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RoPE models: add numerical sanity-check test for RoPE scaling #29808

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merged 4 commits into from
Mar 28, 2024
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RoPE models: add numerical sanity-check test for RoPE scaling #29808

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0be9eed
add hard rope scaling test
gante Mar 22, 2024
5299f57
make fixup
gante Mar 22, 2024
0d39641
quick rope scaling tests
gante Mar 27, 2024
2154b55
add copy statements
gante Mar 28, 2024
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67 changes: 66 additions & 1 deletion tests/models/falcon/test_modeling_falcon.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,11 @@
FalconForTokenClassification,
FalconModel,
)
from transformers.models.falcon.modeling_falcon import (
FalconDynamicNTKScalingRotaryEmbedding,
FalconLinearScalingRotaryEmbedding,
FalconRotaryEmbedding,
)


class FalconModelTester:
Expand Down Expand Up @@ -408,7 +413,8 @@ def test_past_key_values_format(self):
)

@parameterized.expand([("linear",), ("dynamic",)])
def test_model_rope_scaling(self, scaling_type):
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model_rope_scaling_from_config with Llama->Falcon
def test_model_rope_scaling_from_config(self, scaling_type):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
short_input = ids_tensor([1, 10], config.vocab_size)
long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
Expand Down Expand Up @@ -438,6 +444,65 @@ def test_model_rope_scaling(self, scaling_type):
# The output should be different for long inputs
self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))

def test_model_rope_scaling(self):
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@gante gante Mar 27, 2024 •
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This test is the same on all models, with two variations:

  • Llama passes position_ids to RoPE, as opposed to an integer depicting the sequence length. This is due to its torch.compile rework.
  • GPTNeoX has base=config.rotary_emb_base, in RoPE initialization, all other models have base=config.rope_theta. This parameter is the same in GPTNeoX, but it has a different name.

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In this case, we can employ some Copied froms :)

I don't think there's an easy way for the llama test - but for GPTNeox remapping with with x->y should work

config, _ = self.model_tester.prepare_config_and_inputs_for_common()
hidden_size = config.hidden_size
num_heads = config.num_attention_heads
head_dim = hidden_size // num_heads
scaling_factor = 10
short_input_length = 10
long_input_length = int(config.max_position_embeddings * 1.5)

# Inputs
x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device

# Sanity check original RoPE
original_rope = FalconRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rope_theta,
).to(torch_device)
original_cos_short, original_sin_short = original_rope(x, short_input_length)
original_cos_long, original_sin_long = original_rope(x, long_input_length)
torch.testing.assert_close(original_cos_short, original_cos_long[:short_input_length, :])
torch.testing.assert_close(original_sin_short, original_sin_long[:short_input_length, :])

# Sanity check linear RoPE scaling
# New position "x" should match original position with index "x/scaling_factor"
linear_scaling_rope = FalconLinearScalingRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rope_theta,
scaling_factor=scaling_factor,
).to(torch_device)
linear_cos_short, linear_sin_short = linear_scaling_rope(x, short_input_length)
linear_cos_long, linear_sin_long = linear_scaling_rope(x, long_input_length)
torch.testing.assert_close(linear_cos_short, linear_cos_long[:short_input_length, :])
torch.testing.assert_close(linear_sin_short, linear_sin_long[:short_input_length, :])
for new_position in range(0, long_input_length, scaling_factor):
original_position = int(new_position // scaling_factor)
torch.testing.assert_close(linear_cos_long[new_position, :], original_cos_long[original_position, :])
torch.testing.assert_close(linear_sin_long[new_position, :], original_sin_long[original_position, :])

