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体验新版 GitCode,发现更多精彩内容 >>
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提交
9b15efce
编写于
5月 17, 2022
作者:
S
Siming Dai
提交者:
GitHub
5月 17, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refine cpu query (#42803)
上级
b2d8f6df
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
117 addition
and
89 deletion
+117
-89
paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table_inl.cu
.../fluid/framework/fleet/heter_ps/graph_gpu_ps_table_inl.cu
+117
-89
未找到文件。
paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table_inl.cu
浏览文件 @
9b15efce
...
...
@@ -32,10 +32,11 @@ sample_result is to save the neighbor sampling result, its size is len *
sample_size;
*/
__global__
void
get_cpu_id_index
(
int64_t
*
key
,
int64_t
*
val
,
int64_t
*
cpu_key
,
int
*
sum
,
int
*
index
,
int
len
)
{
__global__
void
get_cpu_id_index
(
int64_t
*
key
,
int
*
actual_sample_size
,
int64_t
*
cpu_key
,
int
*
sum
,
int
*
index
,
int
len
)
{
CUDA_KERNEL_LOOP
(
i
,
len
)
{
if
(
val
[
i
]
==
-
1
)
{
if
(
actual_sample_size
[
i
]
==
-
1
)
{
int
old
=
atomicAdd
(
sum
,
1
);
cpu_key
[
old
]
=
key
[
i
];
index
[
old
]
=
i
;
...
...
@@ -44,11 +45,35 @@ __global__ void get_cpu_id_index(int64_t* key, int64_t* val, int64_t* cpu_key,
}
}
__global__
void
get_actual_gpu_ac
(
int
*
gpu_ac
,
int
number_on_cpu
)
{
CUDA_KERNEL_LOOP
(
i
,
number_on_cpu
)
{
gpu_ac
[
i
]
/=
sizeof
(
int64_t
);
}
}
template
<
int
WARP_SIZE
,
int
BLOCK_WARPS
,
int
TILE_SIZE
>
__global__
void
copy_buffer_ac_to_final_place
(
int64_t
*
gpu_buffer
,
int
*
gpu_ac
,
int64_t
*
val
,
int
*
actual_sample_size
,
int
*
index
,
int
*
cumsum_gpu_ac
,
int
number_on_cpu
,
int
sample_size
)
{
assert
(
blockDim
.
x
==
WARP_SIZE
);
assert
(
blockDim
.
y
==
BLOCK_WARPS
);
int
i
=
blockIdx
.
x
*
TILE_SIZE
+
threadIdx
.
y
;
const
int
last_idx
=
min
(
static_cast
<
int
>
(
blockIdx
.
x
+
1
)
*
TILE_SIZE
,
number_on_cpu
);
while
(
i
<
last_idx
)
{
actual_sample_size
[
index
[
i
]]
=
gpu_ac
[
i
];
for
(
int
j
=
threadIdx
.
x
;
j
<
gpu_ac
[
i
];
j
+=
WARP_SIZE
)
{
val
[
index
[
i
]
*
sample_size
+
j
]
=
gpu_buffer
[
cumsum_gpu_ac
[
i
]
+
j
];
}
i
+=
BLOCK_WARPS
;
}
}
template
<
int
WARP_SIZE
,
int
BLOCK_WARPS
,
int
TILE_SIZE
>
__global__
void
neighbor_sample_example_v2
(
GpuPsCommGraph
graph
,
int64_t
*
node_index
,
int
*
actual_size
,
int64_t
*
res
,
int
sample_len
,
int
n
)
{
int
sample_len
,
int
n
,
int
default_value
)
{
assert
(
blockDim
.
x
==
WARP_SIZE
);
assert
(
blockDim
.
y
==
BLOCK_WARPS
);
...
...
@@ -59,7 +84,7 @@ __global__ void neighbor_sample_example_v2(GpuPsCommGraph graph,
while
(
i
<
last_idx
)
{
if
(
node_index
[
i
]
==
-
1
)
{
actual_size
[
i
]
=
0
;
actual_size
[
i
]
=
default_value
;
i
+=
BLOCK_WARPS
;
continue
;
}
...
...
@@ -762,6 +787,10 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2(
auto
d_right
=
memory
::
Alloc
(
place
,
total_gpu
*
sizeof
(
int
));
int
*
d_left_ptr
=
reinterpret_cast
<
int
*>
(
d_left
->
ptr
());
int
*
d_right_ptr
=
reinterpret_cast
<
int
*>
(
d_right
->
ptr
());
int
default_value
=
0
;
if
(
cpu_query_switch
)
{
default_value
=
-
1
;
}
cudaMemsetAsync
(
d_left_ptr
,
-
1
,
total_gpu
*
sizeof
(
int
),
stream
);
cudaMemsetAsync
(
d_right_ptr
,
-
1
,
total_gpu
*
sizeof
(
int
),
stream
);
...
