mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2025-09-06 04:30:03 +00:00
457 lines
No EOL
24 KiB
Python
457 lines
No EOL
24 KiB
Python
'''
|
|
Description :
|
|
Author : Boxin Zhang
|
|
Version : 0.2.5
|
|
Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
|
|
'''
|
|
import torch
|
|
from torch import nn
|
|
from ktransformers.models.modeling_deepseek import DeepseekV2Attention, apply_rotary_pos_emb
|
|
from ktransformers.models.modeling_qwen2_moe import Qwen2MoeAttention
|
|
from ktransformers.models.modeling_qwen3_moe import Qwen3MoeAttention
|
|
from typing import Optional, Tuple
|
|
from ktransformers.operators.base_operator import BaseInjectedModule
|
|
from ktransformers.util.custom_loader import GGUFLoader
|
|
import logging
|
|
from transformers.configuration_utils import PretrainedConfig
|
|
from flashinfer import BatchMLAPagedAttentionWrapper
|
|
from ktransformers.operators.flashinfer_batch_prefill_wrapper import flashInferAttn
|
|
from ktransformers.models.custom_cache import KDeepSeekV3Cache, KGQACache
|
|
logger = logging.getLogger("attention")
|
|
|
|
# Copied from transformers.models.llama.modeling_llama.rotate_half
|
|
def rotate_half(x):
|
|
"""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)
|
|
|
|
class flashinfer_attn(BaseInjectedModule, DeepseekV2Attention):
|
|
def __init__(self,
|
|
key: str,
|
|
gguf_loader : GGUFLoader,
|
|
config: PretrainedConfig,
|
|
orig_module: nn.Module,
|
|
prefill_device: str = "cuda",
|
|
generate_device: str = "cuda",
|
|
chunck_size: int = 1000,
|
|
**kwargs):
|
|
BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
|
|
self.orig_module.__init__(orig_module.config,
|
|
orig_module.layer_idx)
|
|
self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
|
|
|
|
|
|
def get_absorbed(self) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
if not (hasattr(self, 'q_absorb') and hasattr(self, 'out_absorb')):
|
|
kv_b_proj = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
|
|
q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :].reshape(-1, self.kv_lora_rank)
|
|
out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :].reshape(-1, self.kv_lora_rank)
|
|
self.q_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.qk_nope_head_dim,
|
|
bias=False, dtype=q_absorb.dtype, device=q_absorb.device)
|
|
self.q_absorb.weight.data = q_absorb
|
|
self.out_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.v_head_dim,
|
|
bias=False, dtype=out_absorb.dtype, device=out_absorb.device)
|
|
self.out_absorb.weight.data = out_absorb
|
|
#del self.orig_module.kv_b_proj
|
|
q_absorb = self.q_absorb.weight.view(self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank)
|
|
out_absorb = self.out_absorb.weight.view(self.num_heads, self.v_head_dim, self.kv_lora_rank)
|
|
return q_absorb, out_absorb
|
|
|
|
|
|
def forward(self,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: KDeepSeekV3Cache,
|
|
position_ids: torch.Tensor,
|
|
wrapper: BatchMLAPagedAttentionWrapper,
|
|
num_tokens_tensors: torch.Tensor,
|
|
page_idx: torch.Tensor,
|
|
page_offset: torch.Tensor,
|
|
):
|
|
q_len, _ = hidden_states.size()
|
|
|
|
if self.q_lora_rank is None:
|
|
q = self.q_proj(hidden_states, num_tokens_tensors)
|
|
else:
|
|
