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See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129755 Approved by: https://github.com/zou3519 ghstack dependencies: #129752
133 lines
3.5 KiB
Python
133 lines
3.5 KiB
Python
# Eric Jang originally wrote an implementation of MAML in JAX
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# (https://github.com/ericjang/maml-jax).
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# We translated his implementation from JAX to PyTorch.
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import math
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from torch.nn import functional as F
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mpl.use("Agg")
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def net(x, params):
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x = F.linear(x, params[0], params[1])
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x = F.relu(x)
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x = F.linear(x, params[2], params[3])
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x = F.relu(x)
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x = F.linear(x, params[4], params[5])
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return x
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params = [
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torch.Tensor(40, 1).uniform_(-1.0, 1.0).requires_grad_(),
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torch.Tensor(40).zero_().requires_grad_(),
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torch.Tensor(40, 40)
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.uniform_(-1.0 / math.sqrt(40), 1.0 / math.sqrt(40))
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.requires_grad_(),
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torch.Tensor(40).zero_().requires_grad_(),
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torch.Tensor(1, 40)
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.uniform_(-1.0 / math.sqrt(40), 1.0 / math.sqrt(40))
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.requires_grad_(),
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torch.Tensor(1).zero_().requires_grad_(),
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]
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opt = torch.optim.Adam(params, lr=1e-3)
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alpha = 0.1
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K = 20
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losses = []
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num_tasks = 4
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def sample_tasks(outer_batch_size, inner_batch_size):
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# Select amplitude and phase for the task
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As = []
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phases = []
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for _ in range(outer_batch_size):
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As.append(np.random.uniform(low=0.1, high=0.5))
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phases.append(np.random.uniform(low=0.0, high=np.pi))
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def get_batch():
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xs, ys = [], []
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for A, phase in zip(As, phases):
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x = np.random.uniform(low=-5.0, high=5.0, size=(inner_batch_size, 1))
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y = A * np.sin(x + phase)
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xs.append(x)
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ys.append(y)
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return torch.tensor(xs, dtype=torch.float), torch.tensor(ys, dtype=torch.float)
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x1, y1 = get_batch()
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x2, y2 = get_batch()
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return x1, y1, x2, y2
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for it in range(20000):
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loss2 = 0.0
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opt.zero_grad()
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def get_loss_for_task(x1, y1, x2, y2):
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f = net(x1, params)
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loss = F.mse_loss(f, y1)
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# create_graph=True because computing grads here is part of the forward pass.
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# We want to differentiate through the SGD update steps and get higher order
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# derivatives in the backward pass.
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grads = torch.autograd.grad(loss, params, create_graph=True)
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new_params = [(params[i] - alpha * grads[i]) for i in range(len(params))]
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v_f = net(x2, new_params)
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return F.mse_loss(v_f, y2)
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task = sample_tasks(num_tasks, K)
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inner_losses = [
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get_loss_for_task(task[0][i], task[1][i], task[2][i], task[3][i])
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for i in range(num_tasks)
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]
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loss2 = sum(inner_losses) / len(inner_losses)
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loss2.backward()
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opt.step()
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if it % 100 == 0:
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print("Iteration %d -- Outer Loss: %.4f" % (it, loss2))
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losses.append(loss2.detach())
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t_A = torch.tensor(0.0).uniform_(0.1, 0.5)
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t_b = torch.tensor(0.0).uniform_(0.0, math.pi)
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t_x = torch.empty(4, 1).uniform_(-5, 5)
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t_y = t_A * torch.sin(t_x + t_b)
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opt.zero_grad()
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t_params = params
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for k in range(5):
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t_f = net(t_x, t_params)
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t_loss = F.l1_loss(t_f, t_y)
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grads = torch.autograd.grad(t_loss, t_params, create_graph=True)
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t_params = [(t_params[i] - alpha * grads[i]) for i in range(len(params))]
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test_x = torch.arange(-2 * math.pi, 2 * math.pi, step=0.01).unsqueeze(1)
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test_y = t_A * torch.sin(test_x + t_b)
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test_f = net(test_x, t_params)
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plt.plot(test_x.data.numpy(), test_y.data.numpy(), label="sin(x)")
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plt.plot(test_x.data.numpy(), test_f.data.numpy(), label="net(x)")
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plt.plot(t_x.data.numpy(), t_y.data.numpy(), "o", label="Examples")
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plt.legend()
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plt.savefig("maml-sine.png")
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plt.figure()
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plt.plot(np.convolve(losses, [0.05] * 20))
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plt.savefig("losses.png")
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