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yolov5_train_sample

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jason.wang 2025-02-13 14:53:41 +08:00
parent 597ff168f4
commit 4fb0c63f54
1 changed files with 32 additions and 10 deletions
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train.py
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@ -99,6 +99,7 @@ RANK = int(os.getenv("RANK", -1))
WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
GIT_INFO = check_git_info() GIT_INFO = check_git_info()
from tpu_mlir.python.tools.train.tpu_mlir_jit import device, aot_backend
from torch._functorch.aot_autograd import aot_export_joint_simple, aot_export_module from torch._functorch.aot_autograd import aot_export_joint_simple, aot_export_module
import torch.optim as optim import torch.optim as optim
from compile.FxGraphConvertor import fx2mlir from compile.FxGraphConvertor import fx2mlir
@ -454,7 +455,8 @@ def train(hyp, opt, device, callbacks):
if RANK in {-1, 0}: if RANK in {-1, 0}:
pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar
optimizer.zero_grad() optimizer.zero_grad()
zwyjit = JitNet(model, compute_loss) model_opt = torch.compile(model, backend=aot_backend)
# zwyjit = JitNet(model, compute_loss)
for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
callbacks.run("on_train_batch_start") callbacks.run("on_train_batch_start")
ni = i + nb * epoch # number integrated batches (since train start) ni = i + nb * epoch # number integrated batches (since train start)
@ -481,15 +483,13 @@ def train(hyp, opt, device, callbacks):
# Forward # Forward
with torch.cuda.amp.autocast(amp): with torch.cuda.amp.autocast(amp):
# print(1) pred = model_opt(imgs) # forward
# zwy = SophonJointCompile(model, [imgs, targets], trace_joint=True, output_loss_index=0, args=None) loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
# pred = model(imgs) # forward # fx_g, signature = aot_export_module(
# loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size # model, [imgs], trace_joint=False, output_loss_index=0, decompositions=_get_disc_decomp()
# loss, loss_items = zwyjit(imgs, targets.to(device)) # )
fx_g, signature = aot_export_module( # print('fx_g:', fx_g)
zwyjit, [imgs, targets], trace_joint=True, output_loss_index=0, decompositions=_get_disc_decomp()
)
print(fx_g)
if RANK != -1: if RANK != -1:
loss *= WORLD_SIZE # gradient averaged between devices in DDP mode loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
if opt.quad: if opt.quad:
@ -1061,4 +1061,26 @@ def run(**kwargs):
if __name__ == "__main__": if __name__ == "__main__":
opt = parse_opt() opt = parse_opt()
parser = argparse.ArgumentParser()
parser.add_argument("--chip", default="bm1690", choices=['bm1684x', 'bm1690','sg2260'],
help="chip name")
parser.add_argument("--debug", default="print_ori_fx_graph",
help="debug")
parser.add_argument("--cmp", action='store_true',
help="enable cmp")
parser.add_argument("--fast_test", action='store_true',
help="fast_test")
parser.add_argument("--skip_module_num", default=0, type=int,
help='skip_module_num')
parser.add_argument("--exit_at", default=-1, type=int,
help='exit_at')
parser.add_argument("--num_core", default=1, type=int,
help='The numer of TPU cores used for parallel computation')
parser.add_argument("--opt", default=2, type=int,
help='layer group opt')
parser.add_argument("--fp", default="",help="fp")
import tpu_mlir.python.tools.train.tpu_mlir_jit as tpu_mlir_jit
tpu_mlir_jit.args = parser.parse_known_args()[0]
main(opt) main(opt)