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fix failed test cases for blt model #45596

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ydshieh merged 8 commits into from
Apr 29, 2026
+78 −150
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fix failed test cases for blt model #45596

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95a4781
fix 2 failed test cases for blt model on XPU
kaixuanliu Apr 23, 2026
01dd2ed
update
ydshieh Apr 28, 2026
1dc2daa
update
ydshieh Apr 28, 2026
61790a4
update
ydshieh Apr 28, 2026
c1e1da5
update
ydshieh Apr 28, 2026
9a394f6
update
ydshieh Apr 28, 2026
dc624a0
update
ydshieh Apr 28, 2026
6368219
fix another failed cases
kaixuanliu Apr 29, 2026
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6 changes: 3 additions & 3 deletions src/transformers/models/blt/modeling_blt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -670,7 +670,7 @@ def forward(
attention_mask=encoder_attention_mask,
**kwargs,
)
patch_embeds = patch_embeds + cross_attention_output
patch_embeds = patch_embeds + cross_attention_output.to(patch_embeds.device)
encoder_cross_states = patch_embeds
return hidden_states, encoder_cross_states

Expand Down Expand Up @@ -1228,7 +1228,7 @@ def forward(
else:
batch_size, sequence_length = input_ids.shape
encoder_embeds = compute_hash_embeddings(
input_ids,
input_ids.to(self.local_encoder.embed_tokens.weight.device),
self.local_encoder,
self.encoder_hash_tok_embedding,
self.config.encoder_hash_byte_group_nb_functions,
Expand All @@ -1241,7 +1241,7 @@ def forward(
if input_ids is None:
raise ValueError("input_ids is required for entropy-based patching")
_, patch_lengths, _ = self.patcher(
input_ids,
input_ids.to(self.patcher.embed_tokens.weight.device),
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@ydshieh ydshieh Apr 28, 2026

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it's not clear why self.patcher doesn't handle the input device well.

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@ydshieh ydshieh Apr 28, 2026

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Likely the BltPatcher itself is BltPreTrainedModel

patch_size=self.config.patch_size,
threshold=self.config.patching_threshold,
max_patch_length=self.config.max_patch_length,
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/models/blt/modular_blt.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -604,7 +604,7 @@ def forward(
attention_mask=encoder_attention_mask,
**kwargs,
)
patch_embeds = patch_embeds + cross_attention_output
patch_embeds = patch_embeds + cross_attention_output.to(patch_embeds.device)
encoder_cross_states = patch_embeds
return hidden_states, encoder_cross_states

Expand Down Expand Up @@ -955,7 +955,7 @@ def forward(
else:
batch_size, sequence_length = input_ids.shape
encoder_embeds = compute_hash_embeddings(
input_ids,
input_ids.to(self.local_encoder.embed_tokens.weight.device),
self.local_encoder,
self.encoder_hash_tok_embedding,
self.config.encoder_hash_byte_group_nb_functions,
Expand All @@ -968,7 +968,7 @@ def forward(
if input_ids is None:
raise ValueError("input_ids is required for entropy-based patching")
_, patch_lengths, _ = self.patcher(
input_ids,
input_ids.to(self.patcher.embed_tokens.weight.device),
patch_size=self.config.patch_size,
threshold=self.config.patching_threshold,
max_patch_length=self.config.max_patch_length,
Expand Down
216 changes: 72 additions & 144 deletions tests/models/blt/test_modeling_blt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@

from transformers import AutoTokenizer, is_torch_available
from transformers.testing_utils import (
Expectations,
cleanup,
require_torch,
require_torch_accelerator,
Expand Down Expand Up @@ -184,6 +185,10 @@ class BltModelTest(CausalLMModelTest, unittest.TestCase):
def test_generate_from_inputs_embeds(self, _, num_beams):
pass

@pytest.mark.generate
def test_generate_with_quant_cache(self):
self.skipTest("BLT uses EncoderDecoderCache internally and does not support quantized cache")

@pytest.mark.generate
@unittest.skip(
"Blt requires real token IDs for its hash-based embedding computation, making inputs_embeds generation incompatible with identical outputs"
Expand Down Expand Up @@ -260,74 +265,25 @@ def test_model(self):

