Much simpler logic after the overhead.
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849d8821ab
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@ -1397,34 +1397,6 @@ class FlashCausalLM(Model):
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cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size
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total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size
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if max_total_tokens is None:
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if get_support_chunking():
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model_max_length = self.tokenizer.model_max_length
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free_memory = get_free_memory(self.device, MEMORY_FRACTION)
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spare_blocks = (
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# Leave 5% for some wiggle room
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int((free_memory * TGI_WIGGLE_ROOM) // total_cache_size)
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+ batch.num_blocks
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)
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spare_blocks = small_power_of_2(spare_blocks)
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available_blocks = min(model_max_length, spare_blocks)
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batch.num_blocks = available_blocks
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batch.max_blocks = available_blocks
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max_input_tokens = (
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available_blocks - 1
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if max_input_tokens is None
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else max_input_tokens
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)
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max_total_tokens = available_blocks
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else:
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max_total_tokens = sum(len(input_ids) for input_ids in batch.input_ids)
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max_input_tokens = (
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max_total_tokens - 1
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if max_input_tokens is None
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else max_input_tokens
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)
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try:
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self.init_kv_cache(
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batch.num_blocks,
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@ -1459,8 +1431,27 @@ class FlashCausalLM(Model):
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)
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log_master(logger.info, f"KV-cache blocks: {num_blocks}, size: {BLOCK_SIZE}")
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if max_total_tokens is None:
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if get_support_chunking():
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model_max_length = self.tokenizer.model_max_length
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max_input_tokens = (
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min((num_blocks * BLOCK_SIZE - 1), model_max_length)
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if max_input_tokens is None
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else max_input_tokens
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)
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max_total_tokens = num_blocks * BLOCK_SIZE
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else:
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max_total_tokens = sum(len(input_ids) for input_ids in batch.input_ids)
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max_input_tokens = (
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max_total_tokens - 1
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if max_input_tokens is None
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else max_input_tokens
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)
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del batch
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self.kv_cache = []
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empty_cache()
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self.init_kv_cache(
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num_blocks,
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