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https://github.com/ggerganov/llama.cpp.git
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attn
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33dffbc57a
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@ -296,7 +296,9 @@ class Model:
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break
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for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
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data = data_torch.squeeze().numpy()
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# TODO: why do we squeeze here?
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#data = data_torch.squeeze().numpy()
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data = data_torch.numpy()
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# if data ends up empty, it means data_torch was a scalar tensor -> restore
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if len(data.shape) == 0:
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@ -2044,6 +2046,8 @@ class OuteTTSVocoderModel(Model):
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logger.debug(f"Skipping {name!r}")
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return []
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print(f"{self.map_tensor_name(name)} -> {data_torch.shape}")
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return [(self.map_tensor_name(name), data_torch)]
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def set_vocab(self):
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169
src/llama.cpp
169
src/llama.cpp
@ -3055,9 +3055,11 @@ struct llama_model {
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struct ggml_tensor * cls_out_b = nullptr;
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// outetts vocoder
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// TODO: dedup
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struct ggml_tensor * conv_1d = nullptr;
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struct ggml_tensor * conv_1d_b = nullptr;
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// resnet 0
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struct ggml_tensor * posnet_0_norm1 = nullptr;
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struct ggml_tensor * posnet_0_norm1_b = nullptr;
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@ -3070,6 +3072,7 @@ struct llama_model {
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struct ggml_tensor * posnet_0_conv2 = nullptr;
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struct ggml_tensor * posnet_0_conv2_b = nullptr;
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// resnet 1
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struct ggml_tensor * posnet_1_norm1 = nullptr;
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struct ggml_tensor * posnet_1_norm1_b = nullptr;
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@ -3082,6 +3085,48 @@ struct llama_model {
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struct ggml_tensor * posnet_1_conv2 = nullptr;
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struct ggml_tensor * posnet_1_conv2_b = nullptr;
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// attn 2
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struct ggml_tensor * posnet_2_attn_norm = nullptr;
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struct ggml_tensor * posnet_2_attn_norm_b = nullptr;
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struct ggml_tensor * posnet_2_attn_q = nullptr;
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struct ggml_tensor * posnet_2_attn_q_b = nullptr;
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struct ggml_tensor * posnet_2_attn_k = nullptr;
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struct ggml_tensor * posnet_2_attn_k_b = nullptr;
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struct ggml_tensor * posnet_2_attn_v = nullptr;
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struct ggml_tensor * posnet_2_attn_v_b = nullptr;
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struct ggml_tensor * posnet_2_attn_o = nullptr;
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struct ggml_tensor * posnet_2_attn_o_b = nullptr;
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// resnet 3
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struct ggml_tensor * posnet_3_norm1 = nullptr;
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struct ggml_tensor * posnet_3_norm1_b = nullptr;
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struct ggml_tensor * posnet_3_conv1 = nullptr;
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struct ggml_tensor * posnet_3_conv1_b = nullptr;
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struct ggml_tensor * posnet_3_norm2 = nullptr;
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struct ggml_tensor * posnet_3_norm2_b = nullptr;
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struct ggml_tensor * posnet_3_conv2 = nullptr;
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struct ggml_tensor * posnet_3_conv2_b = nullptr;
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// resnet 4
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struct ggml_tensor * posnet_4_norm1 = nullptr;
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struct ggml_tensor * posnet_4_norm1_b = nullptr;
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struct ggml_tensor * posnet_4_conv1 = nullptr;
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struct ggml_tensor * posnet_4_conv1_b = nullptr;
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struct ggml_tensor * posnet_4_norm2 = nullptr;
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struct ggml_tensor * posnet_4_norm2_b = nullptr;
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struct ggml_tensor * posnet_4_conv2 = nullptr;
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struct ggml_tensor * posnet_4_conv2_b = nullptr;
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std::vector<llama_layer> layers;
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// gguf metadata
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@ -7386,6 +7431,11 @@ static const std::map<llm_tensor, llm_tensor_info> llm_tensor_info_mapping = {
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{LLM_TENSOR_POS_NET_NORM2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
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{LLM_TENSOR_POS_NET_CONV1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
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{LLM_TENSOR_POS_NET_CONV2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
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{LLM_TENSOR_POS_NET_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
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{LLM_TENSOR_POS_NET_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_POS_NET_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_POS_NET_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_POS_NET_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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};
