diff --git a/conversion/__init__.py b/conversion/__init__.py index 7ba79a3c50..d8af76a270 100644 --- a/conversion/__init__.py +++ b/conversion/__init__.py @@ -74,6 +74,7 @@ TEXT_MODEL_MAP: dict[str, str] = { "Dots3NoteTextForCausalLM": "dots3", "DotsOCRForCausalLM": "qwen", "DreamModel": "dream", + "EmbeddingGemma2Model": "gemma", "Ernie4_5ForCausalLM": "ernie", "Ernie4_5_ForCausalLM": "ernie", "Ernie4_5_MoeForCausalLM": "ernie", @@ -308,6 +309,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = { "Dots3NoteForCausalLM": "dots3", "Dots3NoteForConditionalGeneration": "dots3", "DotsOCRForCausalLM": "dotsocr", + "EmbeddingGemma2Model": "gemma", "Exaone4_5_ForConditionalGeneration": "exaone", "Gemma3ForConditionalGeneration": "gemma", "Gemma3nForConditionalGeneration": "gemma", diff --git a/conversion/gemma.py b/conversion/gemma.py index 9ec622ed49..2a1f6931ff 100644 --- a/conversion/gemma.py +++ b/conversion/gemma.py @@ -700,7 +700,7 @@ class Gemma4Model(Gemma3Model): self.gguf_writer.add_key_length_swa(head_dim_swa) self.gguf_writer.add_value_length_swa(head_dim_swa) - expert_intermediate_size = self.find_hparam(["expert_intermediate_size", "moe_intermediate_size"]) + expert_intermediate_size = self.find_hparam(["expert_intermediate_size", "moe_intermediate_size"], optional=True) if expert_intermediate_size is not None: self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size) @@ -810,6 +810,28 @@ class Gemma4Model(Gemma3Model): yield from super().modify_tensors(data_torch, name, bid) +@ModelBase.register("EmbeddingGemma2Model") +# TODO: add example model +class EmbeddingGemma2Model(Gemma4Model): + model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING2 + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.hparams["num_kv_shared_layers"] = 0 + + def set_gguf_parameters(self): + super().set_gguf_parameters() + # HF sliding_window is bidirectional, llama.cpp expects the full window size + self.gguf_writer.add_sliding_window(2 * self.hparams["sliding_window"]) + self.gguf_writer.add_embedding_length_out(self.hparams["embedding_dim"]) + self.gguf_writer.add_causal_attention(False) + self._try_set_pooling_type() + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + # default rope on all layers, no rope_freqs needed + return iter(()) + + @ModelBase.register("Gemma4DSparkModel") class Gemma4DSparkModel(DFlashModel): model_arch = gguf.MODEL_ARCH.DFLASH @@ -1030,6 +1052,14 @@ class Gemma4VisionAudioModel(MmprojModel): yield (mapped_name, data_torch) +@ModelBase.register("EmbeddingGemma2Model") +# TODO: add example model +class EmbeddingGemma2VisionAudioModel(Gemma4VisionAudioModel): + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # same towers as Gemma4, but the tensor names have no "model." prefix + yield from super().modify_tensors(data_torch, "model." + name, bid) + + @ModelBase.register("Gemma4UnifiedForConditionalGeneration") @ModelBase.example("hf-tiny-v2/tiny-random-Gemma4UnifiedForConditionalGeneration") class Gemma4UnifiedVisionAudioModel(Gemma4VisionAudioModel): diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 6b872da06d..ea7ddfa576 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -559,6 +559,7 @@ class MODEL_ARCH(IntEnum): GEMMA4 = auto() GEMMA4_ASSISTANT = auto() GEMMA_EMBEDDING = auto() + GEMMA_EMBEDDING2 = auto() STARCODER2 = auto() RWKV6 = auto() RWKV6QWEN2 = auto() @@ -1338,6 +1339,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.GEMMA4: "gemma4", MODEL_ARCH.GEMMA4_ASSISTANT: "gemma4-assistant", MODEL_ARCH.GEMMA_EMBEDDING: "gemma-embedding", + MODEL_ARCH.GEMMA_EMBEDDING2: "gemma-embedding2", MODEL_ARCH.STARCODER2: "starcoder2", MODEL_ARCH.RWKV6: "rwkv6", MODEL_ARCH.RWKV6QWEN2: "rwkv6qwen2", @@ -3471,6 +3473,30 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_PRE_NORM, MODEL_TENSOR.FFN_POST_NORM, ], + MODEL_ARCH.GEMMA_EMBEDDING2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_PRE_NORM, + MODEL_TENSOR.FFN_POST_NORM, + MODEL_TENSOR.LAYER_OUT_SCALE, + MODEL_TENSOR.PER_LAYER_MODEL_PROJ, + MODEL_TENSOR.PER_LAYER_INP_GATE, + MODEL_TENSOR.PER_LAYER_PROJ, + MODEL_TENSOR.PER_LAYER_PROJ_NORM, + MODEL_TENSOR.PER_LAYER_POST_NORM, + ], MODEL_ARCH.STARCODER2: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index cef04d0827..5ac11cb46c 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -88,6 +88,7 @@ class TensorNameMap: "model.lm_head", # dflash "model.transformer.ff_out", # llada "head.decoder", # modern-bert + "embedding_projection", # embeddinggemma2 ), MODEL_TENSOR.DENSE_2_OUT: ( "dense_2_out", # embeddinggemma @@ -753,6 +754,7 @@ class TensorNameMap: MODEL_TENSOR.LAYER_OUT_SCALE: ( "model.layers.