MLX ResNet18 Inference Benchmark

resnet_mlx.py

from huggingface_hub import snapshot_download
import mlx.core as mx
import mlx.nn as nn
import time


class Block(nn.Module):
    def __init__(self, in_dims, dims, stride=1):
        super().__init__()

        self.conv1 = nn.Conv2d(
            in_dims, dims, kernel_size=3, stride=stride, padding=1, bias=False
        )
        self.bn1 = nn.BatchNorm(dims)

        self.conv2 = nn.Conv2d(
            dims, dims, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm(dims)

        self.downsample = []
        if stride != 1:
            self.downsample = [
                nn.Conv2d(in_dims, dims, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm(dims)
            ]

    def __call__(self, x):
        out = nn.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        for l in self.downsample:
            x = l(x)
        out += x
        out = nn.relu(out)
        return out

      
class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm(64)

        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 64, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 128, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 256, 512, num_blocks[3], stride=2)

        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, block, in_dims, dims, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(in_dims, dims, stride))
            in_dims = dims
        return layers

    def __call__(self, x):
        x = nn.relu(self.bn1(self.conv1(x)))
        x = self.maxpool(x)
        for l in self.layer1 + self.layer2 + self.layer3 + self.layer4:
            x = l(x)
        x = mx.mean(x, axis=[1, 2])
        x = self.fc(x)
        return x



def load():
    model = ResNet(Block, [2, 2, 2, 2], num_classes=1000)
    file = "model.safetensors"
    model_path = snapshot_download(
        repo_id="awni/resnet18-mlx",
        allow_patterns=[file],
    )
    model.load_weights(model_path + "/" + file)
    model.eval()
    mx.eval(model)
    return model

if __name__ == "__main__":

    resnet18 = load()

    @mx.compile
    def forward(im):
        return resnet18(im)

    batch_sizes = [1, 2, 4, 8, 16, 32, 64]
    print(f"Batch Size | Images-per-second | Milliseconds-per-image")
    print(f"---- | ---- | ---- ")
    for N in batch_sizes:
        image = mx.random.uniform(shape=(N, 288, 288, 3))

        # Warmup
        for _ in range(5):
            output = forward(image)
            mx.eval(output)

        tic = time.time()
        its = 200
        for _ in range(its):
            output = forward(image)
            mx.async_eval(output)
        mx.eval(output)
        toc = time.time()
        ims_per_sec = N * its / (toc - tic)
        ms_per_im = 1e3 / ims_per_sec
        print(f"{N} | {ims_per_sec:.3f} | {ms_per_im:.3f}")

Times on an M2 Ultra:

Batch Size	Images-per-second	Milliseconds-per-image
1	206.317	4.847
2	452.275	2.211
4	962.070	1.039
8	1391.393	0.719
16	1888.016	0.530
32	2185.299	0.458
64	2292.397	0.436