Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

use stride of 1 in pooling kernels #313

Merged
merged 3 commits into from
Oct 3, 2024
Merged

use stride of 1 in pooling kernels #313

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
815a266
use stride of 1 in pooling kernels
superlopuh Sep 2, 2024
92d26d3
Merge branch 'main' into sasha/stride-one
superlopuh Oct 3, 2024
a1d9448
fix pooling gendata
superlopuh Oct 3, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions Snakefile
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -653,6 +653,7 @@ rule kernel_generate_data_c:
input:
json="kernels/{kernel}/{shape}/params.json",
h="kernels/{kernel}/{shape}/data.h",
gendata="kernels/{kernel}/gendata.py"
output:
"kernels/{kernel}/{shape}/data.c",
wildcard_constraints:
Expand Down
8 changes: 4 additions & 4 deletions kernels/pooling_nchw_max_d1_s2_3x3/baseline.c.template
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,10 +3,10 @@
#include <stdint.h>

void pooling_nchw_max_d1_s2_3x3(const double* restrict x, double* restrict y) {
for (int row = 0; row < H - 3 + 1; row += 2) {
for (int col = 0; col < W - 3 + 1; col += 2) {
int y_row = row / 2;
int y_col = col / 2;
for (int row = 0; row < H - 3 + 1; row += 1) {
for (int col = 0; col < W - 3 + 1; col += 1) {
int y_row = row / 1;
int y_col = col / 1;
int y_index = (y_row * NEW_W) + y_col;
// Load initial value in y
double max_value = -10000.0;
Expand Down
4 changes: 2 additions & 2 deletions kernels/pooling_nchw_max_d1_s2_3x3/data.h.template
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,8 @@

#define N 1
#define C 1
#define H {{(M - 1) * 2 + 3 + 1}}
#define W {{(N - 1) * 2 + 3 + 1}}
#define H {{(M - 1) * 1 + 3 + 1}}
#define W {{(N - 1) * 1 + 3 + 1}}
#define NEW_H {{M}}
#define NEW_W {{N}}

Expand Down
17 changes: 10 additions & 7 deletions kernels/pooling_nchw_max_d1_s2_3x3/gendata.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ def sum_pool_data(

# Define the pooling parameters
pool_size = (3, 3)
stride = 2
stride = 1

new_h = M
new_w = N
Expand All @@ -32,12 +32,15 @@ def sum_pool_data(
y_in = np.random.uniform(rmin, rmax, (n, c, new_h, new_w))
y_out = y_in.copy()

for row in range(0, H - pool_size[0] + 1, stride):
for col in range(0, W - pool_size[1] + 1, stride):
pooling_region = x[:, :, row : row + pool_size[0], col : col + pool_size[1]]
y_out[:, :, row // stride, col // stride] = np.max(
pooling_region, axis=(2, 3)
)
for row in range(new_h):
for col in range(new_w):
pooling_region = x[
:,
:,
row * stride : row * stride + pool_size[0],
col * stride : col * stride + pool_size[1],
]
y_out[:, :, row, col] = np.max(pooling_region, axis=(2, 3))

