Falcon: Add RoPE scaling (#25878)

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

@@ -154,14 +154,14 @@ class OpenLlamaConfig(PretrainedConfig):
 
         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`, "
+                "`rope_scaling` must be a dictionary with with two fields, `type` 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}"
+                f"`rope_scaling`'s type 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/falcon/configuration_falcon.py

@@ -72,6 +72,19 @@ class FalconConfig(PretrainedConfig):
             instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`.
         bias (`bool`, *optional*, defaults to `False`):
             Whether to use bias on Linear layers.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with, when `alibi` is `False`. Pretrained
+            Falcon models with RoPE support up to 2048 tokens.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two 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.
         bos_token_id (`int`, *optional*, defaults to 11):
             The id of the "beginning-of-sequence" token.
         eos_token_id (`int`, *optional*, defaults to 11):
@@ -111,6 +124,9 @@ class FalconConfig(PretrainedConfig):
         multi_query=True,
         parallel_attn=True,
         bias=False,
+        max_position_embeddings=2048,
+        rope_theta=10000.0,
+        rope_scaling=None,
         bos_token_id=11,
         eos_token_id=11,
         **kwargs,
@@ -135,6 +151,10 @@ class FalconConfig(PretrainedConfig):
         self.multi_query = multi_query  # Ignored when new_decoder_architecture is True
         self.parallel_attn = parallel_attn
         self.bias = bias
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
 
         super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
 
@@ -145,3 +165,27 @@ class FalconConfig(PretrainedConfig):
     @property
     def rotary(self):
         return not self.alibi
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if self.rotary:
+            raise ValueError("`rope_scaling` is not supported when `alibi` is `True`.")
+
+        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, `type` 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 type 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/falcon/modeling_falcon.py

@@ -71,20 +71,20 @@ class FalconRotaryEmbedding(nn.Module):
     n_heads_per_partition, seq_len, head_dim]` (e.g. MinGPTAttention format).
     """
 
-    def __init__(self, head_dim: int, base=10000):
+    def __init__(self, head_dim: int, base=10000, max_position_embeddings=2048):
         super().__init__()
-        inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim))
+        self.base = base
+        self.max_position_embeddings = max_position_embeddings
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, head_dim, 2).float() / head_dim))
         self.register_buffer("inv_freq", inv_freq, persistent=False)
         self.head_dim = head_dim
         self.seq_len_cached = -1
         self.cos_cached: torch.Tensor | None = None
         self.sin_cached: torch.Tensor | None = None
 
-    def cos_sin(self, seq_len: int, past_key_values_length: int, device="cpu", dtype=torch.bfloat16) -> torch.Tensor:
-        total_length = seq_len + past_key_values_length
-        if total_length > self.seq_len_cached:
-            self.seq_len_cached = total_length
-            t = torch.arange(total_length, device=device, dtype=self.inv_freq.dtype)
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.seq_len_cached = seq_len
+        t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
         freqs = torch.einsum("i,j->ij", t, self.inv_freq)
         emb = torch.cat((freqs, freqs), dim=-1).to(device)
 
@@ -97,6 +97,10 @@ class FalconRotaryEmbedding(nn.Module):
         self.cos_cached = self.cos_cached.type(dtype)
         self.sin_cached = self.sin_cached.type(dtype)
 
+    def cos_sin(self, seq_len: int, past_key_values_length: int, device="cpu", dtype=torch.bfloat16) -> torch.Tensor:
+        total_length = seq_len + past_key_values_length
+        if total_length > self.seq_len_cached:
+            self._set_cos_sin_cache(total_length, device, dtype)
         return (
             self.cos_cached[:, past_key_values_length : seq_len + past_key_values_length],
             self.sin_cached[:, past_key_values_length : seq_len + past_key_values_length],
@@ -108,6 +112,66 @@ class FalconRotaryEmbedding(nn.Module):
         return (query * cos) + (rotate_half(query) * sin), (key * cos) + (rotate_half(key) * sin)
 
