File size: 6,666 Bytes
12acbba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | // Copyright (c) Microsoft Corporation.
// Licensed under the MIT License.
#include "def.h"
namespace F = torch::nn::functional;
void DepthConvProxy::set_param(
const torch::Tensor& dc_conv1_weight, const torch::Tensor& dc_conv1_bias,
const torch::Tensor& dc_depth_conv_weight, const torch::Tensor& dc_depth_conv_bias,
const torch::Tensor& dc_conv2_weight, const torch::Tensor& dc_conv2_bias,
const torch::Tensor& ffn_conv1_weight, const torch::Tensor& ffn_conv1_bias,
const torch::Tensor& ffn_conv2_weight, const torch::Tensor& ffn_conv2_bias, const bool shortcut)
{
_dc_conv1_weight = dc_conv1_weight;
_dc_conv1_bias = dc_conv1_bias;
_dc_depth_conv_weight = dc_depth_conv_weight;
_dc_conv2_weight = dc_conv2_weight;
_dc_conv2_bias = F::conv2d(dc_depth_conv_bias.reshape({ 1, -1, 1, 1 }), dc_conv2_weight);
_dc_conv2_bias = _dc_conv2_bias.index({ 0, torch::indexing::Slice(), 0, 0 }) + dc_conv2_bias;
_ffn_conv1_weight = ffn_conv1_weight;
_ffn_conv1_bias = ffn_conv1_bias;
_ffn_conv2_weight = ffn_conv2_weight;
_ffn_conv2_bias = ffn_conv2_bias;
_shortcut = shortcut;
}
void DepthConvProxy::set_param_with_adaptor(
const torch::Tensor& dc_conv1_weight, const torch::Tensor& dc_conv1_bias,
const torch::Tensor& dc_depth_conv_weight, const torch::Tensor& dc_depth_conv_bias,
const torch::Tensor& dc_conv2_weight, const torch::Tensor& dc_conv2_bias,
const torch::Tensor& ffn_conv1_weight, const torch::Tensor& ffn_conv1_bias,
const torch::Tensor& ffn_conv2_weight, const torch::Tensor& ffn_conv2_bias,
const torch::Tensor& adaptor_weight, const torch::Tensor& adaptor_bias, const bool shortcut)
{
_dc_conv1_weight = F::conv2d(torch::transpose(adaptor_weight, 0, 1), dc_conv1_weight);
_dc_conv1_weight = torch::transpose(_dc_conv1_weight, 0, 1);
_dc_conv1_weight = torch::cat({ _dc_conv1_weight, adaptor_weight }, 0);
_dc_conv1_bias = F::conv2d(adaptor_bias.reshape({ 1, -1, 1, 1 }), dc_conv1_weight);
_dc_conv1_bias = _dc_conv1_bias.index({ 0, torch::indexing::Slice(), 0, 0 }) + dc_conv1_bias;
_dc_depth_conv_weight = dc_depth_conv_weight;
_dc_conv2_weight = dc_conv2_weight;
_dc_conv2_bias = F::conv2d(dc_depth_conv_bias.reshape({ 1, -1, 1, 1 }), dc_conv2_weight);
_dc_conv2_bias = _dc_conv2_bias.index({ 0, torch::indexing::Slice(), 0, 0 }) + dc_conv2_bias;
_dc_conv2_bias = _dc_conv2_bias + adaptor_bias;
_ffn_conv1_weight = ffn_conv1_weight;
_ffn_conv1_bias = ffn_conv1_bias;
_ffn_conv2_weight = ffn_conv2_weight;
_ffn_conv2_bias = ffn_conv2_bias;
_shortcut = shortcut;
_adaptor = true;
}
std::tuple<torch::Tensor, torch::Tensor> DepthConvProxy::forward_common(const torch::Tensor& x)
{
auto identity = x;
// depthconv
torch::Tensor out;
if (_adaptor) {
// NOTE: Here we always fuse adaptor with the first conv1x1 (when even in_ch > out_ch).