# Sanity check Dynamic NTK RoPE scaling
# Scaling should only be observed after a long input is fed. We can observe that the frequencies increase
# with scaling_factor (or that `inv_freq` decreases)
ntk_scaling_rope = FalconDynamicNTKScalingRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rope_theta,
scaling_factor=scaling_factor,
).to(torch_device)
ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, short_input_length)
ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, long_input_length)
torch.testing.assert_close(ntk_cos_short, original_cos_short)
torch.testing.assert_close(ntk_sin_short, original_sin_short)
with self.assertRaises(AssertionError):
torch.testing.assert_close(ntk_cos_long, original_cos_long)
with self.assertRaises(AssertionError):
torch.testing.assert_close(ntk_sin_long, original_sin_long)
self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())

@require_torch_sdpa
@slow
def test_eager_matches_sdpa_generate(self):
Expand Down
68 changes: 67 additions & 1 deletion tests/models/gpt_neox/test_modeling_gpt_neox.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,11 @@
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
from transformers.models.gpt_neox.modeling_gpt_neox import (
GPTNeoXDynamicNTKScalingRotaryEmbedding,
GPTNeoXLinearScalingRotaryEmbedding,
GPTNeoXRotaryEmbedding,
)


class GPTNeoXModelTester:
Expand Down Expand Up @@ -301,7 +306,8 @@ def test_feed_forward_chunking(self):
pass

@parameterized.expand([("linear",), ("dynamic",)])
def test_model_rope_scaling(self, scaling_type):
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model_rope_scaling_from_config with Llama->GPTNeoX
def test_model_rope_scaling_from_config(self, scaling_type):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
short_input = ids_tensor([1, 10], config.vocab_size)
long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
Expand Down Expand Up @@ -331,6 +337,66 @@ def test_model_rope_scaling(self, scaling_type):
# The output should be different for long inputs
self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))

# Copied from tests.models.falcon.test_modeling_falcon.FalconModelTest.test_model_rope_scaling with Falcon->GPTNeoX, rope_theta->rotary_emb_base
def test_model_rope_scaling(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
hidden_size = config.hidden_size
num_heads = config.num_attention_heads
head_dim = hidden_size // num_heads
scaling_factor = 10
short_input_length = 10
long_input_length = int(config.max_position_embeddings * 1.5)

# Inputs
x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device

# Sanity check original RoPE
original_rope = GPTNeoXRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rotary_emb_base,
).to(torch_device)
original_cos_short, original_sin_short = original_rope(x, short_input_length)
original_cos_long, original_sin_long = original_rope(x, long_input_length)
torch.testing.assert_close(original_cos_short, original_cos_long[:short_input_length, :])
torch.testing.assert_close(original_sin_short, original_sin_long[:short_input_length, :])

# Sanity check linear RoPE scaling
# New position "x" should match original position with index "x/scaling_factor"
linear_scaling_rope = GPTNeoXLinearScalingRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rotary_emb_base,
scaling_factor=scaling_factor,
).to(torch_device)
linear_cos_short, linear_sin_short = linear_scaling_rope(x, short_input_length)
linear_cos_long, linear_sin_long = linear_scaling_rope(x, long_input_length)
torch.testing.assert_close(linear_cos_short, linear_cos_long[:short_input_length, :])
torch.testing.assert_close(linear_sin_short, linear_sin_long[:short_input_length, :])
for new_position in range(0, long_input_length, scaling_factor):
original_position = int(new_position // scaling_factor)
torch.testing.assert_close(linear_cos_long[new_position, :], original_cos_long[original_position, :])
torch.testing.assert_close(linear_sin_long[new_position, :], original_sin_long[original_position, :])

# Sanity check Dynamic NTK RoPE scaling
# Scaling should only be observed after a long input is fed. We can observe that the frequencies increase
# with scaling_factor (or that `inv_freq` decreases)
ntk_scaling_rope = GPTNeoXDynamicNTKScalingRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rotary_emb_base,
scaling_factor=scaling_factor,
).to(torch_device)
ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, short_input_length)
ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, long_input_length)
torch.testing.assert_close(ntk_cos_short, original_cos_short)
torch.testing.assert_close(ntk_sin_short, original_sin_short)
with self.assertRaises(AssertionError):
torch.testing.assert_close(ntk_cos_long, original_cos_long)
with self.assertRaises(AssertionError):
torch.testing.assert_close(ntk_sin_long, original_sin_long)
self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())


