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da1cb6a9ec
separate creating dataset from itererating over the dataset to not create eval data for each eval
510 lines
24 KiB
Python
510 lines
24 KiB
Python
import time, math, os
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start = time.perf_counter()
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from pathlib import Path
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import numpy as np
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from tinygrad import Tensor, Device, dtypes, GlobalCounters, TinyJit
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from tinygrad.nn.state import get_parameters, load_state_dict, safe_load
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from tinygrad.helpers import getenv, Context, prod
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from extra.bench_log import BenchEvent, WallTimeEvent
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def tlog(x): print(f"{x:25s} @ {time.perf_counter()-start:5.2f}s")
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def eval_resnet():
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with WallTimeEvent(BenchEvent.FULL):
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# Resnet50-v1.5
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from extra.models.resnet import ResNet50
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tlog("imports")
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GPUS = [f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 6))]
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for x in GPUS: Device[x]
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tlog("got devices") # NOTE: this is faster with rocm-smi running
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class ResnetRunner:
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def __init__(self, device=None):
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self.mdl = ResNet50()
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for x in get_parameters(self.mdl) if device else []: x.to_(device)
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if (fn:=getenv("RESNET_MODEL", "")): load_state_dict(self.mdl, safe_load(fn))
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else: self.mdl.load_from_pretrained()
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self.input_mean = Tensor([0.485, 0.456, 0.406], device=device).reshape(1, -1, 1, 1)
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self.input_std = Tensor([0.229, 0.224, 0.225], device=device).reshape(1, -1, 1, 1)
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def __call__(self, x:Tensor) -> Tensor:
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x = x.permute([0,3,1,2]).cast(dtypes.float32) / 255.0
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x -= self.input_mean
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x /= self.input_std
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return self.mdl(x).log_softmax().argmax(axis=1).realize()
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mdl = TinyJit(ResnetRunner(GPUS))
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tlog("loaded models")
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# evaluation on the mlperf classes of the validation set from imagenet
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from examples.mlperf.dataloader import batch_load_resnet
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iterator = batch_load_resnet(getenv("BS", 128*6), val=getenv("VAL", 1), shuffle=False, pad_first_batch=True)
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def data_get():
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x,y,cookie = next(iterator)
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return x.shard(GPUS, axis=0).realize(), y, cookie
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n,d = 0,0
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proc = data_get()
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tlog("loaded initial data")
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st = time.perf_counter()
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while proc is not None:
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GlobalCounters.reset()
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proc = (mdl(proc[0]), proc[1], proc[2]) # this frees the images
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run = time.perf_counter()
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# load the next data here
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try: next_proc = data_get()
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except StopIteration: next_proc = None
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nd = time.perf_counter()
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y = np.array(proc[1])
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proc = (proc[0].numpy() == y) & (y != -1) # this realizes the models and frees the cookies
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n += proc.sum()
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d += (y != -1).sum()
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et = time.perf_counter()
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tlog(f"****** {n:5d}/{d:5d} {n*100.0/d:.2f}% -- {(run-st)*1000:7.2f} ms to enqueue, {(et-run)*1000:7.2f} ms to realize ({(nd-run)*1000:7.2f} ms fetching). {(len(proc))/(et-st):8.2f} examples/sec. {GlobalCounters.global_ops*1e-12/(et-st):5.2f} TFLOPS")
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st = et
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proc, next_proc = next_proc, None
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tlog("done")
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def eval_unet3d():
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# UNet3D
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from extra.models.unet3d import UNet3D
