AttributeError: 'dict' object has no attribute 'new_ones'

[cpplyqz]

When I installed teh ESM,I can easy fllow this code:

import torch
import esm

# Load ESM-2 model
model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
batch_converter = alphabet.get_batch_converter()
model.eval()  # disables dropout for deterministic results

# Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
data = [
    ("protein1", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),
    ("protein2", "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),
    ("protein2 with mask","KALTARQQEVFDLIRD<mask>ISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),
    ("protein3",  "K A <mask> I S Q"),
]
batch_labels, batch_strs, batch_tokens = batch_converter(data)
batch_lens = (batch_tokens != alphabet.padding_idx).sum(1)

# Extract per-residue representations (on CPU)
with torch.no_grad():
    results = model(batch_tokens, repr_layers=[33], return_contacts=True)
token_representations = results["representations"][33]

# Generate per-sequence representations via averaging
# NOTE: token 0 is always a beginning-of-sequence token, so the first residue is token 1.
sequence_representations = []
for i, tokens_len in enumerate(batch_lens):
    sequence_representations.append(token_representations[i, 1 : tokens_len - 1].mean(0))

# Look at the unsupervised self-attention map contact predictions
import matplotlib.pyplot as plt
for (_, seq), tokens_len, attention_contacts in zip(data, batch_lens, results["contacts"]):
    plt.matshow(attention_contacts[: tokens_len, : tokens_len])
    plt.title(seq)
    plt.show()

But when I fllowed next code:ESMFold Structure Prediction

import torch
import esm

model = esm.pretrained.esmfold_v1()
model = model.eval().cuda()

# Optionally, uncomment to set a chunk size for axial attention. This can help reduce memory.
# Lower sizes will have lower memory requirements at the cost of increased speed.
# model.set_chunk_size(128)

sequence = "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"
# Multimer prediction can be done with chains separated by ':'

with torch.no_grad():
    output = model.infer_pdb(sequence)

with open("result.pdb", "w") as f:
    f.write(output)

import biotite.structure.io as bsio
struct = bsio.load_structure("result.pdb", extra_fields=["b_factor"])
print(struct.b_factor.mean())  # this will be the pLDDT
# 88.3

I got :

Traceback (most recent call last):
  File "/home/amax/biodata/esm_test/test_esmfold2.py", line 15, in <module>
    output = model.infer_pdb(sequence)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/amax/miniconda3/lib/python3.11/site-packages/esm/esmfold/v1/esmfold.py", line 312, in infer_pdb
    return self.infer_pdbs([sequence], *args, **kwargs)[0]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/amax/miniconda3/lib/python3.11/site-packages/esm/esmfold/v1/esmfold.py", line 307, in infer_pdbs
    output = self.infer(seqs, *args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/amax/miniconda3/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/amax/miniconda3/lib/python3.11/site-packages/esm/esmfold/v1/esmfold.py", line 282, in infer
    output = self.forward(
             ^^^^^^^^^^^^^
  File "/home/amax/miniconda3/lib/python3.11/site-packages/esm/esmfold/v1/esmfold.py", line 180, in forward
    structure: dict = self.trunk(
                      ^^^^^^^^^^^
  File "/home/amax/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/amax/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/amax/miniconda3/lib/python3.11/site-packages/esm/esmfold/v1/trunk.py", line 203, in forward
    structure = self.structure_module(
                ^^^^^^^^^^^^^^^^^^^^^^
  File "/home/amax/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/amax/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/amax/miniconda3/lib/python3.11/site-packages/openfold/model/structure_module.py", line 594, in forward
    mask = s.new_ones(s.shape[:-1])
           ^^^^^^^^^^
AttributeError: 'dict' object has no attribute 'new_ones'

I used openfoldv1.0.0 not the latest version!!! Could any teacher tell me how to solve this problem? Thanks!!!!