feat(server): batch tokenization for flash causal lm (#411)
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@ -79,6 +79,16 @@ class FlashCausalLMBatch(Batch):
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dtype: torch.dtype,
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device: torch.device,
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) -> "FlashCausalLMBatch":
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batch_inputs = []
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max_truncation = 0
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for r in pb.requests:
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batch_inputs.append(r.inputs)
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max_truncation = max(max_truncation, r.truncate)
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batch_tokenized_inputs = tokenizer(
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batch_inputs, truncation=True, max_length=max_truncation
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)["input_ids"]
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position_ids = []
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cu_seqlens = [0]
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max_seqlen = 0
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@ -106,13 +116,13 @@ class FlashCausalLMBatch(Batch):
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max_length = 0
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# Parse batch
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for i, r in enumerate(pb.requests):
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for i, (r, tokenized_input) in enumerate(
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zip(pb.requests, batch_tokenized_inputs)
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):
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# request id -> idx in list mapping
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requests_idx_mapping[r.id] = i
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tokenized_input = tokenizer(
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r.inputs, truncation=True, max_length=r.truncate
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)["input_ids"]
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tokenized_input = tokenized_input[-r.truncate :]
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input_length = len(tokenized_input)
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max_seqlen = max(max_seqlen, input_length)
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@ -134,7 +134,7 @@ def download_weights(
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) -> List[Path]:
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"""Download the safetensors files from the hub"""
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def download_file(filename, tries=5):
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def download_file(filename, tries=5, backoff: int = 5):
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local_file = try_to_load_from_cache(model_id, revision, filename)
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if local_file is not None:
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logger.info(f"File {filename} already present in cache.")
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@ -158,6 +158,8 @@ def download_weights(
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if i + 1 == tries:
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raise e
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logger.error(e)
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logger.info(f"Retrying in {backoff} seconds")
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time.sleep(backoff)
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logger.info(f"Retry {i + 1}/{tries - 1}")
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# We do this instead of using tqdm because we want to parse the logs with the launcher
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