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resnet conv

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Georgi Gerganov 2024-12-10 20:40:52 +02:00
parent ba07b35d6f
commit 7f029b2167
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1 changed files with 26 additions and 0 deletions
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src/llama.cpp
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@ -3061,6 +3061,15 @@ struct llama_model {
struct ggml_tensor * posnet_0_norm1 = nullptr;
struct ggml_tensor * posnet_0_norm1_b = nullptr;
struct ggml_tensor * posnet_0_conv1 = nullptr;
struct ggml_tensor * posnet_0_conv1_b = nullptr;
struct ggml_tensor * posnet_0_norm2 = nullptr;
struct ggml_tensor * posnet_0_norm2_b = nullptr;
struct ggml_tensor * posnet_0_conv2 = nullptr;
struct ggml_tensor * posnet_0_conv2_b = nullptr;
std::vector<llama_layer> layers;
// gguf metadata
@ -7362,6 +7371,9 @@ static const std::map<llm_tensor, llm_tensor_info> llm_tensor_info_mapping = {
{LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
{LLM_TENSOR_CONV1D, {LLM_TENSOR_LAYER_INPUT, GGML_OP_IM2COL}},
{LLM_TENSOR_POS_NET_NORM1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_POS_NET_NORM2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_POS_NET_CONV1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
{LLM_TENSOR_POS_NET_CONV2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
};
// checks if the weight tensor can be used with the specified buffer type and device
@ -9446,6 +9458,15 @@ static bool llm_load_tensors(
model.posnet_0_norm1 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", 0), {768}, 0);
model.posnet_0_norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias", 0), {768}, 0);
model.posnet_0_conv1 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", 0), {3, 768, 768}, 0);
model.posnet_0_conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias", 0), {768}, 0);
model.posnet_0_norm2 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", 0), {768}, 0);
model.posnet_0_norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias", 0), {768}, 0);
model.posnet_0_conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", 0), {3, 768, 768}, 0);
model.posnet_0_conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", 0), {768}, 0);
// output
model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {768}, 0);
model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, 1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
@ -17047,6 +17068,11 @@ struct llm_build_context {
ggml_reshape_2d(ctx0, model.posnet_0_norm1_b, 1, model.posnet_0_norm1_b->ne[0]),
LLM_NORM_GROUP, cb, 0);
cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
cur = ggml_conv_1d_ph(ctx0, model.posnet_0_conv1, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_0_conv1_b, 1, model.posnet_0_conv1_b->ne[0]));
printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]);
//cur = llm_build_norm(ctx0, cur, hparams,