...
@@ -796,14 +825,9 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2(
sizeof
(
int
)
*
(
shard_len
+
shard_len
%
2
));
}
walk_to_dest
(
gpu_id
,
total_gpu
,
h_left
,
h_right
,
d_shard_keys_ptr
,
NULL
);
// For cpu_query_switch, we need global items.
std
::
vector
<
thrust
::
device_vector
<
int64_t
>>
cpu_keys_list
;
std
::
vector
<
thrust
::
device_vector
<
int
>>
cpu_index_list
;
thrust
::
device_vector
<
int64_t
>
tmp1
;
thrust
::
device_vector
<
int
>
tmp2
;
for
(
int
i
=
0
;
i
<
total_gpu
;
++
i
)
{
if
(
h_left
[
i
]
==
-
1
)
{
// Insert empty object
continue
;
}
int
shard_len
=
h_left
[
i
]
==
-
1
?
0
:
h_right
[
i
]
-
h_left
[
i
]
+
1
;
...
...
@@ -832,92 +856,16 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2(
WARP_SIZE
,
BLOCK_WARPS
,
TILE_SIZE
><<<
grid
,
block
,
0
,
resource_
->
remote_stream
(
i
,
gpu_id
)
>>>
(
graph
,
id_array
,
actual_size_array
,
sample_array
,
sample_size
,
shard_len
);
// cpu_graph_table->random_sample_neighbors
// if (cpu_query_switch) {
//}
shard_len
,
default_value
);
}
for
(
int
i
=
0
;
i
<
total_gpu
;
++
i
)
{
if
(
h_left
[
i
]
==
-
1
)
{
if
(
cpu_query_switch
)
{
cpu_keys_list
.
emplace_back
(
tmp1
);
cpu_index_list
.
emplace_back
(
tmp2
);
}
continue
;
}
cudaStreamSynchronize
(
resource_
->
remote_stream
(
i
,
gpu_id
));
if
(
cpu_query_switch
)
{
platform
::
CUDADeviceGuard
guard
(
resource_
->
dev_id
(
i
));
int
shard_len
=
h_left
[
i
]
==
-
1
?
0
:
h_right
[
i
]
-
h_left
[
i
]
+
1
;
auto
&
node
=
path_
[
gpu_id
][
i
].
nodes_
.
back
();
int64_t
*
id_array
=
reinterpret_cast
<
int64_t
*>
(
node
.
val_storage
);
int
*
actual_size_array
=
(
int
*
)(
id_array
+
shard_len
);
int64_t
*
sample_array
=
(
int64_t
*
)(
actual_size_array
+
shard_len
+
shard_len
%
2
);
thrust
::
device_vector
<
int64_t
>
cpu_keys_ptr
(
shard_len
);
thrust
::
device_vector
<
int
>
index_ptr
(
shard_len
+
1
,
0
);
int64_t
*
node_id_array
=
reinterpret_cast
<
int64_t
*>
(
node
.
key_storage
);
int
grid_size2
=
(
shard_len
-
1
)
/
block_size_
+
1
;
get_cpu_id_index
<<<
grid_size2
,
block_size_
,
0
,
resource_
->
remote_stream
(
i
,
gpu_id
)
>>>
(
node_id_array
,
id_array
,
thrust
::
raw_pointer_cast
(
cpu_keys_ptr
.
data
()),
thrust
::
raw_pointer_cast
(
index_ptr
.
data
()),
thrust
::
raw_pointer_cast
(
index_ptr
.
data
())
+
1
,
shard_len
);
cudaStreamSynchronize
(
resource_
->
remote_stream
(
i
,
gpu_id
));
cpu_keys_list
.
emplace_back
(
cpu_keys_ptr
);
cpu_index_list
.
emplace_back
(
index_ptr
);
}
}
if
(
cpu_query_switch
)
{
for
(
int
i
=
0
;
i
<
total_gpu
;
++
i
)
{
if
(
h_left
[
i
]
==
-
1
)
{
continue
;
}
platform
::
CUDADeviceGuard
guard
(
resource_
->
dev_id
(
i
));
auto
shard_len
=
h_right
[
i
]
-
h_left
[
i
]
+
1
;
int
*
cpu_index
=
new
int
[
shard_len
+
1
];
cudaMemcpy
(
cpu_index
,
thrust
::
raw_pointer_cast
(
cpu_index_list
[
i
].
