def test_local_attn_probs(self):
model = GPTNeoModel.from_pretrained(
"valhalla/gpt-neo-random-tiny").eval()
layer = model.h[1].attn.attention.to(torch_device)
hidden_states = self._get_hidden_states()
hidden_states = torch.cat([hidden_states, hidden_states - 0.5], dim=2)
batch_size, seq_length, _ = hidden_states.shape
mask_tokens = 2
attention_mask = torch.ones(batch_size,
seq_length,
device=torch_device,
dtype=torch.long)
attention_mask[:, -mask_tokens:] = 0 # dont attend last mask_tokens
attention_mask = attention_mask.view(batch_size, -1)
attention_mask = attention_mask[:, None, None, :]
attention_mask = (1.0 - attention_mask) * -10000.0
attn_probs = layer(hidden_states,
attention_mask=attention_mask,
output_attentions=True)[-1]
# the last 2 tokens are masked, and should have 0 attn_probs
self.assertTrue(
torch.all(attn_probs[:, :, -mask_tokens:, -mask_tokens:] == 0))
# in loacal attention each token can only attend to the previous window_size tokens (inlcuding itself)
# here window_size is 4, so a token at index 5 can only attend to indcies [2, 3, 4, 5]
# and the attn_probs should be 0 for token [0, 1]
self.assertTrue(torch.all(attn_probs[:, :, 5, 2:6] != 0))
self.assertTrue(torch.all(attn_probs[:, :, 5, :2] == 0))
def test_local_attn_probs(self):
model = GPTNeoModel.from_pretrained(
"valhalla/gpt-neo-random-tiny").eval()
layer = model.h[1].attn.attention.to(torch_device)
hidden_states = self._get_hidden_states()
hidden_states = torch.cat([hidden_states, hidden_states - 0.5], dim=2)
batch_size, seq_length, hidden_size = hidden_states.shape
mask_tokens = 3
attention_mask = torch.ones(batch_size,
seq_length,
device=torch_device,
dtype=torch.long)
attention_mask[:, -mask_tokens:] = 0 # dont atten last mask_tokens
local_causal_mask = GPTNeoAttentionMixin.create_local_attention_mask(
batch_size, seq_length, model.config.window_size, torch_device,
attention_mask)
_, attn_probs = layer(hidden_states,
attention_mask=local_causal_mask,
output_attentions=True)
# the last 3 tokens will be in the last block, and should have 0 attn_probs
self.assertTrue(
torch.all(attn_probs[:, -1, :, -mask_tokens:, -mask_tokens:] == 0))
# the first config.window_size tokens in the first block are always padded
# and should have 0 attn_probs
self.assertTrue(
torch.all(attn_probs[:, 0, :, :model.config.window_size:, :model.
config.window_size] == 0))
def test_model_from_pretrained(self):
for model_name in GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = GPTNeoModel.from_pretrained(model_name)
self.assertIsNotNone(model)