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refactor: introduce GGMLRunnerContext #928

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Merged
leejet merged 4 commits into from
Nov 1, 2025
Merged

refactor: introduce GGMLRunnerContext #928

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95b2949
introduce GGMLRunnerContext
leejet Oct 30, 2025
6ea46b4
add Flash Attention enable control through GGMLRunnerContext
leejet Oct 31, 2025
9e281b0
add conv2d_direct enable control through GGMLRunnerContext
leejet Oct 31, 2025
aba0997
format code
leejet Oct 31, 2025
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91 changes: 44 additions & 47 deletions clip.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -451,16 +451,16 @@ struct CLIPMLP : public GGMLBlock {
}
}

struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
// x: [N, n_token, d_model]
auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);

x = fc1->forward(ctx, x);
if (use_gelu) {
x = ggml_gelu_inplace(ctx, x);
x = ggml_gelu_inplace(ctx->ggml_ctx, x);
} else {
x = ggml_gelu_quick_inplace(ctx, x);
x = ggml_gelu_quick_inplace(ctx->ggml_ctx, x);
}
x = fc2->forward(ctx, x);
return x;
Expand Down Expand Up @@ -488,15 +488,15 @@ struct CLIPLayer : public GGMLBlock {
blocks["mlp"] = std::shared_ptr<GGMLBlock>(new CLIPMLP(d_model, intermediate_size));
}

struct ggml_tensor* forward(struct ggml_context* ctx, ggml_backend_t backend, struct ggml_tensor* x, bool mask = true) {
struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, bool mask = true) {
// x: [N, n_token, d_model]
auto self_attn = std::dynamic_pointer_cast<MultiheadAttention>(blocks["self_attn"]);
auto layer_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm1"]);
auto layer_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm2"]);
auto mlp = std::dynamic_pointer_cast<CLIPMLP>(blocks["mlp"]);

x = ggml_add(ctx, x, self_attn->forward(ctx, backend, layer_norm1->forward(ctx, x), mask));
x = ggml_add(ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
x = ggml_add(ctx->ggml_ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
x = ggml_add(ctx->ggml_ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
return x;
}
};
Expand All @@ -517,8 +517,7 @@ struct CLIPEncoder : public GGMLBlock {
}
}

struct ggml_tensor* forward(struct ggml_context* ctx,
ggml_backend_t backend,
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* x,
int clip_skip = -1,
bool mask = true) {
Expand All @@ -536,7 +535,7 @@ struct CLIPEncoder : public GGMLBlock {
}
std::string name = "layers." + std::to_string(i);
auto layer = std::dynamic_pointer_cast<CLIPLayer>(blocks[name]);
x = layer->forward(ctx, backend, x, mask); // [N, n_token, d_model]
x = layer->forward(ctx, x, mask); // [N, n_token, d_model]
// LOG_DEBUG("layer %d", i);
}
return x;
Expand Down Expand Up @@ -578,20 +577,20 @@ class CLIPEmbeddings : public GGMLBlock {
return params["token_embedding.weight"];
}

struct ggml_tensor* forward(struct ggml_context* ctx,
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* input_ids,
struct ggml_tensor* custom_embed_weight) {
// input_ids: [N, n_token]
auto token_embed_weight = params["token_embedding.weight"];
auto position_embed_weight = params["position_embedding.weight"];

GGML_ASSERT(input_ids->ne[0] == position_embed_weight->ne[1]);
input_ids = ggml_reshape_3d(ctx, input_ids, input_ids->ne[0], 1, input_ids->ne[1]);
auto token_embedding = ggml_get_rows(ctx, custom_embed_weight != nullptr ? custom_embed_weight : token_embed_weight, input_ids);
token_embedding = ggml_reshape_3d(ctx, token_embedding, token_embedding->ne[0], token_embedding->ne[1], token_embedding->ne[3]);
input_ids = ggml_reshape_3d(ctx->ggml_ctx, input_ids, input_ids->ne[0], 1, input_ids->ne[1]);
auto token_embedding = ggml_get_rows(ctx->ggml_ctx, custom_embed_weight != nullptr ? custom_embed_weight : token_embed_weight, input_ids);
token_embedding = ggml_reshape_3d(ctx->ggml_ctx, token_embedding, token_embedding->ne[0], token_embedding->ne[1], token_embedding->ne[3]);

