Alterna · learning material
15.5. Several blocks running
The second block is the result of the first one. It's a repetition of the processing type, but it's not mandatory to reuse the same weights.
What this material covers
- Blocks repeated, weights not automatically divided
Content and key concepts
On the previous page, processing each current separately.
The second block is the result of the first one. It's a repetition of the processing type, but it's not mandatory to reuse the same weights.
The vector obtains positional information, exchanges information and updates its status separately. Residual amounts maintain the same path. The last table gives estimates of the dictionary.
Для изменения рисунка включитеJavaScript; расчёт и теория доступны далее.
Blocks repeated, weights not automatically divided
Stack uses the Ldecoder blocks in sequence. Hidden shape[n,d] is retained and the views are changed. "The same type of block" does not mean the welled weights between layers. After runoff, the final LayerNorm is performed before the weekend head.
In our configuration E[V,d], independent head U[d,V] and bias[V], the final LN contains 2dparameters. Each ReLU-block:4d2attention,4d for two LN,4D2+3dFFN.
P
R
e
L
U
=
2
V
d
+
V
+
2
d
+
L
(
8
d
2
+
7
d
)
.
P_{ReLU}=2Vd+V+2d+L(8d^2+7d).
P
R
e
LU
=
2
V
d
+
V
+
2
d
+
L
(
8
d
2
+
7
d
)
.
For SwiGLU-block, 10d2+4d is produced because FFN is non-bias with 6d2.
Experiment.
Change dept. 1 ≤ 2 , d4 , and heds. Check the formula with the actual count of arrays. The entry parameters are not added for sinusoidal/RoPE; the vocable change after BPE changes E and head.
Boundary.
This program implements the real decoder forward, but not pre-training. No draftout, encoder/cross-attentation, KV-cache and optimized GPU execution. The number of parameters is not the total memory input: snapshots, actions and n2attenationmatrix are stored separately for the purpose of training the machine.
V,d,L=256,4,1
assert 2*V*d+V+2*d+L*(8*d*d+7*d)==2468
assert 2*V*d+V+2*d+L*(10*d*d+4*d)==2488
The code repeats the operations just taken care of.
Completely checked Python-example Chapter
Available with a separate file; the short piece shown does not replace its implementation.
The same type of layer does not mean the same parameters.
From the presentation in the evaluation of the words: How does this issue continue?
Python examples of Chapter 15
Academic base