feat: use dynamic batching when generating #9
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This will increment the number of batches dynamically when you call prefill, and it will reduce the number of batches only when prefill is called again. The intention is to avoid useless recompilation (keeping batch size the same as long as possible). Also note that if a slot was removed, the whole KV cache should be rebuilt, and for now we do not do that.
Since batch size is now dynamic, expected results are now different, so they are updated accordingly.
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Now it is only useful for warmup, since dynamic batching is being used.
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| SEQUENCE_LENGTH = 1024 | ||
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| @pytest.fixture(scope="module") | ||
| def model_name_or_path(): | ||
| os.environ["HF_BATCH_SIZE"] = str(BATCH_SIZE) |
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Maybe we should keep a HF_MAX_BATCH_SIZE somewhere?
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as mentioned below, maybe we'll use it later
| @@ -312,7 +312,10 @@ def __init__( | |||
| tokenizer.padding_side = "left" | |||
| self.tokenizer = tokenizer | |||
| self.special_tokens = self.tokenizer.all_special_ids | |||
| self.slots = [Slot(i, tokenizer, self.model.device) for i in range(self.model.config.batch_size)] | |||
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If we introduce the HF_MAX_BATCH_SIZE maybe we can initialize this list, wdyt?
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I think I need to better understand the usage of the batches, if it can be increased/reduced often, how this affects performance. At that point it will be easier to think about a reasonable algorithm to reduce compilation and batch change overhead as much as possible.
| @@ -350,13 +353,18 @@ def warmup(self, batch: Batch) -> int: | |||
| The maximum number of tokens the model supports. | |||
| """ | |||
| # Just check that the warmup request parameters match the model capacity | |||
| batch_size = self.model.config.batch_size | |||
| # NOTE: later self.model.config.batch_size might become self.model.config.max_batch_size. | |||
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Ok maybe you want to keep it for later 🤗
| # Assign each request to an empty slot | ||
| logger.debug(f"Prefilling {len(batch.requests)} new request(s) with {len(empty_slots)} empty slot(s)") | ||
| logger.debug(f"Prefilling {len(batch.requests)} new request(s) adding to {len(active_slots)} active slot(s)") | ||
| new_slots = [] |
| slot.assign(request, self.model.generation_config) | ||
| new_slots.append(slot) |
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Same comment about new_slots
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same answer, I'll remove it
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Just to be sure: In this PR we are not limiting the maximum batch size the server can handle? If so, can we impl this in a following PR? |
What does this PR do?
This will increment the number of batches dynamically when you call prefill, and it will reduce the number of batches only when prefill is called again.
The intention is to avoid useless recompilation (keeping batch size the same as long as possible).