[pt2e to tosa] face AttributeError #1161
Description
Hi @Jerry-Ge ,
I have run the https://github.com/pytorch/executorch/blob/main/examples/arm/run.sh example done and success, now I am try to modify it to run a quantize int8 pytorch model which need to pass vela on FVP use ARM Ethous U55.
I use the pytorch mnist classification cnn model and quantize to int8 by convert_pt2e. The result of int8 model seems correct.
And I want to export to executorch which backend is ARM U55, but face AttributeError: 'ReshapeAttribute' object has no attribute 'NewshapeAsNumpy'. Did you mean: 'NewShapeAsNumpy'? while doing edge = edge.to_backend(ArmPartitioner).
How could I fix it?
The following code is my export code.
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from torch.ao.quantization import get_default_qconfig_mapping
from torch.quantization.quantize_fx import prepare_fx, convert_fx
from torch.ao.quantization import QuantStub, DeQuantStub
import cv2
import numpy as np
import argparse
import logging
import torch
import torch._export as export
from executorch.backends.arm.arm_backend import ArmPartitioner
from executorch.exir import EdgeCompileConfig
from ..portable.utils import export_to_edge, save_pte_program
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 8, 3, 1)
self.conv2 = nn.Conv2d(8, 16, 3, 1)
self.conv3 = nn.Conv2d(16, 32, 5, 1)
self.fc1 = nn.Linear(32, 64)
self.fc2 = nn.Linear(64, 10)
def forward(self, x):
x = self.conv1(x)
x = F.max_pool2d(x, 2,stride=2)
x = self.conv2(x)
x = F.max_pool2d(x, 2,stride=2)
x = self.conv3(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = self.fc2(x)
output = F.softmax(x, dim=1)
return output
def train(args, model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.cross_entropy(output, target)
loss.backward()
optimizer.step()
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
if args.dry_run:
break
def test(model, device, test_loader):
# model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
def calibrate(model, data_loader):
# model.eval()
with torch.no_grad():
for image, target in data_loader:
model(image)
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=14, metavar='N',
help='number of epochs to train (default: 14)')
parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
help='learning rate (default: 1.0)')
parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--dry-run', action='store_true', default=False,
help='quickly check a single pass')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model', action='store_true', default=False,
help='For Saving the current Model')
parser.add_argument(
"-d",
"--delegate",
action="store_true",
required=False,
default=False,
help="Flag for producing ArmBackend delegated model",
)
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
train_kwargs = {'batch_size': args.batch_size}
test_kwargs = {'batch_size': args.test_batch_size}
if use_cuda:
cuda_kwargs = {'num_workers': 1,
'pin_memory': True,
'shuffle': True}
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
transform=transforms.Compose([
transforms.ToTensor()
])
dataset1 = datasets.MNIST('./data', train=True, download=True,
transform=transform)
dataset2 = datasets.MNIST('./data', train=False,
transform=transform)
train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)
test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
float_model = Net().to(device)
float_model.load_state_dict(torch.load("./pytorch_mnist_cnn_floating.pt"))
float_model.eval()
model_to_quantize = Net().to(device)
model_to_quantize.load_state_dict(torch.load("./pytorch_mnist_cnn_floating.pt"))
model_to_quantize.eval()
from torch._export import capture_pre_autograd_graph
example_inputs = (torch.randn(1, 1, 28,28),)
exported_model = capture_pre_autograd_graph(model_to_quantize, example_inputs)
# or capture with dynamic dimensions
# from torch._export import dynamic_dim
# exported_model = capture_pre_autograd_graph(model_to_quantize, example_inputs, constraints=[dynamic_dim(example_inputs[0], 0)])
from torch.ao.quantization.quantizer.xnnpack_quantizer import (
XNNPACKQuantizer,
get_symmetric_quantization_config,
)
quantizer = XNNPACKQuantizer()
quantizer.set_global(get_symmetric_quantization_config())
from torch.ao.quantization.quantize_pt2e import (
prepare_pt2e,
convert_pt2e,
)
prepared_model = prepare_pt2e(exported_model, quantizer)
print(prepared_model.graph)
calibrate(prepared_model, train_loader)
quantized_model = convert_pt2e(prepared_model)
################################################################
################################################################
# pre-autograd export. eventually this will become torch.export
# model = export.capture_pre_autograd_graph(quantized_model, example_inputs)
print("convert_pt2e(prepared_model)done ")
edge = export_to_edge(
quantized_model,
example_inputs,
edge_compile_config=EdgeCompileConfig(
_check_ir_validity=False,
),
)
print("export_to_edge done ")
logging.info(f"Exported graph:\n{edge.exported_program().graph}")
delegate = args.delegate
model_name = "pytorch_mnist_cnn_ptq_qnnpack"
if delegate is True:
edge = edge.to_backend(ArmPartitioner)
logging.info(f"Lowered graph:\n{edge.exported_program().graph}")
print("edge.to_backend(ArmPartitioner) done ")
exec_prog = edge.to_executorch()
print("edge.to_executorch() done ")
model_name = f"{model_name}" + (
"_arm_delegate" if delegate is True else ""
)
save_pte_program(exec_prog.buffer, model_name)
# delegate = args.delegate
# # model_name = args.model_name + str_qconfig_mapping
# model_name = args.model_name
# if delegate is True:
# edge = edge.to_backend(ArmPartitioner)
# logging.info(f"Lowered graph:\n{edge.exported_program().graph}")
# exec_prog = edge.to_executorch()
# model_name = f"{model_name}" + (
# "_arm_delegate" if delegate is True else ""
# )
# save_pte_program(exec_prog.buffer, model_name)
if __name__ == '__main__':
main()