q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states, num_tokens_tensors), num_tokens_tensors), num_tokens_tensors)
|
|
q = q.view(q_len, self.num_heads, self.q_head_dim)
|
|
q_nope, q_pe = torch.split(
|
|
q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
|
|
)
|
|
|
|
compressed_kv = self.kv_a_proj_with_mqa(hidden_states, num_tokens_tensors)
|
|
compressed_kv, k_pe = torch.split(
|
|
compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
|
|
)
|
|
compressed_kv = compressed_kv.contiguous()
|
|
compressed_kv = self.kv_a_layernorm(compressed_kv, num_tokens_tensors)
|
|
k_pe = k_pe.view(q_len, 1, self.qk_rope_head_dim)
|
|
compressed_kv = compressed_kv.view(q_len, 1, self.kv_lora_rank)
|
|
|
|
cos, sin = self.rotary_emb(q_pe, position_ids.unsqueeze(0))
|
|
q_pe, k_pe = apply_rotary_pos_emb(q_pe.unsqueeze(0), k_pe.unsqueeze(0), cos, sin, unsqueeze_dim=2)
|
|
q_pe = q_pe.squeeze(0)
|
|
if kv_cache is not None:
|
|
|
|
# page_idx, page_offset = kv_cache.get_page_table(position_ids, q_indptr, kv_indptr, kv_indices)
|
|
cache_kwargs = {"sin": sin, "cos": cos, "page_idx": page_idx, "page_offset": page_offset} # Specific to RoPE models
|
|
compressed_kv_with_k_pe = kv_cache.update(compressed_kv.unsqueeze(0), k_pe, self.layer_idx, page_idx, page_offset, cache_kwargs)
|
|
compressed_kv = compressed_kv_with_k_pe [:, :, :, :self.kv_lora_rank].view(-1, kv_cache.page_size, self.kv_lora_rank)
|
|
k_pe = compressed_kv_with_k_pe [:, :, :, self.kv_lora_rank:].view(-1, kv_cache.page_size, self.qk_rope_head_dim)
|
|
|
|
q_absorb, out_absorb = self.get_absorbed()
|
|
q_nope = q_nope.transpose(0, 1) # q_len is 1, no GPU overhead, same below
|
|
q_nope = torch.matmul(q_nope, q_absorb) # batched MM
|
|
q_nope = q_nope.transpose(0, 1)
|
|
# q_nope.squeeze_(1)
|
|
# q_pe.squeeze_(1)
|
|
|
|
attn_output = wrapper.run(q_nope, q_pe, compressed_kv, k_pe).view(q_len, self.num_heads, self.kv_lora_rank)
|
|
attn_output = attn_output.transpose(0, 1)
|
|
attn_output = torch.matmul(attn_output, out_absorb.mT) # [self.num_heads, q_len, self.v_head_dim]
|
|
attn_output = attn_output.transpose(0, 1)
|
|
attn_output = attn_output.reshape(q_len, self.num_heads * self.v_head_dim)
|
|
attn_output = self.o_proj(attn_output, num_tokens_tensors)
|
|
return attn_output
|
|
|
|
class KQwen2MoeAttention(BaseInjectedModule, Qwen2MoeAttention):
|
|
def __init__(self,
|
|
key: str,
|
|
gguf_loader : GGUFLoader,
|
|
config: PretrainedConfig,
|
|
orig_module: nn.Module,
|
|
prefill_device: str = "cuda",
|
|
generate_device: str = "cuda",
|
|
chunck_size: int = 1000,
|
|
**kwargs):
|
|
BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
|
|
self.orig_module.__init__(orig_module.config,
|
|
orig_module.layer_idx)
|
|
self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
|
|
|
|
|
|
# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
|
|
def apply_rotary_pos_emb(self, q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
|
"""Applies Rotary Position Embedding to the query and key tensors.
|
|
|
|
Args:
|
|
q (`torch.Tensor`): The query tensor.
|
|
k (`torch.Tensor`): The key tensor.
|
|
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
|
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
|
position_ids (`torch.Tensor`):
|
|
Deprecated and unused.