{bid}.layer_scalar", # gemma4 + "layers.{bid}.layer_scalar", # embeddinggemma2 "model.blocks.{bid}.embed_skip.a_g", # talkie ), @@ -762,10 +764,12 @@ class TensorNameMap: MODEL_TENSOR.PER_LAYER_MODEL_PROJ: ( "model.per_layer_model_projection", # gemma3n + "ple.per_layer_model_projection", # embeddinggemma2 ), MODEL_TENSOR.PER_LAYER_PROJ_NORM: ( "model.per_layer_projection_norm", # gemma3n + "ple.per_layer_projection_norm", # embeddinggemma2 ), MODEL_TENSOR.ALTUP_PROJ: ( @@ -778,14 +782,17 @@ class TensorNameMap: MODEL_TENSOR.PER_LAYER_INP_GATE: ( "model.layers.{bid}.per_layer_input_gate", # gemma3n + "layers.{bid}.ple_block.per_layer_input_gate", # embeddinggemma2 ), MODEL_TENSOR.PER_LAYER_PROJ: ( "model.layers.{bid}.per_layer_projection", # gemma3n + "layers.{bid}.ple_block.per_layer_projection", # embeddinggemma2 ), MODEL_TENSOR.PER_LAYER_POST_NORM: ( "model.layers.{bid}.post_per_layer_input_norm", # gemma3n + "layers.{bid}.ple_block.post_per_layer_input_norm", # embeddinggemma2 ), MODEL_TENSOR.ALTUP_CORRECT_COEF: ( diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 850737ecaa..e1014ed387 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -60,6 +60,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_GEMMA4, "gemma4" }, { LLM_ARCH_GEMMA4_ASSISTANT, "gemma4-assistant" }, { LLM_ARCH_GEMMA_EMBEDDING, "gemma-embedding" }, + { LLM_ARCH_GEMMA_EMBEDDING2, "gemma-embedding2" }, { LLM_ARCH_STARCODER2, "starcoder2" }, { LLM_ARCH_MAMBA, "mamba" }, { LLM_ARCH_MAMBA2, "mamba2" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index 24068fb7a4..80344d3280 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -65,6 +65,7 @@ enum llm_arch { LLM_ARCH_GEMMA4, LLM_ARCH_GEMMA4_ASSISTANT, LLM_ARCH_GEMMA_EMBEDDING, + LLM_ARCH_GEMMA_EMBEDDING2, LLM_ARCH_STARCODER2, LLM_ARCH_MAMBA, LLM_ARCH_MAMBA2, diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 97e0cee70e..fa379bae88 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -156,6 +156,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_gemma4_assistant(params); case LLM_ARCH_GEMMA_EMBEDDING: return new llama_model_gemma_embedding(params); + case LLM_ARCH_GEMMA_EMBEDDING2: + return new llama_model_gemma_embedding2(params); case LLM_ARCH_STARCODER2: return new llama_model_starcoder2(params); case LLM_ARCH_MAMBA: @@ -2371,6 +2373,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, case LLM_ARCH_WAVTOKENIZER_DEC: case LLM_ARCH_MODERN_BERT: case LLM_ARCH_GEMMA_EMBEDDING: + case LLM_ARCH_GEMMA_EMBEDDING2: case LLM_ARCH_DREAM: case LLM_ARCH_LLADA: case LLM_ARCH_LLADA_MOE: @@ -3152,6 +3155,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_GEMMA4: case LLM_ARCH_GEMMA4_ASSISTANT: case LLM_ARCH_GEMMA_EMBEDDING: + case LLM_ARCH_GEMMA_EMBEDDING2: case LLM_ARCH_STARCODER2: case LLM_ARCH_OPENELM: case LLM_ARCH_GPTNEOX: diff --git a/src/models/gemma-embedding2.cpp b/src/models/gemma-embedding2.cpp new file mode 100644 index 0000000000..4b77664ad3 --- /dev/null +++ b/src/models/gemma-embedding2.cpp @@ -0,0 +1,234 @@ +#include "models.h" + +void llama_model_gemma_embedding2::load_arch_hparams(llama_model_loader & ml) { + hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC; + load_swa_pattern(ml, 6); + + hparams.causal_attn = false; // embeddings do not use causal attention + hparams.f_attention_scale = 1.0f; // same as Gemma4, q_norm makes the scaling unnecessary + + ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); + ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_EMBEDDING_LENGTH_PER_LAYER, hparams.n_embd_per_layer); + + switch (hparams.n_layer()) { + case 24: type = LLM_TYPE_0_3B; break; + default: type = LLM_TYPE_UNKNOWN; + } +} + +void llama_model_gemma_embedding2::load_arch_tensors(llama_model_loader &) { + LLAMA_LOAD_LOCALS; + + const int64_t