Some MHA and RoPE refactoring, llama-3.1 rope_scaling (#91)

eole/bin/convert/convert_HF.py

@@ -466,16 +466,27 @@ def run(cls, args):
             norm_eps = config["layer_norm_eps"]
         else:
             norm_eps = 1e-6
+        rope_config = {}
         if "rope_theta" in config.keys():
-            rope_theta = config["rope_theta"]
-        else:
-            rope_theta = 1e4
+            rope_config["rotary_theta"] = config["rope_theta"]
         if "rotary_dim" in config.keys():
-            rotary_dim = config["rotary_dim"]
+            rope_config["rotary_dim"] = config["rotary_dim"]
         elif "partial_rotary_factor" in config.keys():
-            rotary_dim = int(config["partial_rotary_factor"] * (hidden_size // heads))
-        else:
-            rotary_dim = 0
+            rope_config["rotary_dim"] = int(
+                config["partial_rotary_factor"] * (hidden_size // heads)
+            )
+        if config.get("rope_scaling", None) is not None:
+            rope_config["scaling_type"] = config["rope_scaling"].get("rope_type", None)
+            rope_config["scaling_factor"] = config["rope_scaling"].get("factor", 8.0)
+            rope_config["low_freq_factor"] = config["rope_scaling"].get(
+                "low_freq_factor", 1.0
+            )
+            rope_config["high_freq_factor"] = config["rope_scaling"].get(
+                "high_freq_factor", 4.0
+            )
+            rope_config["original_max_position_embeddings"] = config[
+                "rope_scaling"
+            ].get("original_max_position_embeddings", 8192)
         if "sliding_window" in config.keys():
             sliding_window = config["sliding_window"]
             if sliding_window is None:
@@ -546,8 +557,8 @@ def run(cls, args):
 
         add_qkvbias = False
         add_ffnbias = False
-        rotary_interleave = False
         shared_layer_norm = False
+        rope_config["rotary_interleave"] = False
         position_encoding = {
             "position_encoding_type": "Rotary",
             "n_positions": 0,
@@ -560,7 +571,7 @@ def run(cls, args):
             shared_layer_norm = True
             add_qkvbias = True
             add_ffnbias = True
-            rotary_interleave = False
+            rope_config["rotary_interleave"] = False
         if arch == "GPT2LMHeadModel":
             parallel_residual = False
             shared_layer_norm = True
@@ -1008,9 +1019,7 @@ def get_weight(checkpoint, tensor_name):
                 layer_norm=layer_norm,
                 norm_eps=norm_eps,
                 mlp_activation_fn=mlp_activation_fn,
-                rotary_interleave=rotary_interleave,
-                rotary_theta=rope_theta,
-                rotary_dim=rotary_dim,
+                rope_config=rope_config,
                 sliding_window=sliding_window,
                 heads_kv=heads_kv,
                 head_dim=head_dim,

eole/config/models.py

@@ -157,6 +157,47 @@ class MeanEncoderConfig(EncoderConfig):
     encoder_type: Literal["mean"] = Field(default="mean")
 
 
+class RotaryPositionConfig(Config):
+    """
+    Configuration for rotary position embeddings used in transformer models.
+    """
+
+    rotary_interleave: bool = Field(
+        default=True,
+        description="Interleave the head dimensions when rotary embeddings are applied. "
+        "Otherwise the head dimensions are sliced in half. "
+        "(True=default Llama from Meta (original), "
+        "False= used by all HuggingFace models)",
+    )
+    rotary_theta: int = Field(
+        default=10000,
+        description="Rotary theta base length, 1e4 for Llama2.Mistral, 1e6 for Mixtral",
+    )
+    rotary_dim: int = Field(
+        default=0,
+        description="Rotary dim when model requires it to be different to head dim.",
+    )
+    scaling_type: str | None = Field(
+        default=None,
+        description="Specifies the type of RoPE scaling to be applied, if any.",
+    )
+    scaling_factor: float | None = Field(
+        default=8.0, description="Factor by which to scale RoPE embeddings."
+    )
+    low_freq_factor: float | None = Field(
+        default=1.0,
+        description="Scaling factor applied to the lower frequency components of RoPE.",
+    )
+    high_freq_factor: float | None = Field(
+        default=4.0,
+        description="Scaling factor applied to the higher frequency components of RoPE.",
+    )
+    original_max_position_embeddings: int | None = Field(
+        default=8192,
+        description="Original maximum position embeddings for RoPE scaling.",
+    )
+
+
 class TransformerConfig(Config):
     """
     This base TransformerConfig class regroups parameters than can
@@ -183,21 +224,6 @@ class TransformerConfig(Config):
         default=ActivationFunction.relu,
         description="The activation function to use in MLP layer.",
     )
-    rotary_interleave: bool = Field(
-        default=True,
-        description="Interleave the head dimensions when rotary embeddings are applied. "
-        "Otherwise the head dimensions are sliced in half. "
-        "(True=default Llama from Meta (original), "
-        "False= used by all HuggingFace models)",
-    )
-    rotary_theta: int = Field(
-        default=10000,
-        description="Rotary theta base length, 1e4 for Llama2.Mistral, 1e6 for Mixtral",
-    )
-    rotary_dim: int = Field(
-        default=0,
-        description="Rotary dim when model requires it to be different to head dim.",
-    )
     layer_norm: Literal["standard", "rms"] = Field(
         default="standard",
         description="Type of layer normalization in transformer architecture.",
@@ -249,6 +275,16 @@ class TransformerConfig(Config):
         "Case 2: Max Relative Positions"
         "In the case of position_encoding_type: Relative",
     )
+    rope_config: RotaryPositionConfig | None = Field(
+        default=None, description="Rotary position config, if relevant."
+    )
+
+    @model_validator(mode="after")
+    def _validate_transformer_config(self):
+        if self.position_encoding_type == PositionEncodingType.Rotary:
+            if self.rope_config is None:
+                self.rope_config = RotaryPositionConfig()
+        return self
 