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// checks if the weight tensor can be used with the specified buffer type and device
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@ -9491,6 +9541,45 @@ static bool llm_load_tensors(
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model.posnet_1_conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", 1), {3, 768, 768}, 0);
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model.posnet_1_conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", 1), {768}, 0);
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model.posnet_2_attn_norm = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", 2), {768}, 0);
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model.posnet_2_attn_norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias", 2), {768}, 0);
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model.posnet_2_attn_q = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q, "weight", 2), {1, 768, 768}, 0);
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model.posnet_2_attn_q_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q, "bias", 2), {768}, 0);
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model.posnet_2_attn_k = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K, "weight", 2), {1, 768, 768}, 0);
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model.posnet_2_attn_k_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K, "bias", 2), {768}, 0);
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model.posnet_2_attn_v = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V, "weight", 2), {1, 768, 768}, 0);
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model.posnet_2_attn_v_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V, "bias", 2), {768}, 0);
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model.posnet_2_attn_o = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT, "weight", 2), {1, 768, 768}, 0);
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model.posnet_2_attn_o_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT, "bias", 2), {768}, 0);
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model.posnet_3_norm1 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", 3), {768}, 0);
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model.posnet_3_norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias", 3), {768}, 0);
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model.posnet_3_conv1 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", 3), {3, 768, 768}, 0);
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model.posnet_3_conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias", 3), {768}, 0);
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model.posnet_3_norm2 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", 3), {768}, 0);
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model.posnet_3_norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias", 3), {768}, 0);
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model.posnet_3_conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", 3), {3, 768, 768}, 0);
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model.posnet_3_conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", 3), {768}, 0);
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model.posnet_4_norm1 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", 4), {768}, 0);
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model.posnet_4_norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias", 4), {768}, 0);
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model.posnet_4_conv1 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", 4), {3, 768, 768}, 0);
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model.posnet_4_conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias", 4), {768}, 0);
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model.posnet_4_norm2 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", 4), {768}, 0);
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model.posnet_4_norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias", 4), {768}, 0);
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model.posnet_4_conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", 4), {3, 768, 768}, 0);
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model.posnet_4_conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", 4), {768}, 0);
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// output
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model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {768}, 0);
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model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, 1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
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@ -17088,58 +17177,94 @@ struct llm_build_context {
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cur = ggml_conv_1d_ph(ctx0, model.conv_1d, cur, 1, 1);
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cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.conv_1d_b, 1, model.conv_1d_b->ne[0]));
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inpL = cur;
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// resnet block 0
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{
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struct ggml_tensor * cur_rnet = cur;
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cur_rnet = llm_build_norm(ctx0, cur, hparams,
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cur = llm_build_norm(ctx0, cur, hparams,
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ggml_reshape_2d(ctx0, model.posnet_0_norm1, 1, model.posnet_0_norm1->ne[0]),
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ggml_reshape_2d(ctx0, model.posnet_0_norm1_b, 1, model.posnet_0_norm1_b->ne[0]),
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LLM_NORM_GROUP, cb, 0);
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cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
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cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
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cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_0_conv1, cur_rnet, 1, 1);
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cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_0_conv1_b, 1, model.posnet_0_conv1_b->ne[0]));
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cur = ggml_conv_1d_ph(ctx0, model.posnet_0_conv1, cur, 1, 1);
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cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_0_conv1_b, 1, model.posnet_0_conv1_b->ne[0]));
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cur_rnet = llm_build_norm(ctx0, cur_rnet, hparams,
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cur = llm_build_norm(ctx0, cur, hparams,
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ggml_reshape_2d(ctx0, model.posnet_0_norm2, 1, model.posnet_0_norm2->ne[0]),
