tinygrad/test/test_nn.py

#!/usr/bin/env python
import unittest
import numpy as np
import torch
from tinygrad import Tensor, Device, TinyJit
from tinygrad.ops import UOps
from tinygrad.helpers import CI, Context
from tinygrad.nn import Conv1d, ConvTranspose1d, Conv2d, ConvTranspose2d, Linear, Embedding
from tinygrad.nn import BatchNorm, LayerNorm, LayerNorm2d, GroupNorm, InstanceNorm, RMSNorm, LSTMCell
from tinygrad.nn.state import load_state_dict
from tinygrad.engine.schedule import create_schedule
from tinygrad.engine.realize import run_schedule

@unittest.skipIf(CI and Device.DEFAULT in {"CUDA", "NV"}, "slow")
class TestNN(unittest.TestCase):
  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "no int64 on WebGPU")
  def test_sparse_cat_cross_entropy(self):
    # create in tinygrad
    input_tensor = Tensor.randn(6, 5) # not square to test that mean scaling uses the correct dimension
    target = Tensor([0, 0, 0, 1, 2, 3])  # torch doesn't support target=-1
    torch_input = torch.tensor(input_tensor.numpy())
    torch_target = torch.tensor(target.numpy(), dtype=torch.long)

    for smoothing in [0.0, 0.1, 0.5, 1.0]:
      for ignore_index in [-1, 0, 2]:
        for reduction in ["none", "sum", "mean"]:
          loss = input_tensor.sparse_categorical_crossentropy(target, label_smoothing=smoothing, ignore_index=ignore_index, reduction=reduction)
          torch_loss = torch.nn.CrossEntropyLoss(reduction=reduction, label_smoothing=smoothing, ignore_index=ignore_index)(torch_input, torch_target)
          np.testing.assert_allclose(loss.numpy(), torch_loss.detach().numpy(), atol=1e-5, rtol=1e-6)

          # also test with a batch dimension (of size 1)
          loss = input_tensor.unsqueeze(0).sparse_categorical_crossentropy(
            target.unsqueeze(0), label_smoothing=smoothing, ignore_index=ignore_index, reduction=reduction
          )
          torch_loss = torch.nn.CrossEntropyLoss(reduction=reduction, label_smoothing=smoothing, ignore_index=ignore_index)(
            torch_input.unsqueeze(0).permute(0,2,1), torch_target.unsqueeze(0)
          )
          np.testing.assert_allclose(loss.numpy(), torch_loss.detach().numpy(), atol=1e-5, rtol=1e-6)

  def test_batchnorm2d(self, training=False, threed=False, track_running_stats=True):
    with Tensor.train(training):
      szs = [4, 8, 16, 32]
      for sz in szs:
        # create in tinygrad
        bn = BatchNorm(sz, eps=1e-5, track_running_stats=track_running_stats)
        bn.weight = Tensor.randn(sz)
        bn.bias = Tensor.randn(sz)
        if track_running_stats:
          bn.running_mean = Tensor.randn(sz)
          bn.running_var = Tensor.randn(sz)
          bn.running_var.numpy()[bn.running_var.numpy() < 0] = 0

        # create in torch
        with torch.no_grad():
          if threed:
            tbn = torch.nn.BatchNorm3d(sz, track_running_stats=track_running_stats).eval()
          else:
            tbn = torch.nn.BatchNorm2d(sz, track_running_stats=track_running_stats).eval()
          tbn.training = training
          tbn.weight[:] = torch.tensor(bn.weight.numpy())
          tbn.bias[:] = torch.tensor(bn.bias.numpy())
          if track_running_stats:
            tbn.running_mean[:] = torch.tensor(bn.running_mean.numpy())
            tbn.running_var[:] = torch.tensor(bn.running_var.numpy())

        if track_running_stats:
          np.testing.assert_allclose(bn.running_mean.numpy(), tbn.running_mean.detach().numpy(), rtol=1e-5, atol=1e-6)
          np.testing.assert_allclose(bn.running_var.numpy(), tbn.running_var.detach().numpy(), rtol=1e-5, atol=1e-6)

        # trial
        if threed:
          inn = Tensor.randn(2, sz, 3, 3, 3)
        else:
          inn = Tensor.randn(2, sz, 3, 3)