|
|
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
|
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
|
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
|
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
|
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
|
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
|
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
|
Returns:
|
|
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
|
"""
|
|
cos = cos.unsqueeze(unsqueeze_dim)
|
|
sin = sin.unsqueeze(unsqueeze_dim)
|
|
q_embed = (q * cos) + (rotate_half(q) * sin)
|
|
k_embed = (k * cos) + (rotate_half(k) * sin)
|
|
return q_embed, k_embed
|
|
|
|
|
|
def forward(self,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: KGQACache,
|
|
position_ids: torch.Tensor,
|
|
wrapper: flashInferAttn,
|
|
bsz_tensors: torch.Tensor,
|
|
page_idx: torch.Tensor,
|
|
page_offset: torch.Tensor,
|
|
):
|
|
q_len, _ = hidden_states.size()
|
|
|
|
query_states = self.q_proj(hidden_states, bsz_tensors)
|
|
key_states = self.k_proj(hidden_states, bsz_tensors)
|
|
value_states = self.v_proj(hidden_states, bsz_tensors)
|
|
|
|
|
|
query_states = query_states.view(q_len, self.num_heads, self.head_dim)
|
|
key_states = key_states.view(q_len, self.num_key_value_heads, self.head_dim)
|
|
value_states = value_states.view(q_len, self.num_key_value_heads, self.head_dim)
|
|
|
|
cos, sin = self.rotary_emb(value_states.unsqueeze(0), position_ids.unsqueeze(0))
|
|
query_states, key_states = self.apply_rotary_pos_emb(query_states.unsqueeze(0), key_states.unsqueeze(0), cos, sin, unsqueeze_dim=2)
|
|
|
|
query_states = query_states.view(q_len, self.num_heads, self.head_dim)
|
|
key_states = key_states.view(
|
|
q_len, self.num_key_value_heads, self.head_dim
|
|
)
|
|
value_states = value_states.view(
|
|
q_len, self.num_key_value_heads, self.head_dim
|
|
)
|
|
|
|
k_cache = kv_cache.get_k_cache(self.layer_idx)
|
|
v_cache = kv_cache.get_v_cache(self.layer_idx)
|
|
|
|
|
|
attn_output = wrapper.forward(query_states, k_cache, v_cache, key_states, value_states)
|
|
|
|
|
|
attn_output = self.o_proj(attn_output.view(q_len, self.num_heads * self.head_dim), bsz_tensors)
|
|
|
|
return attn_output
|
|
|
|
class KQwen3MoeAttention(BaseInjectedModule, Qwen3MoeAttention):
|
|
def __init__(self,
|
|
key: str,
|
|
gguf_loader : GGUFLoader,
|
|
config: PretrainedConfig,
|
|
orig_module: nn.Module,
|
|
prefill_device: str = "cuda",
|
|
generate_device: str = "cuda",
|
|
chunck_size: int = 1000,
|
|
**kwargs):
|
|
BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
|
|
self.orig_module.__init__(orig_module.config,
|
|
orig_module.layer_idx)
|
|
self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
|
|
|
|
|
|
# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
|
|
def apply_rotary_pos_emb(self, q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
|
"""Applies Rotary Position Embedding to the query and key tensors.
|
|
|
|
Args:
|
|
q (`torch.Tensor`): The query tensor.
|
|
k (`torch.Tensor`): The key tensor.
|
|
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
|
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
|
position_ids (`torch.Tensor`):
|
|
Deprecated and unused.
|
|
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
|
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
|
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
|
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
|
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
|
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
|
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
|
