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@SZUwishion
SZUwishion committed Sep 19, 2024
1 parent a91dc5c commit 4adf254
Showing 1 changed file with 53 additions and 282 deletions.
335 changes: 53 additions & 282 deletions tools/pnnx/src/ir.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1454,297 +1454,68 @@ void Graph::flops_memops_sum()
if (op->type[0] == 'F')
{
std::string sub_type = op->type.substr(2);
if (sub_type == "adaptive_avg_pool1d")
if (sub_type == "linear")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int l = op->inputs[0]->shape[2];
int o = op->params.at("output_size").ai[0];
flops += n * c * l * o;
memops += n * c * l + n * c * o;
}
else if (sub_type == "adaptive_avg_pool2d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int h = op->inputs[0]->shape[2];
int w = op->inputs[0]->shape[3];
int oh = op->params.at("output_size").ai[0];
int ow = op->params.at("output_size").ai[1];
flops += n * c * h * w * oh * ow;
memops += n * c * h * w + n * c * oh * ow;
}
else if (sub_type == "adaptive_avg_pool3d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int d = op->inputs[0]->shape[2];
int h = op->inputs[0]->shape[3];
int w = op->inputs[0]->shape[4];
int od = op->params.at("output_size").ai[0];
int oh = op->params.at("output_size").ai[1];
int ow = op->params.at("output_size").ai[2];
flops += n * c * d * h * w * od * oh * ow;
memops += n * c * d * h * w + n * c * od * oh * ow;
}
else if (sub_type == "avg_pool1d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int l = op->inputs[0]->shape[2];
int k = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[0] : 1;
int s = op->has_param("stride") ? op->params.at("stride").ai[0] : 1;
int p = op->has_param("padding") ? op->params.at("padding").ai[0] : 0;
int o = (l + 2 * p - k) / s + 1;
flops += n * c * l * k;
memops += n * c * l + n * c * o;
}
else if (sub_type == "avg_pool2d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int h = op->inputs[0]->shape[2];
int w = op->inputs[0]->shape[3];
int kh = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[0] : 1;
int kw = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[1] : 1;
int sh = op->has_param("stride") ? op->params.at("stride").ai[0] : 1;
int sw = op->has_param("stride") ? op->params.at("stride").ai[1] : 1;
int ph = op->has_param("padding") ? op->params.at("padding").ai[0] : 0;
int pw = op->has_param("padding") ? op->params.at("padding").ai[1] : 0;
int oh = (h + 2 * ph - kh) / sh + 1;
int ow = (w + 2 * pw - kw) / sw + 1;
flops += n * c * h * w * kh * kw;
memops += n * c * h * w + n * c * oh * ow;
}
else if (sub_type == "avg_pool3d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int d = op->inputs[0]->shape[2];
int h = op->inputs[0]->shape[3];
int w = op->inputs[0]->shape[4];
int kd = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[0] : 1;
int kh = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[1] : 1;
int kw = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[2] : 1;
int sd = op->has_param("stride") ? op->params.at("stride").ai[0] : 1;
int sh = op->has_param("stride") ? op->params.at("stride").ai[1] : 1;
int sw = op->has_param("stride") ? op->params.at("stride").ai[2] : 1;
int pd = op->has_param("padding") ? op->params.at("padding").ai[0] : 0;
int ph = op->has_param("padding") ? op->params.at("padding").ai[1] : 0;
int pw = op->has_param("padding") ? op->params.at("padding").ai[2] : 0;
int od = (d + 2 * pd - kd) / sd + 1;
int oh = (h + 2 * ph - kh) / sh + 1;
int ow = (w + 2 * pw - kw) / sw + 1;
flops += n * c * d * h * w * kd * kh * kw;
memops += n * c * d * h * w + n * c * od * oh * ow;
}
else if (sub_type == "adaptive_max_pool1d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int l = op->inputs[0]->shape[2];
int o = op->params.at("output_size").ai[0];
flops += n * c * l * o;
memops += n * c * l + n * c * o;
}
else if (sub_type == "adaptive_max_pool2d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int h = op->inputs[0]->shape[2];
int w = op->inputs[0]->shape[3];
int oh = op->params.at("output_size").ai[0];