        # in tinygrad
        outt = bn(inn)

        # in torch
        toutt = tbn(torch.tensor(inn.numpy()))

        # close
        np.testing.assert_allclose(outt.numpy(), toutt.detach().numpy(), rtol=5e-4, atol=1e-6)
        if track_running_stats:
          np.testing.assert_allclose(bn.running_mean.numpy(), tbn.running_mean.detach().numpy(), rtol=1e-5, atol=1e-6)
          np.testing.assert_allclose(bn.running_var.numpy(), tbn.running_var.detach().numpy(), rtol=1e-5, atol=1e-6)

  def test_batchnorm2d_training(self): self.test_batchnorm2d(True, False, True)
  def test_batchnorm2d_no_running_stats(self): self.test_batchnorm2d(False, False, False)
  def test_batchnorm2d_training_no_running_stats(self): self.test_batchnorm2d(True, False, False)
  def test_batchnorm3d(self): self.test_batchnorm2d(False, True, True)
  def test_batchnorm3d_training(self): self.test_batchnorm2d(True, True, True)
  def test_batchnorm3d_no_running_stats(self): self.test_batchnorm2d(False, True, False)
  def test_batchnorm3d_training_no_running_stats(self): self.test_batchnorm2d(True, True, False)

  def test_batchnorm_axis(self):
    sz = (2, 4, 3, 2, 2)
    x = Tensor.randn(sz)
    weight = Tensor.randn(2, 3)
    bias = Tensor.randn(2, 3)
    mean = Tensor.randn(2, 3)
    invstd = Tensor.randn(2, 3)
    a = (x.batchnorm(weight, bias, mean, invstd, axis=(0, 2))
         .permute(1, 0, 2, 3, 4).reshape(4, 6, 2, 2))
    b = (x.permute(1, 0, 2, 3, 4).reshape(4, 6, 2, 2)
         .batchnorm(weight.flatten(), bias.flatten(), mean.flatten(), invstd.flatten()))
    t_x = torch.tensor(x.permute(1, 0, 2, 3, 4).reshape(4, 6, 2, 2).numpy())
    t_weight, t_bias = torch.tensor(weight.flatten().numpy()), torch.tensor(bias.flatten().numpy())
    t_mean, t_invstd = torch.tensor(mean.flatten().numpy()), torch.tensor(invstd.flatten().numpy())
    torch.nn.functional.batch_norm(t_x, t_mean, 1.0 / t_invstd**2, t_weight, t_bias)

    np.testing.assert_allclose(a.numpy(), b.numpy())

  def test_linear(self):
    def _test_linear(x, in_dim, out_dim):
      # create in tinygrad
      model = Linear(in_dim, out_dim)
      z = model(x)

      # create in torch
      with torch.no_grad():
        torch_layer = torch.nn.Linear(in_dim, out_dim).eval()
        torch_layer.weight[:] = torch.tensor(model.weight.numpy(), dtype=torch.float32)
        torch_layer.bias[:] = torch.tensor(model.bias.numpy(), dtype=torch.float32)
        torch_x = torch.tensor(x.numpy(), dtype=torch.float32)
        torch_z = torch_layer(torch_x)

      # test
      np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

    BS, T, in_dim, out_dim = 4, 2, 8, 16
    _test_linear(Tensor.randn(BS, in_dim), in_dim, out_dim)
    _test_linear(Tensor.randn(BS, T, in_dim), in_dim, out_dim) # test with more dims

  def test_conv1d(self):
    BS, C1, W = 4, 16, 224//4
    C2, K, S, P = 64, 7, 2, 1

    # create in tinygrad
    layer = Conv1d(C1, C2, kernel_size=K, stride=S, padding=P)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.Conv1d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.uniform(BS, C1, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

  def test_conv2d(self):
    BS, C1, H, W = 4, 16, 224//4, 224//4
    C2, K, S, P = 64, 7, 2, 1

    # create in tinygrad
    layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.Conv2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.uniform(BS, C1, H, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

  @unittest.skip("Takes too long to compile for Compiled backends")
  def test_conv2d_winograd(self):
    BS, C1, H, W = 2, 8, 16, 16
    C2, K, S, P = 8, 3, 1, 1

    # create in tinygrad
    layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)
    layer.weight.requires_grad = True
    layer.bias.requires_grad = True