Returns:
|
|
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
|
"""
|
|
cos = cos.unsqueeze(unsqueeze_dim)
|
|
sin = sin.unsqueeze(unsqueeze_dim)
|
|
q_embed = (q * cos) + (rotate_half(q) * sin)
|
|
k_embed = (k * cos) + (rotate_half(k) * sin)
|
|
return q_embed, k_embed
|
|
|
|
|
|
def forward(self,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: KGQACache,
|
|
position_ids: torch.Tensor,
|
|
wrapper: flashInferAttn,
|
|
bsz_tensors: torch.Tensor,
|
|
page_idx: torch.Tensor,
|
|
page_offset: torch.Tensor,
|
|
):
|
|
q_len, _ = hidden_states.size()
|
|
|
|
bsz_tensors_q = bsz_tensors * self.num_heads
|
|
bsz_tensors_kv = bsz_tensors * self.num_key_value_heads
|
|
|
|
query_states = self.q_norm(self.q_proj(hidden_states, bsz_tensors), bsz_tensors_q)
|
|
key_states = self.k_norm(self.k_proj(hidden_states, bsz_tensors), bsz_tensors_kv)
|
|
value_states = self.v_proj(hidden_states, bsz_tensors)
|
|
|
|
|
|
query_states = query_states.view(q_len, self.num_heads, self.head_dim)
|
|
key_states = key_states.view(q_len, self.num_key_value_heads, self.head_dim)
|
|
value_states = value_states.view(q_len, self.num_key_value_heads, self.head_dim)
|
|
|
|
cos, sin = self.rotary_emb(value_states.unsqueeze(0), position_ids.unsqueeze(0))
|
|
query_states, key_states = self.apply_rotary_pos_emb(query_states.unsqueeze(0), key_states.unsqueeze(0), cos, sin, unsqueeze_dim=2)
|
|
|
|
query_states = query_states.view(q_len, self.num_heads, self.head_dim)
|
|
key_states = key_states.view(
|
|
q_len, self.num_key_value_heads, self.head_dim
|
|
)
|
|
value_states = value_states.view(
|
|
q_len, self.num_key_value_heads, self.head_dim
|
|
)
|
|
|
|
k_cache = kv_cache.get_k_cache(self.layer_idx)
|
|
v_cache = kv_cache.get_v_cache(self.layer_idx)
|
|
|
|
|
|
attn_output = wrapper.forward(query_states, k_cache, v_cache, key_states, value_states)
|
|
|
|
|
|
attn_output = self.o_proj(attn_output.view(q_len, self.num_heads * self.head_dim), bsz_tensors)
|
|
|
|
return attn_output
|
|
|
|
|
|
class deepseek_torch_attn(BaseInjectedModule, DeepseekV2Attention):
|
|
def __init__(self,
|
|
key: str,
|
|
gguf_loader : GGUFLoader,
|
|
config: PretrainedConfig,
|
|
orig_module: nn.Module,
|
|
prefill_device: str = "cuda",
|
|
generate_device: str = "cuda",
|
|
chunck_size: int = 1000,
|
|
**kwargs):
|
|
BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
|
|
self.orig_module.__init__(orig_module.config,
|
|
orig_module.layer_idx)
|
|
self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
|
|
|
|
|
|
def get_absorbed(self) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
if not (hasattr(self, 'q_absorb') and hasattr(self, 'out_absorb')):
|
|
kv_b_proj = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
|
|
q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :].reshape(-1, self.kv_lora_rank)
|
|
out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :].reshape(-1, self.kv_lora_rank)
|
|
self.q_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.qk_nope_head_dim,
|
|
bias=False, dtype=q_absorb.dtype, device=q_absorb.device)
|
|
self.q_absorb.weight.data = q_absorb
|
|
self.out_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.v_head_dim,
|
|
bias=False, dtype=out_absorb.dtype, device=out_absorb.device)
|
|
self.out_absorb.weight.data = out_absorb
|
|
#del self.orig_module.kv_b_proj
|
|
q_absorb = self.q_absorb.weight.view(self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank)
|
|
out_absorb = self.out_absorb.weight.view(self.num_heads, self.v_head_dim, self.kv_lora_rank)
|
|
return q_absorb, out_absorb
|
|
|
|
|
|
|
|
def forward(self,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: KDeepSeekV3Cache,
