[Assistant Generation] Improve Encoder Decoder by patrickvonplaten · Pull Request #26701 · huggingface/transformers

patrickvonplaten · 2023-10-09T17:52:17Z

What does this PR do?

This PR speeds up assistant generation / speculative decoding for encoder-decoder models such as Distill-Whisper by ~20-30%.

Improvements:

If assistant and model share same encoder, let's allow the user to pass assistant_encoder_outputs so that the inputs are not encoded twice (gives ~20% speed-up)
In the small loop I don't think we have to allocate tensors for the attention mask all the time. This is done automatically by the model if necessary (gives ~3,4% speed-up)
The heuristic to increase / decrease the number of "look-ahead" tokens doesn't work well for whisper, can we maybe allow the user to somehow disable it? Maybe via a config attribute?

patrickvonplaten · 2023-10-09T17:56:04Z

src/transformers/generation/utils.py

-                assistant_model.max_assistant_tokens += 2.0
-            else:
-                assistant_model.max_assistant_tokens = max(1.0, assistant_model.max_assistant_tokens - 1.0)
+            # if n_matches == int(assistant_model.max_assistant_tokens):


The heuristic to increase / decrease the number of "look-ahead" tokens doesn't work well for whisper, can we maybe allow the user to somehow disable it? Maybe via a config attribute?

patrickvonplaten · 2023-10-09T17:56:36Z

src/transformers/generation/utils.py

                    # `new_token_len` can be 1 or 2 (next token in assistant + last token picked by the larger model)
                    new_token_len = candidate_input_ids.shape[1] - prev_seq_len
                    assist_inputs = candidate_input_ids[:, -new_token_len:]
-                    assist_attn = torch.ones_like(candidate_input_ids)


Do we really need this @gante ? Allocating new memory here every time leads to some slow downs that are not insignificant for Distil Whisper

Yes, this makes sense to remove, since it is the default attention mask! 👍

HuggingFaceDocBuilderDev · 2023-10-09T18:11:05Z

The documentation is not available anymore as the PR was closed or merged.

patrickvonplaten · 2023-10-10T11:09:54Z

src/transformers/generation/utils.py

            new_logits = outputs.logits[:, -candidate_length - 1 :]  # excludes the input prompt if present
            if len(logits_processor) > 0:
-                for i in range(candidate_length):
+                for i in range(candidate_length + 1):


That was a bug previously. We forgot to apply the logits processors to the last logit here

Good catch 👀

patrickvonplaten · 2023-10-10T11:10:11Z

src/transformers/generation/utils.py

+                probs = new_logits.softmax(dim=-1)
                selected_tokens = torch.multinomial(probs[0, :, :], num_samples=1).squeeze(1)[None, :]
            else:
-                selected_tokens = new_logits[:, -candidate_length - 1 :, :].argmax(dim=-1)


This is unnecessary here as new_logits is already sliced

…to improve_assistant_generation_enc_dec

gante

Fantastic, thank you for the upgrades @patrickvonplaten 🔥

Only added two minor, optional nits.

gante · 2023-10-10T16:14:06Z

src/transformers/generation/configuration_utils.py


+        > Generation parameters exclusive to [assistant generation](https://arxiv.org/abs/2211.17192)
+
+        max_assistant_tokens (`int`, *optional*, defaults to 5):


Perhaps we can take the chance to give a better name to this variable: assistant_tokens or similar. max_assistant_tokens implies that the assistant will never cross this limit but, as we can see in max_assistant_tokens_schedule (which should also be renamed accordingly), that is not true :)

Poor original naming choice by me :D

Sounds good!

gante · 2023-10-10T16:15:46Z

src/transformers/generation/utils.py

-            assistant_model.max_assistant_tokens = 5  # this value, which will be updated, persists across calls
+        if hasattr(assistant_model, "max_assistant_tokens"):
+            warnings.warn(
+                "Setting `max_assistant_tokens` via `assistant_model.max_assistant_tokens` is deprecated and will be removed in v5. Make sure to set `max_assistant_tokens` via the generation_config instead.",


Perhaps we can deprecate this earlier (like in v4.37)?