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from extra.datasets.kits19 import iterate, sliding_window_inference, get_val_files
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from examples.mlperf.metrics import dice_score
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mdl = UNet3D()
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mdl.load_from_pretrained()
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s = 0
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st = time.perf_counter()
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for i, (image, label) in enumerate(iterate(get_val_files()), start=1):
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mt = time.perf_counter()
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pred, label = sliding_window_inference(mdl, image, label)
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et = time.perf_counter()
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print(f"{(mt-st)*1000:.2f} ms loading data, {(et-mt)*1000:.2f} ms to run model")
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s += dice_score(Tensor(pred), Tensor(label)).mean().item()
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print(f"****** {s:.2f}/{i} {s/i:.5f} Mean DICE score")
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st = time.perf_counter()
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def eval_retinanet():
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# RetinaNet with ResNeXt50_32X4D
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from examples.mlperf.dataloader import batch_load_retinanet
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from extra.datasets.openimages import normalize, download_dataset, BASEDIR
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from extra.models.resnet import ResNeXt50_32X4D
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from extra.models.retinanet import RetinaNet
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from pycocotools.coco import COCO
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from pycocotools.cocoeval import COCOeval
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from contextlib import redirect_stdout
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tlog("imports")
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mdl = RetinaNet(ResNeXt50_32X4D())
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mdl.load_from_pretrained()
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tlog("loaded models")
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coco = COCO(download_dataset(base_dir:=getenv("BASEDIR", BASEDIR), 'validation'))
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coco_eval = COCOeval(coco, iouType="bbox")
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coco_evalimgs, evaluated_imgs, ncats, narea = [], [], len(coco_eval.params.catIds), len(coco_eval.params.areaRng)
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tlog("loaded dataset")
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iterator = batch_load_retinanet(coco, True, Path(base_dir), getenv("BS", 8), shuffle=False)
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def data_get():
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x, img_ids, img_sizes, cookie = next(iterator)
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return x.to(Device.DEFAULT).realize(), img_ids, img_sizes, cookie
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n = 0
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proc = data_get()
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tlog("loaded initial data")
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st = time.perf_counter()
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while proc is not None:
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GlobalCounters.reset()
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proc = (mdl(normalize(proc[0])), proc[1], proc[2], proc[3])
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run = time.perf_counter()
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# load the next data here
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try: next_proc = data_get()
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except StopIteration: next_proc = None
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nd = time.perf_counter()
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predictions, img_ids = mdl.postprocess_detections(proc[0].numpy(), orig_image_sizes=proc[2]), proc[1]
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pd = time.perf_counter()
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coco_results = [{"image_id": img_ids[i], "category_id": label, "bbox": box.tolist(), "score": score}
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for i, prediction in enumerate(predictions) for box, score, label in zip(*prediction.values())]
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with redirect_stdout(None):
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coco_eval.cocoDt = coco.loadRes(coco_results)
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coco_eval.params.imgIds = img_ids
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coco_eval.evaluate()
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evaluated_imgs.extend(img_ids)
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coco_evalimgs.append(np.array(coco_eval.evalImgs).reshape(ncats, narea, len(img_ids)))
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n += len(proc[0])
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et = time.perf_counter()
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tlog(f"****** {(run-st)*1000:7.2f} ms to enqueue, {(et-run)*1000:7.2f} ms to realize ({(nd-run)*1000:7.2f} ms fetching, {(pd-run)*1000:4.2f} ms postprocess_detections). {(len(proc))/(et-st):8.2f} examples/sec. {GlobalCounters.global_ops*1e-12/(et-st):5.2f} TFLOPS")
