mirror of
https://github.com/ml-explore/mlx.git
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144 lines
4.0 KiB
Python
144 lines
4.0 KiB
Python
import time
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import mlx.core as mx
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import mlx.nn
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import mlx.optimizers as opt
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import torch
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def bench_mlx(steps: int = 20) -> float:
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mx.set_default_device(mx.cpu)
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class BenchNetMLX(mlx.nn.Module):
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# simple encoder-decoder net
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def __init__(self, in_channels, hidden_channels=32):
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super().__init__()
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self.net = mlx.nn.Sequential(
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mlx.nn.Conv2d(in_channels, hidden_channels, kernel_size=3, padding=1),
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mlx.nn.ReLU(),
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mlx.nn.Conv2d(
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hidden_channels, 2 * hidden_channels, kernel_size=3, padding=1
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),
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mlx.nn.ReLU(),
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mlx.nn.ConvTranspose2d(
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2 * hidden_channels, hidden_channels, kernel_size=3, padding=1
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),
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mlx.nn.ReLU(),
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mlx.nn.ConvTranspose2d(
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hidden_channels, in_channels, kernel_size=3, padding=1
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),
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)
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def __call__(self, input):
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return self.net(input)
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benchNet = BenchNetMLX(3)
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mx.eval(benchNet.parameters())
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optim = opt.Adam(learning_rate=1e-3)
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inputs = mx.random.normal([10, 256, 256, 3])
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params = benchNet.parameters()
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optim.init(params)
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state = [benchNet.state, optim.state]
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def loss_fn(params, image):
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benchNet.update(params)
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pred_image = benchNet(image)
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return (pred_image - image).abs().mean()
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def step(params, image):
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loss, grads = mx.value_and_grad(loss_fn)(params, image)
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optim.update(benchNet, grads)
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return loss
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total_time = 0.0
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print("MLX:")
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for i in range(steps):
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start_time = time.perf_counter()
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step(benchNet.parameters(), inputs)
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mx.eval(state)
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end_time = time.perf_counter()
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print(f"{i:3d}, time={(end_time-start_time) * 1000:7.2f} ms")
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total_time += (end_time - start_time) * 1000
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return total_time
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def bench_torch(steps: int = 20) -> float:
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device = torch.device("cpu")
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class BenchNetTorch(torch.nn.Module):
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# simple encoder-decoder net
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def __init__(self, in_channels, hidden_channels=32):
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super().__init__()
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self.net = torch.nn.Sequential(
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torch.nn.Conv2d(in_channels, hidden_channels, kernel_size=3, padding=1),
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torch.nn.ReLU(),
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torch.nn.Conv2d(
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hidden_channels, 2 * hidden_channels, kernel_size=3, padding=1
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),
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torch.nn.ReLU(),
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torch.nn.ConvTranspose2d(
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2 * hidden_channels, hidden_channels, kernel_size=3, padding=1
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),
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torch.nn.ReLU(),
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torch.nn.ConvTranspose2d(
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hidden_channels, in_channels, kernel_size=3, padding=1
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),
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)
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def forward(self, input):
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return self.net(input)
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benchNet = BenchNetTorch(3).to(device)
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optim = torch.optim.Adam(benchNet.parameters(), lr=1e-3)
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inputs = torch.randn(10, 3, 256, 256, device=device)
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def loss_fn(pred_image, image):
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return (pred_image - image).abs().mean()
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total_time = 0.0
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print("PyTorch:")
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for i in range(steps):
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start_time = time.perf_counter()
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optim.zero_grad()
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pred_image = benchNet(inputs)
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loss = loss_fn(pred_image, inputs)
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loss.backward()
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optim.step()
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end_time = time.perf_counter()
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print(f"{i:3d}, time={(end_time-start_time) * 1000:7.2f} ms")
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total_time += (end_time - start_time) * 1000
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return total_time
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def main():
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steps = 20
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time_mlx = bench_mlx(steps)
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time_torch = bench_torch(steps)
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print(f"average time of MLX: {time_mlx/steps:9.2f} ms")
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print(f"total time of MLX: {time_mlx:9.2f} ms")
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print(f"average time of PyTorch: {time_torch/steps:9.2f} ms")
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print(f"total time of PyTorch: {time_torch:9.2f} ms")
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diff = time_torch / time_mlx - 1.0
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print(f"torch/mlx diff: {100. * diff:+5.2f}%")
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if __name__ == "__main__":
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main()
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