295 lines
11 KiB
Python
295 lines
11 KiB
Python
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
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import math
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import torch
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import logging
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from typing import Literal, overload, Optional, Tuple
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from torch import Tensor, nn
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from transformers.cache_utils import Cache
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from transformers import LlamaModel, LlamaConfig, LlamaForCausalLM
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from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb, repeat_kv
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_logger = logging.getLogger(__name__)
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AVAILABLE_ATTENTIONS = []
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try:
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from transformers.utils import is_flash_attn_2_available
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if is_flash_attn_2_available():
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AVAILABLE_ATTENTIONS.append("flash_attention_2")
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except Exception as e:
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_logger.warning(f"Error while querying for `flash_attention_2` support: {str(e)}")
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try:
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from .attention.fused import attention as _fused_attention
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def fused_attn_func(q, k, v, softmax_scale=None, causal=False, *args, **kwargs):
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return _fused_attention( q, k, v, causal, softmax_scale )
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AVAILABLE_ATTENTIONS.append("fused_attn")
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except Exception as e:
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_logger.warning(f"Error while querying for `fused_attn` support: {str(e)}")
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is_rocm = any("AMD" in torch.cuda.get_device_properties(i).name for i in range(torch.cuda.device_count()))
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is_ampere_or_newer_gpu = any(torch.cuda.get_device_properties(i).major >= 8 for i in range(torch.cuda.device_count()))
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try:
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if is_rocm:
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# requires pain to set up on Navi3, and for no backwards (training) support
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from flash_attn import flash_attn_func
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AVAILABLE_ATTENTIONS.append("flash_attn")
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elif not is_ampere_or_newer_gpu:
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# Uses https://github.com/ZRayZzz/flash-attention-v100/
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# Currently doesn't work because it's hard-coded to use a head dim of 128, will throw NaNs otherwise...
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from flash_attn_v100 import flash_attn_func as flash_attn_v100_func
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AVAILABLE_ATTENTIONS.append("flash_attn")
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AVAILABLE_ATTENTIONS.append("flash_attn_v100") # needed to signal to use padding
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def flash_attn_func(q, k, v, softmax_scale=None, causal=False, *args, **kwargs):
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return flash_attn_v100_func(
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q,
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k,
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v,
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softmax_scale,
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causal
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)
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else:
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# Borrowed from https://github.com/turboderp/exllamav2/blob/master/exllamav2/attn.py#L32
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# Adapted to provide flash_attn_v1 support
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import flash_attn
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flash_attn_ver = [int(t) for t in flash_attn.__version__.split(".") if t.isdigit()]
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if flash_attn_ver <= [1, 0, 9]:
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AVAILABLE_ATTENTIONS.append("flash_attn")
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from flash_attn.flash_attn_interface import flash_attn_unpadded_func
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from einops import rearrange
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# converts the flash_attn_2 calling convention to flash_attn_1's
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def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False, deterministic=False, *args, **kwargs):
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batch_size, seqlen_q = q.shape[0], q.shape[1]
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seqlen_k = k.shape[1]
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q, k, v = [rearrange(x, 'b s ... -> (b s) ...').contiguous() for x in [q, k, v]]
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cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, device=q.device)
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cu_seqlens_k = cu_seqlens_q
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return flash_attn_unpadded_func(
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q, k, v,
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cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
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dropout_p, softmax_scale, causal, return_attn_probs, deterministic
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)
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has_flash_attn = True
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elif [2, 2, 1] <= flash_attn_ver < [2, 5, 7]:
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AVAILABLE_ATTENTIONS.append("flash_attn")
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from flash_attn import flash_attn_func
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has_flash_attn = True
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elif [2, 5, 7] <= flash_attn_ver:
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AVAILABLE_ATTENTIONS.append("flash_attn")
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from flash_attn import flash_attn_func, flash_attn_with_kvcache
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signature = list(inspect.signature(flash_attn_func).parameters)
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has_flash_attn_with_window = "window_size" in signature
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has_flash_attn_with_softcap = "softcap" in signature
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import flash_attn_2_cuda as flash_attn_cuda
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has_flash_attn = True
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has_flash_attn_with_paged = True
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except Exception as e:
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_logger.warning(f"Error while querying for `flash_attn` support: {str(e)}")
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try:
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from xformers.ops.fmha import memory_efficient_attention
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from xformers.ops.fmha.attn_bias import LowerTriangularFromBottomRightMask, LowerTriangularMask
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AVAILABLE_ATTENTIONS.append("xformers")
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except Exception as e:
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_logger.warning(f"Error while importing `xformers`: {str(e)}")
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# to-do: find a better way to query for if there's available kernels since these return true regardless
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if torch.backends.cuda.flash_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("flash")
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if torch.backends.cuda.mem_efficient_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("mem_efficient")
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if torch.backends.cuda.math_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("math")
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if torch.backends.cuda.cudnn_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("cudnn")
