[llava 13, 14, 15, 16/N] #4643
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[llava 13, 14, 15, 16/N] #4643
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As titled. [ghstack-poisoned]
…reuse" As titled. [ghstack-poisoned]
…reuse" As titled. [ghstack-poisoned]
…reuse" As titled. [ghstack-poisoned]
…reuse" As titled. [ghstack-poisoned]
This refactoring is needed in order to extract out prefill() and run_model_step() out from runner so that these APIs become replaceable and easy to plugin and use. * prefill(): For the case where parallel prefill is enabled or not using kv cache, the model is able to accept a large block (more than 1) of tokens. For the other case where we have kv cache but parallel prefill is not enabled, we can only feed in 1 token every time. * run_model_step(): This function should not update the input. Instead it should run the model differently, depending on whether kv cache is enabled. This should return the next token directly. All the input update needs to happen in the generation loop. [ghstack-poisoned]
This refactoring is needed in order to extract out prefill() and run_model_step() out from runner so that these APIs become replaceable and easy to plugin and use. * prefill(): For the case where parallel prefill is enabled or not using kv cache, the model is able to accept a large block (more than 1) of tokens. For the other case where we have kv cache but parallel prefill is not enabled, we can only feed in 1 token every time. * run_model_step(): This function should not update the input. Instead it should run the model differently, depending on whether kv cache is enabled. This should return the next token directly. All the input update needs to happen in the generation loop. [ghstack-poisoned]
This refactoring is needed in order to extract out prefill() and run_model_step() out from runner so that these APIs become replaceable and easy to plugin and use. * prefill(): For the case where parallel prefill is enabled or not using kv cache, the model is able to accept a large block (more than 1) of tokens. For the other case where we have kv cache but parallel prefill is not enabled, we can only feed in 1 token every time. * run_model_step(): This function should not update the input. Instead it should run the model differently, depending on whether kv cache is enabled. This should return the next token directly. All the input update needs to happen in the generation loop. [ghstack-poisoned]
Last PR #4556 refactored run_model_step() so that it is suitable to be extracted out as a separate class. This new `TextDecoderRunner` provides 2 APIs: * step(tokens, start_pos) This API takes one or more tokens with start_pos and feed them into Module. Return a tensor of logits. * logits_to_token(logits) This API samples the result and returns a token. We don't expect this logic to change across different runners. [ghstack-poisoned]
This refactoring is needed in order to extract out prefill() and run_model_step() out from runner so that these APIs become replaceable and easy to plugin and use. * prefill(): For the case where parallel prefill is enabled or not using kv cache, the model is able to accept a large block (more than 1) of tokens. For the other case where we have kv cache but parallel prefill is not enabled, we can only feed in 1 token every time. * run_model_step(): This function should not update the input. Instead it should run the model differently, depending on whether kv cache is enabled. This should return the next token directly. All the input update needs to happen in the generation loop. Differential Revision: [D60840327](https://our.internmc.facebook.com/intern/diff/D60840327) [ghstack-poisoned]
Last PR #4556 refactored run_model_step() so that it is suitable to be extracted out as a separate class. This new `TextDecoderRunner` provides 2 APIs: * step(tokens, start_pos) This API takes one or more tokens with start_pos and feed them into Module. Return a tensor of logits. * logits_to_token(logits) This API samples the result and returns a token. We don't expect this logic to change across different runners. Differential Revision: [D60856571](https://our.internmc.facebook.com/intern/diff/D60856571) [ghstack-poisoned]
…ner" Last PR #4556 refactored run_model_step() so that it is suitable to be extracted out as a separate class. This new `TextDecoderRunner` provides 2 APIs: * step(tokens, start_pos) This API takes one or more tokens with start_pos and feed them into Module. Return a tensor of logits. * logits_to_token(logits) This API samples the result and returns a token. We don't expect this logic to change across different runners. Differential Revision: [D60856571](https://our.internmc.facebook.com/intern/diff/D60856571) [ghstack-poisoned]
Last PR #4556 refactored run_model_step() so that it is suitable to be extracted out as a separate class. This new `TextDecoderRunner` provides 2 APIs: * step(tokens, start_pos) This API takes one or more tokens with start_pos and feed them into Module. Return a tensor of logits. * logits_to_token(logits) This API samples the result and returns a token. We don't expect this logic to change across different runners. Differential Revision: [D60856571](https://our.internmc.facebook.com/intern/diff/D60856571) [ghstack-poisoned]
Depends on whether parallel or sequential prefill is chosen, prefill() calls `TextDecoderRunner.step()` to prefill prompt tokens to LLM. [ghstack-poisoned]
…to a new class" Depends on whether parallel or sequential prefill is chosen, prefill() calls `TextDecoderRunner.step()` to prefill prompt tokens to LLM. Differential Revision: [D60927756](https://our.internmc.facebook.com/intern/diff/D60927756) [ghstack-poisoned]
Depends on whether parallel or sequential prefill is chosen, prefill() calls `TextDecoderRunner.step()` to prefill prompt tokens to LLM. Differential Revision: [D60927756](https://our.internmc.facebook.com/intern/diff/D60927756) [ghstack-poisoned]
…to a new class" Depends on whether parallel or sequential prefill is chosen, prefill() calls `TextDecoderRunner.step()` to prefill prompt tokens to LLM. Differential Revision: [D60927756](https://our.internmc.facebook.com/intern/diff/D60927756) [ghstack-poisoned]
Depends on whether parallel or sequential prefill is chosen, prefill() calls `TextDecoderRunner.step()` to prefill prompt tokens to LLM. Differential Revision: [D60927756](https://our.internmc.facebook.com/intern/diff/D60927756) [ghstack-poisoned]
…to a new class" Depends on whether parallel or sequential prefill is chosen, prefill() calls `TextDecoderRunner.step()` to prefill prompt tokens to LLM. Differential Revision: [D60927756](https://our.internmc.facebook.com/intern/diff/D60927756) [ghstack-poisoned]
Depends on whether parallel or sequential prefill is chosen, prefill() calls `TextDecoderRunner.step()` to prefill prompt tokens to LLM. Differential Revision: [D60927756](https://our.internmc.facebook.com/intern/diff/D60927756) [ghstack-poisoned]
So that it can be reused [ghstack-poisoned]
So that it can be reused Differential Revision: [D60938984](https://our.internmc.facebook.com/intern/diff/D60938984) [ghstack-poisoned]
So that it can be reused Differential Revision: [D60938984](https://our.internmc.facebook.com/intern/diff/D60938984) [ghstack-poisoned]
Differential Revision: D60938984 Pull Request resolved: #4588
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/4643
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New FailureAs of commit e79c362 with merge base 92edd04 ( NEW FAILURE - The following job has failed:
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