n_embd_per_layer = hparams.n_embd_per_layer; + const int64_t n_embd_out = hparams.n_embd_out(); + + if (n_embd_head_k != n_embd_head_v) { + throw std::runtime_error("EmbeddingGemma2 requires n_embd_head_k == n_embd_head_v"); + } + if (hparams.n_embd_head_k_swa != hparams.n_embd_head_v_swa) { + throw std::runtime_error("EmbeddingGemma2 requires n_embd_head_k_swa == n_embd_head_v_swa"); + } + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + per_layer_model_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_MODEL_PROJ, "weight", 0), {n_embd, n_embd_per_layer * n_layer}, 0); + per_layer_proj_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ_NORM, "weight", 0), {n_embd_per_layer}, 0); + + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + // projects the final hidden state to the embedding dimension + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_embd_out}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + const int64_t n_head = hparams.n_head(i); + const int64_t n_embd_head = hparams.n_embd_head_k(i); + const int64_t n_embd_k = hparams.n_embd_k_gqa(i); + const int64_t n_embd_v = hparams.n_embd_v_gqa(i); + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head * n_head, n_embd}, 0); + + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head}, 0); + layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0); + + layer.per_layer_inp_gate = create_tensor(tn(LLM_TENSOR_PER_LAYER_INP_GATE, "weight", i), {n_embd, n_embd_per_layer}, 0); + layer.per_layer_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ, "weight", i), {n_embd_per_layer, n_embd}, 0); + layer.per_layer_post_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_POST_NORM, "weight", i), {n_embd}, 0); + + layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), {1u}, 0); + } +} + +std::unique_ptr llama_model_gemma_embedding2::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +llama_model_gemma_embedding2::graph::graph(const llama_model & model, const llm_graph_params & params) : + llm_graph_context(params), + model(model) { + ggml_tensor * cur; + ggml_tensor * inpL; + + // important: do not normalize weights for raw embeddings input (i.e. encoded image embeddings) + inpL = build_inp_embd(model.tok_embd, sqrtf(n_embd)); + cb(inpL, "inp_scaled", -1); + + // inp_pos - contains the positions + ggml_tensor * inp_pos = build_inp_pos(); + + auto * inp_attn = build_attn_inp_no_cache(); + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + // inp_per_layer shape: [n_embd_per_layer, n_tokens, n_layer] + ggml_tensor * inp_per_layer = build_inp_per_layer(inpL); + + for (int il = 0; il < n_layer; ++il) { + const int64_t n_embd_head = hparams.n_embd_head_k(il); + const int64_t n_head = hparams.n_head(il); + const int64_t n_head_kv = hparams.n_head_kv(il); + + const float freq_base_l = model.get_rope_freq_base(cparams, il); + const float freq_scale_l = model.get_rope_freq_scale(cparams, il); + const int n_rot_l = hparams.n_rot(il); + + // norm + cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // self-attention + { + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s); + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s); + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il); + Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il); + Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps); + cb(Qcur, "Qcur_normed", il); + cb(Kcur, "Kcur_normed", il); + cb(Vcur, "Vcur_normed", il); + + Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, + ext_factor, attn_factor, beta_fast, beta_slow); + Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(Qcur, "Qcur_pos", il); + cb(Kcur, "Kcur_pos", il); + + cur = build_attn(inp_attn, + model.layers[il].wo, nullptr, model.layers[il].wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, hparams.f_attention_scale, il); + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); + } + + cur = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "attn_post_norm", il); + + ggml_tensor * attn_out = ggml_add(ctx0, cur, inpL); + cb(attn_out, "attn_out", il); + + // feed-forward network + cur = build_norm(attn_out, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + cur = build_ffn(cur, + model.layers[il].ffn_up, nullptr, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, nullptr, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, nullptr, model.layers[il].ffn_down_s, + nullptr, + LLM_FFN_GELU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + + cur = build_norm(cur, model.layers[il].ffn_post_norm, nullptr, LLM_NORM_RMS, -1); + cb(cur, "ffn_post_norm", il); + + cur = ggml_add(ctx0, cur, attn_out); + + // per-layer embedding + { + ggml_tensor * pe_in = cur; + cb(cur, "pe_in", il); + + cur = build_lora_mm(model.layers[il].per_layer_inp_gate, cur); // [n_embd_per_layer, n_tokens] + cur = ggml_gelu(ctx0, cur); + + ggml_tensor * inp_this_layer = ggml_view_2d(ctx0, inp_per_layer, + inp_per_layer->ne[0], inp_per_layer->ne[1], inp_per_layer->nb[1], il * inp_per_layer->nb[2]); + + if (il == n_layer - 1 && inp_out_ids) { + inp_this_layer = ggml_get_rows(ctx0, inp_this_layer, inp_out_ids); + } + + cur = ggml_mul(ctx0, cur, inp_this_layer); + cur = build_lora_mm(model.layers[il].per_layer_proj, cur); // [n_embd, n_tokens] + cur = build_norm(cur, model.layers[il].per_layer_post_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "per_layer_embd_out", il); + + cur = ggml_add(ctx0, pe_in, cur); + } + + // layer_scalar + cur = ggml_mul(ctx0, cur, model.layers[il].out_scale); + cb(cur, "out_scaled", il); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = build_norm(inpL, model.output_norm, nullptr, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + + // projecting per token is equivalent to projecting after mean pooling + cur = build_lora_mm(model.output, cur, model.output_s); + cb(cur, "result_embd", -1); + res->t_embd = cur; + + ggml_build_forward_expand(gf, cur); +} + +// equivalent to EmbeddingGemma2TextPLE in python code +// this model has no per-layer token embeddings, the per-layer inputs come only from the projection +// inpL shape: [n_embd, n_tokens] +// output shape: [n_embd_per_layer, n_tokens, n_layer] +ggml_tensor * llama_model_gemma_embedding2::graph::build_inp_per_layer(ggml_tensor * inpL) { + const int64_t n_embd_per_layer = hparams.n_embd_per_layer; + + ggml_tensor * cur = ggml_mul_mat(ctx0, model.per_layer_model_proj, inpL); // [n_embd_per_layer * n_layer, n_tokens] + cur = ggml_scale(ctx0, cur, 1.0f / sqrtf((float) n_embd)); + cur = ggml_reshape_3d(ctx0, cur, n_embd_per_layer, n_layer, n_tokens); + + cur = build_norm(cur, model.per_layer_proj_norm, nullptr, LLM_NORM_RMS, -1); + cb(cur, "inp_per_layer", -1); + + // permute to shape: [n_embd_per_layer, n_tokens, n_layer] + cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3)); + return cur; +} diff --git a/src/models/models.h b/src/models/models.h index 387a4adcb2..1b589ddcf3 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -914,6 +914,23 @@ struct llama_model_gemma_embedding : public llama_model_base { }; +struct llama_model_gemma_embedding2 : public llama_model_base { + llama_model_gemma_embedding2(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + + struct graph : public llm_graph_context { + const llama_model & model; + + graph(const llama_model & model, const llm_graph_params & params); + + ggml_tensor * build_inp_per_layer(ggml_tensor * inpL); + }; + + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; + + struct llama_model_starcoder2 : public llama_model_base { llama_model_starcoder2(const struct llama_model_params & params) : llama_model_base(params) {} void load_arch_hparams(llama_model_loader & ml) override; diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp index a46e32d7ff..e74d8767cc 100644 --- a/tests/test-llama-archs.cpp +++ b/tests/test-llama-archs.cpp @@ -729,7 +729,7 @@ static bool arch_supported(const llm_arch arch) { if (arch == LLM_ARCH_GRANITE_SWITCH) { return false; // FIXME adapter fixture } - if (arch == LLM_ARCH_LLAMA_EMBED || arch == LLM_ARCH_GEMMA_EMBEDDING || arch == LLM_ARCH_T5ENCODER) { + if (arch == LLM_ARCH_LLAMA_EMBED || arch == LLM_ARCH_GEMMA_EMBEDDING || arch == LLM_ARCH_GEMMA_EMBEDDING2 || arch == LLM_ARCH_T5ENCODER) { return false; // FIXME Embedding (?) models produce inconsistent results. } if (arch == LLM_ARCH_RWKV6 || arch == LLM_ARCH_RWKV6QWEN2 || arch == LLM_ARCH_RWKV7 || arch == LLM_ARCH_ARWKV7) {