File size: 2,459 Bytes
5ee43e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | import argparse
import logging
import time
import torch
from transformers import AutoTokenizer, DebertaForSequenceClassification
import torch_neuronx # ensures Neuron backend is available
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def main():
parser = argparse.ArgumentParser(
description="DeBERTa sequence-classification with torch.compile on Neuron"
)
parser.add_argument(
"--model",
type=str,
default="microsoft/deberta-base",
help="DeBERTa model name on Hugging Face Hub",
)
parser.add_argument("--batch-size", type=int, default=1, help="Batch size")
args = parser.parse_args()
torch.set_default_dtype(torch.float32)
torch.manual_seed(42)
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(args.model)
model = DebertaForSequenceClassification.from_pretrained(
args.model, torch_dtype=torch.float32, attn_implementation="eager"
)
model.eval()
# Tokenize sample text
text = "DeBERTa improves BERT and RoBERTa using disentangled attention."
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
# Pre-run once to fix shapes before compilation
with torch.no_grad():
logits = model(**inputs).logits
# Compile forward pass (allow graph breaks to avoid instruction-limit)
model.forward = torch.compile(model.forward, backend="neuron", fullgraph=False)
# Warmup
warmup_start = time.time()
with torch.no_grad():
_ = model(**inputs)
warmup_time = time.time() - warmup_start
# Actual run
run_start = time.time()
with torch.no_grad():
logits = model(**inputs).logits
run_time = time.time() - run_start
# Decode result
predicted_class_id = logits.argmax().item()
predicted_label = model.config.id2label[predicted_class_id]
logger.info("Warmup: %.2f s, Run: %.4f s", warmup_time, run_time)
logger.info("Predicted label: %s", predicted_label)
if __name__ == "__main__":
main()
"""
torch._dynamo.exc.TorchRuntimeError: Dynamo failed to run FX node with fake tensors: call_function <built-in function linear>(*(FakeTensor(..., device='neuron:0', size=(1, 18, 768)), Parameter(FakeTensor(..., size=(2304, 768), requires_grad=True)), None), **{}): got RuntimeError('Unhandled FakeTensor Device Propagation for aten.mm.default, found two different devices neuron:0, cpu')
""" |