data
()),
(
shard_len
+
1
)
*
sizeof
(
int
),
cudaMemcpyDeviceToHost
);
if
(
cpu_index
[
0
]
>
0
)
{
int
number_on_cpu
=
cpu_index
[
0
];
int64_t
*
cpu_keys
=
new
int64_t
[
number_on_cpu
];
cudaMemcpy
(
cpu_keys
,
thrust
::
raw_pointer_cast
(
cpu_keys_list
[
i
].
data
()),
number_on_cpu
*
sizeof
(
int64_t
),
cudaMemcpyDeviceToHost
);
std
::
vector
<
std
::
shared_ptr
<
char
>>
buffers
(
number_on_cpu
);
std
::
vector
<
int
>
ac
(
number_on_cpu
);
auto
status
=
cpu_graph_table
->
random_sample_neighbors
(
0
,
cpu_keys
,
sample_size
,
buffers
,
ac
,
false
);
auto
&
node
=
path_
[
gpu_id
][
i
].
nodes_
.
back
();
// display_sample_res(node.key_storage,node.val_storage,shard_len,sample_size);
int64_t
*
id_array
=
reinterpret_cast
<
int64_t
*>
(
node
.
val_storage
);
int
*
actual_size_array
=
(
int
*
)(
id_array
+
shard_len
);
int64_t
*
sample_array
=
(
int64_t
*
)(
actual_size_array
+
shard_len
+
shard_len
%
2
);
for
(
int
j
=
0
;
j
<
number_on_cpu
;
j
++
)
{
int
offset
=
cpu_index
[
j
+
1
]
*
sample_size
;
ac
[
j
]
=
ac
[
j
]
/
sizeof
(
int64_t
);
/*
std::cerr<<"for cpu key "<<cpu_keys[j]<<" ac_size = "<<ac[j];
int64_t *sss = (int64_t*)(buffers[j].get());
for(int t = 0; t < ac[j]; t++){
std::cerr<<" sampled neighbor ****** "<<sss[t];
}
std::cerr<<"index = "<<cpu_index[j+1]<<std::endl;
*/
cudaMemcpy
(
sample_array
+
offset
,
(
int64_t
*
)(
buffers
[
j
].
get
()),
sizeof
(
int64_t
)
*
ac
[
j
],
cudaMemcpyHostToDevice
);
cudaMemcpy
(
actual_size_array
+
cpu_index
[
j
+
1
],
ac
.
data
()
+
j
,
sizeof
(
int
),
cudaMemcpyHostToDevice
);
// display_sample_res(node.key_storage,node.val_storage,shard_len,sample_size);
}
delete
[]
cpu_keys
;
}
delete
[]
cpu_index
;
}
}
move_neighbor_sample_result_to_source_gpu
(
gpu_id
,
total_gpu
,
sample_size
,
h_left
,
h_right
,
d_shard_vals_ptr
,
d_shard_actual_sample_size_ptr
);
...
...
@@ -925,15 +873,95 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2(
d_shard_vals_ptr
,
val
,
d_shard_actual_sample_size_ptr
,
actual_sample_size
,
d_idx_ptr
,
sample_size
,
len
);
cudaStreamSynchronize
(
stream
);
if
(
cpu_query_switch
)
{
// Get cpu keys and corresponding position.
thrust
::
device_vector
<
int64_t
>
t_cpu_keys
(
len
);
thrust
::
device_vector
<
int
>
t_index
(
len
+
1
,
0
);
get_cpu_id_index
<<<
grid_size
,
block_size_
,
0
,
stream
>>>
(
key
,
actual_sample_size
,
thrust
::
raw_pointer_cast
(
t_cpu_keys
.
data
()),
thrust
::
raw_pointer_cast
(
t_index
.
data
()),
thrust
::
raw_pointer_cast
(
t_index
.
data
())
+
1
,
len
);
cudaStreamSynchronize
(
stream
);
int
number_on_cpu
=
0
;
cudaMemcpy
(
&
number_on_cpu
,
thrust
::
raw_pointer_cast
(
t_index
.
data
()),
sizeof
(
int
),
cudaMemcpyDeviceToHost
);
if
(
number_on_cpu
>
0
)
{
int64_t
*
cpu_keys
=
new
int64_t
[
number_on_cpu
];
cudaMemcpy
(
cpu_keys
,
thrust
::
raw_pointer_cast
(
t_cpu_keys
.
