_cond_pre_and_text, out_cond_pre_and_text], dim=0) + return x + else: + x = torch.cat([x[0:1, :, :-denoise_width], out_cond_pre_and_text[:, :, -denoise_width:]], dim=2) + x = torch.cat([x[0:1], x[0:1]], dim=0) + return x + else: + raise NotImplementedError + + +def _build_dit_model(config: MagiConfig): + """Builds the model""" + device = "cuda" if env_is_true("SKIP_LOAD_MODEL") else "meta" + with torch.device(device): + model = VideoDiTModel( + config=config, pre_process=mpu.is_pipeline_first_stage(), post_process=mpu.is_pipeline_last_stage() + ) + # print_rank_0(model) + + # Print number of parameters. + param_count = sum([p.nelement() for p in model.parameters()]) + model_size_gb = sum([p.nelement() * p.element_size() for p in model.parameters()]) / (1024**3) + print_per_rank( + f"(cp, pp) rank ({mpu.get_cp_rank()}, {mpu.get_pp_rank()}): param count {param_count}, model size {model_size_gb:.2f} GB".format( + mpu.get_cp_rank(), mpu.get_pp_rank(), param_count, model_size_gb + ) + ) + + return model + + +def _high_precision_promoter(module: VideoDiTModel): + module.x_embedder.float() + module.y_embedder.float() + module.t_embedder.float() + module.final_linear.float() + module.rope.float() + for name, sub_module in module.named_modules(): + # skip qk_layernorm_xattn + if "_xattn" in name: + continue + # high precision qk_layernorm by default + if "q_layernorm" in name or "k_layernorm" in name: + sub_module.float() + if "self_attn_post_norm" in name or "mlp_post_norm" in name: + sub_module.float() + if "final_layernorm" in name: + sub_module.float() + return module + + +def get_dit(config: MagiConfig): + """Build and load VideoDiT model""" + model = _build_dit_model(config) + print_rank_0("Build DiTModel successfully") + + mem_allocated_gb = torch.cuda.memory_allocated() / 1024**3 + mem_reserved_gb = torch.cuda.memory_reserved() / 1024**3 + print_rank_0( + f"After build_dit_model, memory allocated: {mem_allocated_gb:.2f} GB, memory reserved: {mem_reserved_gb:.2f} GB" + ) + + # To avoid Error in debug mode, set default iteration to 0 + if not env_is_true("SKIP_LOAD_MODEL"): + model = load_checkpoint(model) + mem_allocated_gb = torch.cuda.memory_allocated() / 1024**3 + mem_reserved_gb = torch.cuda.memory_reserved() / 1024**3 + print_rank_0( + f"After load_checkpoint, memory allocated: {mem_allocated_gb:.2f} GB, memory reserved: {mem_reserved_gb:.2f} GB" + ) + + model = _high_precision_promoter(model) + mem_allocated_gb = torch.cuda.memory_allocated() / 1024**3 + mem_reserved_gb = torch.cuda.memory_reserved() / 1024**3 + print_rank_0( + f"After high_precision_promoter, memory allocated: {mem_allocated_gb:.2f} GB, memory reserved: {mem_reserved_gb:.2f} GB" + ) + + model.eval() + gc.collect() + torch.cuda.empty_cache() + + print_rank_0("Load checkpoint successfully") + return model diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/dit/dit_module.py b/FlowCache/FlowCache4MAGI-1/inference/model/dit/dit_module.py new file mode 100644 index 0000000000000000000000000000000000000000..0aab4dbfac5223a37e6d98fdfc1a9042c7ef80f5 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/model/dit/dit_module.py @@ -0,0 +1,1599 @@ +# 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. + +import math +import numbers +from functools import partial +from typing import Callable, List, Optional, Tuple, Dict, Set +import flashinfer +import torch +import torch.distributed +import torch.nn as nn +import torch.nn.functional as F +import triton +import triton.language as tl +from einops import rearrange +from flash_attn import flash_attn_varlen_func +from flash_attn.flash_attn_interface import flash_attn_func +from flash_attn.layers.rotary import apply_rotary_emb as flash_apply_rotary_emb +from flashinfer.gemm import bmm_fp8 + +try: + from magi_attention.functional import flex_flash_attn_func + + flex_attention = flex_flash_attn_func +except: + flex_attention = None + +from torch import Tensor +from torch.nn import Parameter + +from inference.common import EngineConfig, InferenceParams, ModelConfig, ModelMetaArgs, PackedCrossAttnParams, divide +from inference.infra.distributed import parallel_state +from inference.infra.parallelism import CSOHelper, UlyssesScheduler, cso_communication + +########################################################## +# TimestepEmbedder +########################################################## +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, model_config: ModelConfig, frequency_embedding_size=256): + super().__init__() + + self.data_type = model_config.params_dtype + hidden_size = model_config.hidden_size + + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, int(hidden_size * model_config.cond_hidden_ratio), bias=True), + nn.SiLU(), + nn.Linear( + int(hidden_size * model_config.cond_hidden_ratio), int(hidden_size * model_config.cond_hidden_ratio), bias=True + ), + ) + self.frequency_embedding_size = frequency_embedding_size + + # rescale the timestep for the general transport model + self.timestep_rescale_factor = 1000 + + @staticmethod + def timestep_embedding(t, dim, max_period=10000, timestep_rescale_factor=1): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to( + device=t.device + ) + args = t[:, None].float() * freqs[None] * timestep_rescale_factor + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t = t.to(torch.float32) + t_freq = self.timestep_embedding( + t, self.frequency_embedding_size, timestep_rescale_factor=self.timestep_rescale_factor + ) + t_emb = self.mlp(t_freq.to(self.data_type)) + return t_emb + + +########################################################## +# CaptionEmbedder +########################################################## +class CaptionEmbedder(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + + def __init__(self, model_config: ModelConfig): + super().__init__() + + in_channels = model_config.caption_channels + hidden_size = model_config.hidden_size + caption_max_length = model_config.caption_max_length + + self.y_proj_xattn = nn.Sequential( + nn.Linear(in_channels, int(hidden_size * model_config.xattn_cond_hidden_ratio), bias=True), nn.SiLU() + ) + + self.y_proj_adaln = nn.Sequential(nn.Linear(in_channels, int(hidden_size * model_config.cond_hidden_ratio), bias=True)) + + self.null_caption_embedding = Parameter(torch.empty(caption_max_length, in_channels)) + + def caption_drop(self, caption, caption_dropout_mask): + """ + Drops labels to enable classifier-free guidance. + caption.shape = (N, 1, cap_len, C) + """ + dropped_caption = torch.where( + caption_dropout_mask[:, None, None, None], # (N, 1, 1, 1) + self.null_caption_embedding[None, None, :], # (1, 1, cap_len, C) + caption, # (N, 1, cap_len, C) + ) + return dropped_caption + + def caption_drop_single_token(self, caption_dropout_mask): + dropped_caption = torch.where( + caption_dropout_mask[:, None, None], # (N, 1, 1) + self.null_caption_embedding[None, -1, :], # (1, 1, C) + self.null_caption_embedding[None, -2, :], # (1, 1, C) + ) + return dropped_caption # (N, 1, C) + + def forward(self, caption, train, caption_dropout_mask=None): + if train and caption_dropout_mask is not None: + caption = self.caption_drop(caption, caption_dropout_mask) + caption_xattn = self.y_proj_xattn(caption) + if caption_dropout_mask is not None: + caption = self.caption_drop_single_token(caption_dropout_mask) + + caption_adaln = self.y_proj_adaln(caption) + return caption_xattn, caption_adaln + + +########################################################## +# FinalLinear +########################################################## +class FinalLinear(nn.Module): + """ + The final linear layer of DiT. + """ + + def __init__(self, hidden_size, patch_size, t_patch_size, out_channels): + super().__init__() + self.linear = nn.Linear(hidden_size, patch_size * patch_size * t_patch_size * out_channels, bias=False) + + def forward(self, x): + x = self.linear(x) + return x + + +########################################################## +# AdaModulateLayer +########################################################## +class AdaModulateLayer(torch.nn.Module): + def __init__(self, model_config: ModelConfig): + super().__init__() + self.model_config = model_config + + self.gate_num_chunks = 2 + self.act = nn.SiLU() + self.proj = nn.Sequential( + nn.Linear( + int(self.model_config.hidden_size * self.model_config.cond_hidden_ratio), + int(self.model_config.hidden_size * self.model_config.cond_gating_ratio * self.gate_num_chunks), + bias=True, + dtype=self.model_config.params_dtype, + ) + ) + + def forward(self, c): + c = self.act(c) + return self.proj(c) + + +########################################################## +# bias_modulate_add +########################################################## +@triton.jit +def range_mod_kernel_fwd( + X, # pointer to the input + MAP, # map x index to gating index + GATINGS, # pointer to the gatings + Y, # pointer to the output + M, # number of rows in X, unused + N, # number of columns in X + stride_xm, # how much to increase the pointer when moving by 1 row in X + stride_xn, # how much to increase the pointer when moving by 1 column in X + stride_gm, # how much to increase the pointer when moving by 1 row in GATINGS + stride_gn, # how much to increase the pointer when moving by 1 column in GATINGS + stride_ym, # how much to increase the pointer when moving by 1 row in Y + stride_yn, # how much to increase the pointer when moving by 1 column in Y + BLOCK_SIZE: tl.constexpr, # number of columns in a block +): + # Map the program id to the row of X and Y it should compute. + row = tl.program_id(0) + + cur_X = X + row * stride_xm + x_cols = tl.arange(0, BLOCK_SIZE) * stride_xn + x_mask = x_cols < N * stride_xn + x = tl.load(cur_X + x_cols, mask=x_mask, other=0.0) + + cur_MAP = MAP + row + gating_index = tl.load(cur_MAP) + cur_GATING = GATINGS + gating_index * stride_gm + gating_cols = tl.arange(0, BLOCK_SIZE) * stride_gn + gating_mask = gating_cols < N * stride_gn + gating = tl.load(cur_GATING + gating_cols, mask=gating_mask, other=0.0) + + cur_Y = Y + row * stride_ym + y_cols = tl.arange(0, BLOCK_SIZE) * stride_yn + y_mask = y_cols < N * stride_yn + tl.store(cur_Y + y_cols, x * gating, mask=y_mask) + + +def range_mod_triton(x, c_mapping, gatings): + """ + Inputs: + x: (s, b, h). Tensor of inputs embedding (images or latent representations of images) + c_mapping: (s, b). Tensor of condition map + gatings: (b, denoising_range_num, h). Tensor of condition embedding + """ + + assert x.is_cuda, "x is not on cuda" + assert c_mapping.is_cuda, "c_mapping is not on cuda" + assert gatings.is_cuda, "gatings is not on cuda" + + # TODO: use 3D tensor for x, c_mapping, and gatings + s, b, h = x.shape + x = x.transpose(0, 1).flatten(0, 1) + c_mapping = c_mapping.transpose(0, 1).flatten(0, 1) + gatings = gatings.flatten(0, 1) + + assert x.dim() == 2, f"x must be a 2D tensor but got {x.dim()}D" + assert c_mapping.dim() == 1, f"c_mapping must be a 1D tensor but got {c_mapping.dim()}D" + assert gatings.dim() == 2, f"gatings must be a 2D tensor but got {gatings.dim()}D" + + M, N = x.shape + if c_mapping.size(0) != M: + import pdb; pdb.set_trace() # noqa: T201 + assert c_mapping.size(0) == M, "c_mapping must have the same number of rows as x" + + # Less than 64KB per feature: enqueue fused kernel + MAX_FUSED_SIZE = 65536 // x.element_size() + BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N)) + if N > BLOCK_SIZE: + raise RuntimeError("range_mod_triton doesn't support feature dim >= 64KB.") + + MAP = c_mapping + y = torch.empty_like(x) + + range_mod_kernel_fwd[(M,)]( + x, + MAP, + gatings, + y, + M, + N, + x.stride(0), + x.stride(1), + gatings.stride(0), + gatings.stride(1), + y.stride(0), + y.stride(1), + BLOCK_SIZE=BLOCK_SIZE, + ) + y = y.reshape(b, s, h).transpose(0, 1) + + return y + + +def bias_modulate_add( + x: torch.Tensor, residual: torch.Tensor, condition_map: torch.Tensor, gate: torch.Tensor, post_norm: torch.nn.Module +): + assert gate.shape[-1] == x.shape[-1] + + original_dtype = x.dtype + x = x.float() + residual = residual.float() + gate = gate.float() + + try: + x = range_mod_triton(x, condition_map, gate) + except RuntimeError as e: + print(f"RuntimeError in range_mod_triton: {e}") + import pdb;pdb.set_trace() + + x = post_norm(x) + x = x + residual + x = x.to(original_dtype) + + return x + + +########################################################## +# FusedLayerNorm +########################################################## +def make_viewless_tensor(inp, requires_grad): + # return tensor as-is, if not a 'view' + if inp._base is None: + return inp + + out = torch.empty((1,), dtype=inp.dtype, device=inp.device, requires_grad=requires_grad) + out.data = inp.data + return out + + +class FusedLayerNorm(torch.nn.Module): + + """ + Layer Norm, fused into a single CUDA kernel. + Borrow from: https://github.com/NVIDIA/Megatron-LM/blob/6501752396e9cc360ce894cda4b2217a58c1c09d/megatron/core/fusions/fused_layer_norm.py#L30 + + Args: + hidden_size (int): Transformer hidden dimension. + + eps (float): Epsilon added to denominator, for numerical stability. + + zero_centered_gamma (bool): Adjust LayerNorm weights such that they are + centered around zero. This improves numerical stability. + + model_config (ModelConfig): Transformer config. Include to match custom + layer norm interfaces. + + normalization (str): Normalization type, used for Transformer Engine. + Must equal 'LayerNorm' here. + """ + + def __init__(self, model_config: ModelConfig, hidden_size: int): + super().__init__() + + self.zero_centered_gamma = model_config.apply_layernorm_1p + if isinstance(hidden_size, numbers.Integral): + hidden_size = (hidden_size,) + self.hidden_size = torch.Size(hidden_size) + self.eps = model_config.layernorm_epsilon + self.weight = Parameter(torch.empty(*hidden_size, dtype=model_config.params_dtype)) + self.bias = Parameter(torch.empty(*hidden_size, dtype=model_config.params_dtype)) + + def forward(self, input: Tensor) -> Tensor: + weight = self.weight + 1 if self.zero_centered_gamma else self.weight + return torch.nn.functional.layer_norm(input, self.hidden_size, weight, self.bias, self.eps) + + +def softcap(x: torch.Tensor, cap: int): + return (cap * torch.tanh(x.float() / cap)).to(x.dtype) + + +def div_clamp_to(x: torch.Tensor, scale: torch.Tensor): + fp8_min = torch.finfo(torch.float8_e4m3fn).min + fp8_max = torch.finfo(torch.float8_e4m3fn).max + prefix_shape = x.shape[:-1] + last_shape = x.shape[-1] + x = x.flatten().reshape(-1, last_shape) + # Split x into 256 MB parts to avoid big memory peak + part_size = 256 * 1024 * 1024 // last_shape + part_num = (x.shape[0] + part_size - 1) // part_size + return ( + torch.cat( + [ + torch.clamp(x[i * part_size : (i + 1) * part_size].float() / scale.float(), fp8_min, fp8_max).bfloat16() + for i in range(part_num) + ], + dim=0, + ) + .to(torch.float8_e4m3fn) + .reshape(*prefix_shape, last_shape) + .contiguous() + ) + + +########################################################## +# CustomLayerNormLinear +########################################################## +class CustomLayerNormLinear(torch.nn.Module): + def __init__( + self, + input_size: int, + output_size_q: int, + output_size_kv: int, + layer_number: int, + model_config: ModelConfig, + engine_config: EngineConfig, + ): + super().__init__() + self.layer_norm = torch.nn.LayerNorm(input_size, eps=model_config.layernorm_epsilon, dtype=model_config.params_dtype) + + self.layer_number = layer_number + layers = {"q": output_size_q, "qx": output_size_q, "k": output_size_kv, "v": output_size_kv} + + for name, output_size in layers.items(): + if not engine_config.fp8_quant or self.layer_number == 0 or self.layer_number == model_config.num_layers - 1: + setattr(self, name, torch.nn.Linear(input_size, output_size, bias=False, dtype=model_config.params_dtype)) + else: + setattr(self, name, PerTensorQuantizedFp8Linear(input_size, output_size)) + + def forward_ln(self, hidden_states): + return self.layer_norm(hidden_states) + + def forward_q(self, hidden_states): + return self.q(hidden_states) + + def forward_qx(self, hidden_states): + return self.qx(hidden_states) + + def forward_k(self, hidden_states): + return self.k(hidden_states) + + def forward_v(self, hidden_states): + return self.v(hidden_states) + + +########################################################## +# PerTensorQuantizedFp8Linear +########################################################## +class PerTensorQuantizedFp8Linear(torch.nn.Module): + # The bias and device parameter is not used; it is included for compatibility with Linear's parameters. + def __init__(self, in_features: int, out_features: int, bias=False, dtype=torch.bfloat16, device=None) -> None: + super().__init__() + + self.in_features = in_features + self.out_features = out_features + self.finfo = torch.finfo(torch.float8_e4m3fn) + self.output_dtype = dtype + + self.weight = Parameter(torch.empty((1, out_features, in_features), dtype=torch.float8_e4m3fn)) + self.weight_scale = Parameter(torch.empty(1, dtype=torch.float32)) + self.input_scale = Parameter(torch.empty(in_features, dtype=torch.float32)) + + def forward(self, input: torch.Tensor): + input = div_clamp_to(input, self.input_scale) + + prefix_shape = input.shape[:-1] + # column major weight + return bmm_fp8( + input.reshape(1, -1, self.in_features), + self.weight.transpose(-2, -1), + self.input_scale, + self.weight_scale, + dtype=self.output_dtype, + ).reshape(prefix_shape + (self.out_features,)) + + +########################################################## +# PerChannelQuantizedFp8Linear +########################################################## +class PerChannelQuantizedFp8Linear(torch.nn.Module): + # The bias and device parameter is not used; it is included for compatibility with Linear's parameters. + def __init__(self, in_features: int, out_features: int, bias=False, dtype=torch.bfloat16, device=None) -> None: + super().__init__() + + self.in_features = in_features + self.out_features = out_features + self.output_dtype = dtype + self.finfo = torch.finfo(torch.float8_e4m3fn) + + self.weight = Parameter(torch.empty((1, out_features, in_features), dtype=torch.float8_e4m3fn)) + self.weight_scale = Parameter(torch.empty(1, dtype=torch.float32)) + self.input_scale = Parameter(torch.empty(1, dtype=torch.float32)) + self.smooth_scale = Parameter(torch.empty(1, in_features, dtype=torch.float32)) + + def forward(self, x): + x = div_clamp_to(x, self.smooth_scale.to(torch.float32)) + + prefix_shape = x.shape[:-1] + return bmm_fp8( + x.reshape(1, -1, self.in_features), + self.weight.transpose(-2, -1), + self.input_scale, + self.weight_scale, + dtype=self.output_dtype, + ).reshape(prefix_shape + (self.out_features,)) + + +########################################################## +# CustomMLP +########################################################## +class CustomMLP(torch.nn.Module): + """ + CustomMLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + + + Returns an output and a bias to be added to the output. + + We use the following notation: + h: hidden size + p: number of tensor model parallel partitions + b: batch size + s: sequence length + """ + + def __init__(self, model_config: ModelConfig, engine_config: EngineConfig, layer_number: int, input_size: int = None): + super().__init__() + + self.model_config: ModelConfig = model_config + self.engine_config: EngineConfig = engine_config + self.layer_number = layer_number + + self.input_size = input_size if input_size != None else self.model_config.hidden_size + self.layer_norm = torch.nn.LayerNorm( + self.input_size, eps=self.model_config.layernorm_epsilon, dtype=self.model_config.params_dtype + ) + + submodules_linear_fc1 = torch.nn.Linear + if self.engine_config.fp8_quant and self.layer_number != 0 and self.layer_number != model_config.num_layers - 1: + submodules_linear_fc1 = PerTensorQuantizedFp8Linear + + if self.model_config.gated_linear_unit: + self.linear_fc1 = submodules_linear_fc1( + self.input_size, 2 * self.model_config.ffn_hidden_size, bias=False, dtype=self.model_config.params_dtype + ) + else: + self.linear_fc1 = submodules_linear_fc1( + self.input_size, self.model_config.ffn_hidden_size, bias=False, dtype=self.model_config.params_dtype + ) + + submodules_linear_fc2 = torch.nn.Linear + if engine_config.fp8_quant and self.layer_number != 0 and self.layer_number != model_config.num_layers - 1: + submodules_linear_fc2 = PerChannelQuantizedFp8Linear + + self.linear_fc2 = submodules_linear_fc2( + self.model_config.ffn_hidden_size, self.model_config.hidden_size, bias=False, dtype=self.model_config.params_dtype + ) + + def forward(self, hidden_states): + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.linear_fc1(hidden_states) + if self.model_config.gated_linear_unit: + hidden_states = flashinfer.activation.silu_and_mul(hidden_states) + else: + hidden_states = torch.nn.functional.gelu(hidden_states) + hidden_states = self.linear_fc2(hidden_states) + + return hidden_states + + +########################################################## +# LearnableRotaryEmbeddingCat +########################################################## +def ndgrid(*tensors) -> Tuple[torch.Tensor, ...]: + """generate N-D grid in dimension order. + + The ndgrid function is like meshgrid except that the order of the first two input arguments are switched. + + That is, the statement + [X1,X2,X3] = ndgrid(x1,x2,x3) + + produces the same result as + + [X2,X1,X3] = meshgrid(x2,x1,x3) + + This naming is based on MATLAB, the purpose is to avoid confusion due to torch's change to make + torch.meshgrid behaviour move from matching ndgrid ('ij') indexing to numpy meshgrid defaults of ('xy'). + + """ + try: + return torch.meshgrid(*tensors, indexing="ij") + except TypeError: + # old PyTorch < 1.10 will follow this path as it does not have indexing arg, + # the old behaviour of meshgrid was 'ij' + return torch.meshgrid(*tensors) + + +def pixel_freq_bands( + num_bands: int, max_freq: float = 224.0, linear_bands: bool = True, device: Optional[torch.device] = None +): + if linear_bands: + bands = torch.linspace(1.0, max_freq / 2, num_bands, dtype=torch.float32, device=device) + else: + bands = 2 ** torch.linspace(0, math.log(max_freq, 2) - 1, num_bands, dtype=torch.float32, device=device) + return bands * torch.pi + + +def freq_bands( + num_bands: int, temperature: float = 10000.0, step: int = 2, device: Optional[torch.device] = None +) -> torch.Tensor: + exp = torch.arange(0, num_bands, step, dtype=torch.int64, device=device).to(torch.float32) / num_bands + bands = 1.0 / (temperature**exp) + return bands + + +def build_fourier_pos_embed( + feat_shape: List[int], + bands: Optional[torch.Tensor] = None, + num_bands: int = 64, + max_res: int = 224, + temperature: float = 10000.0, + linear_bands: bool = False, + include_grid: bool = False, + in_pixels: bool = True, + ref_feat_shape: Optional[List[int]] = None, + dtype: torch.dtype = torch.float32, + device: Optional[torch.device] = None, +) -> List[torch.Tensor]: + """ + + Args: + feat_shape: Feature shape for embedding. + bands: Pre-calculated frequency bands. + num_bands: Number of frequency bands (determines output dim). + max_res: Maximum resolution for pixel based freq. + temperature: Temperature for non-pixel freq. + linear_bands: Linear band spacing for pixel based freq. + include_grid: Include the spatial grid in output. + in_pixels: Output in pixel freq. + ref_feat_shape: Reference feature shape for resize / fine-tune. + dtype: Output dtype. + device: Output device. + + Returns: + + """ + if bands is None: + if in_pixels: + bands = pixel_freq_bands(num_bands, float(max_res), linear_bands=linear_bands, device=device) + else: + bands = freq_bands(num_bands, temperature=temperature, step=1, device=device) + else: + if device is None: + device = bands.device + if dtype is None: + dtype = bands.dtype + + if in_pixels: + t = [torch.linspace(-1.0, 1.0, steps=s, device=device, dtype=torch.float32) for s in feat_shape] + else: + t = [torch.arange(s, device=device, dtype=torch.int64).to(torch.float32) for s in feat_shape] + # align spatial center (H/2,W/2) to (0,0) + t[1] = t[1] - (feat_shape[1] - 1) / 2 + t[2] = t[2] - (feat_shape[2] - 1) / 2 + if ref_feat_shape is not None: + # eva's scheme for resizing rope embeddings (ref shape = pretrain) + # aligning to the endpoint e.g [0,1,2] -> [0, 0.4, 0.8, 1.2, 1.6, 2] + t_rescaled = [] + for x, f, r in zip(t, feat_shape, ref_feat_shape): + # deal with image input + if f == 1: + assert r == 1, "ref_feat_shape must be 1 when feat_shape is 1" + t_rescaled.append(x) + else: + t_rescaled.append(x / (f - 1) * (r - 1)) + t = t_rescaled + + grid = torch.stack(ndgrid(t), dim=-1) + grid = grid.unsqueeze(-1) + pos = grid * bands + + pos_sin, pos_cos = pos.sin().to(dtype=dtype), pos.cos().to(dtype) + out = [grid, pos_sin, pos_cos] if include_grid else [pos_sin, pos_cos] + return out + + +def build_rotary_pos_embed( + feat_shape: List[int], + bands: Optional[torch.Tensor] = None, + dim: int = 64, + max_res: int = 224, + temperature: float = 10000.0, + linear_bands: bool = False, + in_pixels: bool = True, + ref_feat_shape: Optional[List[int]] = None, + dtype: torch.dtype = torch.float32, + device: Optional[torch.device] = None, +): + """ + + Args: + feat_shape: Spatial shape of the target tensor for embedding. + bands: Optional pre-generated frequency bands + dim: Output dimension of embedding tensor. + max_res: Maximum resolution for pixel mode. + temperature: Temperature (inv freq) for non-pixel mode + linear_bands: Linearly (instead of log) spaced bands for pixel mode + in_pixels: Pixel vs language (inv freq) mode. + dtype: Output dtype. + device: Output device. + + Returns: + + """ + sin_emb, cos_emb = build_fourier_pos_embed( + feat_shape, + bands=bands, + num_bands=dim // 8, + max_res=max_res, + temperature=temperature, + linear_bands=linear_bands, + in_pixels=in_pixels, + ref_feat_shape=ref_feat_shape, + device=device, + dtype=dtype, + ) + num_spatial_dim = 1 + # this would be much nicer as a .numel() call to torch.Size(), but torchscript sucks + for x in feat_shape: + num_spatial_dim *= x + + sin_emb = sin_emb.reshape(num_spatial_dim, -1) + cos_emb = cos_emb.reshape(num_spatial_dim, -1) + return sin_emb, cos_emb + + +class LearnableRotaryEmbeddingCat(nn.Module): + """Rotary position embedding w/ concatenatd sin & cos + + The following impl/resources were referenced for this impl: + * https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py + * https://blog.eleuther.ai/rotary-embeddings/ + """ + + def __init__( + self, + dim, + max_res=224, + temperature=10000, + in_pixels=True, + linear_bands: bool = False, + feat_shape: Optional[List[int]] = None, + ref_feat_shape: Optional[List[int]] = None, + ): + super().