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[GPTNeoX] Fix BC issue with 4.36 #28602

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Jan 21, 2024
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[GPTNeoX] Fix BC issue with 4.36 #28602

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e73962c
fix dtype issue
ArthurZucker Jan 19, 2024
0deb451
add a test
ArthurZucker Jan 19, 2024
ca1742e
update copied from mentions
ArthurZucker Jan 19, 2024
3fd7e35
nits
ArthurZucker Jan 19, 2024
cd83e62
fixup
ArthurZucker Jan 19, 2024
4c6ef80
fix copies
ArthurZucker Jan 19, 2024
5046253
Apply suggestions from code review
ArthurZucker Jan 19, 2024
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22 changes: 11 additions & 11 deletions src/transformers/models/gpt_neox/modeling_gpt_neox.py
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Original file line number Diff line number Diff line change
Expand Up @@ -526,8 +526,8 @@ def attention_mask_func(attention_scores, ltor_mask):
return attention_scores


# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with LlamaRotary->GPTNeoXRotary
class GPTNeoXRotaryEmbedding(nn.Module):
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()

Expand All @@ -549,24 +549,24 @@ def _set_cos_sin_cache(self, seq_len, device, dtype):
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
self.register_buffer("cos_cached", emb.cos(), persistent=False)
self.register_buffer("sin_cached", emb.sin(), persistent=False)

def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

return (
self.cos_cached[:seq_len].to(dtype=x.dtype),
self.sin_cached[:seq_len].to(dtype=x.dtype),
self.cos_cached[:seq_len],
self.sin_cached[:seq_len],
)


# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->GPTNeoX
class GPTNeoXLinearScalingRotaryEmbedding(GPTNeoXRotaryEmbedding):
"""GPTNeoXRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding.__init__
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
Expand All @@ -579,14 +579,14 @@ def _set_cos_sin_cache(self, seq_len, device, dtype):
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
self.register_buffer("cos_cached", emb.cos(), persistent=False)
self.register_buffer("sin_cached", emb.sin(), persistent=False)


# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->GPTNeoX
class GPTNeoXDynamicNTKScalingRotaryEmbedding(GPTNeoXRotaryEmbedding):
"""GPTNeoXRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""

# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding.__init__
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
Expand All @@ -606,8 +606,8 @@ def _set_cos_sin_cache(self, seq_len, device, dtype):
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
self.register_buffer("cos_cached", emb.cos(), persistent=False)
self.register_buffer("sin_cached", emb.sin(), persistent=False)


def rotate_half(x):
Expand Down
9 changes: 5 additions & 4 deletions src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py
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Original file line number Diff line number Diff line change
Expand Up @@ -235,6 +235,7 @@ def _attn(self, query, key, value, attention_mask=None, head_mask=None):

# Copied from transformers.models.gpt_neox.modeling_gpt_neox.GPTNeoXRotaryEmbedding with GPTNeoXRotaryEmbedding->RotaryEmbedding
class RotaryEmbedding(nn.Module):
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()

Expand All @@ -256,17 +257,17 @@ def _set_cos_sin_cache(self, seq_len, device, dtype):
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
self.register_buffer("cos_cached", emb.cos(), persistent=False)
self.register_buffer("sin_cached", emb.sin(), persistent=False)

def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

return (
self.cos_cached[:seq_len].to(dtype=x.dtype),
self.sin_cached[:seq_len].to(dtype=x.dtype),
self.cos_cached[:seq_len],
self.sin_cached[:seq_len],
)


Expand Down
10 changes: 10 additions & 0 deletions 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 @@ -355,3 +355,13 @@ def test_lm_generate_gptneox(self):
output_str = tokenizer.batch_decode(output_ids)[0]

self.assertEqual(output_str, expected_output)

def pythia_integration_test(self):
model_name_or_path = "EleutherAI/pythia-70m"
model = GPTNeoXForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16).to(torch_device)
EXPECTED_LOGITS = torch.tensor([1069.0000, 228.7500, 1072.0000, 1072.0000, 1069.0000, 1068.0000, 1068.0000, 1071.0000, 1071.0000, 1071.0000, 1073.0000, 1070.0000, 1071.0000, 1075.0000, 1073.0000, 1075.0000, 1074.0000, 1069.0000, 1072.0000, 1071.0000, 1071.0000, 1071.0000, 1070.0000, 1069.0000, 1069.0000, 1069.0000, 1070.0000, 1075.0000, 1073.0000, 1074.0000]) # fmt: skip
input_ids = [29, 93, 303, 64, 5478, 49651, 10394, 187, 34, 12939, 875]
# alternative: tokenizer('<|im_start|>system\nA chat between')
input_ids = torch.as_tensor(input_ids)[None].to(torch_device)
outputs = model(input_ids)["logits"][:, -1][0, :30]
self.assertTrue(torch.allclose(EXPECTED_LOGITS, outputs, atol=1e-5))
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