    # create in torch
    torch_layer = torch.nn.Conv2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
    torch_layer.weight = torch.nn.Parameter(torch.tensor(layer.weight.numpy(), dtype=torch.float32))
    torch_layer.bias = torch.nn.Parameter(torch.tensor(layer.bias.numpy(), dtype=torch.float32))

    # test
    x = Tensor.uniform(BS, C1, H, W, requires_grad=True)

    with Context(WINO=1):
      z = layer(x)

    torch_x = torch.tensor(x.numpy(), requires_grad=True)
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

    m = z.mean()
    m.backward()
    gw = layer.weight.grad.realize()
    gb = layer.bias.grad.realize()
    gx = x.grad.realize()

    torch_z.mean().backward()
    np.testing.assert_allclose(gw.numpy(), torch_layer.weight.grad.numpy(), atol=5e-4, rtol=1e-5)
    np.testing.assert_allclose(gb.numpy(), torch_layer.bias.grad.numpy(), atol=5e-4, rtol=1e-5)
    np.testing.assert_allclose(gx.numpy(), torch_x.grad.numpy(), atol=5e-4, rtol=1e-5)

  @unittest.skipIf(CI and Device.DEFAULT == "WEBGPU", "runs out of memory in CI")
  def test_conv_transpose1d(self):
    BS, C1, W = 4, 16, 224//4
    C2, K, S, P = 64, 7, 2, 1

    # create in tinygrad
    layer = ConvTranspose1d(C1, C2, kernel_size=K, stride=S, padding=P)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.ConvTranspose1d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.uniform(BS, C1, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

  @unittest.skipIf(CI and Device.DEFAULT == "WEBGPU", "runs out of memory in CI")
  def test_conv_transpose2d(self):
    BS, C1, H, W = 4, 16, 224//4, 224//4
    C2, K, S, P = 64, 7, 2, 1

    # create in tinygrad
    layer = ConvTranspose2d(C1, C2, kernel_size=K, stride=S, padding=P)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.ConvTranspose2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.uniform(BS, C1, H, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

  def test_groupnorm(self):
    BS, H, W, C, G = 20, 10, 10, 6, 3

    # create in torch
    torch_layer = torch.nn.GroupNorm(G, C).eval()

    # create in tinygrad
    layer = GroupNorm(G, C)
    layer.weight = Tensor(torch_layer.weight.detach().numpy(), requires_grad=True)
    layer.bias = Tensor(torch_layer.bias.detach().numpy(), requires_grad=True)

    for _ in range(10):
      # forward
      x = Tensor.randn(BS, C, H, W, requires_grad=True)
      z = layer(x)
      torch_x = torch.tensor(x.numpy(), requires_grad=True)
      torch_z = torch_layer(torch_x)
      np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-6, rtol=5e-6)

      # backward
      z.sum().backward()
      torch_z.sum().backward(retain_graph=True)
      np.testing.assert_allclose(x.grad.numpy(), torch_x.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
      np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
      np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=5e-4, rtol=5e-4)

  def test_layernorm(self):
    N, C, H, W = 20, 5, 10, 10

    # create in torch
    torch_layer = torch.nn.LayerNorm([H, W]).eval()

    # create in tinygrad
    layer = LayerNorm([H, W])
    layer.weight = Tensor(torch_layer.weight.detach().numpy(), requires_grad=True)
    layer.bias = Tensor(torch_layer.bias.detach().numpy(), requires_grad=True)

    for _ in range(10):
      # forward
      x = Tensor.randn(N, C, H, W, requires_grad=True)
      z = layer(x)
      torch_x = torch.tensor(x.numpy(), requires_grad=True)
      torch_z = torch_layer(torch_x)
      np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-6, rtol=5e-6)

      # backward
      z.sum().backward()
      torch_z.sum().backward(retain_graph=True)
      np.testing.assert_allclose(x.grad.numpy(), torch_x.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
      np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
      np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=5e-4, rtol=5e-4)

  def test_layernorm_2d(self):
    N, C, H, W = 20, 5, 10, 10

    # create in torch
    torch_layer = torch.nn.LayerNorm([C]).eval()

    # create in tinygrad
    layer = LayerNorm2d(C)
    layer.weight = Tensor(torch_layer.weight.detach().numpy(), requires_grad=True)
    layer.bias = Tensor(torch_layer.bias.detach().numpy(), requires_grad=True)

    for _ in range(10):
      # forward
      x = Tensor.randn(N, C, H, W, requires_grad=True)
      z = layer(x)
      torch_x = torch.tensor(x.numpy(), requires_grad=True)
      torch_z = torch_layer(torch_x.permute(0,2,3,1)).permute(0,3,1,2)
      np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-6, rtol=5e-6)