int ow = op->params.at("output_size").ai[1];
flops += n * c * h * w * oh * ow;
memops += n * c * h * w + n * c * oh * ow;
}
else if (sub_type == "adaptive_max_pool3d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int d = op->inputs[0]->shape[2];
int h = op->inputs[0]->shape[3];
int w = op->inputs[0]->shape[4];
int od = op->params.at("output_size").ai[0];
int oh = op->params.at("output_size").ai[1];
int ow = op->params.at("output_size").ai[2];
flops += n * c * d * h * w * od * oh * ow;
memops += n * c * d * h * w + n * c * od * oh * ow;
}
else if (sub_type == "max_pool1d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int l = op->inputs[0]->shape[2];
int k = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[0] : 1;
int s = op->has_param("stride") ? op->params.at("stride").ai[0] : 1;
int p = op->has_param("padding") ? op->params.at("padding").ai[0] : 0;
int o = (l + 2 * p - k) / s + 1;
flops += n * c * l * k;
memops += n * c * l + n * c * o;
}
else if (sub_type == "max_pool2d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int h = op->inputs[0]->shape[2];
int w = op->inputs[0]->shape[3];
int kh = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[0] : 1;
int kw = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[1] : 1;
int sh = op->has_param("stride") ? op->params.at("stride").ai[0] : 1;
int sw = op->has_param("stride") ? op->params.at("stride").ai[1] : 1;
int ph = op->has_param("padding") ? op->params.at("padding").ai[0] : 0;
int pw = op->has_param("padding") ? op->params.at("padding").ai[1] : 0;
int oh = (h + 2 * ph - kh) / sh + 1;
int ow = (w + 2 * pw - kw) / sw + 1;
flops += n * c * h * w * kh * kw;
memops += n * c * h * w + n * c * oh * ow;
}
else if (sub_type == "max_pool3d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int d = op->inputs[0]->shape[2];
int h = op->inputs[0]->shape[3];
int w = op->inputs[0]->shape[4];
int kd = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[0] : 1;
int kh = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[1] : 1;
int kw = op->has_param("kernel_size") ? op->params.at("kernel_size").ai[2] : 1;
int sd = op->has_param("stride") ? op->params.at("stride").ai[0] : 1;
int sh = op->has_param("stride") ? op->params.at("stride").ai[1] : 1;
int sw = op->has_param("stride") ? op->params.at("stride").ai[2] : 1;
int pd = op->has_param("padding") ? op->params.at("padding").ai[0] : 0;
int ph = op->has_param("padding") ? op->params.at("padding").ai[1] : 0;
int pw = op->has_param("padding") ? op->params.at("padding").ai[2] : 0;
int od = (d + 2 * pd - kd) / sd + 1;
int oh = (h + 2 * ph - kh) / sh + 1;
int ow = (w + 2 * pw - kw) / sw + 1;
flops += n * c * d * h * w * kd * kh * kw;
memops += n * c * d * h * w + n * c * od * oh * ow;
}
else if (sub_type == "prelu" || sub_type == "leaky_relu")
{

}
else if (sub_type == "conv1d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int l = op->inputs[0]->shape[2];
int k = op->inputs[1]->shape[0];
int o = op->outputs[0]->shape[2];
flops += 2 * n * c * l * k * o;
memops += 2 * n * c * l * k + n * o;
}
else if (sub_type == "conv2d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int h = op->inputs[0]->shape[2];
int w = op->inputs[0]->shape[3];
int kh = op->inputs[1]->shape[2];
int kw = op->inputs[1]->shape[3];
int o = op->outputs[0]->shape[2];
int s = op->has_param("stride") ? op->params.at("stride").ai[0] : 1;
int p = op->has_param("padding") ? op->params.at("padding").ai[0] : 0;
int g = op->has_param("groups") ? op->params.at("groups").i : 1;
flops += 2 * n * c * h * w * kh * kw * o / g;
memops += 2 * n * c * h * w * kh * kw / g + n * o * h * w;
}
else if (sub_type == "conv3d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int d = op->inputs[0]->shape[2];
int h = op->inputs[0]->shape[3];
int w = op->inputs[0]->shape[4];
int kd = op->inputs[1]->shape[2];
int kh = op->inputs[1]->shape[3];
int kw = op->inputs[1]->shape[4];
int o = op->outputs[0]->shape[2];
int s = op->has_param("stride") ? op->params.at("stride").ai[0] : 1;