__init__() + self.dim = dim + self.max_res = max_res + self.temperature = temperature + self.in_pixels = in_pixels + self.linear_bands = linear_bands + self.feat_shape = feat_shape + self.ref_feat_shape = ref_feat_shape + self.bands = nn.Parameter(self.get_default_bands()) + + def get_default_bands(self): + if self.in_pixels: + bands = pixel_freq_bands( + self.dim // 8, float(self.max_res), linear_bands=self.linear_bands, devicse=torch.cuda.current_device() + ) + else: + bands = freq_bands(self.dim // 8, temperature=self.temperature, step=1, device=torch.cuda.current_device()) + return bands + + def get_embed(self, shape: Optional[List[int]], ref_feat_shape: Optional[List[int]] = None): + # rebuild bands and embeddings every call, use if target shape changes + embeds = build_rotary_pos_embed( + feat_shape=shape, + bands=self.bands, # use learned bands + dim=self.dim, + max_res=self.max_res, + linear_bands=self.linear_bands, + in_pixels=self.in_pixels, + ref_feat_shape=ref_feat_shape if ref_feat_shape else self.ref_feat_shape, + temperature=self.temperature, + device=torch.cuda.current_device(), + ) + return torch.cat(embeds, -1) + + +########################################################## +# Attention +########################################################## +class Attention(torch.nn.Module): + """ + Attention layer abstract class. + """ + + def __init__(self, model_config: ModelConfig, engine_config: EngineConfig, layer_number: int): + super().__init__() + + self.model_config: ModelConfig = model_config + self.engine_config: EngineConfig = engine_config + self.layer_number = layer_number + + self.hidden_size_per_attention_head = self.model_config.kv_channels + # num_query_groups and num_attention_heads are different for GQA + self.query_projection_size = self.model_config.kv_channels * self.model_config.num_attention_heads + self.kv_projection_size = self.model_config.kv_channels * self.model_config.num_query_groups + + # Per attention head and per partition values. + world_size = parallel_state.get_tp_world_size(with_context_parallel=True) + if world_size > self.model_config.num_query_groups and world_size % self.model_config.num_query_groups == 0: + self.num_query_groups_per_partition = 1 + else: + self.num_query_groups_per_partition = divide(self.model_config.num_query_groups, world_size) + + def _allocate_key_and_value_memory(self, sequence_length, batch_size, dtype): + """Allocate memory to store kv cache during inference.""" + + if self.engine_config.kv_offload: + return torch.empty( + sequence_length * batch_size, + self.num_query_groups_per_partition, + self.hidden_size_per_attention_head * 2, + dtype=dtype, + device=torch.cpu.current_device(), + pin_memory=True, + ) + else: + return torch.empty( + sequence_length * batch_size, + self.num_query_groups_per_partition, + self.hidden_size_per_attention_head * 2, + dtype=dtype, + device=torch.cuda.current_device(), + ) + + +########################################################## +# FullyParallelAttention +########################################################## +def split_tensor_along_last_dim( + tensor: torch.Tensor, num_partitions: int, contiguous_split_chunks: bool = False +) -> List[torch.Tensor]: + """Split a tensor along its last dimension. + + Args: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + + Returns: + A list of Tensors + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = divide(tensor.size()[last_dim], num_partitions) + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + +class FullyParallelAttention(Attention): + def __init__(self, model_config: ModelConfig, engine_config: EngineConfig, layer_number: int): + super().__init__(model_config=model_config, engine_config=engine_config, layer_number=layer_number) + + # output 2x query, one for self-attn, one for cross-attn with condition + self.linear_qkv = CustomLayerNormLinear( + input_size=self.model_config.hidden_size, + output_size_q=self.query_projection_size, + output_size_kv=self.kv_projection_size, + layer_number=self.layer_number, + model_config=self.model_config, + engine_config=self.engine_config, + ) + + # kv from condition, e.g., caption + self.linear_kv_xattn = torch.nn.Linear( + int(self.model_config.hidden_size * self.model_config.xattn_cond_hidden_ratio), # 6144 + 2 * self.kv_projection_size, # 2048 + dtype=self.model_config.params_dtype, + bias=False, + ) + + # Output. + self.adapt_linear_quant = ( + self.engine_config.fp8_quant and self.layer_number != 0 and self.layer_number != model_config.num_layers - 1 + ) + submodules_linear_proj = PerChannelQuantizedFp8Linear if self.adapt_linear_quant else torch.nn.Linear + self.linear_proj = submodules_linear_proj( + 2 * self.query_projection_size, self.model_config.hidden_size, dtype=self.model_config.params_dtype, bias=False + ) + + self.q_layernorm = FusedLayerNorm(model_config=self.model_config, hidden_size=self.hidden_size_per_attention_head) + self.q_layernorm_xattn = FusedLayerNorm( + model_config=self.model_config, hidden_size=self.hidden_size_per_attention_head + ) + self.k_layernorm = FusedLayerNorm(model_config=self.model_config, hidden_size=self.hidden_size_per_attention_head) + self.k_layernorm_xattn = FusedLayerNorm( + model_config=self.model_config, hidden_size=self.hidden_size_per_attention_head + ) + + self.attn_weights_history = [] + + def _full_adjust_key_and_value( + self, inference_params: InferenceParams, key_and_value: torch.Tensor, meta_args: ModelMetaArgs + ): + """ + Saves the generated key and value tensors to the end of the buffers in inference_params. + Returns the full size keys and values from the provided inference_params + + Returns a tuple: (key, value) + """ + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + inf_max_seq_length = inference_params.max_sequence_length + inf_max_batch_size = inference_params.max_batch_size + + if self.layer_number not in inference_params.key_value_memory_dict: + inference_key_and_value_memory = self._allocate_key_and_value_memory( + inf_max_seq_length, inf_max_batch_size, key_and_value.dtype + ) + inference_params.key_value_memory_dict[self.layer_number] = inference_key_and_value_memory + else: + # Get the pre-allocated buffers for this layer + inference_key_and_value_memory = inference_params.key_value_memory_dict[self.layer_number] + + sequence_start = meta_args.slice_point * meta_args.clip_token_nums * inf_max_batch_size + # Only take clean kv cache here, but for partial reuse, the kv of the currently denoising chunk is not passed to forward, so this part of kv is also needed + get_key_and_value = inference_key_and_value_memory[:sequence_start, ...].cuda() + + # Copy key and values. + if inference_params.update_kv_cache: + key_and_value_total = key_and_value + + clip_size = ( + key_and_value_total.size(0) - meta_args.clip_token_nums * inf_max_batch_size + if meta_args.distill_nearly_clean_chunk + else key_and_value_total.size(0) + ) + sequence_end = sequence_start + clip_size + assert sequence_end <= inference_key_and_value_memory.size(0) + # update kv cache + inference_key_and_value_memory[sequence_start:sequence_end, ...] = key_and_value_total[:clip_size] + + return torch.cat([get_key_and_value, key_and_value], dim=0) + + def _custom_adjust_key_and_value( + self, inference_params: InferenceParams, key_and_value: torch.Tensor, meta_args: ModelMetaArgs + ): + """ + Saves the generated key and value tensors to the end of the buffers in inference_params. + Returns the full size keys and values from the provided inference_params + + Returns a tuple: (key, value) + """ + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + + # 1. The principle is to update the kv cache for whichever chunk is passed in + inf_max_seq_length = inference_params.max_sequence_length + inf_max_batch_size = inference_params.max_batch_size + + if self.layer_number not in inference_params.key_value_memory_dict: + inference_key_and_value_memory = self._allocate_key_and_value_memory( + inf_max_seq_length, inf_max_batch_size, key_and_value.dtype + ) + inference_params.key_value_memory_dict[self.layer_number] = inference_key_and_value_memory + else: + # Get the pre-allocated buffers for this layer + inference_key_and_value_memory = inference_params.key_value_memory_dict[self.layer_number] + + chunk_start = meta_args.start_chunk_id + chunk_end = meta_args.end_chunk_id + if meta_args.distill_nearly_clean_chunk: + chunk_end -= 1 + + sequence_start = chunk_start * meta_args.clip_token_nums * inf_max_batch_size + sequence_end = chunk_end * meta_args.clip_token_nums * inf_max_batch_size + # 1. Update values in inference_key_and_value_memory + clip_size = ( + key_and_value.size(0) - meta_args.clip_token_nums * inf_max_batch_size + if meta_args.distill_nearly_clean_chunk + else key_and_value.size(0) + ) + try: + inference_key_and_value_memory[sequence_start:sequence_end, ...] = key_and_value[:clip_size] + except Exception as e: + print(f"Error updating inference key and value memory: {e}") + import pdb; pdb.set_trace() + + # 2. Concatenate kv values from previous chunks + key_and_value_total = key_and_value + past_chunk_kv = inference_key_and_value_memory[:sequence_start, ...].cuda() + key_and_value_total = torch.cat([past_chunk_kv, key_and_value], dim=0) + + return key_and_value_total + + def _compresskv_adjust_key_and_value( + self, inference_params: InferenceParams, key_and_value: torch.Tensor, meta_args: ModelMetaArgs + ): + inf_max_seq_length = inference_params.max_sequence_length + inf_max_batch_size = inference_params.max_batch_size + + if self.layer_number not in inference_params.key_value_memory_dict: + inference_key_and_value_memory = self._allocate_key_and_value_memory( + meta_args.total_cache_len, inf_max_batch_size, key_and_value.dtype + ) + inference_params.key_value_memory_dict[self.layer_number] = inference_key_and_value_memory + else: + inference_key_and_value_memory = inference_params.key_value_memory_dict[self.layer_number] + + tracker = inference_params.kv_chunk_tracker + + # Calculate the chunk range being processed + chunk_start = meta_args.start_chunk_id + chunk_end = meta_args.end_chunk_id + if meta_args.distill_nearly_clean_chunk: + chunk_end -= 1 + + current_chunk_ids = list(range(chunk_start, chunk_end)) # e.g., [3, 4, 5] + + if len(current_chunk_ids) > 0: + # Allocate kv cache ranges, skip if already allocated + tracker.register_chunks(current_chunk_ids) + + # === Split key_and_value by chunk === + tokens_per_chunk = meta_args.clip_token_nums + # Split tensor: one segment per chunk + chunk_tensors = [] + start_idx = 0 + for i, cid in enumerate(current_chunk_ids): + chunk_len = tokens_per_chunk + end_idx = start_idx + chunk_len + chunk_tensors.append(key_and_value[start_idx:end_idx, ...]) + start_idx = end_idx + + # === Write each chunk to its allocated position === + for cid, chunk_kv in zip(current_chunk_ids, chunk_tensors): + s, e = tracker.get_range(cid) + target_length = e - s + assert chunk_kv.size(0) == target_length, f"Chunk size mismatch: chunk {cid}, expected {target_length}, got {chunk_kv.size(0)}" + + inference_key_and_value_memory[s : s + chunk_kv.size(0), ...] = chunk_kv + + + # === Concatenate past KV === + past_ranges = tracker.get_all_ranges_previous(current_chunk_ids) + past_chunks = [] + for s, e in past_ranges: + past_chunks.append(inference_key_and_value_memory[s:e, ...].cuda()) + + if past_chunks: + past_kv = torch.cat(past_chunks, dim=0) + key_and_value_total = torch.cat([past_kv, key_and_value], dim=0) + else: + key_and_value_total = key_and_value.cuda() + + return key_and_value_total + + def adjust_key_and_value_for_inference( + self, key_and_value: torch.Tensor, inference_params: InferenceParams, meta_args: ModelMetaArgs + ): + if inference_params is None: + return torch.chunk(key_and_value, 2, dim=-1) + + # Only update kvcache when necessary, include 3 conditions: + # 1. extract prefix video clean feature + # 2. the first chunk of current kv is clean, we need to save their feature + # 3. previous chunk is clean and we need to save/load their feature + + # Priority: compress_kv > save_kvcache_every_forward > full_adjust + if meta_args.compress_kv: + key_and_value = self._compresskv_adjust_key_and_value(inference_params, key_and_value, meta_args) + elif meta_args.save_kvcache_every_forward: + key_and_value = self._custom_adjust_key_and_value(inference_params, key_and_value, meta_args) + elif (meta_args.extract_prefix_video_feature or meta_args.fwd_extra_1st_chunk or meta_args.slice_point > 0) and \ + not meta_args.save_kvcache_every_forward: + key_and_value = self._full_adjust_key_and_value(inference_params, key_and_value, meta_args) + key, value = torch.chunk(key_and_value, 2, dim=-1) + return key.contiguous(), value.contiguous() + + # ===================== + # Get Query for core attn + # [sq, b, (hn hd)] -> [(sq b), hn, hd] + # ===================== + + def get_q(self, mixed_qqkv: torch.Tensor, cos_emb: torch.Tensor, sin_emb: torch.Tensor): + query = self.linear_qkv.forward_q(mixed_qqkv) + query = query.reshape(query.size(0), query.size(1), -1, self.hidden_size_per_attention_head) + assert self.q_layernorm is not None + original_dtype = query.dtype + query = query.float() + query = self.q_layernorm(query) + query = query.transpose(0, 1).contiguous() + query = flash_apply_rotary_emb(query, cos_emb, sin_emb) + query = query.to(original_dtype) + return rearrange(query, "b sq hn hd -> (sq b) hn hd").contiguous() + + # ===================== + # Get Key for core attn + # [sq, b, (hn hd)] -> [(sq b), hn, hd] + # ===================== + + def get_k(self, mixed_qqkv: torch.Tensor, cos_emb: torch.Tensor, sin_emb: torch.Tensor): + key = self.linear_qkv.forward_k(mixed_qqkv) + key = key.reshape(key.size(0), key.size(1), -1, self.hidden_size_per_attention_head) + assert self.k_layernorm is not None + original_dtype = key.dtype + key = key.float() + key = self.k_layernorm(key) + key = key.transpose(0, 1).contiguous() + key = flash_apply_rotary_emb(key, cos_emb, sin_emb) + key = key.to(original_dtype) + return rearrange(key, "b sq hn hd -> (sq b) hn hd").contiguous() + + # ===================== + # Get Value for core attn + # [sq, b, (hn hd)] -> [(sq b), hn, hd] + # ===================== + + def get_v(self, mixed_qqkv: torch.Tensor): + value = self.linear_qkv.forward_v(mixed_qqkv) + return rearrange(value, "sq b (hn hd) -> (sq b) hn hd", hd=self.hidden_size_per_attention_head).contiguous() + + def get_kv(self, mixed_qqkv: torch.Tensor, cos_emb: torch.Tensor, sin_emb: torch.Tensor): + # Get KV together for better performance when encoutering cpu-bound, mainly used by cuda graph + key = self.get_k(mixed_qqkv, cos_emb, sin_emb) + value = self.get_v(mixed_qqkv) + # [(sq b), hn, hd] -> [(sq b), hn, 2 * hd] + return torch.cat([key, value], dim=-1) + + def get_qkv(self, mixed_qqkv: torch.Tensor, cos_emb: torch.Tensor, sin_emb: torch.Tensor): + # Get QKV together for better performance when encoutering cpu-bound, mainly used by cuda graph + q = self.get_q(mixed_qqkv, cos_emb, sin_emb) + k = self.get_k(mixed_qqkv, cos_emb, sin_emb) + v = self.get_v(mixed_qqkv) + return q, k, v + + def get_xqkv(self, mixed_qqkv: torch.Tensor, key_value_states: torch.Tensor): + query_xattn = self.linear_qkv.forward_qx(mixed_qqkv) + query_xattn = rearrange(query_xattn, "sq b (hn hd) -> (b sq) hn hd", hd=self.hidden_size_per_attention_head) + query_xattn = self.q_layernorm_xattn(query_xattn) + + # [y_total_token, h] --> [y_total_token, 2*hp] + mixed_kv_xattn = torch.concat( + [torch.matmul(key_value_states, w.t()) for w in torch.chunk(self.linear_kv_xattn.weight, 8, axis=0)], axis=1 + ) + # [y_total_token, 2*hn*hd] --> [y_total_token, hn, 2*hd] + mixed_kv_xattn = mixed_kv_xattn.view(key_value_states.shape[0], -1, 2 * self.hidden_size_per_attention_head) + + # [y_total_token, hn, 2*hd] --> 2 [y_total_token, hn, hd] + (key_xattn, value_xattn) = split_tensor_along_last_dim(mixed_kv_xattn, 2) + + key_xattn = self.k_layernorm_xattn(key_xattn) + return query_xattn, key_xattn, value_xattn + + + def core_attention(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, bs: int, meta_args: ModelMetaArgs): + + # (sq b) hn hd -> b sq hn hd + query = query.reshape(-1, bs, query.shape[1], query.shape[2]).transpose(0, 1).contiguous() + # (sq b) hn hd -> b sq hn hd + key = key.reshape(-1, bs, key.shape[1], key.shape[2]).transpose(0, 1).contiguous() + # (sq b) hn hd -> b sq hn hd + value = value.reshape(-1, bs, value.shape[1], value.shape[2]).transpose(0, 1).contiguous() + + if torch.cuda.get_device_capability()[0] >= 9 and flex_attention is not None: + core_attn_out, _ = flex_attention( + query.flatten(0, 1), + key.flatten(0, 1), + value.flatten(0, 1), + meta_args.core_attn_params.q_range, + meta_args.core_attn_params.k_range, + max_seqlen_q=meta_args.core_attn_params.max_seqlen_q, + max_seqlen_k=meta_args.core_attn_params.max_seqlen_k, + softmax_scale=None, + deterministic=torch.are_deterministic_algorithms_enabled(), + disable_fwd_atomic_reduction=True, + ) + # (b sq) hn hd -> (sq b) hn hd + core_attn_out = rearrange(core_attn_out, "(b sq) h d -> (sq b) h d", b=bs) + else: + # NOTE(lml): We convert multi denoising_range_num input into multi batch_size input at third time forward under 3_cfg mode, thus could not support normal multi batch_size input. We use an assert statement to ensure that it is still in this situation, thereby guaranteeing the correct use of q_range and k_range later on. + assert not (bs > 1 and meta_args.denoising_range_num > 1) + q_range = meta_args.core_attn_params.np_q_range + k_range = meta_args.core_attn_params.np_k_range + core_attn_outs = [] + q_seqlen = query.shape[1] + + try: + # Adapt to flowcache case where only a single chunk is passed + if q_seqlen == meta_args.clip_token_nums: + q = query + i = meta_args.start_chunk_id - meta_args.slice_point + k = key[:, k_range[i, 0] : k_range[i, 1]] + v = value[:, k_range[i, 0] : k_range[i, 1]] + o = flash_attn_func(q=q, k=k, v=v, deterministic=torch.are_deterministic_algorithms_enabled()) + o = rearrange(o, "b sq h d -> (sq b) h d", b=bs) + core_attn_outs.append(o) + # Original + else: + for i in range(meta_args.denoising_range_num): # chunk_end - chunk_start + if bs == 1: + q = query[:, q_range[i, 0] : q_range[i, 1]] + k = key[:, k_range[i, 0] : k_range[i, 1]] + v = value[:, k_range[i, 0] : k_range[i, 1]] + else: + assert i == 0 + q = query[:, q_range[0, 0] : q_range[0, 1]] + k = key[:, k_range[0, 0] : k_range[0, 1]] + v = value[:, k_range[0, 0] : k_range[0, 1]] + + o = flash_attn_func(q=q, k=k, v=v, deterministic=torch.are_deterministic_algorithms_enabled()) + o = rearrange(o, "b sq h d -> (sq b) h d", b=bs) + core_attn_outs.append(o) + except RuntimeError as e: + print(f"RuntimeError in core_attention: {e}") + import pdb; pdb.set_trace() + + core_attn_out = torch.cat(core_attn_outs, dim=0) + return core_attn_out + + def full_attention(self, bs: int, meta_args: ModelMetaArgs, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, i: int): + # NOTE(lml): full_attention is used under cp_shuffle_overlap strategy. We further limit it to the case of bs=1, so that we do not need to pay attention to the arrangement of sq and bs dimensions. + assert bs == 1 + if torch.cuda.get_device_capability()[0] >= 9 and flex_attention is not None: + q_range = meta_args.core_attn_params.q_range[i : i + 1] - meta_args.core_attn_params.q_range[i, 0] + k_range = meta_args.core_attn_params.k_range[i : i + 1] + o, _ = flex_attention( + q, + k, + v, + q_ranges=q_range, + k_ranges=k_range, + max_seqlen_q=meta_args.core_attn_params.max_seqlen_q, + max_seqlen_k=meta_args.core_attn_params.max_seqlen_k, + softmax_scale=None, + deterministic=torch.are_deterministic_algorithms_enabled(), + disable_fwd_atomic_reduction=True, + ) + else: + k_range = meta_args.core_attn_params.np_k_range[i : i + 1] + k = k[k_range[0, 0] : k_range[0, 1]] + v = v[k_range[0, 0] : k_range[0, 1]] + o = flash_attn_func( + q=q.unsqueeze(0), + k=k.unsqueeze(0), + v=v.unsqueeze(0), + deterministic=torch.are_deterministic_algorithms_enabled(), + ).flatten(0, 1) + return o + + def cross_attention( + self, + mixed_qqkv: torch.Tensor, + key_value_states: torch.Tensor, + cross_attn_params: PackedCrossAttnParams, + get_xqkv_func: Callable, + ): + # ================= + # cross-attn for aggragating caption / condition + # ================= + query_xattn, key_xattn, value_xattn = get_xqkv_func(mixed_qqkv, key_value_states) + + if torch.cuda.get_device_capability()[0] >= 9 and flex_attention is not None: + xattn_out, _ = flex_attention( + query_xattn, + key_xattn, + value_xattn, + cross_attn_params.q_ranges, + cross_attn_params.kv_ranges, + max_seqlen_q=cross_attn_params.max_seqlen_q, + max_seqlen_k=cross_attn_params.max_seqlen_kv, + softmax_scale=None, + deterministic=False, + disable_fwd_atomic_reduction=True, + ) + else: + xattn_out = flash_attn_varlen_func( + query_xattn, # [b*sq, hn, hd] + key_xattn, # [y_total_token, hn, hd] + value_xattn, # [y_total_token, hn, hd] + cu_seqlens_q=cross_attn_params.cu_seqlens_q, + cu_seqlens_k=cross_attn_params.cu_seqlens_kv, + max_seqlen_q=cross_attn_params.max_seqlen_q, + max_seqlen_k=cross_attn_params.max_seqlen_kv, + deterministic=torch.are_deterministic_algorithms_enabled(), + ) + + batch_size = mixed_qqkv.shape[1] + xattn_out = rearrange(xattn_out, "(b sq) hn hd -> sq b (hn hd)", b=batch_size).contiguous() + return xattn_out + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor, + inference_params: InferenceParams, + rotary_pos_emb: torch.Tensor, + meta_args: ModelMetaArgs, + ): + assert rotary_pos_emb is not None, "FullyParallelAttention needs rotary_pos_emb" + sin_emb, cos_emb = rotary_pos_emb.tensor_split(2, -1) + batch_size = hidden_states.shape[1] + # All comminications operate on dimensions shaped as (cp * sq * b) + batch_cp_split_sizes = None if meta_args.cp_split_sizes is None else [x * batch_size for x in meta_args.cp_split_sizes] + + # Attention heads [sq, b, h] --> [sq, b, q + qx + k + v] + mixed_qqkv = self.linear_qkv.forward_ln(hidden_states) + + # ===================== + # Function wrapper + # ===================== + get_kv_func = self.get_kv + get_q_func = self.get_q + get_qkv_func = self.get_qkv + get_xqkv_func = self.get_xqkv + + # ===================== + # Parallel Strategy + # ===================== + if self.engine_config.cp_strategy == "none": + assert self.engine_config.cp_size == 1 + key_and_value = get_kv_func(mixed_qqkv, cos_emb, sin_emb) + query = get_q_func(mixed_qqkv, cos_emb, sin_emb) + + key, value = self.adjust_key_and_value_for_inference(key_and_value, inference_params, meta_args) + + # Save current query for subsequent compression + self._last_query = query.detach().clone() + + core_attn_out = self.core_attention(query, key, value, batch_size, meta_args) + core_attn_out = rearrange(core_attn_out, "(sq b) hn hd -> sq b (hn hd)", b=batch_size) + xattn_out = self.cross_attention(mixed_qqkv, key_value_states, meta_args.cross_attn_params, get_xqkv_func) + + elif self.engine_config.cp_strategy == "cp_ulysses": + get_kv_func = partial(get_kv_func, mixed_qqkv, cos_emb, sin_emb) + get_q_func = partial(get_q_func, mixed_qqkv, cos_emb, sin_emb) + get_qkv_func = partial(get_qkv_func, mixed_qqkv, cos_emb, sin_emb) + kv_cache_func = partial( + self.adjust_key_and_value_for_inference, inference_params=inference_params, meta_args=meta_args + ) + if meta_args.enable_cuda_graph and meta_args.denoising_range_num <= 3: + # Temporal solution for first chunk opt + core_attn_out, xattn_out = UlyssesScheduler.get_attn_and_xattn_with_fused_qkv_comm( + get_qkv_func, + kv_cache_func, + partial(self.core_attention, bs=batch_size, meta_args=meta_args), + partial(self.cross_attention, mixed_qqkv, key_value_states, meta_args.cross_attn_params, get_xqkv_func), + self.engine_config.ulysses_overlap_degree, + batch_size, + self.engine_config.cp_size, + batch_cp_split_sizes, + ) + else: + core_attn_out, xattn_out = UlyssesScheduler.get_attn_and_xattn_with_fused_kv_comm( + get_q_func, + get_kv_func, + kv_cache_func, + partial(self.core_attention, bs=batch_size, meta_args=meta_args), + partial(self.cross_attention, mixed_qqkv, key_value_states, meta_args.cross_attn_params, get_xqkv_func), + self.engine_config.ulysses_overlap_degree, + batch_size, + self.engine_config.cp_size, + batch_cp_split_sizes, + ) + + elif self.engine_config.cp_strategy == "cp_shuffle_overlap": + key_and_value = self.get_kv(mixed_qqkv, cos_emb, sin_emb) + key_and_value, handle_kv = cso_communication(key_and_value, self.engine_config.cp_size, batch_cp_split_sizes, "kv") + + query = get_q_func(mixed_qqkv, cos_emb, sin_emb) + cso_helper = CSOHelper(meta_args.denoising_range_num, self.engine_config.cp_size, batch_cp_split_sizes) + query, handle_q = cso_helper.split_query_for_overlap(query) + + handle_kv.wait() + # NOTE(lml): rearrange and unpad key_and_value for later attention compute under cp_shuffle_overlap strategy, and we should split sqb into sq and b when support multi batch_size input. + key_and_value = ( + rearrange( + key_and_value, + "(cp dn sqb) hn nhd -> dn (cp sqb) hn nhd", + dn=meta_args.denoising_range_num, + cp=self.engine_config.cp_size, + )[:, : meta_args.clip_token_nums] + .flatten(0, 1) + .contiguous() + ) + key, value = self.adjust_key_and_value_for_inference(key_and_value, inference_params, meta_args) + + handle_q.wait() + core_attn_out, handle_attn = cso_helper.overlap( + partial(self.full_attention, hidden_states.shape[1], meta_args), query, key, value + ) + xattn_out = self.cross_attention(mixed_qqkv, key_value_states, meta_args.cross_attn_params, get_xqkv_func) + + handle_attn.wait() + core_attn_out = rearrange( + torch.concat(core_attn_out, dim=0), + "(dn cp sq b) hn hd -> (dn sq) b (cp hn hd)", + cp=self.engine_config.cp_size, + b=hidden_states.shape[1], + dn=meta_args.denoising_range_num, + ) + else: + raise ValueError(f"Unsupported cp_strategy: {self.engine_config.cp_strategy}") + + return core_attn_out, xattn_out + + +########################################################## +# TransformerLayer +########################################################## +class TransformerLayer(torch.nn.Module): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__(self, model_config: ModelConfig, engine_config: EngineConfig, layer_number: int = 1): + super().