def test_topk_equals_length_attention_masked(self):
d_model = 32
n_heads = 4
improved_transformer = TransformerEncoder([
TransformerEncoderLayer(
AttentionLayer(
ImprovedClusteredAttention(clusters=10, topk=20), d_model,
n_heads), d_model, n_heads) for i in range(6)
])
full_transformer = TransformerEncoder([
TransformerEncoderLayer(
AttentionLayer(FullAttention(), d_model, n_heads), d_model,
n_heads) for i in range(6)
])
full_transformer = full_transformer.to("cuda")
improved_transformer = improved_transformer.to("cuda")
improved_transformer.load_state_dict(full_transformer.state_dict())
improved_transformer.eval()
full_transformer.eval()
x = torch.rand(100, 20, d_model).to("cuda")
lengths = x.new_full((100, ), 20, dtype=torch.int64)
lengths[1] = 5
lengths[10] = 10
length_mask = LengthMask(lengths=lengths, max_len=20)
y_full = improved_transformer(x, length_mask=length_mask)
y_improved = full_transformer(x, length_mask=length_mask)
self.assertLess(
torch.max(torch.abs(y_improved[1, :5] - y_full[1, :5])), 1e-4)
self.assertLess(
torch.max(torch.abs(y_improved[10, :10] - y_full[10, :10])), 1e-4)
def test_clustered_attention_forward(self):
d_model = 128
n_heads = 4
transformer = TransformerEncoder([
TransformerEncoderLayer(
AttentionLayer(ClusteredAttention(clusters=10), d_model,
n_heads), d_model, n_heads) for i in range(6)
])
x = transformer(torch.rand(100, 20, d_model))
self.assertEqual(x.shape, (100, 20, d_model))
def test_full_attention_forward(self):
d_model = 128
n_heads = 4
transformer = TransformerEncoder([
TransformerEncoderLayer(
AttentionLayer(FullAttention(), d_model, n_heads), d_model,
n_heads) for i in range(6)
])
x = transformer(torch.rand(10, 7, d_model))
self.assertEqual(x.shape, (10, 7, d_model))
def test_improved_clustered_attention_forward(self):
d_model = 128
n_heads = 4
transformer = TransformerEncoder([
TransformerEncoderLayer(
AttentionLayer(ImprovedClusteredAttention(clusters=10, topk=5),
d_model, n_heads), d_model, n_heads)
for i in range(6)
])
x = torch.rand(100, 20, d_model)
y = transformer(x)
self.assertEqual(y.shape, (100, 20, d_model))
def test_full_attention_forward(self):
d_model = 128
n_heads = 4
transformer = TransformerEncoder([
TransformerEncoderLayer(
AttentionLayer(ClusteredAttention(clusters=10), d_model,
n_heads), d_model, n_heads) for i in range(6)
])
transformer = transformer.to("cuda")
x = torch.rand(100, 20, d_model).to("cuda")
y = transformer(x)
self.assertEqual(y.shape, (100, 20, d_model))
def test_topk_equals_length_attention(self):
d_model = 32
n_heads = 4
improved_transformer = TransformerEncoder([
TransformerEncoderLayer(
AttentionLayer(
ImprovedClusteredAttention(clusters=10, topk=20), d_model,
n_heads), d_model, n_heads) for i in range(6)
])
full_transformer = TransformerEncoder([
TransformerEncoderLayer(
AttentionLayer(FullAttention(), d_model, n_heads), d_model,
n_heads) for i in range(6)
])
full_transformer = full_transformer.to("cuda")
improved_transformer = improved_transformer.to("cuda")
improved_transformer.load_state_dict(full_transformer.state_dict())
improved_transformer.eval()
full_transformer.eval()
x = torch.rand(100, 20, d_model).to("cuda")
y_full = improved_transformer(x)
y_improved = full_transformer(x)
self.assertLess(torch.max(torch.abs(y_improved - y_full)), 1e-4)
def test_improved_clustered_attention_forward(self):
d_model = 128
n_heads = 4
transformer = TransformerEncoder([
TransformerEncoderLayer(
AttentionLayer(
ReformerAttention(
chunk_size=32,
rounds=4,
bits=8,
masked=False,
), d_model, n_heads), d_model, n_heads) for i in range(6)
])
x = torch.rand(12, 128, d_model)
y = transformer(x)
self.assertEqual(y.shape, (12, 128, d_model))