     @computed_field
     @property

eole/decoders/transformer_decoder.py

@@ -10,7 +10,8 @@
     TransformerDecoderBase,
 )
 from eole.modules.multi_headed_attn import ContextMHA
-from eole.constants import LayerNorm
+from eole.constants import LayerNorm, PositionEncodingType
+from eole.modules.rope import RotaryPosition
 
 
 class TransformerDecoderLayer(TransformerDecoderLayerBase):
@@ -57,6 +58,7 @@ def _forward(
         step=None,
         future=False,
         return_attn=False,
+        position_embeddings=None,
     ):
         """A naive forward pass for transformer decoder.
 
@@ -70,6 +72,7 @@ def _forward(
             step (int or None): stepwise decoding counter
             future (bool): If set True, do not apply future_mask.
             return_attn (bool) : if set True requires attns output
+            position_embeddings (FloatTensor): rotary position encodings, if any
 
         Returns:
             (FloatTensor, FloatTensor):
@@ -98,6 +101,7 @@ def _forward(
             sliding_window=self.sliding_window,
             step=step,
             return_attn=return_attn,
+            position_embeddings=position_embeddings,
         )
 
         if self.dropout_p > 0:
@@ -149,6 +153,9 @@ def __init__(
             model_config, running_config=running_config
         )
 
+        if model_config.position_encoding_type == PositionEncodingType.Rotary:
+            self.rope = RotaryPosition(model_config)
+
         self.transformer_layers = nn.ModuleList(
             [
                 TransformerDecoderLayer(
@@ -191,6 +198,15 @@ def forward(self, emb, **kwargs):
                     {"keys": torch.tensor([]), "values": torch.tensor([])},
                 )
 
+        if hasattr(self, "rope"):
+            position_embeddings = self.rope(
+                emb,
+                step=step,
+                device=emb.device,
+            )
+        else:
+            position_embeddings = None
+
         with_align = kwargs.pop("with_align", False)
         return_attn = with_align or kwargs.pop("return_attn", False)
 
@@ -205,6 +221,7 @@ def forward(self, emb, **kwargs):
                 step=step,
                 with_align=with_align,
                 return_attn=return_attn,
+                position_embeddings=position_embeddings,
             )
             if attn_align is not None:
                 attn_aligns.append(attn_align)

eole/decoders/transformer_lm_decoder.py

@@ -10,7 +10,8 @@
     TransformerDecoderLayerBase,
     TransformerDecoderBase,
 )
-from eole.constants import LayerNorm
+from eole.constants import LayerNorm, PositionEncodingType
+from eole.modules.rope import RotaryPosition
 
 
 class TransformerLMDecoderLayer(TransformerDecoderLayerBase):
@@ -19,7 +20,15 @@ class TransformerLMDecoderLayer(TransformerDecoderLayerBase):
          See TransformerDecoderLayerBase
     """
 
-    def _forward(self, layer_in, pad_mask, step=None, future=False, return_attn=False):
+    def _forward(
+        self,
+        layer_in,
+        pad_mask,
+        step=None,
+        future=False,
+        return_attn=False,
+        position_embeddings=None,
+    ):
         """A naive forward pass for transformer decoder.
 
         # T: could be 1 in the case of stepwise decoding or tgt_len
@@ -31,6 +40,7 @@ def _forward(self, layer_in, pad_mask, step=None, future=False, return_attn=Fals
             step (int or None): stepwise decoding counter
             future (bool): If set True, do not apply future_mask.
             return_attn (bool): If set True return attn
+            position_embeddings (FloatTensor): rotary position encodings, if any
 
         Returns:
             (FloatTensor, FloatTensor):
@@ -59,6 +69,7 @@ def _forward(self, layer_in, pad_mask, step=None, future=False, return_attn=Fals
             sliding_window=self.sliding_window,
             step=step,
             return_attn=return_attn,
+            position_embeddings=position_embeddings,
         )
         if self.dropout_p > 0:
             attn_output = self.dropout(attn_output)
@@ -91,6 +102,9 @@ def __init__(
     ):
         super(TransformerLMDecoder, self).__init__(model_config)
 
+        if model_config.position_encoding_type == PositionEncodingType.Rotary:
+            self.rope = RotaryPosition(model_config)
+
         self.transformer_layers = nn.ModuleList(
             [
                 TransformerLMDecoderLayer(
@@ -112,6 +126,16 @@ def forward(self, emb, **kwargs):
         pad_mask = kwargs.pop("tgt_pad_mask", None)
         assert pad_mask is not None, "TransformerLMDecoder requires a pad mask"
         step = kwargs.pop("step", None)
+
+        if hasattr(self, "rope"):
+            position_embeddings = self.rope(
+                emb,
+                step=step,
+                device=emb.device,
+            )
+        else:
+            position_embeddings = None
+
         if step == 0:
             # decoding mode.
             # Initialize KV and key_pad_mask cache.
@@ -140,6 +164,7 @@ def forward(self, emb, **kwargs):
                 step=step,
                 with_align=with_align,
                 return_attn=return_attn,
+                position_embeddings=position_embeddings,
             )
 
         emb = self.layer_norm(emb)
@@ -160,7 +185,3 @@ def _init_cache(self, device, mask):
                         "key_pad_mask": mask,
                     },
                 )
-                if hasattr(layer.self_attn, "rope"):
-                    layer.self_attn.rope = layer.self_attn.rope.to(device)
-                    layer.self_attn.cos = layer.self_attn.cos.to(device)
-                    layer.self_attn.sin = layer.self_attn.sin.to(device)

eole/encoders/transformer.py

@@ -7,7 +7,8 @@
 from eole.encoders.encoder import EncoderBase
 from eole.modules.multi_headed_attn import SelfMHA
 from eole.modules.transformer_mlp import MLP
-from eole.constants import LayerNorm
+from eole.constants import LayerNorm, PositionEncodingType
+from eole.modules.rope import RotaryPosition
 
 
 class TransformerEncoderLayer(nn.Module):
@@ -45,18 +46,21 @@ def __init__(
             running_config=running_config,
         )
 
-    def forward(self, layer_in, mask):
+    def forward(self, layer_in, mask, position_embeddings=None):
         """
         Args:
             layer_in (FloatTensor): ``(batch_size, src_len, model_dim)``
             mask (LongTensor): ``(batch_size, 1, src_len)``
+            position_embeddings (FloatTensor): rotary position encodings, if any
 
         Returns:
             (FloatTensor):
             * layer_out ``(batch_size, src_len, model_dim)``
         """
         norm_layer_in = self.input_layernorm(layer_in)
-        context, _ = self.self_attn(norm_layer_in, mask=mask)
+        context, _ = self.self_attn(
+            norm_layer_in, mask=mask, position_embeddings=position_embeddings
+        )
         if self.dropout_p > 0:
             context = self.dropout(context)
         if self.parallel_residual:
@@ -98,6 +102,9 @@ def __init__(
     ):
         super(TransformerEncoder, self).__init__()
 
+        if model_config.position_encoding_type == PositionEncodingType.Rotary:
+            self.rope = RotaryPosition(model_config)
+
         self.transformer_layers = nn.ModuleList(
             [
                 TransformerEncoderLayer(
@@ -130,8 +137,13 @@ def forward(self, emb, mask=None):
         # 1 to be expanded to number of heads in MHA
         # Run the forward pass of every layer of the tranformer.
 
+        if hasattr(self, "rope"):
+            position_embeddings = self.rope(emb, step=0, device=emb.device)
+        else:
+            position_embeddings = None
+
         for layer in self.transformer_layers:
-            enc_out = layer(enc_out, mask)
+            enc_out = layer(enc_out, mask, position_embeddings=position_embeddings)
         enc_out = self.layer_norm(enc_out)
         return enc_out, None