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if AVAILABLE_ATTENTIONS:
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AVAILABLE_ATTENTIONS.append("sdpa")
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class LlamaAttention_Adapted(LlamaAttention):
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def __init__(self, *args, **kwargs):
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if 'mode' in kwargs:
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self.mode = kwargs['mode']
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kwargs.pop("mode")
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else:
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self.mode = "math"
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if self.mode == "math":
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self.mode = torch.nn.attention.SDPBackend.MATH
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elif self.mode == "mem_efficient":
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self.mode = torch.nn.attention.SDPBackend.EFFICIENT_ATTENTION
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elif self.mode == "flash":
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self.mode = torch.nn.attention.SDPBackend.FLASH_ATTENTION
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elif self.mode == "cudnn":
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self.mode = torch.nn.attention.SDPBackend.CUDNN_ATTENTION
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super().__init__(*args, **kwargs)
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# Adapted from LlamaAttention.forward
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if output_attentions:
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# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
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return super().forward(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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)
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dropout_rate = self.attention_dropout if self.training else 0.0
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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if position_embeddings is None:
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cos, sin = self.rotary_emb(value_states, position_ids)
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else:
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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if self.mode in ["xformers", "flash_attn"]:
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# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
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# to be able to avoid many of these transpose/reshape/view.
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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"""
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# In PEFT, usually we cast the layer norms in float32 for training stability reasons
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# therefore the input hidden states gets silently casted in float32. Hence, we need
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# cast them back in the correct dtype just to be sure everything works as expected.
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# This might slowdown training & inference so it is recommended to not cast the LayerNorms
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# in fp32. (LlamaRMSNorm handles it correctly)
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input_dtype = query_states.dtype
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if input_dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = torch.get_autocast_gpu_dtype()
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# Handle the case where the model is quantized
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elif hasattr(self.config, "_pre_quantization_dtype"):
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target_dtype = self.config._pre_quantization_dtype
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else:
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target_dtype = self.q_proj.weight.dtype
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query_states = query_states.to(target_dtype)
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key_states = key_states.to(target_dtype)
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value_states = value_states.to(target_dtype)
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"""
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if self.mode == "flash_attn":
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attn_output = flash_attn_func(
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query_states,
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key_states,
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value_states,
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causal=True,
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softmax_scale=1.0 / math.sqrt(self.head_dim),
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dropout_p=dropout_rate,
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)
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attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
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elif self.mode == "xformers":
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attn_output = memory_efficient_attention(
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query_states,
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key_states,
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value_states,
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attn_bias = LowerTriangularMask() if attention_mask is None or attention_mask[0, 0, 0, 1] == 0 else None,
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scale = 1.0 / math.sqrt(self.head_dim),
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p=dropout_rate
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)
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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return attn_output, None, past_key_value
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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causal_mask = attention_mask
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if attention_mask is not None:
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causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
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# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
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# Reference: https://github.com/pytorch/pytorch/issues/112577.
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if query_states.device.type == "cuda" and causal_mask is not None:
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query_states = query_states.contiguous()
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key_states = key_states.contiguous()
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value_states = value_states.contiguous()
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# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
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# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
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is_causal = True if causal_mask is None and q_len > 1 else False
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if self.mode in ["fused_attn"]:
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attn_output = fused_attn_func(
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query_states,
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key_states,
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value_states,
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causal=True,
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softmax_scale=1.0 / math.sqrt(self.head_dim),
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dropout_p=dropout_rate,
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)
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else:
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with torch.nn.attention.sdpa_kernel(self.mode):
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attn_output = torch.nn.functional.scaled_dot_product_attention(
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query_states,
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key_states,
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value_states,
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attn_mask=causal_mask,
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dropout_p=dropout_rate,
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is_causal=is_causal,
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.view(bsz, q_len, -1)
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attn_output = self.o_proj(attn_output)
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return attn_output, None, past_key_value |