__init__() + self.model_config = model_config + self.engine_config = engine_config + self.layer_number = layer_number + self._get_layer_offset() + ## [Module 1: ada_modulate_layer + self.ada_modulate_layer = AdaModulateLayer(model_config=self.model_config) + + ## [Module 2: SelfAttention] + self.self_attention = FullyParallelAttention( + model_config=self.model_config, engine_config=self.engine_config, layer_number=self.layer_number + ) + + ## [Module 3: SelfAttention PostNorm] + self.self_attn_post_norm = FusedLayerNorm(model_config=self.model_config, hidden_size=self.model_config.hidden_size) + + ## [Module 4: MLP block] + self.mlp = CustomMLP(model_config=self.model_config, engine_config=self.engine_config, layer_number=self.layer_number) + + ## [Module 5: MLP PostNorm] + self.mlp_post_norm = FusedLayerNorm(model_config=self.model_config, hidden_size=self.model_config.hidden_size) + + def _get_layer_offset(self): + pipeline_rank = parallel_state.get_pp_rank() + + num_layers_per_pipeline_rank = self.model_config.num_layers // parallel_state.get_pp_world_size() + + # Each stage gets a contiguous set of layers. + if parallel_state.get_pp_world_size() > 1: + offset = pipeline_rank * num_layers_per_pipeline_rank + else: + offset = 0 + + return offset + + def forward( + self, + hidden_states: torch.Tensor, + condition: torch.Tensor, + condition_map: torch.Tensor, + y_xattn_flat: torch.Tensor, + rotary_pos_emb: torch.Tensor, + inference_params: InferenceParams, + meta_args: ModelMetaArgs, + ): + # hidden_states: [s/cp/sp, b, h] + residual = hidden_states + + # Self attention. + core_attn_out, cross_attn_out = self.self_attention( + hidden_states, + key_value_states=y_xattn_flat, + inference_params=inference_params, + rotary_pos_emb=rotary_pos_emb, + meta_args=meta_args, + ) + hidden_states = self.attn_post_process(core_attn_out, cross_attn_out, residual, condition, condition_map) + + return hidden_states + + def attn_post_process( + self, + core_attn_out: torch.Tensor, + cross_attn_out: torch.Tensor, + residual: torch.Tensor, + condition: torch.Tensor, + condition_map: torch.Tensor, + ): + hidden_states = self.attn_linear_proj(core_attn_out, cross_attn_out) + hidden_states = self.gating_and_mlp(hidden_states, residual, condition, condition_map) + return hidden_states + + def attn_linear_proj(self, core_attn_out: torch.Tensor, cross_attn_out: torch.Tensor): + # ============================================ + # attention post-process , output. [sq, b, h] + # ============================================ + + attn_out = torch.concat([core_attn_out, cross_attn_out], dim=2) + # NOTE: hn=8 is hardcoded to align with TP8 traning and TP1 inference + attn_out = rearrange(attn_out, "sq b (n hn hd) -> sq b (hn n hd)", n=2, hn=8) + if self.self_attention.adapt_linear_quant: + attn_out = self.self_attention.linear_proj(attn_out) + else: + # Use high-precision for non-quantized linear projection + with torch.autocast(device_type="cuda", dtype=torch.float32): + attn_out = self.self_attention.linear_proj(attn_out) + + return attn_out + + def gating_and_mlp( + self, hidden_states: torch.Tensor, residual: torch.Tensor, condition: torch.Tensor, condition_map: torch.Tensor + ): + gate_output = self.ada_modulate_layer(condition) + softcap_gate_cap = 1.0 + gate_output = softcap(gate_output, softcap_gate_cap) + gate_msa, gate_mlp = gate_output.chunk(2, dim=-1) + + # Residual connection for self-attention. + hidden_states = bias_modulate_add(hidden_states, residual, condition_map, gate_msa, self.self_attn_post_norm).to( + self.model_config.params_dtype + ) + + residual = hidden_states + hidden_states = self.mlp(hidden_states) + # Residual connection for MLP. + hidden_states = bias_modulate_add(hidden_states, residual, condition_map, gate_mlp, self.mlp_post_norm).to( + self.model_config.params_dtype + ) + return hidden_states + + +########################################################## +# TransformerBlock +########################################################## +class TransformerBlock(torch.nn.Module): + """Transformer class.""" + + def __init__( + self, model_config: ModelConfig, engine_config: EngineConfig, pre_process: bool = True, post_process: bool = True + ): + super().__init__() + + self.model_config = model_config + self.engine_config = engine_config + self.pre_process = pre_process + self.post_process = post_process + + # required for pipeline parallel schedules + self.input_tensor = None + + layer_number = self.model_config.num_layers // parallel_state.get_pp_world_size() + # offset is implicit in TransformerLayer + self.layers = torch.nn.ModuleList( + [ + TransformerLayer(model_config=self.model_config, engine_config=self.engine_config, layer_number=i) + for i in range(layer_number) + ] + ) + if self.post_process: + # Final layer norm before output. + self.final_layernorm = FusedLayerNorm(model_config=self.model_config, hidden_size=self.model_config.hidden_size) + + def set_input_tensor(self, input_tensor: Tensor): + """Set input tensor to be used instead of forward()'s input. + + When doing pipeline parallelism the input from the previous + stage comes from communication, not from the input, so the + model's forward_step_func won't have it. This function is thus + used by internal code to bypass the input provided by the + forward_step_func""" + self.input_tensor = input_tensor + + @torch.no_grad() + def forward( + self, + hidden_states: Tensor, + condition: Tensor, + condition_map: Tensor, + y_xattn_flat: Tensor, + rotary_pos_emb: Tensor, + inference_params: InferenceParams, + meta_args: ModelMetaArgs, + ) -> torch.Tensor: + if not self.pre_process: + assert self.input_tensor is not None, "please call set_input_tensor for pp" + hidden_states = self.input_tensor + + for layer in self.layers: + hidden_states = layer( + hidden_states=hidden_states, + condition=condition, + condition_map=condition_map, + y_xattn_flat=y_xattn_flat, + rotary_pos_emb=rotary_pos_emb, + inference_params=inference_params, + meta_args=meta_args, + ) + + # Final layer norm. + if self.post_process: + hidden_states = self.final_layernorm(hidden_states.float()) + + return hidden_states \ No newline at end of file diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/t5/__init__.py b/FlowCache/FlowCache4MAGI-1/inference/model/t5/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1b0a916be0148ab30dac23af283700ba551f72ac --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/model/t5/__init__.py @@ -0,0 +1,17 @@ +# 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 .t5_model import T5Embedder + +__all__ = ["T5Embedder"] diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/t5/__pycache__/__init__.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/model/t5/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e20a457ac8442cd79dbc9cf29005b756fa92bc74 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/model/t5/__pycache__/__init__.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/t5/__pycache__/t5_model.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/model/t5/__pycache__/t5_model.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b32cd3130fcd59d91ecfc28178db7f60e773d9e2 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/model/t5/__pycache__/t5_model.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/t5/t5_model.py b/FlowCache/FlowCache4MAGI-1/inference/model/t5/t5_model.py new file mode 100644 index 0000000000000000000000000000000000000000..1f3ccbee178b51cd0cfa781d0479ff961e848f41 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/model/t5/t5_model.py @@ -0,0 +1,286 @@ +# 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. + +import html +import os +import re +import urllib.parse as ul + +import ftfy +import torch +from bs4 import BeautifulSoup +from huggingface_hub import hf_hub_download +from transformers import AutoTokenizer, T5EncoderModel + + +def save_model_as_safetensors(model): + from safetensors.torch import save_file + state_dict = model.state_dict() + for k in state_dict: + state_dict[k] = state_dict[k].contiguous() + + save_file(state_dict, "/path/to/t5/model.safetensors") + +class T5Embedder: + available_models = ["t5-v1_1-xxl"] + bad_punct_regex = re.compile( + r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}" + ) # noqa + + def __init__( + self, + device, + dir_or_name="t5-v1_1-xxl", + *, + local_cache=False, + cache_dir=None, + hf_token=None, + use_text_preprocessing=True, + t5_model_kwargs=None, + torch_dtype=None, + use_offload_folder=None, + model_max_length=120, + ): + self.device = torch.device(device) + self.torch_dtype = torch_dtype or torch.bfloat16 + if t5_model_kwargs is None: + t5_model_kwargs = {"low_cpu_mem_usage": True, "torch_dtype": self.torch_dtype} + if use_offload_folder is not None: + t5_model_kwargs["offload_folder"] = use_offload_folder + t5_model_kwargs["device_map"] = { + "shared": self.device, + "encoder.embed_tokens": self.device, + "encoder.block.0": self.device, + "encoder.block.1": self.device, + "encoder.block.2": self.device, + "encoder.block.3": self.device, + "encoder.block.4": self.device, + "encoder.block.5": self.device, + "encoder.block.6": self.device, + "encoder.block.7": self.device, + "encoder.block.8": self.device, + "encoder.block.9": self.device, + "encoder.block.10": self.device, + "encoder.block.11": self.device, + "encoder.block.12": "disk", + "encoder.block.13": "disk", + "encoder.block.14": "disk", + "encoder.block.15": "disk", + "encoder.block.16": "disk", + "encoder.block.17": "disk", + "encoder.block.18": "disk", + "encoder.block.19": "disk", + "encoder.block.20": "disk", + "encoder.block.21": "disk", + "encoder.block.22": "disk", + "encoder.block.23": "disk", + "encoder.final_layer_norm": "disk", + "encoder.dropout": "disk", + } + else: + t5_model_kwargs["device_map"] = {"shared": self.device, "encoder": self.device} + self.use_text_preprocessing = use_text_preprocessing + self.hf_token = hf_token + self.cache_dir = cache_dir or os.path.expanduser("~/.cache/IF_") + self.dir_or_name = dir_or_name + tokenizer_path, path = dir_or_name, dir_or_name + if local_cache: + cache_dir = os.path.join(self.cache_dir, dir_or_name) + tokenizer_path, path = cache_dir, cache_dir + elif dir_or_name in self.available_models: + cache_dir = os.path.join(self.cache_dir, dir_or_name) + for filename in [ + "config.json", + "special_tokens_map.json", + "spiece.model", + "tokenizer_config.json", + "pytorch_model.bin.index.json", + "pytorch_model-00001-of-00002.bin", + "pytorch_model-00002-of-00002.bin", + ]: + hf_hub_download( + repo_id=f"DeepFloyd/{dir_or_name}", + filename=filename, + cache_dir=cache_dir, + force_filename=filename, + token=self.hf_token, + ) + tokenizer_path, path = cache_dir, cache_dir + else: + cache_dir = os.path.join(self.cache_dir, "t5-v1_1-xxl") + for filename in ["config.json", "special_tokens_map.json", "spiece.model", "tokenizer_config.json"]: + hf_hub_download( + repo_id="DeepFloyd/t5-v1_1-xxl", + filename=filename, + cache_dir=cache_dir, + force_filename=filename, + token=self.hf_token, + ) + tokenizer_path = cache_dir + + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + self.model = T5EncoderModel.from_pretrained(path, **t5_model_kwargs).eval() + self.model_max_length = model_max_length + + + def get_text_embeddings(self, texts): + texts = [self.text_preprocessing(text) for text in texts] + + text_tokens_and_mask = self.tokenizer( + texts, + max_length=self.model_max_length, + padding="max_length", + truncation=True, + return_attention_mask=True, + add_special_tokens=True, + return_tensors="pt", + ) + + text_tokens_and_mask["input_ids"] = text_tokens_and_mask["input_ids"] + text_tokens_and_mask["attention_mask"] = text_tokens_and_mask["attention_mask"] + + with torch.no_grad(): + text_encoder_embs = self.model( + input_ids=text_tokens_and_mask["input_ids"].to(self.device), + attention_mask=text_tokens_and_mask["attention_mask"].to(self.device), + )["last_hidden_state"].detach() + return text_encoder_embs, text_tokens_and_mask["attention_mask"].to(self.device) + + def text_preprocessing(self, text): + if self.use_text_preprocessing: + # The exact text cleaning as was in the training stage: + text = self.clean_caption(text) + text = self.clean_caption(text) + return text + else: + return text.lower().strip() + + @staticmethod + def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + def clean_caption(self, caption): + caption = str(caption) + caption = ul.unquote_plus(caption) + caption = caption.strip().lower() + caption = re.sub("", "person", caption) + # urls: + caption = re.sub( + r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa + "", + caption, + ) # regex for urls + caption = re.sub( + r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa + "", + caption, + ) # regex for urls + # html: + caption = BeautifulSoup(caption, features="html.parser").text + + # @ + caption = re.sub(r"@[\w\d]+\b", "", caption) + + # 31C0—31EF CJK Strokes + # 31F0—31FF Katakana Phonetic Extensions + # 3200—32FF Enclosed CJK Letters and Months + # 3300—33FF CJK Compatibility + # 3400—4DBF CJK Unified Ideographs Extension A + # 4DC0—4DFF Yijing Hexagram Symbols + # 4E00—9FFF CJK Unified Ideographs + caption = re.sub(r"[\u31c0-\u31ef]+", "", caption) + caption = re.sub(r"[\u31f0-\u31ff]+", "", caption) + caption = re.sub(r"[\u3200-\u32ff]+", "", caption) + caption = re.sub(r"[\u3300-\u33ff]+", "", caption) + caption = re.sub(r"[\u3400-\u4dbf]+", "", caption) + caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption) + caption = re.sub(r"[\u4e00-\u9fff]+", "", caption) + ####################################################### + + # все виды тире / all types of dash --> "-" + caption = re.sub( + r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa + "-", + caption, + ) + + # кавычки к одному стандарту + caption = re.sub(r"[`´«»“”¨]", '"', caption) + caption = re.sub(r"[‘’]", "'", caption) + + # " + caption = re.sub(r""?", "", caption) + # & + caption = re.sub(r"&", "", caption) + + # ip adresses: + caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) + + # article ids: + caption = re.sub(r"\d:\d\d\s+$", "", caption) + + # \n + caption = re.sub(r"\\n", " ", caption) + + # "#123" + caption = re.sub(r"#\d{1,3}\b", "", caption) + # "#12345.." + caption = re.sub(r"#\d{5,}\b", "", caption) + # "123456.." + caption = re.sub(r"\b\d{6,}\b", "", caption) + # filenames: + caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) + + # + caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" + caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" + + caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT + caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " + + # this-is-my-cute-cat / this_is_my_cute_cat + regex2 = re.compile(r"(?:\-|\_)") + if len(re.findall(regex2, caption)) > 3: + caption = re.sub(regex2, " ", caption) + + caption = self.basic_clean(caption) + + caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 + caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc + caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 + + caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) + caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) + caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) + caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) + caption = re.sub(r"\bpage\s+\d+\b", "", caption) + + caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... + + caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) + + caption = re.sub(r"\b\s+\:\s+", r": ", caption) + caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) + caption = re.sub(r"\s+", " ", caption) + + caption.strip() + + caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) + caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) + caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) + caption = re.sub(r"^\.\S+$", "", caption) + + return caption.strip() diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/vae/__init__.py b/FlowCache/FlowCache4MAGI-1/inference/model/vae/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7bc68d8a76ae0ccc0b43d5faa4cafc5c7d1e1c2d --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/model/vae/__init__.py @@ -0,0 +1,18 @@ +# 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 .vae_model import AutoModel, VideoTokenizerABC, ViTVAE +from .vae_module import DiagonalGaussianDistribution + +__all__ = ["AutoModel", "VideoTokenizerABC", "ViTVAE", "DiagonalGaussianDistribution"] diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/vae/__pycache__/__init__.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/model/vae/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1bb9bb10ec18ac42dfedf3d2babbdb728a14d1e1 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/model/vae/__pycache__/__init__.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/vae/__pycache__/vae_model.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/model/vae/__pycache__/vae_model.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9a447c822f0afd4419a8c52b12a1abdf09fe96a5 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/model/vae/__pycache__/vae_model.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/vae/__pycache__/vae_module.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/model/vae/__pycache__/vae_module.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bad6bae604842a9c56464bf000f2b57a2c486fae Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/model/vae/__pycache__/vae_module.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/vae/vae_model.py b/FlowCache/FlowCache4MAGI-1/inference/model/vae/vae_model.py new file mode 100644 index 0000000000000000000000000000000000000000..b8812c11b0b084ff65a3d74c1d485a0a038d28b0 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/model/vae/vae_model.py @@ -0,0 +1,361 @@ +# 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. + +import json +import os +from abc import ABC, abstractmethod +from typing import Literal + +import torch +from diffusers import ConfigMixin, ModelMixin +from diffusers.configuration_utils import register_to_config + +from inference.infra.parallelism import TileProcessor + +from .vae_module import DiagonalGaussianDistribution, ViTDecoder, ViTEncoder + + +class VideoTokenizerABC(ABC): + """ + Abstract base class for video tokenizers. + + This class defines the interface for video tokenizers and provides common methods and properties. + """ + + @property + @abstractmethod + def spatial_downsample_factor(self): + """ + Property representing the spatial downsample factor. + + Returns: + int: The spatial downsample factor. + """ + raise NotImplementedError + + @property + @abstractmethod + def temporal_downsample_factor(self): + """ + Property representing the temporal downsample factor. + + Returns: + int: The temporal downsample factor. + """ + raise NotImplementedError + + @property + def first_frame_as_image(self): + """ + Property representing the first frame as image. + For tokenizer like CausalVAE, Omnitokenizer, the first frame is treated as image. + in this case if the temporal downsample factor is 4, the input should be 4*x+1, and encoded tensor would be x+1. + for example encode 65 frames to 17 frames. and decode 17 frames to 65 frames. + + Returns: + bool: The first frame as image. + """ + return False + + @property + def allow_spatial_tiling(self): + """ + Determines whether spatial tiling is allowed or not. + + Returns: + bool: True if spatial tiling is allowed, False otherwise. + """ + return True + + @abstractmethod + def encode(self, x) -> torch.Tensor: + """ + Abstract method for encoding the input tensor. + + Args: + x (torch.Tensor [N C T H W] range[-1, 1]): The input tensor to be encoded. + + Returns: + torch.Tensor: The encoded tensor. + """ + raise NotImplementedError + + @abstractmethod + def decode(self, x) -> torch.Tensor: + """ + Abstract method for decoding the input tensor. + + Args: + x (torch.Tensor [N C T H W]): The input tensor to be decoded. + + Returns: + torch.Tensor [N C T H W] range[-1, 1]: The decoded tensor. + """ + raise NotImplementedError + + def tile_processor( + self, + tile_sample_min_height=256, + tile_sample_min_width=256, + tile_sample_min_length=16, + spatial_tile_overlap_factor: float = 0.25, + temporal_tile_overlap_factor: float = 0, + parallel_group: torch.distributed.ProcessGroup = None, + ) -> TileProcessor: + """ + Property representing the tiled encoder or decoder. + + Returns: + TileProcessor: The tiled encoder or decoder. + """ + return TileProcessor( + encode_fn=self.encode, + decode_fn=self.decode, + tile_sample_min_height=tile_sample_min_height, + tile_sample_min_width=tile_sample_min_width, + tile_sample_min_length=tile_sample_min_length, + spatial_tile_overlap_factor=spatial_tile_overlap_factor, + temporal_tile_overlap_factor=temporal_tile_overlap_factor, + sr_ratio=getattr(self, 'sr_ratio', 1), + spatial_downsample_factor=self.spatial_downsample_factor, + temporal_downsample_factor=self.temporal_downsample_factor, + first_frame_as_image=self.first_frame_as_image, + parallel_group=parallel_group, + ) + + @torch.inference_mode() + def tiled_encode_3d( + self, + x, + tile_sample_min_height=256, + tile_sample_min_width=256, + tile_sample_min_length: int = 16, + spatial_tile_overlap_factor: float = 0.25, + temporal_tile_overlap_factor: float = 0, + allow_spatial_tiling: bool = None, + verbose: bool = False, + parallel_group: torch.distributed.ProcessGroup = None, + ) -> torch.Tensor: + """ + Encodes the input tensor `x` using tiled encoding. + + Args: + x (torch.Tensor shape:[N C T H W]): The input tensor to be encoded. + tile_sample_min_height (int, optional): The minimum height of each tile sample. Defaults to 256. + tile_sample_min_width (int, optional): The minimum width of each tile sample. Defaults to 256. + tile_sample_min_length (int, optional): The minimum length of each tile sample. Defaults to 16. + spatial_tile_overlap_factor (float, optional): Overlap factor for spatial tiles. Defaults to 0.25. + temporal_tile_overlap_factor (float, optional): Overlap factor for temporal tiles. Defaults to 0. + allow_spatial_tiling (bool, optional): Whether spatial tiling is allowed. Defaults to None. + verbose (bool, optional): Whether to print verbose information. Defaults to False. + parallel_group (torch.distributed.ProcessGroup, optional): Distributed encoding group. Defaults to None. + Returns: + torch.Tensor: The encoded tensor. + """ + allow_spatial_tiling = allow_spatial_tiling if allow_spatial_tiling is not None else self.allow_spatial_tiling + if not allow_spatial_tiling: + tile_sample_min_height = 100000 + tile_sample_min_width = 100000 + return self.tile_processor( + tile_sample_min_height=tile_sample_min_height, + tile_sample_min_width=tile_sample_min_width, + tile_sample_min_length=tile_sample_min_length, + spatial_tile_overlap_factor=spatial_tile_overlap_factor, + temporal_tile_overlap_factor=temporal_tile_overlap_factor, + parallel_group=parallel_group, + ).tiled_encode(x, verbose) + + @torch.inference_mode() + def tiled_decode_3d( + self, + x, + tile_sample_min_height=256, + tile_sample_min_width=256, + tile_sample_min_length: int = 16, + spatial_tile_overlap_factor: float = 0.25, + temporal_tile_overlap_factor: float = 0, + allow_spatial_tiling: bool = None, + verbose: bool = False, + parallel_group: torch.distributed.ProcessGroup = None, + ) -> torch.Tensor: + """ + Decodes the input tensor using the tile autoencoder. + + Args: + x (torch.Tensor): The input tensor to be decoded. + tile_sample_min_height (int, optional): The minimum height of each tile sample. Defaults to 256. + tile_sample_min_width (int, optional): The minimum width of each tile sample. Defaults to 256. + tile_sample_min_length (int, optional): The minimum length of each tile sample. Defaults to 16. + spatial_tile_overlap_factor (float, optional): Overlap factor for spatial tiles. Defaults to 0.25. + temporal_tile_overlap_factor (float, optional): Overlap factor for temporal tiles. Defaults to 0. + allow_spatial_tiling (bool, optional): Whether spatial tiling is allowed. Defaults to None. + verbose (bool, optional): Whether to print verbose information. Defaults to False. + parallel_group (torch.distributed.ProcessGroup, optional): Distributed decoding group. Defaults to None. + Returns: + torch.Tensor shape:[N C T H W]: The decoded tensor. + """ + allow_spatial_tiling = allow_spatial_tiling if allow_spatial_tiling is not None else self.allow_spatial_tiling + if not allow_spatial_tiling: + tile_sample_min_height = 100000 + tile_sample_min_width = 100000 + return self.tile_processor( + tile_sample_min_height=tile_sample_min_height, + tile_sample_min_width=tile_sample_min_width, + tile_sample_min_length=tile_sample_min_length, + spatial_tile_overlap_factor=spatial_tile_overlap_factor, + temporal_tile_overlap_factor=temporal_tile_overlap_factor, + parallel_group=parallel_group, + ).tiled_decode(x, verbose) + + +class ViTVAE(ModelMixin, ConfigMixin, VideoTokenizerABC): + @register_to_config + def __init__(self, ddconfig: dict, model_type: Literal['vit', 'vit_ncthw'] = 'vit'): + super().