452 lines
16 KiB
C++
452 lines
16 KiB
C++
#include "models.h"
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// Helpers for MobileNetV5 Blocks
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// RMS Norm 2D - normalizes over channels for each spatial position
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ggml_tensor * clip_graph_mobilenetv5::rms_norm_2d(ggml_tensor * inp, ggml_tensor * weight, float eps) {
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// inp: [W, H, C, B]
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ggml_tensor * cur = ggml_permute(ctx0, inp, 2, 1, 0, 3);
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cur = ggml_cont(ctx0, cur);
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cur = ggml_rms_norm(ctx0, cur, eps);
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if (weight) {
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cur = ggml_mul(ctx0, cur, weight);
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}
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cur = ggml_permute(ctx0, cur, 2, 1, 0, 3);
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cur = ggml_cont(ctx0, cur);
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return cur;
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}
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// Conv2dSame padding - asymmetric SAME padding like PyTorch/TF
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ggml_tensor* clip_graph_mobilenetv5::pad_same_2d(ggml_tensor* inp, int kernel_h, int kernel_w, int stride_h, int stride_w, int dilation_h, int dilation_w) {
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const int64_t ih = inp->ne[1]; // height
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const int64_t iw = inp->ne[0]; // width
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// Calculate output size (ceil division)
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const int64_t oh = (ih + stride_h - 1) / stride_h;
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const int64_t ow = (iw + stride_w - 1) / stride_w;
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// Calculate padding needed
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const int64_t pad_h = std::max((int64_t)0, (oh - 1) * stride_h + (kernel_h - 1) * dilation_h + 1 - ih);
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const int64_t pad_w = std::max((int64_t)0, (ow - 1) * stride_w + (kernel_w - 1) * dilation_w + 1 - iw);
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// Split padding asymmetrically
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const int pad_h_top = pad_h / 2;
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const int pad_h_bottom = pad_h - pad_h_top;
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const int pad_w_left = pad_w / 2;
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const int pad_w_right = pad_w - pad_w_left;
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// Apply padding if needed
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// ggml_pad_ext: (ctx, tensor, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3)
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// For [W, H, C, B]: p0=width, p1=height, p2=channels, p3=batch
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if (pad_h > 0 || pad_w > 0) {
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inp = ggml_pad_ext(ctx0, inp,
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pad_w_left, pad_w_right, // width padding (dim 0)
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pad_h_top, pad_h_bottom, // height padding (dim 1)
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0, 0, // no channel padding (dim 2)
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0, 0); // no batch padding (dim 3)
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}
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return inp;
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}
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// Edge Residual Block (Stage 0)
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ggml_tensor * clip_graph_mobilenetv5::build_edge_residual(ggml_tensor * inp, const mobilenetv5_block & block, int stride) {
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ggml_tensor * cur = inp;
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// 1. Expansion Conv (3x3)
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if (stride == 2) {
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// Case: Downsampling (Block 0)
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// Replicates Conv2dSame(kernel=3, stride=2)
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cur = pad_same_2d(cur, 3, 3, stride, stride);
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cur = ggml_conv_2d_direct(ctx0, block.s0_conv_exp_w, cur, stride, stride, 0, 0, 1, 1);
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} else {
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// Case: Normal 3x3 Block (Block 1, 2)
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// Replicates Conv2d(kernel=3, stride=1, padding=1)
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cur = ggml_conv_2d_direct(ctx0, block.s0_conv_exp_w, cur, stride, stride, 1, 1, 1, 1);
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}
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// BN + Activation
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if (block.s0_bn1_w) cur = rms_norm_2d(cur, block.s0_bn1_w);
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cur = ggml_gelu(ctx0, cur);
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// 2. Pointwise Linear Conv (1x1)
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// 1x1 Convs usually have padding=0 and stride=1
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cur = ggml_conv_2d_direct(ctx0, block.s0_conv_pwl_w, cur, 1, 1, 0, 0, 1, 1);
