From edb18b6e8f5ea6509ad43057f8bb98fc557dbc4e Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Fri, 25 Apr 2025 14:31:42 +0200 Subject: [PATCH] clip : fix pixtral on some GPU backends (#13097) * clip : fix pixtral on some GPU backends * refactor inp_raw set * rm outdated comment * fix dynamic size * add TODO --- examples/llava/clip.cpp | 68 ++++++++++++++++++++++++++------------ tests/test-backend-ops.cpp | 2 ++ 2 files changed, 48 insertions(+), 22 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 9a5ab7c81..da8a590f0 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -554,15 +554,15 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im } // implementation of the 2D RoPE without adding a new op in ggml +// this is not efficient (use double the memory), but works on all backends +// TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065 static ggml_tensor * build_rope_2d( - ggml_cgraph * gf, ggml_context * ctx0, ggml_tensor * cur, ggml_tensor * pos_h, ggml_tensor * pos_w, const float freq_base ) { - ggml_tensor * tmp; const int64_t n_dim = cur->ne[0]; const int64_t n_head = cur->ne[1]; const int64_t n_pos = cur->ne[2]; @@ -571,18 +571,23 @@ static ggml_tensor * build_rope_2d( // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3 // first half of cur will use 1e-0, 1e-2 (even) // second half of cur will use 1e-1, 1e-3 (odd) - // - // for the first half, the trick here is to rotate n_dim/2, so inv_freq will be even + // the trick here is to rotate just half of n_dim, so inv_freq will automatically be even // ^ don't ask me why, it's math! -2(2i) / n_dim == -2i / (n_dim/2) // then for the second half, we use freq_scale to shift the inv_freq // ^ why? replace (2i) with (2i+1) in the above equation const float freq_scale_odd = std::pow(freq_base, (float)-2/n_dim); // first half + ggml_tensor * first; { - cur = ggml_rope_ext_inplace( + first = ggml_view_3d(ctx0, cur, + n_dim/2, n_head, n_pos, + ggml_row_size(cur->type, n_dim), + ggml_row_size(cur->type, n_dim*n_head), + 0); + first = ggml_rope_ext( ctx0, - cur, + first, pos_h, // positions nullptr, // freq factors n_dim/2, // n_dims @@ -592,15 +597,17 @@ static ggml_tensor * build_rope_2d( } // second half + ggml_tensor * second; { - tmp = ggml_view_3d(ctx0, cur, + second = ggml_view_3d(ctx0, cur, n_dim/2, n_head, n_pos, ggml_row_size(cur->type, n_dim), ggml_row_size(cur->type, n_dim*n_head), n_dim/2 * ggml_element_size(cur)); - tmp = ggml_rope_ext_inplace( + second = ggml_cont(ctx0, second); // copy, because ggml_rope don't play well with non-contiguous tensors + second = ggml_rope_ext( ctx0, - tmp, + second, pos_w, // positions nullptr, // freq factors n_dim/2, // n_dims @@ -608,10 +615,9 @@ static ggml_tensor * build_rope_2d( freq_scale_odd, 0.0f, 1.0f, 0.0f, 0.0f ); - // calculate inplace (modify cur directly) - ggml_build_forward_expand(gf, tmp); } + cur = ggml_concat(ctx0, first, second, 0); return cur; } @@ -680,13 +686,13 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i struct ggml_tensor * Q = ggml_mul_mat(ctx0, model.layers[il].q_w, cur); Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_patches); - Q = build_rope_2d(gf, ctx0, Q, pos_h, pos_w, hparams.rope_theta); + Q = build_rope_2d(ctx0, Q, pos_h, pos_w, hparams.rope_theta); Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); struct ggml_tensor * K = ggml_mul_mat(ctx0, model.layers[il].k_w, cur); K = ggml_reshape_3d(ctx0, K, d_head, n_head, num_patches); - K = build_rope_2d(gf, ctx0, K, pos_h, pos_w, hparams.rope_theta); + K = build_rope_2d(ctx0, K, pos_h, pos_w, hparams.rope_theta); K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); struct ggml_tensor * V = ggml_mul_mat(ctx0, model.layers[il].v_w, cur); @@ -2796,10 +2802,15 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const auto & model = ctx->vision_model; const auto & hparams = model.hparams; + // TODO @ngxson : this is ugly, need to refactor later + bool support_dynamic_size = ctx->has_minicpmv_projector + || ctx->has_qwen2vl_merger + || ctx->proj_type == PROJECTOR_TYPE_PIXTRAL; + const int image_size = hparams.image_size; int image_size_width = image_size; int image_size_height = image_size; - if (ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger) { + if (support_dynamic_size) { image_size_width = imgs.entries[0]->nx; image_size_height = imgs.entries[0]->ny; } @@ -2811,9 +2822,20 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima { struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw"); - float * data = (float *)malloc(ggml_nbytes(inp_raw)); + std::vector inp_data(ggml_nelements(inp_raw)); + float * data = inp_data.data(); + + // layout of data (note: the channel dim is unrolled to better visualize the layout): + // + // ┌──W──┐ + // │ H │ channel = R + // ├─────┤ │ + // │ H │ channel = G + // ├─────┤ │ + // │ H │ channel = B + // └─────┘ │ + // ──────┘ x B - // TODO @ngxson : this whole code block is ugly, will need to be refactored for (size_t i = 0; i < imgs.entries.size(); i++) { const int nx = imgs.entries[i]->nx; const int ny = imgs.entries[i]->ny; @@ -2828,17 +2850,19 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const int n = nx * ny; for (int b = 0; b < batch_size; b++) { - for (int k = 0; k < 3; k++) { - for (int y = 0; y < ny; y++) { - for (int x = 0; x < nx; x++) { - data[(b * 3 * n) + k * n + y * nx + x] = imgs.entries[b]->buf[3 * (y * nx + x) + k]; - } + float * batch_entry = data + b * (3*n); + for (int y = 0; y < ny; y++) { + for (int x = 0; x < nx; x++) { + size_t base_src = 3*(y * nx + x); // idx of the first channel + size_t base_dst = y * nx + x; // idx of the first channel + batch_entry[ base_dst] = imgs.entries[b]->buf[base_src ]; + batch_entry[1*n + base_dst] = imgs.entries[b]->buf[base_src + 1]; + batch_entry[2*n + base_dst] = imgs.entries[b]->buf[base_src + 2]; } } } } ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw)); - free(data); } if (ctx->has_minicpmv_projector) { { diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 61751755b..d70acb771 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2606,6 +2606,8 @@ struct test_rope : public test_case { } else { out = ggml_rope_ext_back(ctx, a, pos, freq, n_dims, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f); } + + // TODO: add test with a non-contiguous view as input ; this case is needed for build_rope_2d in clip.cpp } ggml_set_name(out, "out");