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[Feature] Support new blackwell decode attention #7949

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54 changes: 54 additions & 0 deletions custom_ops/gpu_ops/send_cache.cu
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Original file line number Diff line number Diff line change
@@ -0,0 +1,54 @@
// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "helper.h"
#include "paddle/extension.h"
#include "paddle/phi/backends/context_pool.h"
#include "paddle/phi/core/memory/memcpy.h"
#include "remote_cache_kv_ipc.h"

void SendCacheFunc(const paddle::Tensor& qkv,
const paddle::optional<paddle::Tensor>& kv_signal_data) {
const char* fmt_write_cache_completed_signal_str =
std::getenv("FLAGS_fmt_write_cache_completed_signal");
const char* FLAGS_use_pd_disaggregation_per_chunk =
std::getenv("FLAGS_use_pd_disaggregation_per_chunk");
if (fmt_write_cache_completed_signal_str &&
(std::strcmp(fmt_write_cache_completed_signal_str, "true") == 0 ||
std::strcmp(fmt_write_cache_completed_signal_str, "1") == 0)) {
if (FLAGS_use_pd_disaggregation_per_chunk &&
(std::strcmp(FLAGS_use_pd_disaggregation_per_chunk, "true") == 0 ||
std::strcmp(FLAGS_use_pd_disaggregation_per_chunk, "1") == 0)) {
cudaLaunchHostFunc(
qkv.stream(),
&(RemoteCacheKvIpc::
save_cache_kv_complete_signal_layerwise_per_query),
(void*)nullptr);
} else {
if (kv_signal_data) {
cudaLaunchHostFunc(
qkv.stream(),
&RemoteCacheKvIpc::save_cache_kv_complete_signal_layerwise,
(void*)(const_cast<int64_t*>(
kv_signal_data.get().data<int64_t>())));
}
}
}
}

PD_BUILD_STATIC_OP(send_cache)
.Inputs({"qkv", paddle::Optional("kv_signal_data")})
.Outputs({"qkv_out"})
.SetInplaceMap({{"qkv", "qkv_out"}})
.SetKernelFn(PD_KERNEL(SendCacheFunc));
1 change: 1 addition & 0 deletions custom_ops/setup_ops.py
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Expand Up @@ -339,6 +339,7 @@ def find_end_files(directory, end_str):
"gpu_ops/gelu_tanh.cu",
"gpu_ops/reasoning_phase_token_constraint.cu",
"gpu_ops/get_attn_mask_q.cu",
"gpu_ops/send_cache.cu",
]
sm_versions = get_sm_version(archs)
# Some kernels in this file require SM75+ instructions. Exclude them when building SM70 (V100).
Expand Down
20 changes: 16 additions & 4 deletions fastdeploy/cache_manager/cache_messager.py
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Expand Up @@ -226,6 +226,8 @@ def __init__(
scale_block_bytes = math.prod(key_cache_scale.shape[1:])
if key_cache_scale.dtype == paddle.bfloat16 or key_cache_scale.dtype == paddle.float16:
scale_block_bytes *= 2
elif key_cache_scale.dtype == paddle.float32:
scale_block_bytes *= 4
logger.info(f"scale_block_bytes: {scale_block_bytes}, dtype: {key_cache_scale.dtype}")
logger.info(
f"layers {num_layers} cache_shape: {cache_shape}, max_block_num: {max_block_num}, "
Expand Down Expand Up @@ -571,6 +573,8 @@ def __init__(
scale_block_bytes = math.prod(key_cache_scale.shape[1:])
if key_cache_scale.dtype == paddle.bfloat16 or key_cache_scale.dtype == paddle.float16:
scale_block_bytes *= 2
elif key_cache_scale.dtype == paddle.float32:
scale_block_bytes *= 4
logger.info(f"scale_block_bytes: {scale_block_bytes}, dtype: {key_cache_scale.dtype}")
logger.info(
f"layers {num_layers} cache_shape: {cache_shape}, max_block_num: {max_block_num}, "
Expand Down Expand Up @@ -936,6 +940,8 @@ def main():
if args.value_cache_shape:
value_cache_shape_list = [int(i) for i in args.value_cache_shape.split(",")]
total_gpu_blocks = key_cache_shape_list[0]
# key_cache_shape: [num_blocks, kv_head_num, block_size, head_dim]
block_size = key_cache_shape_list[2]
num_extra_layer_gpu_blocks = int(total_gpu_blocks * speculative_config.num_gpu_block_expand_ratio)
gpu_cache_kvs = {}
gpu_cache_k_tensors = []
Expand Down Expand Up @@ -973,10 +979,14 @@ def main():
f"key_caches_{i}_rank{rank}.device{device}",
)
if args.cache_dtype == "block_wise_fp8":
_scale_dtype = (
"float32" if envs.FD_ATTENTION_BACKEND == "FLASH_MASK_ATTN_BLACKWELL" else paddle.get_default_dtype()
)
_scale_last_dim = 4 if envs.FD_ATTENTION_BACKEND == "FLASH_MASK_ATTN_BLACKWELL" else key_cache_shape[2]
gpu_cache_kvs[f"key_cache_scales_{i}_rank{rank}_device{device}"] = paddle.full(
shape=[num_gpu_blocks, key_cache_shape[1], key_cache_shape[2]],
shape=[num_gpu_blocks, key_cache_shape[1], _scale_last_dim],
fill_value=0,
dtype=paddle.get_default_dtype(),
dtype=_scale_dtype,
)
set_data_ipc(
gpu_cache_kvs[f"key_cache_scales_{i}_rank{rank}_device{device}"],
Expand All @@ -996,9 +1006,9 @@ def main():
)
if args.cache_dtype == "block_wise_fp8":
gpu_cache_kvs[f"value_cache_scales_{i}_rank{rank}_device{device}"] = paddle.full(
shape=[num_gpu_blocks, value_cache_shape[1], value_cache_shape[2]],
shape=[num_gpu_blocks, value_cache_shape[1], _scale_last_dim],
fill_value=0,
dtype=paddle.get_default_dtype(),
dtype=_scale_dtype,
)
set_data_ipc(
gpu_cache_kvs[f"value_cache_scales_{i}_rank{rank}_device{device}"],
Expand All @@ -1023,6 +1033,7 @@ def main():
gpu_id=device,
rdma_port=args.rdma_port,
cache_dtype=args.cache_dtype,
block_size=block_size,
)
else:
cache_messager = CacheMessager(
Expand All @@ -1038,6 +1049,7 @@ def main():
gpu_id=device,
rdma_port=args.rdma_port,
cache_dtype=args.cache_dtype,
block_size=block_size,
)

