add transformer with dropout, fix transformer ffm, layernorm order

python/mlx/nn/layers/transformer.py

@@ -7,7 +7,49 @@ import mlx.core as mx
 from mlx.nn.layers.base import Module
 from mlx.nn.layers.linear import Linear
 from mlx.nn.layers.normalization import LayerNorm
+from mlx.nn.layers.dropout import Dropout
+from mlx.nn.layers.positional_encoding import SinusoidalPositionalEncoding
 
+class MyPosEncoding(SinusoidalPositionalEncoding):
+    def __init__(
+        self,
+        dims: int,
+        min_freq: float = 0.0001,
+        max_freq: float = 1,
+        scale: Optional[float] = None,
+        cos_first: bool = False,
+        full_turns: bool = False,
+    ):
+        super().__init__(
+            dims,
+            min_freq=min_freq,
+            max_freq=max_freq,
+            scale=scale,
+            cos_first=cos_first,
+            full_turns=full_turns
+        )
+        self.dims = dims
+    
+    def __call__(self, x):
+        seq_length = x.shape[1]  # Assuming x.shape [batch_size, sequence_length, embedding_dim]
+        position = mx.arange(seq_length)[..., None] * self._sigmas
+        
+        # Generate positional encodings
+        div_term = mx.exp(mx.arange(0, self.dims, 2) * -(math.log(10000.0) / self.dims))
+        sinusoid_inp = position * div_term
+        
+        y = mx.zeros((seq_length, self.dims))
+        if self.cos_first:
+            y[:, 0::2] = mx.cos(sinusoid_inp) 
+            y[:, 1::2] = mx.sin(sinusoid_inp) 
+        else:
+            y[:, 0::2] = mx.sin(sinusoid_inp) 
+            y[:, 1::2] = mx.cos(sinusoid_inp) 
+                    
+        if self.scale != 1:
+            y = y * self.scale
+
+        return x + y
 
 class MultiHeadAttention(Module):
     """Implements the scaled dot product attention with multiple heads.
@@ -107,17 +149,41 @@ class TransformerEncoderLayer(Module):
         self.linear2 = Linear(mlp_dims, dims)
 
     def __call__(self, x, mask):
-        y = self.ln1(x)
+        y = self.attention(x, x, x, mask)
-        y = self.attention(y, y, y, mask)
+        y = self.ln1(x + y)
-        x = x + y
+        
-
-        y = self.ln2(x)
         y = self.linear1(y)
         y = mx.maximum(y, 0)
         y = self.linear2(y)
-        x = x + y
 
-        return x
+        y = self.ln2(x + y)
+        return y
+
+
+class TransformerEncoderLayerWithDropout(Module):
+    def __init__(self, dims: int, num_heads: int, mlp_dims: Optional[int] = None, dropout_rate: float = 0.1):
+        super().__init__()
+        mlp_dims = mlp_dims or dims * 4
+        self.attention = MultiHeadAttention(dims, num_heads)
+        self.ln1 = LayerNorm(dims)
+        self.ln2 = LayerNorm(dims)
+        self.linear1 = Linear(dims, mlp_dims)
+        self.linear2 = Linear(mlp_dims, dims)
+        self.dropout1 = Dropout(dropout_rate)
+        self.dropout2 = Dropout(dropout_rate)
+
+    def __call__(self, x, mask):
+        y = self.attention(x, x, x, mask)
+        y = self.dropout1(y)
+        y = self.ln1(x + y)
+        
+        y = self.linear1(y)
+        y = mx.maximum(y, 0)
+        y = self.linear2(y)
+        y = self.dropout2(y)
+
+        y = self.ln2(x + y)
+        return y
 
 
 class TransformerEncoder(Module):
@@ -139,6 +205,26 @@ class TransformerEncoder(Module):
         return x
 
 
+class TransformerEncoderWithDropout(Module):
+    def __init__(
+        self, num_layers: int, dims: int, num_heads: int, mlp_dims: Optional[int] = None
+    ):
+        super().__init__()
+        self.layers = [
+            TransformerEncoderLayerWithDropout(dims, num_heads, mlp_dims)
+            for i in range(num_layers)
+        ]
+        self.ln = LayerNorm(dims)
+
+    def __call__(self, x, mask):
+        for l in self.layers:
+            x = l(x, mask)
+        x = self.ln(x)
+
+        return x
+
+
+
 class TransformerDecoderLayer(Module):
     def __init__(self, dims: int, num_heads: int, mlp_dims: Optional[int] = None):
         super().__init__()
@@ -152,21 +238,51 @@ class TransformerDecoderLayer(Module):
         self.linear2 = Linear(mlp_dims, dims)
 
     def __call__(self, x, memory, x_mask, memory_mask):
-        y = self.ln1(x)
+        y = self.self_attention(x, x, x, x_mask)
-        y = self.self_attention(y, y, y, x_mask)
+        x = self.ln1(x + y)
-        x = x + y
+        
+        y = self.cross_attention(y, memory, memory, memory_mask)
+        x = self.ln1(x + y)
 
-        y = self.ln2(x)
+        y = self.linear1(x)
-        y = self.cross_attention(x, memory, memory, memory_mask)
-        x = x + y
-
-        y = self.ln3(x)
-        y = self.linear1(y)
         y = mx.maximum(y, 0)
         y = self.linear2(y)
-        x = x + y
+        y = self.ln3(x + y)
 
-        return x
+        return y
+
+
+class TransformerDecoderLayerWithDropout(Module):
+    def __init__(self, dims: int, num_heads: int, mlp_dims: Optional[int] = None, dropout_rate: float = 0.1):
+        super().__init__()
+        mlp_dims = mlp_dims or dims * 4
+        self.self_attention = MultiHeadAttention(dims, num_heads)
+        self.cross_attention = MultiHeadAttention(dims, num_heads)
+        self.ln1 = LayerNorm(dims)
+        self.ln2 = LayerNorm(dims)
+        self.ln3 = LayerNorm(dims)
+        self.linear1 = Linear(dims, mlp_dims)
+        self.linear2 = Linear(mlp_dims, dims)
+        self.dropout1 = Dropout(dropout_rate)
+        self.dropout2 = Dropout(dropout_rate)
+        self.dropout3 = Dropout(dropout_rate)
+
+    def __call__(self, x, memory, x_mask, memory_mask):
+        y = self.self_attention(x, x, x, x_mask)
+        y = self.dropout1(y)
+        x = self.ln1(x + y)
+        
+        y = self.cross_attention(y, memory, memory, memory_mask)
+        y = self.dropout2(y)
+        x = self.ln1(x + y)
+
+        y = self.linear1(x)
+        y = mx.maximum(y, 0)
+        y = self.linear2(y)
+        y = self.dropout3(y)
+        y = self.ln3(x + y)
+
+        return y
 
 
 class TransformerDecoder(Module):
@@ -188,6 +304,25 @@ class TransformerDecoder(Module):
         return x
 
 
+class TransformerDecoderWithDropout(Module):
+    def __init__(
+        self, num_layers: int, dims: int, num_heads: int, mlp_dims: Optional[int] = None
+    ):
+        super().__init__()
+        self.layers = [
+            TransformerDecoderLayerWithDropout(dims, num_heads, mlp_dims)
+            for i in range(num_layers)
+        ]
+        self.ln = LayerNorm(dims)
+
+    def __call__(self, x, memory, x_mask, memory_mask):
+        for l in self.layers:
+            x = l(x, memory, x_mask, memory_mask)
+        x = self.ln(x)
+
+        return x
+
+
 class Transformer(Module):
     def __init__(
         self,