19.2 Different examples are seen in the same models

Content and key concepts

On the previous page, size and quality are different.
Copy the weights on the devices and divide the data. It is important to take into account the different number of currents when combining average errors.
You can divide data, tables, or steps. Partial results need to be linked. Different patterns first turn into vectors of appropriate width, and experts are selected along a limited route.
Для изменения рисунка включитеJavaScript; расчёт и теория доступны далее.
Replicas see different examples and agree on gradiants
In sinchronous data paralla, each replica stores complete parameters, processes local mini-batch, and receives local gradient. The collective reducion forms a gradiant global batch and then replices perform the same optimizer step.
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\qquad \theta' = \operatorname{Opt}(\theta,g,state).
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If the number valid tokens on replicas is different because of padding/masking, the average of the finished local means can give the wrong weight. A reliable contract dislodies the gradient sum and denominator valid tags. Gradient accumulation increases the effective batch without additional replic, but changes the number forward/backward to optimizer step.
That's what the model shows.
The diagram shows scatter examples, local forward/backward, all-ruce and a single high barrier. The Optimizer state may be replicable or hard; the latter is already changing the memory/communication contract, although the mathematical global gradient remains the same.
Scope of applicability.
Double replicas does not guarantee double througput: communication and input pipeline become bottleneck. A big batch may require another lightning-rate class and influence optimization. Asynchronus variants have a steel gradiants and are not described by this formula.
replica_grads_19b=[[1.0,3.0],[3.0,1.0]]
global_grad_19b=[sum(row_19b[j_19b] for row_19b in replica_grads_19b)/len(replica_grads_19b) for j_19b in range(2)]
weights_19b=[5.0,5.0]; lr_19b=0.1
updated_19b=[w_19b-lr_19b*g_19b for w_19b,g_19b in zip(weights_19b,global_grad_19b)]
assert global_grad_19b==[2.0,2.0] and updated_19b==[4.8,4.8]
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.
Token-weated mean is replicating a global target.
One table is divided into parts. How does this question continue?
Python examples of Chapter 19
Academic base