Enable distributed sample image generation on multi-GPU enviroment

library/train_util.py

@@ -19,7 +19,7 @@
     Tuple,
     Union,
 )
-from accelerate import Accelerator, InitProcessGroupKwargs, DistributedDataParallelKwargs
+from accelerate import Accelerator, InitProcessGroupKwargs, DistributedDataParallelKwargs, PartialState
 import gc
 import glob
 import math
@@ -4636,7 +4636,6 @@
 
     return prompt_dict
 
-
 def sample_images_common(
     pipe_class,
     accelerator: Accelerator,
@@ -4654,6 +4653,7 @@
     """
     StableDiffusionLongPromptWeightingPipelineの改造版を使うようにしたので、clip skipおよびプロンプトの重みづけに対応した
     """
+
     if steps == 0:
         if not args.sample_at_first:
             return
@@ -4668,13 +4668,15 @@
             if steps % args.sample_every_n_steps != 0 or epoch is not None:  # steps is not divisible or end of epoch
                 return
 
+    distributed_state = PartialState() #testing implementation of multi gpu distributed inference
+
     print(f"\ngenerating sample images at step / サンプル画像生成 ステップ: {steps}")
     if not os.path.isfile(args.sample_prompts):
         print(f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}")
         return
 
     org_vae_device = vae.device  # CPUにいるはず
-    vae.to(device)
+    vae.to(distributed_state.device)
 
     # unwrap unet and text_encoder(s)
     unet = accelerator.unwrap_model(unet)
@@ -4700,12 +4702,11 @@
         with open(args.sample_prompts, "r", encoding="utf-8") as f:
             prompts = json.load(f)
 
-    schedulers: dict = {}
+    # schedulers: dict = {}  cannot find where this is used
     default_scheduler = get_my_scheduler(
         sample_sampler=args.sample_sampler,
         v_parameterization=args.v_parameterization,
     )
-    schedulers[args.sample_sampler] = default_scheduler
 
     pipeline = pipe_class(
         text_encoder=text_encoder,
@@ -4718,114 +4719,135 @@
         requires_safety_checker=False,
         clip_skip=args.clip_skip,
     )
-    pipeline.to(device)
-
+    pipeline.to(distributed_state.device)
     save_dir = args.output_dir + "/sample"
     os.makedirs(save_dir, exist_ok=True)
+
+    # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processess available (number of devices available)
+    # prompt_dicts are assigned to lists based on order of processes, to attempt to time the image creation time to match enum order. Probably only works when steps and sampler are identical.
+    per_process_prompts = generate_per_device_prompt_list(prompts, num_of_processes = distributed_state.num_processes, prompt_replacement = prompt_replacement)
 
     rng_state = torch.get_rng_state()
     cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None
+    # True random sample image generation
+    torch.seed()
+    torch.cuda.seed()
 
     with torch.no_grad():
-        # with accelerator.autocast():
-        for i, prompt_dict in enumerate(prompts):
-            if not accelerator.is_main_process:
-                continue
-
-            if isinstance(prompt_dict, str):
-                prompt_dict = line_to_prompt_dict(prompt_dict)
-
-            assert isinstance(prompt_dict, dict)
-            negative_prompt = prompt_dict.get("negative_prompt")
-            sample_steps = prompt_dict.get("sample_steps", 30)
-            width = prompt_dict.get("width", 512)
-            height = prompt_dict.get("height", 512)
-            scale = prompt_dict.get("scale", 7.5)
-            seed = prompt_dict.get("seed")
-            controlnet_image = prompt_dict.get("controlnet_image")
-            prompt: str = prompt_dict.get("prompt", "")
-            sampler_name: str = prompt_dict.get("sample_sampler", args.sample_sampler)
-
-            if seed is not None:
-                torch.manual_seed(seed)
-                torch.cuda.manual_seed(seed)
-
-            scheduler = schedulers.get(sampler_name)
-            if scheduler is None:
-                scheduler = get_my_scheduler(
-                    sample_sampler=sampler_name,
-                    v_parameterization=args.v_parameterization,
-                )
-                schedulers[sampler_name] = scheduler
-            pipeline.scheduler = scheduler
-
-            if prompt_replacement is not None:
-                prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1])
-                if negative_prompt is not None:
-                    negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])
-
-            if controlnet_image is not None:
-                controlnet_image = Image.open(controlnet_image).convert("RGB")
-                controlnet_image = controlnet_image.resize((width, height), Image.LANCZOS)
-
-            height = max(64, height - height % 8)  # round to divisible by 8
-            width = max(64, width - width % 8)  # round to divisible by 8
-            print(f"prompt: {prompt}")
-            print(f"negative_prompt: {negative_prompt}")
-            print(f"height: {height}")
-            print(f"width: {width}")
-            print(f"sample_steps: {sample_steps}")
-            print(f"scale: {scale}")
-            print(f"sample_sampler: {sampler_name}")
-            if seed is not None:
-                print(f"seed: {seed}")
-            with accelerator.autocast():
-                latents = pipeline(
-                    prompt=prompt,
-                    height=height,
-                    width=width,
-                    num_inference_steps=sample_steps,
-                    guidance_scale=scale,
-                    negative_prompt=negative_prompt,
-                    controlnet=controlnet,
-                    controlnet_image=controlnet_image,
-                )
+        with distributed_state.split_between_processes(per_process_prompts) as prompt_dict_lists:
+             for prompt_dict in prompt_dict_lists[0]:
+                 sample_image_inference(accelerator, args, pipeline, save_dir, prompt_dict, epoch, steps, controlnet=controlnet)
 