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st = et
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proc, next_proc = next_proc, None
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coco_eval.params.imgIds = evaluated_imgs
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coco_eval._paramsEval.imgIds = evaluated_imgs
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coco_eval.evalImgs = list(np.concatenate(coco_evalimgs, -1).flatten())
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coco_eval.accumulate()
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coco_eval.summarize()
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tlog("done")
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def eval_rnnt():
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# RNN-T
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from extra.models.rnnt import RNNT
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mdl = RNNT()
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mdl.load_from_pretrained()
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from extra.datasets.librispeech import iterate
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from examples.mlperf.metrics import word_error_rate
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LABELS = [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
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c = 0
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scores = 0
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words = 0
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st = time.perf_counter()
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for X, Y in iterate():
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mt = time.perf_counter()
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tt = mdl.decode(Tensor(X[0]), Tensor([X[1]]))
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et = time.perf_counter()
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print(f"{(mt-st)*1000:.2f} ms loading data, {(et-mt)*1000:.2f} ms to run model")
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for n, t in enumerate(tt):
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tnp = np.array(t)
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_, scores_, words_ = word_error_rate(["".join([LABELS[int(tnp[i])] for i in range(tnp.shape[0])])], [Y[n]])
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scores += scores_
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words += words_
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c += len(tt)
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print(f"WER: {scores/words}, {words} words, raw scores: {scores}, c: {c}")
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st = time.perf_counter()
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def eval_bert():
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# Bert-QA
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from extra.models.bert import BertForQuestionAnswering
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mdl = BertForQuestionAnswering()
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mdl.load_from_pretrained()
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@TinyJit
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def run(input_ids, input_mask, segment_ids):
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return mdl(input_ids, input_mask, segment_ids).realize()
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from extra.datasets.squad import iterate
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from examples.mlperf.helpers import get_bert_qa_prediction
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from examples.mlperf.metrics import f1_score
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from transformers import BertTokenizer
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tokenizer = BertTokenizer(str(Path(__file__).parents[2] / "extra/weights/bert_vocab.txt"))
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c = 0
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f1 = 0.0
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st = time.perf_counter()
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for X, Y in iterate(tokenizer):
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mt = time.perf_counter()
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outs = []
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for x in X:
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outs.append(run(Tensor(x["input_ids"]), Tensor(x["input_mask"]), Tensor(x["segment_ids"])).numpy())
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et = time.perf_counter()
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print(f"{(mt-st)*1000:.2f} ms loading data, {(et-mt)*1000:.2f} ms to run model over {len(X)} features")
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pred = get_bert_qa_prediction(X, Y, outs)
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print(f"pred: {pred}\nans: {Y['answers']}")
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f1 += max([f1_score(pred, ans) for ans in Y["answers"]])
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c += 1
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print(f"f1: {f1/c}, raw: {f1}, c: {c}\n")
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st = time.perf_counter()
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def eval_llama3():
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from extra.models.llama import Transformer
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from examples.llama3 import MODEL_PARAMS, load, convert_from_huggingface
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from tinygrad.helpers import tqdm
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BASEDIR = Path(getenv("BASEDIR", "/raid/datasets/c4/"))
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BS = getenv("BS", 4)
