vbatts/llama.cpp
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Georgi Gerganov 2024-12-10 22:05:44 +02:00
parent 3046fde420
commit 435cfd788b
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1 changed files with 72 additions and 1 deletions
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src/llama.cpp
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@ -1631,7 +1631,7 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_POS_NET_CONV1, "pos_net.%d.conv1" },
{ LLM_TENSOR_POS_NET_CONV2, "pos_net.%d.conv2" },
{ LLM_TENSOR_POS_NET_NORM, "pos_net.norm" },
{ LLM_TENSOR_POS_NET_NORM, "pos_net.%d.norm" },
{ LLM_TENSOR_POS_NET_NORM1, "pos_net.%d.norm1" },
{ LLM_TENSOR_POS_NET_NORM2, "pos_net.%d.norm2" },
{ LLM_TENSOR_POS_NET_ATTN_NORM, "pos_net.%d.attn_norm" },
@ -3127,6 +3127,10 @@ struct llama_model {
struct ggml_tensor * posnet_4_conv2 = nullptr;
struct ggml_tensor * posnet_4_conv2_b = nullptr;
// resnet 5
struct ggml_tensor * posnet_5_norm = nullptr;
struct ggml_tensor * posnet_5_norm_b = nullptr;
std::vector<llama_layer> layers;
// gguf metadata
@ -7427,6 +7431,7 @@ static const std::map<llm_tensor, llm_tensor_info> llm_tensor_info_mapping = {
// this tensor is loaded for T5, but never used
{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_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{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}},
@ -9580,6 +9585,9 @@ static bool llm_load_tensors(
model.posnet_4_conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", 4), {3, 768, 768}, 0);
model.posnet_4_conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", 4), {768}, 0);
model.posnet_5_norm = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", 5), {768}, 0);
model.posnet_5_norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias", 5), {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);
@ -17267,6 +17275,69 @@ struct llm_build_context {
cur = ggml_add(ctx0, cur, inpL);
}
inpL = cur;
// resnet block 3
{
cur = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_3_norm1, 1, model.posnet_3_norm1->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_3_norm1_b, 1, model.posnet_3_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_3_conv1, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_3_conv1_b, 1, model.posnet_3_conv1_b->ne[0]));
cur = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_3_norm2, 1, model.posnet_3_norm2->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_3_norm2_b, 1, model.posnet_3_norm2_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_3_conv2, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_3_conv2_b, 1, model.posnet_3_conv2_b->ne[0]));
cur = ggml_add(ctx0, cur, inpL);
}
inpL = cur;
// resnet block 4
{
cur = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_4_norm1, 1, model.posnet_4_norm1->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_4_norm1_b, 1, model.posnet_4_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_4_conv1, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_4_conv1_b, 1, model.posnet_4_conv1_b->ne[0]));
cur = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_4_norm2, 1, model.posnet_4_norm2->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_4_norm2_b, 1, model.posnet_4_norm2_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_4_conv2, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_4_conv2_b, 1, model.posnet_4_conv2_b->ne[0]));
cur = ggml_add(ctx0, cur, inpL);
}
// normalize block 5
{
cur = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_5_norm, 1, model.posnet_5_norm->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_5_norm_b, 1, model.posnet_5_norm_b->ne[0]),
LLM_NORM_GROUP, cb, 0);
}
printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]);
//cur = llm_build_norm(ctx0, cur, hparams,