|
|
position_ids: torch.Tensor,
|
|
wrapper: None,
|
|
num_tokens_tensors: torch.Tensor,
|
|
page_idx: torch.Tensor,
|
|
page_offset: torch.Tensor,
|
|
attention_masks: Optional[list[torch.Tensor]] = None,
|
|
q_indptr: Optional[torch.Tensor] = None,
|
|
kv_indices: Optional[torch.Tensor] = None,
|
|
kv_indptr: Optional[torch.Tensor] = None,
|
|
bsz_tensors: Optional[torch.Tensor] = None,
|
|
last_page_len: Optional[torch.Tensor] = None,
|
|
):
|
|
# range bsz_tensors
|
|
final_attention_output = torch.tensor([], device=hidden_states.device)
|
|
for i in range(bsz_tensors[0]):
|
|
batch_num_tokens_tensors = q_indptr[i+1] - q_indptr[i]
|
|
batch_last_page_len = last_page_len[i]
|
|
# kv_total_len is kv_len, batch_compressed_kv is compressed_kv, batch_k_pe is k_pe
|
|
batch_page_idx = page_idx[q_indptr[i]:q_indptr[i+1]]
|
|
batch_page_offset = page_offset[q_indptr[i]:q_indptr[i+1]]
|
|
# kv_page_nums is the number of pages for the current batch
|
|
kv_page_nums = kv_indptr[i+1] - kv_indptr[i]
|
|
# kv_total_len is the total length of the kv cache for the current batch (kv_len for algorithm)
|
|
kv_total_len = kv_page_nums * kv_cache.page_size
|
|
if batch_last_page_len is not None:
|
|
kv_total_len = kv_total_len - (kv_cache.page_size - batch_last_page_len)
|
|
# print(f"kv_total_len's shape {kv_total_len.shape}")
|
|
# kv_index is the index of the kv cache pages for the current batch
|
|
kv_index = kv_indices[kv_indptr[i]:kv_indptr[i+1]]
|
|
# we can index [kv_index, page_offset_indices] to get the kv cache for the current batch
|
|
# from q_indptr[i] to q_indptr[i+1] is the range of the current batch
|
|
batch_hidden_states = hidden_states[q_indptr[i]:q_indptr[i+1]]
|
|
batch_position_ids = position_ids[q_indptr[i]:q_indptr[i+1]]
|
|
q_len, _ = batch_hidden_states.size()
|
|
# print("q_len -> ", q_len)
|
|
|
|
if self.q_lora_rank is None:
|
|
q = self.q_proj(batch_hidden_states, batch_num_tokens_tensors)
|
|
else:
|
|
q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(batch_hidden_states, batch_num_tokens_tensors), batch_num_tokens_tensors), batch_num_tokens_tensors)
|
|
# for v3, bsz, q_len, num_heads(128), qk_head_dim(192=128(nope)+64(rope))
|
|
q = q.view(q_len, self.num_heads, self.q_head_dim)
|
|
# q_nope is [q_len, num_heads(128), qk_nope_head_dim(128)]
|
|
# q_pe is [q_len, num_heads(128), qk_rope_head_dim(64)]
|
|
q_nope, q_pe = torch.split(
|
|
q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
|
|
)
|
|
# compressed_kv is [q_len, kv_lora_rank(512) + rope(64)]
|
|
compressed_kv = self.kv_a_proj_with_mqa(batch_hidden_states, batch_num_tokens_tensors)
|
|
# compressed_kv is [q_len, kv_lora_rank(512)], k_pe is [q_len, rope(64)]
|
|
compressed_kv, k_pe = torch.split(
|
|
compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
|
|
)
|
|
compressed_kv = compressed_kv.contiguous()
|
|
compressed_kv = self.kv_a_layernorm(compressed_kv, batch_num_tokens_tensors)
|
|
# k_pe is [q_len, 1, qk_rope_head_dim(64)]
|
|
k_pe = k_pe.view(q_len, 1, self.qk_rope_head_dim)
|
|
# compressed_kv is [q_len, 1, kv_lora_rank(512)]
|
|
compressed_kv = compressed_kv.view(q_len, 1, self.kv_lora_rank)
|
|
|
|
cos, sin = self.rotary_emb(q_pe, batch_position_ids.unsqueeze(0))
|
|
# print(f"q_pe shape{q_pe.shape}, k_pe shape {k_pe.shape}")
|
|
q_pe, k_pe = apply_rotary_pos_emb(q_pe.unsqueeze(0), k_pe.unsqueeze(0), cos, sin, unsqueeze_dim=2)
|
|
q_pe = q_pe.squeeze(0)
|
|
# q_pe is [num_heads(128), q_len, qk_rope_head_dim(64)]
|
|
q_pe.transpose_(0, 1)
|
|
if kv_cache is not None:
|
|