cache_ready_signal_data = np.zeros(shape=[args.mp_num], dtype=np.int32)
Expand Down
53 changes: 38 additions & 15 deletions fastdeploy/cache_manager/cache_transfer_manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -175,10 +175,16 @@ def __init__(self, args):
# compute cache bytes
self.cache_dtype = args.cache_dtype
self.cache_item_bytes = CacheConfig.get_cache_bytes(self.cache_dtype)
self.scale_item_bytes = CacheConfig.get_cache_bytes(paddle.get_default_dtype())
self.has_cache_scale = self.cache_dtype == "block_wise_fp8"
if self.has_cache_scale:
self.cache_scale_shape = [self.num_gpu_blocks, self.head_num, self.block_size]
if envs.FD_ATTENTION_BACKEND == "FLASH_MASK_ATTN_BLACKWELL":
self.scale_item_bytes = CacheConfig.get_cache_bytes("float32")
self.cache_scale_shape = [self.num_gpu_blocks, self.head_num, 4]
else:
self.scale_item_bytes = CacheConfig.get_cache_bytes(paddle.get_default_dtype())
self.cache_scale_shape = [self.num_gpu_blocks, self.head_num, self.block_size]
else:
self.scale_item_bytes = CacheConfig.get_cache_bytes(paddle.get_default_dtype())

# kv cache storage
self.storage_backend_type = args.kvcache_storage_backend
Expand Down Expand Up @@ -416,7 +422,9 @@ def _init_storage_buffer(self, args):
self.storage_backend.register_buffer(write_buffer, cache_buffer_total_bytes)

if self.has_cache_scale:
self.scale_buffer_stride_bytes = layer_num * self.head_num * self.block_size * self.scale_item_bytes
self.scale_buffer_stride_bytes = (
layer_num * self.head_num * self.cache_scale_shape[-1] * self.scale_item_bytes
)
scale_buffer_total_bytes = block_num * self.scale_buffer_stride_bytes * 2
logger.info(
f"Creating scale cpu buffer cache for all layers: {scale_buffer_total_bytes / 1024 ** 3:.2f}GB"
Expand Down Expand Up @@ -474,10 +482,16 @@ def _init_gpu_cache(self):
set_data_ipc(key_cache, key_name)