 
+class FalconLinearScalingRotaryEmbedding(FalconRotaryEmbedding):
+    """FalconRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, head_dim: int, base=10000, max_position_embeddings=2048, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(head_dim, base, max_position_embeddings)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.seq_len_cached = seq_len
+        t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+        # This line is the only difference from FalconRotaryEmbedding._set_cos_sin_cache
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1).to(device)
+
+        if dtype in [torch.float16, torch.bfloat16]:
+            emb = emb.float()
+
+        self.cos_cached = emb.cos()[None, :, :]
+        self.sin_cached = emb.sin()[None, :, :]
+
+        self.cos_cached = self.cos_cached.type(dtype)
+        self.sin_cached = self.sin_cached.type(dtype)
+
+
+class FalconDynamicNTKScalingRotaryEmbedding(FalconRotaryEmbedding):
+    """
+    FalconRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla
+    """
+
+    def __init__(self, head_dim: int, base=10000, max_position_embeddings=2048, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(head_dim, base, max_position_embeddings)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.seq_len_cached = seq_len
+
+        # This if block is the only difference from FalconRotaryEmbedding._set_cos_sin_cache
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.head_dim / (self.head_dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.head_dim, 2).float().to(device) / self.head_dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1).to(device)
+
+        if dtype in [torch.float16, torch.bfloat16]:
+            emb = emb.float()
+
+        self.cos_cached = emb.cos()[None, :, :]
+        self.sin_cached = emb.sin()[None, :, :]
+
+        self.cos_cached = self.cos_cached.type(dtype)
+        self.sin_cached = self.sin_cached.type(dtype)
+
+
 def _make_causal_mask(
     input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
 ) -> torch.BoolTensor:
@@ -191,6 +255,7 @@ class FalconAttention(nn.Module):
     def __init__(self, config: FalconConfig):
         super().__init__()
 
+        self.config = config
         self.hidden_size = config.hidden_size
         self.num_heads = config.num_attention_heads
         self.head_dim = self.hidden_size // self.num_heads
@@ -203,7 +268,7 @@ class FalconAttention(nn.Module):
                 f" {self.num_heads})."
             )
 
-        self.maybe_rotary = FalconRotaryEmbedding(config.head_dim) if config.rotary else lambda q, k, t: (q, k)
+        self.maybe_rotary = self._init_rope() if config.rotary else lambda q, k, t: (q, k)
 
         # Layer-wise attention scaling
         self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
@@ -221,6 +286,34 @@ class FalconAttention(nn.Module):
         self.attention_dropout = nn.Dropout(config.attention_dropout)
         self.num_kv_heads = config.num_kv_heads if (self.new_decoder_architecture or not self.multi_query) else 1
 
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            rotary_emb = FalconRotaryEmbedding(
+                self.head_dim,
+                base=self.config.rope_theta,
+                max_position_embeddings=self.config.max_position_embeddings,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                rotary_emb = FalconLinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    base=self.config.rope_theta,
+                    max_position_embeddings=self.config.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                )
+            elif scaling_type == "dynamic":
+                rotary_emb = FalconDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    base=self.config.rope_theta,
+                    max_position_embeddings=self.config.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+        return rotary_emb
+
     def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Split the last dimension into (num_heads, head_dim), results share same memory storage as `fused_qkv`

src/transformers/models/gpt_neox/configuration_gpt_neox.py

@@ -163,14 +163,14 @@ class GPTNeoXConfig(PretrainedConfig):
 
         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`, "
+                "`rope_scaling` must be a dictionary with with two fields, `type` 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}"
+                f"`rope_scaling`'s type 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/configuration_llama.py

@@ -165,14 +165,14 @@ class LlamaConfig(PretrainedConfig):
 
         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`, "
+                "`rope_scaling` must be a dictionary with with two fields, `type` 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}"
+                f"`rope_scaling`'s type 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}")

tests/models/falcon/test_modeling_falcon.py

@@ -17,7 +17,9 @@
 
 import unittest
 
-from transformers import AutoTokenizer, FalconConfig, is_torch_available
+from parameterized import parameterized
+
+from transformers import AutoTokenizer, FalconConfig, is_torch_available, set_seed
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from ...generation.test_utils import GenerationTesterMixin
@@ -410,6 +412,37 @@ class FalconModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
                     past_kv[i][1].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
                 )
 
+    @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 = FalconModel(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 = FalconModel(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 FalconLanguageGenerationTest(unittest.TestCase):