Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

VQNet runtimeError: Tensors in different devices #38

Open
302850047 opened this issue May 27, 2024 · 1 comment
Open

VQNet runtimeError: Tensors in different devices #38

302850047 opened this issue May 27, 2024 · 1 comment

Comments

@302850047
Copy link

import os
import pickle
import tarfile
from tqdm import tqdm
import matplotlib
import numpy as np
import pyqpanda as pq
import pyvqnet.nn as nn
from keras.utils import to_categorical
import tensorflow as tf
from pyvqnet.data.data import data_generator
from pyvqnet.nn.linear import Linear
from pyvqnet.nn.loss import CrossEntropyLoss
from pyvqnet.nn.module import Module
from pyvqnet.optim.adam import Adam
from pyvqnet.qnn.measure import expval
from pyvqnet.qnn.quantumlayer import QuantumLayerMultiProcess
from pyvqnet.tensor.tensor import QTensor
import keras
from pyvqnet import DEV_GPU_0
from pyvqnet.tensor import *
from pyvqnet.tensor import tensor
import warnings
warnings.filterwarnings('ignore')
try:
matplotlib.use("TkAgg")
except: # pylint:disable=bare-except
print("Can not use matplot TkAgg")

try:
import urllib.request
except ImportError:
raise ImportError("You should use Python 3.x")

def load_local_cifar10(path):
with tarfile.open(path, 'r:gz') as tar:
tar.extractall()
data_dir = './cifar-10-batches-py'
x_train, y_train, x_test, y_test = [], [], [], []

for batch in range(1, 6):
    with open(os.path.join(data_dir, f'data_batch_{batch}'), 'rb') as f:
        dict = pickle.load(f, encoding='bytes')
        x_train.append(dict[b'data'])
        y_train.append(dict[b'labels'])

with open(os.path.join(data_dir, 'test_batch'), 'rb') as f:
    dict = pickle.load(f, encoding='bytes')
    x_test = dict[b'data']
    y_test = dict[b'labels']

x_train = np.concatenate(x_train)
y_train = np.concatenate(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)

x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)
# 将 x_train 转换为 QTensor
# x_train = tensor.to_tensor(x_train)

# # 将 y_train 转换为 QTensor
# y_train = tensor.to_tensor(y_train)

# # 将 x_test 转换为 QTensor
# x_test = tensor.to_tensor(x_test)

# # 将 y_test 转换为 QTensor
# y_test = tensor.to_tensor(y_test)

# x_train = x_train.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
# x_test = x_test.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
x_train = x_train.reshape([-1,3,32,32])
x_test = x_test.reshape([-1,3,32,32])
# return (x_train, y_train), (x_test, y_test)
# 数据预处理
# x_train = x_train.astype('float32') / 255.0
# x_test = x_test.astype('float32') / 255.0
# y_train = to_categorical(y_train, 10)
# y_test = to_categorical(y_test, 10)

# # 确定本地路径
# local_cifar10_path = 'cifar-10-python.tar.gz'  # 确保路径正确
# (x_train, y_train), (x_test, y_test) = load_local_cifar10(local_cifar10_path)

return (x_train, y_train), (x_test, y_test)

def RotCircuit(para, qlist):

if isinstance(para, QTensor):
    para = QTensor._to_numpy(para)
if para.ndim > 1:
    raise ValueError(" dim of paramters in Rot should be 1")
if para.shape[0] != 3:
    raise ValueError(" numbers of paramters in Rot should be 3")

cir = pq.QCircuit()
cir.insert(pq.RZ(qlist, para[2]))
cir.insert(pq.RY(qlist, para[1]))
cir.insert(pq.RZ(qlist, para[0]))

return cir

def build_RotCircuit(qubits, weights):
cir = pq.QCircuit()
cir.insert(RotCircuit(weights[0:3], qubits[0]))
cir.insert(RotCircuit(weights[3:6], qubits[1]))
cir.insert(RotCircuit(weights[6:9], qubits[2]))
cir.insert(RotCircuit(weights[9:12], qubits[3]))
cir.insert(RotCircuit(weights[12:15], qubits[4]))
cir.insert(RotCircuit(weights[15:18], qubits[5]))
cir.insert(RotCircuit(weights[18:21], qubits[6]))
cir.insert(RotCircuit(weights[21:24], qubits[7]))
cir.insert(RotCircuit(weights[24:27], qubits[8]))
cir.insert(RotCircuit(weights[27:30], qubits[9]))
cir.insert(RotCircuit(weights[30:33], qubits[10]))
cir.insert(RotCircuit(weights[33:36], qubits[11]))
cir.insert(RotCircuit(weights[36:39], qubits[12]))
cir.insert(RotCircuit(weights[39:42], qubits[13]))
cir.insert(RotCircuit(weights[42:45], qubits[14]))
cir.insert(RotCircuit(weights[45:48], qubits[15]))