      # backward
      z.sum().backward()
      torch_z.sum().backward(retain_graph=True)
      np.testing.assert_allclose(x.grad.numpy(), torch_x.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
      np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
      np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=5e-4, rtol=5e-4)

  def test_instancenorm_2d(self):
    N, C, H, W = 20, 10, 10, 10

    # create in torch
    torch_layer = torch.nn.InstanceNorm2d(C, affine=True).eval()

    # create in tinygrad
    layer = InstanceNorm(C)
    layer.weight = Tensor(torch_layer.weight.detach().numpy(), requires_grad=True)
    layer.bias = Tensor(torch_layer.bias.detach().numpy(), requires_grad=True)

    for _ in range(10):
      # forward
      x = Tensor.randn(N, C, H, W, requires_grad=True)
      z = layer(x)
      torch_x = torch.tensor(x.numpy(), requires_grad=True)
      torch_z = torch_layer(torch_x)
      np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-6, rtol=5e-6)

      # backward
      z.sum().backward()
      torch_z.sum().backward(retain_graph=True)
      np.testing.assert_allclose(x.grad.numpy(), torch_x.grad.detach().numpy(), atol=1e-3, rtol=1e-3)
      np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=1e-3, rtol=1e-3)
      np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=1e-3, rtol=1e-3)

  def test_instancenorm_3d(self):
    N, C, D, H, W = 20, 10, 10, 10, 10

    # create in torch
    torch_layer = torch.nn.InstanceNorm3d(C, affine=True).eval()

    # create in tinygrad
    layer = InstanceNorm(C)
    layer.weight = Tensor(torch_layer.weight.detach().numpy(), requires_grad=True)
    layer.bias = Tensor(torch_layer.bias.detach().numpy(), requires_grad=True)

    for _ in range(10):
      # forward
      x = Tensor.randn(N, C, D, H, W, requires_grad=True)
      z = layer(x)
      torch_x = torch.tensor(x.numpy(), requires_grad=True)
      torch_z = torch_layer(torch_x)
      np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-6, rtol=5e-6)

      # backward
      z.sum().backward()
      torch_z.sum().backward(retain_graph=True)
      np.testing.assert_allclose(x.grad.numpy(), torch_x.grad.detach().numpy(), atol=1e-3, rtol=1e-3)
      np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=2e-3, rtol=1e-3)
      np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=1e-3, rtol=1e-3)

  def test_rmsnorm(self):
    class TorchRMSNorm(torch.nn.Module):
      # https://github.com/meta-llama/llama/blob/be327c427cc5e89cc1d3ab3d3fec4484df771245/llama/model.py#L34C1-L77C36
      def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = torch.nn.Parameter(torch.ones(dim))

      def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

      def forward(self, x):
        output = self._norm(x.float()).type_as(x)
        return output * self.weight

    B, T, embed_size = 4, 10, 20
    torch_layer = TorchRMSNorm(embed_size)
    layer = RMSNorm(embed_size)
    layer.weight.requires_grad = True

    for _ in range(10):
      # forward
      x = Tensor.randn(B, T, embed_size, requires_grad=True)
      z = layer(x)
      torch_x = torch.tensor(x.numpy(), requires_grad=True)
      torch_z = torch_layer(torch_x)
      np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-6, rtol=5e-6)

      # backward
      z.sum().backward()
      torch_z.sum().backward(retain_graph=True)
      np.testing.assert_allclose(x.grad.numpy(), torch_x.grad.detach().numpy(), atol=1e-3, rtol=1e-3)
      np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=2e-3, rtol=1e-3)

  def test_embedding(self):
    B, T, embed_size, vocab_size = 4, 10, 20, 28

    # create in tinygrad
    layer = Embedding(vocab_size, embed_size)

    with torch.no_grad():
      torch_layer = torch.nn.Embedding(vocab_size, embed_size).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)

    # test
    x = Tensor(np.random.randint(0, vocab_size, (B, T)))
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8)

    # test with empty input length
    x = Tensor(np.random.randint(0, vocab_size, (B, 0)))
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8)