int p = op->has_param("padding") ? op->params.at("padding").ai[0] : 0;
int g = op->has_param("groups") ? op->params.at("groups").i : 1;
flops += 2 * n * c * d * h * w * kd * kh * kw * o / g;
memops += 2 * n * c * d * h * w * kd * kh * kw / g + n * o * d * h * w;
}
else if (sub_type == "conv_transpose1d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int l = op->inputs[0]->shape[2];
int k = op->inputs[1]->shape[0];
int o = op->outputs[0]->shape[2];
flops += 2 * n * c * l * k * o;
memops += 2 * n * c * l * k + n * o;
}
else if (sub_type == "conv_transpose2d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int h = op->inputs[0]->shape[2];
int w = op->inputs[0]->shape[3];
int kh = op->inputs[1]->shape[2];
int kw = op->inputs[1]->shape[3];
int o = op->outputs[0]->shape[2];
int s = op->has_param("stride") ? op->params.at("stride").ai[0] : 1;
int p = op->has_param("padding") ? op->params.at("padding").ai[0] : 0;
int g = op->has_param("groups") ? op->params.at("groups").i : 1;
flops += 2 * n * c * h * w * kh * kw * o / g;
memops += 2 * n * c * h * w * kh * kw / g + n * o * h * w;
}
else if (sub_type == "conv_transpose3d")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int d = op->inputs[0]->shape[2];
int h = op->inputs[0]->shape[3];
int w = op->inputs[0]->shape[4];
int kd = op->inputs[1]->shape[2];
int kh = op->inputs[1]->shape[3];
int kw = op->inputs[1]->shape[4];
int o = op->outputs[0]->shape[2];
int s = op->has_param("stride") ? op->params.at("stride").ai[0] : 1;
int p = op->has_param("padding") ? op->params.at("padding").ai[0] : 0;
int g = op->has_param("groups") ? op->params.at("groups").i : 1;
flops += 2 * n * c * d * h * w * kd * kh * kw * o / g;
memops += 2 * n * c * d * h * w * kd * kh * kw / g + n * o * d * h * w;
}
else if (sub_type == "embedding")
{
/*todo*/
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>());
int out_features = op->attrs.at("data").shape[0];
flops += input_size * out_features;
if(op->has_param("bias"))
{
flops += out_features;
}
memops += input_size + output_size;
}
else if (sub_type == "linear")
else if (sub_type == "avgpool1d"
|| sub_type == "avgpool2d"
|| sub_type == "avgpool3d"
|| sub_type == "adaptive_avgpool1d"
|| sub_type == "adaptive_avgpool2d"
|| sub_type == "adaptive_avgpool3d")
{
int n = op->inputs[0]->shape[0];
int i = op->inputs[0]->shape[1];
int o = op->outputs[0]->shape[1];
flops += 2 * n * i * o;
memops += 2 * n * i + n * o;
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>());
flops += input_size;
memops += input_size + output_size;
}
else if (sub_type == "log_softmax")
else if (sub_type == "prelu"
|| sub_type == "elu"
|| sub_type == "leaky_relu"
|| sub_type == "gelu"
|| sub_type == "silu"
|| sub_type == "softmax")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int l = op->inputs[0]->shape[2];
extra_flops += 2 * n * c * l;
extra_memops += 2 * n * c * l;
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>());
extra_flops += input_size;
extra_memops += input_size + output_size;
}
else if (sub_type == "logsigmoid")
else if (sub_type == "unsample"
|| sub_type == "upsample_nearest"
|| sub_type == "upsample_bilinear")
{
int n = op->inputs[0]->shape[0];
int c = op->inputs[0]->shape[1];
int l = op->inputs[0]->shape[2];
extra_flops += 2 * n * c * l;
extra_memops += 2 * n * c * l;
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>());
extra_flops += output_size;
extra_memops += input_size + output_size;
}
else if (sub_type == "scaled_dot_product_attention")
else if (sub_type == "interpolate")
{
int n = op->inputs[0]->shape[0];
int l = op->inputs[0]->shape[1];
int d = op->inputs[0]->shape[2];
flops += 2 * n * l * l + n * l * d + n * l * l * d;
memops += 2 * n * l * d + 3 * n * l * l + n * l;
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> scale_factor = op->params.at("scale_factor").ai;
extra_flops += input_size * std::accumulate(scale_factor.begin(), scale_factor.end(), 1, std::multiplies<int>());
extra_memops += input_size + output_size;
}
}

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