return cir

def CRXCircuit(para, control_qlists, rot_qlists):
cir = pq.QCircuit()
cir.insert(pq.RX(rot_qlists, para))
cir.set_control(control_qlists)
return cir

def build_CRotCircuit(qubits, weights):
cir = pq.QCircuit()
cir.insert(CRXCircuit(weights[0], qubits[0], qubits[1]))
cir.insert(CRXCircuit(weights[1], qubits[1], qubits[2]))
cir.insert(CRXCircuit(weights[2], qubits[2], qubits[3]))
cir.insert(CRXCircuit(weights[3], qubits[3], qubits[4]))
cir.insert(CRXCircuit(weights[4], qubits[4], qubits[5]))
cir.insert(CRXCircuit(weights[5], qubits[5], qubits[6]))
cir.insert(CRXCircuit(weights[6], qubits[6], qubits[7]))
cir.insert(CRXCircuit(weights[7], qubits[7], qubits[8]))
cir.insert(CRXCircuit(weights[8], qubits[8], qubits[9]))
cir.insert(CRXCircuit(weights[9], qubits[9], qubits[10]))
cir.insert(CRXCircuit(weights[10], qubits[10], qubits[11]))
cir.insert(CRXCircuit(weights[11], qubits[11], qubits[12]))
cir.insert(CRXCircuit(weights[12], qubits[12], qubits[13]))
cir.insert(CRXCircuit(weights[13], qubits[13], qubits[14]))
cir.insert(CRXCircuit(weights[14], qubits[14], qubits[15]))
cir.insert(CRXCircuit(weights[15], qubits[15], qubits[0]))

return cir

def build_qmlp_circuit(x, weights, qubits, clist, machine):
cir = pq.QCircuit()
num_qubits = len(qubits)
for i in range(num_qubits):
cir.insert(pq.RX(qubits[i], x[i]))

cir.insert(build_RotCircuit(qubits, weights[0:48]))
cir.insert(build_CRotCircuit(qubits, weights[48:64]))

for i in range(num_qubits):
    cir.insert(pq.RX(qubits[i], x[i]))

cir.insert(build_RotCircuit(qubits, weights[64:112]))
cir.insert(build_CRotCircuit(qubits, weights[112:128]))

prog = pq.QProg()
prog.insert(cir)
# print(prog)
# exit()

exp_vals = []
for position in range(num_qubits):
    pauli_str = {"Z" + str(position): 1.0}
    exp2 = expval(machine, prog, pauli_str, qubits)
    exp_vals.append(exp2)

return exp_vals

def build_multiprocess_qmlp_circuit(x, weights, num_qubits, num_clist):
machine = pq.CPUQVM()
machine.init_qvm()
qubits = machine.qAlloc_many(num_qubits)
cir = pq.QCircuit()
for i in range(num_qubits):
cir.insert(pq.RX(qubits[i], x[i]))

cir.insert(build_RotCircuit(qubits, weights[0:48]))
cir.insert(build_CRotCircuit(qubits, weights[48:64]))

for i in range(num_qubits):
    cir.insert(pq.RX(qubits[i], x[i]))

cir.insert(build_RotCircuit(qubits, weights[64:112]))
cir.insert(build_CRotCircuit(qubits, weights[112:128]))

prog = pq.QProg()
prog.insert(cir)
# print(prog)
# exit()

exp_vals = []
for position in range(num_qubits):
    pauli_str = {"Z" + str(position): 1.0}
    exp2 = expval(machine, prog, pauli_str, qubits)
    exp_vals.append(exp2)

return exp_vals

class QMLPModel(Module):

def init(self):

super(QMLPModel, self).init()

self.ave_pool2d = AvgPool2D([7, 7], [7, 7], "valid")

