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code format fix
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@SZUwishion
SZUwishion committed Oct 15, 2024
1 parent 3fce044 commit 5ff6210
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Showing 2 changed files with 67 additions and 67 deletions.
132 changes: 66 additions & 66 deletions tools/pnnx/src/ir.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1461,18 +1461,18 @@ void Graph::flops_memops_sum()
int output_size = std::accumulate(output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
int out_features = op->attrs.at("data").shape[0];
flops += input_size * out_features;
if(op->has_param("bias"))
if (op->has_param("bias"))
{
flops += out_features;
}
memops += input_size + output_size;
}
else if (sub_type == "avgpool1d"
|| sub_type == "avgpool2d"
|| sub_type == "avgpool3d"
|| sub_type == "adaptive_avgpool1d"
|| sub_type == "adaptive_avgpool2d"
|| sub_type == "adaptive_avgpool3d")
|| sub_type == "avgpool2d"
|| sub_type == "avgpool3d"
|| sub_type == "adaptive_avgpool1d"
|| sub_type == "adaptive_avgpool2d"
|| sub_type == "adaptive_avgpool3d")
{
std::vector<int> input_shape = op->inputs[0]->shape;
std::vector<int> output_shape = op->outputs[0]->shape;
Expand All @@ -1482,11 +1482,11 @@ void Graph::flops_memops_sum()
memops += input_size + output_size;
}
else if (sub_type == "prelu"
|| sub_type == "elu"
|| sub_type == "leaky_relu"
|| sub_type == "gelu"
|| sub_type == "silu"
|| sub_type == "softmax")
|| sub_type == "elu"
|| sub_type == "leaky_relu"
|| sub_type == "gelu"
|| sub_type == "silu"
|| sub_type == "softmax")
{
std::vector<int> input_shape = op->inputs[0]->shape;
std::vector<int> output_shape = op->outputs[0]->shape;
Expand All @@ -1496,8 +1496,8 @@ void Graph::flops_memops_sum()
extra_memops += input_size + output_size;
}
else if (sub_type == "unsample"
|| sub_type == "upsample_nearest"
|| sub_type == "upsample_bilinear")
|| sub_type == "upsample_nearest"
|| sub_type == "upsample_bilinear")
{
std::vector<int> input_shape = op->inputs[0]->shape;
std::vector<int> output_shape = op->outputs[0]->shape;
Expand Down Expand Up @@ -1534,7 +1534,7 @@ void Graph::flops_memops_sum()
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int num_elements = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
if((op->has_param("affine") && op->params.at("affine").b)
if ((op->has_param("affine") && op->params.at("affine").b)
|| (op->has_param("elementwise_affine") && op->params.at("elementwise_affine").b))
{
extra_flops += 2 * num_elements;
Expand All @@ -1547,11 +1547,11 @@ void Graph::flops_memops_sum()
}
}
else if (sub_type == "Conv1d"
|| sub_type == "Conv2d"
|| sub_type == "Conv3d"
|| sub_type == "ConvTranspose1d"
|| sub_type == "ConvTranspose2d"
|| sub_type == "ConvTranspose3d")
|| sub_type == "Conv2d"
|| sub_type == "Conv3d"
|| sub_type == "ConvTranspose1d"
|| sub_type == "ConvTranspose2d"
|| sub_type == "ConvTranspose3d")
{
int c = op->params.at("in_channels").i;
std::vector<int> k = op->params.at("kernel_size").ai;
Expand All @@ -1561,17 +1561,17 @@ void Graph::flops_memops_sum()
int input_size = std::accumulate(input_shape.begin(), input_shape.end(), 1, std::multiplies<int>());
int output_size = std::accumulate(output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
int kernel_size = std::accumulate(k.begin() + 2, k.end(), 1, std::multiplies<int>());
flops += output_size * c * kernel_size / g;
flops += output_size * c * kernel_size / g;
memops += input_size + output_size + std::accumulate(k.begin(), k.end(), 1, std::multiplies<int>()) * c / g;
if(op->has_param("bias"))
if (op->has_param("bias"))
{
flops += output_size;
memops += output_size;
}
}
else if (sub_type == "AvgPool1d"
|| sub_type == "AvgPool2d"
|| sub_type == "AvgPool3d")
|| sub_type == "AvgPool2d"
|| sub_type == "AvgPool3d")
{
std::vector<int> input_shape = op->inputs[0]->shape;
std::vector<int> output_shape = op->outputs[0]->shape;
Expand All @@ -1581,31 +1581,31 @@ void Graph::flops_memops_sum()
memops += input_size + output_size;
}
else if (sub_type == "AdaptiveAvgPool1d"
|| sub_type == "AdaptiveAvgPool2d"
|| sub_type == "AdaptiveAvgPool3d")
|| sub_type == "AdaptiveAvgPool2d"
|| sub_type == "AdaptiveAvgPool3d")
{
std::vector<int> input_shape = op->inputs[0]->shape;
std::vector<int> output_shape = op->outputs[0]->shape;
int input_size = std::accumulate(input_shape.begin(), input_shape.end(), 1, std::multiplies<int>());
int output_size = std::accumulate(output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
std::vector<int> kernel_size;
for(size_t i = 2; i < input_shape.size(); i++)
for (size_t i = 2; i < input_shape.size(); i++)
{
kernel_size.emplace_back(output_shape[i] / input_shape[i]);
}
flops += (std::accumulate(kernel_size.begin(), kernel_size.end(), 1, std::multiplies<int>()) + 1) * output_size;
memops += input_size + output_size;
}
else if(sub_type == "PReLU"
|| sub_type == "ELU"
|| sub_type == "LeakyReLU"
|| sub_type == "GELU")
else if (sub_type == "PReLU"
|| sub_type == "ELU"
|| sub_type == "LeakyReLU"
|| sub_type == "GELU")
{
std::vector<int> shape = op->outputs[0]->shape;
int n = shape[0];
int num_elements = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
extra_flops += num_elements;
if(sub_type == "PReLU")
if (sub_type == "PReLU")
{
extra_memops += 2 * num_elements + n * op->params["num_parameters"].i;
}
Expand All @@ -1614,7 +1614,7 @@ void Graph::flops_memops_sum()
extra_memops += 2 * num_elements;
}
}
else if(sub_type == "Tanh")
else if (sub_type == "Tanh")
{
std::vector<int> shape = op->outputs[0]->shape;
int num_elements = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
Expand All @@ -1634,75 +1634,75 @@ void Graph::flops_memops_sum()
memops += input_size + output_size + output_size * (bias ? 1 : 0);
}
else if (sub_type == "Upsample"
|| sub_type == "UnsampleBilinear2d"
|| sub_type == "UnsampleNearest2d")
|| sub_type == "UnsampleBilinear2d"
|| sub_type == "UnsampleNearest2d")
{
std::vector<int> input_shape = op->inputs[0]->shape;
int input_size = std::accumulate(input_shape.begin(), input_shape.end(), 1, std::multiplies<int>());
std::vector<int> output_shape = op->outputs[0]->shape;
int output_size = std::accumulate(output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
std::string mode;
if(sub_type == "Unsample")
if (sub_type == "Unsample")
{
mode = op->has_param("mode") ? op->params.at("mode").s : "nearest";
}
else if(sub_type == "UnsampleBilinear2d")
else if (sub_type == "UnsampleBilinear2d")
{
mode = "bilinear";
}
else if(sub_type == "UnsampleNearest2d")
else if (sub_type == "UnsampleNearest2d")
{
mode = "nearest";
}