__init__() + + if model_type == 'vit': + self.encoder = ViTEncoder(**ddconfig) + self.decoder = ViTDecoder(**ddconfig) + elif model_type == 'vit_ncthw': + from videotokenizer.modules.vit_ncthw import ViTDecoderNCTHW, ViTEncoderNCTHW + + self.encoder = ViTEncoderNCTHW(**ddconfig) + self.decoder = ViTDecoderNCTHW(**ddconfig) + else: + raise ValueError(f"model_type {model_type} not supported") + + if 'patch_length' in ddconfig: + self._temporal_downsample_factor = ddconfig['patch_length'] + else: + self._temporal_downsample_factor = 1 + + if 'patch_size' in ddconfig: + self._spatial_downsample_factor = ddconfig['patch_size'] + else: + self._spatial_downsample_factor = 8 + + @property + def spatial_downsample_factor(self): + return self._spatial_downsample_factor + + @property + def temporal_downsample_factor(self): + return self._temporal_downsample_factor + + def init_from_ckpt(self, path, ignore_keys=list()): + raise NotImplementedError + + def encode(self, x, sample_posterior=True): + """ + Encode the input video. + + Args: + x (torch.Tensor): Input video tensor has shape N C T H W + + Returns: + tuple: Tuple containing the quantized tensor, embedding loss, and additional information. + """ + N, C, T, H, W = x.shape + if T == 1 and self._temporal_downsample_factor > 1: + x = x.expand(-1, -1, 4, -1, -1) + x = self.encoder(x) + posterior = DiagonalGaussianDistribution(x) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + + return z[:, :, :1, :, :].type(x.dtype) + else: + x = self.encoder(x) + posterior = DiagonalGaussianDistribution(x) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + + return z.type(x.dtype) + + def decode(self, x): + """ + Decode the quantized tensor. + + Args: + quant (torch.Tensor): Quantized tensor. + + Returns: + torch.Tensor: Decoded tensor. + """ + N, C, T, H, W = x.shape + if T == 1: + x = x.expand(-1, -1, 1, -1, -1) + x = self.decoder(x) + x = x[:, :, :1, :, :] + return x + else: + x = self.decoder(x) + return x + + def forward(self, x, sample_posterior=True): + x = self.encoder(x) + posterior = DiagonalGaussianDistribution(x) + + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + + dec = self.decoder(z) + return dec, posterior + + def get_last_layer(self): + """ + Get the last layer of the decoder. + + Returns: + torch.Tensor: Last layer of the decoder. + """ + return self.decoder.last_layer.weight + + @property + def allow_spatial_tiling(self): + return False + + +class AutoModel: + r""" + :class:`~models.AutoModel` is a generic model class + that will be instantiated as one of the base model classes of the library + when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)` + + + This class cannot be instantiated using `__init__()` (throws an error). + """ + + def __init__(self): + raise EnvironmentError( + "AutoModel is designed to be instantiated " + "using the `AutoModel.from_pretrained(pretrained_model_name_or_path)` method." + ) + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs) -> VideoTokenizerABC: + config = os.path.join(pretrained_model_name_or_path, 'config.json') + if not os.path.exists(config): + raise ValueError("Can't find a model config file at {}.".format(config)) + # Load config + with open(config, 'r') as json_file: + config_dict = json.load(json_file) + assert config_dict['_class_name'] == 'ViTVAE' + return ViTVAE.from_pretrained(pretrained_model_name_or_path, *args, **kwargs) diff --git a/FlowCache/FlowCache4MAGI-1/inference/model/vae/vae_module.py b/FlowCache/FlowCache4MAGI-1/inference/model/vae/vae_module.py new file mode 100644 index 0000000000000000000000000000000000000000..eb4501b045bd11fde4920ea316a39b792eca92e1 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/model/vae/vae_module.py @@ -0,0 +1,757 @@ +# 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. + +import math +from functools import lru_cache +from typing import List, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +from einops import rearrange +from flash_attn import flash_attn_func, flash_attn_qkvpacked_func +from timm.models.layers import to_2tuple, trunc_normal_ + +################################################### +# modified 3D rotary embedding from timm +################################################### + + +def ndgrid(*tensors) -> Tuple[torch.Tensor, ...]: + """generate N-D grid in dimension order. + + The ndgrid function is like meshgrid except that the order of the first two input arguments are switched. + + That is, the statement + [X1,X2,X3] = ndgrid(x1,x2,x3) + + produces the same result as + + [X2,X1,X3] = meshgrid(x2,x1,x3) + + This naming is based on MATLAB, the purpose is to avoid confusion due to torch's change to make + torch.meshgrid behaviour move from matching ndgrid ('ij') indexing to numpy meshgrid defaults of ('xy'). + + """ + try: + return torch.meshgrid(*tensors, indexing='ij') + except TypeError: + # old PyTorch < 1.10 will follow this path as it does not have indexing arg, + # the old behaviour of meshgrid was 'ij' + return torch.meshgrid(*tensors) + + +def freq_bands( + num_bands: int, temperature: float = 10000.0, step: int = 2, device: Optional[torch.device] = None +) -> torch.Tensor: + exp = torch.arange(0, num_bands, step, dtype=torch.int64, device=device).to(torch.float32) / num_bands + bands = 1.0 / (temperature**exp) + return bands + + +def pixel_freq_bands( + num_bands: int, max_freq: float = 224.0, linear_bands: bool = True, device: Optional[torch.device] = None +): + if linear_bands: + bands = torch.linspace(1.0, max_freq / 2, num_bands, dtype=torch.float32, device=device) + else: + bands = 2 ** torch.linspace(0, math.log(max_freq, 2) - 1, num_bands, dtype=torch.float32, device=device) + return bands * torch.pi + + +def build_fourier_pos_embed( + feat_shape: List[int], + bands: Optional[torch.Tensor] = None, + num_bands: int = 64, + max_res: int = 224, + temperature: float = 10000.0, + linear_bands: bool = False, + include_grid: bool = False, + in_pixels: bool = True, + ref_feat_shape: Optional[List[int]] = None, + dtype: torch.dtype = torch.float32, + device: Optional[torch.device] = None, + center_imgidx=True, +) -> List[torch.Tensor]: + """ + + Args: + feat_shape: Feature shape for embedding. + bands: Pre-calculated frequency bands. + num_bands: Number of frequency bands (determines output dim). + max_res: Maximum resolution for pixel based freq. + temperature: Temperature for non-pixel freq. + linear_bands: Linear band spacing for pixel based freq. + include_grid: Include the spatial grid in output. + in_pixels: Output in pixel freq. + ref_feat_shape: Reference feature shape for resize / fine-tune. + dtype: Output dtype. + device: Output device. + + Returns: + + """ + if bands is None: + if in_pixels: + bands = pixel_freq_bands(num_bands, float(max_res), linear_bands=linear_bands, device=device) + else: + bands = freq_bands(num_bands, temperature=temperature, step=1, device=device) + else: + if device is None: + device = bands.device + if dtype is None: + dtype = bands.dtype + + if in_pixels: + t = [torch.linspace(-1.0, 1.0, steps=s, device=device, dtype=torch.float32) for s in feat_shape] + else: + if center_imgidx: + t = [ + torch.arange(s, device=device, dtype=torch.int64).to(torch.float32) - (s - 1) / 2 + if len(feat_shape) == 2 or i != 0 + else torch.arange(s, device=device, dtype=torch.int64).to(torch.float32) + for i, s in enumerate(feat_shape) + ] + else: + t = [torch.arange(s, device=device, dtype=torch.int64).to(torch.float32) for s in feat_shape] + + if ref_feat_shape is not None: + assert len(feat_shape) == len(ref_feat_shape), 'shape must be in same dimension' + # eva's scheme for resizing rope embeddings (ref shape = pretrain) + t = [x / f * r for x, f, r in zip(t, feat_shape, ref_feat_shape)] + + grid = torch.stack(ndgrid(t), dim=-1) + grid = grid.unsqueeze(-1) + pos = grid * bands + pos_sin, pos_cos = pos.sin().to(dtype=dtype), pos.cos().to(dtype) + out = [grid, pos_sin, pos_cos] if include_grid else [pos_sin, pos_cos] + return out + + +def rot(x): + return torch.stack([-x[..., 1::2], x[..., ::2]], -1).reshape(x.shape) + + +def apply_rot_embed(x: torch.Tensor, sin_emb, cos_emb): + if sin_emb.ndim == 3: + return x * cos_emb.unsqueeze(1).expand_as(x) + rot(x) * sin_emb.unsqueeze(1).expand_as(x) + # import ipdb; ipdb.set_trace() + return x * cos_emb + rot(x) * sin_emb + + +def build_rotary_pos_embed( + feat_shape: List[int], + bands: Optional[torch.Tensor] = None, + dim: int = 64, + max_res: int = 224, + temperature: float = 10000.0, + linear_bands: bool = False, + in_pixels: bool = True, + ref_feat_shape: Optional[List[int]] = None, + dtype: torch.dtype = torch.float32, + device: Optional[torch.device] = None, + center_imgidx=True, +): + """ + + Args: + feat_shape: Spatial shape of the target tensor for embedding. + bands: Optional pre-generated frequency bands + dim: Output dimension of embedding tensor. + max_res: Maximum resolution for pixel mode. + temperature: Temperature (inv freq) for non-pixel mode + linear_bands: Linearly (instead of log) spaced bands for pixel mode + in_pixels: Pixel vs language (inv freq) mode. + dtype: Output dtype. + device: Output device. + + Returns: + + """ + sin_emb, cos_emb = build_fourier_pos_embed( + feat_shape, + bands=bands, + num_bands=dim // (len(feat_shape) * 2), + max_res=max_res, + temperature=temperature, + linear_bands=linear_bands, + in_pixels=in_pixels, + ref_feat_shape=ref_feat_shape, + device=device, + dtype=dtype, + center_imgidx=center_imgidx, + ) + num_spatial_dim = 1 + # this would be much nicer as a .numel() call to torch.Size(), but torchscript sucks + for x in feat_shape: + num_spatial_dim *= x + sin_emb = sin_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1) + cos_emb = cos_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1) + return sin_emb, cos_emb + + +################################################### +# Mlp +################################################### +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +################################################### +# ManualLayerNorm +################################################### +class ManualLayerNorm(nn.Module): + def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True): + super(ManualLayerNorm, self).__init__() + self.normalized_shape = normalized_shape + self.eps = eps + self.elementwise_affine = elementwise_affine + + def forward(self, x): + mean = x.mean(dim=-1, keepdim=True) + std = x.std(dim=-1, keepdim=True, unbiased=False) + + x_normalized = (x - mean) / (std + self.eps) + + return x_normalized + + +################################################### +# Attention +################################################### +@lru_cache(maxsize=50) +def cache_rotary_emb(feat_shape, device='cuda', dim=64, dtype=torch.bfloat16, max_res=512, ref_feat_shape=(4, 16, 16)): + return build_rotary_pos_embed( + feat_shape=feat_shape, + dim=dim, + max_res=max_res, + in_pixels=False, + ref_feat_shape=ref_feat_shape, + device=device, + dtype=dtype, + ) + + +class Attention(nn.Module): + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, ln_in_attn=False, use_rope=False + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop_rate = attn_drop + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + if ln_in_attn: + self.qkv_norm = ManualLayerNorm(head_dim, elementwise_affine=False) + else: + self.qkv_norm = nn.Identity() + self.use_rope = use_rope + + def forward(self, x, feat_shape=None): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + + qkv = self.qkv_norm(qkv) + q, k, v = qkv.chunk(3, dim=2) + if self.use_rope: + assert feat_shape is not None + q, k, v = qkv.chunk(3, dim=2) + rope_emb = cache_rotary_emb(feat_shape=feat_shape, dim=C // self.num_heads, device=x.device, dtype=x.dtype) + sin_emb = rope_emb[0].unsqueeze(0).unsqueeze(2) + cos_emb = rope_emb[1].unsqueeze(0).unsqueeze(2) + print(q.shape, sin_emb.shape) + q[:, 1:, :] = apply_rot_embed(q[:, 1:, :], sin_emb, cos_emb).bfloat16() + k[:, 1:, :] = apply_rot_embed(k[:, 1:, :], sin_emb, cos_emb).bfloat16() + x = flash_attn_func(q, k, v, dropout_p=self.attn_drop_rate) + else: + x = flash_attn_qkvpacked_func(qkv=qkv.bfloat16(), dropout_p=self.attn_drop_rate) + # x = v + x = x.reshape(B, N, C) + # import ipdb; ipdb.set_trace() + x = self.proj(x) + x = self.proj_drop(x) + return x + + +################################################### +# Block +################################################### +class Block(nn.Module): + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ln_in_attn=False, + use_rope=False, + ): + super().__init__() + if not ln_in_attn: + self.norm1 = norm_layer(dim) + else: + self.norm1 = nn.Identity() + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ln_in_attn=ln_in_attn, + use_rope=use_rope, + ) + self.drop_path = nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x, feat_shape=None): + x = x + self.drop_path(self.attn(self.norm1(x), feat_shape=feat_shape)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +################################################### +# PatchEmbed +################################################### +class PatchEmbed(nn.Module): + """Image to Patch Embedding""" + + def __init__(self, video_size=224, video_length=16, patch_size=16, patch_length=1, in_chans=3, embed_dim=768): + super().__init__() + video_size = to_2tuple(video_size) + patch_size = to_2tuple(patch_size) + + num_patches = (video_length // patch_length) * (video_size[1] // patch_size[1]) * (video_size[0] // patch_size[0]) + + self.video_size = video_size + self.patch_size = patch_size + + self.video_length = video_length + self.patch_length = patch_length + + self.num_patches = num_patches + + self.proj = nn.Conv3d( + in_chans, + embed_dim, + kernel_size=(patch_length, patch_size[0], patch_size[1]), + stride=(patch_length, patch_size[0], patch_size[1]), + ) + + def forward(self, x): + """ + Forward pass of the PatchEmbed module. + + Args: + x (torch.Tensor): Input tensor of shape (B, C, T, H, W), where + B is the batch size, C is the number of channels, T is the + number of frames, H is the height, and W is the width. + + Returns: + torch.Tensor: Output tensor of shape (B, L, C'), where B is the + batch size, L is the number of tokens, and C' is the number + of output channels after flattening and transposing. + """ + B, C, T, H, W = x.shape + + x = self.proj(x) + return x + + +################################################### +# ViTEncoder +################################################### +def resize_pos_embed(posemb, src_shape, target_shape): + posemb = posemb.reshape(1, src_shape[0], src_shape[1], src_shape[2], -1) + posemb = posemb.permute(0, 4, 1, 2, 3) + posemb = nn.functional.interpolate(posemb, size=target_shape, mode='trilinear', align_corners=False) + posemb = posemb.permute(0, 2, 3, 4, 1) + posemb = posemb.reshape(1, target_shape[0] * target_shape[1] * target_shape[2], -1) + return posemb + + +class ViTEncoder(nn.Module): + """Vision Transformer with support for patch or hybrid CNN input stage""" + + def __init__( + self, + video_size=256, + video_length=16, + patch_size=8, + patch_length=4, + in_chans=3, + z_chans=4, + double_z=True, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.0, + norm_layer=nn.LayerNorm, + with_cls_token=True, + norm_code=False, + ln_in_attn=False, + conv_last_layer=False, + use_rope=False, + use_final_proj=False, + ): + super().__init__() + + conv_last_layer = False # duplicate argument + + # self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + + self.latent_size = video_size // patch_size + self.latent_length = video_length // patch_length + + self.patch_embed = PatchEmbed( + video_size=video_size, + video_length=video_length, + patch_size=patch_size, + patch_length=patch_length, + in_chans=in_chans, + embed_dim=embed_dim, + ) + + num_patches = self.patch_embed.num_patches + self.with_cls_token = with_cls_token + if with_cls_token: + self.cls_token_nums = 1 + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + else: + self.cls_token_nums = 0 + self.cls_token = None + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.cls_token_nums, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList( + [ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + ln_in_attn=ln_in_attn, + use_rope=use_rope, + ) + for i in range(depth) + ] + ) + self.norm = norm_layer(embed_dim) + + self.norm_code = norm_code + + self.out_channels = z_chans * 2 if double_z else z_chans + self.last_layer = nn.Linear(embed_dim, self.out_channels, bias=True) + + trunc_normal_(self.pos_embed, std=0.02) + + if self.with_cls_token: + trunc_normal_(self.cls_token, std=0.02) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def forward(self, x): + B = x.shape[0] + # B C T H W -> B C T/pT H/pH W//pW + x = self.patch_embed(x) + latentT, latentH, latentW = x.shape[2], x.shape[3], x.shape[4] + # B C T/pT H/pH W//pW -> B (T/pT H/pH W//pW) C + x = x.flatten(2).transpose(1, 2) + + if self.with_cls_token: + cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + + if latentT != self.latent_length or latentH != self.latent_size or latentW != self.latent_size: + pos_embed = resize_pos_embed( + self.pos_embed[:, 1:, :], + src_shape=(self.latent_length, self.latent_size, self.latent_size), + target_shape=(latentT, latentH, latentW), + ) + pos_embed = torch.cat((self.pos_embed[:, 0:1, :], pos_embed), dim=1) + else: + pos_embed = self.pos_embed + + x = x + pos_embed + x = self.pos_drop(x) + + for idx, blk in enumerate(self.blocks): + x = blk(x, feat_shape=(latentT, latentH, latentW)) + + x = self.norm(x) + x = self.last_layer(x) + + if self.with_cls_token: + x = x[:, 1:] # remove cls_token + + # B L C - > B , lT, lH, lW, zC + x = x.reshape(B, latentT, latentH, latentW, self.out_channels) + + # B , lT, lH, lW, zC -> B, zC, lT, lH, lW + x = x.permute(0, 4, 1, 2, 3) + if self.norm_code: + prev_dtype = x.dtype + x = x.float() + x = x / torch.norm(x, dim=1, keepdim=True) + x = x.to(prev_dtype) + return x + + def freeze_pretrain(self): + # Freeze all parameters + for param in self.parameters(): + param.requires_grad = False + + +################################################### +# ViTDecoder +################################################### +class ViTDecoder(nn.Module): + """Vision Transformer with support for patch or hybrid CNN input stage""" + + def __init__( + self, + video_size=256, + video_length=16, + patch_size=8, + patch_length=4, + in_chans=3, + z_chans=4, + double_z=True, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.0, + norm_layer=nn.LayerNorm, + with_cls_token=True, + norm_code=False, + ln_in_attn=False, + conv_last_layer=False, + use_rope=False, + use_final_proj=False, + ): + super().__init__() + + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + + self.latent_size = video_size // patch_size + self.latent_length = video_length // patch_length + self.patch_size = patch_size + self.patch_length = patch_length + + self.proj_in = nn.Linear(z_chans, embed_dim) + + num_patches = self.latent_size * self.latent_size * self.latent_length + + self.with_cls_token = with_cls_token + if with_cls_token: + self.cls_token_nums = 1 + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + else: + self.cls_token_nums = 0 + self.cls_token = None + + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.cls_token_nums, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList( + [ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + ln_in_attn=ln_in_attn, + use_rope=use_rope, + ) + for i in range(depth) + ] + ) + self.norm = norm_layer(embed_dim) + + assert conv_last_layer == True, "Only support conv_last_layer=True" + + self.unpatch_channels = embed_dim // (patch_size * patch_size * patch_length) + self.final_proj = nn.Identity() + self.final_norm = nn.Identity() + + self.use_final_proj = use_final_proj + if self.use_final_proj: + self.unpatch_channels = 4 + self.final_proj = nn.Linear(embed_dim, self.unpatch_channels * (patch_size * patch_size * patch_length), bias=True) + self.final_norm = norm_layer(self.unpatch_channels * (patch_size * patch_size * patch_length)) + + self.last_layer = nn.Conv3d(in_channels=self.unpatch_channels, out_channels=3, kernel_size=3, stride=1, padding=1) + + trunc_normal_(self.pos_embed, std=0.02) + + if self.with_cls_token: + trunc_normal_(self.cls_token, std=0.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def forward(self, x): + B, C, latentT, latentH, latentW = x.shape # x: (B, C, latentT, latentH, latenW) + x = x.permute(0, 2, 3, 4, 1) # x: (B, latentT, latentH, latenW, C) + + x = x.reshape(B, -1, C) + + x = self.proj_in(x) + + if self.with_cls_token: + cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + + if latentT != self.latent_length or latentH != self.latent_size or latentW != self.latent_size: + pos_embed = resize_pos_embed( + self.pos_embed[:, 1:, :], + src_shape=(self.latent_length, self.latent_size, self.latent_size), + target_shape=(latentT, latentH, latentW), + ) + pos_embed = torch.cat((self.pos_embed[:, 0:1, :], pos_embed), dim=1) + else: + pos_embed = self.pos_embed + + x = x + pos_embed + x = self.pos_drop(x) + + for idx, blk in enumerate(self.blocks): + x = blk(x, feat_shape=(latentT, latentH, latentW)) + + x = self.norm(x) + + if self.with_cls_token: + x = x[:, 1:] # remove cls_token + # B L C - > B, lT, lH, lW, pT, pH, pW, C + if self.use_final_proj: + x = self.final_proj(x) + x = self.final_norm(x) + x = x.reshape(B, latentT, latentH, latentW, self.patch_length, self.patch_size, self.patch_size, self.unpatch_channels) + x = rearrange(x, 'B lT lH lW pT pH pW C -> B C (lT pT) (lH pH) (lW pW)', C=self.unpatch_channels) + + x = self.last_layer(x) + return x + + +################################################### +# DiagonalGaussianDistribution +################################################### +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.0]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3], + ) + + def nll(self, sample, dims=[1, 2, 3]): + if self.deterministic: + return torch.Tensor([0.0]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims) + + def mode(self): + return self.mean diff --git 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(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. + +""" +Cache management module for MAGI inference. + +This module provides optimized implementations for: +- TeaCache: Full output reuse (all chunks together) +- ChunkWiseCache: Per-chunk output reuse (used in FlowCache) +- KVCacheCompressor: Dynamic KV cache compression +""" + +from .base import CacheStrategy, OutputCache, KVCompressor +from .cachereuse import TeaCache, ChunkWiseCache +from .kv_compressor import KVCacheCompressor +from .utils import generate_dynamic_kv_range + +__all__ = [ + "CacheStrategy", + "OutputCache", + "KVCompressor", + "TeaCache", + "ChunkWiseCache", + "generate_dynamic_kv_range", +] diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/__init__.