model: add support for qwen3vl series (#16780)
* support qwen3vl series. Co-authored-by: Thireus ☠ <Thireus@users.noreply.github.com> Co-authored-by: yairpatch <yairpatch@users.noreply.github.com> Co-authored-by: LETS-BEE <LETS-BEE@users.noreply.github.com> * bugfix: fix the arch check for qwen3vl-moe. * use build_ffn * optimize deepstack structure * optimize deepstack feature saving * Revert "optimize deepstack feature saving" for temporal fix This reverts commit f321b9fdf13e59527408152e73b1071e19a87e71. * code clean * use fused qkv in clip * clean up / rm is_deepstack_layers for simplification * add test model * move test model to "big" section * fix imrope check * remove trailing whitespace * fix rope fail * metal : add imrope support * add imrope support for sycl * vulkan: add imrope w/o check * fix vulkan * webgpu: add imrope w/o check * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * fix tensor mapping --------- Co-authored-by: Thireus ☠ <Thireus@users.noreply.github.com> Co-authored-by: yairpatch <yairpatch@users.noreply.github.com> Co-authored-by: LETS-BEE <LETS-BEE@users.noreply.github.com> Co-authored-by: Xuan Son Nguyen <son@huggingface.co> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
co-authored by
Thireus ☠
yairpatch
LETS-BEE
Sigbjørn Skjæret
Xuan Son Nguyen
Georgi Gerganov
parent
dcca0d3ab8
commit
d261223d24
@@ -1056,6 +1056,7 @@ struct vk_op_rope_push_constants {
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uint32_t s1;
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uint32_t s2;
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int32_t sections[4];
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uint32_t is_imrope;
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uint32_t is_back;
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uint32_t set_rows_stride;
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};
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@@ -9927,6 +9928,8 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, cons
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memcpy(sections, (int32_t *) dst->op_params + 11, sizeof(int)*4);
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}
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const bool is_imrope = mode == GGML_ROPE_TYPE_IMROPE;
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float corr_dims[2];
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ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
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@@ -9948,7 +9951,7 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, cons
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(uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1],
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freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1]}, theta_scale,
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src2 != nullptr, (uint32_t)src0->ne[2], s1, s2,
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{ sections[0], sections[1], sections[2], sections[3] }, backprop, set_rows_stride,
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{ sections[0], sections[1], sections[2], sections[3] }, is_imrope, backprop, set_rows_stride,
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}, dryrun);
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}
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@@ -27,6 +27,7 @@ layout (push_constant) uniform parameter {
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uint s1;
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uint s2;
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int sections[4];
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uint is_imrope;
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uint is_back;
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uint set_rows_stride;
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} p;
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@@ -32,17 +32,29 @@ void main() {
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const uint sector = (i0 / 2) % sect_dims;
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float theta_base = 0.0;
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if (sector < p.sections[0]) {
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theta_base = data_pos[channel_x]*pow(p.theta_scale, i0/2.0f);
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}
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else if (sector >= p.sections[0] && sector < sec_w) {
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theta_base = data_pos[channel_x + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
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}
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else if (sector >= sec_w && sector < sec_w + p.sections[2]) {
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theta_base = data_pos[channel_x + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
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}
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else if (sector >= sec_w + p.sections[2]) {
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theta_base = data_pos[channel_x + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
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if (p.is_imrope != 0) {
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if (sector % 3 == 1 && sector < 3 * p.sections[1]) {
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theta_base = data_pos[channel_x + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
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} else if (sector % 3 == 2 && sector < 3 * p.sections[2]) {
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theta_base = data_pos[channel_x + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
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} else if (sector % 3 == 0 && sector < 3 * p.sections[0]) {
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theta_base = data_pos[channel_x]*pow(p.theta_scale, i0/2.0f);
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} else {
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theta_base = data_pos[channel_x + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
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}
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} else {
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if (sector < p.sections[0]) {
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theta_base = data_pos[channel_x]*pow(p.theta_scale, i0/2.0f);
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}
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else if (sector >= p.sections[0] && sector < sec_w) {
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theta_base = data_pos[channel_x + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
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}
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else if (sector >= sec_w && sector < sec_w + p.sections[2]) {
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theta_base = data_pos[channel_x + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
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}
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else if (sector >= sec_w + p.sections[2]) {
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theta_base = data_pos[channel_x + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
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}
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}
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const float freq_factor = p.has_ff != 0 ? data_ff[i0/2] : 1.0f;
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