I haven't seen users fiddling with this internal variable :)

gante · 2023-10-10T16:18:29Z

src/transformers/generation/utils.py

                    # `new_token_len` can be 1 or 2 (next token in assistant + last token picked by the larger model)
                    new_token_len = candidate_input_ids.shape[1] - prev_seq_len
                    assist_inputs = candidate_input_ids[:, -new_token_len:]
-                    assist_attn = torch.ones_like(candidate_input_ids)


Yes, this makes sense to remove, since it is the default attention mask! 👍

gante · 2023-10-10T16:19:07Z

src/transformers/generation/utils.py

            new_logits = outputs.logits[:, -candidate_length - 1 :]  # excludes the input prompt if present
            if len(logits_processor) > 0:
-                for i in range(candidate_length):
+                for i in range(candidate_length + 1):


Good catch 👀

…to improve_assistant_generation_enc_dec

examples/research_projects/jax-projects/big_bird/bigbird_flax.py

examples/research_projects/jax-projects/big_bird/train.py

examples/research_projects/vqgan-clip/VQGAN_CLIP.py

…into improve_assistant_generation_enc_dec

patrickvonplaten · 2023-10-11T13:30:13Z

The failing Hub test seems to be flaky.

This PR is ready for a final review.

patrickvonplaten · 2023-10-11T13:30:51Z

src/transformers/models/biogpt/modeling_biogpt.py

            inputs_embeds = self.embed_tokens(input) * self.embed_scale

        if attention_mask is None:
-            attention_mask = torch.ones(inputs_embeds.shape[:2], dtype=torch.bool, device=inputs_embeds.device)


This was a bug previously. The attention_mask should be equal to input_embeds + past_key_values length.

ArthurZucker

Very clean thanks for improving the performances!

ArthurZucker · 2023-10-11T13:40:23Z

src/transformers/generation/configuration_utils.py

+            - `"_heuristic_`: When all _speculative_ tokens are correct, increase `num_assistant_tokens` by 2 else
+              reduce by 1


wondering if the schedule parameters should be hard coded but fine for me

[Assistant Generation] Improve enc dec

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patrickvonplaten marked this pull request as draft October 9, 2023 17:53

patrickvonplaten commented Oct 9, 2023

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patrickvonplaten added 2 commits October 9, 2023 21:11

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Fix logit processor checks

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patrickvonplaten added 2 commits October 10, 2023 12:19

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Merge branch 'main' of https://github.com/huggingface/transformers in…

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…to improve_assistant_generation_enc_dec

patrickvonplaten marked this pull request as ready for review October 10, 2023 12:20

make style

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patrickvonplaten requested a review from gante October 10, 2023 13:55

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patrickvonplaten added 3 commits October 11, 2023 11:22

Merge branch 'main' of https://github.com/huggingface/transformers in…

aa096d4

…to improve_assistant_generation_enc_dec

fix deprecation

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fix generation test

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patrickvonplaten changed the title ~~[Assistant Generation] Improve enc dec~~ [Assistant Generation] Improve Encoder Decoder Oct 11, 2023

patrickvonplaten added 3 commits October 11, 2023 12:57

xxxMerge branch 'main' of https://github.com/huggingface/transformers …

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…into improve_assistant_generation_enc_dec

fix biogpt

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make style

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patrickvonplaten commented Oct 11, 2023

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patrickvonplaten requested review from ArthurZucker and LysandreJik October 11, 2023 13:31

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patrickvonplaten merged commit da69de1 into main Oct 11, 2023

patrickvonplaten deleted the improve_assistant_generation_enc_dec branch October 11, 2023 13:52


		> Generation parameters exclusive to [assistant generation](https://arxiv.org/abs/2211.17192)

		max_assistant_tokens (`int`, optional, defaults to 5):

		- `"_heuristic_`: When all _speculative_ tokens are correct, increase `num_assistant_tokens` by 2 else
		reduce by 1

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