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if (block.s0_bn2_w) cur = rms_norm_2d(cur, block.s0_bn2_w);
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// 3. Residual Connection
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// Only apply residual if spatial dimensions and channels match (stride 1)
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if (stride == 1 && inp->ne[2] == cur->ne[2] && inp->ne[0] == cur->ne[0]) {
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cur = ggml_add(ctx0, cur, inp);
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}
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return cur;
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}
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// Universal Inverted Residual Block (Stage 1+)
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ggml_tensor * clip_graph_mobilenetv5::build_inverted_residual(ggml_tensor * inp, const mobilenetv5_block & block, int stride) {
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ggml_tensor * cur = inp;
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// 1. Depthwise Start (Optional)
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// NOTE: dw_start always has stride=1 (no downsampling here)
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if (block.dw_start_w) {
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int k = block.dw_start_w->ne[0]; // 3 or 5
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int p = k / 2;
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cur = ggml_conv_2d_dw(ctx0, block.dw_start_w, cur, 1, 1, p, p, 1, 1);
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if (block.dw_start_bn_w) cur = rms_norm_2d(cur, block.dw_start_bn_w);
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}
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// 2. Pointwise Expansion (1x1)
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if (block.pw_exp_w) {
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// Standard 1x1 conv, pad=0, stride=1
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cur = ggml_conv_2d_direct(ctx0, block.pw_exp_w, cur, 1, 1, 0, 0, 1, 1);
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if (block.pw_exp_bn_w) cur = rms_norm_2d(cur, block.pw_exp_bn_w);
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cur = ggml_gelu(ctx0, cur);
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}
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// 3. Depthwise Mid (Optional)
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// NOTE: dw_mid is where downsampling happens (stride=2 for first block of stage)
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if (block.dw_mid_w) {
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int k = block.dw_mid_w->ne[0]; // 3 or 5
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if (stride > 1) {
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// Case: Stride 2 (Downsample) -> Use Asymmetric "Same" Padding
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cur = pad_same_2d(cur, k, k, stride, stride);
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cur = ggml_conv_2d_dw(ctx0, block.dw_mid_w, cur, stride, stride, 0, 0, 1, 1); // pad=0
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} else {
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// Case: Stride 1 -> Use Standard Symmetric Padding
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int p = k / 2;
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cur = ggml_conv_2d_dw(ctx0, block.dw_mid_w, cur, stride, stride, p, p, 1, 1);
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}
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if (block.dw_mid_bn_w) cur = rms_norm_2d(cur, block.dw_mid_bn_w);
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cur = ggml_gelu(ctx0, cur);
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}
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// 4. Pointwise Projection (1x1)
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if (block.pw_proj_w) {
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cur = ggml_conv_2d_direct(ctx0, block.pw_proj_w, cur, 1, 1, 0, 0, 1, 1);
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if (block.pw_proj_bn_w) cur = rms_norm_2d(cur, block.pw_proj_bn_w);
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}
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// Apply Layer Scaling if present
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if (block.layer_scale_w) {
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cur = ggml_mul(ctx0, cur, block.layer_scale_w);
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}
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// 5. Residual Connection
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bool same_spatial = (inp->ne[0] == cur->ne[0]) && (inp->ne[1] == cur->ne[1]);
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bool same_channel = (inp->ne[2] == cur->ne[2]);
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if (same_spatial && same_channel) {
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cur = ggml_add(ctx0, cur, inp);
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}
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return cur;
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}
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// Attention Block (MQA)
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ggml_tensor * clip_graph_mobilenetv5::build_mobilenet_attn(ggml_tensor * inp, const mobilenetv5_block & block) {
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ggml_tensor * cur = inp;
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// Norm
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if (block.attn_norm_w) {
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cur = rms_norm_2d(cur, block.attn_norm_w, 1e-6f);
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}