@require_torch
class GPTNeoXLanguageGenerationTest(unittest.TestCase):
Expand Down
70 changes: 69 additions & 1 deletion tests/models/llama/test_modeling_llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,11 @@
LlamaModel,
LlamaTokenizer,
)
from transformers.models.llama.modeling_llama import (
LlamaDynamicNTKScalingRotaryEmbedding,
LlamaLinearScalingRotaryEmbedding,
LlamaRotaryEmbedding,
)


class LlamaModelTester:
Expand Down Expand Up @@ -370,7 +375,7 @@ def test_save_load_fast_init_from_base(self):
pass

@parameterized.expand([("linear",), ("dynamic",)])
def test_model_rope_scaling(self, scaling_type):
def test_model_rope_scaling_from_config(self, scaling_type):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
short_input = ids_tensor([1, 10], config.vocab_size)
long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
Expand Down Expand Up @@ -400,6 +405,69 @@ def test_model_rope_scaling(self, scaling_type):
# The output should be different for long inputs
self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))

def test_model_rope_scaling(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
hidden_size = config.hidden_size
num_heads = config.num_attention_heads
head_dim = hidden_size // num_heads
scaling_factor = 10
short_input_length = 10
long_input_length = int(config.max_position_embeddings * 1.5)

# Inputs
x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device
position_ids_short = torch.arange(short_input_length, dtype=torch.long, device=torch_device)
position_ids_short = position_ids_short.unsqueeze(0)
position_ids_long = torch.arange(long_input_length, dtype=torch.long, device=torch_device)
position_ids_long = position_ids_long.unsqueeze(0)

# Sanity check original RoPE
original_rope = LlamaRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rope_theta,
).to(torch_device)
original_cos_short, original_sin_short = original_rope(x, position_ids_short)
original_cos_long, original_sin_long = original_rope(x, position_ids_long)
torch.testing.assert_close(original_cos_short, original_cos_long[:, :short_input_length, :])
torch.testing.assert_close(original_sin_short, original_sin_long[:, :short_input_length, :])

# Sanity check linear RoPE scaling
# New position "x" should match original position with index "x/scaling_factor"
linear_scaling_rope = LlamaLinearScalingRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rope_theta,
scaling_factor=scaling_factor,
).to(torch_device)
linear_cos_short, linear_sin_short = linear_scaling_rope(x, position_ids_short)
linear_cos_long, linear_sin_long = linear_scaling_rope(x, position_ids_long)
torch.testing.assert_close(linear_cos_short, linear_cos_long[:, :short_input_length, :])
torch.testing.assert_close(linear_sin_short, linear_sin_long[:, :short_input_length, :])
for new_position in range(0, long_input_length, scaling_factor):
original_position = int(new_position // scaling_factor)
torch.testing.assert_close(linear_cos_long[:, new_position, :], original_cos_long[:, original_position, :])
torch.testing.assert_close(linear_sin_long[:, new_position, :], original_sin_long[:, original_position, :])

# Sanity check Dynamic NTK RoPE scaling
# Scaling should only be observed after a long input is fed. We can observe that the frequencies increase
# with scaling_factor (or that `inv_freq` decreases)
ntk_scaling_rope = LlamaDynamicNTKScalingRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rope_theta,
scaling_factor=scaling_factor,
).to(torch_device)
ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, position_ids_short)
ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, position_ids_long)
torch.testing.assert_close(ntk_cos_short, original_cos_short)
torch.testing.assert_close(ntk_sin_short, original_sin_short)
with self.assertRaises(AssertionError):
torch.testing.assert_close(ntk_cos_long, original_cos_long)
with self.assertRaises(AssertionError):
torch.testing.assert_close(ntk_sin_long, original_sin_long)
self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())