yield Define("N", n)
yield Define("C", c)
Expand Down
4 changes: 2 additions & 2 deletions kernels/pooling_nchw_max_d1_s2_3x3/linalg.mlir.template
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
func.func public @pooling_nchw_max_d1_s2_3x3(%X : tensor<1x1x{{(M - 1) * 2 + 3 + 1}}x{{(N - 1) * 2 + 3 + 1}}xf64> {"llvm.noalias"}, %Y : tensor<1x1x{{M}}x{{N}}xf64> {"llvm.noalias"}) -> tensor<1x1x{{M}}x{{N}}xf64> {
func.func public @pooling_nchw_max_d1_s2_3x3(%X : tensor<1x1x{{(M - 1) * 1 + 3 + 1}}x{{(N - 1) * 1 + 3 + 1}}xf64> {"llvm.noalias"}, %Y : tensor<1x1x{{M}}x{{N}}xf64> {"llvm.noalias"}) -> tensor<1x1x{{M}}x{{N}}xf64> {
%min_val = arith.constant -10000.0 : f64
%zeros = linalg.fill ins(%min_val : f64) outs(%Y : tensor<1x1x{{M}}x{{N}}xf64>) -> tensor<1x1x{{M}}x{{N}}xf64>
%kernel = tensor.empty() : tensor<3x3xf64>
%res = linalg.pooling_nchw_max {"dilations" = dense<1> : vector<2xi64>, "strides" = dense<2> : vector<2xi64>} ins(%X, %kernel : tensor<1x1x{{(M - 1) * 2 + 3 + 1}}x{{(N - 1) * 2 + 3 + 1}}xf64>, tensor<3x3xf64>) outs(%zeros : tensor<1x1x{{M}}x{{N}}xf64>) -> tensor<1x1x{{M}}x{{N}}xf64>
%res = linalg.pooling_nchw_max {"dilations" = dense<1> : vector<2xi64>, "strides" = dense<1> : vector<2xi64>} ins(%X, %kernel : tensor<1x1x{{(M - 1) * 1 + 3 + 1}}x{{(N - 1) * 1 + 3 + 1}}xf64>, tensor<3x3xf64>) outs(%zeros : tensor<1x1x{{M}}x{{N}}xf64>) -> tensor<1x1x{{M}}x{{N}}xf64>
func.return %res : tensor<1x1x{{M}}x{{N}}xf64>
}
8 changes: 4 additions & 4 deletions kernels/pooling_nchw_sum_d1_s2_3x3/baseline.c.template
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,12 +3,12 @@
#include <stdint.h>

void pooling_nchw_sum_d1_s2_3x3(const double* restrict x, double* restrict y) {
for (int row = 0; row < H - 3 + 1; row += 2) {
for (int col = 0; col < W - 3 + 1; col += 2) {
for (int row = 0; row < H - 3 + 1; row += 1) {
for (int col = 0; col < W - 3 + 1; col += 1) {
for (int i = 0; i < N; i++) {
for (int j = 0; j < C; j++) {
int y_row = row / 2;
int y_col = col / 2;
int y_row = row / 1;
int y_col = col / 1;
int y_index = (i * (C * NEW_H * NEW_W)) + (j * (NEW_H * NEW_W)) +
(y_row * NEW_W) + y_col;
double sum = 0.0;
Expand Down
4 changes: 2 additions & 2 deletions kernels/pooling_nchw_sum_d1_s2_3x3/data.h.template
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,8 @@

#define N 1
#define C 1
#define H {{(M - 1) * 2 + 3 + 1}}
#define W {{(N - 1) * 2 + 3 + 1}}
#define H {{(M - 1) * 1 + 3 + 1}}
#define W {{(N - 1) * 1 + 3 + 1}}
#define NEW_H {{M}}
#define NEW_W {{N}}

Expand Down
17 changes: 10 additions & 7 deletions kernels/pooling_nchw_sum_d1_s2_3x3/gendata.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ def sum_pool_data(

# Define the pooling parameters
pool_size = (3, 3)
stride = 2
stride = 1

new_h = M
new_w = N
Expand All @@ -32,12 +32,15 @@ def sum_pool_data(
y_in = np.random.uniform(rmin, rmax, (n, c, new_h, new_w))
y_out = y_in.copy()

for row in range(0, H - pool_size[0] + 1, stride):
for col in range(0, W - pool_size[1] + 1, stride):
pooling_region = x[:, :, row : row + pool_size[0], col : col + pool_size[1]]
y_out[:, :, row // stride, col // stride] = np.sum(
pooling_region, axis=(2, 3)
)
for row in range(new_h):
for col in range(new_w):
pooling_region = x[
:,
:,
row * stride : row * stride + pool_size[0],
col * stride : col * stride + pool_size[1],
]
y_out[:, :, row, col] = np.sum(pooling_region, axis=(2, 3))

