Llama/GPTNeoX: add RoPE scaling (#24653)

* add rope_scaling

* tmp commit

* add gptneox

* add tests

* GPTNeoX can now handle long inputs, so the pipeline test was wrong

* Update src/transformers/models/open_llama/configuration_open_llama.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* remove ntk

* remove redundant validation

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/models/gpt_neox/configuration_gpt_neox.py

@@ -78,6 +78,15 @@ class GPTNeoXConfig(PretrainedConfig):
         use_parallel_residual (`bool`, *optional*, defaults to `True`):
             Whether to use a "parallel" formulation in each Transformer layer, which can provide a slight training
             speedup at large scales (e.g. 20B).
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
+            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
+            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+
         Example:
 
     ```python
@@ -115,6 +124,7 @@ class GPTNeoXConfig(PretrainedConfig):
         eos_token_id=2,
         tie_word_embeddings=False,
         use_parallel_residual=True,
+        rope_scaling=None,
         **kwargs,
     ):
         super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@@ -135,7 +145,32 @@ class GPTNeoXConfig(PretrainedConfig):
         self.use_cache = use_cache
         self.tie_word_embeddings = tie_word_embeddings
         self.use_parallel_residual = use_parallel_residual
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
         if self.hidden_size % self.num_attention_heads != 0:
             raise ValueError(
                 "The hidden size is not divisble by the number of attention heads! Make sure to update them!"
             )
+
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")

src/transformers/models/gpt_neox/modeling_gpt_neox.py

@@ -86,6 +86,7 @@ class GPTNeoXPreTrainedModel(PreTrainedModel):
 class GPTNeoXAttention(nn.Module):
     def __init__(self, config):
         super().__init__()
+        self.config = config
         self.num_attention_heads = config.num_attention_heads
         self.hidden_size = config.hidden_size
         if self.hidden_size % self.num_attention_heads != 0:
@@ -94,18 +95,11 @@ class GPTNeoXAttention(nn.Module):
             )
         self.head_size = self.hidden_size // self.num_attention_heads
         self.rotary_ndims = int(self.head_size * config.rotary_pct)
-        max_positions = config.max_position_embeddings
+        self._init_bias(config.max_position_embeddings)
-        self.register_buffer(
+
-            "bias",
-            torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
-                1, 1, max_positions, max_positions
-            ),
-            persistent=False,
-        )
         self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False)
-        self.rotary_emb = RotaryEmbedding(
+        self._init_rope()
-            self.rotary_ndims, config.max_position_embeddings, base=config.rotary_emb_base
+
-        )
         self.register_buffer(
             "norm_factor",
             torch.sqrt(torch.tensor(self.head_size, dtype=torch.float32)).to(torch.get_default_dtype()),
@@ -113,9 +107,44 @@ class GPTNeoXAttention(nn.Module):
         )
         self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size)
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-
         self.attention_dropout = nn.Dropout(config.attention_dropout)
 
+    def _init_bias(self, max_positions, device=None):
+        self.register_buffer(
+            "bias",
+            torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
+                1, 1, max_positions, max_positions
+            ),
+            persistent=False,
+        )
+        if device is not None:
+            self.bias = self.bias.to(device)
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = GPTNeoXRotaryEmbedding(
+                self.rotary_ndims, self.config.max_position_embeddings, base=self.config.rotary_emb_base
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = GPTNeoXLinearScalingRotaryEmbedding(
+                    self.rotary_ndims,
+                    self.config.max_position_embeddings,
+                    base=self.config.rotary_emb_base,
+                    scaling_factor=scaling_factor,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = GPTNeoXDynamicNTKScalingRotaryEmbedding(
+                    self.rotary_ndims,
+                    self.config.max_position_embeddings,
+                    base=self.config.rotary_emb_base,
+                    scaling_factor=scaling_factor,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
     def forward(
         self,
         hidden_states: torch.FloatTensor,
@@ -210,6 +239,9 @@ class GPTNeoXAttention(nn.Module):
         batch_size, num_attention_heads, query_length, attn_head_size = query.size()
         key_length = key.size(-2)
 
+        # dynamically increase the causal mask with the key length, if needed.
+        if key_length > self.bias.shape[-1]:
+            self._init_bias(key_length, device=key.device)
         causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
 
         query = query.view(batch_size * num_attention_heads, query_length, attn_head_size)
@@ -258,15 +290,23 @@ def attention_mask_func(attention_scores, ltor_mask):
     return attention_scores
 
