UserWarning: 1MPS: no support for int64 reduction ops, casting it to int32

Hi I am kind of new in pytorch. I was trying run this benchmark code in pytorch on Mac M2 but i am geting user warning.

Benchmark Code

import torch
from torch import nn
from import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor

print(f"PyTorch version: {torch.__version__}")

# Check PyTorch has access to MPS (Metal Performance Shader, Apple's GPU architecture)
print(f"Is MPS (Metal Performance Shader) built? {torch.backends.mps.is_built()}")
print(f"Is MPS available? {torch.backends.mps.is_available()}")

# Set the device
device = "mps" if torch.backends.mps.is_available() else "cpu"
device = torch.device(device)
print(f"Using device: {device}")

# Define the CNN model
class HandwritingRecognitionModel(nn.Module):
    def __init__(self):

        # Define the convolutional layers
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)

        # Define the pooling and dropout layers
        self.pool = nn.MaxPool2d(2, 2)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)

        # Define the fully connected layers
        self.fc1 = nn.Linear(32 * 7 * 7, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        # Pass the input through the convolutional layers
        x = self.conv1(x)
        x = self.pool(x)
        x = self.dropout1(x)
        x = self.conv2(x)
        x = self.pool(x)
        x = self.dropout2(x)

        # Reshape the output for the fully connected layers
        x = x.view(-1, 32 * 7 * 7)

        # Pass the output through the fully connected layers
        x = self.fc1(x)
        x = self.fc2(x)

        # Return the final output
        return x

# Load the MNIST dataset
train_dataset = MNIST("./data", train=True, download=True, transform=ToTensor())
test_dataset = MNIST("./data", train=False, download=True, transform=ToTensor())

# Define the data loaders
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# Define the model
model = HandwritingRecognitionModel().to(device)

# Define the loss function and optimizer
loss_fn = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

# Train the model for 10 epochs
for epoch in range(10):
    # Set the model to training mode

    # Iterate over the training data
    for images, labels in train_loader:
        images, labels =,
        # Pass the input through the model
        outputs = model(images)
        # Compute the loss
        loss = loss_fn(outputs, labels)

        # Backpropagate the error
        # Update the model parameters

    # Set the model to evaluation mode

    # Evaluate the model on the validation set
    with torch.no_grad():
        correct = 0
        total = 0

        for images, labels in test_loader:
            images, labels =,
            # Pass the input through the model
            outputs = model(images)

            # Get the predicted labels
            _, predicted = torch.max(, 1)

            # Update the total and correct counts
            total += labels.size(0)
            correct += (predicted == labels).sum()

        # Print the accuracy
        print(f"Epoch {epoch + 1}: Accuracy = {100 * correct / total:.2f}%")


/Users/alex/miniconda3/envs/torch/lib/python3.10/site-packages/torch/autograd/ UserWarning: 1MPS: no support for int64 reduction ops, casting it to int32 (Triggered internally at /Users/runner/work/_temp/anaconda/conda-bld/pytorch_1677226422231/work/aten/src/ATen/native/mps/operations/
  Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass

Can somebody explain me this warning? It is import? And how to get rid of it?

This warning was added in this PR recently and mentions the internal downcasting of int64 values to int32 due to the lack of reduction ops natively supporting int64.
This warning should be raised only once but I don’t know if you could also suppress it without disabling all warnings.

Btw. it seems the condition was also wrong and the warning was raised when it shouldn’t.
This PR should fix it and would show up in the next nightly build.