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# Copyright 2022 MosaicML LLM Foundry authors
# SPDX-License-Identifier: Apache-2.0
# This file is copied and modified from
# https://github.com/huggingface/transformers/blob/fe3c8ab1af558b95f67f5fafc0c55f09fd2b09db/src/transformers/models/llama/modeling_llama.py
# See the clearly denoted code blocks for the main modifications (there are a few others like type ignores, and error messages)
import logging
from typing import Callable, Optional, Tuple
import torch
import torch.nn.functional as F
from transformers.models.llama.modeling_llama import LlamaAttention
from llmfoundry.models.layers.attention import (
scaled_multihead_dot_product_attention, triton_flash_attn_fn)
log = logging.getLogger(__name__)
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""Equivalent of torch.repeat_interleave(x, dim=1,
repeats=n_rep).
The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to
(batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :,
None, :, :].expand(batch, num_key_value_heads,
n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen,
head_dim)
def rotate_half(x: torch.Tensor) -> torch.Tensor:
"""Rotates half the hidden dims of the input."""
x1 = x[..., :x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(
q: torch.Tensor,
k: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
position_ids: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
def get_llama_attention_patch_fn(patch_fn_name: str = 'torch') -> Callable:
if patch_fn_name == 'torch':
return llama_attention_patch_torch
elif patch_fn_name == 'triton':
return llama_attention_patch_triton
else:
raise ValueError(
f'Unrecognized llama attention patch function: {patch_fn_name}')
def llama_attention_patch_torch(
self: LlamaAttention,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = 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]]]:
if use_cache:
raise NotImplementedError(
'use_cache is not yet supported when patching Llama attention.')
bsz, q_len, _ = hidden_states.size()
if self.config.pretraining_tp > 1:
key_value_slicing = (self.num_key_value_heads *
self.head_dim) // self.config.pretraining_tp
query_slices = self.q_proj.weight.split(
(self.num_heads * self.head_dim) // self.config.pretraining_tp,
dim=0)
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
query_states = [
F.linear(hidden_states, query_slices[i])
for i in range(self.config.pretraining_tp)
]
query_states = torch.cat(query_states, dim=-1)
key_states = [
F.linear(hidden_states, key_slices[i])
for i in range(self.config.pretraining_tp)
]
key_states = torch.cat(key_states, dim=-1)
value_states = [
F.linear(hidden_states, value_slices[i])
for i in range(self.config.pretraining_tp)
]
value_states = torch.cat(value_states, dim=-1)
else:
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads,
self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads,
self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
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)
### MAIN MODIFICATIONS START HERE ###
query_states = query_states.transpose(1, 2).view(
bsz, q_len, self.num_heads * self.head_dim)
key_states = key_states.transpose(1, 2).view(
bsz, q_len, self.num_key_value_heads * self.head_dim)
value_states = value_states.transpose(1, 2).view(
bsz, q_len, self.num_key_value_heads * self.head_dim)
attn_output, attn_weights, _ = scaled_multihead_dot_product_attention(
query=query_states,
key=key_states,
value=value_states,
n_heads=self.num_heads,
kv_n_heads=self.num_key_value_heads,
past_key_value=None,
softmax_scale=None,
attn_bias=attention_mask,
key_padding_mask=None,
is_causal=False, # The causal mask is propagated from LLamaForCausalLM
dropout_p=0,
training=self.training,
needs_weights=False,
)
### MAIN MODIFICATIONS END HERE ###
if self.config.pretraining_tp > 1:
attn_output = attn_output.split(self.hidden_size //
self.config.pretraining_tp,
dim=2)
o_proj_slices = self.o_proj.weight.split(self.hidden_size //
self.config.pretraining_tp,
dim=1)
attn_output = sum([
F.linear(attn_output[i], o_proj_slices[i])
for i in range(self.config.pretraining_tp)
])
else:
attn_output = self.o_proj(attn_output)
assert isinstance(attn_output, torch.Tensor)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, None
def llama_attention_patch_triton(
self: LlamaAttention,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = 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]]]:
if use_cache:
raise NotImplementedError(
'use_cache is not yet supported when patching Llama attention.')
# output_attentions is not support for triton attention
if output_attentions:
raise NotImplementedError(
'output_attentions is not supported when patching Llama attention with triton attention.'
)
bsz, q_len, _ = hidden_states.size()
if self.config.pretraining_tp > 1:
key_value_slicing = (self.num_key_value_heads *
self.head_dim) // self.config.pretraining_tp
query_slices = self.q_proj.weight.split(
(self.num_heads * self.head_dim) // self.config.pretraining_tp,
dim=0)
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
query_states = [
F.linear(hidden_states, query_slices[i])
for i in range(self.config.pretraining_tp)
]
query_states = torch.cat(query_states, dim=-1)
key_states = [
F.linear(hidden_states, key_slices[i])
for i in range(self.config.pretraining_tp)
]
key_states = torch.cat(key_states, dim=-1)
value_states = [
F.linear(hidden_states, value_slices[i])
for i in range(self.config.pretraining_tp)
]
value_states = torch.cat(value_states, dim=-1)
else:
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads,
self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads,
self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
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)
### MAIN MODIFICATIONS START HERE ###
query_states = query_states.transpose(1, 2).view(
bsz, q_len, self.num_heads * self.head_dim)
key_states = key_states.transpose(1, 2).view(
bsz, q_len, self.num_key_value_heads * self.head_dim)
value_states = value_states.transpose(1, 2).view(
bsz, q_len, self.num_key_value_heads * self.head_dim)
attn_output, _, _ = triton_flash_attn_fn(
query=query_states,
key=key_states,
value=value_states,
n_heads=self.num_heads,
kv_n_heads=self.num_key_value_heads,
past_key_value=None,
softmax_scale=None,
attn_bias=attention_mask,
key_padding_mask=None,
is_causal=False, # The causal mask is propagated from LLamaForCausalLM
dropout_p=0,
training=self.training,
needs_weights=False,
)
### MAIN MODIFICATIONS END HERE ###
if self.config.pretraining_tp > 1:
attn_output = attn_output.split(self.hidden_size //
self.config.pretraining_tp,
dim=2)
o_proj_slices = self.o_proj.weight.split(self.hidden_size //
self.config.pretraining_tp,
dim=1)
attn_output = sum([
F.linear(attn_output[i], o_proj_slices[i])
for i in range(self.config.pretraining_tp)
])
else:
attn_output = self.o_proj(attn_output)
assert isinstance(attn_output, torch.Tensor)
return attn_output, None, None
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