// token_embedding + position_embedding
auto x = ggml_add(ctx,
auto x = ggml_add(ctx->ggml_ctx,
token_embedding,
position_embed_weight); // [N, n_token, embed_dim]
return x;
Expand Down Expand Up @@ -629,7 +628,7 @@ class CLIPVisionEmbeddings : public GGMLBlock {
num_positions = num_patches + 1;
}

struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* pixel_values) {
struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* pixel_values) {
// pixel_values: [N, num_channels, image_size, image_size]
// return: [N, num_positions, embed_dim]
GGML_ASSERT(pixel_values->ne[0] == image_size && pixel_values->ne[1] == image_size && pixel_values->ne[2] == num_channels);
Expand All @@ -641,18 +640,18 @@ class CLIPVisionEmbeddings : public GGMLBlock {
// concat(patch_embedding, class_embedding) + position_embedding
struct ggml_tensor* patch_embedding;
int64_t N = pixel_values->ne[3];
patch_embedding = ggml_ext_conv_2d(ctx, pixel_values, patch_embed_weight, nullptr, patch_size, patch_size); // [N, embed_dim, image_size // pacht_size, image_size // pacht_size]
patch_embedding = ggml_reshape_3d(ctx, patch_embedding, num_patches, embed_dim, N); // [N, embed_dim, num_patches]
patch_embedding = ggml_cont(ctx, ggml_permute(ctx, patch_embedding, 1, 0, 2, 3)); // [N, num_patches, embed_dim]
patch_embedding = ggml_reshape_4d(ctx, patch_embedding, 1, embed_dim, num_patches, N); // [N, num_patches, embed_dim, 1]

struct ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, embed_dim, N);
class_embedding = ggml_repeat(ctx, class_embed_weight, class_embedding); // [N, embed_dim]
class_embedding = ggml_reshape_4d(ctx, class_embedding, 1, embed_dim, 1, N); // [N, 1, embed_dim, 1]

struct ggml_tensor* x = ggml_concat(ctx, class_embedding, patch_embedding, 2); // [N, num_positions, embed_dim, 1]
x = ggml_reshape_3d(ctx, x, embed_dim, num_positions, N); // [N, num_positions, embed_dim]
x = ggml_add(ctx, x, position_embed_weight);
patch_embedding = ggml_ext_conv_2d(ctx->ggml_ctx, pixel_values, patch_embed_weight, nullptr, patch_size, patch_size); // [N, embed_dim, image_size // pacht_size, image_size // pacht_size]
patch_embedding = ggml_reshape_3d(ctx->ggml_ctx, patch_embedding, num_patches, embed_dim, N); // [N, embed_dim, num_patches]
patch_embedding = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, patch_embedding, 1, 0, 2, 3)); // [N, num_patches, embed_dim]
patch_embedding = ggml_reshape_4d(ctx->ggml_ctx, patch_embedding, 1, embed_dim, num_patches, N); // [N, num_patches, embed_dim, 1]

struct ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx->ggml_ctx, GGML_TYPE_F32, embed_dim, N);
class_embedding = ggml_repeat(ctx->ggml_ctx, class_embed_weight, class_embedding); // [N, embed_dim]
class_embedding = ggml_reshape_4d(ctx->ggml_ctx, class_embedding, 1, embed_dim, 1, N); // [N, 1, embed_dim, 1]

struct ggml_tensor* x = ggml_concat(ctx->ggml_ctx, class_embedding, patch_embedding, 2); // [N, num_positions, embed_dim, 1]
x = ggml_reshape_3d(ctx->ggml_ctx, x, embed_dim, num_positions, N); // [N, num_positions, embed_dim]
x = ggml_add(ctx->ggml_ctx, x, position_embed_weight);
return x; // [N, num_positions, embed_dim]
}
};
Expand Down Expand Up @@ -714,8 +713,7 @@ class CLIPTextModel : public GGMLBlock {
return embeddings->get_token_embed_weight();
}

struct ggml_tensor* forward(struct ggml_context* ctx,
ggml_backend_t backend,
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* input_ids,
struct ggml_tensor* tkn_embeddings,
size_t max_token_idx = 0,
Expand All @@ -727,16 +725,16 @@ class CLIPTextModel : public GGMLBlock {
auto final_layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["final_layer_norm"]);

auto x = embeddings->forward(ctx, input_ids, tkn_embeddings); // [N, n_token, hidden_size]
x = encoder->forward(ctx, backend, x, return_pooled ? -1 : clip_skip, true);
x = encoder->forward(ctx, x, return_pooled ? -1 : clip_skip, true);
if (return_pooled || with_final_ln) {
x = final_layer_norm->forward(ctx, x);
}

if (return_pooled) {
auto text_projection = params["text_projection"];
ggml_tensor* pooled = ggml_view_1d(ctx, x, hidden_size, x->nb[1] * max_token_idx);
ggml_tensor* pooled = ggml_view_1d(ctx->ggml_ctx, x, hidden_size, x->nb[1] * max_token_idx);
if (text_projection != nullptr) {
pooled = ggml_ext_linear(ctx, pooled, text_projection, nullptr);
pooled = ggml_ext_linear(ctx->ggml_ctx, pooled, text_projection, nullptr);
} else {
LOG_DEBUG("identity projection");
}
Expand Down Expand Up @@ -779,8 +777,7 @@ class CLIPVisionModel : public GGMLBlock {
blocks["post_layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
}

