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feat(server): batch tokenization for flash causal lm
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@ -79,6 +79,16 @@ class FlashCausalLMBatch(Batch):
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dtype: torch.dtype,
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dtype: torch.dtype,
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device: torch.device,
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device: torch.device,
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) -> "FlashCausalLMBatch":
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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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position_ids = []
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cu_seqlens = [0]
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cu_seqlens = [0]
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max_seqlen = 0
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max_seqlen = 0
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@ -106,13 +116,11 @@ class FlashCausalLMBatch(Batch):
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max_length = 0
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max_length = 0
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# Parse batch
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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(zip(pb.requests, batch_tokenized_inputs)):
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# request id -> idx in list mapping
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# request id -> idx in list mapping
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requests_idx_mapping[r.id] = i
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requests_idx_mapping[r.id] = i
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tokenized_input = tokenizer(
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tokenized_input = tokenized_input[-r.truncate:]
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r.inputs, truncation=True, max_length=r.truncate
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)["input_ids"]
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input_length = len(tokenized_input)
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input_length = len(tokenized_input)
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max_seqlen = max(max_seqlen, input_length)
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max_seqlen = max(max_seqlen, input_length)
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