// It brings larger MACs, but it faster on A100 due to lower memory cost.
auto out_identity = F::conv2d(identity, _dc_conv1_weight);
auto chunks = torch::chunk(out_identity, 2, 1);
out = chunks[0];
identity = chunks[1];
} else {
out = F::conv2d(identity, _dc_conv1_weight);
}
out = bias_wsilu_depthwise_conv2d_cuda(out, _dc_depth_conv_weight, _dc_conv1_bias);
out = F::conv2d(out, _dc_conv2_weight);
if (_shortcut) {
bias_shortcut_2_cuda(out, _dc_conv2_bias, identity);
} else {
bias_shortcut_cuda(out, _dc_conv2_bias, identity);
identity = out;
}
// ffn
out = F::conv2d(out, _ffn_conv1_weight);
bias_wsilu_chunk_add_cuda(out, _ffn_conv1_bias);
out = F::conv2d(out, _ffn_conv2_weight);
return { out, identity };
}
torch::Tensor DepthConvProxy::forward(const torch::Tensor& x)
{
auto [out, identity] = forward_common(x);
bias_shortcut_cuda(out, _ffn_conv2_bias, identity);
return out;
}
torch::Tensor DepthConvProxy::forward_with_quant_step(const torch::Tensor& x,
const torch::Tensor& quant_step)
{
auto [out, identity] = forward_common(x);
bias_shortcut_with_quant_step_cuda(out, _ffn_conv2_bias, quant_step, identity);
return out;
}
torch::Tensor DepthConvProxy::forward_with_cat(const torch::Tensor& x, const torch::Tensor& to_cat,
const bool cat_at_front)
{
auto [t, identity] = forward_common(x);
auto t_shape = t.sizes();
auto B = t_shape[0];
auto C = t_shape[1];
auto H = t_shape[2];
auto W = t_shape[3];
auto add_ch = to_cat.sizes()[1];
auto out = torch::empty({ B, C + add_ch, H, W }, t.options());
if (cat_at_front) {
auto t_out = out.narrow(1, add_ch, C);
bias_shortcut_no_inplace_cuda(t_out, t, _ffn_conv2_bias, identity);
out.narrow(1, 0, add_ch) = to_cat;
} else {
auto t_out = out.narrow(1, 0, C);
bias_shortcut_no_inplace_cuda(t_out, t, _ffn_conv2_bias, identity);
out.narrow(1, C, add_ch) = to_cat;
}
return out;
}
void SubpelConv2xProxy::set_param(const torch::Tensor& weight, const torch::Tensor& bias,
const int padding)
{
_weight = weight;
_bias = bias;
_padding = padding;
}
torch::Tensor SubpelConv2xProxy::forward(const torch::Tensor& x)
{
auto t = F::conv2d(x, _weight, F::Conv2dFuncOptions().padding(_padding));
auto t_shape = t.sizes();
auto B = t_shape[0];
auto C = t_shape[1];
auto H = t_shape[2];
auto W = t_shape[3];
auto out = torch::empty({ B, C / 4, H * 2, W * 2 }, t.options());
assert(B == 1);
bias_pixel_shuffle_2_cuda(out, t, _bias, C, H * W, W);
return out;
}
torch::Tensor SubpelConv2xProxy::forward_with_cat(const torch::Tensor& x, const torch::Tensor& to_cat,
const bool cat_at_front)
{
auto t = F::conv2d(x, _weight, F::Conv2dFuncOptions().padding(_padding));
auto t_shape = t.sizes();
auto B = t_shape[0];
auto C = t_shape[1];
auto H = t_shape[2];
auto W = t_shape[3];
auto add_ch = to_cat.sizes()[1];
auto out = torch::empty({ B, add_ch + C / 4, H * 2, W * 2 }, t.options());
assert(B == 1);
if (cat_at_front) {
auto t_out = out.narrow(1, add_ch, C / 4);
bias_pixel_shuffle_2_cuda(t_out, t, _bias, C, H * W, W);
out.narrow(1, 0, add_ch) = to_cat;
} else {
auto t_out = out.narrow(1, 0, C / 4);
bias_pixel_shuffle_2_cuda(t_out, t, _bias, C, H * W, W);
out.narrow(1, C / 4, add_ch) = to_cat;
}
return out;
}
|