yield Define("N", n)
yield Define("C", c)
Expand Down
4 changes: 2 additions & 2 deletions kernels/pooling_nchw_sum_d1_s2_3x3/linalg.mlir.template
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
func.func public @pooling_nchw_sum_d1_s2_3x3(%X : tensor<1x1x{{(M - 1) * 2 + 3 + 1}}x{{(N - 1) * 2 + 3 + 1}}xf64> {"llvm.noalias"}, %Y : tensor<1x1x{{M}}x{{N}}xf64> {"llvm.noalias"}) -> tensor<1x1x{{M}}x{{N}}xf64> {
func.func public @pooling_nchw_sum_d1_s2_3x3(%X : tensor<1x1x{{(M - 1) * 1 + 3 + 1}}x{{(N - 1) * 1 + 3 + 1}}xf64> {"llvm.noalias"}, %Y : tensor<1x1x{{M}}x{{N}}xf64> {"llvm.noalias"}) -> tensor<1x1x{{M}}x{{N}}xf64> {
%zero = arith.constant 0.0 : f64
%zeros = linalg.fill ins(%zero : f64) outs(%Y : tensor<1x1x{{M}}x{{N}}xf64>) -> tensor<1x1x{{M}}x{{N}}xf64>
%kernel = tensor.empty() : tensor<3x3xf64>
%res = linalg.pooling_nchw_sum {"dilations" = dense<1> : vector<2xi64>, "strides" = dense<2> : vector<2xi64>} ins(%X, %kernel : tensor<1x1x{{(M - 1) * 2 + 3 + 1}}x{{(N - 1) * 2 + 3 + 1}}xf64>, tensor<3x3xf64>) outs(%zeros : tensor<1x1x{{M}}x{{N}}xf64>) -> tensor<1x1x{{M}}x{{N}}xf64>
%res = linalg.pooling_nchw_sum {"dilations" = dense<1> : vector<2xi64>, "strides" = dense<1> : vector<2xi64>} ins(%X, %kernel : tensor<1x1x{{(M - 1) * 1 + 3 + 1}}x{{(N - 1) * 1 + 3 + 1}}xf64>, tensor<3x3xf64>) outs(%zeros : tensor<1x1x{{M}}x{{N}}xf64>) -> tensor<1x1x{{M}}x{{N}}xf64>
func.return %res : tensor<1x1x{{M}}x{{N}}xf64>
}
6 changes: 3 additions & 3 deletions results/kernels.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,9 +25,9 @@ matmul,4x16x8xf64,linalg_xdsl,708,1493,1490,2.811418685121107,0.0,512,578,1625,0
matmul_transb,4x16x16xf32,baseline,3386,4184,4181,2.539660056657224,1.4921875,0,706,1793,0.20850561134081513,0.3935340022296544,1794,1528,1024,0.5298287064382753,0,64,1.0,1.0,1,0.0,1794,0.5561066336019839,1432,0,0,0.42291789722386297,0,799,0.0,0.9527466036621383,0.0
matmul_transb,4x16x16xf32,snitch_stream,871,1660,1657,2.648367952522255,0.0,0,674,1785,0.7738231917336394,0.9519774011299436,708,0,0,0.8128587830080367,0,32,2.1325301204819276,2.1325301204819276,1,0.0,332,0.7793427230046949,94,0,0,0.1079219288174512,0,790,0.0,0.9207807118254879,0.0
matmul_transb,4x16x16xf32,snrt,849,1612,1609,2.648367952522255,0.0,0,674,1785,0.7938751472320377,0.9519774011299436,708,0,0,0.833922261484099,0,32,2.1325301204819276,2.1325301204819276,1,0.0,332,0.8924731182795699,40,0,0,0.04711425206124853,0,764,0.0,0.8810365135453475,0.0
pooling_nchw_max_d1_s2_3x3,4x4xf64,baseline,442,1213,1210,0.993103448275862,1.1192660550458715,0,145,144,0.32805429864253394,0.5370370370370371,270,122,109,0.6108597285067874,0,16,1.0,1.0,1,0.0,270,0.903010033444816,29,0,0,0.06561085972850679,0,772,0.0,0.6764705882352942,0.0