 
-class RotaryEmbedding(torch.nn.Module):
+class GPTNeoXRotaryEmbedding(torch.nn.Module):
     def __init__(self, dim, max_position_embeddings, base=10000, device=None):
         super().__init__()
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
         self.register_buffer("inv_freq", inv_freq)
 
         # Build here to make `torch.jit.trace` work.
-        self.max_seq_len_cached = max_position_embeddings
+        self._set_cos_sin_cache(seq_len=max_position_embeddings, device=self.inv_freq.device)
-        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+
+    def _set_cos_sin_cache(self, seq_len, device):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
         freqs = torch.einsum("i,j->ij", 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)
@@ -275,16 +315,54 @@ class RotaryEmbedding(torch.nn.Module):
 
     def forward(self, x, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
-        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
         if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device)
+        return self.cos_cached[:seq_len, ...].to(x.device), self.sin_cached[:seq_len, ...].to(x.device)
+
+
+class GPTNeoXLinearScalingRotaryEmbedding(GPTNeoXRotaryEmbedding):
+    """GPTNeoXRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device):
         self.max_seq_len_cached = seq_len
-            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
         freqs = torch.einsum("i,j->ij", 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).to(x.device)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.cos_cached = emb.cos()[None, None, :, :]
+        self.sin_cached = emb.sin()[None, None, :, :]
+
+
+class GPTNeoXDynamicNTKScalingRotaryEmbedding(GPTNeoXRotaryEmbedding):
+    """GPTNeoXRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", 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.cos_cached = emb.cos()[None, None, :, :]
         self.sin_cached = emb.sin()[None, None, :, :]
-        return self.cos_cached[:seq_len, ...].to(x.device), self.sin_cached[:seq_len, ...].to(x.device)
 
 
 def rotate_half(x):

src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py

@@ -238,16 +238,24 @@ class GPTNeoXJapaneseAttention(nn.Module):
         return attn_output, attn_weights
 
 
-# Copied from transformers.models.gpt_neox.modeling_gpt_neox.RotaryEmbedding
+# Copied from transformers.models.gpt_neox.modeling_gpt_neox.GPTNeoXRotaryEmbedding
 class RotaryEmbedding(torch.nn.Module):
     def __init__(self, dim, max_position_embeddings, base=10000, device=None):
         super().__init__()
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
         self.register_buffer("inv_freq", inv_freq)
 
         # Build here to make `torch.jit.trace` work.
-        self.max_seq_len_cached = max_position_embeddings
+        self._set_cos_sin_cache(seq_len=max_position_embeddings, device=self.inv_freq.device)
-        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+
+    def _set_cos_sin_cache(self, seq_len, device):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
         freqs = torch.einsum("i,j->ij", 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)
@@ -256,15 +264,8 @@ class RotaryEmbedding(torch.nn.Module):
 
     def forward(self, x, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
-        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
         if seq_len > self.max_seq_len_cached:
-            self.max_seq_len_cached = seq_len
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device)
-            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
-            freqs = torch.einsum("i,j->ij", 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).to(x.device)
-            self.cos_cached = emb.cos()[None, None, :, :]
-            self.sin_cached = emb.sin()[None, None, :, :]
         return self.cos_cached[:seq_len, ...].to(x.device), self.sin_cached[:seq_len, ...].to(x.device)
 
 

src/transformers/models/llama/configuration_llama.py

@@ -64,6 +64,15 @@ class LlamaConfig(PretrainedConfig):
             relevant if `config.is_decoder=True`.
         tie_word_embeddings(`bool`, *optional*, defaults to `False`):
             Whether to tie weight embeddings
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
+            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
+            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+
         Example:
 
     ```python
@@ -97,6 +106,7 @@ class LlamaConfig(PretrainedConfig):
         bos_token_id=1,
         eos_token_id=2,
         tie_word_embeddings=False,
+        rope_scaling=None,
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -109,6 +119,9 @@ class LlamaConfig(PretrainedConfig):
         self.initializer_range = initializer_range
         self.rms_norm_eps = rms_norm_eps
         self.use_cache = use_cache
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
         super().__init__(
             pad_token_id=pad_token_id,
             bos_token_id=bos_token_id,
@@ -116,3 +129,24 @@ class LlamaConfig(PretrainedConfig):
             tie_word_embeddings=tie_word_embeddings,
             **kwargs,
         )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")

src/transformers/models/llama/modeling_llama.py

@@ -91,36 +91,84 @@ class LlamaRMSNorm(nn.Module):
 class LlamaRotaryEmbedding(torch.nn.Module):
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
         self.register_buffer("inv_freq", inv_freq)
 