# self.quantum_circuit = QuantumLayer(build_qmlp_circuit, 128, "CPU", 16, diff_method="finite_diff")

self.quantum_circuit = QuantumLayerMultiProcess(build_multiprocess_qmlp_circuit, 128,

16, 1, diff_method="finite_diff")

self.linear = Linear(16, 10)

def forward(self, x):

bsz = x.shape[0]

x = self.ave_pool2d(x)

input_data = x.reshape([bsz, 16])

quanutum_result = self.quantum_circuit(input_data)

result = self.linear(quanutum_result)

return result

class QMLPModel(Module):

def init(self):

super(QMLPModel, self).init()

# 添加一个简单的CNN来降维并抽取特征

self.ave_pool2d = nn.AvgPool2D([7, 7], [7, 7], "valid")

self.cnn_layers = nn.Sequential(

# 假设输入为CIFAR图像:[batch_size, 3, 32, 32]

nn.Conv2D(3, 16, kernel_size=(3,3)),

nn.ReLu(),

nn.MaxPool2D([2,2],[2,2]),

# 现在尺寸是:[batch_size, 16, 16, 16]

nn.Conv2D(16, 8, kernel_size=(3,3)),

nn.ReLu(),

nn.MaxPool2D([2,2],[2,2]),

# 最终尺寸是:[batch_size, 8, 8, 8]

)

# 确保修改量子电路或其他模型结构以适应新的输入尺寸

self.quantum_circuit = QuantumLayerMultiProcess(build_multiprocess_qmlp_circuit, 128, 16, 1, diff_method="finite_diff")

# 经过量子层后，再连接到最终的分类层

self.linear = Linear(16, 10)

def forward(self, x):

bsz = x.shape[0]

x = self.ave_pool2d(x)

# 此处你可能需要根据量子电路的输入要求进一步处理x

input_data = x.reshape([bsz, 16]) # 确保这一步匹配量子电路的期待输入格式

quantum_result = self.quantum_circuit(input_data)

result = self.linear(quantum_result)

return result

class QMLPModel(nn.Module):

def init(self):

super(QMLPModel, self).init()

# 假设输入是经过适当预处理的CIFAR图像：[batch_size, channels, height, width]

self.ave_pool2d = nn.AvgPool2D([3, 3], [1, 1], "valid")

self.cnn_layers = nn.Sequential(

nn.Conv2D(3, 16, kernel_size=(3, 3), stride=(1, 1), padding='same'),

nn.ReLu(),

nn.AvgPool2D(kernel=[2, 2], stride=[2, 2]),

# 由于使用'same' padding和stride=2，现在尺寸是：[batch_size, 16, 16, 16]

nn.Conv2D(16, 8, kernel_size=(3, 3), stride=(1, 1), padding='same'),

nn.ReLu(),

nn.AvgPool2D(kernel=[2, 2], stride=[2, 2]),

# 再次使用'same' padding和stride=2，现在尺寸是：[batch_size, 8, 8, 8]

)

# 需调整QuantumLayerMultiProcess参数来适配CNN层的输出

self.quantum_circuit = QuantumLayerMultiProcess(build_multiprocess_qmlp_circuit, 128, 16, 1, diff_method="finite_diff")

# 假定我们展平了CNN层的输出，并适配到了量子层

self.linear = nn.Linear(16, 10) # 输出10个类别

def forward(self, x):

# x = self.cnn_layers(x)

# # 展平特征以适配量子层

# x = x.view(x.size(0), -1)

# quanutum_result = self.quantum_circuit(x)

# result = self.linear(quanutum_result)

# return result

bsz = x.shape[0]

x = self.ave_pool2d(x)

input_data = x.reshape([bsz, 2700])

quanutum_result = self.quantum_circuit(input_data)

result = self.linear(quanutum_result)

return result

class QMLPModel(Module):
def init(self):
super(QMLPModel, self).init()
self.ave_pool2d = nn.AvgPool2D([7, 7], [7, 7], "valid")
# self.quantum_circuit = QuantumLayer(build_qmlp_circuit, 128, "CPU", 16, diff_method="finite_diff")
self.quantum_circuit = QuantumLayerMultiProcess(build_multiprocess_qmlp_circuit, 128,
16, 1, diff_method="finite_diff")
self.linear = Linear(16, 10)