pooling_nchw_max_d1_s2_3x3,4x4xf64,linalg_xdsl,275,1023,1020,0.9943820224719101,0.0,0,178,177,0.6472727272727272,0.9888888888888889,180,0,0,0.6545454545454545,0,0,3.214285714285714,3.2142857142857144,1,0.0,56,0.5283018867924528,50,0,0,0.18181818181818182,0,749,0.0,0.8363636363636364,0.0
pooling_nchw_sum_d1_s2_3x3,4x4xf64,baseline,582,1341,1338,2.9767441860465116,1.1018518518518519,0,129,384,0.22164948453608246,0.5098814229249012,253,119,108,0.43470790378006874,0,16,1.0,1.0,1,0.0,253,0.9730769230769231,7,0,0,0.012027491408934709,0,760,0.0,0.44673539518900346,0.0
pooling_nchw_max_d1_s2_3x3,4x4xf64,baseline,584,1328,1325,0.995575221238938,1.1226415094339623,0,226,225,0.386986301369863,0.6330532212885154,357,119,106,0.6113013698630136,0,25,1.0,1.0,1,0.0,357,0.9153846153846154,33,0,0,0.05650684931506849,0,745,0.0,0.6678082191780821,0.0
pooling_nchw_max_d1_s2_3x3,4x4xf64,linalg_xdsl,275,1018,1015,0.9943820224719101,0.0,0,178,177,0.6472727272727272,0.9888888888888889,180,0,0,0.6545454545454545,0,0,3.214285714285714,3.2142857142857144,1,0.0,56,0.5283018867924528,50,0,0,0.18181818181818182,0,744,0.0,0.8363636363636364,0.0
pooling_nchw_sum_d1_s2_3x3,4x4xf64,baseline,902,1647,1644,2.985074626865672,1.1904761904761905,0,201,600,0.22283813747228381,0.6072507552870091,331,125,105,0.3669623059866962,0,25,1.0,1.0,1,0.0,331,0.914364640883978,31,0,0,0.03436807095343681,0,746,0.0,0.401330376940133,0.0
pooling_nchw_sum_d1_s2_3x3,4x4xf64,linalg_xdsl,271,1046,1043,2.6797752808988764,0.0,0,178,477,0.6568265682656826,0.9888888888888889,180,0,0,0.6642066420664207,0,0,3.214285714285714,3.2142857142857144,1,0.0,56,0.5384615384615384,48,0,0,0.17712177121771217,0,776,0.0,0.8413284132841328,0.0
relu,4x4xf64,baseline,142,892,889,0.9444444444444444,1.0,0,18,17,0.1267605633802817,0.36,50,16,16,0.352112676056338,0,16,1.0,1.0,1,0.0,50,0.8771929824561403,7,0,0,0.04929577464788732,0,751,0.0,0.4014084507042253,0.0
relu,4x4xf64,linalg_xdsl,72,817,814,0.9444444444444444,0.0,0,18,17,0.25,0.9,20,0,0,0.2777777777777778,0,0,3.333333333333333,3.3333333333333335,1,0.0,6,0.25,18,0,0,0.25,0,746,0.0,0.5277777777777778,0.0
Expand Down
6 changes: 3 additions & 3 deletions results/kernels.fast.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,9 +25,9 @@ matmul,4x16x8xf64,linalg_xdsl,708,1493,1490,2.811418685121107,0.0,512,578,1625,0
matmul_transb,4x16x16xf32,baseline,3386,4184,4181,2.539660056657224,1.4921875,0,706,1793,0.20850561134081513,0.3935340022296544,1794,1528,1024,0.5298287064382753,0,64,1.0,1.0,1,0.0,1794,0.5561066336019839,1432,0,0,0.42291789722386297,0,799,0.0,0.9527466036621383,0.0
matmul_transb,4x16x16xf32,snitch_stream,871,1660,1657,2.648367952522255,0.0,0,674,1785,0.7738231917336394,0.9519774011299436,708,0,0,0.8128587830080367,0,32,2.1325301204819276,2.1325301204819276,1,0.0,332,0.7793427230046949,94,0,0,0.1079219288174512,0,790,0.0,0.9207807118254879,0.0
