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September 7, 2024 20:02
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MLX ResNet18 Inference Benchmark
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| 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}") |
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Times on an M2 Ultra: