add value residual learning

README.md

@@ -201,3 +201,14 @@ preds = model(time_series)
     url     = {https://api.semanticscholar.org/CorpusID:265018962}
 }
 ```
+
+```bibtex
+@article{Zhou2024ValueRL,
+    title   = {Value Residual Learning For Alleviating Attention Concentration In Transformers},
+    author  = {Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan},
+    journal = {ArXiv},
+    year    = {2024},
+    volume  = {abs/2410.17897},
+    url     = {https://api.semanticscholar.org/CorpusID:273532030}
+}
+```

iTransformer/attend.py

@@ -10,6 +10,8 @@
 
 from einops import rearrange, repeat
 
+from torch.nn.attention import SDPBackend
+
 # constants
 
 EfficientAttentionConfig = namedtuple('EfficientAttentionConfig', ['enable_flash', 'enable_math', 'enable_mem_efficient'])
@@ -92,9 +94,18 @@ def flash_attn(
 
         config = self.cuda_config if is_cuda else self.cpu_config
 
+        str_to_backend = dict(
+            enable_flash = SDPBackend.FLASH_ATTENTION,
+            enable_mem_efficient = SDPBackend.EFFICIENT_ATTENTION,
+            enable_math = SDPBackend.MATH,
+            enable_cudnn = SDPBackend.CUDNN_ATTENTION
+        )
+
+        sdpa_backends = [str_to_backend[enable_str] for enable_str, enable in config._asdict().items() if enable]
+
         # pytorch 2.0 flash attn: q, k, v, mask, dropout, causal, softmax_scale
 
-        with torch.backends.cuda.sdp_kernel(**config._asdict()):
+        with torch.nn.attention.sdpa_kernel(sdpa_backends):
             out = F.scaled_dot_product_attention(
                 q, k, v,
                 is_causal = self.causal,

iTransformer/iTransformer.py

@@ -35,7 +35,8 @@ def __init__(
         dim_head = 32,
         heads = 4,
         dropout = 0.,
-        flash = True
+        flash = True,
+        learned_value_residual_mix = False
     ):
         super().__init__()
         self.scale = dim_head ** -0.5
@@ -46,6 +47,12 @@ def __init__(
             Rearrange('b n (qkv h d) -> qkv b h n d', qkv = 3, h = heads)
         )
 
+        self.to_value_residual_mix = nn.Sequential(
+            nn.Linear(dim, heads, bias = False),
+            Rearrange('b n h -> b h n 1'),
+            nn.Sigmoid()
+        ) if learned_value_residual_mix else None
+
         self.to_v_gates = nn.Sequential(
             nn.Linear(dim, heads, bias = False),
             nn.Sigmoid(),
@@ -60,13 +67,25 @@ def __init__(
             nn.Dropout(dropout)
         )
 
-    def forward(self, x):
+    def forward(
+        self,
+        x,
+        value_residual = None
+    ):
         q, k, v = self.to_qkv(x)
 
+        orig_v = v
+
+        if exists(self.to_value_residual_mix):
+            assert exists(value_residual)
+            mix = self.to_value_residual_mix(x)
+            v = v.lerp(value_residual, mix)
+
         out = self.attend(q, k, v)
 
         out = out * self.to_v_gates(x)
-        return self.to_out(out)
+
+        return self.to_out(out), orig_v
 
 # feedforward
 
@@ -120,9 +139,11 @@ def __init__(
         self.num_tokens_per_variate = num_tokens_per_variate
 
         self.layers = ModuleList([])
-        for _ in range(depth):
+        for i in range(depth):
+            is_first = i == 0
+
             self.layers.append(ModuleList([
-                Attention(dim, dim_head = dim_head, heads = heads, dropout = attn_dropout, flash = flash_attn),
+                Attention(dim, dim_head = dim_head, heads = heads, dropout = attn_dropout, flash = flash_attn, learned_value_residual_mix = not is_first),
                 nn.LayerNorm(dim),
                 FeedForward(dim, mult = ff_mult, dropout = ff_dropout),
                 nn.LayerNorm(dim)
@@ -180,10 +201,20 @@ def forward(
             m = repeat(self.mem_tokens, 'm d -> b m d', b = x.shape[0])
             x, mem_ps = pack([m, x], 'b * d')
 
+        # value residual learning
+        # https://arxiv.org/abs/2410.17897
+
+        first_values = None
+
         # attention and feedforward layers
 
         for attn, attn_post_norm, ff, ff_post_norm in self.layers:
-            x = attn(x) + x
+
+            attn_out, values = attn(x, value_residual = first_values)
+            first_values = default(first_values, values)
+
+            x = x + attn_out
+
             x = attn_post_norm(x)
             x = ff(x) + x
             x = ff_post_norm(x)

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'iTransformer',
   packages = find_packages(exclude=[]),
-  version = '0.6.0',
+  version = '0.7.0',
   license='MIT',
   description = 'iTransformer - Inverted Transformer Are Effective for Time Series Forecasting',
   author = 'Phil Wang',
@@ -19,10 +19,10 @@
   ],
   install_requires=[
     'beartype',
-    'einops>=0.7.0',
+    'einops>=0.8.0',
     'gateloop-transformer>=0.2.3',
     'rotary-embedding-torch',
-    'torch>=2.1',
+    'torch>=2.3',
   ],
   classifiers=[
     'Development Status :: 4 - Beta',