if self.cache_dtype == "block_wise_fp8":
_scale_dtype = (
"float32"
if envs.FD_ATTENTION_BACKEND == "FLASH_MASK_ATTN_BLACKWELL"
else paddle.get_default_dtype()
)
_scale_last_dim = self.cache_scale_shape[-1]
key_cache_scales = paddle.full(
shape=[num_gpu_blocks, self.key_cache_shape[1], self.key_cache_shape[2]],
shape=[num_gpu_blocks, self.key_cache_shape[1], _scale_last_dim],
fill_value=0,
dtype=paddle.get_default_dtype(),
dtype=_scale_dtype,
)
set_data_ipc(key_cache_scales, key_cache_scales_name)
if self.value_cache_shape:
Expand All @@ -486,9 +500,9 @@ def _init_gpu_cache(self):

if self.cache_dtype == "block_wise_fp8":
value_cache_scales = paddle.full(
shape=[num_gpu_blocks, self.value_cache_shape[1], self.value_cache_shape[2]],
shape=[num_gpu_blocks, self.value_cache_shape[1], _scale_last_dim],
fill_value=0,
dtype=paddle.get_default_dtype(),
dtype=_scale_dtype,
)
set_data_ipc(value_cache_scales, value_cache_scales_name)
else:
Expand All @@ -497,21 +511,27 @@ def _init_gpu_cache(self):
val_cache = paddle.empty(shape=[], dtype=cache_type)
key_cache = share_external_data_(key_cache, key_name, key_cache_shape, True)
if self.cache_dtype == "block_wise_fp8":
key_cache_scales = paddle.empty(shape=[], dtype=paddle.get_default_dtype())
_scale_dtype = (
"float32"
if envs.FD_ATTENTION_BACKEND == "FLASH_MASK_ATTN_BLACKWELL"
else paddle.get_default_dtype()
)
_scale_last_dim = self.cache_scale_shape[-1]
key_cache_scales = paddle.empty(shape=[], dtype=_scale_dtype)
key_cache_scales = share_external_data_(
key_cache_scales,
key_cache_scales_name,
[num_gpu_blocks, self.key_cache_shape[1], self.key_cache_shape[2]],
[num_gpu_blocks, self.key_cache_shape[1], _scale_last_dim],
True,
)
if self.value_cache_shape:
val_cache = share_external_data_(val_cache, val_name, value_cache_shape, True)
if self.cache_dtype == "block_wise_fp8":
value_cache_scales = paddle.empty(shape=[], dtype=paddle.get_default_dtype())
value_cache_scales = paddle.empty(shape=[], dtype=_scale_dtype)
value_cache_scales = share_external_data_(
value_cache_scales,
value_cache_scales_name,
[num_gpu_blocks, self.value_cache_shape[1], self.value_cache_shape[2]],
[num_gpu_blocks, self.value_cache_shape[1], _scale_last_dim],
True,
)

Expand Down Expand Up @@ -572,10 +592,13 @@ def _init_cpu_cache(self):
value_need_to_allocate_bytes = self.num_cpu_blocks * cache_item_bytes * value_cache_size
logger.info("Initializing swap space (cpu cache) for all layers.")
if self.cache_dtype == "block_wise_fp8":
cache_scales = paddle.empty(shape=[], dtype=paddle.get_default_dtype())
cache_scales_size = self.key_cache_shape[1] * self.key_cache_shape[2]
scales_key_need_to_allocate_bytes = self.num_cpu_blocks * cache_scales.element_size() * cache_scales_size
scales_value_need_to_allocate_bytes = self.num_cpu_blocks * cache_scales.element_size() * cache_scales_size
_scale_dtype = (
"float32" if envs.FD_ATTENTION_BACKEND == "FLASH_MASK_ATTN_BLACKWELL" else paddle.get_default_dtype()
)
_scale_item_bytes = CacheConfig.get_cache_bytes(_scale_dtype)
cache_scales_size = self.key_cache_shape[1] * self.cache_scale_shape[-1]
scales_key_need_to_allocate_bytes = self.num_cpu_blocks * _scale_item_bytes * cache_scales_size
scales_value_need_to_allocate_bytes = self.num_cpu_blocks * _scale_item_bytes * cache_scales_size
self.k_dst_ptrs = []
self.v_dst_ptrs = []
self.k_scales_ptrs = []
Expand Down
2 changes: 2 additions & 0 deletions fastdeploy/model_executor/forward_meta.py
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Expand Up @@ -164,6 +164,8 @@ class ForwardMeta:

real_bsz: int = 0

seq_lens_kv: paddle.Tensor = None

def clear_caches(self):
"""Safely clean up the caches"""
if self.caches:
Expand Down
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