-            image = pipeline.latents_to_image(latents)[0]
-
-            ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime())
-            num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}"
-            seed_suffix = "" if seed is None else f"_{seed}"
-            img_filename = (
-                f"{'' if args.output_name is None else args.output_name + '_'}{ts_str}_{num_suffix}_{i:02d}{seed_suffix}.png"
-            )
-
-            image.save(os.path.join(save_dir, img_filename))
-
-            # wandb有効時のみログを送信
-            try:
-                wandb_tracker = accelerator.get_tracker("wandb")
-                try:
-                    import wandb
-                except ImportError:  # 事前に一度確認するのでここはエラー出ないはず
-                    raise ImportError("No wandb / wandb がインストールされていないようです")
-
-                wandb_tracker.log({f"sample_{i}": wandb.Image(image)})
-            except:  # wandb 無効時
-                pass
 
     # clear pipeline and cache to reduce vram usage
     del pipeline
-    torch.cuda.empty_cache()
 
+    with torch.cuda.device(torch.cuda.current_device()):
+        torch.cuda.empty_cache()
+
     torch.set_rng_state(rng_state)
     if cuda_rng_state is not None:
         torch.cuda.set_rng_state(cuda_rng_state)
     vae.to(org_vae_device)
 
+def generate_per_device_prompt_list(prompts, num_of_processes, prompt_replacement=None):
+
+    # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processess available (number of devices available)
+    # prompt_dicts are assigned to lists based on order of processes, to attempt to time the image creation time to match enum order. Probably only works when steps and sampler are identical.
+    per_process_prompts = [[] for i in range(num_of_processes)]
+    for i, prompt in enumerate(prompts):
+        if isinstance(prompt, str):
+            prompt = line_to_prompt_dict(prompt)
+        assert isinstance(prompt, dict)
+        prompt.pop("subset", None) # Clean up subset key
+        prompt["enum"] = i
+        # Adds an enumerator to the dict based on prompt position. Used later to name image files. Also cleanup of extra data in original prompt dict.
+        if prompt_replacement is not None:
+            prompt["prompt"] = prompt["prompt"].replace(prompt_replacement[0], prompt_replacement[1])
+            if prompt["negative_prompt"] is not None:
+                prompt["negative_prompt"] = prompt["negative_prompt"].replace(prompt_replacement[0], prompt_replacement[1])
+        # Refactor prompt replacement to here in order to simplify sample_image_inference function.
+        per_process_prompts[i % num_of_processes].append(prompt)
+    return per_process_prompts
+
+def sample_image_inference(accelerator: Accelerator, args: argparse.Namespace, pipeline, save_dir, prompt_dict, epoch, steps, controlnet=None):
+    assert isinstance(prompt_dict, dict)
+    negative_prompt = prompt_dict.get("negative_prompt")
+    sample_steps = prompt_dict.get("sample_steps", 30)
+    width = prompt_dict.get("width", 512)
+    height = prompt_dict.get("height", 512)
+    scale = prompt_dict.get("scale", 7.5)
+    seed = prompt_dict.get("seed")
+    controlnet_image = prompt_dict.get("controlnet_image")
+    prompt: str = prompt_dict.get("prompt", "")
+    sampler_name: str = prompt_dict.get("sample_sampler", args.sample_sampler)
+
+    if seed is not None:
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+
+    scheduler = get_my_scheduler(
+        sample_sampler=sampler_name,
+        v_parameterization=args.v_parameterization,
+    )
+    pipeline.scheduler = scheduler
+
+    if controlnet_image is not None:
+        controlnet_image = Image.open(controlnet_image).convert("RGB")
+        controlnet_image = controlnet_image.resize((width, height), Image.LANCZOS)
+
+    height = max(64, height - height % 8)  # round to divisible by 8
+    width = max(64, width - width % 8)  # round to divisible by 8
+    print(f"\nprompt: {prompt}")
+    print(f"negative_prompt: {negative_prompt}")
+    print(f"height: {height}")
+    print(f"width: {width}")
+    print(f"sample_steps: {sample_steps}")
+    print(f"scale: {scale}")
+    print(f"sample_sampler: {sampler_name}")
+    if seed is not None:
+        print(f"seed: {seed}")
+    with accelerator.autocast():
+        latents = pipeline(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=sample_steps,
+            guidance_scale=scale,
+            negative_prompt=negative_prompt,
+            controlnet=controlnet,
+            controlnet_image=controlnet_image,
+        )
+    image = pipeline.latents_to_image(latents)[0]
+    # adding accelerator.wait_for_everyone() here should sync up and ensure that sample images are saved in the same order as the original prompt list
+    ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime())
+    num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}"
+    seed_suffix = "" if seed is None else f"_{seed}"
+    i: int = prompt_dict["enum"]
+    img_filename = (
+        f"{'' if args.output_name is None else args.output_name + '_'}{num_suffix}_{i:02d}_{ts_str}{seed_suffix}.png"
+    )
+
+    image.save(os.path.join(save_dir, img_filename))
+    if seed is not None:
+        torch.seed()
+        torch.cuda.seed()
+    # wandb有効時のみログを送信
+    try:
+        wandb_tracker = accelerator.get_tracker("wandb")
+        try:
+            import wandb
+        except ImportError:  # 事前に一度確認するのでここはエラー出ないはず
+            raise ImportError("No wandb / wandb がインストールされていないようです")
 
+        wandb_tracker.log({f"sample_{i}": wandb.Image(image)})
+    except:  # wandb 無効時
+        pass
 # endregion
 
+
+
+
 # region 前処理用