matmul_transb,4x16x16xf32,snrt,849,1612,1609,2.648367952522255,0.0,0,674,1785,0.7938751472320377,0.9519774011299436,708,0,0,0.833922261484099,0,32,2.1325301204819276,2.1325301204819276,1,0.0,332,0.8924731182795699,40,0,0,0.04711425206124853,0,764,0.0,0.8810365135453475,0.0
pooling_nchw_max_d1_s2_3x3,4x4xf64,baseline,442,1213,1210,0.993103448275862,1.1192660550458715,0,145,144,0.32805429864253394,0.5370370370370371,270,122,109,0.6108597285067874,0,16,1.0,1.0,1,0.0,270,0.903010033444816,29,0,0,0.06561085972850679,0,772,0.0,0.6764705882352942,0.0
pooling_nchw_max_d1_s2_3x3,4x4xf64,linalg_xdsl,275,1023,1020,0.9943820224719101,0.0,0,178,177,0.6472727272727272,0.9888888888888889,180,0,0,0.6545454545454545,0,0,3.214285714285714,3.2142857142857144,1,0.0,56,0.5283018867924528,50,0,0,0.18181818181818182,0,749,0.0,0.8363636363636364,0.0
pooling_nchw_sum_d1_s2_3x3,4x4xf64,baseline,582,1341,1338,2.9767441860465116,1.1018518518518519,0,129,384,0.22164948453608246,0.5098814229249012,253,119,108,0.43470790378006874,0,16,1.0,1.0,1,0.0,253,0.9730769230769231,7,0,0,0.012027491408934709,0,760,0.0,0.44673539518900346,0.0
pooling_nchw_max_d1_s2_3x3,4x4xf64,baseline,584,1328,1325,0.995575221238938,1.1226415094339623,0,226,225,0.386986301369863,0.6330532212885154,357,119,106,0.6113013698630136,0,25,1.0,1.0,1,0.0,357,0.9153846153846154,33,0,0,0.05650684931506849,0,745,0.0,0.6678082191780821,0.0
pooling_nchw_max_d1_s2_3x3,4x4xf64,linalg_xdsl,275,1018,1015,0.9943820224719101,0.0,0,178,177,0.6472727272727272,0.9888888888888889,180,0,0,0.6545454545454545,0,0,3.214285714285714,3.2142857142857144,1,0.0,56,0.5283018867924528,50,0,0,0.18181818181818182,0,744,0.0,0.8363636363636364,0.0
pooling_nchw_sum_d1_s2_3x3,4x4xf64,baseline,902,1647,1644,2.985074626865672,1.1904761904761905,0,201,600,0.22283813747228381,0.6072507552870091,331,125,105,0.3669623059866962,0,25,1.0,1.0,1,0.0,331,0.914364640883978,31,0,0,0.03436807095343681,0,746,0.0,0.401330376940133,0.0
pooling_nchw_sum_d1_s2_3x3,4x4xf64,linalg_xdsl,271,1046,1043,2.6797752808988764,0.0,0,178,477,0.6568265682656826,0.9888888888888889,180,0,0,0.6642066420664207,0,0,3.214285714285714,3.2142857142857144,1,0.0,56,0.5384615384615384,48,0,0,0.17712177121771217,0,776,0.0,0.8413284132841328,0.0
relu,4x4xf64,baseline,142,892,889,0.9444444444444444,1.0,0,18,17,0.1267605633802817,0.36,50,16,16,0.352112676056338,0,16,1.0,1.0,1,0.0,50,0.8771929824561403,7,0,0,0.04929577464788732,0,751,0.0,0.4014084507042253,0.0
relu,4x4xf64,linalg_xdsl,72,817,814,0.9444444444444444,0.0,0,18,17,0.25,0.9,20,0,0,0.2777777777777778,0,0,3.333333333333333,3.3333333333333335,1,0.0,6,0.25,18,0,0,0.25,0,746,0.0,0.5277777777777778,0.0
Expand Down
4 changes: 2 additions & 2 deletions results/pivoted.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,8 @@ dense 8x8xf64,3206,3530,,2741,2723
fill 4x4xf64,50,,63,,
matmul 4x16x8xf64,2495,,708,,
matmul_transb 4x16x16xf32,3386,,,871,849