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..734f947e977a8d5a5c004cf7b8474e74e244ce76 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/__init__.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/base.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/base.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d37cab9e274f2bf60a7774b15b58350c9936ebdd Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/base.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/cachereuse.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/cachereuse.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d052ad2761769970dddc76f62e0eb7ad95daf8c3 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/cachereuse.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/cachereuse.cpython-312.pyc b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/cachereuse.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..29e2604dc20b6e36701433308e131575c79bb3de Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/cachereuse.cpython-312.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/kv_compressor.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/kv_compressor.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..339797698425c22ddb78556ed4463b5fc0f943fa Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/kv_compressor.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/utils.cpython-310.pyc b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f12113951ae5c3c5ab073924042852a1e0049ae0 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/__pycache__/utils.cpython-310.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/base.py b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/base.py new file mode 100644 index 0000000000000000000000000000000000000000..f78ea8006b97acd54c5face1f47fb79d560a499d --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/base.py @@ -0,0 +1,171 @@ +# 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. + +""" +Base classes for cache management strategies. +""" + +from abc import ABC, abstractmethod +from typing import Dict, Optional, Tuple +import torch + + +class CacheStrategy(ABC): + """ + Abstract base class for cache management strategies. + + All cache implementations should inherit from this class and implement + the required methods. + """ + + def __init__(self, enabled: bool = True): + """ + Initialize the cache strategy. + + Args: + enabled: Whether this cache strategy is enabled + """ + self.enabled = enabled + + @abstractmethod + def reset(self): + """ + Reset the cache state. + + This method should clear all internal state and prepare the cache + for a new inference run. + """ + pass + + def reset_if_enabled(self): + """Reset the cache if it is enabled.""" + if self.enabled: + self.reset() + + +class OutputCache(CacheStrategy): + """ + Abstract base class for output reuse strategies. + + Output caching strategies determine when model outputs can be reused + based on input similarity metrics. + """ + + @abstractmethod + def should_reuse( + self, + chunk_id: int, + step: int, + current_features: torch.Tensor, + **kwargs + ) -> bool: + """ + Determine whether the output for a chunk should be reused. + + Args: + chunk_id: The ID of the current chunk + step: The current denoising step + current_features: Feature tensor for the current input + **kwargs: Additional arguments specific to the implementation + + Returns: + True if the output should be reused, False otherwise + """ + pass + + @abstractmethod + def update_residual( + self, + chunk_id: int, + residual: torch.Tensor + ): + """ + Update the residual for a chunk. + + When outputs are reused, the residual from the previous step is + applied to the current input. + + Args: + chunk_id: The ID of the chunk + residual: The residual tensor to store + """ + pass + + @abstractmethod + def get_residual(self, chunk_id: int) -> Optional[torch.Tensor]: + """ + Get the stored residual for a chunk. + + Args: + chunk_id: The ID of the chunk + + Returns: + The residual tensor if available, None otherwise + """ + pass + + +class KVCompressor(CacheStrategy): + """ + Abstract base class for KV cache compression strategies. + + KV cache compression manages memory usage by selectively compressing + KV caches from completed chunks. + """ + + @abstractmethod + def should_compress( + self, + current_chunk_id: int, + cache_used: int, + cache_capacity: int, + **kwargs + ) -> bool: + """ + Determine whether KV cache compression should be triggered. + + Args: + current_chunk_id: The ID of the most recently completed chunk + cache_used: Current KV cache usage in tokens + cache_capacity: Total KV cache capacity in tokens + **kwargs: Additional arguments specific to the implementation + + Returns: + True if compression should be performed, False otherwise + """ + pass + + @abstractmethod + def compress( + self, + inference_params, + chunk_tracker, + clean_chunk_ids: list, + active_chunk_ids: list, + **kwargs + ) -> Dict[int, Tuple[int, int]]: + """ + Compress KV caches for specified chunks. + + Args: + inference_params: Inference parameters containing KV cache + chunk_tracker: Tracker managing chunk ranges + clean_chunk_ids: List of chunk IDs to compress + active_chunk_ids: List of chunk IDs to keep uncompressed + **kwargs: Additional arguments + + Returns: + Dictionary mapping chunk_id to (start, end) ranges after compression + """ + pass diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/cachereuse.py b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/cachereuse.py new file mode 100644 index 0000000000000000000000000000000000000000..43a9e61a175deb1567c293af749a99e2c759264f --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/cachereuse.py @@ -0,0 +1,602 @@ +# 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. + +""" +Cache reuse implementations for output optimization. + +This module provides two caching strategies: +- TeaCache: Full output reuse (all chunks together) +- ChunkWiseCache: Per-chunk output reuse (for FlowCache) +""" + +import json +import os + +from einops import rearrange +import torch +from typing import Dict, List, Optional, Tuple +from .base import OutputCache + + +class TeaCache(OutputCache): + """ + TeaCache implementation with full output reuse. + + This cache computes the relative L1 distance between current and previous + modulated inputs. When the accumulated distance is below threshold, the + output is reused and only the residual is applied. + + All chunks are treated as a single unit for reuse decisions. + + Attributes: + rel_l1_thresh: Threshold for relative L1 distance + warmup_steps: Number of warmup steps before reuse can happen + log: Whether to log reuse decisions + accumulated_rel_l1_distance: Accumulated relative L1 distance + previous_modulated_input: Previous input features + previous_residual: Previous residual for reuse + reuse_times: Number of times output was reused + previous_output: Output from previous stage + cnt: Current step counter + num_steps: Total number of steps + """ + + def __init__( + self, + rel_l1_thresh: float = 0.01, + warmup_steps: int = 0, + log: bool = False + ): + super().__init__(enabled=True) + self.rel_l1_thresh = rel_l1_thresh + self.warmup_steps = warmup_steps + self.log = log + + # State variables + self.accumulated_rel_l1_distance = 0.0 + self.previous_modulated_input = None + self.previous_residual = None + self.reuse_times = 0 + self.previous_output = None + self.cnt = 0 + self.num_steps = 0 + self.should_calc = True + + def reset(self): + """Reset all cache state.""" + self.accumulated_rel_l1_distance = 0.0 + self.previous_modulated_input = None + self.previous_residual = None + self.reuse_times = 0 + self.previous_output = None + self.cnt = 0 + self.should_calc = True + + def compute_feature_metric( + self, + x: torch.Tensor, + x_embedder, + x_rescale_factor: float, + half_channel_vae: bool, + params_dtype: torch.dtype + ) -> torch.Tensor: + """ + Compute feature metric from input tensor. + + Args: + x: Input tensor [N, C, T, H, W] + x_embedder: Model's x_embedder module + x_rescale_factor: Rescale factor for x + half_channel_vae: Whether VAE uses half channels + params_dtype: Model's parameter dtype for final conversion + + Returns: + Feature tensor of shape [(T*H*W), N, C] + """ + metric_x = x.clone() + metric_x = metric_x * x_rescale_factor + + if half_channel_vae: + assert metric_x.shape[1] == 16, "Expected 16 channels for half-channel VAE" + metric_x = torch.cat([metric_x, metric_x], dim=1) + + metric_x = metric_x.float() + metric_x = x_embedder(metric_x) + metric_x = metric_x.to(params_dtype) + metric_x = rearrange(metric_x, "N C T H W -> (T H W) N C").contiguous() + + return metric_x + + def should_reuse( + self, + chunk_id: int, + step: int, + current_features: torch.Tensor, + denoise_step_per_stage: int, + num_chunks_current: int, + num_chunks_previous: int, + **kwargs + ) -> bool: + """ + Determine whether to reuse output based on feature similarity. + + Args: + chunk_id: Current chunk ID (not used in simple mode) + step: Current denoising step + current_features: Current input features + denoise_step_per_stage: Steps per denoising stage + num_chunks_current: Number of chunks in current stage + num_chunks_previous: Number of chunks in previous stage + + Returns: + True if output should be reused, False if should calculate + """ + # Always calculate first and last steps, and during warmup + if self.cnt == 0 or self.cnt == self.num_steps - 1 or self.cnt < self.warmup_steps: + self.should_calc = True + self.accumulated_rel_l1_distance = 0 + if self.log: + print(f"Calculate output at step {self.cnt}") + return False + + # Compute feature difference + a1 = current_features.clone() + a2 = self.previous_modulated_input.clone() + + # Handle chunk count changes across stages + if self.cnt % denoise_step_per_stage == 0: + dim1 = a1.shape[0] + dim2 = a2.shape[0] + + if dim1 > dim2: + # Next stage has more chunks, truncate to match + a1 = a1[:dim2] + elif dim1 < dim2: + # Next stage has fewer chunks, take tail part + a2 = a2[-dim1:] + + # Compute relative L1 distance + rel_l1 = ((a1 - a2).abs().mean() / a2.abs().mean()).cpu().item() + self.accumulated_rel_l1_distance += rel_l1 + + # Decide whether to reuse + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + if self.cnt % denoise_step_per_stage == 0 and dim1 > dim2: + # Only calculate new chunk when crossing stage + self.should_calc = True + if self.log: + print(f"Partly reuse output at step {self.cnt}, only calculate new chunk") + return False + else: + # Full reuse + self.reuse_times += 1 + if self.log: + print(f"Reuse output at step {self.cnt}") + self.should_calc = False + return True + else: + # Threshold exceeded, recalculate + if self.log: + print(f"Calculate output at step {self.cnt}") + self.should_calc = True + self.accumulated_rel_l1_distance = 0 + return False + + def update_residual(self, chunk_id: int, residual: torch.Tensor): + """ + Update the residual for reuse. + + Args: + chunk_id: Chunk ID (not used in simple mode, residual applies to all) + residual: Residual tensor to store + """ + self.previous_residual = residual + + def get_residual(self, chunk_id: int) -> Optional[torch.Tensor]: + """ + Get the stored residual. + + Args: + chunk_id: Chunk ID (not used in simple mode) + + Returns: + The residual tensor or None + """ + return self.previous_residual + + def increment_step(self): + """Increment step counter and print statistics if done.""" + self.cnt += 1 + if self.cnt == self.num_steps: + print(f"Reuse output account for {self.reuse_times} / {self.num_steps} steps, " + f"ratio: {self.reuse_times / self.num_steps:.2%}") + self.cnt = 0 + + def store_previous_features(self, features: torch.Tensor): + """Store current features as previous for next step.""" + self.previous_modulated_input = features.clone() + + def get_previous_features(self) -> Optional[torch.Tensor]: + """Get the stored previous features.""" + return self.previous_modulated_input + + def prepare_for_next_stage(self): + """Store output for use in next stage.""" + pass # Handled in integrate_velocity + + +class ChunkWiseCache(OutputCache): + """ + Chunk-wise output cache implementation for FlowCache. + + This cache tracks reuse decisions separately for each chunk, allowing + finer-grained control over which chunks to skip. + + Attributes: + rel_l1_thresh: Threshold for relative L1 distance + warmup_steps: Number of warmup steps per chunk before reuse can happen + discard_nearly_clean_chunk: Whether to skip nearly-clean chunk + log: Whether to log reuse decisions + chunk_accumulated_rel_l1: Per-chunk accumulated L1 distance + chunk_reuse_flags: Per-chunk reuse flags for current step + prev_metric_chunks: Previous features per chunk + previous_residual: Per-chunk residuals + """ + + def __init__( + self, + rel_l1_thresh: float = 0.01, + warmup_steps: int = 0, + discard_nearly_clean_chunk: bool = False, + log: bool = False, + metric_stats_path: Optional[str] = None, + ): + super().__init__(enabled=True) + self.rel_l1_thresh = rel_l1_thresh + self.warmup_steps = warmup_steps + self.discard_nearly_clean_chunk = discard_nearly_clean_chunk + self.log = log + self.metric_stats_path = metric_stats_path + self.metric_records = [] + self.execution_records = [] + self.chunk_execution_counts: Dict[int, Dict[str, int]] = {} + + # State variables + self.chunk_accumulated_rel_l1: Dict[int, float] = {} + self.chunk_reuse_flags: Dict[int, bool] = {} + self.prev_metric_chunks: Dict[int, torch.Tensor] = {} + self.previous_residual: Dict[int, torch.Tensor] = {} + + self.cnt = 0 + self.num_steps = 0 + + def reset(self): + """Reset all cache state.""" + self.chunk_accumulated_rel_l1.clear() + self.chunk_reuse_flags.clear() + self.prev_metric_chunks.clear() + self.previous_residual.clear() + self.metric_records.clear() + self.execution_records.clear() + self.chunk_execution_counts.clear() + self.cnt = 0 + + def initialize_chunk_state(self, chunk_num: int): + """Initialize state for all chunks.""" + if len(self.chunk_accumulated_rel_l1) != chunk_num: + self.chunk_accumulated_rel_l1 = {i: 0.0 for i in range(chunk_num)} + self.previous_residual = {i: None for i in range(chunk_num)} + + # Reset reuse flags for each step + self.chunk_reuse_flags = {i: False for i in range(chunk_num)} + + def compute_feature_metric( + self, + x: torch.Tensor, + x_embedder, + x_rescale_factor: float, + half_channel_vae: bool, + chunk_token_nums: int, + params_dtype: torch.dtype, + offset: int = 0, + fwd_extra_1st_chunk: bool = False, + distill_nearly_clean_chunk: bool = False + ) -> Tuple[Dict[int, torch.Tensor], int]: + """ + Compute feature metric for each chunk. + + Following source code logic: + 1. Compute metric_x from input x + 2. Handle fwd_extra_1st_chunk: slice off first chunk if needed + 3. Handle distill_nearly_clean_chunk: slice off last chunk if needed + 4. Split into chunks + + Args: + x: Input tensor [N, C, T, H, W] + x_embedder: Model's x_embedder module + x_rescale_factor: Rescale factor for x + half_channel_vae: Whether VAE uses half channels + chunk_token_nums: Number of tokens per chunk + params_dtype: Model's parameter dtype for final conversion + offset: Offset for chunk_id (to match x_chunks indexing) + fwd_extra_1st_chunk: Whether to slice off first chunk (always False) + distill_nearly_clean_chunk: Whether to slice off last chunk + + Returns: + Tuple of (metric_chunks dict, num_chunks_for_x) + """ + from einops import rearrange + + # 1. Compute metric_x from input x + metric_x = x.clone() + metric_x = metric_x * x_rescale_factor + + if half_channel_vae: + assert metric_x.shape[1] == 16 + metric_x = torch.cat([metric_x, metric_x], dim=1) + + metric_x = metric_x.float() + metric_x = x_embedder(metric_x) + metric_x = metric_x.to(params_dtype) + metric_x = rearrange(metric_x, "N C T H W -> (T H W) N C").contiguous() + + # 2. Handle fwd_extra_1st_chunk: slice off first chunk if needed + # Note: fwd_extra_1st_chunk is always False in current implementation + if fwd_extra_1st_chunk: + metric_x = metric_x[chunk_token_nums:, :, :] + + # 3. Handle distill_nearly_clean_chunk: slice off last chunk if needed + if distill_nearly_clean_chunk: + metric_x = metric_x[:-chunk_token_nums, :, :] + + # 4. Split into chunks + assert metric_x.shape[0] % chunk_token_nums == 0 + num_chunks = metric_x.shape[0] // chunk_token_nums + + metric_chunks = {} + for i in range(num_chunks): + start = i * chunk_token_nums + end = start + chunk_token_nums + metric_chunks[offset + i] = metric_x[start:end] + + # Return num_chunks for x_chunks iteration (matching source code) + return metric_chunks, num_chunks + + def should_reuse( + self, + chunk_id: int, + step: int, + current_features: torch.Tensor, + chunk_denoise_count: Dict[int, int], + current_num_chunks: int, + previous_num_chunks: int, + **kwargs + ) -> bool: + """ + Determine whether to reuse output for a specific chunk. + + Args: + chunk_id: The chunk ID to check + step: Current denoising step + current_features: Current features for all chunks + chunk_denoise_count: Denoising steps completed per chunk + current_num_chunks: Number of chunks in current stage + previous_num_chunks: Number of chunks in previous stage + + Returns: + True if output should be reused, False otherwise + """ + # First and last steps always calculate + if self.cnt == 0 or self.cnt == self.num_steps - 1: + self.record_metric_decision(chunk_id, step, None, None, False, "first_or_last_step", **kwargs) + return False + + # Check if chunk exists in both current and previous + if chunk_id not in current_features or chunk_id not in self.prev_metric_chunks: + self.record_metric_decision(chunk_id, step, None, None, False, "missing_previous_features", **kwargs) + return False + + # Apply warmup: skip reuse during warmup period + if self._should_skip_reuse(chunk_id, chunk_denoise_count): + self.chunk_accumulated_rel_l1[chunk_id] = 0.0 + self.record_metric_decision(chunk_id, step, None, 0.0, False, "warmup", **kwargs) + return False + + # Compute relative L1 distance + curr_feat = current_features[chunk_id] + prev_feat = self.prev_metric_chunks[chunk_id] + + diff = (curr_feat - prev_feat).abs().mean() + denom = prev_feat.abs().mean() + 1e-8 + rel_l1 = (diff / denom).item() + delta_l1_norm = (curr_feat - prev_feat).abs().sum().item() + prev_feat_l1_norm = prev_feat.abs().sum().item() + rel_l1_ratio = delta_l1_norm / max(prev_feat_l1_norm, 1e-8) + + # Accumulate and check threshold + accumulated = self.chunk_accumulated_rel_l1[chunk_id] + rel_l1 + + if accumulated < self.rel_l1_thresh: + self.chunk_accumulated_rel_l1[chunk_id] = accumulated + self.chunk_reuse_flags[chunk_id] = True + self.record_metric_decision( + chunk_id, step, rel_l1, accumulated, True, "below_threshold", + delta_l1_norm=delta_l1_norm, + prev_feat_l1_norm=prev_feat_l1_norm, + rel_l1_ratio=rel_l1_ratio, + **kwargs, + ) + return True + else: + self.chunk_accumulated_rel_l1[chunk_id] = 0.0 + self.chunk_reuse_flags[chunk_id] = False + self.record_metric_decision( + chunk_id, step, rel_l1, accumulated, False, "threshold_exceeded", + delta_l1_norm=delta_l1_norm, + prev_feat_l1_norm=prev_feat_l1_norm, + rel_l1_ratio=rel_l1_ratio, + **kwargs, + ) + return False + + def record_metric_decision( + self, + chunk_id: int, + step: int, + rel_l1: Optional[float], + accumulated_rel_l1: Optional[float], + reused: bool, + decision_reason: str, + **kwargs + ): + if not self.metric_stats_path: + return + + chunk_offset = kwargs.get("chunk_offset", 0) + record = { + "infer_idx": kwargs.get("infer_idx"), + "cur_denoise_step": kwargs.get("cur_denoise_step", step), + "denoise_stage": kwargs.get("denoise_stage"), + "denoise_idx": kwargs.get("denoise_idx"), + "chunk_idx": chunk_id, + "generated_chunk_idx": chunk_id - chunk_offset, + "chunk_denoise_count": kwargs.get("chunk_denoise_count_value"), + "flowcache_rel_l1": rel_l1, + "flowcache_rel_l1_ratio": kwargs.get("rel_l1_ratio"), + "flowcache_delta_l1_norm": kwargs.get("delta_l1_norm"), + "flowcache_prev_feat_l1_norm": kwargs.get("prev_feat_l1_norm"), + "flowcache_accumulated_rel_l1": accumulated_rel_l1, + "rel_l1_thresh": self.rel_l1_thresh, + "reused": bool(reused), + "decision_reason": decision_reason, + } + self.metric_records.append(record) + + def record_actual_execution( + self, + chunk_id: int, + reused: bool, + **kwargs + ): + stats = self.chunk_execution_counts.setdefault( + chunk_id, + {"reuse_steps": 0, "compute_steps": 0, "total_steps": 0}, + ) + if reused: + stats["reuse_steps"] += 1 + else: + stats["compute_steps"] += 1 + stats["total_steps"] += 1 + + if not self.metric_stats_path: + return + + chunk_offset = kwargs.get("chunk_offset", 0) + self.execution_records.append({ + "infer_idx": kwargs.get("infer_idx"), + "cur_denoise_step": kwargs.get("cur_denoise_step"), + "denoise_stage": kwargs.get("denoise_stage"), + "denoise_idx": kwargs.get("denoise_idx"), + "chunk_idx": chunk_id, + "generated_chunk_idx": chunk_id - chunk_offset, + "reused": bool(reused), + "execution": "reuse" if reused else "compute", + }) + + def get_execution_summary(self): + summary = {} + for chunk_id, stats in sorted(self.chunk_execution_counts.items()): + total_steps = stats["total_steps"] + reuse_steps = stats["reuse_steps"] + compute_steps = stats["compute_steps"] + summary[str(chunk_id)] = { + "chunk_idx": chunk_id, + "reuse_steps": reuse_steps, + "compute_steps": compute_steps, + "total_steps": total_steps, + "reuse_rate": reuse_steps / total_steps if total_steps else 0.0, + "compute_rate": compute_steps / total_steps if total_steps else 0.0, + } + return summary + + def _should_skip_reuse( + self, + chunk_id: int, + chunk_denoise_count: Dict[int, int] + ) -> bool: + """ + Check if reuse should be skipped for this chunk. + + During warmup period, chunks are always recalculated. + + Args: + chunk_id: Chunk to check + chunk_denoise_count: Steps completed per chunk + + Returns: + True if should skip reuse (i.e., in warmup period) + """ + return chunk_denoise_count[chunk_id] < self.warmup_steps + + def update_residual(self, chunk_id: int, residual: torch.Tensor): + """Update the residual for a specific chunk.""" + self.previous_residual[chunk_id] = residual + + def get_residual(self, chunk_id: int) -> Optional[torch.Tensor]: + """Get the stored residual for a chunk.""" + return self.previous_residual.get(chunk_id) + + def store_previous_features(self, metric_chunks: Dict[int, torch.Tensor]): + """Store current features as previous for next step.""" + self.prev_metric_chunks = { + i: f.clone().detach() for i, f in metric_chunks.items() + } + + def increment_step(self): + """Increment step counter.""" + self.cnt += 1 + if self.cnt == self.num_steps: + self.cnt = 0 + + def set_total_steps(self, num_steps: int): + """Set total number of steps.""" + self.num_steps = num_steps + + def save_metric_stats(self): + if not self.metric_stats_path: + return + save_dir = os.path.dirname(self.metric_stats_path) + if save_dir: + os.makedirs(save_dir, exist_ok=True) + + payload = { + "description": ( + "FlowCache original per-chunk reuse metric. flowcache_rel_l1 = " + "mean(abs(x_embedder(X_t_current) - x_embedder(X_t_previous))) / " + "(mean(abs(x_embedder(X_t_previous))) + 1e-8). " + "flowcache_rel_l1_ratio = sum(abs(delta)) / sum(abs(previous_feature)); " + "flowcache_accumulated_rel_l1 is the accumulated value compared with rel_l1_thresh. " + "chunk_execution_summary is counted at the actual integrate step and includes every " + "per-chunk reuse or compute execution." + ), + "chunk_execution_summary": self.get_execution_summary(), + "execution_records": self.execution_records, + "records": self.metric_records, + } + if self.metric_stats_path.endswith((".pt", ".pth")): + torch.save(payload, self.metric_stats_path) + else: + with open(self.metric_stats_path, "w") as f: + json.dump(payload, f, indent=2) + print(f"Saved FlowCache metric stats to {self.metric_stats_path}") diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/kv_compressor.py b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/kv_compressor.