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SMALL = getenv("SMALL", 0)
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SEQLEN = getenv("SEQLEN", 8192)
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MODEL_PATH = Path(getenv("MODEL_PATH", "/raid/weights/llama31_8b/"))
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params = MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]
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params = params | {"vocab_size": 32000} if not SMALL else params
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if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: params['n_layers'] = llama_layers
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model = Transformer(**params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
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# load weights
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weights = load(str(MODEL_PATH / "model.safetensors.index.json"))
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if "model.embed_tokens.weight" in weights:
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print("converting from huggingface format")
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weights = convert_from_huggingface(weights, params["n_layers"], params["n_heads"], params["n_kv_heads"])
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load_state_dict(model, weights, strict=False, consume=True)
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@TinyJit
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def eval_step(model, tokens):
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logits:Tensor = model(tokens[:, :-1], start_pos=0, temperature=math.nan)
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loss = logits.sparse_categorical_crossentropy(tokens[:, 1:])
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return loss.flatten().float()
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from examples.mlperf.dataloader import get_llama3_dataset, iterate_llama3_dataset
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eval_dataset = get_llama3_dataset(5760, SEQLEN, BASEDIR, val=True, small=bool(SMALL))
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iter = iterate_llama3_dataset(eval_dataset, BS)
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losses = []
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for tokens in tqdm(iter, total=5760//BS):
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GlobalCounters.reset()
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losses += eval_step(model, tokens).tolist()
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tqdm.write(f"loss: {np.mean(losses)}")
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log_perplexity = np.mean(losses)
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print(f"Log Perplexity: {log_perplexity}")
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# NOTE: BEAM hangs on 8xmi300x with DECODE_BS=384 in final realize below; function is declared here for external testing
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@TinyJit
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def vae_decode(x:Tensor, vae, disable_beam=False) -> Tensor:
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from examples.stable_diffusion import AutoencoderKL
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assert isinstance(vae, AutoencoderKL)
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x = vae.post_quant_conv(1./0.18215 * x)
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x = vae.decoder.conv_in(x)
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x = vae.decoder.mid(x)
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for i, l in enumerate(vae.decoder.up[::-1]):
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print("decode", x.shape)
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for b in l['block']: x = b(x)
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if 'upsample' in l:
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bs,c,py,px = x.shape
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x = x.reshape(bs, c, py, 1, px, 1).expand(bs, c, py, 2, px, 2).reshape(bs, c, py*2, px*2)
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x = l['upsample']['conv'](x)
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if i == len(vae.decoder.up) - 1 and disable_beam:
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with Context(BEAM=0): x.realize()
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else: x.realize()
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x = vae.decoder.conv_out(vae.decoder.norm_out(x).swish())
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x = ((x + 1.0) / 2.0).clip(0.0, 1.0)
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return x
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def eval_stable_diffusion():
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import csv, PIL, sys
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from tqdm import tqdm
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from examples.mlperf.initializers import init_stable_diffusion, gelu_erf
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from examples.stable_diffusion import AutoencoderKL
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from extra.models.unet import UNetModel
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from tinygrad.nn.state import load_state_dict, torch_load
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from tinygrad.helpers import BEAM
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from extra.models import clip
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from extra.models.clip import FrozenOpenClipEmbedder
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from extra.models.clip import OpenClipEncoder
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from extra.models.inception import FidInceptionV3