def forward(self, x):
    bsz = x.shape[0]
    x = self.ave_pool2d(x)
    input_data = x.reshape([bsz, 16*3])
    quanutum_result = self.quantum_circuit(input_data)
    result = self.linear(quanutum_result)
    return result

def vqnet_test_QMLPModel():
# 设置训练集和验证集大小
# train_size = 5000
# eval_size = 1000

# 使用提供的函数加载CIFAR数据
local_cifar10_path = '/data3/gaolanmei_2024/AutoPET/AutoPET/data/cifar-10-python.tar.gz'  # 确保路径正确
(x_train, y_train), (x_test, y_test) = load_local_cifar10(local_cifar10_path)

# 截取一部分数据用于快速测试
# x_train = x_train[:train_size]
# y_train = y_train[:train_size]
# x_test = x_test[:eval_size]
# y_test = y_test[:eval_size]

print(f"x_train length: {len(x_train)}")
print(f"y_train length: {len(x_test)}")
# print(f"Batch size: {batch_size}")
model = QMLPModel()  # 确保QMLPModel可以处理CIFAR数据的输入维度
# model.toGPU(DEV_GPU_0)
optimizer = Adam(model.parameters(), lr=0.001)
loss_func = CrossEntropyLoss()
loss_list = []

epochs = 30
model = model.to_gpu(DEV_GPU_0)

batch_size = 16
# model.toGPU(DEV_GPU_0)
for epoch in range(1, epochs):
    total_loss = []

    correct = 0
    n_train = 0

    # 使用 data_generator 按批次生成数据
    data_iter = data_generator(x_train, y_train, batch_size=batch_size, shuffle=True)

    for x, y in tqdm(data_iter):

        x = QTensor(x)
        y = QTensor(y)
        x = x.to_gpu(DEV_GPU_0)
        y = y.to_gpu(DEV_GPU_0)
        # x.toGPU(DEV_GPU_0)
        # y.toGPU(DEV_GPU_0)
        optimizer.zero_grad()
        output = model(x)
        loss = loss_func(y, output)
        loss_np = loss.to_numpy()
        np_output = output.to_numpy()
        y = y.to_numpy()
        mask = (np_output.argmax(1) == y.argmax(1))
        correct += np.sum(np.array(mask))
        n_train += batch_size
        loss.backward()
        optimizer._step()
        total_loss.append(loss_np)
        train_loss_list.append(np.sum(total_loss) / len(total_loss))
        train_acc_list.append(np.sum(correct) / n_train)
        print("train epoch {:.0f} loss is : {:.10f}".format(epoch, train_loss_list[-1]))
        
    print("##########################")
    print(f"Train Accuracy: {correct / n_train}")
    loss_list.append(np.sum(total_loss) / len(total_loss))
    print("epoch: ", epoch)
    print("{:.0f} loss is : {:.10f}".format(epoch, loss_list[-1]))

if name == "main":
vqnet_test_QMLPModel()

我的代码是上面这个，但是报了tensor存在不同的device
Traceback (most recent call last):
File "/data3/gaolanmei_2024/AutoPET/AutoPET/data/main.py", line 386, in
vqnet_test_QMLPModel()
File "/data3/gaolanmei_2024/AutoPET/AutoPET/data/main.py", line 364, in vqnet_test_QMLPModel
output = model(x)
File "pyvqnet/nn/module.py", line 613, in pyvqnet.nn.module.Module.call
File "/data3/gaolanmei_2024/AutoPET/AutoPET/data/main.py", line 313, in forward
result = self.linear(quanutum_result)
File "pyvqnet/nn/module.py", line 613, in pyvqnet.nn.module.Module.call
File "pyvqnet/nn/linear.py", line 106, in pyvqnet.nn.linear.Linear.forward
RuntimeError: VQNet runtimeError: Tensors in different devices ( FILE: /root/yxy/vqnet2.0.8/package/1016-linux/py39/vqnet/src/tensor/tensor_utils.cpp. LINE: 3153. FUNC: mult2D_templates
@kevzos
Copy link
Contributor

kevzos commented Jun 5, 2024

@302850047 可以使用QuantumLayerV2 代替 QuantumLayerMultiProcess 进行量子变分线路计算。 QuantumLayerMultiProcess当前实现缺少不同设备上的数据处理功能。

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
2 participants
@kevzos @302850047