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Skip barriers of noops #19

Merged
merged 1 commit into from
Aug 14, 2024
Merged

Skip barriers of noops #19

merged 1 commit into from
Aug 14, 2024

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ikawrakow
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GGML_OP_RESHAPE, GGML_OP_VIEW, GGML_OP_PERMUTE, GGML_OP_TRANSPOSE, along with GGML_OP_NONE, are all noops in ggml. I.e., nothing happens. But ggml still has a thread barrier after them, which wastes time. The waste is not too bad for large models where computations are long compared to the time taken for thread synchronization. But for small models skipping those unnecessary waits makes a noticeable difference.

Let's look at a really tiny model - the 99M parameter TriLM ternary model quantized with IQ2_TN. The following table compares performance for PP-512 and TG-128 with and without the change in this PR

CPU threads test t/s (main) t/s (PR) Speedup
Ryzen-7950X 16 pp512 11386.75 ± 19.08 11587.58 ± 34.26 1.018
Ryzen-7950X 8 tg128 1312.25 ± 1.02 1460.80 ± 1.69 1.113
M2-Max 8 pp512 7642.81 ± 22.07 7680.29 ± 9.29 1.005
M2-Max 8 tg128 992.83 ± 18.17 1096.47 ± 14.45 1.104

So, basically, for such a small model ggml spends 10% of its time waiting for threads to pass through a barrier after a noop when generating tokens.

There are other barriers that can be eliminated. E.g., the typical attention block involves matrix multiplications of the Q, K and V tensors with the same activations, so there is no need to synchronize threads after each such matrix multiplications. In a similar way, in the feed-forward portion of the network the ffn_up and ffn_gate tensors multiply the same activations, so one can save another barrier there. This is left for a future PR.

GGML_OP_RESHAPE, GGML_OP_VIEW, GGML_OP_PERMUTE, GGML_OP_TRANSPOSE,
along with GGML_OP_NONE, are all noops. I.e., nothinh happens.
But ggml still has a barrier after them, which wastes time.
The waste is not too bad for large models where computations are
long compared to the time taken for thread synchronization.
But for small models skipping those unnecessary waits makes
a significant difference. E.g., for the 99M TriLMamodel,
TG-500 goes up to 1426 t/s from 1240 t/s.
@ikawrakow ikawrakow merged commit 6c5384f into main Aug 14, 2024
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