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config = {}
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GPUS = config["GPUS"] = [f"{Device.DEFAULT}:{i}" for i in range(getenv("GPUS", 1))]
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for x in GPUS: Device[x]
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print(f"running eval on {GPUS}")
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seed = config["seed"] = getenv("SEED", 12345)
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CKPTDIR = config["CKPTDIR"] = Path(getenv("CKPTDIR", "./checkpoints"))
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DATADIR = config["DATADIR"] = Path(getenv("DATADIR", "./datasets"))
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CONTEXT_BS = config["CONTEXT_BS"] = getenv("CONTEXT_BS", 1 * len(GPUS))
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DENOISE_BS = config["DENOISE_BS"] = getenv("DENOISE_BS", 1 * len(GPUS))
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DECODE_BS = config["DECODE_BS"] = getenv("DECODE_BS", 1 * len(GPUS))
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INCEPTION_BS = config["INCEPTION_BS"] = getenv("INCEPTION_BS", 1 * len(GPUS))
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CLIP_BS = config["CLIP_BS"] = getenv("CLIP_BS", 1 * len(GPUS))
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EVAL_CKPT_DIR = config["EVAL_CKPT_DIR"] = getenv("EVAL_CKPT_DIR", "")
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STOP_IF_CONVERGED = config["STOP_IF_CONVERGED"] = getenv("STOP_IF_CONVERGED", 0)
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if (WANDB := getenv("WANDB", "")):
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import wandb
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wandb.init(config=config, project="MLPerf-Stable-Diffusion")
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assert EVAL_CKPT_DIR != "", "provide a directory with checkpoints to be evaluated"
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print(f"running eval on checkpoints in {EVAL_CKPT_DIR}\nSEED={seed}")
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eval_queue:list[tuple[int, Path]] = []
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for p in Path(EVAL_CKPT_DIR).iterdir():
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if p.name.endswith(".safetensors"):
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ckpt_iteration = p.name.split(".safetensors")[0]
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assert ckpt_iteration.isdigit(), f"invalid checkpoint name: {p.name}, expected <digits>.safetensors"
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eval_queue.append((int(ckpt_iteration), p))
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assert len(eval_queue), f'no files ending with ".safetensors" were found in {EVAL_CKPT_DIR}'
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print(sorted(eval_queue, reverse=True))
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Tensor.manual_seed(seed) # seed for weight initialization
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model, unet, sqrt_alphas_cumprod, sqrt_one_minus_alphas_cumprod = init_stable_diffusion("v2-mlperf-eval", CKPTDIR / "sd" / "512-base-ema.ckpt", GPUS)
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# load prompts for generating images for validation; 2 MB of data total
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with open(DATADIR / "coco2014" / "val2014_30k.tsv") as f:
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reader = csv.DictReader(f, delimiter="\t")
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eval_inputs:list[dict] = [{"image_id": int(row["image_id"]), "id": int(row["id"]), "caption": row["caption"]} for row in reader]
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assert len(eval_inputs) == 30_000
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# NOTE: the clip weights are the same between model.cond_stage_model and clip_encoder
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eval_timesteps = list(reversed(range(1, 1000, 20)))
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original_device, Device.DEFAULT = Device.DEFAULT, "CPU"
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# The choice of alphas_prev[0] = alphas_cumprod[0] seems arbitrary, but it's how the mlperf ref does it:
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# alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
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eval_alphas_prev = model.alphas_cumprod[0:1].cat(model.alphas_cumprod[list(range(1, 1000, 20))[:-1]]).to(GPUS).realize()
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inception = FidInceptionV3().load_from_pretrained(CKPTDIR / "inception" / "pt_inception-2015-12-05-6726825d.pth")
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vision_cfg = {'width': 1280, 'layers': 32, 'd_head': 80, 'image_size': 224, 'patch_size': 14}
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text_cfg = {'width': 1024, 'n_heads': 16, 'layers': 24, 'vocab_size': 49408, 'ctx_length': 77}
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clip.gelu = gelu_erf
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clip_encoder = OpenClipEncoder(1024, text_cfg, vision_cfg)
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loaded = torch_load(CKPTDIR / "clip" / "open_clip_pytorch_model.bin")
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loaded.update({"attn_mask": clip_encoder.attn_mask, "mean": clip_encoder.mean, "std": clip_encoder.std})
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load_state_dict(clip_encoder, loaded)
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Device.DEFAULT=original_device
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@TinyJit
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def denoise_step(x:Tensor, x_x:Tensor, t_t:Tensor, uc_c:Tensor, sqrt_alphas_cumprod_t:Tensor, sqrt_one_minus_alphas_cumprod_t:Tensor,