if(mode == "nearest")
if (mode == "nearest")
{
extra_flops += input_size;
extra_memops += input_size + output_size;
}
else if(mode == "linear")
else if (mode == "linear")
{
extra_flops += 5 * output_size;
extra_memops += 2 * input_size + output_size;
}
else if(mode == "bilinear")
else if (mode == "bilinear")
{
extra_flops += 11 * output_size;
extra_memops += 4 * input_size + output_size;
}
else if(mode == "bicubic")
else if (mode == "bicubic")
{
extra_flops += (224 + 35) * output_size;
extra_memops += 16 * input_size + output_size;
}
else if(mode == "trilinear")
else if (mode == "trilinear")
{
extra_flops += (13 * 2 + 5) * input_size;
extra_memops += 8 * input_size + output_size;
}
}
else if(sub_type == "RNN")
else if (sub_type == "RNN")
{
bool bi = op->has_param("bidirectional") && op->params.at("bidirectional").b;
bool bias = op->has_param("bias") && op->params.at("bias").b;
int input_size = op->params.at("input_size").i;
int hidden_size = op->params.at("hidden_size").i;
int flops1 = hidden_size * (input_size + hidden_size) + hidden_size;
if(bias)
if (bias)
{
flops1 += 2 * hidden_size;
}
if(bi)
if (bi)
{
flops1 *= 2;
}

int num_layers = op->params.at("num_layers").i;
int flops2 = 0;
if(bi)
if (bi)
{
flops2 = 3 * hidden_size * hidden_size + hidden_size;
if(bias)
if (bias)
{
flops2 += 2 * hidden_size;
}
Expand All @@ -1711,7 +1711,7 @@ void Graph::flops_memops_sum()
else
{
flops2 = 2 * hidden_size * hidden_size + hidden_size;
if(bias)
if (bias)
{
flops2 += 2 * hidden_size;
}
Expand All @@ -1723,34 +1723,34 @@ void Graph::flops_memops_sum()
flops += (flops1 + flops2) * num_steps * batch_size;
memops += num_steps * batch_size * input_size;
memops += 2 * num_steps * batch_size * hidden_size * num_layers * (bi ? 2 : 1);
if(bias)
if (bias)
{
memops += 2 * hidden_size * num_layers * (bi ? 2 : 1);
}
}
else if(sub_type == "LSTM")
else if (sub_type == "LSTM")
{
bool bi = op->has_param("bidirectional") && op->params.at("bidirectional").b;
bool bias = op->has_param("bias") && op->params.at("bias").b;
int input_size = op->params.at("input_size").i;
int hidden_size = op->params.at("hidden_size").i;
int flops1 = 4 * hidden_size * (input_size + hidden_size) + 4 * hidden_size;
if(bias)
if (bias)
{
flops1 += 8 * hidden_size;
}
if(bi)
if (bi)
{
flops1 *= 2;
}
flops1 += 4 * hidden_size;

