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cleanup comment
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@ -6,7 +6,6 @@ from typing import Dict, Optional
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from text_generation_server.utils.log import log_master
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REQUEST_LOGPROBS = os.getenv("REQUEST_LOGPROBS", "0").lower() in {"1", "true"}
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ATTENTION = os.environ["ATTENTION"]
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# default_prefix_caching = "1" if ATTENTION in {"flashinfer", "flashdecoding"} else "0"
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PREFIX_CACHING = os.environ["PREFIX_CACHING"].lower() in {
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@ -36,12 +36,11 @@ def tgi_flash_attention_forward(
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softcap: Optional[float] = None,
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**kwargs, # This is needed to "absorb" other args passed by Transformers modeling
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):
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# from pdb import set_trace; set_trace()
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kv_cache = kv_cache[module.layer_idx]
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query_states = query_states.transpose(1, 2).squeeze(dim=0)
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key_states = key_states.transpose(1, 2).squeeze(dim=0)
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value_states = value_states.transpose(1, 2).squeeze(dim=0)
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# from pdb import set_trace; set_trace()
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# Take care of updating the cache in-place
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kv_cache.store(key=key_states, value=value_states, slots=slots, kv_scales=kv_scales)
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@ -49,7 +48,6 @@ def tgi_flash_attention_forward(
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_, num_heads, head_dim = query_states.shape
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softmax_scale = 1 / math.sqrt(head_dim) if softmax_scale is None else softmax_scale
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sliding_window = -1 if sliding_window is None else sliding_window
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# from pdb import set_trace; set_trace()
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if cu_seqlen_prefill is not None:
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attn_output = attention(
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@ -108,7 +106,6 @@ class TransformersFlashCausalLM(FlashCausalLM):
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tokenizer_class=AutoTokenizer,
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kv_cache_dtype: Optional[torch.dtype] = None,
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):
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# # from pdb import set_trace; set_trace()
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self.quantize = quantize
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self.process_group, rank, world_size = initialize_torch_distributed()
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@ -266,7 +263,6 @@ class TransformersFlashCausalLM(FlashCausalLM):
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prefill_cache_indices=None, # not used, but passed to match original signature
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adapter_data=None, # not supported, but passed to match original signature
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):
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# from pdb import set_trace; set_trace()
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# A value of `None` (i.e. no logit slicing) translates to `0` in Transformers
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logits_to_keep = lm_head_indices if lm_head_indices is not None else 0
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