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alpha_prev:Tensor, unet:UNetModel, GPUS) -> Tensor:
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out_uncond, out = unet(x_x, t_t, uc_c).to("CPU").reshape(-1, 2, 4, 64, 64).chunk(2, dim=1)
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out_uncond = out_uncond.squeeze(1).shard(GPUS,axis=0)
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out = out.squeeze(1).shard(GPUS,axis=0)
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v_t = out_uncond + 8.0 * (out - out_uncond)
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e_t = sqrt_alphas_cumprod_t * v_t + sqrt_one_minus_alphas_cumprod_t * x
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pred_x0 = sqrt_alphas_cumprod_t * x - sqrt_one_minus_alphas_cumprod_t * v_t
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dir_xt = (1. - alpha_prev).sqrt() * e_t
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x_prev = alpha_prev.sqrt() * pred_x0 + dir_xt
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return x_prev.realize()
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def shard_tensor(t:Tensor) -> Tensor: return t.shard(GPUS, axis=0) if len(GPUS) > 1 else t.to(GPUS[0])
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def get_batch(whole:Tensor, i:int, bs:int) -> tuple[Tensor, int]:
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batch = whole[i: i + bs].to("CPU")
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if (unpadded_bs:=batch.shape[0]) < bs:
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batch = batch.cat(batch[-1:].expand(bs - unpadded_bs, *batch[-1].shape))
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return batch, unpadded_bs
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@Tensor.train(mode=False)
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def eval_unet(eval_inputs:list[dict], unet:UNetModel, cond_stage:FrozenOpenClipEmbedder, first_stage:AutoencoderKL,
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inception:FidInceptionV3, clip:OpenClipEncoder) -> tuple[float, float]:
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# Eval is divided into 5 jits, one per model
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# It doesn't make sense to merge these jits, e.g. unet repeats 50 times in isolation; images fork to separate inception/clip
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|
# We're generating and scoring 30,000 images per eval, and all the data can flow through one jit at a time
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# To maximize throughput for each jit, we have only one model/jit on the GPU at a time, and pool outputs from each jit off-GPU
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for model in (unet, first_stage, inception, clip):
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Tensor.realize(*[p.to_("CPU") for p in get_parameters(model)])
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|
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uc_written = False
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models = (cond_stage, unet, first_stage, inception, clip)
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jits = (jit_context:=TinyJit(cond_stage.embed_tokens), denoise_step, vae_decode, jit_inception:=TinyJit(inception),
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jit_clip:=TinyJit(clip.get_clip_score))
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all_bs = (CONTEXT_BS, DENOISE_BS, DECODE_BS, INCEPTION_BS, CLIP_BS)
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if (EVAL_SAMPLES:=getenv("EVAL_SAMPLES", 0)) and EVAL_SAMPLES > 0:
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eval_inputs = eval_inputs[0:EVAL_SAMPLES]
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output_shapes = [(ns:=len(eval_inputs),77), (ns,77,1024), (ns,4,64,64), (ns,3,512,512), (ns,2048), (ns,)]
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# Writing progress to disk lets us resume eval if we crash
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stages = ["tokens", "embeds", "latents", "imgs", "inception", "clip"]
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disk_tensor_names, disk_tensor_shapes = stages + ["end", "uc"], output_shapes + [(6,), (1,77,1024)]
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if not all(os.path.exists(f"{EVAL_CKPT_DIR}/{name}.bytes") for name in disk_tensor_names):
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for name, shape in zip(disk_tensor_names, disk_tensor_shapes):
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file = Path(f"{EVAL_CKPT_DIR}/{name}.bytes")
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file.unlink(missing_ok=True)
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with file.open("wb") as f: f.truncate(prod(shape) * 4)
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progress = {name: Tensor.empty(*shape, device=f"disk:{EVAL_CKPT_DIR}/{name}.bytes", dtype=dtypes.int if name in {"tokens", "end"} else dtypes.float)
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for name, shape in zip(disk_tensor_names, disk_tensor_shapes)}
|
|
|
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def embed_tokens(tokens:Tensor) -> Tensor:
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|
nonlocal uc_written
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|
if not uc_written:
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|
with Context(BEAM=0): progress["uc"].assign(cond_stage.embed_tokens(cond_stage.tokenize("").to(GPUS)).to("CPU").realize()).realize()
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|
uc_written = True
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|
return jit_context(shard_tensor(tokens))
|
|
|
|
def generate_latents(embeds:Tensor) -> Tensor:
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|
