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【Hackathon No.56&38】deformable_conv_v1 算子实现 float16 数据类型支持&前向运行加速 #46111

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merged 24 commits into from
Oct 10, 2022
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【Hackathon No.56&38】deformable_conv_v1 算子实现 float16 数据类型支持&前向运行加速 #46111

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6b51055
add support fp16 for deformable
Rayman96 Sep 16, 2022
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fix error push
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fix slow part
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set datetype
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2820e9b
fix im2col_step bug and codestyle
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use fastAtomicAdd
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acc for ModulatedDeformableCol2im
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Update deformable_conv_kernel.cu
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Merge branch 'develop' into for_deformable_fp16
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82 changes: 43 additions & 39 deletions paddle/phi/kernels/cpu/deformable_conv_grad_kernel.cc
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Expand Up @@ -43,6 +43,7 @@ inline void ModulatedDeformableCol2imCPUKernel(
const int height_col,
const int width_col,
T* grad_im) {
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
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不要求支持CPU的FP16 Kernel，因此这个文件暂时不要修改。

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有的函数由于cc和cu文件共用，就在cpu的文件中增加了少量代码以适配。

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猜测有没有一种可能，单测出错的原因是因为你这里使用了MT？

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猜测有没有一种可能，单测出错的原因是因为你这里使用了MT？

好像有可能🤔 我在cc文件里不用MT试试

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猜测有没有一种可能，单测出错的原因是因为你这里使用了MT？

仔细看了下单测的过程，应该不是由于kernel中代码的问题。是因为op_test内check_grad使用的是(y_neg-y_pos) / delta /2的数值方式，而y_neg和y_pos本身是float16的小数位不够，精度不够高，导致分子无法表示真实差值。感觉在float16类型是可能用数值定义法来测试好像不太行😂。
目前我是通过人工核验float16和float32情况下的grad一致性，因为float32是可以利用数值法核验的。
image

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目前我是通过人工核验float16和float32情况下的grad一致性，因为float32是可以利用数值法核验的。

「人工校验」的方式，是说和python实现的reference版本结果进行对比？确保如下2个方面：
（1）np数据需要显式定义数据类型，默认是double
（2）reference实现也务必使用float16作为输入输出、但是使用float类型进行计算，保证reference实现和cuda kernel中实现的方式是一致。

另外，conv类算子可能本身误差较大，我看fp32的单测，max_relative_error已经设置到0.05、0.1这么大了。

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目前我是通过人工核验float16和float32情况下的grad一致性，因为float32是可以利用数值法核验的。

「人工校验」的方式，是说和python实现的reference版本结果进行对比？确保如下2个方面： （1）np数据需要显式定义数据类型，默认是double （2）reference实现也务必使用float16作为输入输出、但是使用float类型进行计算，保证reference实现和cuda kernel中实现的方式是一致。

另外，conv类算子可能本身误差较大，我看fp32的单测，max_relative_error已经设置到0.05、0.1这么大了。

不是的，是直接使用c++版本的float32和float16二者的grad打印出来进行比较，可以参考这里的截图。https://github.com/PaddlePaddle/Paddle/pull/46111#issuecomment-1253283724。
二者一致应该可以在人工上确认我fp16的实现上应该是没问题的。
ps：np目前python版只有前向infer的实现，fp32和fp16是没有问题的。目前单测的后向计算grad都是统一使用op_test.py中的数值定义法（本身存在精度不够和误差大的问题），这也是造成grad无法通过单测的原因。

for (int thread = 0; thread < num_kernels; thread++) {
const int j = (thread / width_col / height_col / batch_size) % kernel_w;
const int i =
Expand All @@ -67,17 +68,17 @@ inline void ModulatedDeformableCol2imCPUKernel(
((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out;
const int data_mask_hw_ptr =
((i * kernel_w + j) * height_col + h_out) * width_col + w_out;
const T offset_h = data_offset_ptr[data_offset_h_ptr];
const T offset_w = data_offset_ptr[data_offset_w_ptr];
const T cur_inv_h_data = h_in + i * dilation_h + offset_h;
const T cur_inv_w_data = w_in + j * dilation_w + offset_w;
const MT offset_h = static_cast<MT>(data_offset_ptr[data_offset_h_ptr]);
const MT offset_w = static_cast<MT>(data_offset_ptr[data_offset_w_ptr]);
const MT cur_inv_h_data = h_in + i * dilation_h + offset_h;
const MT cur_inv_w_data = w_in + j * dilation_w + offset_w;