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ggml_reshape_2d(ctx0, model.posnet_0_norm2_b, 1, model.posnet_0_norm2_b->ne[0]),
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LLM_NORM_GROUP, cb, 0);
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cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
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cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
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cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_0_conv2, cur_rnet, 1, 1);
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cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_0_conv2_b, 1, model.posnet_0_conv2_b->ne[0]));
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cur = ggml_conv_1d_ph(ctx0, model.posnet_0_conv2, cur, 1, 1);
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cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_0_conv2_b, 1, model.posnet_0_conv2_b->ne[0]));
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cur = ggml_add(ctx0, cur_rnet, cur);
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cur = ggml_add(ctx0, cur, inpL);
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}
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inpL = cur;
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// resnet block 1
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{
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struct ggml_tensor * cur_rnet = cur;
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cur_rnet = llm_build_norm(ctx0, cur, hparams,
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cur = llm_build_norm(ctx0, cur, hparams,
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ggml_reshape_2d(ctx0, model.posnet_1_norm1, 1, model.posnet_1_norm1->ne[0]),
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ggml_reshape_2d(ctx0, model.posnet_1_norm1_b, 1, model.posnet_1_norm1_b->ne[0]),
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LLM_NORM_GROUP, cb, 0);
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cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
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cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
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cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_1_conv1, cur_rnet, 1, 1);
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cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_1_conv1_b, 1, model.posnet_1_conv1_b->ne[0]));
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cur = ggml_conv_1d_ph(ctx0, model.posnet_1_conv1, cur, 1, 1);
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cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_1_conv1_b, 1, model.posnet_1_conv1_b->ne[0]));
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cur_rnet = llm_build_norm(ctx0, cur_rnet, hparams,
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cur = llm_build_norm(ctx0, cur, hparams,
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ggml_reshape_2d(ctx0, model.posnet_1_norm2, 1, model.posnet_1_norm2->ne[0]),
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ggml_reshape_2d(ctx0, model.posnet_1_norm2_b, 1, model.posnet_1_norm2_b->ne[0]),
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LLM_NORM_GROUP, cb, 0);
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cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
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cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
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cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_1_conv2, cur_rnet, 1, 1);
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cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_1_conv2_b, 1, model.posnet_1_conv2_b->ne[0]));
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cur = ggml_conv_1d_ph(ctx0, model.posnet_1_conv2, cur, 1, 1);
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cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_1_conv2_b, 1, model.posnet_1_conv2_b->ne[0]));
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cur = ggml_add(ctx0, cur_rnet, cur);
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cur = ggml_add(ctx0, cur, inpL);
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}
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inpL = cur;
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// attention block
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{
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cur = llm_build_norm(ctx0, cur, hparams,
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ggml_reshape_2d(ctx0, model.posnet_2_attn_norm, 1, model.posnet_2_attn_norm->ne[0]),
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ggml_reshape_2d(ctx0, model.posnet_2_attn_norm_b, 1, model.posnet_2_attn_norm_b->ne[0]),
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LLM_NORM_GROUP, cb, 0);
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struct ggml_tensor * q;
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struct ggml_tensor * k;
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struct ggml_tensor * v;
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q = ggml_conv_1d_ph(ctx0, model.posnet_2_attn_q, cur, 1, 1);
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k = ggml_conv_1d_ph(ctx0, model.posnet_2_attn_k, cur, 1, 1);
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v = ggml_conv_1d_ph(ctx0, model.posnet_2_attn_v, cur, 1, 1);
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q = ggml_add(ctx0, q, ggml_reshape_2d(ctx0, model.posnet_2_attn_q_b, 1, model.posnet_2_attn_q_b->ne[0]));
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k = ggml_add(ctx0, k, ggml_reshape_2d(ctx0, model.posnet_2_attn_k_b, 1, model.posnet_2_attn_k_b->ne[0]));
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v = ggml_add(ctx0, v, ggml_reshape_2d(ctx0, model.posnet_2_attn_v_b, 1, model.posnet_2_attn_v_b->ne[0]));
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q = ggml_cont(ctx0, ggml_transpose(ctx0, q));
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k = ggml_cont(ctx0, ggml_transpose(ctx0, k));
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struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
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kq = ggml_soft_max_ext(ctx0, kq, nullptr, 1.0f/sqrtf(float(768)), 0.0f);
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cur = ggml_mul_mat(ctx0, kq, v);
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cur = ggml_conv_1d_ph(ctx0, model.posnet_2_attn_o, cur, 1, 1);
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cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_2_attn_o_b, 1, model.posnet_2_attn_o_b->ne[0]));
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cur = ggml_add(ctx0, cur, inpL);
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}
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printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]);
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