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd43d2b864f1f1f478542525bd2014c8c572772 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/kv_compressor.py @@ -0,0 +1,390 @@ +# 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. + +""" +KV Cache Compression module. +""" + +import torch +from typing import Dict, List, Optional, Tuple, Any +from .base import KVCompressor +from .utils import ( + identify_compressible_chunks, + check_compress_condition, + get_latent_spatial_dims, +) + + +class KVCacheCompressor(KVCompressor): + """ + Manages KV cache compression for memory-efficient inference. + + This compressor identifies clean chunks (completed denoising) and compresses + their KV caches using the configured compression strategy (e.g., R1KV). + + Attributes: + total_cache_len: Total cache capacity in tokens + tokens_per_chunk: Number of tokens per chunk + budget_cache_len: Target cache size after compression + compression_config: Configuration for compression strategy + kv_compressed: Whether compression has been performed + chunk_query_states: Query states for each layer (used for compression) + """ + + def __init__( + self, + total_cache_len: int, + tokens_per_chunk: int, + budget_chunk_nums: int, + window_size: int = 4, + compression_config: Optional[Dict[str, Any]] = None + ): + """ + Initialize the KV cache compressor. + + Args: + total_cache_len: Total cache capacity in tokens + tokens_per_chunk: Number of tokens per chunk + budget_chunk_nums: Target number of chunks after compression + window_size: Window size for denoising stages + compression_config: Configuration for compression strategy + """ + super().__init__(enabled=True) + self.total_cache_len = total_cache_len + self.tokens_per_chunk = tokens_per_chunk + self.budget_cache_len = (budget_chunk_nums - 1) * tokens_per_chunk + self.window_size = window_size + self.compression_config = compression_config or {} + + self.kv_compressed = False + self.chunk_query_states: Dict[int, torch.Tensor] = {} + + def reset(self): + """Reset compression state.""" + self.kv_compressed = False + self.chunk_query_states.clear() + + def should_compress( + self, + tracker, + chunk_num: int, + chunk_start: int, + transport_input, + chunk_denoise_count: Dict[int, int], + **kwargs + ) -> bool: + """ + Check if compression should be triggered. + + Args: + tracker: ChunkKVRangeTracker instance + chunk_num: Total number of chunks + chunk_start: Current chunk being processed + transport_input: Transport input + chunk_denoise_count: Denoising steps per chunk + + Returns: + True if compression should be performed + """ + return check_compress_condition( + tracker=tracker, + total_cache_len=self.total_cache_len, + chunk_num=chunk_num, + chunk_start=chunk_start, + transport_input=transport_input, + chunk_denoise_count=chunk_denoise_count, + window_size=self.window_size + ) + + def compress( + self, + model, + inference_params, + tracker, + transport_input, + chunk_start: int, + chunk_denoise_count: Dict[int, int], + query_states_dict: Optional[Dict[int, torch.Tensor]] = None, + **kwargs + ) -> Dict[int, Tuple[int, int]]: + """ + Perform KV cache compression. + + Args: + model: DiT model with videodit_blocks + inference_params: Inference parameters containing KV cache + tracker: ChunkKVRangeTracker instance + transport_input: Transport input + chunk_start: Current chunk being processed + chunk_denoise_count: Denoising steps per chunk + + Returns: + Dictionary mapping chunk_id to (start, end) ranges after compression + """ + # Identify chunks to compress + chunk_offset = self._get_chunk_offset(transport_input) + clean_chunk_ids, active_chunk_ids = identify_compressible_chunks( + tracker=tracker, + chunk_start=chunk_start, + transport_input=transport_input, + chunk_denoise_count=chunk_denoise_count, + chunk_offset=chunk_offset + ) + + if len(clean_chunk_ids) < 2: + # Need at least 2 chunks to compress + return {} + + # Compress for each layer + final_chunk_ids = [] + final_lengths = [] + + for layer in model.videodit_blocks.layers: + if not hasattr(layer.self_attention, 'kv_cluster'): + continue + + # import pdb; pdb.set_trace() + layer_result = self._compress_layer( + layer=layer, + inference_params=inference_params, + tracker=tracker, + clean_chunk_ids=clean_chunk_ids, + active_chunk_ids=active_chunk_ids, + transport_input=transport_input, + query_states_dict=query_states_dict + ) + + # Store result from first layer for chunk metadata + if layer.self_attention.layer_number == 0: + final_chunk_ids = layer_result['chunk_ids'] + final_lengths = layer_result['lengths'] + + # Update tracker ranges (shared across layers) + new_ranges = self._compute_new_ranges( + final_chunk_ids, final_lengths + ) + tracker.update_ranges_after_compression(new_ranges) + + # Mark as compressed + self.kv_compressed = True + + return new_ranges + + def _compress_layer( + self, + layer, + inference_params, + tracker, + clean_chunk_ids: List[int], + active_chunk_ids: List[int], + transport_input, + query_states_dict: Optional[Dict[int, torch.Tensor]] = None + ) -> Dict[str, Any]: + """ + Compress KV cache for a single layer. + + Args: + layer: Transformer layer + inference_params: Inference parameters + tracker: ChunkKVRangeTracker + clean_chunk_ids: Chunks to compress + active_chunk_ids: Chunks to keep uncompressed + transport_input: Transport input + query_states_dict: Query states for each layer (from transport) + + Returns: + Dictionary with compression results + """ + kv_cluster = layer.self_attention.kv_cluster + layer_num = layer.self_attention.layer_number + + # Extract KV caches for clean chunks + clean_kv_list = [] + clean_lengths = [] + for cid in clean_chunk_ids: + s, e = tracker.get_range(cid) + chunk_kv = inference_params.key_value_memory_dict[layer_num][s:e, ...] + clean_kv_list.append(chunk_kv) + clean_lengths.append(e - s) + + # Concatenate and split into key and value + clean_kv = torch.cat(clean_kv_list, dim=0) + key_clean, value_clean = torch.chunk(clean_kv, 2, dim=-1) + + # Extract KV caches for active chunks + active_kv_list = [] + active_lengths = [] + for cid in active_chunk_ids: + s, e = tracker.get_range(cid) + chunk_kv = inference_params.key_value_memory_dict[layer_num][s:e, ...] + active_kv_list.append(chunk_kv) + active_lengths.append(e - s) + + # Get query states for compression + query_states = query_states_dict.get(layer_num) if query_states_dict else None + if query_states is None: + raise RuntimeError(f"Query states not available for layer {layer_num}") + + # Set compression budget + total_clean_tokens = sum(clean_lengths) + kv_cluster.budget = max( + total_clean_tokens - self.tokens_per_chunk, + self.tokens_per_chunk + ) + + # Get latent dimensions + H, W = get_latent_spatial_dims(transport_input, layer.model_config) + T = self.tokens_per_chunk // (H * W) + + # Perform compression + key_compressed, value_compressed, indices = kv_cluster.update_kv( + key_states=key_clean, + query_states=query_states, + value_states=value_clean, + clean_chunk_tokens=total_clean_tokens, + latent_size_t=T, + latent_size_h=H, + latent_size_w=W, + ) + + # Reassemble KV cache + final_kv_parts = [] + final_chunk_ids = [] + final_lengths = [] + + # Add compressed part + compressed_kv = torch.cat([key_compressed, value_compressed], dim=-1) + final_kv_parts.append(compressed_kv) + + # Compute compressed lengths per chunk + all_lengths_after_compress = self._compute_compressed_lengths( + indices, clean_chunk_ids, clean_lengths, total_clean_tokens + ) + final_chunk_ids.extend(clean_chunk_ids) + final_lengths.extend(all_lengths_after_compress) + + # Add active (uncompressed) chunks + for i, chunk_kv in enumerate(active_kv_list): + final_kv_parts.append(chunk_kv) + final_chunk_ids.append(active_chunk_ids[i]) + final_lengths.append(active_lengths[i]) + + # Concatenate and update KV cache + final_kv = torch.cat(final_kv_parts, dim=0) + total_kv_len = final_kv.size(0) + + inference_params.key_value_memory_dict[layer_num][:total_kv_len, ...] = final_kv + inference_params.key_value_memory_dict[layer_num][total_kv_len:, ...] = 0.0 + + return { + 'chunk_ids': final_chunk_ids, + 'lengths': final_lengths + } + + def _compute_compressed_lengths( + self, + indices: torch.Tensor, + clean_chunk_ids: List[int], + clean_lengths: List[int], + total_clean_tokens: int + ) -> List[int]: + """ + Compute the compressed length for each chunk. + + Args: + indices: Selected token indices [num_to_keep, num_kv_heads, head_dim] + clean_chunk_ids: IDs of chunks that were compressed + clean_lengths: Original lengths of compressed chunks + total_clean_tokens: Total tokens before compression + + Returns: + List of compressed lengths per chunk + """ + # TODO: This has an issue - different heads keep different ranges + # But it's fine since we attend to all previous chunks' KV cache + indices_1d = indices[:, 0, 0] # shape: (num_to_keep,) + + all_lengths_after_compress = [] + start_idx = 0 + + for chunk_len in clean_lengths: + end_idx = start_idx + chunk_len + # Count selected tokens in this chunk's range + mask = (indices_1d >= start_idx) & (indices_1d < min(end_idx, total_clean_tokens)) + kept_in_chunk = mask.sum().item() + all_lengths_after_compress.append(kept_in_chunk) + start_idx = end_idx + + return all_lengths_after_compress + + def _compute_new_ranges( + self, + chunk_ids: List[int], + lengths: List[int] + ) -> Dict[int, Tuple[int, int]]: + """ + Compute new chunk ranges after compression. + + Args: + chunk_ids: List of chunk IDs in order + lengths: Compressed lengths for each chunk + + Returns: + Dictionary mapping chunk_id to (start, end) range + """ + new_ranges = {} + current_start = 0 + + for cid, length in zip(chunk_ids, lengths): + new_end = current_start + length + new_ranges[cid] = (current_start, new_end) + current_start = new_end + + return new_ranges + + def _get_chunk_offset(self, transport_input) -> int: + """ + Get the number of prefix video chunks. + + Args: + transport_input: Transport input + + Returns: + Number of prefix video chunks + """ + if transport_input.prefix_video is not None: + return transport_input.prefix_video.size(2) // transport_input.chunk_width + return 0 + + def store_query_states(self, layer_num: int, query_states: torch.Tensor): + """ + Store query states for later compression. + + Args: + layer_num: Layer number + query_states: Query tensor to store + """ + self.chunk_query_states[layer_num] = query_states + + def get_query_states(self, layer_num: int) -> Optional[torch.Tensor]: + """ + Get stored query states for a layer. + + Args: + layer_num: Layer number + + Returns: + Query tensor or None if not available + """ + return self.chunk_query_states.get(layer_num) diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/utils.py b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7a83cf9781d9df9041cc5e225f0cc3d20810bbb2 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/pipeline/cache/utils.py @@ -0,0 +1,390 @@ +# 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. + +""" +Utility functions for cache management. +""" + +import math +import torch +from typing import Dict, List, Tuple, Optional, Any +from inference.common import PackedCrossAttnParams + + +def generate_dynamic_kv_range( + tracker, + current_chunk_id: int, + x_chunks_keys: List[int], + chunk_token_nums: int, + near_clean_chunk_idx: int = -1 +) -> torch.Tensor: + """ + Generate dynamic KV ranges for chunks after compression. + + This function computes the KV range each chunk should attend to, + taking into account the compressed KV cache layout. + + Args: + tracker: ChunkKVRangeTracker instance managing chunk ranges + current_chunk_id: The chunk being processed + x_chunks_keys: List of all chunk keys being processed + chunk_token_nums: Number of tokens per chunk + near_clean_chunk_idx: Index of the nearly-clean chunk (-1 if not present) + + Returns: + Tensor of shape [num_chunks, 2] with KV ranges for each chunk + """ + kv_ranges = [] + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + # Process normal chunks (excluding near_clean_chunk) + normal_chunks = [chunk_id for chunk_id in x_chunks_keys if chunk_id != near_clean_chunk_idx] + + for chunk_id in normal_chunks: + # Normal chunk: needs to see itself and all previous chunks + all_chunk_ids = tracker.get_all_chunk_ids() + list(normal_chunks) + chunks_to_include = [cid for cid in all_chunk_ids if cid <= chunk_id] + + # Calculate based on actual compressed ranges in tracker + total_tokens = 0 + for cid in chunks_to_include: + if cid in tracker.get_all_chunk_ids(): + # Use compressed actual range + s, e = tracker.get_range(cid) + total_tokens = max(total_tokens, e) + else: + # Newly entered chunk not yet registered, but size is known + total_tokens += chunk_token_nums + + range_start = 0 + range_end = total_tokens + kv_ranges.append([range_start, range_end]) + + # Handle near_clean_chunk (always last if present) + if near_clean_chunk_idx != -1: + # Calculate end position of last normal chunk + last_normal_chunk_end = 0 + all_chunk_ids = tracker.get_all_chunk_ids() + normal_chunks + for cid in all_chunk_ids: + if cid in tracker.get_all_chunk_ids(): + s, e = tracker.get_range(cid) + last_normal_chunk_end = max(last_normal_chunk_end, e) + else: + # Newly entered chunk not yet registered + last_normal_chunk_end += chunk_token_nums + + # near_clean_chunk range: (last_normal_chunk_end, last_normal_chunk_end + chunk_token_nums] + range_start = last_normal_chunk_end + range_end = last_normal_chunk_end + chunk_token_nums + kv_ranges.append([range_start, range_end]) + + return torch.tensor(kv_ranges, device=device, dtype=torch.int32) + + +def identify_compressible_chunks( + tracker, + chunk_start: int, + transport_input, + chunk_denoise_count: Dict[int, int], + chunk_offset: int = 0 +) -> Tuple[List[int], List[int]]: + """ + Identify which chunks can be compressed and which should remain active. + + A chunk can be compressed if: + - It's a prefix video chunk (always clean) + - It's a generated chunk that has completed all denoising steps + + Args: + tracker: ChunkKVRangeTracker instance + chunk_start: Current chunk being processed + transport_input: Transport input containing chunk info + chunk_denoise_count: Dictionary mapping chunk_id to denoising steps completed + chunk_offset: Number of prefix video chunks + + Returns: + Tuple of (clean_chunk_ids, active_chunk_ids) + """ + all_chunk_ids = tracker.get_all_chunk_ids() + + clean_chunks = [] + for cid in all_chunk_ids: + if cid < chunk_offset: + # Prefix video chunks are always clean + clean_chunks.append(cid) + elif cid <= chunk_start: + # Generated chunks need to check denoising completion + if chunk_denoise_count[cid] == transport_input.num_steps: + clean_chunks.append(cid) + + active_chunks = [cid for cid in all_chunk_ids if cid not in clean_chunks] + + return clean_chunks, active_chunks + + +def check_compress_condition( + tracker, + total_cache_len: int, + chunk_num: int, + chunk_start: int, + transport_input, + chunk_denoise_count: Dict[int, int], + window_size: int = 4 +) -> bool: + """ + Check if KV cache compression should be triggered. + + Compression is triggered when: + 1. Cache is full (next_free_idx >= total_cache_len) + 2. More chunks are yet to enter (registered_count < chunk_num) + 3. Next chunk is about to enter (last chunk's steps == num_steps/window_size) + + Args: + tracker: ChunkKVRangeTracker instance + total_cache_len: Total cache capacity in tokens + chunk_num: Total number of chunks + chunk_start: Current chunk being processed + transport_input: Transport input containing parameters + chunk_denoise_count: Dictionary mapping chunk_id to denoising steps + window_size: Window size for denoising stages (default: 4) + + Returns: + True if compression should be performed, False otherwise + """ + all_chunk_ids = tracker.get_all_chunk_ids() + if len(all_chunk_ids) == 0: + return False + + registered_chunk_count = len(all_chunk_ids) + cache_full = tracker.next_free_idx >= total_cache_len + has_more_chunks = registered_chunk_count < chunk_num + last_chunk_id = all_chunk_ids[-1] + + # Calculate steps per stage + steps_per_stage = transport_input.num_steps // window_size + next_chunk_will_enter = chunk_denoise_count[last_chunk_id] == steps_per_stage + + should_compress = cache_full and has_more_chunks and next_chunk_will_enter + return should_compress + + +def get_embedding_and_meta_with_chunk_info( + model_self, + x: torch.Tensor, + t: torch.Tensor, + y: torch.Tensor, + caption_dropout_mask, + xattn_mask, + kv_range: torch.Tensor, + **kwargs +) -> tuple: + """ + Compute embeddings and meta information with chunk-aware processing. + + This is a unified version of the get_embedding_and_meta function that + properly handles chunk-based processing with dynamic KV ranges. + + Args: + model_self: The DiT model instance + x: Input tensor [N, C, T, H, W] + t: Timestep tensor [N, range_num] + y: Text conditioning tensor + caption_dropout_mask: Dropout mask for captions + xattn_mask: Cross-attention mask + kv_range: KV range tensor + **kwargs: Additional arguments including: + - range_num: Total number of chunks + - denoising_range_num: Number of chunks being denoised + - slice_point: Starting chunk index + - start_chunk_id: First chunk to process + - end_chunk_id: Last chunk to process (exclusive) + - distill_nearly_clean_chunk: Whether to add nearly-clean chunk + - chunk_token_nums: Tokens per chunk + - chunk_width: Width of each chunk in frames + - num_steps: Total denoising steps + + Returns: + Tuple of (x, condition, condition_map, rope, y_xattn_flat, xattn_mask_cuda, + H, W, ardf_meta, cross_attn_params) + """ + # ========== Part 1: Embed x ========== + x = model_self.x_embedder(x) # [N, C, T, H, W] + batch_size, _, T, H, W = x.shape + + # Prepare necessary variables + range_num = kwargs["range_num"] + denoising_range_num = kwargs["denoising_range_num"] + slice_point = kwargs.get("slice_point", 0) + frame_in_range = T // denoising_range_num + + # distill_nearly_clean_chunk adds one extra chunk + T_total = (range_num + kwargs.get("distill_nearly_clean_chunk", False)) * frame_in_range + + # ========== Part 2: Compute rotary positional embedding ========== + rescale_factor = math.sqrt((H * W) / (16 * 16)) + rope = model_self.rope.get_embed( + shape=[T_total, H, W], + ref_feat_shape=[T_total, H / rescale_factor, W / rescale_factor] + ) + # Rope shape: (T*H*W, head_dim) - cut to current chunk range + rope = rope[ + kwargs["start_chunk_id"] * frame_in_range * H * W : + kwargs["end_chunk_id"] * frame_in_range * H * W + ] + + # ========== Part 3: Embed t ========== + assert t.shape[0] == batch_size, f"Invalid t shape: {t.shape[0]} != {batch_size}" + assert t.shape[1] == denoising_range_num, f"Invalid t shape: {t.shape[1]} != {denoising_range_num}" + + t_flat = t.flatten() # (N * denoising_range_num,) + t = model_self.t_embedder(t_flat) # (N, D) + + if model_self.engine_config.distill: + distill_dt_scalar = 2 + if kwargs["num_steps"] == 12: + base_chunk_step = 4 + distill_dt_factor = base_chunk_step / kwargs["distill_interval"] * distill_dt_scalar + else: + distill_dt_factor = kwargs["num_steps"] / 4 * distill_dt_scalar + + distill_dt = torch.ones_like(t_flat) * distill_dt_factor + distill_dt_embed = model_self.t_embedder(distill_dt) + t = t + distill_dt_embed + + t = t.reshape(batch_size, denoising_range_num, -1) # (N, range_num, D) + + # ========== Part 4: Embed y, prepare condition and y_xattn_flat ========== + y_xattn, y_adaln = model_self.y_embedder(y, model_self.training, caption_dropout_mask) + + assert xattn_mask is not None + xattn_mask = xattn_mask.squeeze(1).squeeze(1) + + # condition: (N, range_num, D) + y_adaln = y_adaln.squeeze(1) # (N, D) + condition = t + y_adaln.unsqueeze(1) + + assert condition.shape[0] == batch_size + assert condition.shape[1] == denoising_range_num + + seqlen_per_chunk = (T * H * W) // denoising_range_num + condition_map = torch.arange(batch_size * denoising_range_num, device=x.device) + condition_map = torch.repeat_interleave(condition_map, seqlen_per_chunk) + condition_map = condition_map.reshape(batch_size, -1).transpose(0, 1).contiguous() + + # y_xattn_flat: (total_token, D) + y_xattn_flat = torch.masked_select( + y_xattn.squeeze(1), + xattn_mask.unsqueeze(-1).bool() + ).reshape(-1, y_xattn.shape[-1]) + + xattn_mask_for_cuda_graph = None + + # ========== Part 5: Prepare cross_attn_params ========== + xattn_mask = xattn_mask.reshape(xattn_mask.shape[0], -1) + y_index = torch.sum(xattn_mask, dim=-1) + clip_token_nums = H * W * frame_in_range + + cu_seqlens_q = torch.Tensor( + [0] + ([clip_token_nums] * denoising_range_num * batch_size) + ).to(torch.int64).to(x.device) + cu_seqlens_k = torch.cat( + [y_index.new_tensor([0]), y_index] + ).to(torch.int64).to(x.device) + cu_seqlens_q = cu_seqlens_q.cumsum(-1).to(torch.int32) + cu_seqlens_k = cu_seqlens_k.cumsum(-1).to(torch.int32) + + assert cu_seqlens_q.shape == cu_seqlens_k.shape, \ + f"cu_seqlens_q.shape: {cu_seqlens_q.shape}, cu_seqlens_k.shape: {cu_seqlens_k.shape}" + + xattn_q_ranges = torch.cat( + [cu_seqlens_q[:-1].unsqueeze(1), cu_seqlens_q[1:].unsqueeze(1)], dim=1 + ) + xattn_k_ranges = torch.cat( + [cu_seqlens_k[:-1].unsqueeze(1), cu_seqlens_k[1:].unsqueeze(1)], dim=1 + ) + assert xattn_q_ranges.shape == xattn_k_ranges.shape, \ + f"xattn_q_ranges.shape: {xattn_q_ranges.shape}, xattn_k_ranges.shape: {xattn_k_ranges.shape}" + + cross_attn_params = PackedCrossAttnParams( + q_ranges=xattn_q_ranges, + kv_ranges=xattn_k_ranges, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_kv=cu_seqlens_k, + max_seqlen_q=clip_token_nums, + max_seqlen_kv=model_self.caption_max_length, + ) + + # ========== Part 6: Prepare core_attn related q/kv range ========== + q_range = torch.cat( + [cu_seqlens_q[:-1].unsqueeze(1), cu_seqlens_q[1:].unsqueeze(1)], dim=1 + ) + flat_kv = torch.unique(kv_range, sorted=True) + max_seqlen_k = (flat_kv[-1] - flat_kv[0]).cpu().item() + + ardf_meta = dict( + clip_token_nums=clip_token_nums, + slice_point=slice_point, + range_num=range_num, + denoising_range_num=denoising_range_num, + q_range=q_range, + k_range=kv_range, + max_seqlen_q=clip_token_nums, + max_seqlen_k=max_seqlen_k, + ) + + return (x, condition, condition_map, rope, y_xattn_flat, + xattn_mask_for_cuda_graph, H, W, ardf_meta, cross_attn_params) + + +def compute_chunk_token_nums( + transport_input, + model_config, + chunk_width: int +) -> int: + """ + Calculate the number of tokens in one chunk. + + Args: + transport_input: Transport input containing latent dimensions + model_config: Model configuration + chunk_width: Number of frames per chunk + + Returns: + Number of tokens per chunk + """ + patch_size = model_config.patch_size + latent_h = transport_input.latent_size[3] // patch_size + latent_w = transport_input.latent_size[4] // patch_size + + return chunk_width * latent_h * latent_w + + +def get_latent_spatial_dims( + transport_input, + model_config +) -> Tuple[int, int]: + """ + Get the spatial dimensions of latent in patch units. + + Args: + transport_input: Transport input containing latent dimensions + model_config: Model configuration + + Returns: + Tuple of (height_patches, width_patches) + """ + patch_size = model_config.patch_size + h = transport_input.latent_size[3] // patch_size + w = transport_input.latent_size[4] // patch_size + return h, w diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/.DS_Store b/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..81ddf723ab2ad7484c6b094f87561851f2a00842 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/.DS_Store differ diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/__init__.py b/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..271690aebf11d70f2405296aac31012d08bae88f --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/__init__.py @@ -0,0 +1,9 @@ +""" +This package provides efficient decoding-time KV cache compression methods. +""" + +__version__ = "0.1.0" + +from .monkeypatch import replace_magi + 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0000000000000000000000000000000000000000..fcd7a518cde39d1f61e9f83c159662b63c6ed759 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/kv_compressor.py @@ -0,0 +1,314 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import List +import os + +from inference.pipeline.kvcompress.utils import cal_similarity, compute_attention_scores + + +class KVCompressor: + def __init__( + self, + kernel_size=7, + mix_lambda=0.07, + compress_strategy="token", + query_granularity="chunk", + score_weighting_method="default", + power=3, + **kwargs, + ): + self.kernel_size = kernel_size + self.mix_lambda = mix_lambda + assert compress_strategy in ["token", "frame", "chunk"] + assert query_granularity in ["token", "frame", "chunk"] + self.compress_strategy = compress_strategy + self.query_granularity = query_granularity + self.score_weighting_method = score_weighting_method + self.power = power + + def update_kv( + # The passed kv is the kv cache of all chunks + self, + key_states, + query_states, + value_states, + clean_chunk_tokens, + latent_size_t, + latent_size_h, + latent_size_w + ): + if self.query_granularity == "token": + # Take 50 tokens + query_states = query_states[- 50 : ] + elif self.query_granularity == "frame": + query_states = query_states[- latent_size_h * latent_size_w : ] + elif self.query_granularity == "chunk": + pass + else: + raise ValueError("Invalid query granularity") + + if self.compress_strategy == "token": + return self.update_kv_token( + key_states, + query_states, + value_states, + clean_chunk_tokens, + each_chunk_tokens=latent_size_t * latent_size_h * latent_size_w, + ) + elif self.compress_strategy == "frame": + return self.update_kv_frame_chunk( + key_states, + query_states, + value_states, + clean_chunk_tokens, + together_size=latent_size_h * latent_size_w, + ) + elif self.compress_strategy == "chunk": + return self.update_kv_frame_chunk( + key_states, + query_states, + value_states, + clean_chunk_tokens, + together_size=latent_size_t * latent_size_h * latent_size_w, + ) + else: + raise ValueError("Invalid compress strategy") + + def update_kv_token( + self, + key_states, + query_states, + value_states, + clean_chunk_tokens, + each_chunk_tokens, + ): + each_chunk_tokens = int(each_chunk_tokens) + head_dim = query_states.shape[-1] + kv_cache_len = key_states.shape[0] + + attn_weights = compute_attention_scores(query_states, key_states[:clean_chunk_tokens]) + attn_weights_sum = ( + nn.functional.softmax( + attn_weights[:, :, : clean_chunk_tokens], + dim=-1, + dtype=torch.float32, + ) + .mean(dim=-2) + .to(query_states.dtype) + ) + + attn_cache = F.max_pool1d( + attn_weights_sum, + kernel_size=self.kernel_size, + padding=self.kernel_size // 2, + stride=1, + ).to('cpu') + + similarity_cos = cal_similarity(key_states[:clean_chunk_tokens, :, :]).to('cpu') + + final_score = attn_cache * self.mix_lambda - similarity_cos * (1 - self.mix_lambda) + + # Ensure final score is non-negative for weighting + min_scores_per_head = final_score.min(dim=-1, keepdim=True).values # (num_kv_heads, 1) + final_score = final_score - min_scores_per_head + + # Note that final_score contains negative numbers + # Apply different weighting methods to final_score, relatively making tokens at later positions more likely to be selected + if self.score_weighting_method == "no_weight": + print("Using no weighting method") + pass + + elif self.score_weighting_method == "hard_code": + print("Using hard code weighting method") + final_score[:, :each_chunk_tokens] -= 1e6 + + elif self.score_weighting_method == "exponential": + print("Using exponential weighting method") + seq_len = final_score.shape[1] + positions = torch.arange(seq_len, dtype=torch.float32, device=final_score.device) / (seq_len - 1) if seq_len > 1 else torch.zeros(1) + decay_rate = 2.0 + # Normalize to [0.1, 1.0] range + exponential_values = 1 - torch.exp(-decay_rate * positions) + max_value = 1 - torch.exp(torch.tensor(-decay_rate, device=final_score.device)) # Value when positions=1 + weights = 0.1 + 0.9 * (exponential_values / max_value) + final_score = final_score * weights.unsqueeze(0) + + elif self.score_weighting_method == "polynomial": + print(f"Using polynomial weighting method, power={self.power}") + seq_len = final_score.shape[1] + positions = torch.arange(seq_len, dtype=torch.float32, device=final_score.device) / (seq_len - 1) if seq_len > 1 else torch.zeros(1) + # Normalize to [0.1, 1.0] range + weights = 0.1 + 0.9 * (positions ** self.power) + final_score = final_score * weights.unsqueeze(0) + elif self.score_weighting_method == "upper_convex_polynomial": + print(f"Using upper convex polynomial weighting method, power={self.power}") + seq_len = final_score.shape[1] + positions = torch.arange(seq_len, dtype=torch.float32, device=final_score.device) / (seq_len - 1) if seq_len > 1 else torch.zeros(1) + max_value = 2.0 + # Construct upper convex n-th degree polynomial: w(x) = max_value * (1 - (1-x)^n) + weights = max_value * (1 - (1 - positions) ** self.power) + final_score = final_score * weights.unsqueeze(0) + + elif self.score_weighting_method == "gaussian": + print("Using gaussian weighting method") + # Emphasize previous information more + seq_len = final_score.shape[1] + + positions = torch.arange(seq_len, dtype=torch.float32, device=final_score.device) + sigma = seq_len / 4.0 # ← Adjustable! Smaller values emphasize the beginning more + + gaussian_decay = torch.exp(-0.5 * (positions / sigma) ** 2) + min_decay = torch.exp(torch.tensor(-0.5 * ((seq_len - 1) / sigma) ** 2, device=final_score.device)) + + # Map [min_decay, 1.0] → [0.1, 1.0] + weights = 0.1 + 0.9 * ((gaussian_decay - min_decay) / (1.0 - min_decay)) + final_score = final_score * weights.unsqueeze(0) + else: + raise ValueError(f"Unknown score weighting method: {self.score_weighting_method}") + + # Calculate number of tokens to keep + num_to_keep = self.budget + + # Select top-k tokens + try: + indices = final_score.topk(num_to_keep, dim=-1).indices # shape: (num_kv_heads, num_to_keep) + del final_score + except RuntimeError: + import pdb; pdb.set_trace() + indices = indices.unsqueeze(-1).expand(-1, -1, head_dim).permute(1, 0, 2) # shape: (num_to_keep, num_kv_heads, head_dim) + + indices = indices.to(key_states.device) + + # Compress non-recent parts + k_past_compress = key_states[:clean_chunk_tokens, :, :].gather(dim=0, index=indices) + v_past_compress = value_states[:clean_chunk_tokens, :, :].gather(dim=0, index=indices) + k_cur = key_states[clean_chunk_tokens :, :, :] + v_cur = value_states[clean_chunk_tokens :, :, :] + + key_compress = torch.cat([k_past_compress, k_cur], dim=0) + value_compress = torch.cat([v_past_compress, v_cur], dim=0) + + return key_compress, value_compress, indices + + def update_kv_frame_chunk( + self, + key_states, + query_states, + value_states, + clean_chunk_tokens, + together_size, + ): + head_dim = query_states.shape[-1] + kv_cache_len = key_states.shape[0] + + # ========== Compression Logic ========== + + # Step 1: Compute attention weights + attn_weights = compute_attention_scores(query_states, key_states) + attn_weights_sum = ( + nn.functional.softmax( + attn_weights[:, :, : clean_chunk_tokens], + dim=-1, + dtype=torch.float32, + ) + .mean(dim=-2) # shape: (num_kv_heads, clean_chunk_tokens) + .to(query_states.dtype) + ) + + # Step 2: Pooling to get "importance" of each token + attn_cache = F.max_pool1d( + attn_weights_sum, + kernel_size=self.kernel_size, + padding=self.kernel_size // 2, + stride=1, + ).to('cpu') # shape: (num_kv_heads, clean_chunk_tokens) + + # Step 3: Compute similarity between tokens + similarity_cos = cal_similarity(key_states[:clean_chunk_tokens, :, :]).to('cpu') + + # Step 4: Compute final score for each token + final_score_per_token = attn_cache * self.mix_lambda - similarity_cos * (1 - self.mix_lambda) + # shape: (num_kv_heads, clean_chunk_tokens) + + # ========== Frame-wise or Chunk-wise Aggregation ========== + # In the code below, chunk is also referred to as frame; they are conceptually consistent, just differing in how many tokens are aggregated into one frame/chunk + + assert clean_chunk_tokens % together_size == 0 + num_frames = clean_chunk_tokens // together_size + + # Reshape to (num_kv_heads, num_frames, together_size) + score_frames = final_score_per_token.view( + key_states.shape[1], num_frames, together_size + ) + + # Aggregate scores for each frame + frame_scores = score_frames.mean(dim=-1) # shape: (num_kv_heads, num_frames) + + # Calculate number of frames to keep + assert self.budget % together_size == 0 + num_frames_to_keep = self.budget // together_size + + # Select top-k frames for each head + frame_indices = frame_scores.topk(num_frames_to_keep, dim=-1).indices + # shape: (num_kv_heads, num_frames_to_keep) + + + # Convert frame_indices to token indices + # frame_indices: frame id selected by each head + + # offset: [0, 1, ..., together_size-1] + token_offsets = torch.arange(together_size, device=key_states.device) + frame_indices_expanded = frame_indices.unsqueeze(-1) * together_size + token_indices_per_head = frame_indices_expanded + token_offsets # shape: (num_heads, num_frames_to_keep, together_size) + token_indices_flat = token_indices_per_head.view(key_states.shape[1], -1) # (num_heads, K * together_size) + indices_gather = token_indices_flat.permute(1, 0).unsqueeze(-1).expand(-1, -1, head_dim) # shape: (kept_tokens, num_kv_heads, head_dim) + + # Gather from key/value + k_past_compress = key_states[:clean_chunk_tokens, :, :].gather(dim=0, index=indices_gather) + v_past_compress = value_states[:clean_chunk_tokens, :, :].gather(dim=0, index=indices_gather) + + # ========== Concatenate Recent Parts ========== + k_cur = key_states[clean_chunk_tokens:, :, :] + v_cur = value_states[clean_chunk_tokens:, :, :] + + key_compress = torch.cat([k_past_compress, k_cur], dim=0) + value_compress = torch.cat([v_past_compress, v_cur], dim=0) + + return key_compress, value_compress, indices_gather # token indices + + +def plot_tensor_values(tensor_1d: torch.Tensor, title: str = "Tensor Values", save_path: str = None, + xlabel: str = "Position", ylabel: str = "Value", figsize: tuple = (10, 6)): + try: + import matplotlib.pyplot as plt + import numpy as np + except ImportError: + print("Warning: matplotlib not available, skipping plot") + return + + # Ensure input is a 1D tensor + if tensor_1d.dim() != 1: + raise ValueError(f"Input tensor must be 1D, got {tensor_1d.dim()}D") + + # Convert to numpy array + values = tensor_1d.detach().cpu().float().numpy() + positions = np.arange(len(values)) + + # Create plot + plt.figure(figsize=figsize) + plt.plot(positions, values, 'b-', linewidth=2, markersize=4, alpha=0.8) + plt.xlabel(xlabel, fontsize=12) + plt.ylabel(ylabel, fontsize=12) + plt.title(title, fontsize=14) + + # Adjust layout + plt.tight_layout() + + # Save plot + if save_path is not None: + os.makedirs(os.path.dirname(save_path), exist_ok=True) + plt.savefig(save_path, dpi=100, bbox_inches='tight') + print(f"Plot saved to: {save_path}") + + plt.close() \ No newline at end of file diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/modeling.py b/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/modeling.py new file mode 100644 index 0000000000000000000000000000000000000000..0ebec5d78478d33f86036559fd7c6f66b733ef65 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/modeling.py @@ -0,0 +1,54 @@ +import torch +from .kv_compressor import KVCompressor +from inference.model.dit.dit_module import CustomLayerNormLinear, FusedLayerNorm, PerChannelQuantizedFp8Linear, Attention +from inference.common import EngineConfig, InferenceParams, ModelConfig, ModelMetaArgs + +def MagiAttention_init( + self, model_config: ModelConfig, engine_config: EngineConfig, layer_number: int, compression_config: dict +): + Attention.__init__(self, model_config, engine_config, layer_number) + # super().__init__(model_config=model_config, engine_config=engine_config, layer_number=layer_number) + + # output 2x query, one for self-attn, one for cross-attn with condition + self.linear_qkv = CustomLayerNormLinear( + input_size=self.model_config.hidden_size, + output_size_q=self.query_projection_size, + output_size_kv=self.kv_projection_size, + layer_number=self.layer_number, + model_config=self.model_config, + engine_config=self.engine_config, + ) + + # kv from condition, e.g., caption + self.linear_kv_xattn = torch.nn.Linear( + int(self.model_config.hidden_size * self.model_config.xattn_cond_hidden_ratio), # 6144 + 2 * self.kv_projection_size, # 2048 + dtype=self.model_config.params_dtype, + bias=False, + ) + + # Output. + self.adapt_linear_quant = ( + self.engine_config.fp8_quant and self.layer_number != 0 and self.layer_number != model_config.num_layers - 1 + ) + submodules_linear_proj = PerChannelQuantizedFp8Linear if self.adapt_linear_quant else torch.nn.Linear + self.linear_proj = submodules_linear_proj( + 2 * self.query_projection_size, self.model_config.hidden_size, dtype=self.model_config.params_dtype, bias=False + ) + + self.q_layernorm = FusedLayerNorm(model_config=self.model_config, hidden_size=self.hidden_size_per_attention_head) + self.q_layernorm_xattn = FusedLayerNorm( + model_config=self.model_config, hidden_size=self.hidden_size_per_attention_head + ) + self.k_layernorm = FusedLayerNorm(model_config=self.model_config, hidden_size=self.hidden_size_per_attention_head) + self.k_layernorm_xattn = FusedLayerNorm( + model_config=self.model_config, hidden_size=self.hidden_size_per_attention_head + ) + + self.attn_weights_history = [] + + # =============== New logic start =============== + self.kv_cluster = KVCompressor( + **compression_config["method_config"] + ) + # =============== New logic end ================= \ No newline at end of file diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/monkeypatch.py b/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/monkeypatch.py new file mode 100644 index 0000000000000000000000000000000000000000..154d59435406156a93909fd3d015334e47b76dd3 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/monkeypatch.py @@ -0,0 +1,8 @@ +from .modeling import MagiAttention_init + +def replace_magi(compression_config): + from inference.model.dit import VideoDiTModel, FullyParallelAttention + def init_wrapper(self, model_config, engine_config, layer_number): + MagiAttention_init(self, model_config, engine_config, layer_number, compression_config) + + FullyParallelAttention.__init__ = init_wrapper \ No newline at end of file diff --git a/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/utils.py b/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c5748d277836e2deb6bc4aaa503ccef829f58ee8 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/inference/pipeline/kvcompress/utils.py @@ -0,0 +1,208 @@ +import math +import torch +import time +import matplotlib.pyplot as plt +import numpy as np +from typing import List, Tuple, Dict + +################################################################# +###################### kv cache utilities ####################### +################################################################# + +def compute_attention_scores(query_states, key_states_cpu, pooling="max"): + """ + query_states: [q_len, q_heads, head_dim] on GPU + key_states_cpu: [kv_len, kv_heads, head_dim] on CPU + """ + + q_len, q_heads, head_dim = query_states.shape + kv_len, kv_heads, _ = key_states_cpu.shape + query_group_size = q_heads // kv_heads + + device = query_states.device # GPU + + # print(f"Before computing attention scores, GPU memory usage: {torch.cuda.memory_allocated() / 1024 ** 3:.1f} GB") + + if query_group_size == 1: + chunk_size = 12150 + + attn_weights = torch.empty(kv_heads, q_len, kv_len, device=device, dtype=query_states.dtype) + + for i in range(0, kv_len, chunk_size): + end_i = min(i + chunk_size, kv_len) + k_chunk = key_states_cpu[i:end_i].to(device) # Transfer small chunk to GPU + + attn_chunk = torch.bmm( + query_states.transpose(0, 1), # [kv_heads, q_len, head_dim] + k_chunk.transpose(1, 2) # [kv_heads, head_dim, chunk_size] + ) / math.sqrt(head_dim) # [kv_heads, q_len, chunk_size] + + attn_weights[:, :, i:end_i] = attn_chunk + del k_chunk, attn_chunk + + return attn_weights + + else: + # query_states: [q_len, q_heads, head_dim] -> reshape to group + # We group by query_group, but still compute key in chunks + query_states = query_states.view(q_len, kv_heads, query_group_size, head_dim) + # [q_len, kv_heads, g, head_dim] -> permute to [kv_heads, g, q_len, head_dim] + query_states = query_states.permute(1, 2, 0, 3).contiguous() # [kv_heads, g, q_len, head_dim] + + if pooling == "mean": + attn_weights_sum = None + count = 0 + elif pooling == "max": + attn_weights_max = None + else: + raise ValueError("Pooling method not supported") + + for g in range(query_group_size): + q_group = query_states[:, g, :, :] # [kv_heads, q_len, head_dim] + + chunk_size = 12150 + group_attn = torch.empty(kv_heads, q_len, kv_len, device=device, dtype=query_states.dtype) + + for i in range(0, kv_len, chunk_size): + end_i = min(i + chunk_size, kv_len) + k_chunk = key_states_cpu[i:end_i].to(device) # [chunk_size, kv_heads, head_dim] + k_chunk = k_chunk.permute(1, 2, 0) # [kv_heads, head_dim, chunk_size] + attn_chunk = torch.bmm(q_group, k_chunk) / math.sqrt(head_dim) + group_attn[:, :, i:end_i] = attn_chunk + del k_chunk, attn_chunk + + # Apply pooling over query_group_size dimension + if pooling == "mean": + if attn_weights_sum is None: + attn_weights_sum = group_attn + else: + attn_weights_sum += group_attn + count += 1 + elif pooling == "max": + if attn_weights_max is None: + attn_weights_max = group_attn + else: + attn_weights_max = torch.max(attn_weights_max, group_attn) + + del group_attn + + if pooling == "mean": + attn_weights = attn_weights_sum / count + del attn_weights_sum + elif pooling == "max": + attn_weights = attn_weights_max + del attn_weights_max + + return attn_weights + + +# def cal_similarity( +# key_states, +# ): +# # key_states shape: [kv_len, kv_heads, head_dim] +# start = time.time() +# k = key_states.permute(1, 0, 2).to('cuda') # shape: [kv_heads, kv_len, head_dim] +# num_heads = k.shape[0] + +# k_norm = k / (k.norm(dim=-1, keepdim=True) + 1e-8) +# similarity_cos = torch.matmul(k_norm, k_norm.transpose(-1, -2)).to('cpu') + +# for h in range(num_heads): +# similarity_cos[h].fill_diagonal_(0.0) + +# end = time.time() +# return similarity_cos.mean(dim=1).softmax(dim=-1) + + +def cal_similarity( + key_states, +): + # [kv_len, H, D] → [H, kv_len, D] + k = key_states.permute(1, 0, 2).to('cuda') + H, L, D = k.shape + + # L2 normalize each key vector per head + k_norm = k / (k.norm(dim=-1, keepdim=True) + 1e-8) # [H, L, D] + + # Step 1: Compute sum of all keys per head → [H, D] + k_sum = k_norm.sum(dim=1) # Σ_j k_j + + # Step 2: For each key i, compute k_i ⋅ (Σ_j k_j) → [H, L] + # That is: (k_norm @ k_sum.T) → use bmm for batch + # k_norm: [H, L, D], k_sum.unsqueeze(-1): [H, D, 1] → bmm → [H, L, 1] + dot_with_sum = torch.bmm(k_norm, k_sum.unsqueeze(-1)).squeeze(-1) # [H, L] + + # Step 3: Apply correction for diagonal (since cos(k_i, k_i) = 1 was included in sum) + # Original: fill_diagonal_(0) then mean(dim=1) ⇒ (total_sum - 1) / L + if L == 1: + mean_sim = torch.zeros(H, 1, device=k.device) # or handle specially + else: + mean_sim = (dot_with_sum - 1.0) / L # [H, L] ← strictly equivalent to original + + avg_sim = mean_sim + + # Step 5: Softmax → final importance-like distribution + result = avg_sim.softmax(dim=-1).to('cpu') # move small result to CPU + + return result + + +class ChunkKVRangeTracker: + def __init__(self, total_cache_len: int, clip_token_nums: int, max_batch_size: int): + self.total_cache_len = total_cache_len + self.clip_token_nums = clip_token_nums + self.max_batch_size = max_batch_size + self.tokens_per_chunk = clip_token_nums * max_batch_size + self.chunk_ranges: Dict[int, Tuple[int, int]] = {} # chunk_id -> (start, end) + self.next_free_idx = 0 # For sequential allocation when not compressed + self.registered_chunks_ordered: List[int] = [] # Maintain registration order for compression and concatenation + + def register_chunks(self, chunk_ids: List[int]): + """Batch register multiple chunks and allocate original space""" + for cid in chunk_ids: + if cid in self.chunk_ranges: + continue + start = self.next_free_idx + end = start + self.tokens_per_chunk + if end > self.total_cache_len: + import pdb; pdb.set_trace() + raise ValueError("KV cache is full") + self.chunk_ranges[cid] = (start, end) + self.registered_chunks_ordered.append(cid) + self.next_free_idx = end + + def get_range(self, chunk_id: int) -> Tuple[int, int]: + if chunk_id not in self.chunk_ranges: + raise KeyError(f"Chunk {chunk_id} not registered. Call register_chunks first.") + return self.chunk_ranges[chunk_id] + + def get_all_ranges_previous(self, current_chunk_ids: List[int]) -> List[Tuple[int, int]]: + # Get KV ranges of all previous chunks + ranges = [] + if len(current_chunk_ids) > 0: + min_chunk_id = min(current_chunk_ids) + for cid in self.registered_chunks_ordered: + if cid >= min_chunk_id: + continue + ranges.append(self.chunk_ranges[cid]) + else: + # To adapt to MAGI-1's original logic, should return ranges of all registered chunks + for cid in self.registered_chunks_ordered: + ranges.append(self.chunk_ranges[cid]) + return ranges + + def get_all_chunk_ids(self) -> List[int]: + return self.registered_chunks_ordered.copy() + + def update_ranges_after_compression(self, new_ranges: Dict[int, Tuple[int, int]]): + """Update each chunk's range based on actual compressed length""" + # Update chunk_ranges + for cid, (start, end) in new_ranges.items(): + if cid in self.chunk_ranges: + self.chunk_ranges[cid] = (start, end) + + # Update next_free_idx to maximum end + if new_ranges: + self.next_free_idx = max(end for start, end in new_ranges.values()) + else: + self.next_free_idx = 0 \ No newline at end of file diff --git a/FlowCache/FlowCache4MAGI-1/outputs/a_woman_dancing/flowcache_metric_stats_2026-05-19_09-18-01.json b/FlowCache/FlowCache4MAGI-1/outputs/a_woman_dancing/flowcache_metric_stats_2026-05-19_09-18-01.json new file mode 100644 index 0000000000000000000000000000000000000000..2f29778a033f4993f571b1c7cb5e4e018057f165 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/outputs/a_woman_dancing/flowcache_metric_stats_2026-05-19_09-18-01.json @@ -0,0 +1,10698 @@ +{ + "description": "FlowCache original per-chunk reuse metric. flowcache_rel_l1 = mean(abs(x_embedder(X_t_current) - x_embedder(X_t_previous))) / (mean(abs(x_embedder(X_t_previous))) + 1e-8). flowcache_rel_l1_ratio = sum(abs(delta)) / sum(abs(previous_feature)); flowcache_accumulated_rel_l1 is the accumulated value compared with rel_l1_thresh.", + 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0.09912109375, + "flowcache_rel_l1_ratio": 0.09943181818181818, + "flowcache_delta_l1_norm": 143360.0, + "flowcache_prev_feat_l1_norm": 1441792.0, + "flowcache_accumulated_rel_l1": 0.09912109375, + "rel_l1_thresh": 0.015, + "reused": false, + "decision_reason": "threshold_exceeded" + }, + { + "infer_idx": 0, + "cur_denoise_step": 206, + "denoise_stage": 12, + "denoise_idx": 14, + "chunk_idx": 9, + "generated_chunk_idx": 9, + "chunk_denoise_count": 62, + "flowcache_rel_l1": 0.10498046875, + "flowcache_rel_l1_ratio": 0.10508241758241758, + "flowcache_delta_l1_norm": 156672.0, + "flowcache_prev_feat_l1_norm": 1490944.0, + "flowcache_accumulated_rel_l1": 0.10498046875, + "rel_l1_thresh": 0.015, + "reused": false, + "decision_reason": "threshold_exceeded" + } + ] +} \ No newline at end of file diff --git a/FlowCache/FlowCache4MAGI-1/outputs/a_woman_dancing/infer_2026-05-19_09-18-01.log b/FlowCache/FlowCache4MAGI-1/outputs/a_woman_dancing/infer_2026-05-19_09-18-01.log new file mode 100644 index 0000000000000000000000000000000000000000..ddb800627d565cd51be2bc47e2c77f1dac980815 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/outputs/a_woman_dancing/infer_2026-05-19_09-18-01.log @@ -0,0 +1,223 @@ +/home/dyvm6xra/dyvm6xrauser11/miniforge3/envs/magi/lib/python3.10/site-packages/timm/models/layers/__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers + warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning) +[W519 09:18:09.994595319 CUDAAllocatorConfig.h:28] Warning: expandable_segments not supported on this platform (function operator()) +[2026-05-19 09:18:09,596 - INFO] Initialize torch distribution and model parallel successfully +[2026-05-19 09:18:09,597 - INFO] MagiConfig(model_config=ModelConfig(model_name='videodit_ardf', num_layers=34, hidden_size=3072, ffn_hidden_size=12288, num_attention_heads=24, num_query_groups=8, kv_channels=128, layernorm_epsilon=1e-06, apply_layernorm_1p=True, x_rescale_factor=1, half_channel_vae=False, params_dtype=torch.bfloat16, patch_size=2, t_patch_size=1, in_channels=16, out_channels=16, cond_hidden_ratio=0.25, caption_channels=4096, caption_max_length=800, xattn_cond_hidden_ratio=1.0, cond_gating_ratio=1.0, gated_linear_unit=False), runtime_config=RuntimeConfig(cfg_number=1, cfg_t_range=[0.0, 0.0217, 0.1, 0.3, 0.999], prev_chunk_scales=[1.5, 1.5, 1.5, 1.0, 1.0], text_scales=[7.5, 7.5, 7.5, 0.0, 0.0], noise2clean_kvrange=[], clean_chunk_kvrange=1, clean_t=0.9999, seed=1234, num_frames=240, video_size_h=720, video_size_w=720, num_steps=64, window_size=4, fps=24, chunk_width=6, t5_pretrained='./downloads/t5_pretrained', t5_device='cuda', vae_pretrained='./downloads/vae', scale_factor=0.18215, temporal_downsample_factor=4, load='./downloads/4.5B_distill'), engine_config=EngineConfig(distributed_backend='nccl', distributed_timeout_minutes=15, pp_size=1, cp_size=1, cp_strategy='none', ulysses_overlap_degree=1, fp8_quant=False, distill_nearly_clean_chunk_threshold=0.3, shortcut_mode='8,16,16', distill=True, kv_offload=False, enable_cuda_graph=False)) +[2026-05-19 09:18:09,597 - INFO] Precompute validation prompt embeddings +You are using the default legacy behaviour of the . This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 +Loading additional config: {'rel_l1_thresh': 0.015, 'warmup_steps': 5, 'discard_nearly_clean_chunk': True, 'compress_kv_cache': True, 'total_cache_chunk_nums': 5, 'compress_strategy': 'token', 'mix_lambda': 0.07, 'query_granularity': 'frame', 'score_weighting_method': 'no_weight', 'power': 3, 'log': True, 'print_peak_memory': True, 'debug': False} +Added to args: rel_l1_thresh = 0.015 +Added to args: warmup_steps = 5 +Added to args: discard_nearly_clean_chunk = True +Added to args: compress_kv_cache = True +Added to args: total_cache_chunk_nums = 5 +Added to args: compress_strategy = token +Added to args: mix_lambda = 0.07 +Added to args: query_granularity = frame +Added to args: score_weighting_method = no_weight +Added to args: power = 3 +Added to args: log = True +Added to args: print_peak_memory = True +Added to args: debug = False +Running on GPU: NVIDIA H800 +GPU Memory before pipeline: 0.00 GB + Loading checkpoint shards: 0%| | 0/2 [00:00. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 +Loading additional config: {'rel_l1_thresh': 0.015, 'warmup_steps': 5, 'discard_nearly_clean_chunk': True, 'compress_kv_cache': True, 'total_cache_chunk_nums': 5, 'compress_strategy': 'token', 'mix_lambda': 0.07, 'query_granularity': 'frame', 'score_weighting_method': 'no_weight', 'power': 3, 'log': True, 'print_peak_memory': True, 'debug': False} +Added to args: rel_l1_thresh = 0.015 +Added to args: warmup_steps = 5 +Added to args: discard_nearly_clean_chunk = True +Added to args: compress_kv_cache = True +Added to args: total_cache_chunk_nums = 5 +Added to args: compress_strategy = token +Added to args: mix_lambda = 0.07 +Added to args: query_granularity = frame +Added to args: score_weighting_method = no_weight +Added to args: power = 3 +Added to args: log = True +Added to args: print_peak_memory = True +Added to args: debug = False +Running on GPU: NVIDIA H800 +GPU Memory before pipeline: 0.00 GB + Loading checkpoint shards: 0%| | 0/2 [00:00 >(tee -a "$LOG_FILE") 2>&1 + +echo "🚀 Starting multi-GPU benchmark sampling" +echo "🎮 GPUs: $DEVICES" + +# Run sampling +python sample_video.py "$YAML_CONFIG" + +if [ $? -eq 0 ]; then + echo "✅ Sampling completed successfully." +else + echo "❌ Sampling failed. Check log: $LOG_FILE" + exit 1 +fi + +echo "---" +echo "🎉 All sampling tasks completed." diff --git a/FlowCache/FlowCache4MAGI-1/scripts/sample/flowcache_vbench.sh b/FlowCache/FlowCache4MAGI-1/scripts/sample/flowcache_vbench.sh new file mode 100644 index 0000000000000000000000000000000000000000..85dc6ea8c4f96940985d61c6418f51546af9e825 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/scripts/sample/flowcache_vbench.sh @@ -0,0 +1,80 @@ +#!/bin/bash + +# FlowCache VBench sampling script +# Usage: bash flowcache_vbench.sh [yaml_config_path] +# Default config: yaml_config/sample/flowcache_vbench.yaml + +export PAD_HQ=1 +export PAD_DURATION=1 +export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True +export OFFLOAD_T5_CACHE=true +export OFFLOAD_VAE_CACHE=true +export TORCH_CUDA_ARCH_LIST="8.9;9.0" + +MAGI_ROOT=$(git rev-parse --show-toplevel) +export PYTHONPATH="$MAGI_ROOT:$PYTHONPATH" +export MAGI_ROOT="$MAGI_ROOT" + +# YAML config file path (can be overridden via command line argument) +YAML_CONFIG="${1:-yaml_config/sample/flowcache_vbench.yaml}" + +if [ ! -f "$YAML_CONFIG" ]; then + echo "❌ YAML config file not found: $YAML_CONFIG" + exit 1 +fi + +echo "📋 Using YAML config: $YAML_CONFIG" + +# Create log directory +LOG_DIR="./logs" +mkdir -p "$LOG_DIR" +LOG_FILE="$LOG_DIR/flowcache_vbench_$(date +%Y%m%d_%H%M%S).log" +exec > >(tee -a "$LOG_FILE") 2>&1 + +echo "🚀 Starting multi-GPU benchmark sampling" + +# Define list of dimensions to process +DIMENSIONS=("overall_consistency" "subject_consistency" "scene") + +echo "🔢 Total dimensions to process: ${#DIMENSIONS[@]}" +echo "📋 Dimensions: ${DIMENSIONS[*]}" + +# Loop through each dimension +for DIMENSION in "${DIMENSIONS[@]}"; do + echo "🔍 Processing dimension: $DIMENSION" + + # Use Python to temporarily modify the dimension in YAML, then run sampling + python3 -c " +import yaml +import sys + +# Read YAML config +with open('$YAML_CONFIG', 'r') as f: + config = yaml.safe_load(f) + +# Modify dimension +config['dimension'] = '$DIMENSION' + +# Save to temporary file +temp_config = '$YAML_CONFIG.tmp' +with open(temp_config, 'w') as f: + yaml.dump(config, f, default_flow_style=False) +print(temp_config) +" > /tmp/temp_config_path.txt + + TEMP_CONFIG=$(cat /tmp/temp_config_path.txt) + python sample_video.py "$TEMP_CONFIG" + rm "$TEMP_CONFIG" + + if [ $? -eq 0 ]; then + echo "✅ Completed: $DIMENSION" + else + echo "❌ Failed: $DIMENSION" + echo "🛑 Script paused due to error. Fix the issue and rerun." + exit 1 + fi + + echo "---" +done + +echo "🎉 All sampling tasks completed." diff --git a/FlowCache/FlowCache4MAGI-1/scripts/sample/teacache_physicsiq.sh b/FlowCache/FlowCache4MAGI-1/scripts/sample/teacache_physicsiq.sh new file mode 100644 index 0000000000000000000000000000000000000000..d4d0ffc2ce5fe7c329ff0cb766d56a28d6b289b4 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/scripts/sample/teacache_physicsiq.sh @@ -0,0 +1,53 @@ +#!/bin/bash + +# TeaCache PhysicsIQ sampling script +# Usage: bash teacache_physicsiq.sh [yaml_config_path] +# Default config: yaml_config/sample/teacache_physicsiq.yaml + +export DEVICES="0,1,2,3,4,5,6,7" + +export PAD_HQ=1 +export PAD_DURATION=1 +export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True +export OFFLOAD_T5_CACHE=true +export OFFLOAD_VAE_CACHE=true +export TORCH_CUDA_ARCH_LIST="8.9;9.0" + +MAGI_ROOT=$(git rev-parse --show-toplevel) +export PYTHONPATH="$MAGI_ROOT:$PYTHONPATH" +export MAGI_ROOT="$MAGI_ROOT" + +export XDG_CACHE_HOME="/path/to/tmp" +mkdir -p "$XDG_CACHE_HOME" + +# YAML config file path (can be overridden via command line argument) +YAML_CONFIG="${1:-yaml_config/sample/teacache_physicsiq.yaml}" + +if [ ! -f "$YAML_CONFIG" ]; then + echo "❌ YAML config file not found: $YAML_CONFIG" + exit 1 +fi + +echo "📋 Using YAML config: $YAML_CONFIG" + +# Create log directory +LOG_DIR="./logs" +mkdir -p "$LOG_DIR" +LOG_FILE="$LOG_DIR/teacache_physicsiq_$(date +%Y%m%d_%H%M%S).log" +exec > >(tee -a "$LOG_FILE") 2>&1 + +echo "🚀 Starting multi-GPU benchmark sampling" +echo "🎮 GPUs: $DEVICES" + +# Run sampling +python sample_video.py "$YAML_CONFIG" + +if [ $? -eq 0 ]; then + echo "✅ Sampling completed successfully." +else + echo "❌ Sampling failed. Check log: $LOG_FILE" + exit 1 +fi + +echo "---" +echo "🎉 All sampling tasks completed." diff --git a/FlowCache/FlowCache4MAGI-1/scripts/sample/teacache_vbench.sh b/FlowCache/FlowCache4MAGI-1/scripts/sample/teacache_vbench.sh new file mode 100644 index 0000000000000000000000000000000000000000..196d31a912ad4f83040091376e2032e9c78f0dab --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/scripts/sample/teacache_vbench.sh @@ -0,0 +1,82 @@ +#!/bin/bash + +# TeaCache VBench sampling script +# Usage: bash teacache_vbench.sh [yaml_config_path] +# Default config: yaml_config/sample/teacache_vbench.yaml + +export DEVICES="4,5,7" + +export PAD_HQ=1 +export PAD_DURATION=1 +export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True +export OFFLOAD_T5_CACHE=true +export OFFLOAD_VAE_CACHE=true +export TORCH_CUDA_ARCH_LIST="8.9;9.0" + +MAGI_ROOT=$(git rev-parse --show-toplevel) +export PYTHONPATH="$MAGI_ROOT:$PYTHONPATH" +export MAGI_ROOT="$MAGI_ROOT" + +# YAML config file path (can be overridden via command line argument) +YAML_CONFIG="${1:-yaml_config/sample/teacache_vbench.yaml}" + +if [ ! -f "$YAML_CONFIG" ]; then + echo "❌ YAML config file not found: $YAML_CONFIG" + exit 1 +fi + +echo "📋 Using YAML config: $YAML_CONFIG" + +# Create log directory +LOG_DIR="./logs" +mkdir -p "$LOG_DIR" +LOG_FILE="$LOG_DIR/teacache_vbench_$(date +%Y%m%d_%H%M%S).log" +exec > >(tee -a "$LOG_FILE") 2>&1 + +echo "🚀 Starting multi-GPU benchmark sampling" +echo "🎮 GPUs: $DEVICES" + +# Define list of dimensions to process +DIMENSIONS=("overall_consistency" "subject_consistency" "scene") + +echo "🔢 Total dimensions to process: ${#DIMENSIONS[@]}" + +# Loop through each dimension +for DIMENSION in "${DIMENSIONS[@]}"; do + echo "📌 Processing dimension: $DIMENSION" + + # Use Python to temporarily modify the dimension in YAML, then run sampling + python3 -c " +import yaml +import sys + +# Read YAML config +with open('$YAML_CONFIG', 'r') as f: + config = yaml.safe_load(f) + +# Modify dimension +config['dimension'] = '$DIMENSION' + +# Save to temporary file +temp_config = '$YAML_CONFIG.tmp' +with open(temp_config, 'w') as f: + yaml.dump(config, f, default_flow_style=False) +print(temp_config) +" > /tmp/temp_config_path.txt + + TEMP_CONFIG=$(cat /tmp/temp_config_path.txt) + python sample_video.py "$TEMP_CONFIG" + rm "$TEMP_CONFIG" + + if [ $? -eq 0 ]; then + echo "✅ Successfully completed: $DIMENSION" + else + echo "❌ Failed: $DIMENSION" + echo "🛑 Script paused due to error. Fix the issue and rerun." + exit 1 + fi + + echo "---" +done + +echo "🎉 All sampling tasks completed." diff --git a/FlowCache/FlowCache4MAGI-1/scripts/single_run/flowcache_t2v.sh b/FlowCache/FlowCache4MAGI-1/scripts/single_run/flowcache_t2v.sh new file mode 100644 index 0000000000000000000000000000000000000000..c7bf9910fcc4c4bcf401f9ef16c444808d495936 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/scripts/single_run/flowcache_t2v.sh @@ -0,0 +1,119 @@ +# Copyright (c) 2025 SandAI. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +export MASTER_ADDR=localhost +export MASTER_PORT=6005 +export GPUS_PER_NODE=1 +export NNODES=1 +export WORLD_SIZE=1 +export CUDA_VISIBLE_DEVICES=0 + +export PAD_HQ=1 +export PAD_DURATION=1 + +export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True +export OFFLOAD_T5_CACHE=true +export OFFLOAD_VAE_CACHE=true +export TORCH_CUDA_ARCH_LIST="8.9;9.0" + +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +MAGI_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)" +cd "$MAGI_ROOT" + +PROMPT="${PROMPT:-a woman dancing.}" +TIMESTAMP="${RUN_ID:-$(date "+%Y-%m-%d_%H-%M-%S")}" +PROMPT_DIR_NAME="${PROMPT_DIR_NAME:-$(python3 - "$PROMPT" <<'PY' +import re +import sys +import unicodedata + +prompt = unicodedata.normalize("NFKC", sys.argv[1]).strip() +prompt = re.sub(r"[\\/:\*\?\"<>\|\x00-\x1f]+", "_", prompt) +prompt = re.sub(r"\s+", "_", prompt) +prompt = prompt.strip("._") +print((prompt or "prompt")[:120]) +PY +)}" +OUTPUT_ROOT="${OUTPUT_ROOT:-outputs}" +EXP_DIR="${RUN_DIR:-$OUTPUT_ROOT/${PROMPT_DIR_NAME}_$TIMESTAMP}" +mkdir -p "$EXP_DIR" + +OUTPUT_PATH="${OUTPUT_PATH:-$EXP_DIR/output_$TIMESTAMP.mp4}" +RESIDUAL_JSON="${RESIDUAL_JSON:-$EXP_DIR/residual_stats_$TIMESTAMP.json}" +RESIDUAL_PNG="${RESIDUAL_PNG:-$EXP_DIR/residual_norms_$TIMESTAMP.png}" +L1_REL_JSON="${L1_REL_JSON:-$EXP_DIR/l1_rel_stats_$TIMESTAMP.json}" +L1_REL_PNG="${L1_REL_PNG:-$EXP_DIR/l1_rel_$TIMESTAMP.png}" +L1_REL_RATIO_PNG="${L1_REL_RATIO_PNG:-$EXP_DIR/l1_rel_ratio_$TIMESTAMP.png}" +X_EMBEDDER_L1_REL_PNG="${X_EMBEDDER_L1_REL_PNG:-$EXP_DIR/x_embedder_l1_rel_$TIMESTAMP.png}" +X_EMBEDDER_L1_REL_RATIO_PNG="${X_EMBEDDER_L1_REL_RATIO_PNG:-$EXP_DIR/x_embedder_l1_rel_ratio_$TIMESTAMP.png}" +FLOWCACHE_METRIC_JSON="${FLOWCACHE_METRIC_JSON:-$EXP_DIR/flowcache_metric_stats_$TIMESTAMP.json}" +FLOWCACHE_REL_L1_PNG="${FLOWCACHE_REL_L1_PNG:-$EXP_DIR/flowcache_rel_l1_$TIMESTAMP.png}" +FLOWCACHE_REL_L1_RATIO_PNG="${FLOWCACHE_REL_L1_RATIO_PNG:-$EXP_DIR/flowcache_rel_l1_ratio_$TIMESTAMP.png}" +FLOWCACHE_ACCUMULATED_REL_L1_PNG="${FLOWCACHE_ACCUMULATED_REL_L1_PNG:-$EXP_DIR/flowcache_accumulated_rel_l1_$TIMESTAMP.png}" +LOG_FILE="${LOG_FILE:-$EXP_DIR/infer_$TIMESTAMP.log}" + +export PYTHONPATH="$MAGI_ROOT:${PYTHONPATH:-}" +python3 inference/pipeline/flowcache.py \ + --config_file config/single_run/flowcache_t2v.json \ + --mode t2v \ + --prompt "$PROMPT" \ + --output_path "$OUTPUT_PATH" \ + --additional_config yaml_config/single_run/config.yaml \ + --residual_stats_path "$RESIDUAL_JSON" \ + --l1_rel_stats_path "$L1_REL_JSON" \ + --flowcache_metric_stats_path "$FLOWCACHE_METRIC_JSON" \ + 2>&1 | tee "$LOG_FILE" + +python3 tools/plot_residual_norms.py "$RESIDUAL_JSON" -o "$RESIDUAL_PNG" +python3 tools/plot_l1_rel.py "$L1_REL_JSON" -o "$L1_REL_PNG" +python3 tools/plot_l1_rel.py "$L1_REL_JSON" --y-field l1_rel_ratio -o "$L1_REL_RATIO_PNG" +python3 tools/plot_l1_rel.py "$L1_REL_JSON" --y-field x_embedder_l1_rel -o "$X_EMBEDDER_L1_REL_PNG" +python3 tools/plot_l1_rel.py "$L1_REL_JSON" --y-field x_embedder_l1_rel_ratio -o "$X_EMBEDDER_L1_REL_RATIO_PNG" +python3 tools/plot_l1_rel.py "$FLOWCACHE_METRIC_JSON" --x-field cur_denoise_step --y-field flowcache_rel_l1 -o "$FLOWCACHE_REL_L1_PNG" +python3 tools/plot_l1_rel.py "$FLOWCACHE_METRIC_JSON" --x-field cur_denoise_step --y-field flowcache_rel_l1_ratio -o "$FLOWCACHE_REL_L1_RATIO_PNG" +python3 tools/plot_l1_rel.py "$FLOWCACHE_METRIC_JSON" --x-field cur_denoise_step --y-field flowcache_accumulated_rel_l1 -o "$FLOWCACHE_ACCUMULATED_REL_L1_PNG" + +python3 - "$FLOWCACHE_METRIC_JSON" <<'PY' +import json +import sys + +with open(sys.argv[1], "r") as f: + payload = json.load(f) + +summary = payload.get("chunk_execution_summary", {}) +print("FlowCache actual execution summary:") +for chunk_id in sorted(summary, key=lambda value: int(value)): + item = summary[chunk_id] + print( + " chunk {chunk_idx}: reuse={reuse_steps}, compute={compute_steps}, " + "total={total_steps}, reuse_rate={reuse_rate:.2%}".format(**item) + ) +PY + +echo "Done." +echo " log: $LOG_FILE" +echo " video: $OUTPUT_PATH" +echo " residual json: $RESIDUAL_JSON" +echo " residual plot: $RESIDUAL_PNG" +echo " L1 rel json: $L1_REL_JSON" +echo " L1 rel plot: $L1_REL_PNG" +echo " L1 rel ratio plot: $L1_REL_RATIO_PNG" +echo " x_embedder L1 rel plot: $X_EMBEDDER_L1_REL_PNG" +echo " x_embedder L1 rel ratio plot: $X_EMBEDDER_L1_REL_RATIO_PNG" +echo " FlowCache metric json: $FLOWCACHE_METRIC_JSON" +echo " FlowCache rel L1 plot: $FLOWCACHE_REL_L1_PNG" +echo " FlowCache rel L1 ratio plot: $FLOWCACHE_REL_L1_RATIO_PNG" +echo " FlowCache accumulated rel L1 plot: $FLOWCACHE_ACCUMULATED_REL_L1_PNG" diff --git a/FlowCache/FlowCache4MAGI-1/scripts/single_run/flowcache_v2v.sh b/FlowCache/FlowCache4MAGI-1/scripts/single_run/flowcache_v2v.sh new file mode 100644 index 0000000000000000000000000000000000000000..e6867ddc351383f193685fdea417c6f57058e0f9 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/scripts/single_run/flowcache_v2v.sh @@ -0,0 +1,51 @@ +# Copyright (c) 2025 SandAI. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +export MASTER_ADDR=localhost +export MASTER_PORT=6001 +export GPUS_PER_NODE=1 +export NNODES=1 +export WORLD_SIZE=1 +export CUDA_VISIBLE_DEVICES=7 + +export PAD_HQ=1 +export PAD_DURATION=1 + +export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True +export OFFLOAD_T5_CACHE=true +export OFFLOAD_VAE_CACHE=true +export TORCH_CUDA_ARCH_LIST="8.9;9.0" + +MAGI_ROOT=$(git rev-parse --show-toplevel) + + +OUTPUT_NAME=flowcache +TIMESTAMP=$(date "+%Y-%m-%d_%H-%M-%S") +EXP_DIR="/path/to/output/magi/${TIMESTAMP}_${OUTPUT_NAME}" +mkdir -p "$EXP_DIR" + +LOG_FILE="$EXP_DIR/log_${TIMESTAMP}.log" +OUTPUT_PATH="$EXP_DIR/output.mp4" + +export PYTHONPATH="$MAGI_ROOT:$PYTHONPATH" +python3 inference/pipeline/flowcache.py \ + --config_file config/single_run/flowcache_v2v.json \ + --mode v2v \ + --prompt "Two pillows on a table and two grabber tools hanging above them from which a brown tennis ball and an orange block are suspended. The grabber tools let go of the ball and block. Static shot with no camera movement." \ + --prefix_video_path "/path/to/physicsiq/conditioning_video.mp4" \ + --output_path $OUTPUT_PATH \ + --additional_config addconfig/config.yaml \ + 2>&1 | tee $LOG_FILE + +# a cat sitting on the grass diff --git a/FlowCache/FlowCache4MAGI-1/scripts/single_run/teacache_t2v.sh b/FlowCache/FlowCache4MAGI-1/scripts/single_run/teacache_t2v.sh new file mode 100644 index 0000000000000000000000000000000000000000..e01f11f55fdbcb27fdebb14fffd4940517668db0 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/scripts/single_run/teacache_t2v.sh @@ -0,0 +1,50 @@ +# Copyright (c) 2025 SandAI. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +export MASTER_ADDR=localhost +export MASTER_PORT=6002 +export GPUS_PER_NODE=1 +export NNODES=1 +export WORLD_SIZE=1 +export CUDA_VISIBLE_DEVICES=2 + +export PAD_HQ=1 +export PAD_DURATION=1 + +export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True +export OFFLOAD_T5_CACHE=true +export OFFLOAD_VAE_CACHE=true +export TORCH_CUDA_ARCH_LIST="8.9;9.0" + +MAGI_ROOT=$(git rev-parse --show-toplevel) + + +OUTPUT_NAME=allreuse +TIMESTAMP=$(date "+%Y-%m-%d_%H-%M-%S") +EXP_DIR="/path/to/output/magi/${TIMESTAMP}_${OUTPUT_NAME}" +mkdir -p "$EXP_DIR" + +LOG_FILE="$EXP_DIR/log_${TIMESTAMP}.log" +exec > >(tee -a "$LOG_FILE") 2>&1 +OUTPUT_PATH="$EXP_DIR/output.mp4" + +export PYTHONPATH="$MAGI_ROOT:$PYTHONPATH" +python3 inference/pipeline/teacache_all.py \ + --rel_l1_thresh 0.01 \ + --warmup_steps 5 \ + --config_file config/single_run/flowcache_t2v.json \ + --mode t2v \ + --prompt "A fantasy landscape" \ + --log \ + --output_path $OUTPUT_PATH \ \ No newline at end of file diff --git a/FlowCache/FlowCache4MAGI-1/scripts/single_run/teacache_v2v.sh b/FlowCache/FlowCache4MAGI-1/scripts/single_run/teacache_v2v.sh new file mode 100644 index 0000000000000000000000000000000000000000..d99fb479b9811b6ca93d23a73cdf9f3654ecf4eb --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/scripts/single_run/teacache_v2v.sh @@ -0,0 +1,52 @@ +# Copyright (c) 2025 SandAI. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +export MASTER_ADDR=localhost +export MASTER_PORT=6012 +export GPUS_PER_NODE=1 +export NNODES=1 +export WORLD_SIZE=1 +export CUDA_VISIBLE_DEVICES=1 +export CUDA_HOME="/usr/local/cuda-12.1" + +export PAD_HQ=1 +export PAD_DURATION=1 + +export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True +export OFFLOAD_T5_CACHE=true +export OFFLOAD_VAE_CACHE=true +export TORCH_CUDA_ARCH_LIST="8.9;9.0" + +MAGI_ROOT=$(git rev-parse --show-toplevel) + + +OUTPUT_NAME=allreuse +TIMESTAMP=$(date "+%Y-%m-%d_%H-%M-%S") +EXP_DIR="/path/to/output/magi/${TIMESTAMP}_${OUTPUT_NAME}" +mkdir -p "$EXP_DIR" + +LOG_FILE="$EXP_DIR/log_${TIMESTAMP}.log" +exec > >(tee -a "$LOG_FILE") 2>&1 +OUTPUT_PATH="$EXP_DIR/output.mp4" + +export PYTHONPATH="$MAGI_ROOT:$PYTHONPATH" +python3 inference/pipeline/teacache_all.py \ + --rel_l1_thresh 0.01 \ + --warmup_steps 5 \ + --config_file config/single_run/all_reuse.json \ + --mode v2v \ + --prompt "Two pillows on a table and two grabber tools hanging above them from which a brown tennis ball and an orange block are suspended. The grabber tools let go of the ball and block. Static shot with no camera movement." \ + --prefix_video_path "/path/to/physicsiq/conditioning_video.mp4" \ + --output_path $OUTPUT_PATH \ + --log \ diff --git a/FlowCache/FlowCache4MAGI-1/tools/__pycache__/plot_l1_rel.cpython-312.pyc b/FlowCache/FlowCache4MAGI-1/tools/__pycache__/plot_l1_rel.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1e85d201a85bc30be7690f33a0276cc3134397c8 Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/tools/__pycache__/plot_l1_rel.cpython-312.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/tools/__pycache__/plot_residual_norms.cpython-312.pyc b/FlowCache/FlowCache4MAGI-1/tools/__pycache__/plot_residual_norms.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8453b79cd8122069b9c3886294fe9399dd435f7c Binary files /dev/null and b/FlowCache/FlowCache4MAGI-1/tools/__pycache__/plot_residual_norms.cpython-312.pyc differ diff --git a/FlowCache/FlowCache4MAGI-1/yaml_config/sample/flowcache_physicsiq.yaml b/FlowCache/FlowCache4MAGI-1/yaml_config/sample/flowcache_physicsiq.yaml new file mode 100644 index 0000000000000000000000000000000000000000..81433ce4c0d4aaedf738e6502e90ca9c5c93fb20 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/yaml_config/sample/flowcache_physicsiq.yaml @@ -0,0 +1,36 @@ +# FlowCache PhysicsIQ configuration file +# Usage: bash scripts/sample/flowcache_physicsiq.sh + +# Basic configuration +benchmark: physicsiq +config_file: config/sample/5s_physicsiq.json + +# GPU configuration +gpus: all + +# PhysicsIQ dataset configuration +physicsiq_data_dir: /path/to/physicsiq + +# Output path configuration +base_save_path: /path/to/output/physicsiq + +# Reuse strategy configuration +reuse_strategy: chunkwise +rel_l1_thresh: 0.01 +warmup_steps: 5 + +# KV cache compression configuration +compress_kv_cache: true +total_cache_chunk_nums: 6 +compress_strategy: token +query_granularity: token +mix_lambda: 0.07 +score_weighting_method: no_weight +power: 3 + +# Sampling range control +start: 150 +end: 200 + +# Log configuration +log: false diff --git a/FlowCache/FlowCache4MAGI-1/yaml_config/sample/flowcache_vbench.yaml b/FlowCache/FlowCache4MAGI-1/yaml_config/sample/flowcache_vbench.yaml new file mode 100644 index 0000000000000000000000000000000000000000..85cd1d240495f6b169bedfcde8a407166f6b3f32 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/yaml_config/sample/flowcache_vbench.yaml @@ -0,0 +1,36 @@ +# FlowCache VBench configuration file +# Usage: bash scripts/sample/flowcache_vbench.sh + +# Basic configuration +benchmark: vbench +config_file: config/sample/vbench.json + +# GPU configuration +gpus: all + +# VBench dataset configuration +vbench_prompt_dir: downloads/vbench/prompts_per_dimension + +# Dimension configuration (specify the current dimension to process) +dimension: overall_consistency # Options: subject_consistency, scene, object_class, multiple_objects, color, spatial_relationship, temporal_style, human_action, temporal_flickering, appearance_style + +# Output path configuration +base_save_path: outputs/vbench + +# Reuse strategy configuration +reuse_strategy: chunkwise +rel_l1_thresh: 0.01 +warmup_steps: 5 + +# KV cache compression configuration +compress_kv_cache: true +total_cache_chunk_nums: 6 +budget_cache_chunk_nums: 1 +compress_strategy: token +query_granularity: chunk +mix_lambda: 0.07 +score_weighting_method: no_weight +discard_nearly_clean_chunk: true + +# Log configuration +log: false diff --git a/FlowCache/FlowCache4MAGI-1/yaml_config/sample/teacache_physicsiq.yaml b/FlowCache/FlowCache4MAGI-1/yaml_config/sample/teacache_physicsiq.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f0dbfcb540039e515a0196bf90b360317b814ca9 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/yaml_config/sample/teacache_physicsiq.yaml @@ -0,0 +1,23 @@ +# TeaCache PhysicsIQ configuration file +# Usage: bash scripts/sample/teacache_physicsiq.sh + +# Basic configuration +benchmark: physicsiq +config_file: config/sample/5s_physicsiq.json + +# GPU configuration +gpus: all + +# PhysicsIQ dataset configuration +physicsiq_data_dir: /path/to/physicsiq + +# Output path configuration +base_save_path: /path/to/output/physicsiq + +# Reuse strategy configuration +reuse_strategy: all +rel_l1_thresh: 0.01 +warmup_steps: 5 + +# Log configuration +log: false diff --git a/FlowCache/FlowCache4MAGI-1/yaml_config/sample/teacache_vbench.yaml b/FlowCache/FlowCache4MAGI-1/yaml_config/sample/teacache_vbench.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f6783e30de2788738b58a1c85124931a3f7d8a2b --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/yaml_config/sample/teacache_vbench.yaml @@ -0,0 +1,26 @@ +# TeaCache VBench configuration file +# Usage: bash scripts/sample/teacache_vbench.sh + +# Basic configuration +benchmark: vbench +config_file: config/sample/vbench.json + +# GPU configuration +gpus: all + +# VBench dataset configuration +vbench_prompt_dir: downloads/vbench/prompts_per_dimension + +# Dimension configuration (specify the current dimension to process) +dimension: overall_consistency # Options: subject_consistency, scene, object_class, multiple_objects, color, spatial_relationship, temporal_style, human_action, temporal_flickering, appearance_style + +# Output path configuration +base_save_path: /path/to/output/vbench + +# Reuse strategy configuration +reuse_strategy: all +rel_l1_thresh: 0.01 +warmup_steps: 5 + +# Log configuration +log: false diff --git a/FlowCache/FlowCache4MAGI-1/yaml_config/single_run/config.yaml b/FlowCache/FlowCache4MAGI-1/yaml_config/single_run/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..78463d84ae51ccc251f9272651f299a997feba54 --- /dev/null +++ b/FlowCache/FlowCache4MAGI-1/yaml_config/single_run/config.yaml @@ -0,0 +1,15 @@ +rel_l1_thresh: 0.015 +warmup_steps: 5 +discard_nearly_clean_chunk: true + +compress_kv_cache: true +total_cache_chunk_nums: 5 +compress_strategy: token +mix_lambda: 0.07 +query_granularity: frame +score_weighting_method: no_weight +power: 3 + +log: true +print_peak_memory: true +debug: false \ No newline at end of file