release palm lite version, thanks to @conceptofmind

palm_pytorch/palm_lite.py

@@ -97,7 +97,7 @@ def __init__(self, dim, dim_head=64, heads=8, ff_mult=4):
 
         attn_inner_dim = dim_head * heads
         ff_inner_dim = dim * ff_mult
-        self.fused_dims = (attn_inner_dim, dim_head, dim_head, (ff_inner_dim * 2))
+        self.fused_dims = (attn_inner_dim, dim_head, (ff_inner_dim * 2))
 
         self.heads = heads
         self.scale = dim_head**-0.5
@@ -139,9 +139,9 @@ def forward(self, x):
 
         x = self.norm(x)
 
-        # attention queries, keys, values, and feedforward inner
+        # attention queries, keys or values (shared key / values is a personal discovery of mine), and feedforward inner
 
-        q, k, v, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1)
+        q, kv, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1)
 
         # split heads
         # they use multi-query single-key-value attention, yet another Noam Shazeer paper
@@ -156,7 +156,7 @@ def forward(self, x):
 
         # similarity
 
-        sim = einsum("b h i d, b j d -> b h i j", q, k)
+        sim = einsum("b h i d, b j d -> b h i j", q, kv)
 
         # add the alibi bias
 
@@ -170,7 +170,7 @@ def forward(self, x):
         # attention
 
         attn = sim.softmax(dim=-1)
-        out = einsum("b h i j, b j d -> b h i d", attn, v)
+        out = einsum("b h i j, b j d -> b h i d", attn, kv)
 
         # merge heads
 
@@ -197,21 +197,21 @@ def PaLM(*, dim, num_tokens, depth, dim_head=64, heads=8, ff_mult=4):
 
 # For testing functionality of the model
 
-# if __name__ == "__main__":
+if __name__ == "__main__":
 
-#     palm = PaLM(
-#         num_tokens = 20000,
-#         dim = 512,
-#         depth = 1,
-#         heads = 8,
-#         dim_head = 64,
-#     )
+    palm = PaLM(
+        num_tokens = 20000,
+        dim = 512,
+        depth = 1,
+        heads = 8,
+        dim_head = 64,
+    )
 
-#     tokens = torch.randint(0, 20000, (1, 2048))
-#     logits = palm(tokens) # (1, 2048, 20000)
+    tokens = torch.randint(0, 20000, (1, 2048))
+    logits = palm(tokens) # (1, 2048, 20000)
 
-#     n_params_torch = sum(
-#         p.numel() for p in palm.parameters() if p.requires_grad
-#     )
+    n_params_torch = sum(
+        p.numel() for p in palm.parameters() if p.requires_grad
+    )
 
-#     print(f"Number of parameters in torch model: {n_params_torch}")
+    print(f"Number of parameters in torch model: {n_params_torch}")

setup.py

@@ -3,11 +3,12 @@
 setup(
     name="PaLM-pytorch",
     packages=find_packages(exclude=[]),
-    version="0.1.0",
+    version="0.2.0",
     license="MIT",
     description="PaLM: Scaling Language Modeling with Pathways - Pytorch",
     author="Phil Wang",
     author_email="[email protected]",
+    long_description_content_type = 'text/markdown',
     url="https://github.com/lucidrains/PaLM-pytorch",
     keywords=[
         "artificial general intelligence",