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// 1. Q Calculation
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ggml_tensor * q = ggml_conv_2d_direct(ctx0, block.attn_q_w, cur, 1, 1, 0, 0, 1, 1);
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// 2. K Calculation (Downsampled)
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// Uses Conv2dSame(640, 640, kernel_size=(3, 3), stride=(2, 2), groups=640)
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ggml_tensor * k_inp = cur;
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if (block.attn_k_dw_w) {
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int k_size = block.attn_k_dw_w->ne[0]; // Usually 3
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k_inp = pad_same_2d(cur, k_size, k_size, 2, 2); // Apply SAME padding
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k_inp = ggml_conv_2d_dw(ctx0, block.attn_k_dw_w, k_inp, 2, 2, 0, 0, 1, 1); // padding=0
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if (block.attn_k_norm_w) {
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k_inp = rms_norm_2d(k_inp, block.attn_k_norm_w, 1e-6f);
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}
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}
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ggml_tensor * k = ggml_conv_2d_direct(ctx0, block.attn_k_w, k_inp, 1, 1, 0, 0, 1, 1);
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// 3. V Calculation (Downsampled)
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// Uses Conv2dSame(640, 640, kernel_size=(3, 3), stride=(2, 2), groups=640)
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ggml_tensor * v_inp = cur;
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if (block.attn_v_dw_w) {
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int v_size = block.attn_v_dw_w->ne[0]; // Usually 3
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v_inp = pad_same_2d(cur, v_size, v_size, 2, 2); // Apply SAME padding
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v_inp = ggml_conv_2d_dw(ctx0, block.attn_v_dw_w, v_inp, 2, 2, 0, 0, 1, 1); // padding=0
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if (block.attn_v_norm_w) {
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v_inp = rms_norm_2d(v_inp, block.attn_v_norm_w, 1e-6f);
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}
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}
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ggml_tensor * v = ggml_conv_2d_direct(ctx0, block.attn_v_w, v_inp, 1, 1, 0, 0, 1, 1);
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const int W = cur->ne[0]; const int H = cur->ne[1]; const int B = cur->ne[3];
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const int D = k->ne[2]; // Head dimension
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const int n_head = q->ne[2] / D;
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const int N = W * H;
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// Process Q: [W, H, D*n_head, B] -> [D, N, n_head, B]
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q = ggml_reshape_3d(ctx0, q, N, D*n_head, B);
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q = ggml_reshape_4d(ctx0, q, N, D, n_head, B);
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q = ggml_permute(ctx0, q, 1, 0, 2, 3); // [D, N, n_head, B]
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q = ggml_cont(ctx0, q);
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const int Wk = k->ne[0]; const int Hk = k->ne[1];
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const int M = Wk * Hk;
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// Process K: [Wk, Hk, D, B] -> [D, M, 1, B]
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k = ggml_reshape_3d(ctx0, k, M, D, B);
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k = ggml_reshape_4d(ctx0, k, M, D, 1, B);
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k = ggml_permute(ctx0, k, 1, 0, 2, 3); // [D, M, 1, B]
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k = ggml_cont(ctx0, k);
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// Process V: [Wk, Hk, D, B] -> [M, D, 1, B]
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v = ggml_reshape_3d(ctx0, v, M, D, B);
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v = ggml_reshape_4d(ctx0, v, M, D, 1, B);
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v = ggml_cont(ctx0, v); // [M, D, 1, B]
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// Multi-Query Attention
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float scale = 1.0f / sqrtf((float)D);
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// Step 1: Compute Q @ K.T
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ggml_tensor * scores = ggml_mul_mat(ctx0, k, q);
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scores = ggml_scale(ctx0, scores, scale);
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scores = ggml_soft_max(ctx0, scores);
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ggml_tensor * kqv = ggml_mul_mat(ctx0, v, scores);
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kqv = ggml_permute(ctx0, kqv, 1, 0, 2, 3);
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kqv = ggml_cont(ctx0, kqv);
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kqv = ggml_reshape_3d(ctx0, kqv, N, D * n_head, B);
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kqv = ggml_reshape_4d(ctx0, kqv, W, H, D * n_head, B);
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kqv = ggml_cont(ctx0, kqv);
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// Output projection
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cur = ggml_conv_2d_direct(ctx0, block.attn_o_w, kqv, 1, 1, 0, 0, 1, 1);
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// Residual & Layer Scale
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if (inp->ne[0] == cur->ne[0] && inp->ne[2] == cur->ne[2]) {
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if (block.layer_scale_w) {