         # Build here to make `torch.jit.trace` work.
-        self.max_seq_len_cached = max_position_embeddings
+        self._set_cos_sin_cache(
-        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
         freqs = torch.einsum("i,j->ij", 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)
-        dtype = torch.get_default_dtype()
         self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
         self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
 
     def forward(self, x, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
-        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
         if seq_len > self.max_seq_len_cached:
-            self.max_seq_len_cached = seq_len
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
-            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
+
-            freqs = torch.einsum("i,j->ij", 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).to(x.device)
-            self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(x.dtype), persistent=False)
-            self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(x.dtype), persistent=False)
         return (
             self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
             self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
         )
 
 
+class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    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)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    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)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
@@ -176,7 +224,24 @@ class LlamaAttention(nn.Module):
         self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
         self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
         self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
             self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
 
     def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
         return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

src/transformers/models/open_llama/configuration_open_llama.py

@@ -67,6 +67,15 @@ class OpenLlamaConfig(PretrainedConfig):
             relevant if `config.is_decoder=True`.
         tie_word_embeddings(`bool`, *optional*, defaults to `False`):
             Whether to tie weight embeddings
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
+            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
+            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+
         Example:
 
     ```python
@@ -104,6 +113,7 @@ class OpenLlamaConfig(PretrainedConfig):
         attention_dropout_prob=0.1,
         use_stable_embedding=True,
         shared_input_output_embedding=True,
+        rope_scaling=None,
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -123,6 +133,9 @@ class OpenLlamaConfig(PretrainedConfig):
         self.attention_dropout_prob = attention_dropout_prob
         self.use_stable_embedding = use_stable_embedding
         self.shared_input_output_embedding = shared_input_output_embedding
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
         super().__init__(
             pad_token_id=pad_token_id,
             bos_token_id=bos_token_id,
@@ -130,3 +143,25 @@ class OpenLlamaConfig(PretrainedConfig):
             tie_word_embeddings=tie_word_embeddings,
             **kwargs,
         )
+
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")

src/transformers/models/open_llama/modeling_open_llama.py

@@ -102,36 +102,86 @@ class OpenLlamaRMSNorm(nn.Module):
 class OpenLlamaRotaryEmbedding(torch.nn.Module):
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
         self.register_buffer("inv_freq", inv_freq)
 
         # Build here to make `torch.jit.trace` work.
-        self.max_seq_len_cached = max_position_embeddings
+        self._set_cos_sin_cache(
-        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
         freqs = torch.einsum("i,j->ij", 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)
-        dtype = torch.get_default_dtype()
         self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
         self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
 
     def forward(self, x, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
-        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
         if seq_len > self.max_seq_len_cached:
-            self.max_seq_len_cached = seq_len
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
-            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
+
-            freqs = torch.einsum("i,j->ij", 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).to(x.device)
-            self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(x.dtype), persistent=False)
-            self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(x.dtype), persistent=False)
         return (
             self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
             self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
         )
 
 
+# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->OpenLlama
+class OpenLlamaLinearScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
+    """OpenLlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    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)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->OpenLlama
+class OpenLlamaDynamicNTKScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
+    """OpenLlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    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)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
@@ -190,7 +240,27 @@ class OpenLlamaAttention(nn.Module):
         self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
         self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
         self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-        self.rotary_emb = OpenLlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+        self._init_rope()
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaAttention._init_rope with Llama->OpenLlama
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = OpenLlamaRotaryEmbedding(
+                self.head_dim, max_position_embeddings=self.max_position_embeddings
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = OpenLlamaLinearScalingRotaryEmbedding(
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = OpenLlamaDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
 
     def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
         return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

tests/models/gpt_neox/test_modeling_gpt_neox.py

@@ -17,7 +17,9 @@
 
 import unittest
 
-from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available
+from parameterized import parameterized
+
+from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from ...generation.test_utils import GenerationTesterMixin
@@ -49,7 +51,7 @@ class GPTNeoXModelTester:
         use_token_type_ids=True,
         use_labels=True,
         vocab_size=99,
-        hidden_size=32,
+        hidden_size=64,
         num_hidden_layers=5,
         num_attention_heads=4,
         intermediate_size=37,
@@ -298,6 +300,37 @@ class GPTNeoXModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
     def test_feed_forward_chunking(self):
         pass
 