int num_layers = op->params.at("num_layers").i;
int flops2 = 0;
if(bi)
if (bi)
{
flops2 = 12 * hidden_size * hidden_size + 4 * hidden_size;
if(bias)
if (bias)
{
flops2 += 8 * hidden_size;
}
Expand All @@ -1760,7 +1760,7 @@ void Graph::flops_memops_sum()
else
{
flops2 = 4 * hidden_size * hidden_size + 4 * hidden_size;
if(bias)
if (bias)
{
flops2 += 8 * hidden_size;
}
Expand All @@ -1773,7 +1773,7 @@ void Graph::flops_memops_sum()
flops += (flops1 + flops2) * num_steps * batch_size;
memops += num_steps * batch_size * input_size;
memops += 2 * num_steps * batch_size * hidden_size * num_layers * (bi ? 2 : 1);
if(bias)
if (bias)
{
memops += 8 * hidden_size * num_layers * (bi ? 2 : 1);
}
Expand All @@ -1785,22 +1785,22 @@ void Graph::flops_memops_sum()
int input_size = op->params.at("input_size").i;
int hidden_size = op->params.at("hidden_size").i;
int flops1 = 3 * hidden_size * (input_size + hidden_size) + 3 * hidden_size;
if(bias)
if (bias)
{
flops1 += 6 * hidden_size;
}
flops1 += 4 * hidden_size;
if(bi)
if (bi)
{
flops1 *= 2;
}

int num_layers = op->params.at("num_layers").i;
int flops2 = 0;
if(bi)
if (bi)
{
flops2 = 9 * hidden_size * hidden_size + 3 * hidden_size;
if(bias)
if (bias)
{
flops2 += 6 * hidden_size;
}
Expand All @@ -1810,7 +1810,7 @@ void Graph::flops_memops_sum()
else
{
flops2 = 6 * hidden_size * hidden_size + 3 * hidden_size;
if(bias)
if (bias)
{
flops2 += 6 * hidden_size;
}
Expand All @@ -1823,12 +1823,12 @@ void Graph::flops_memops_sum()
flops += (flops1 + flops2) * num_steps * batch_size;
memops += num_steps * batch_size * input_size;
memops += 2 * num_steps * batch_size * hidden_size * num_layers * (bi ? 2 : 1);
if(bias)
if (bias)
{
memops += 6 * hidden_size * num_layers * (bi ? 2 : 1);
}
}
else if(sub_type == "MultiheadAttention")
else if (sub_type == "MultiheadAttention")
{
bool batch_first = op->has_param("batch_first") && op->params.at("batch_first").b;
int batch_size = batch_first ? op->inputs[0]->shape[0] : op->inputs[0]->shape[1];
Expand Down Expand Up @@ -1883,7 +1883,7 @@ void Graph::flops_memops_sum()
else if (op->type.substr(0, 5) == "torch")
{
std::string sub_type = op->type.substr(6);
if(sub_type == "matmul"
if (sub_type == "matmul"
|| sub_type == "mm"
|| sub_type == "bmm")
{
Expand All @@ -1897,7 +1897,7 @@ void Graph::flops_memops_sum()
memops += input_size_1 + input_size_2 + output_size;
}
else if (sub_type == "addmm"
|| sub_type == "baddbmm")
|| sub_type == "baddbmm")
{
std::vector<int> input_shape = op->inputs[0]->shape;
std::vector<int> mat_shape_1 = op->inputs[1]->shape;
Expand All @@ -1911,7 +1911,7 @@ void Graph::flops_memops_sum()
memops += input_size + mat_size_1 + mat_size_2 + output_size;
}
else if (sub_type == "mul"
|| sub_type == "add")
|| sub_type == "add")
{
std::vector<int> input_shape_1 = op->inputs[0]->shape;
std::vector<int> input_shape_2 = op->inputs[1]->shape;
Expand Down
2 changes: 1 addition & 1 deletion tools/pnnx/src/main.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -362,7 +362,7 @@ int main(int argc, char** argv)
pnnx_graph.save(pnnxparampath, pnnxbinpath);

pnnx_graph.python(pnnxpypath, pnnxbinpath);

pnnx_graph.flops_memops_sum();
fprintf(stderr, "float ops = %.3fM\n", double(pnnx_graph.flops) / 1e6);
fprintf(stderr, "mem ops = %.3fM\n", double(pnnx_graph.memops) / 1e6);
Expand Down

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