T cur_top_grad = data_col[thread];
MT cur_top_grad = static_cast<MT>(data_col[thread]);
if (data_mask) {
const T* data_mask_ptr =
data_mask + (b * deformable_group + deformable_group_index) *
kernel_h * kernel_w * height_col * width_col;
const T mask = data_mask_ptr[data_mask_hw_ptr];
const MT mask = static_cast<MT>(data_mask_ptr[data_mask_hw_ptr]);
cur_top_grad *= mask;
}
const int cur_h = static_cast<int>(cur_inv_h_data);
Expand All @@ -89,22 +90,23 @@ inline void ModulatedDeformableCol2imCPUKernel(
abs(cur_inv_w_data - (cur_w + dx)) < 1) {
int cur_bottom_grad_pos =
((b * channels + c) * height + cur_h + dy) * width + cur_w + dx;
T weight = DmcnGetGradientWeight(cur_inv_h_data,
cur_inv_w_data,
cur_h + dy,
cur_w + dx,
height,
width);
MT weight = DmcnGetGradientWeight(cur_inv_h_data,
cur_inv_w_data,
cur_h + dy,
cur_w + dx,
height,
width);

*(grad_im + cur_bottom_grad_pos) =
*(grad_im + cur_bottom_grad_pos) + weight * cur_top_grad;
*(grad_im + cur_bottom_grad_pos) +
static_cast<T>(weight * cur_top_grad);
}
}
}
}
}

template <typename T, typename Context>
template <typename T, typename MT, typename Context>
void ModulatedDeformableCol2im(const Context& dev_ctx,
const T* data_col,
const T* data_offset,
Expand All @@ -116,7 +118,7 @@ void ModulatedDeformableCol2im(const Context& dev_ctx,
const std::vector<int>& stride,
const std::vector<int>& dilation,
const int deformable_group,
T* grad_im) {
MT* grad_im) {
int channel_per_deformable_group = im_shape[0] / deformable_group;
int num_kernels = col_shape[0] * col_shape[1] * col_shape[2] * col_shape[3];

Expand Down Expand Up @@ -169,8 +171,9 @@ void ModulatedDeformableCol2imCoordCPUKernel(
const int width_col,
T* grad_offset,
T* grad_mask) {
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
for (int i = 0; i < num_kernels; i++) {
T val = 0, mval = 0;
MT val = 0, mval = 0;
const int w = i % width_col;
const int h = (i / width_col) % height_col;
const int c = (i / width_col / height_col) % offset_channels;
Expand Down Expand Up @@ -215,40 +218,41 @@ void ModulatedDeformableCol2imCoordCPUKernel(
const int data_offset_w_ptr =
(((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col +
w_out);
const T offset_h = data_offset_ptr[data_offset_h_ptr];
const T offset_w = data_offset_ptr[data_offset_w_ptr];
T inv_h = h_in + i * dilation_h + offset_h;
T inv_w = w_in + j * dilation_w + offset_w;
const MT offset_h = static_cast<MT>(data_offset_ptr[data_offset_h_ptr]);
const MT offset_w = static_cast<MT>(data_offset_ptr[data_offset_w_ptr]);
MT inv_h = h_in + i * dilation_h + offset_h;
MT inv_w = w_in + j * dilation_w + offset_w;
if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) {
inv_h = inv_w = -2;
} else {
mval += data_col_ptr[col_pos] *
funcs::DmcnIm2colBilinear(data_im_ptr + cnt * height * width,
width,
height,
width,
inv_h,
inv_w);
mval +=
static_cast<MT>(data_col_ptr[col_pos]) *
funcs::DmcnIm2colBilinear<T, MT>(data_im_ptr + cnt * height * width,
width,
height,
width,
inv_h,
inv_w);
}
const T weight =
DmcnGetCoordinateWeight(inv_h,
inv_w,
height,
width,
data_im_ptr + cnt * height * width,
width,
bp_dir);
const MT weight =
DmcnGetCoordinateWeight<T, MT>(inv_h,
inv_w,
height,
width,
data_im_ptr + cnt * height * width,
width,
bp_dir);
if (data_mask_ptr) {
const int data_mask_hw_ptr =
(((i * kernel_w + j) * height_col + h_out) * width_col + w_out);
const T mask = data_mask_ptr[data_mask_hw_ptr];
val += weight * data_col_ptr[col_pos] * mask;
const MT mask = static_cast<MT>(data_mask_ptr[data_mask_hw_ptr]);
val += weight * static_cast<MT>(data_col_ptr[col_pos]) * mask;
} else {
val += weight * data_col_ptr[col_pos];
val += weight * static_cast<MT>(data_col_ptr[col_pos]);
}
cnt += 1;
}
grad_offset[i] = val;
grad_offset[i] = static_cast<T>(val);
if (grad_mask && offset_c % 2 == 0)
grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h *
kernel_w +
Expand Down
25 changes: 14 additions & 11 deletions paddle/phi/kernels/funcs/deformable_conv_functor.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,10 @@
// limitations under the License.

