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Fix tflongformer int dtype (#18907)

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* Use int64 throughout TFLongFormer

* make style

* Do some more fixed casting in TFLongFormer

* Fix some wonky "is None" conditionals

* Cast all the dtypes, salt the earth

* Fix copies to TFLED as well and do some casting there

* dtype fix in TFLongformer test

* Make fixup

* Expand tolerances on the LED tests too (I think this is a TF32 thing)

* Expand test tolerances for LED a tiny bit (probably a Tensorfloat thing again)
This commit is contained in:
Matt
2022-09-12 17:51:10 +01:00
committed by GitHub
parent f7ceda345d
commit c126a239bc
4 changed files with 137 additions and 84 deletions
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4
tests/models/led/test_modeling_tf_led.py
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@@ -412,7 +412,7 @@ class TFLEDModelIntegrationTest(unittest.TestCase):
expected_slice = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]],
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-3)
def test_inference_with_head(self):
model = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384")
@@ -428,4 +428,4 @@ class TFLEDModelIntegrationTest(unittest.TestCase):
expected_slice = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]],
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-3, rtol=1e-3)

106
tests/models/longformer/test_modeling_tf_longformer.py
View File

@@ -115,7 +115,7 @@ class TFLongformerModelTester:
):
model = TFLongformerModel(config=config)
attention_mask = tf.ones(input_ids.shape, dtype=tf.dtypes.int32)
attention_mask = tf.ones(input_ids.shape, dtype=tf.int64)
output_with_mask = model(input_ids, attention_mask=attention_mask)[0]
output_without_mask = model(input_ids)[0]
tf.debugging.assert_near(output_with_mask[0, 0, :5], output_without_mask[0, 0, :5], rtol=1e-4)
@@ -403,26 +403,24 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
# first row => [0.4983, 2.6918, -0.0071, 1.0492, 0.0000, 0.0000, 0.0000]
tf.debugging.assert_near(padded_hidden_states[0, 0, 0, :4], chunked_hidden_states[0, 0, 0], rtol=1e-3)
tf.debugging.assert_near(padded_hidden_states[0, 0, 0, 4:], tf.zeros((3,), dtype=tf.dtypes.float32), rtol=1e-3)
tf.debugging.assert_near(padded_hidden_states[0, 0, 0, 4:], tf.zeros((3,), dtype=tf.float32), rtol=1e-3)
# last row => [0.0000, 0.0000, 0.0000, 2.0514, -1.1600, 0.5372, 0.2629]
tf.debugging.assert_near(padded_hidden_states[0, 0, -1, 3:], chunked_hidden_states[0, 0, -1], rtol=1e-3)
tf.debugging.assert_near(
padded_hidden_states[0, 0, -1, :3], tf.zeros((3,), dtype=tf.dtypes.float32), rtol=1e-3
)
tf.debugging.assert_near(padded_hidden_states[0, 0, -1, :3], tf.zeros((3,), dtype=tf.float32), rtol=1e-3)
def test_pad_and_transpose_last_two_dims(self):
hidden_states = self._get_hidden_states()
self.assertEqual(shape_list(hidden_states), [1, 4, 8])
# pad along seq length dim
paddings = tf.constant([[0, 0], [0, 0], [0, 1], [0, 0]], dtype=tf.dtypes.int32)
paddings = tf.constant([[0, 0], [0, 0], [0, 1], [0, 0]], dtype=tf.int64)
hidden_states = TFLongformerSelfAttention._chunk(hidden_states, window_overlap=2)
padded_hidden_states = TFLongformerSelfAttention._pad_and_transpose_last_two_dims(hidden_states, paddings)
self.assertTrue(shape_list(padded_hidden_states) == [1, 1, 8, 5])
expected_added_dim = tf.zeros((5,), dtype=tf.dtypes.float32)
expected_added_dim = tf.zeros((5,), dtype=tf.float32)
tf.debugging.assert_near(expected_added_dim, padded_hidden_states[0, 0, -1, :], rtol=1e-6)
tf.debugging.assert_near(
hidden_states[0, 0, -1, :], tf.reshape(padded_hidden_states, (1, -1))[0, 24:32], rtol=1e-6
@@ -441,10 +439,10 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
hid_states_3 = TFLongformerSelfAttention._mask_invalid_locations(hidden_states[:, :, :, :3], 2)
hid_states_4 = TFLongformerSelfAttention._mask_invalid_locations(hidden_states[:, :, 2:, :], 2)
self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_1), tf.dtypes.int32)) == 8)
self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_2), tf.dtypes.int32)) == 24)
self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_3), tf.dtypes.int32)) == 24)
self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_4), tf.dtypes.int32)) == 12)
self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_1), tf.int64)) == 8)
self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_2), tf.int64)) == 24)
self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_3), tf.int64)) == 24)
self.assertTrue(tf.math.reduce_sum(tf.cast(tf.math.is_inf(hid_states_4), tf.int64)) == 12)
def test_chunk(self):
hidden_states = self._get_hidden_states()
@@ -456,12 +454,14 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
chunked_hidden_states = TFLongformerSelfAttention._chunk(hidden_states, window_overlap=2)
# expected slices across chunk and seq length dim
expected_slice_along_seq_length = tf.convert_to_tensor([0.4983, -0.7584, -1.6944], dtype=tf.dtypes.float32)
expected_slice_along_chunk = tf.convert_to_tensor([0.4983, -1.8348, -0.7584, 2.0514], dtype=tf.dtypes.float32)
expected_slice_along_seq_length = tf.convert_to_tensor([0.4983, -0.7584, -1.6944], dtype=tf.float32)
expected_slice_along_chunk = tf.convert_to_tensor([0.4983, -1.8348, -0.7584, 2.0514], dtype=tf.float32)
self.assertTrue(shape_list(chunked_hidden_states) == [1, 3, 4, 4])
tf.debugging.assert_near(chunked_hidden_states[0, :, 0, 0], expected_slice_along_seq_length, rtol=1e-3)
tf.debugging.assert_near(chunked_hidden_states[0, 0, :, 0], expected_slice_along_chunk, rtol=1e-3)
tf.debugging.assert_near(
chunked_hidden_states[0, :, 0, 0], expected_slice_along_seq_length, rtol=1e-3, atol=1e-4
)
tf.debugging.assert_near(chunked_hidden_states[0, 0, :, 0], expected_slice_along_chunk, rtol=1e-3, atol=1e-4)
def test_layer_local_attn(self):
model = TFLongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny")
@@ -469,7 +469,7 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
hidden_states = self._get_hidden_states()
batch_size, seq_length, hidden_size = hidden_states.shape
attention_mask = tf.zeros((batch_size, seq_length), dtype=tf.dtypes.float32)
attention_mask = tf.zeros((batch_size, seq_length), dtype=tf.float32)
is_index_global_attn = tf.math.greater(attention_mask, 1)
is_global_attn = tf.math.reduce_any(is_index_global_attn)
@@ -483,11 +483,11 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
)[0]
expected_slice = tf.convert_to_tensor(
[0.00188, 0.012196, -0.017051, -0.025571, -0.02996, 0.017297, -0.011521, 0.004848], dtype=tf.dtypes.float32
[0.00188, 0.012196, -0.017051, -0.025571, -0.02996, 0.017297, -0.011521, 0.004848], dtype=tf.float32
)
self.assertEqual(output_hidden_states.shape, (1, 4, 8))
tf.debugging.assert_near(output_hidden_states[0, 1], expected_slice, rtol=1e-3)
tf.debugging.assert_near(output_hidden_states[0, 1], expected_slice, rtol=1e-3, atol=1e-4)
def test_layer_global_attn(self):
model = TFLongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny")
@@ -498,8 +498,8 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
batch_size, seq_length, hidden_size = hidden_states.shape
# create attn mask
attention_mask_1 = tf.zeros((1, 1, 1, seq_length), dtype=tf.dtypes.float32)