data
()),
number_on_cpu
*
sizeof
(
int64_t
),
cudaMemcpyDeviceToHost
);
std
::
vector
<
std
::
shared_ptr
<
char
>>
buffers
(
number_on_cpu
);
std
::
vector
<
int
>
ac
(
number_on_cpu
);
auto
status
=
cpu_graph_table
->
random_sample_neighbors
(
0
,
cpu_keys
,
sample_size
,
buffers
,
ac
,
false
);
int
total_cpu_sample_size
=
std
::
accumulate
(
ac
.
begin
(),
ac
.
end
(),
0
);
total_cpu_sample_size
/=
sizeof
(
int64_t
);
// Merge buffers into one int64_t vector.
int64_t
*
merge_buffers
=
new
int64_t
[
total_cpu_sample_size
];
int
start
=
0
;
for
(
int
j
=
0
;
j
<
number_on_cpu
;
j
++
)
{
memcpy
(
merge_buffers
+
start
,
(
int64_t
*
)(
buffers
[
j
].
get
()),
ac
[
j
]);
start
+=
ac
[
j
]
/
sizeof
(
int64_t
);
}
// Copy merge_buffers to gpu.
thrust
::
device_vector
<
int64_t
>
gpu_buffers
(
total_cpu_sample_size
);
thrust
::
device_vector
<
int
>
gpu_ac
(
number_on_cpu
);
int64_t
*
gpu_buffers_ptr
=
thrust
::
raw_pointer_cast
(
gpu_buffers
.
data
());
int
*
gpu_ac_ptr
=
thrust
::
raw_pointer_cast
(
gpu_ac
.
data
());
cudaMemcpyAsync
(
gpu_buffers_ptr
,
merge_buffers
,
total_cpu_sample_size
*
sizeof
(
int64_t
),
cudaMemcpyHostToDevice
,
stream
);
cudaMemcpyAsync
(
gpu_ac_ptr
,
ac
.
data
(),
number_on_cpu
*
sizeof
(
int
),
cudaMemcpyHostToDevice
,
stream
);
// Copy gpu_buffers and gpu_ac using kernel.
// Kernel divide for gpu_ac_ptr.
int
grid_size2
=
(
number_on_cpu
-
1
)
/
block_size_
+
1
;
get_actual_gpu_ac
<<<
grid_size2
,
block_size_
,
0
,
stream
>>>
(
gpu_ac_ptr
,
number_on_cpu
);
cudaStreamSynchronize
(
stream
);
thrust
::
device_vector
<
int
>
cumsum_gpu_ac
(
number_on_cpu
);
thrust
::
exclusive_scan
(
gpu_ac
.
begin
(),
gpu_ac
.
end
(),
cumsum_gpu_ac
.
begin
(),
0
);
constexpr
int
WARP_SIZE_
=
32
;
constexpr
int
BLOCK_WARPS_
=
128
/
WARP_SIZE_
;
constexpr
int
TILE_SIZE_
=
BLOCK_WARPS_
*
16
;
const
dim3
block2
(
WARP_SIZE_
,
BLOCK_WARPS_
);
const
dim3
grid2
((
number_on_cpu
+
TILE_SIZE_
-
1
)
/
TILE_SIZE_
);
copy_buffer_ac_to_final_place
<
WARP_SIZE_
,
BLOCK_WARPS_
,
TILE_SIZE_
><<<
grid2
,
block2
,
0
,
stream
>>>
(
gpu_buffers_ptr
,
gpu_ac_ptr
,
val
,
actual_sample_size
,
thrust
::
raw_pointer_cast
(
t_index
.
data
())
+
1
,
thrust
::
raw_pointer_cast
(
cumsum_gpu_ac
.
data
()),
number_on_cpu
,
sample_size
);
delete
[]
merge_buffers
;
delete
[]
cpu_keys
;
}
}
{
cudaStreamSynchronize
(
stream
);
platform
::
CUDAPlace
place
=
platform
::
CUDAPlace
(
resource_
->
dev_id
(
gpu_id
));
platform
::
CUDADeviceGuard
guard
(
resource_
->
dev_id
(
gpu_id
));
thrust
::
device_vector
<
int
>
t_actual_sample_size
(
len
);
thrust
::
copy
(
actual_sample_size
,
actual_sample_size
+
len
,
t_actual_sample_size
.
begin
());
int
total_sample_size
=
thrust
::
reduce
(
t_actual_sample_size
.
begin
(),
t_actual_sample_size
.
end
());
result
.
actual_val_mem
=
memory
::
AllocShared
(
place
,
total_sample_size
*
sizeof
(
int64_t
));
result
.
actual_val
=
(
int64_t
*
)(
result
.
actual_val_mem
)
->
ptr
();
...
...
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