#include "paddle/phi/kernels/funcs/deformable_conv_functor.h"

#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/amp_type_traits.h"

namespace phi {
namespace funcs {
Expand Down Expand Up @@ -42,6 +44,7 @@ inline void ModulatedDeformableIm2colCPUKernel(
const int height_col,
const int width_col,
T* data_col) {
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
for (int i = 0; i < num_kernels; i++) {
const int w_col = i % width_col;
const int h_col = (i / width_col) % height_col;
Expand Down Expand Up @@ -76,22 +79,22 @@ inline void ModulatedDeformableIm2colCPUKernel(
((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col +
w_col;

const T offset_h = data_offset_ptr[data_offset_h_ptr];
const T offset_w = data_offset_ptr[data_offset_w_ptr];
T val = static_cast<T>(0);
const T h_im = h_in + i * dilation_h + offset_h;
const T w_im = w_in + j * dilation_w + offset_w;
const MT offset_h = static_cast<MT>(data_offset_ptr[data_offset_h_ptr]);
const MT offset_w = static_cast<MT>(data_offset_ptr[data_offset_w_ptr]);
MT val = static_cast<MT>(0);
const MT h_im = h_in + i * dilation_h + offset_h;
const MT w_im = w_in + j * dilation_w + offset_w;
if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) {
val =
DmcnIm2colBilinear(data_im_ptr, width, height, width, h_im, w_im);
val = DmcnIm2colBilinear<T, MT>(
data_im_ptr, width, height, width, h_im, w_im);
}
*data_col_ptr = val;
if (data_mask_ptr) {
const int data_mask_hw_ptr =
((i * kernel_w + j) * height_col + h_col) * width_col + w_col;
const T mask = data_mask_ptr[data_mask_hw_ptr];
*data_col_ptr *= mask;
const MT mask = static_cast<MT>(data_mask_ptr[data_mask_hw_ptr]);
val *= mask;
}
*data_col_ptr = static_cast<T>(val);
data_col_ptr += batch_size * height_col * width_col;
}
}
Expand Down
41 changes: 30 additions & 11 deletions paddle/phi/kernels/funcs/deformable_conv_functor.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -12,8 +12,11 @@
// See the License for the specific language governing permissions and
// limitations under the License.

#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/kernels/funcs/deformable_conv_functor.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/common/amp_type_traits.h"
#include "paddle/phi/common/float16.h"

namespace phi {
namespace funcs {
Expand Down Expand Up @@ -51,6 +54,8 @@ __global__ void ModulatedDeformableIm2colGpuKernel(
T* data_col) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int offset = blockDim.x * gridDim.x;

using MT = typename phi::dtype::MPTypeTrait<T>::Type;
for (size_t i = index; i < nthreads; i += offset) {
const int w_col = i % width_col;
const int h_col = (i / width_col) % height_col;
Expand Down Expand Up @@ -85,22 +90,22 @@ __global__ void ModulatedDeformableIm2colGpuKernel(
((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col +
w_col;

