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test / src /llama_flash_attn_monkey_patch.py

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	from typing import List, Optional, Tuple

	import torch

	import transformers
	from transformers.models.llama.modeling_llama import apply_rotary_pos_emb

	from einops import rearrange

	from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
	from flash_attn.bert_padding import unpad_input, pad_input


	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""Input shape: Batch x Time x Channel
	attention_mask: [bsz, q_len]
	"""
	bsz, q_len, _ = hidden_states.size()

	query_states = (
	self.q_proj(hidden_states)
	.view(bsz, q_len, self.num_heads, self.head_dim)
	.transpose(1, 2)
	)
	key_states = (
	self.k_proj(hidden_states)
	.view(bsz, q_len, self.num_heads, self.head_dim)
	.transpose(1, 2)
	)
	value_states = (
	self.v_proj(hidden_states)
	.view(bsz, q_len, self.num_heads, self.head_dim)
	.transpose(1, 2)
	)
	# [bsz, q_len, nh, hd]
	# [bsz, nh, q_len, hd]

	kv_seq_len = key_states.shape[-2]
	assert past_key_value is None, "past_key_value is not supported"

	cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
	query_states, key_states = apply_rotary_pos_emb(
	query_states, key_states, cos, sin, position_ids
	)
	# [bsz, nh, t, hd]
	assert not output_attentions, "output_attentions is not supported"
	assert not use_cache, "use_cache is not supported"

	# Flash attention codes from
	# https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py

	# transform the data into the format required by flash attention
	qkv = torch.stack(
	[query_states, key_states, value_states], dim=2
	) # [bsz, nh, 3, q_len, hd]
	qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd]
	# We have disabled _prepare_decoder_attention_mask in LlamaModel
	# the attention_mask should be the same as the key_padding_mask
	key_padding_mask = attention_mask

	if key_padding_mask is None:
	qkv = rearrange(qkv, "b s ... -> (b s) ...")
	max_s = q_len
	cu_q_lens = torch.arange(
	0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device
	)
	output = flash_attn_unpadded_qkvpacked_func(
	qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
	)
	output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
	else:
	nheads = qkv.shape[-2]
	x = rearrange(qkv, "b s three h d -> b s (three h d)")
	x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
	x_unpad = rearrange(
	x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads
	)
	output_unpad = flash_attn_unpadded_qkvpacked_func(
	x_unpad, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
	)
	output = rearrange(
	pad_input(
	rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, bsz, q_len
	),
	"b s (h d) -> b s h d",
	h=nheads,
	)
	return self.o_proj(rearrange(output, "b s h d -> b s (h d)")), None, None


	# Disable the transformation of the attention mask in LlamaModel as the flash attention
	# requires the attention mask to be the same as the key_padding_mask
	def _prepare_decoder_attention_mask(
	self, attention_mask, input_shape, inputs_embeds, past_key_values_length
	):
	# [bsz, seq_len]
	return attention_mask


	def replace_llama_attn_with_flash_attn():
	print("Replacing original LLaMa attention with flash attention", flush=True)
	transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (
	_prepare_decoder_attention_mask
	)
	transformers.models.llama.modeling_llama.LlamaAttention.forward = forward