+    @parameterized.expand([("linear",), ("dynamic",)])
+    def test_model_rope_scaling(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)
+
+        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
+        original_model = GPTNeoXModel(config)
+        original_model.to(torch_device)
+        original_model.eval()
+        original_short_output = original_model(short_input).last_hidden_state
+        original_long_output = original_model(long_input).last_hidden_state
+
+        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
+        config.rope_scaling = {"type": scaling_type, "factor": 10.0}
+        scaled_model = GPTNeoXModel(config)
+        scaled_model.to(torch_device)
+        scaled_model.eval()
+        scaled_short_output = scaled_model(short_input).last_hidden_state
+        scaled_long_output = scaled_model(long_input).last_hidden_state
+
+        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
+        # maximum sequence length, so the outputs for the short input should match.
+        if scaling_type == "dynamic":
+            self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
+        else:
+            self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
+
+        # The output should be different for long inputs
+        self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
+
 
 @require_torch
 class GPTNeoXLanguageGenerationTest(unittest.TestCase):

tests/models/llama/test_modeling_llama.py

@@ -17,7 +17,9 @@
 
 import unittest
 
-from transformers import LlamaConfig, is_torch_available
+from parameterized import parameterized
+
+from transformers import LlamaConfig, is_torch_available, set_seed
 from transformers.testing_utils import require_torch, torch_device
 
 from ...generation.test_utils import GenerationTesterMixin
@@ -332,3 +334,34 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
     @unittest.skip("LLaMA buffers include complex numbers, which breaks this test")
     def test_save_load_fast_init_from_base(self):
         pass
+
+    @parameterized.expand([("linear",), ("dynamic",)])
+    def test_model_rope_scaling(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)
+
+        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
+        original_model = LlamaModel(config)
+        original_model.to(torch_device)
+        original_model.eval()
+        original_short_output = original_model(short_input).last_hidden_state
+        original_long_output = original_model(long_input).last_hidden_state
+
+        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
+        config.rope_scaling = {"type": scaling_type, "factor": 10.0}
+        scaled_model = LlamaModel(config)
+        scaled_model.to(torch_device)
+        scaled_model.eval()
+        scaled_short_output = scaled_model(short_input).last_hidden_state
+        scaled_long_output = scaled_model(long_input).last_hidden_state
+
+        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
+        # maximum sequence length, so the outputs for the short input should match.
+        if scaling_type == "dynamic":
+            self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
+        else:
+            self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
+
+        # The output should be different for long inputs
+        self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))

tests/models/open_llama/test_modeling_open_llama.py

@@ -17,7 +17,9 @@
 
 import unittest
 
-from transformers import OpenLlamaConfig, is_torch_available
+from parameterized import parameterized
+
+from transformers import OpenLlamaConfig, is_torch_available, set_seed
 from transformers.testing_utils import require_torch, torch_device
 
 from ...generation.test_utils import GenerationTesterMixin
@@ -335,3 +337,34 @@ class OpenLlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
     @unittest.skip("Open-Llama buffers include complex numbers, which breaks this test")
     def test_save_load_fast_init_from_base(self):
         pass
+
+    @parameterized.expand([("linear",), ("dynamic",)])
+    def test_model_rope_scaling(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)
+
+        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
+        original_model = OpenLlamaModel(config)
+        original_model.to(torch_device)
+        original_model.eval()
+        original_short_output = original_model(short_input).last_hidden_state
+        original_long_output = original_model(long_input).last_hidden_state
+
+        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
+        config.rope_scaling = {"type": scaling_type, "factor": 10.0}
+        scaled_model = OpenLlamaModel(config)
+        scaled_model.to(torch_device)
+        scaled_model.eval()
+        scaled_short_output = scaled_model(short_input).last_hidden_state
+        scaled_long_output = scaled_model(long_input).last_hidden_state
+
+        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
+        # maximum sequence length, so the outputs for the short input should match.
+        if scaling_type == "dynamic":
+            self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
+        else:
+            self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
+
+        # The output should be different for long inputs
+        self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))

tests/pipelines/test_pipelines_text_generation.py

@@ -240,7 +240,7 @@ class TextGenerationPipelineTests(unittest.TestCase):
         # We don't care about infinite range models.
         # They already work.
         # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
-        EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS = ["RwkvForCausalLM", "XGLMForCausalLM"]
+        EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS = ["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"]
         if (
             tokenizer.model_max_length < 10000
             and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS