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deconvolution dynamic weight #5119

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Oct 31, 2023
Merged

deconvolution dynamic weight #5119

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1623ec9
deconvolution dynamic weight
nihui Oct 30, 2023
c78bb2b
pnnx conversion
nihui Oct 31, 2023
5002853
wip
nihui Oct 31, 2023
d19508b
fix build
nihui Oct 31, 2023
3498208
deconv1d dynamic weight
nihui Oct 31, 2023
f559fff
test++
nihui Oct 31, 2023
247691a
fix
nihui Oct 31, 2023
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4 changes: 4 additions & 0 deletions docs/developer-guide/operators.md
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Original file line number Diff line number Diff line change
Expand Up @@ -529,6 +529,7 @@ y = activation(x3, act_type, act_params)
| 19 | output_pad_bottom| int | output_pad_right | |
| 20 | output_w | int | 0 | |
| 21 | output_h | int | output_w | |
| 28 | dynamic_weight| int | 0 | |

| weight | type | shape |
| ------------- | ----- | --------------------- |
Expand Down Expand Up @@ -558,6 +559,7 @@ y = activation(x3, act_type, act_params)
| 15 | pad_right | int | pad_left | |
| 18 | output_pad_right| int | 0 | |
| 20 | output_w | int | 0 | |
| 28 | dynamic_weight| int | 0 | |

| weight | type | shape |
| ------------- | ----- | --------------------- |
Expand Down Expand Up @@ -638,6 +640,7 @@ y = activation(x3, act_type, act_params)
| 19 | output_pad_bottom| int | output_pad_right | |
| 20 | output_w | int | 0 | |
| 21 | output_h | int | output_w | |
| 28 | dynamic_weight| int | 0 | |

| weight | type | shape |
| ------------- | ----- | --------------------- |
Expand Down Expand Up @@ -668,6 +671,7 @@ y = activation(x3, act_type, act_params)
| 15 | pad_right | int | pad_left | |
| 18 | output_pad_right| int | 0 | |
| 20 | output_w | int | 0 | |
| 28 | dynamic_weight| int | 0 | |

| weight | type | shape |
| ------------- | ----- | --------------------- |
Expand Down
4 changes: 2 additions & 2 deletions src/layer/arm/convolution_arm.cpp
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Expand Up @@ -806,9 +806,9 @@ int Convolution_arm::forward(const std::vector<Mat>& bottom_blobs, std::vector<M
pd.set(1, _kernel_w);
pd.set(11, _kernel_h);
pd.set(2, dilation_w);
pd.set(21, dilation_h);
pd.set(12, dilation_h);
pd.set(3, stride_w);
pd.set(31, stride_h);
pd.set(13, stride_h);
pd.set(4, pad_left);
pd.set(15, pad_right);
pd.set(14, pad_top);
Expand Down
166 changes: 144 additions & 22 deletions src/layer/arm/deconvolution_arm.cpp
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Expand Up @@ -49,6 +49,9 @@ Deconvolution_arm::Deconvolution_arm()

int Deconvolution_arm::create_pipeline(const Option& opt)
{
if (dynamic_weight)
return 0;

activation = create_activation_layer(activation_type, activation_params, opt);

#if NCNN_ARM82
Expand Down Expand Up @@ -750,6 +753,146 @@ int Deconvolution_arm::forward(const Mat& bottom_blob, Mat& top_blob, const Opti
return 0;
}

int Deconvolution_arm::forward(const std::vector<Mat>& bottom_blobs, std::vector<Mat>& top_blobs, const Option& opt) const
{
const Mat& bottom_blob = bottom_blobs[0];
const Mat& _weight_data = bottom_blobs[1];
Mat& top_blob = top_blobs[0];

const int _num_input = bottom_blob.c * bottom_blob.elempack;
const int _kernel_w = _weight_data.w;
const int _kernel_h = _weight_data.h;
const int _num_output = _weight_data.d * 1;

Mat weight_data_flattened;
flatten(_weight_data, weight_data_flattened, opt);
if (weight_data_flattened.empty())
return -100;

#if NCNN_ARM82
if (opt.use_fp16_storage && cpu_support_arm_asimdhp() && weight_data_flattened.elembits() == 16)
{
Mat weight_data_flattened_fp32;
cast_float16_to_float32(weight_data_flattened, weight_data_flattened_fp32, opt);
weight_data_flattened = weight_data_flattened_fp32;
}
#endif // NCNN_ARM82
#if NCNN_BF16
if (opt.use_bf16_storage && weight_data_flattened.elembits() == 16)
{
Mat weight_data_flattened_fp32;
cast_bfloat16_to_float32(weight_data_flattened, weight_data_flattened_fp32, opt);
weight_data_flattened = weight_data_flattened_fp32;
}
#endif // NCNN_BF16

// weight_data_flattened as pack1
weight_data_flattened.w *= weight_data_flattened.elempack;
weight_data_flattened.elemsize /= weight_data_flattened.elempack;
weight_data_flattened.elempack = 1;

// transpose group-inch/group-outch/group-kh-kw to group-outch/group-inch/group-kh-kw
Mat weight_data_transposed;
{
weight_data_transposed.create(_kernel_w * _kernel_h * _num_output * _num_input / 1, 4u, opt.workspace_allocator);
if (weight_data_transposed.empty())
return -100;

const int outch_g = _num_output / 1;
const int inch_g = _num_input / 1;
const int maxk = _kernel_h * _kernel_w;

for (int g = 0; g < 1; g++)
{
// reorder weight from inch-outch to outch-inch
float* wg2 = (float*)weight_data_transposed + g * outch_g * inch_g * maxk;
const float* wg = (const float*)weight_data_flattened + g * inch_g * outch_g * maxk;
for (int i = 0; i < outch_g; i++)
{
for (int j = 0; j < inch_g; j++)
{
for (int k = 0; k < maxk; k++)
{
wg2[(i * inch_g + j) * maxk + k] = wg[(j * outch_g + i) * maxk + k];
}
}
}
}
}

Mat bias_data_flattened;
if (bias_term)
{
const Mat& _bias_data = bottom_blobs[2];
flatten(_bias_data, bias_data_flattened, opt);
if (bias_data_flattened.empty())
return -100;

#if NCNN_ARM82
if (opt.use_fp16_storage && cpu_support_arm_asimdhp() && bias_data_flattened.elembits() == 16)
{
Mat bias_data_flattened_fp32;
cast_float16_to_float32(bias_data_flattened, bias_data_flattened_fp32, opt);
bias_data_flattened = bias_data_flattened_fp32;
}
#endif // NCNN_ARM82
#if NCNN_BF16
if (opt.use_bf16_storage && bias_data_flattened.elembits() == 16)
{
Mat bias_data_flattened_fp32;
cast_bfloat16_to_float32(bias_data_flattened, bias_data_flattened_fp32, opt);
bias_data_flattened = bias_data_flattened_fp32;
}
#endif // NCNN_BF16

// bias_data_flattened as pack1
bias_data_flattened.w *= bias_data_flattened.elempack;
bias_data_flattened.elemsize /= bias_data_flattened.elempack;
bias_data_flattened.elempack = 1;
}

ncnn::Layer* op = ncnn::create_layer(ncnn::LayerType::Deconvolution);

ncnn::ParamDict pd;
pd.set(0, _num_output);
pd.set(1, _kernel_w);
pd.set(11, _kernel_h);
pd.set(2, dilation_w);
pd.set(12, dilation_h);
pd.set(3, stride_w);
pd.set(13, stride_h);
pd.set(4, pad_left);
pd.set(15, pad_right);
pd.set(14, pad_top);
pd.set(16, pad_bottom);
pd.set(18, output_pad_right);
pd.set(19, output_pad_bottom);
pd.set(20, output_w);
pd.set(21, output_h);
pd.set(5, bias_term);
pd.set(6, weight_data_transposed.w);
pd.set(9, activation_type);
pd.set(10, activation_params);

op->load_param(pd);

ncnn::Mat weights[2];
weights[0] = weight_data_transposed;
weights[1] = bias_data_flattened;

op->load_model(ncnn::ModelBinFromMatArray(weights));

op->create_pipeline(opt);

op->forward(bottom_blob, top_blob, opt);

op->destroy_pipeline(opt);

delete op;

return 0;
}

#if NCNN_BF16
int Deconvolution_arm::create_pipeline_bf16s(const Option& opt)
{
Expand Down Expand Up @@ -1167,28 +1310,7 @@ int Deconvolution_arm::forward_bf16s(const Mat& bottom_blob, Mat& top_blob, cons
kptr += maxk;
}

if (activation_type == 1)
{
sum = std::max(sum, 0.f);
}
else if (activation_type == 2)
{
float slope = activation_params[0];
sum = sum > 0.f ? sum : sum * slope;
}
else if (activation_type == 3)
{
float min = activation_params[0];
float max = activation_params[1];
if (sum < min)
sum = min;
if (sum > max)
sum = max;
}
else if (activation_type == 4)
{
sum = 1.f / (1.f + expf(-sum));
}
sum = activation_ss(sum, activation_type, activation_params);

outptr[j] = float32_to_bfloat16(sum);
}
Expand Down
2 changes: 2 additions & 0 deletions src/layer/arm/deconvolution_arm.h
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Expand Up @@ -29,6 +29,8 @@ class Deconvolution_arm : virtual public Deconvolution

virtual int forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const;

virtual int forward(const std::vector<Mat>& bottom_blobs, std::vector<Mat>& top_blobs, const Option& opt) const;

protected:
#if NCNN_ARM82
int create_pipeline_fp16s(const Option& opt);
Expand Down
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