🚨 Llama: update rope scaling to match static cache changes (#29143)

src/transformers/models/deprecated/open_llama/modeling_open_llama.py

@@ -100,7 +100,7 @@ class OpenLlamaRotaryEmbedding(nn.Module):
         )
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->OpenLlama
+# Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->OpenLlama
 class OpenLlamaLinearScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
     """OpenLlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
 
@@ -120,7 +120,7 @@ class OpenLlamaLinearScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->OpenLlama
+# Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->OpenLlama
 class OpenLlamaDynamicNTKScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
     """OpenLlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
 

src/transformers/models/falcon/modeling_falcon.py

@@ -167,7 +167,8 @@ class FalconRotaryEmbedding(nn.Module):
         )
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Falcon
+# copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Falcon
+# TODO @joao no longer copied from LLama after static cache, fix me (copied -> Copied)
 class FalconLinearScalingRotaryEmbedding(FalconRotaryEmbedding):
     """FalconRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
 
@@ -187,7 +188,8 @@ class FalconLinearScalingRotaryEmbedding(FalconRotaryEmbedding):
         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Falcon
+# copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Falcon
+# TODO @joao no longer copied from LLama after static cache, fix me (copied -> Copied)
 class FalconDynamicNTKScalingRotaryEmbedding(FalconRotaryEmbedding):
     """FalconRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
 

src/transformers/models/llama/modeling_llama.py

@@ -94,7 +94,6 @@ ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
 class LlamaRotaryEmbedding(nn.Module):
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
-
         self.dim = dim
         self.max_position_embeddings = max_position_embeddings
         self.base = base
@@ -118,6 +117,9 @@ class LlamaRotaryEmbedding(nn.Module):
         return self._cos_cached
 
     def forward(self, x, position_ids, seq_len=None):
+        if seq_len is not None:
+            logger.warning_once("The `seq_len` argument is deprecated and unused. It will be removed in v4.40.")
+
         # x: [bs, num_attention_heads, seq_len, head_size]
         inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
         position_ids_expanded = position_ids[:, None, :].float()
@@ -138,16 +140,11 @@ class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
         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=torch.int64).type_as(self.inv_freq)
-        t = t / self.scaling_factor
-
-        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)
+    def forward(self, x, position_ids, seq_len=None):
+        # difference to the original RoPE: a scaling factor is aplied to the position ids
+        position_ids = position_ids.float() / self.scaling_factor
+        cos, sin = super().forward(x, position_ids, seq_len)
+        return cos, sin
 
 
 class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
@@ -157,23 +154,20 @@ class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
         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
-
+    def forward(self, x, position_ids, seq_len=None):
+        # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
+        seq_len = torch.max(position_ids) + 1
         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, dtype=torch.int64).float().to(device) / self.dim))
-            self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
+            inv_freq = 1.0 / (
+                base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim)
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: this may break with compilation
 
-        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)
+        cos, sin = super().forward(x, position_ids, seq_len)
+        return cos, sin
 
 
 def rotate_half(x):
@@ -183,7 +177,7 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
 
     Args:
@@ -191,9 +185,8 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
         k (`torch.Tensor`): The key tensor.
         cos (`torch.Tensor`): The cosine part of the rotary embedding.
         sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`):
-            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
-            used to pass offsetted position ids when working with a KV-cache.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
         unsqueeze_dim (`int`, *optional*, defaults to 1):
             The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
             sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
@@ -360,8 +353,8 @@ class LlamaAttention(nn.Module):
         value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
 
         past_key_value = getattr(self, "past_key_value", past_key_value)
-        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
         if past_key_value is not None:
             # sin and cos are specific to RoPE models; position_ids needed for the static cache
@@ -447,8 +440,8 @@ class LlamaFlashAttention2(LlamaAttention):
         key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
         value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
 
-        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
         past_key_value = getattr(self, "past_key_value", past_key_value)
 
@@ -645,8 +638,8 @@ class LlamaSdpaAttention(LlamaAttention):
         key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
         value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
 
-        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
         past_key_value = getattr(self, "past_key_value", past_key_value)
 

src/transformers/models/persimmon/modeling_persimmon.py

@@ -77,7 +77,7 @@ class PersimmonRotaryEmbedding(nn.Module):
         )
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Persimmon
+# Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->Persimmon
 class PersimmonLinearScalingRotaryEmbedding(PersimmonRotaryEmbedding):
     """PersimmonRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
 
@@ -97,7 +97,7 @@ class PersimmonLinearScalingRotaryEmbedding(PersimmonRotaryEmbedding):
         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Persimmon
+# Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->Persimmon
 class PersimmonDynamicNTKScalingRotaryEmbedding(PersimmonRotaryEmbedding):
     """PersimmonRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
 

src/transformers/models/phi/modeling_phi.py

@@ -120,7 +120,7 @@ class PhiRotaryEmbedding(nn.Module):
         )
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Phi
+# Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->Phi
 class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
     """PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
 
@@ -140,7 +140,7 @@ class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Phi
+# Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->Phi
 class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
     """PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
 

src/transformers/models/stablelm/modeling_stablelm.py

@@ -103,7 +103,7 @@ class StableLmRotaryEmbedding(nn.Module):
         )
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->StableLm
+# Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->StableLm
 class StableLmLinearScalingRotaryEmbedding(StableLmRotaryEmbedding):
     """StableLmRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
 
@@ -123,7 +123,7 @@ class StableLmLinearScalingRotaryEmbedding(StableLmRotaryEmbedding):
         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->StableLm
+# Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->StableLm
 class StableLmDynamicNTKScalingRotaryEmbedding(StableLmRotaryEmbedding):
     """StableLmRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
 

tests/models/llama/test_modeling_llama.py

@@ -362,7 +362,6 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
         pass
 
     @parameterized.expand([("linear",), ("dynamic",)])
-    @unittest.skip("TODO @gante fix this for Llama")
     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)