@slow
def test_model_logits(self):
EXPECTED_OUTPUT = torch.tensor(
[
[
-10.4948,
-10.7065,
-6.1813,
-10.5545,
-10.3428,
-9.1493,
-8.4937,
-8.6382,
-9.2159,
-9.5907,
-9.3679,
-8.4184,
-9.0655,
-3.4436,
2.9616,
-10.3157,
-6.3723,
-6.0133,
-9.7100,
-9.2128,
-8.8064,
-9.8179,
-9.7516,
-9.4681,
-9.7715,
-9.4897,
-9.0491,
-9.8098,
-9.4648,
-9.3294,
],
[
-13.3010,
-13.1910,
-5.7230,
-13.2895,
-13.4864,
-8.7140,
-7.0275,
-7.0182,
-10.1362,
-10.3762,
-9.9086,
-7.8049,
-8.8660,
-5.2711,
-3.5778,
-12.5346,
-9.1609,
-6.7925,
-10.3717,
-9.2650,
-10.6393,
-11.4807,
-11.2128,
-10.9615,
-10.5806,
-10.8873,
-11.0651,
-11.3471,
-10.5437,
-9.9688,
],
]
).to(torch_device)
# fmt: off
EXPECTED_OUTPUT = Expectations(
{
(None, None): torch.tensor(
[
[-10.5000, -10.6875, -6.2500, -10.5625, -10.3125, -9.1875, -8.5000, -8.5625, -9.1875, -9.6250, -9.3750, -8.5000, -9.1250, -3.3906, 2.9688, -10.3125, -6.4688, -6.0312, -9.7500, -9.1875, -8.8125, -9.8750, -9.8125, -9.5000, -9.8125, -9.5000, -9.0625, -9.8125, -9.5000, -9.3750],
[-13.2500, -13.1250, -5.6875, -13.1875, -13.3750, -8.6875, -6.9688, -6.9375, -10.0625, -10.3125, -9.8125, -7.7188, -8.8125, -5.2188, -3.5000, -12.4375, -9.0625, -6.6250, -10.3125, -9.1875, -10.6250, -11.4375, -11.1250, -10.8750, -10.5000, -10.8750, -11.0000, -11.3125, -10.5000, -9.8750],
]
),
("xpu", None): torch.tensor(
[
[-10.4375, -10.6875, -6.1875, -10.5000, -10.3125, -9.1250, -8.4375, -8.6250, -9.1875, -9.5625, -9.3125, -8.4375, -9.0625, -3.4375, 2.9531, -10.2500, -6.4062, -6.0000, -9.6875, -9.1875, -8.8125, -9.8125, -9.7500, -9.4375, -9.7500, -9.4375, -9.0000, -9.8125, -9.4375, -9.3125],
[-13.3125, -13.2500, -5.5938, -13.3125, -13.5000, -8.7500, -7.0625, -7.0312, -10.1875, -10.3750, -9.9375, -7.8438, -8.8750, -5.3438, -3.5938, -12.5625, -9.2500, -6.8125, -10.3750, -9.3125, -10.6875, -11.5625, -11.3125, -11.0000, -10.6250, -10.9375, -11.0625, -11.3750, -10.5625, -10.0000],
]
),
}
).get_expectation()
EXPECTED_OUTPUT = EXPECTED_OUTPUT.to(torch_device)
# fmt: on

input_ids = [1, 42, 21, 12, 43, 23, 1, 4]

Expand All @@ -336,14 +292,21 @@ def test_model_logits(self):
with torch.no_grad():
output = model(torch.tensor([input_ids]).to(torch_device))[0]

torch.testing.assert_close(EXPECTED_OUTPUT, output[0, :2, :30], rtol=1e-4, atol=1e-4)
torch.testing.assert_close(EXPECTED_OUTPUT, output[0, :2, :30].to(torch_device), rtol=1e-3, atol=1e-3)

@slow
@require_torch_bf16
def test_model_bf16(self):
"""Test Blt model with bfloat16 precision."""
NUM_TOKENS_TO_GENERATE = 200
EXPECTED_TEXT = "my name is alex and i am a student at the university of michigan in the college of arts and sciences. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan m"
# fmt: off
EXPECTED_TEXT = Expectations(
{
(None, None): "my name is alex and i am a student at the university of michigan. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan math club and the michigan computer s",
("xpu", None): "my name is alex and i am a student at the university of michigan. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan math club and the michigan computer s",
}
)
# fmt: on

prompt = "my name is"

Expand All @@ -360,81 +323,32 @@ def test_model_bf16(self):
)

output_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXT)
self.assertEqual(output_text, EXPECTED_TEXT.get_expectation())

@slow
@require_torch_bf16
def test_model_logits_bf16(self):
"""Test Blt model logits with bfloat16 precision."""