cache_kwargs = {"sin": sin, "cos": cos, "page_idx": batch_page_idx, "page_offset": batch_page_offset} # Specific to RoPE models
|
|
compressed_kv_with_k_pe = kv_cache.update(compressed_kv.unsqueeze(0), k_pe, self.layer_idx, batch_page_idx, batch_page_offset, cache_kwargs)
|
|
compressed_kv = compressed_kv_with_k_pe [:, :, :, :self.kv_lora_rank].view(-1, kv_cache.page_size, self.kv_lora_rank)
|
|
k_pe = compressed_kv_with_k_pe [:, :, :, self.kv_lora_rank:].view(-1, kv_cache.page_size, self.qk_rope_head_dim)
|
|
# q_absorb is [num_heads(128), qk_nope_head_dim(128), kv_lora_rank(512)]
|
|
# out_absorb is [num_heads(128), kv_lora_rank(512), v_head_dim(128)] v_head_dim is also the nope dim
|
|
q_absorb, out_absorb = self.get_absorbed()
|
|
# q_nope is [num_heads(128), q_len, qk_nope_head_dim(128)]
|
|
q_nope = q_nope.transpose(0, 1) # q_len is 1, no GPU overhead, same below
|
|
# q_nope is [num_heads(128), q_len, kv_lora_rank(512)]
|
|
q_nope = torch.matmul(q_nope, q_absorb) # batched MM
|
|
|
|
# # q_nope is [q_len, num_heads(128), kv_lora_rank(512)]
|
|
# q_nope = q_nope.transpose(0, 1)
|
|
|
|
# we need to index out the compressed_kv and k_pe for the current batch
|
|
batch_compressed_kv = None
|
|
batch_k_pe = None
|
|
for page_index in kv_index:
|
|
if kv_total_len > kv_cache.page_size:
|
|
tmp_compressed_kv = compressed_kv[page_index, 0:kv_cache.page_size, :]
|
|
tmp_k_pe = k_pe[page_index, 0:kv_cache.page_size, :]
|
|
if batch_compressed_kv is None or batch_k_pe is None:
|
|
batch_compressed_kv = tmp_compressed_kv
|
|
batch_k_pe = tmp_k_pe
|
|
else:
|
|
batch_compressed_kv = torch.cat((batch_compressed_kv, tmp_compressed_kv), dim=0)
|
|
batch_k_pe = torch.cat((batch_k_pe, tmp_k_pe), dim=0)
|
|
kv_total_len -= kv_cache.page_size
|
|
else:
|
|
tmp_compressed_kv = compressed_kv[page_index, 0:kv_total_len, :]
|
|
tmp_k_pe = k_pe[page_index, 0:kv_total_len, :]
|
|
if batch_compressed_kv is None or batch_k_pe is None:
|
|
batch_compressed_kv = tmp_compressed_kv
|
|
batch_k_pe = tmp_k_pe
|
|
else:
|
|
batch_compressed_kv = torch.cat((batch_compressed_kv, tmp_compressed_kv), dim=0)
|
|
batch_k_pe = torch.cat((batch_k_pe, tmp_k_pe), dim=0)
|
|
break
|
|
# batch_compressed_kv is [kv_total_len(k_len), kv_lora_rank(512)]
|
|
# batch_k_pe is [kv_total_len(k_len), qk_rope_head_dim(64)]
|
|
attention_weights = (torch.matmul(q_pe,batch_k_pe.mT) + torch.matmul(q_nope, batch_compressed_kv.mT)) * self.softmax_scale
|
|
# attention_weights is [num_heads(128), q_len, k_len]
|
|
|
|
# attention_weights = attention_weights.transpose(0,1).unsqueeze(0).squeeze(-1).expand(q_len,-1,-1).transpose(0,1)
|
|
|
|
# attention_masks[i] is [q_len, k_len]
|
|
|
|
attention_weights = (attention_weights + attention_masks[i])
|
|
# attention_weights shape is [num_heads(128), q_len, k_len]
|
|
attention_weights = nn.functional.softmax(attention_weights,dim=-1,dtype=torch.float32).to(q_pe.dtype)
|
|
attn_output = torch.matmul(attention_weights, batch_compressed_kv) # [num_heads(128),q_len, lora_rank(512)]
|
|
# out_absorb shape is [num_heads(128), kv_lora_rank(512), v_head_dim(128)]
|
|
out_absorb = out_absorb.transpose(1,2)
|
|
# q for q_len, n for num_heads, h for v_head_dim, v for kv_lora_rank
|
|
attn_output = torch.matmul(attn_output, out_absorb) # [num_heads(128), q_len, v_head_dim(128)]
|
|
attn_output = attn_output.transpose(0, 1) # [q_len, num_heads(128), v_head_dim(128)]
|
|
attn_output = attn_output.reshape(q_len, self.num_heads * self.v_head_dim)
|
|
attn_output = self.o_proj(attn_output, batch_num_tokens_tensors)
|
|
final_attention_output = torch.cat((final_attention_output, attn_output), dim=0)
|
|
return final_attention_output |