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cur = ggml_mul(ctx0, cur, block.layer_scale_w);
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}
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cur = ggml_add(ctx0, cur, inp);
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}
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return cur;
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}
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ggml_cgraph * clip_graph_mobilenetv5::build() {
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ggml_tensor * inp = build_inp_raw();
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// 1. Stem - Conv2dSame(3, 64, kernel_size=(3, 3), stride=(2, 2))
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ggml_tensor * cur = pad_same_2d(inp, 3, 3, 2, 2); // Apply SAME padding
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cur = ggml_conv_2d_direct(ctx0, model.mobilenet_stem_conv_w, cur, 2, 2, 0, 0, 1, 1); // padding=0
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if (model.mobilenet_stem_conv_b) {
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cur = ggml_add(ctx0, cur, model.mobilenet_stem_conv_b);
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}
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if (model.mobilenet_stem_norm_w) cur = rms_norm_2d(cur, model.mobilenet_stem_norm_w);
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cur = ggml_gelu(ctx0, cur);
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// 2. Blocks
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std::vector<ggml_tensor*> intermediate_features;
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const int total_blocks = model.mobilenet_blocks.size();
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auto is_stage_start = [&](int i) {
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if (i == 0) return true;
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for (int end_idx : model.mobilenet_stage_ends) {
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if (i == end_idx + 1) return true;
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}
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return false;
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};
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auto is_fusion_point = [&](int i) {
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if (model.mobilenet_stage_ends.size() >= 4) {
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if (i == model.mobilenet_stage_ends[2]) return true; // End of Stage 2
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if (i == model.mobilenet_stage_ends[3]) return true; // End of Stage 3
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} else {
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if (i == total_blocks - 1) return true;
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}
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return false;
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};
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for (int i = 0; i < total_blocks; i++) {
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const auto & block = model.mobilenet_blocks[i];
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int stride = is_stage_start(i) ? 2 : 1;
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if (block.s0_conv_exp_w) cur = build_edge_residual(cur, block, stride);
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else if (block.attn_q_w) cur = build_mobilenet_attn(cur, block);
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else cur = build_inverted_residual(cur, block, stride);
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if (is_fusion_point(i)) {
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intermediate_features.push_back(cur);
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}
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}
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// 3. Multi-Scale Fusion Adapter (MSFA)
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if (!intermediate_features.empty()) {
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// A. Reference Resolution: PyTorch implementation uses inputs[0]
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// We assume intermediate_features[0] is the "High Resolution" target.
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// In MobileNet designs, this is typically the feature map with the smallest stride (e.g. 32x32).
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ggml_tensor* target_feat = intermediate_features[0];
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int high_res_w = target_feat->ne[0];
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int high_res_h = target_feat->ne[1];
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std::vector<ggml_tensor*> resized_feats;
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// B. Resize inputs to match inputs[0] (High Resolution)
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for (auto feat : intermediate_features) {
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int feat_w = feat->ne[0];
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int feat_h = feat->ne[1];
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// PyTorch: if feat_size < high_resolution: interpolate
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if (feat_w < high_res_w || feat_h < high_res_h) {
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// Calculate scale factor.
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// Note: PyTorch 'nearest' works on arbitrary float scales.
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// ggml_upscale generally takes integer factors or target sizes depending on helper.
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// Assuming standard power-of-2 scaling (e.g. 16 -> 32 means scale=2).