EXPECTED_OUTPUT = torch.tensor(
[
[
-10.5000,
-10.6875,
-6.1875,
-10.5625,
-10.3125,
-9.1875,
-8.5000,
-8.6875,
-9.1875,
-9.5625,
-9.3750,
-8.5000,
-9.0625,
-3.4219,
2.9531,
-10.3125,
-6.4062,
-6.0000,
-9.6875,
-9.1875,
-8.8125,
-9.8125,
-9.7500,
-9.4375,
-9.8125,
-9.5000,
-9.0000,
-9.8125,
-9.4375,
-9.3125,
],
[
-13.2500,
-13.1875,
-5.6875,
-13.3125,
-13.5000,
-8.7500,
-7.0625,
-7.0312,
-10.1250,
-10.3750,
-9.8750,
-7.8438,
-8.8750,
-5.2812,
-3.5625,
-12.5000,
-9.1875,
-6.8125,
-10.3750,
-9.3125,
-10.6250,
-11.5000,
-11.2500,
-11.0000,
-10.5625,
-10.8750,
-11.0625,
-11.3750,
-10.5625,
-10.0000,
],
]
).to(torch_device)
# fmt: off
EXPECTED_OUTPUT = Expectations(
{
(None, None): torch.tensor(
[
[-10.5000, -10.6875, -6.2500, -10.5625, -10.3125, -9.1875, -8.5000, -8.5625, -9.1875, -9.6250, -9.3750, -8.5000, -9.1250, -3.3906, 2.9688, -10.3125, -6.4688, -6.0312, -9.7500, -9.1875, -8.8125, -9.8750, -9.8125, -9.5000, -9.8125, -9.5000, -9.0625, -9.8125, -9.5000, -9.3750],
[-13.2500, -13.1250, -5.6875, -13.1875, -13.3750, -8.6875, -6.9688, -6.9375, -10.0625, -10.3125, -9.8125, -7.7188, -8.8125, -5.2188, -3.5000, -12.4375, -9.0625, -6.6250, -10.3125, -9.1875, -10.6250, -11.4375, -11.1250, -10.8750, -10.5000, -10.8750, -11.0000, -11.3125, -10.5000, -9.8750],
]
),
("xpu", None): torch.tensor(
[
[-10.4375, -10.6875, -6.1875, -10.5000, -10.3125, -9.1250, -8.4375, -8.6250, -9.1875, -9.5625, -9.3125, -8.4375, -9.0625, -3.4375, 2.9531, -10.2500, -6.4062, -6.0000, -9.6875, -9.1875, -8.8125, -9.8125, -9.7500, -9.4375, -9.7500, -9.4375, -9.0000, -9.8125, -9.4375, -9.3125],
[-13.3125, -13.2500, -5.5938, -13.3125, -13.5000, -8.7500, -7.0625, -7.0312, -10.1875, -10.3750, -9.9375, -7.8438, -8.8750, -5.3438, -3.5938, -12.5625, -9.2500, -6.8125, -10.3750, -9.3125, -10.6875, -11.5625, -11.3125, -11.0000, -10.6250, -10.9375, -11.0625, -11.3750, -10.5625, -10.0000],
]
),
}
).get_expectation()
EXPECTED_OUTPUT = EXPECTED_OUTPUT.to(torch_device)
# fmt: on

input_ids = [1, 42, 21, 12, 43, 23, 1, 4]

Expand All @@ -445,13 +359,20 @@ def test_model_logits_bf16(self):
with torch.no_grad():
output = model(torch.tensor([input_ids]).to(torch_device))[0]

torch.testing.assert_close(EXPECTED_OUTPUT, output[0, :2, :30], rtol=1e-3, atol=1e-3)
torch.testing.assert_close(EXPECTED_OUTPUT, output[0, :2, :30].to(torch_device), rtol=1e-3, atol=1e-3)

@slow
def test_model_eager(self):
"""Test Blt model with bfloat16 precision using eager attention implementation."""
NUM_TOKENS_TO_GENERATE = 200
EXPECTED_TEXT = "my name is alex and i am a student at the university of michigan. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan math club and the michigan computer s"
# fmt: off
EXPECTED_TEXT = Expectations(
{
(None, None): "my name is alex and i am a student at the university of michigan. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan math club and the michigan computer s",
("xpu", None): "my name is alex and i am a student at the university of michigan in the college of arts and sciences. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan m",
}
)
# fmt: on

prompt = "my name is"

Expand All @@ -466,14 +387,21 @@ def test_model_eager(self):
)

output_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXT)
self.assertEqual(output_text, EXPECTED_TEXT.get_expectation())

@slow
@require_torch_bf16
def test_model_bf16_static_cache(self):
"""Test Blt model with bfloat16 precision and static cache."""
NUM_TOKENS_TO_GENERATE = 200
EXPECTED_TEXT = "my name is alex and i am a student at the university of michigan in the college of arts and sciences. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan m"
# fmt: off
EXPECTED_TEXT = Expectations(
{
(None, None): "my name is alex and i am a student at the university of michigan. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan math club and the michigan computer s",
("xpu", None): "my name is alex and i am a student at the university of michigan. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan math club and the michigan computer s",
}
)
# fmt: on

prompt = "my name is"

Expand All @@ -492,4 +420,4 @@ def test_model_bf16_static_cache(self):
)

output_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXT)
self.assertEqual(output_text, EXPECTED_TEXT.get_expectation())
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