uc_c = Tensor.stack(progress["uc"].to("CPU").expand(bs, 77, 1024), embeds, dim=1).reshape(-1, 77, 1024)
|
|
uc_c = shard_tensor(uc_c)
|
|
x = shard_tensor(Tensor.randn(bs,4,64,64))
|
|
for step_idx, timestep in enumerate(tqdm(eval_timesteps)):
|
|
reversed_idx = Tensor([50 - step_idx - 1], device=GPUS)
|
|
alpha_prev = eval_alphas_prev[reversed_idx]
|
|
ts = Tensor.full(bs, fill_value=timestep, dtype=dtypes.int, device="CPU")
|
|
ts_ts = shard_tensor(ts.cat(ts))
|
|
ts = shard_tensor(ts)
|
|
sqrt_alphas_cumprod_t = sqrt_alphas_cumprod[ts].reshape(bs, 1, 1, 1)
|
|
sqrt_one_minus_alphas_cumprod_t = sqrt_one_minus_alphas_cumprod[ts].reshape(bs, 1, 1, 1)
|
|
x_x = shard_tensor(Tensor.stack(x.to("CPU"), x.to("CPU"), dim=1).reshape(-1, 4, 64, 64))
|
|
x.assign(denoise_step(x, x_x, ts_ts, uc_c, sqrt_alphas_cumprod_t, sqrt_one_minus_alphas_cumprod_t, alpha_prev, unet, GPUS)).realize()
|
|
return x
|
|
|
|
def decode_latents(latents:Tensor) -> Tensor: return vae_decode(shard_tensor(latents), first_stage, disable_beam=True)
|
|
def generate_inception(imgs:Tensor) -> Tensor: return jit_inception(shard_tensor(imgs))[:,:,0,0]
|
|
|
|
def calc_clip_scores(batch:Tensor, batch_tokens:Tensor) -> Tensor:
|
|
# Tensor.interpolate does not yet support bicubic, so we use PIL
|
|
batch = (batch.to(GPUS[0]).permute(0,2,3,1) * 255).clip(0, 255).cast(dtypes.uint8).numpy()
|
|
batch = [np.array(PIL.Image.fromarray(batch[i]).resize((224,224), PIL.Image.BICUBIC)) for i in range(bs)]
|
|
batch = shard_tensor(Tensor(np.stack(batch, axis=0).transpose(0,3,1,2), device="CPU").realize())
|
|
batch = batch.cast(dtypes.float) / 255
|
|
batch = (batch - model.mean) / model.std
|
|
batch = jit_clip(shard_tensor(batch_tokens), batch)
|
|
return batch
|
|
|
|
callbacks = (embed_tokens, generate_latents, decode_latents, generate_inception, calc_clip_scores)
|
|
|
|
# save every forward pass output to disk; NOTE: this needs ~100 GB disk space because 30k images are large
|
|
def stage_progress(stage_idx:int) -> int: return progress["end"].to("CPU")[stage_idx].item()
|
|
if stage_progress(0) < len(eval_inputs):
|
|
tokens = []
|
|
for i in tqdm(range(0, len(eval_inputs), CONTEXT_BS)):
|
|
subset = [cond_stage.tokenize(row["caption"], device="CPU") for row in eval_inputs[i: i+CONTEXT_BS]]
|
|
tokens.append(Tensor.cat(*subset, dim=0).realize())
|
|
progress["tokens"].assign(Tensor.cat(*tokens, dim=0).realize()).realize()
|
|
progress["end"][0:1].assign(Tensor([len(eval_inputs)], dtype=dtypes.int)).realize()
|
|
prev_stage = "tokens"
|
|
tokens = progress["tokens"]
|
|
|
|
# wrapper code for every model
|
|
for stage_idx, model, jit, bs, callback in zip(range(1,6), models, jits, all_bs, callbacks):
|
|
stage = stages[stage_idx]
|
|
if stage_progress(stage_idx) >= len(eval_inputs):
|
|
prev_stage = stage
|
|
continue # use cache
|
|
t0 = time.perf_counter()
|
|
print(f"starting eval with model: {model}")
|
|
if stage_idx == 1: inputs = tokens
|
|
elif stage_idx == 5: inputs = progress["imgs"]
|
|
else: inputs = progress[prev_stage]
|
|
|
|
Tensor.realize(*[p.to_(GPUS) for p in get_parameters(model)])
|
|
for batch_idx in tqdm(range(stage_progress(stage_idx), inputs.shape[0], bs)):
|
|
t1 = time.perf_counter()
|
|
batch, unpadded_bs = get_batch(inputs, batch_idx, bs)
|
|
if isinstance(model, OpenClipEncoder): batch = callback(batch, get_batch(tokens, batch_idx, bs)[0].realize())
|
|
else: batch = callback(batch)
|
|
# to(GPUS[0]) is necessary for this to work, without that the result is still on GPUS, probably due to a bug
|
|
batch = batch.to(GPUS[0]).to("CPU")[0:unpadded_bs].realize()
|
|
progress[stage][batch_idx: batch_idx + bs].assign(batch).realize()
|
|
# keep track of what our last output was, so we can resume from there if we crash in this loop
|
|
progress["end"][stage_idx: stage_idx + 1].assign(Tensor([batch_idx + bs], dtype=dtypes.int)).realize()
|
|
print(f"model: {model}, batch_idx: {batch_idx}, elapsed: {(time.perf_counter() - t1):.2f}")
|
|
del batch
|
|
|
|
jit.reset()
|
|
Tensor.realize(*[p.to_("CPU") for p in get_parameters(model)])
|
|
print(f"done with model: {model}, elapsed: {(time.perf_counter() - t0):.2f}")
|
|
prev_stage = stage
|
|
|
|
inception_stats_fn = str(DATADIR / "coco2014" / "val2014_30k_stats.npz")
|
|
fid_score = inception.compute_score(progress["inception"].to("CPU"), inception_stats_fn)
|
|
clip_score = progress["clip"].to(GPUS[0]).mean().item()
|
|
for name in disk_tensor_names:
|
|
Path(f"{EVAL_CKPT_DIR}/{name}.bytes").unlink(missing_ok=True)
|
|
|
|
if EVAL_SAMPLES and BEAM:
|
|
print("BEAM COMPLETE", flush=True) # allows wrapper script to detect BEAM search completion and retry if it failed
|
|
sys.exit() # Don't eval additional models; we don't care about clip/fid scores when running BEAM on eval sample subset
|
|
|
|
return clip_score, fid_score
|
|
|
|
# evaluate checkpoints in reverse chronological order
|
|
for ckpt_iteration, p in sorted(eval_queue, reverse=True):
|
|
unet_ckpt = safe_load(p)
|
|
load_state_dict(unet, unet_ckpt)
|
|
clip_score, fid_score = eval_unet(eval_inputs, unet, model.cond_stage_model, model.first_stage_model, inception, clip_encoder)
|
|
converged = True if clip_score >= 0.15 and fid_score <= 90 else False
|
|
print(f"eval results for {EVAL_CKPT_DIR}/{p.name}: clip={clip_score}, fid={fid_score}, converged={converged}")
|
|
if WANDB:
|
|
wandb.log({"eval/ckpt_iteration": ckpt_iteration, "eval/clip_score": clip_score, "eval/fid_score": fid_score})
|
|
if converged and STOP_IF_CONVERGED:
|
|
print(f"Convergence detected, exiting early before evaluating other checkpoints due to STOP_IF_CONVERGED={STOP_IF_CONVERGED}")
|
|
sys.exit()
|
|
|
|
# for testing
|
|
return clip_score, fid_score, ckpt_iteration
|
|
|
|
if __name__ == "__main__":
|
|
# inference only
|
|
Tensor.training = False
|
|
|
|
models = getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert").split(",")
|
|
for m in models:
|
|
nm = f"eval_{m}"
|
|
if nm in globals():
|
|
print(f"eval {m}")
|
|
globals()[nm]()
|