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int scale_w = high_res_w / feat_w;
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// int scale_h = high_res_h / feat_h;
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// Safety check for non-integer scaling if strictly replicating
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GGML_ASSERT(high_res_w % feat_w == 0);
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// Upsample (Nearest Neighbor)
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// 2 is the scale factor
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feat = ggml_upscale(ctx0, feat, scale_w, ggml_scale_mode::GGML_SCALE_MODE_NEAREST);
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}
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resized_feats.push_back(feat);
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}
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// C. Concatenate at High Resolution (Channel Dim = 2 in ggml)
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cur = resized_feats[0];
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for (size_t k = 1; k < resized_feats.size(); ++k) {
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cur = ggml_concat(ctx0, cur, resized_feats[k], 2);
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}
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// D. FFN (UniversalInvertedResidual)
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// Structure: Expand Conv -> Norm -> GELU -> Project Conv -> Norm
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// 1. Expansion
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if (model.msfa_ffn_expand_w) {
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// 1x1 Conv
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cur = ggml_conv_2d_direct(ctx0, model.msfa_ffn_expand_w, cur, 1, 1, 0, 0, 1, 1);
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if (model.msfa_ffn_expand_bn) {
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cur = rms_norm_2d(cur, model.msfa_ffn_expand_bn);
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}
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cur = ggml_gelu(ctx0, cur);
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}
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// 2. Projection (No DW because kernel_size=0)
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if (model.msfa_ffn_project_w) {
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// 1x1 Conv
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cur = ggml_conv_2d_direct(ctx0, model.msfa_ffn_project_w, cur, 1, 1, 0, 0, 1, 1);
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// UniversalInvertedResidual typically has a norm after projection
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if (model.msfa_ffn_project_bn) {
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cur = rms_norm_2d(cur, model.msfa_ffn_project_bn);
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}
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}
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// E. Final Downsample to Target Resolution (Output Resolution)
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// PyTorch: matches self.output_resolution (e.g. 16x16)
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const int target_out_res = 16;
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int current_w = cur->ne[0];
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if (current_w > target_out_res) {
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int s = current_w / target_out_res;
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GGML_ASSERT(current_w % target_out_res == 0);
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// Avg Pool: Kernel=s, Stride=s
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cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, s, s, s, s, 0, 0);
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}
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// F. Final Norm
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if (model.msfa_concat_norm_w) {
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cur = rms_norm_2d(cur, model.msfa_concat_norm_w);
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}
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}
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// 4. Gemma 3n Multimodal Projection (Embedder)
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// Input: 'cur' is [Width, Height, Channels, Batch]
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int W = cur->ne[0];
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int H = cur->ne[1];
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int C = cur->ne[2];
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int B = cur->ne[3];
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GGML_ASSERT(C == hparams.n_embd);
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// 1. Permute and Flatten to [Channels, Tokens, Batch]
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// PyTorch expects (Batch, Seq, Hidden), GGML usually processes (Hidden, Seq, Batch)
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cur = ggml_permute(ctx0, cur, 2, 1, 0, 3); // -> [C, H, W, B]
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cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); // -> [C, W, H, B]
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cur = ggml_cont(ctx0, cur);
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cur = ggml_reshape_3d(ctx0, cur, C, W*H, B);
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cur = ggml_cont(ctx0, cur);
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// 2. FEATURE SCALING
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// PyTorch: vision_outputs *= self.config.vision_config.hidden_size**0.5
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const float scale_factor = sqrtf((float)C);
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cur = ggml_scale(ctx0, cur, scale_factor);
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// 3. SOFT EMBEDDING NORM
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// PyTorch: self._norm(x) * self.weight
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// We must normalize regardless, then multiply if weight exists.
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{
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const float eps = 1e-6f; // Gemma3n uses 1e-6
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cur = ggml_rms_norm(ctx0, cur, eps);
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if (model.mm_soft_emb_norm_w) {
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// Weight shape is (2048,) -> Element-wise broadcast multiply
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cur = ggml_mul(ctx0, cur, model.mm_soft_emb_norm_w);
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}
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}
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// 4. PROJECTION
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// PyTorch: embedding_projection = nn.Linear(vision_hidden, text_hidden, bias=False)
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// Weight stored as [out_features, in_features] = [text_hidden_size, vision_hidden_size]
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if (model.mm_input_proj_w) {
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cur = ggml_mul_mat(ctx0, model.mm_input_proj_w, cur);
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}
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// 5. POST PROJECTION NORM
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// PyTorch: embedding_post_projection_norm = Gemma3nRMSNorm(..., with_scale=False)
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// with_scale=False means weight is registered as buffer with value 1.0
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// So output = rms_norm(x) * 1.0 = rms_norm(x), magnitude ~1
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{
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const float eps = 1e-6f;
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cur = ggml_rms_norm(ctx0, cur, eps);
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if (model.mm_post_proj_norm_w) {
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// If weight is loaded, multiply (should be ~1.0 anyway)
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cur = ggml_mul(ctx0, cur, model.mm_post_proj_norm_w);
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}
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}
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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