struct ggml_tensor* forward(struct ggml_context* ctx,
ggml_backend_t backend,
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* pixel_values,
bool return_pooled = true,
int clip_skip = -1) {
Expand All @@ -792,14 +789,14 @@ class CLIPVisionModel : public GGMLBlock {

auto x = embeddings->forward(ctx, pixel_values); // [N, num_positions, embed_dim]
x = pre_layernorm->forward(ctx, x);
x = encoder->forward(ctx, backend, x, clip_skip, false);
x = encoder->forward(ctx, x, clip_skip, false);
// print_ggml_tensor(x, true, "ClipVisionModel x: ");
auto last_hidden_state = x;
x = post_layernorm->forward(ctx, x); // [N, n_token, hidden_size]

GGML_ASSERT(x->ne[3] == 1);
if (return_pooled) {
ggml_tensor* pooled = ggml_cont(ctx, ggml_view_2d(ctx, x, x->ne[0], x->ne[2], x->nb[2], 0));
ggml_tensor* pooled = ggml_cont(ctx->ggml_ctx, ggml_view_2d(ctx->ggml_ctx, x, x->ne[0], x->ne[2], x->nb[2], 0));
return pooled; // [N, hidden_size]
} else {
// return x; // [N, n_token, hidden_size]
Expand Down Expand Up @@ -831,12 +828,12 @@ class CLIPProjection : public UnaryBlock {
out_features(out_features),
transpose_weight(transpose_weight) {}

struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) override {
struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
struct ggml_tensor* w = params["weight"];
if (transpose_weight) {
w = ggml_cont(ctx, ggml_transpose(ctx, w));
w = ggml_cont(ctx->ggml_ctx, ggml_transpose(ctx->ggml_ctx, w));
}
return ggml_ext_linear(ctx, x, w, nullptr);
return ggml_ext_linear(ctx->ggml_ctx, x, w, nullptr);
}
};

Expand All @@ -860,8 +857,7 @@ class CLIPVisionModelProjection : public GGMLBlock {
blocks["visual_projection"] = std::shared_ptr<GGMLBlock>(new CLIPProjection(hidden_size, projection_dim, transpose_proj_w));
}

struct ggml_tensor* forward(struct ggml_context* ctx,
ggml_backend_t backend,
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* pixel_values,
bool return_pooled = true,
int clip_skip = -1) {
Expand All @@ -870,7 +866,7 @@ class CLIPVisionModelProjection : public GGMLBlock {
auto vision_model = std::dynamic_pointer_cast<CLIPVisionModel>(blocks["vision_model"]);
auto visual_projection = std::dynamic_pointer_cast<CLIPProjection>(blocks["visual_projection"]);

auto x = vision_model->forward(ctx, backend, pixel_values, return_pooled, clip_skip); // [N, hidden_size] or [N, n_token, hidden_size]
auto x = vision_model->forward(ctx, pixel_values, return_pooled, clip_skip); // [N, hidden_size] or [N, n_token, hidden_size]

if (return_pooled) {
x = visual_projection->forward(ctx, x); // [N, projection_dim]
Expand Down Expand Up @@ -902,8 +898,7 @@ struct CLIPTextModelRunner : public GGMLRunner {
model.get_param_tensors(tensors, prefix);
}

struct ggml_tensor* forward(struct ggml_context* ctx,
ggml_backend_t backend,
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* input_ids,
struct ggml_tensor* embeddings,
size_t max_token_idx = 0,
Expand All @@ -913,10 +908,10 @@ struct CLIPTextModelRunner : public GGMLRunner {
size_t n_token = input_ids->ne[0];
if (input_ids->ne[0] > model.n_token) {
GGML_ASSERT(input_ids->ne[0] % model.n_token == 0);
input_ids = ggml_reshape_2d(ctx, input_ids, model.n_token, input_ids->ne[0] / model.n_token);
input_ids = ggml_reshape_2d(ctx->ggml_ctx, input_ids, model.n_token, input_ids->ne[0] / model.n_token);
}

return model.forward(ctx, backend, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
return model.forward(ctx, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
}

struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
Expand All @@ -943,7 +938,9 @@ struct CLIPTextModelRunner : public GGMLRunner {
embeddings = ggml_concat(compute_ctx, token_embed_weight, custom_embeddings, 1);
}

struct ggml_tensor* hidden_states = forward(compute_ctx, runtime_backend, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
auto runner_ctx = get_context();

struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);

ggml_build_forward_expand(gf, hidden_states);

Expand Down
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