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# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Optional
import torch
import torch.distributed as dist
import torch.nn.functional as F
from mmengine import MessageHub
from transformers.cache_utils import Cache
from xtuner.model.transformers_models.deepseek_v2.modeling_deepseek import \
apply_rotary_pos_emb
from xtuner.parallel.sequence import (get_sequence_parallel_world_size,
post_process_for_sequence_parallel_attn,
pre_process_for_sequence_parallel_attn)
from .attention import flash_attn_wo_mask, varlen_flash_attn
def deepseek_attn_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
):
# DeepseekV2FlashAttention2 attention does not support output_attentions
if 'padding_mask' in kwargs:
warnings.warn(
'Passing `padding_mask` is deprecated and will be removed in '
'v4.37. Please make sure use `attention_mask` instead.`')
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop('padding_mask')
output_attentions = False
bsz, q_len, _ = hidden_states.size()
if self.q_lora_rank is None:
q = self.q_proj(hidden_states)
else:
q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
q_nope, q_pe = torch.split(
q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
compressed_kv, k_pe = torch.split(
compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
kv = (
self.kv_b_proj(self.kv_a_layernorm(compressed_kv)).view(
bsz, q_len, self.num_heads,
self.qk_nope_head_dim + self.v_head_dim).transpose(1, 2))
k_nope, value_states = torch.split(
kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
kv_seq_len = value_states.shape[-2]
kv_seq_len = value_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value.get_usable_length(kv_seq_len,
self.layer_idx)
assert position_ids is not None, '`position_ids` should not be None.'
if self.training:
cos, sin = self.rotary_emb(
value_states, seq_len=position_ids.max() + 1)
else:
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
query_states[:, :, :, :self.qk_nope_head_dim] = q_nope
query_states[:, :, :, self.qk_nope_head_dim:] = q_pe
key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
key_states[:, :, :, :self.qk_nope_head_dim] = k_nope
key_states[:, :, :, self.qk_nope_head_dim:] = k_pe
if self.q_head_dim != self.v_head_dim:
value_states = F.pad(value_states,
[0, self.q_head_dim - self.v_head_dim])
if past_key_value is not None:
cache_kwargs = {'sin': sin, 'cos': cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs)
# Reashape to the expected shape for Flash Attention
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training
# stability reasons therefore the input hidden states gets silently
# casted in float32. Hence, we need cast them back in the correct dtype
# just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not
# cast the LayerNorms in fp32. (DeepseekV2RMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
# Handle the case where the model is quantized
if hasattr(self.config, '_pre_quantization_dtype'):
target_dtype = self.config._pre_quantization_dtype
elif torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
else:
target_dtype = self.q_a_proj.weight.dtype
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
enable_sequence_parallel = (
dist.is_initialized() and get_sequence_parallel_world_size() > 1
and self.training)
if enable_sequence_parallel:
query_states, key_states, value_states = \
pre_process_for_sequence_parallel_attn(
query_states, key_states, value_states)
attn_output = self._flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
query_states.shape[1],
dropout=dropout_rate,
softmax_scale=self.softmax_scale,
)
if enable_sequence_parallel:
attn_output = post_process_for_sequence_parallel_attn(attn_output)
if self.q_head_dim != self.v_head_dim:
attn_output = attn_output[:, :, :, :self.v_head_dim]
attn_output = attn_output.reshape(bsz, q_len, self.num_heads *
self.v_head_dim).contiguous()
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def deepseek_varlen_attn_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
):
is_training = self.training
message_hub = MessageHub.get_instance('varlen_attn_args')
rank = dist.get_rank()
cumulative_len = message_hub.get_info(f'cumulative_len_rank_{rank}')
max_seqlen = message_hub.get_info(f'max_seqlen_rank_{rank}')
assert is_training == (cumulative_len is not None) == (
past_key_value is None)
output_attentions = False
bsz, q_len, _ = hidden_states.size()
if self.q_lora_rank is None:
q = self.q_proj(hidden_states)
else:
q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
q_nope, q_pe = torch.split(
q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
compressed_kv, k_pe = torch.split(
compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
kv = (
self.kv_b_proj(self.kv_a_layernorm(compressed_kv)).view(
bsz, q_len, self.num_heads,
self.qk_nope_head_dim + self.v_head_dim).transpose(1, 2))
k_nope, value_states = torch.split(
kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
kv_seq_len = value_states.shape[-2]
kv_seq_len = value_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value.get_usable_length(kv_seq_len,
self.layer_idx)
assert position_ids is not None, '`position_ids` should not be None.'
if self.training:
cos, sin = self.rotary_emb(
value_states, seq_len=position_ids.max() + 1)
else:
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
query_states[:, :, :, :self.qk_nope_head_dim] = q_nope
query_states[:, :, :, self.qk_nope_head_dim:] = q_pe
key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
key_states[:, :, :, :self.qk_nope_head_dim] = k_nope
key_states[:, :, :, self.qk_nope_head_dim:] = k_pe
if self.q_head_dim != self.v_head_dim:
value_states = F.pad(value_states,
[0, self.q_head_dim - self.v_head_dim])
if past_key_value is not None:
cache_kwargs = {'sin': sin, 'cos': cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs)
# In PEFT, usually we cast the layer norms in float32 for training
# stability reasons therefore the input hidden states gets silently
# casted in float32. Hence, we need cast them back in the correct dtype
# just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not
# cast the LayerNorms in fp32. (DeepseekV2RMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
# Handle the case where the model is quantized
if hasattr(self.config, '_pre_quantization_dtype'):
target_dtype = self.config._pre_quantization_dtype
elif torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
else:
target_dtype = self.q_a_proj.weight.dtype
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
# Reashape to the expected shape for Flash Attention
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
# ----------------- varlen flash attention forward ----------------------#
dropout_rate = self.attention_dropout if self.training else 0.0
if not self._flash_attn_uses_top_left_mask:
causal = self.is_causal
else:
causal = self.is_causal and q_len != 1
if is_training:
attn_output = varlen_flash_attn(
query_states,
key_states,
value_states,
cumulative_len,
max_seqlen,
causal=causal,
dropout_p=dropout_rate,
training=True)
else:
attn_output = flash_attn_wo_mask(
query_states,
key_states,
value_states,
causal=causal,
dropout_p=dropout_rate,
training=False)
# ---------------- varlen flash attention forward end ------------------ #
if self.q_head_dim != self.v_head_dim:
attn_output = attn_output[:, :, :, :self.v_head_dim]
attn_output = attn_output.reshape(bsz, q_len,
self.num_heads * self.v_head_dim)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
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