pooling_nchw_max_d1_s2_3x3 4x4xf64,442,,275,,
pooling_nchw_sum_d1_s2_3x3 4x4xf64,582,,271,,
pooling_nchw_max_d1_s2_3x3 4x4xf64,584,,275,,
pooling_nchw_sum_d1_s2_3x3 4x4xf64,902,,271,,
relu 4x4xf64,142,,72,,
relu 4x8xf32,297,210,,67,85
saxpy 64xf32,634,634,,,140
Expand Down
4 changes: 2 additions & 2 deletions results/pivoted.fast.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,8 @@ dense 8x8xf64,3206,3530,,2741,2723
fill 4x4xf64,50,,63,,
matmul 4x16x8xf64,2495,,708,,
matmul_transb 4x16x16xf32,3386,,,871,849
pooling_nchw_max_d1_s2_3x3 4x4xf64,442,,275,,
pooling_nchw_sum_d1_s2_3x3 4x4xf64,582,,271,,
pooling_nchw_max_d1_s2_3x3 4x4xf64,584,,275,,
pooling_nchw_sum_d1_s2_3x3 4x4xf64,902,,271,,
relu 4x4xf64,142,,72,,
relu 4x8xf32,297,210,,67,85
saxpy 64xf32,634,634,,,140
Expand Down
2 changes: 1 addition & 1 deletion results/pivoted_fpu.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ dense 8x8xf64,0.20,0.18,,0.26,0.26
fill 4x4xf64,0.02,,0.29,,
matmul 4x16x8xf64,0.21,,0.82,,
matmul_transb 4x16x16xf32,0.21,,,0.77,0.79
pooling_nchw_max_d1_s2_3x3 4x4xf64,0.33,,0.65,,
pooling_nchw_max_d1_s2_3x3 4x4xf64,0.39,,0.65,,
pooling_nchw_sum_d1_s2_3x3 4x4xf64,0.22,,0.66,,
relu 4x4xf64,0.13,,0.25,,
relu 4x8xf32,0.33,0.16,,0.28,0.22
Expand Down
2 changes: 1 addition & 1 deletion results/pivoted_fpu.fast.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ dense 8x8xf64,0.20,0.18,,0.26,0.26
fill 4x4xf64,0.02,,0.29,,
matmul 4x16x8xf64,0.21,,0.82,,
matmul_transb 4x16x16xf32,0.21,,,0.77,0.79
pooling_nchw_max_d1_s2_3x3 4x4xf64,0.33,,0.65,,
pooling_nchw_max_d1_s2_3x3 4x4xf64,0.39,,0.65,,
pooling_nchw_sum_d1_s2_3x3 4x4xf64,0.22,,0.66,,
relu 4x4xf64,0.13,,0.25,,
relu 4x8xf32,0.33,0.16,,0.28,0.22
Expand Down
4 changes: 2 additions & 2 deletions results/pivoted_ipc.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,8 @@ dense 8x8xf64,0.51,0.55,,0.39,0.33
fill 4x4xf64,0.46,,0.56,,
matmul 4x16x8xf64,0.56,,0.93,,
matmul_transb 4x16x16xf32,0.95,,,0.92,0.88
pooling_nchw_max_d1_s2_3x3 4x4xf64,0.68,,0.84,,
pooling_nchw_sum_d1_s2_3x3 4x4xf64,0.45,,0.84,,
pooling_nchw_max_d1_s2_3x3 4x4xf64,0.67,,0.84,,
pooling_nchw_sum_d1_s2_3x3 4x4xf64,0.40,,0.84,,
relu 4x4xf64,0.40,,0.53,,
relu 4x8xf32,0.57,0.51,,0.57,0.40
saxpy 64xf32,0.93,0.93,,,0.65
Expand Down
4 changes: 2 additions & 2 deletions results/pivoted_ipc.fast.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,8 @@ dense 8x8xf64,0.51,0.55,,0.39,0.33
fill 4x4xf64,0.46,,0.56,,
matmul 4x16x8xf64,0.56,,0.93,,
matmul_transb 4x16x16xf32,0.95,,,0.92,0.88
pooling_nchw_max_d1_s2_3x3 4x4xf64,0.68,,0.84,,
pooling_nchw_sum_d1_s2_3x3 4x4xf64,0.45,,0.84,,
pooling_nchw_max_d1_s2_3x3 4x4xf64,0.67,,0.84,,
pooling_nchw_sum_d1_s2_3x3 4x4xf64,0.40,,0.84,,
relu 4x4xf64,0.40,,0.53,,
relu 4x8xf32,0.57,0.51,,0.57,0.40
saxpy 64xf32,0.93,0.93,,,0.65
Expand Down
Loading