attention_mask_2 = tf.zeros((1, 1, 1, seq_length), dtype=tf.dtypes.float32)
attention_mask_1 = tf.zeros((1, 1, 1, seq_length), dtype=tf.float32)
attention_mask_2 = tf.zeros((1, 1, 1, seq_length), dtype=tf.float32)
attention_mask_1 = tf.where(tf.range(4)[None, :, None, None] > 1, 10000.0, attention_mask_1)
attention_mask_1 = tf.where(tf.range(4)[None, :, None, None] > 2, -10000.0, attention_mask_1)
@@ -525,15 +525,15 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
self.assertEqual(output_hidden_states.shape, (2, 4, 8))
expected_slice_0 = tf.convert_to_tensor(
[-0.06508, -0.039306, 0.030934, -0.03417, -0.00656, -0.01553, -0.02088, -0.04938], dtype=tf.dtypes.float32
[-0.06508, -0.039306, 0.030934, -0.03417, -0.00656, -0.01553, -0.02088, -0.04938], dtype=tf.float32
)
expected_slice_1 = tf.convert_to_tensor(
[-0.04055, -0.038399, 0.0396, -0.03735, -0.03415, 0.01357, 0.00145, -0.05709], dtype=tf.dtypes.float32
[-0.04055, -0.038399, 0.0396, -0.03735, -0.03415, 0.01357, 0.00145, -0.05709], dtype=tf.float32
)
tf.debugging.assert_near(output_hidden_states[0, 2], expected_slice_0, rtol=1e-3)
tf.debugging.assert_near(output_hidden_states[1, -2], expected_slice_1, rtol=1e-3)
tf.debugging.assert_near(output_hidden_states[0, 2], expected_slice_0, rtol=1e-3, atol=1e-4)
tf.debugging.assert_near(output_hidden_states[1, -2], expected_slice_1, rtol=1e-3, atol=1e-4)
def test_layer_attn_probs(self):
model = TFLongformerModel.from_pretrained("patrickvonplaten/longformer-random-tiny")
@@ -542,8 +542,8 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
batch_size, seq_length, hidden_size = hidden_states.shape
# create attn mask
attention_mask_1 = tf.zeros((1, 1, 1, seq_length), dtype=tf.dtypes.float32)
attention_mask_2 = tf.zeros((1, 1, 1, seq_length), dtype=tf.dtypes.float32)
attention_mask_1 = tf.zeros((1, 1, 1, seq_length), dtype=tf.float32)
attention_mask_2 = tf.zeros((1, 1, 1, seq_length), dtype=tf.float32)
attention_mask_1 = tf.where(tf.range(4)[None, :, None, None] > 1, 10000.0, attention_mask_1)
attention_mask_1 = tf.where(tf.range(4)[None, :, None, None] > 2, -10000.0, attention_mask_1)
@@ -584,18 +584,16 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
tf.debugging.assert_near(
local_attentions[0, 0, 0, :],
tf.convert_to_tensor(
[0.3328, 0.0000, 0.0000, 0.0000, 0.0000, 0.3355, 0.3318, 0.0000], dtype=tf.dtypes.float32
),
tf.convert_to_tensor([0.3328, 0.0000, 0.0000, 0.0000, 0.0000, 0.3355, 0.3318, 0.0000], dtype=tf.float32),
rtol=1e-3,
atol=1e-4,
)
tf.debugging.assert_near(
local_attentions[1, 0, 0, :],
tf.convert_to_tensor(
[0.2492, 0.2502, 0.2502, 0.0000, 0.0000, 0.2505, 0.0000, 0.0000], dtype=tf.dtypes.float32
),
tf.convert_to_tensor([0.2492, 0.2502, 0.2502, 0.0000, 0.0000, 0.2505, 0.0000, 0.0000], dtype=tf.float32),
rtol=1e-3,
atol=1e-4,
)
# All the global attention weights must sum to 1.
@@ -603,13 +601,15 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
tf.debugging.assert_near(
global_attentions[0, 0, 1, :],
tf.convert_to_tensor([0.2500, 0.2500, 0.2500, 0.2500], dtype=tf.dtypes.float32),
tf.convert_to_tensor([0.2500, 0.2500, 0.2500, 0.2500], dtype=tf.float32),
rtol=1e-3,
atol=1e-4,
)
tf.debugging.assert_near(
global_attentions[1, 0, 0, :],
tf.convert_to_tensor([0.2497, 0.2500, 0.2499, 0.2504], dtype=tf.dtypes.float32),
tf.convert_to_tensor([0.2497, 0.2500, 0.2499, 0.2504], dtype=tf.float32),
rtol=1e-3,
atol=1e-4,
)
@slow
@@ -617,31 +617,31 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
model = TFLongformerModel.from_pretrained("allenai/longformer-base-4096")
# 'Hello world!'
input_ids = tf.convert_to_tensor([[0, 20920, 232, 328, 1437, 2]], dtype=tf.dtypes.int32)
