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resnet

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Georgi Gerganov 2024-12-10 20:50:13 +02:00
parent 7f029b2167
commit 33dffbc57a
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1 changed files with 76 additions and 7 deletions
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src/llama.cpp
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@ -3070,6 +3070,18 @@ struct llama_model {
struct ggml_tensor * posnet_0_conv2 = nullptr; struct ggml_tensor * posnet_0_conv2 = nullptr;
struct ggml_tensor * posnet_0_conv2_b = nullptr; struct ggml_tensor * posnet_0_conv2_b = nullptr;
struct ggml_tensor * posnet_1_norm1 = nullptr;
struct ggml_tensor * posnet_1_norm1_b = nullptr;
struct ggml_tensor * posnet_1_conv1 = nullptr;
struct ggml_tensor * posnet_1_conv1_b = nullptr;
struct ggml_tensor * posnet_1_norm2 = nullptr;
struct ggml_tensor * posnet_1_norm2_b = nullptr;
struct ggml_tensor * posnet_1_conv2 = nullptr;
struct ggml_tensor * posnet_1_conv2_b = nullptr;
std::vector<llama_layer> layers; std::vector<llama_layer> layers;
// gguf metadata // gguf metadata
@ -9467,6 +9479,18 @@ static bool llm_load_tensors(
model.posnet_0_conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", 0), {3, 768, 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); model.posnet_0_conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", 0), {768}, 0);
model.posnet_1_norm1 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", 1), {768}, 0);
model.posnet_1_norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias", 1), {768}, 0);
model.posnet_1_conv1 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", 1), {3, 768, 768}, 0);
model.posnet_1_conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias", 1), {768}, 0);
model.posnet_1_norm2 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", 1), {768}, 0);
model.posnet_1_norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias", 1), {768}, 0);
model.posnet_1_conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", 1), {3, 768, 768}, 0);
model.posnet_1_conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", 1), {768}, 0);
// output // output
model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {768}, 0); 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); model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, 1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
@ -17060,18 +17084,63 @@ struct llm_build_context {
printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]); printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]);
printf("conv1d: %d %d %d\n", model.conv_1d->ne[0], model.conv_1d->ne[1], model.conv_1d->ne[2]); printf("conv1d: %d %d %d\n", model.conv_1d->ne[0], model.conv_1d->ne[1], model.conv_1d->ne[2]);
cur = ggml_conv_1d_ph(ctx0, model.conv_1d, cur, 1, 1); cur = ggml_conv_1d_ph(ctx0, model.conv_1d, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.conv_1d_b, 1, model.conv_1d_b->ne[0])); cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.conv_1d_b, 1, model.conv_1d_b->ne[0]));
cur = llm_build_norm(ctx0, cur, hparams, // resnet block 0
{
struct ggml_tensor * cur_rnet = cur;
cur_rnet = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_0_norm1, 1, model.posnet_0_norm1->ne[0]), ggml_reshape_2d(ctx0, model.posnet_0_norm1, 1, model.posnet_0_norm1->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_0_norm1_b, 1, model.posnet_0_norm1_b->ne[0]), ggml_reshape_2d(ctx0, model.posnet_0_norm1_b, 1, model.posnet_0_norm1_b->ne[0]),
LLM_NORM_GROUP, cb, 0); LLM_NORM_GROUP, cb, 0);
cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur); cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
cur = ggml_conv_1d_ph(ctx0, model.posnet_0_conv1, cur, 1, 1); cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_0_conv1, cur_rnet, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_0_conv1_b, 1, model.posnet_0_conv1_b->ne[0])); cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_0_conv1_b, 1, model.posnet_0_conv1_b->ne[0]));
cur_rnet = llm_build_norm(ctx0, cur_rnet, hparams,
ggml_reshape_2d(ctx0, model.posnet_0_norm2, 1, model.posnet_0_norm2->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_0_norm2_b, 1, model.posnet_0_norm2_b->ne[0]),
LLM_NORM_GROUP, cb, 0);
cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_0_conv2, cur_rnet, 1, 1);
cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_0_conv2_b, 1, model.posnet_0_conv2_b->ne[0]));
cur = ggml_add(ctx0, cur_rnet, cur);
}
// resnet block 1
{
struct ggml_tensor * cur_rnet = cur;
cur_rnet = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_1_norm1, 1, model.posnet_1_norm1->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_1_norm1_b, 1, model.posnet_1_norm1_b->ne[0]),
LLM_NORM_GROUP, cb, 0);
cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_1_conv1, cur_rnet, 1, 1);
cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_1_conv1_b, 1, model.posnet_1_conv1_b->ne[0]));
cur_rnet = llm_build_norm(ctx0, cur_rnet, hparams,
ggml_reshape_2d(ctx0, model.posnet_1_norm2, 1, model.posnet_1_norm2->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_1_norm2_b, 1, model.posnet_1_norm2_b->ne[0]),
LLM_NORM_GROUP, cb, 0);
cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_1_conv2, cur_rnet, 1, 1);
cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_1_conv2_b, 1, model.posnet_1_conv2_b->ne[0]));
cur = ggml_add(ctx0, cur_rnet, cur);
}
printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]); printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]);