    # test with jit enabled
    @TinyJit
    def layer_jit(x):
      return layer(x).realize()

    for _ in range(3):
      x = Tensor(np.random.randint(0, vocab_size, (B, T)))
      z = layer_jit(x)
      torch_x = torch.tensor(x.numpy())
      torch_z = torch_layer(torch_x)
      np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8)

  def test_embedding_one_kernel(self):
    layer = Embedding(20, 30)
    layer.weight = Tensor.zeros_like(layer.weight).contiguous()
    a = Tensor([[1, 5, 9, 11],
                [12, 19, 8, 1]])
    result = layer(a)
    schedule = create_schedule([result.lazydata])
    self.assertEqual(3, len([item for item in schedule if item.ast.op is UOps.SINK]), "first run realizes arange, weight, and embedding")
    run_schedule(schedule)

    b = Tensor([[1, 2, 3],
                [4, 5, 6],
                [7, 8, 9]])
    result = layer(b)
    schedule = create_schedule([result.lazydata])
    self.assertEqual(1, len([item for item in schedule if item.ast.op is UOps.SINK]), "second run realizes embedding only")
    run_schedule(schedule)

  def test_load_state_dict(self):
    layer = Conv2d(3, 5, kernel_size=3)

    state_dict = {
      'weight': Tensor.randn(5, 3, 3, 3),
      'bias': Tensor.randn(5),
    }
    load_state_dict(layer, state_dict)

    np.testing.assert_allclose(layer.weight.numpy(), state_dict['weight'].numpy())
    np.testing.assert_allclose(layer.bias.numpy(), state_dict['bias'].numpy())

  @unittest.skipIf(CI and Device.DEFAULT in {"GPU", "CUDA", "METAL"}, "no GPU CI")
  def test_load_state_dict_sharded(self):
    devices = (f"{Device.DEFAULT}:1", f"{Device.DEFAULT}:2")

    layer = Conv2d(3, 5, kernel_size=3)
    layer.weight.shard_(devices, -1)
    layer.bias.shard_(devices, None)
    state_dict = {
      'weight': Tensor.randn(5, 3, 3, 3).shard(devices, -1),
      'bias': Tensor.randn(5).shard(devices, None),
    }
    load_state_dict(layer, state_dict)

    self.assertEqual(layer.weight.device, devices)
    self.assertEqual(layer.bias.device, devices)
    np.testing.assert_allclose(layer.weight.numpy(), state_dict['weight'].numpy())
    np.testing.assert_allclose(layer.bias.numpy(), state_dict['bias'].numpy())

  def test_lstm_cell(self):
    layer = LSTMCell(32, 16)
    with torch.no_grad():
      torch_layer = torch.nn.LSTMCell(32, 16)
      layer.weight_hh.assign(torch_layer.weight_hh.numpy())
      layer.weight_ih.assign(torch_layer.weight_ih.numpy())
      layer.bias_hh.assign(torch_layer.bias_hh.numpy())
      layer.bias_ih.assign(torch_layer.bias_ih.numpy())

      inp = Tensor.randn(1, 32)
      out_h, out_c = layer(inp)
      torch_out_h, torch_out_c = torch_layer(torch.tensor(inp.numpy()))
      np.testing.assert_allclose(out_h.numpy(), torch_out_h.numpy(), atol=1e-6)
      np.testing.assert_allclose(out_c.numpy(), torch_out_c.numpy(), atol=1e-6)

      out_h, out_c = layer(inp, (out_h, out_c))
      torch_out_h, torch_out_c = torch_layer(torch.tensor(inp.numpy()), (torch_out_h, torch_out_c))
      np.testing.assert_allclose(out_h.numpy(), torch_out_h.numpy(), atol=1e-6)
      np.testing.assert_allclose(out_c.numpy(), torch_out_c.numpy(), atol=1e-6)

  def test_lstm_cell_no_bias(self):
    layer = LSTMCell(32, 16, bias=False)
    inp = Tensor.randn(1, 32)
    out_h, out_c = layer(inp)
    out_h.realize()
    out_c.realize()
    h = Tensor.randn(1, 16)
    c = Tensor.randn(1, 16)
    out_h, out_c = layer(inp, (h, c))
    out_h.realize()
    out_c.realize()
    assert layer.bias_hh is None
    assert layer.bias_ih is None

if __name__ == '__main__':
  unittest.main()