attention_mask = tf.ones(shape_list(input_ids), dtype=tf.dtypes.int32)
input_ids = tf.convert_to_tensor([[0, 20920, 232, 328, 1437, 2]], dtype=tf.int64)
attention_mask = tf.ones(shape_list(input_ids), dtype=tf.int64)
output = model(input_ids, attention_mask=attention_mask)[0]
output_without_mask = model(input_ids)[0]
expected_output_slice = tf.convert_to_tensor(
[0.0549, 0.1087, -0.1119, -0.0368, 0.0250], dtype=tf.dtypes.float32
)
expected_output_slice = tf.convert_to_tensor([0.0549, 0.1087, -0.1119, -0.0368, 0.0250], dtype=tf.float32)
tf.debugging.assert_near(output[0, 0, -5:], expected_output_slice, rtol=1e-3)
tf.debugging.assert_near(output_without_mask[0, 0, -5:], expected_output_slice, rtol=1e-3)
tf.debugging.assert_near(output[0, 0, -5:], expected_output_slice, rtol=1e-3, atol=1e-4)
tf.debugging.assert_near(output_without_mask[0, 0, -5:], expected_output_slice, rtol=1e-3, atol=1e-4)
@slow
def test_inference_no_head_long(self):
model = TFLongformerModel.from_pretrained("allenai/longformer-base-4096")
# 'Hello world! ' repeated 1000 times
input_ids = tf.convert_to_tensor([[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=tf.dtypes.int32)
input_ids = tf.convert_to_tensor([[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=tf.int64)
attention_mask = tf.ones(shape_list(input_ids), dtype=tf.dtypes.int32)
global_attention_mask = tf.zeros(shape_list(input_ids), dtype=tf.dtypes.int32)
attention_mask = tf.ones(shape_list(input_ids), dtype=tf.int64)
global_attention_mask = tf.zeros(shape_list(input_ids), dtype=tf.int64)
# Set global attention on a few random positions
global_attention_mask = tf.tensor_scatter_nd_update(
global_attention_mask, tf.constant([[0, 1], [0, 4], [0, 21]]), tf.constant([1, 1, 1])
global_attention_mask,
tf.constant([[0, 1], [0, 4], [0, 21]], dtype=tf.int64),
tf.constant([1, 1, 1], dtype=tf.int64),
)
output = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)[0]
@@ -650,15 +650,15 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
expected_output_mean = tf.constant(0.024267)
# assert close
tf.debugging.assert_near(tf.reduce_sum(output), expected_output_sum, rtol=1e-4)
tf.debugging.assert_near(tf.reduce_mean(output), expected_output_mean, rtol=1e-4)
tf.debugging.assert_near(tf.reduce_sum(output), expected_output_sum, rtol=1e-4, atol=1e-4)
tf.debugging.assert_near(tf.reduce_mean(output), expected_output_mean, rtol=1e-4, atol=1e-4)
@slow
def test_inference_masked_lm_long(self):
model = TFLongformerForMaskedLM.from_pretrained("allenai/longformer-base-4096")
# 'Hello world! ' repeated 1000 times
input_ids = tf.convert_to_tensor([[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=tf.dtypes.int32)
input_ids = tf.convert_to_tensor([[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=tf.int64)
output = model(input_ids, labels=input_ids)
loss = output.loss
@@ -669,9 +669,13 @@ class TFLongformerModelIntegrationTest(unittest.TestCase):
expected_prediction_scores_mean = tf.constant(-3.03477)
# assert close
tf.debugging.assert_near(tf.reduce_mean(loss), expected_loss, rtol=1e-4)
tf.debugging.assert_near(tf.reduce_sum(prediction_scores), expected_prediction_scores_sum, rtol=1e-4)
tf.debugging.assert_near(tf.reduce_mean(prediction_scores), expected_prediction_scores_mean, rtol=1e-4)
tf.debugging.assert_near(tf.reduce_mean(loss), expected_loss, rtol=1e-4, atol=1e-4)
tf.debugging.assert_near(
tf.reduce_sum(prediction_scores), expected_prediction_scores_sum, rtol=1e-4, atol=1e-4
)
tf.debugging.assert_near(
tf.reduce_mean(prediction_scores), expected_prediction_scores_mean, rtol=1e-4, atol=1e-4
)
@slow
def test_inference_masked_lm(self):