const T offset_h = data_offset_ptr[data_offset_h_ptr];
const T offset_w = data_offset_ptr[data_offset_w_ptr];
T val = static_cast<T>(0);
const T h_im = h_in + i * dilation_h + offset_h;
const T w_im = w_in + j * dilation_w + offset_w;
const MT offset_h = static_cast<MT>(data_offset_ptr[data_offset_h_ptr]);
const MT offset_w = static_cast<MT>(data_offset_ptr[data_offset_w_ptr]);
MT val = static_cast<MT>(0);
const MT h_im = h_in + i * dilation_h + offset_h;
const MT w_im = w_in + j * dilation_w + offset_w;
if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) {
val =
DmcnIm2colBilinear(data_im_ptr, width, height, width, h_im, w_im);
val = DmcnIm2colBilinear<T, MT>(
data_im_ptr, width, height, width, h_im, w_im);
}
*data_col_ptr = val;
if (data_mask_ptr) {
const int data_mask_hw_ptr =
((i * kernel_w + j) * height_col + h_col) * width_col + w_col;
const T mask = data_mask_ptr[data_mask_hw_ptr];
*data_col_ptr *= mask;
const MT mask = static_cast<MT>(data_mask_ptr[data_mask_hw_ptr]);
val *= mask;
}
*data_col_ptr = static_cast<T>(val);
data_col_ptr += batch_size * height_col * width_col;
}
}
Expand Down Expand Up @@ -164,6 +169,20 @@ template void ModulatedDeformableIm2col(
const int deformable_groups,
float* data_col);

template void ModulatedDeformableIm2col(
const phi::GPUContext& dev_ctx,
const phi::dtype::float16* data_im,
const phi::dtype::float16* data_offset,
const phi::dtype::float16* data_mask,
const std::vector<int64_t>& im_shape,
const std::vector<int64_t>& col_shape,
const std::vector<int64_t>& filter_shape,
const std::vector<int>& paddings,
const std::vector<int>& strides,
const std::vector<int>& dilations,
const int deformable_groups,
phi::dtype::float16* data_col);

template void ModulatedDeformableIm2col(
const phi::GPUContext& dev_ctx,
const double* data_im,
Expand Down
55 changes: 29 additions & 26 deletions paddle/phi/kernels/funcs/deformable_conv_functor.h
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Original file line number Diff line number Diff line change
Expand Up @@ -14,44 +14,47 @@

#pragma once

#include "paddle/phi/common/amp_type_traits.h"
#include "paddle/phi/common/float16.h"
#include "paddle/phi/core/dense_tensor.h"

namespace phi {
namespace funcs {

template <typename T>
HOSTDEVICE T DmcnIm2colBilinear(const T* bottom_data,
const int data_width,
const int height,
const int width,
T h,
T w) {
template <typename T, typename MT>
HOSTDEVICE MT DmcnIm2colBilinear(const T* bottom_data,
const int data_width,
const int height,
const int width,
MT h,
MT w) {
int h_low = floor(h);
int w_low = floor(w);
int h_high = h_low + 1;
int w_high = w_low + 1;

T lh = h - h_low;
T lw = w - w_low;
T hh = 1 - lh;
T hw = 1 - lw;
MT lh = h - h_low;
MT lw = w - w_low;
MT hh = 1 - lh;
MT hw = 1 - lw;

T v1 =
(h_low >= 0 && w_low >= 0) ? bottom_data[h_low * data_width + w_low] : 0;
T v2 = (h_low >= 0 && w_high <= width - 1)
? bottom_data[h_low * data_width + w_high]
: 0;
T v3 = (h_high <= height - 1 && w_low >= 0)
? bottom_data[h_high * data_width + w_low]
: 0;
T v4 = (h_high <= height - 1 && w_high <= width - 1)
? bottom_data[h_high * data_width + w_high]
: 0;
MT v1 = (h_low >= 0 && w_low >= 0)
? static_cast<MT>(bottom_data[h_low * data_width + w_low])
: 0;
MT v2 = (h_low >= 0 && w_high <= width - 1)
? static_cast<MT>(bottom_data[h_low * data_width + w_high])
: 0;
MT v3 = (h_high <= height - 1 && w_low >= 0)
? static_cast<MT>(bottom_data[h_high * data_width + w_low])
: 0;
MT v4 = (h_high <= height - 1 && w_high <= width - 1)
? static_cast<MT>(bottom_data[h_high * data_width + w_high])
: 0;

T w1 = hh * hw;
T w2 = hh * lw;
T w3 = lh * hw;
T w4 = lh * lw;
MT w1 = hh * hw;
MT w2 = hh * lw;
MT w3 = lh * hw;
MT w4 = lh * lw;

return w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4;
}
Expand Down
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