@require_flash_attn
@require_torch_gpu
@require_bitsandbytes
Expand Down
69 changes: 67 additions & 2 deletions tests/models/persimmon/test_modeling_persimmon.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,11 @@
PersimmonForSequenceClassification,
PersimmonModel,
)
from transformers.models.persimmon.modeling_persimmon import (
PersimmonDynamicNTKScalingRotaryEmbedding,
PersimmonLinearScalingRotaryEmbedding,
PersimmonRotaryEmbedding,
)


# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester with Llama->Persimmon
Expand Down Expand Up @@ -365,8 +370,8 @@ def test_save_load_fast_init_from_base(self):
pass

@parameterized.expand([("linear",), ("dynamic",)])
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model_rope_scaling with Llama->Persimmon
def test_model_rope_scaling(self, scaling_type):
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model_rope_scaling_from_config with Llama->Persimmon
def test_model_rope_scaling_from_config(self, scaling_type):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
short_input = ids_tensor([1, 10], config.vocab_size)
long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
Expand Down Expand Up @@ -396,6 +401,66 @@ def test_model_rope_scaling(self, scaling_type):
# The output should be different for long inputs
self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))

# Copied from tests.models.falcon.test_modeling_falcon.FalconModelTest.test_model_rope_scaling with Falcon->Persimmon
def test_model_rope_scaling(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
hidden_size = config.hidden_size
num_heads = config.num_attention_heads
head_dim = hidden_size // num_heads
scaling_factor = 10
short_input_length = 10
long_input_length = int(config.max_position_embeddings * 1.5)

# Inputs
x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device

# Sanity check original RoPE
original_rope = PersimmonRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rope_theta,
).to(torch_device)
original_cos_short, original_sin_short = original_rope(x, short_input_length)
original_cos_long, original_sin_long = original_rope(x, long_input_length)
torch.testing.assert_close(original_cos_short, original_cos_long[:short_input_length, :])
torch.testing.assert_close(original_sin_short, original_sin_long[:short_input_length, :])

# Sanity check linear RoPE scaling
# New position "x" should match original position with index "x/scaling_factor"
linear_scaling_rope = PersimmonLinearScalingRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rope_theta,
scaling_factor=scaling_factor,
).to(torch_device)
linear_cos_short, linear_sin_short = linear_scaling_rope(x, short_input_length)
linear_cos_long, linear_sin_long = linear_scaling_rope(x, long_input_length)
torch.testing.assert_close(linear_cos_short, linear_cos_long[:short_input_length, :])
torch.testing.assert_close(linear_sin_short, linear_sin_long[:short_input_length, :])
for new_position in range(0, long_input_length, scaling_factor):
original_position = int(new_position // scaling_factor)
torch.testing.assert_close(linear_cos_long[new_position, :], original_cos_long[original_position, :])
torch.testing.assert_close(linear_sin_long[new_position, :], original_sin_long[original_position, :])

# Sanity check Dynamic NTK RoPE scaling
# Scaling should only be observed after a long input is fed. We can observe that the frequencies increase
# with scaling_factor (or that `inv_freq` decreases)
ntk_scaling_rope = PersimmonDynamicNTKScalingRotaryEmbedding(
head_dim,
max_position_embeddings=config.max_position_embeddings,
base=config.rope_theta,
scaling_factor=scaling_factor,
).to(torch_device)
ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, short_input_length)
ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, long_input_length)
torch.testing.assert_close(ntk_cos_short, original_cos_short)
torch.testing.assert_close(ntk_sin_short, original_sin_short)
with self.assertRaises(AssertionError):
torch.testing.assert_close(ntk_cos_long, original_cos_long)
with self.assertRaises(AssertionError):
torch.testing.assert_close(ntk_sin_long, original_sin_long)
self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())


@require_torch
class PersimmonIntegrationTest(unittest.TestCase):
Expand Down
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