diff --git a/README.md b/README.md index 47d41ebfc..f4088c05e 100644 --- a/README.md +++ b/README.md @@ -107,7 +107,6 @@ Typically finetunes of the base models below are supported as well. - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187) - [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim) -- [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410) - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553) - [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index bd303150a..d534b5163 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1148,45 +1148,6 @@ class RefactModel(Model): return tensors -@Model.register("PersimmonForCausalLM") -class PersimmonModel(Model): - model_arch = gguf.MODEL_ARCH.PERSIMMON - - def set_gguf_parameters(self): - block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) - head_count = self.hparams["num_attention_heads"] - head_count_kv = head_count - hidden_size = self.hparams["hidden_size"] - - self.gguf_writer.add_name('persimmon-8b-chat') - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_embedding_length(hidden_size) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - - # NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller - # than the head size? - # ref: https://github.com/ggerganov/llama.cpp/pull/4889 - # self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) - self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) - - self.gguf_writer.add_head_count(head_count) - self.gguf_writer.add_head_count_kv(head_count_kv) - self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) - - def set_vocab(self): - self._set_vocab_sentencepiece() - # self.gguf_writer.add_bos_token_id(71013) - # self.gguf_writer.add_eos_token_id(71013) - - def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: - del name, new_name, bid, n_dims # unused - - # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) - return True - - @Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM") class StableLMModel(Model): model_arch = gguf.MODEL_ARCH.STABLELM diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py deleted file mode 100755 index 07dcade74..000000000 --- a/convert-persimmon-to-gguf.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import logging -import argparse -import os -import sys -from pathlib import Path -from pprint import pprint - -import torch -from sentencepiece import SentencePieceProcessor - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) -import gguf - -logger = logging.getLogger("persimmon-to-gguf") - - -def _flatten_dict(dct, tensors, prefix=None): - assert isinstance(dct, dict) - for key in dct.keys(): - new_prefix = prefix + '.' + key if prefix is not None else key - if isinstance(dct[key], torch.Tensor): - tensors[new_prefix] = dct[key] - elif isinstance(dct[key], dict): - _flatten_dict(dct[key], tensors, new_prefix) - else: - raise ValueError(type(dct[key])) - return None - - -def _get_sentencepiece_tokenizer_info(dir_model: Path): - tokenizer_path = dir_model / 'adept_vocab.model' - logger.info('getting sentencepiece tokenizer from', tokenizer_path) - tokenizer = SentencePieceProcessor(str(tokenizer_path)) - logger.info('adding tokens') - tokens: list[bytes] = [] - scores: list[float] = [] - toktypes: list[int] = [] - - for i in range(tokenizer.vocab_size()): - text: bytes - score: float - - piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") - score = tokenizer.get_score(i) - - toktype = 1 - if tokenizer.is_unknown(i): - toktype = 2 - if tokenizer.is_control(i): - toktype = 3 - if tokenizer.is_unused(i): - toktype = 5 - if tokenizer.is_byte(i): - toktype = 6 - - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - pass - return tokens, scores, toktypes - - -def main(): - parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("--ckpt-path", type=Path, help="path to persimmon checkpoint .pt file") - parser.add_argument("--model-dir", type=Path, help="directory containing model e.g. 8b_chat_model_release") - parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory") - parser.add_argument("--verbose", action="store_true", help="increase output verbosity") - args = parser.parse_args() - logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) - sys.path.append(str(args.adept_inference_dir)) - persimmon_model = torch.load(args.ckpt_path) - hparams = persimmon_model['args'] - pprint(hparams) - tensors: dict[str, torch.Tensor] = {} - _flatten_dict(persimmon_model['model'], tensors, None) - - arch = gguf.MODEL_ARCH.PERSIMMON - gguf_writer = gguf.GGUFWriter(args.outfile, gguf.MODEL_ARCH_NAMES[arch]) - - block_count = hparams.num_layers - head_count = hparams.num_attention_heads - head_count_kv = head_count - ctx_length = hparams.seq_length - hidden_size = hparams.hidden_size - - gguf_writer.add_name('persimmon-8b-chat') - gguf_writer.add_context_length(ctx_length) - gguf_writer.add_embedding_length(hidden_size) - gguf_writer.add_block_count(block_count) - gguf_writer.add_feed_forward_length(hparams.ffn_hidden_size) - # ref: https://github.com/ggerganov/llama.cpp/pull/4889/commits/eea19039fc52ea2dbd1aab45b59ab4e3e29a3443 - gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) - gguf_writer.add_head_count(head_count) - gguf_writer.add_head_count_kv(head_count_kv) - gguf_writer.add_rope_freq_base(hparams.rotary_emb_base) - gguf_writer.add_layer_norm_eps(hparams.layernorm_epsilon) - - tokens, scores, toktypes = _get_sentencepiece_tokenizer_info(args.model_dir) - gguf_writer.add_tokenizer_model('llama') - gguf_writer.add_tokenizer_pre('default') - gguf_writer.add_token_list(tokens) - gguf_writer.add_token_scores(scores) - gguf_writer.add_token_types(toktypes) - gguf_writer.add_bos_token_id(71013) - gguf_writer.add_eos_token_id(71013) - - tensor_map = gguf.get_tensor_name_map(arch, block_count) - logger.info(tensor_map) - for name in tensors.keys(): - data_torch = tensors[name] - if name.endswith(".self_attention.rotary_emb.inv_freq"): - continue - old_dtype = data_torch.dtype - # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) - data = data_torch.to(torch.float32).squeeze().numpy() - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor '{name}'") - - n_dims = len(data.shape) - logger.debug(f"{new_name}, n_dims = {str(n_dims)}, {str(old_dtype)} --> {str(data.dtype)}") - gguf_writer.add_tensor(new_name, data) - logger.info("gguf: write header") - gguf_writer.write_header_to_file() - logger.info("gguf: write metadata") - gguf_writer.write_kv_data_to_file() - logger.info("gguf: write tensors") - gguf_writer.write_tensors_to_file() - - gguf_writer.close() - - logger.info(f"gguf: model successfully exported to '{args.outfile}'") - - -if __name__ == '__main__': - main() diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 978fcada3..692120f4d 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -115,7 +115,6 @@ class MODEL_ARCH(IntEnum): GPTNEOX = auto() MPT = auto() STARCODER = auto() - PERSIMMON = auto() REFACT = auto() BERT = auto() NOMIC_BERT = auto() @@ -193,7 +192,6 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.GPTNEOX: "gptneox", MODEL_ARCH.MPT: "mpt", MODEL_ARCH.STARCODER: "starcoder", - MODEL_ARCH.PERSIMMON: "persimmon", MODEL_ARCH.REFACT: "refact", MODEL_ARCH.BERT: "bert", MODEL_ARCH.NOMIC_BERT: "nomic-bert", @@ -426,20 +424,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - MODEL_TENSOR.ATTN_Q_NORM, - MODEL_TENSOR.ATTN_K_NORM, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], MODEL_ARCH.REFACT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -756,9 +740,6 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.ROPE_FREQS, - ], MODEL_ARCH.QWEN: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, diff --git a/llama.cpp b/llama.cpp index 2025e4558..863961f15 100644 --- a/llama.cpp +++ b/llama.cpp @@ -202,7 +202,6 @@ enum llm_arch { LLM_ARCH_GPTNEOX, LLM_ARCH_MPT, LLM_ARCH_STARCODER, - LLM_ARCH_PERSIMMON, LLM_ARCH_REFACT, LLM_ARCH_BERT, LLM_ARCH_NOMIC_BERT, @@ -239,7 +238,6 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_MPT, "mpt" }, { LLM_ARCH_BAICHUAN, "baichuan" }, { LLM_ARCH_STARCODER, "starcoder" }, - { LLM_ARCH_PERSIMMON, "persimmon" }, { LLM_ARCH_REFACT, "refact" }, { LLM_ARCH_BERT, "bert" }, { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, @@ -595,23 +593,6 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, - { - LLM_ARCH_PERSIMMON, - { - { LLM_TENSOR_TOKEN_EMBD, "token_embd"}, - { LLM_TENSOR_OUTPUT_NORM, "output_norm"}, - { LLM_TENSOR_OUTPUT, "output"}, - { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm"}, - { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv"}, - { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output"}, - { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"}, - { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm"}, - { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm"}, - { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down"}, - { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up"}, - { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd"}, - }, - }, { LLM_ARCH_MPT, { @@ -3967,14 +3948,6 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; - case LLM_ARCH_PERSIMMON: - { - ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); - switch (hparams.n_layer) { - case 36: model.type = e_model::MODEL_8B; break; - default: model.type = e_model::MODEL_UNKNOWN; - } - } break; case LLM_ARCH_REFACT: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -5221,47 +5194,6 @@ static bool llm_load_tensors( layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; - case LLM_ARCH_PERSIMMON: - { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } - - for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - - auto & layer = model.layers[i]; - - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); - - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); - - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64}); - - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}); - } - } break; case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: { @@ -7923,213 +7855,6 @@ struct llm_build_context { return gf; } - struct ggml_cgraph * build_persimmon() { - struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); - - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head/2 == hparams.n_rot); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); - - // inp_pos - contains the positions - struct ggml_tensor * inp_pos = build_inp_pos(); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * residual = inpL; - - cur = llm_build_norm(ctx0, inpL, hparams, - model.layers[il].attn_norm, - model.layers[il].attn_norm_b, - LLM_NORM, cb, il); - cb(cur, "attn_norm", il); - - // self attention - { - cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - cb(cur, "wqkv", il); - - cur = ggml_add(ctx0, cur, model.layers[il].bqkv); - cb(cur, "bqkv", il); - - // split qkv - GGML_ASSERT(n_head_kv == n_head); - - struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens); - cb(tmpqkv, "tmpqkv", il); - - struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2)); - cb(tmpqkv_perm, "tmpqkv", il); - - struct ggml_tensor * tmpq = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - 0 - ); - cb(tmpq, "tmpq", il); - - struct ggml_tensor * tmpk = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens - ); - cb(tmpk, "tmpk", il); - - // Q/K Layernorm - tmpq = llm_build_norm(ctx0, tmpq, hparams, - model.layers[il].attn_q_norm, - model.layers[il].attn_q_norm_b, - LLM_NORM, cb, il); - cb(tmpq, "tmpq", il); - - tmpk = llm_build_norm(ctx0, tmpk, hparams, - model.layers[il].attn_k_norm, - model.layers[il].attn_k_norm_b, - LLM_NORM, cb, il); - cb(tmpk, "tmpk", il); - - // RoPE the first n_rot of q/k, pass the other half, and concat. - struct ggml_tensor * qrot = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, - ggml_element_size(tmpq) * n_embd_head, - ggml_element_size(tmpq) * n_embd_head * n_head, - 0 - ); - cb(qrot, "qrot", il); - - struct ggml_tensor * krot = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, - ggml_element_size(tmpk) * n_embd_head, - ggml_element_size(tmpk) * n_embd_head * n_head, - 0 - ); - cb(krot, "krot", il); - - // get the second half of tmpq, e.g tmpq[n_rot:, :, :] - struct ggml_tensor * qpass = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, - ggml_element_size(tmpq) * n_embd_head, - ggml_element_size(tmpq) * n_embd_head * n_head, - ggml_element_size(tmpq) * n_rot - ); - cb(qpass, "qpass", il); - - struct ggml_tensor * kpass = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, - ggml_element_size(tmpk) * n_embd_head, - ggml_element_size(tmpk) * n_embd_head * n_head, - ggml_element_size(tmpk) * n_rot - ); - cb(kpass, "kpass", il); - - struct ggml_tensor * qrotated = ggml_rope_custom( - ctx0, qrot, inp_pos, n_rot, rope_type, 0, n_orig_ctx, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(qrotated, "qrotated", il); - - struct ggml_tensor * krotated = ggml_rope_custom( - ctx0, krot, inp_pos, n_rot, rope_type, 0, n_orig_ctx, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(krotated, "krotated", il); - - // ggml currently only supports concatenation on dim=2 - // so we need to permute qrot, qpass, concat, then permute back. - qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3)); - cb(qrotated, "qrotated", il); - - krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3)); - cb(krotated, "krotated", il); - - qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3)); - cb(qpass, "qpass", il); - - kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3)); - cb(kpass, "kpass", il); - - struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass); - cb(Qcur, "Qcur", il); - - struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass); - cb(Kcur, "Kcur", il); - - struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3)); - cb(Q, "Q", il); - - Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); - cb(Kcur, "Kcur", il); - - struct ggml_tensor * Vcur = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2 - ); - cb(Vcur, "Vcur", il); - - cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, - model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Q, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - } - - if (il == n_layer - 1) { - // skip computing output for unused tokens - struct ggml_tensor * inp_out_ids = build_inp_out_ids(); - cur = ggml_get_rows(ctx0, cur, inp_out_ids); - residual = ggml_get_rows(ctx0, residual, inp_out_ids); - } - - struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur); - cb(ffn_inp, "ffn_inp", il); - - // feed-forward network - { - cur = llm_build_norm(ctx0, ffn_inp, hparams, - model.layers[il].ffn_norm, - model.layers[il].ffn_norm_b, - LLM_NORM, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, model.layers[il].ffn_up_b, - NULL, NULL, - model.layers[il].ffn_down, model.layers[il].ffn_down_b, - NULL, - LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il); - cb(cur, "ffn_out", il); - } - - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); - - inpL = cur; - } - - cur = inpL; - - cur = llm_build_norm(ctx0, cur, hparams, - model.output_norm, - model.output_norm_b, - LLM_NORM, cb, -1); - cb(cur, "result_norm", -1); - - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - return gf; - } - struct ggml_cgraph * build_refact() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -10898,10 +10623,6 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_starcoder(); } break; - case LLM_ARCH_PERSIMMON: - { - result = llm.build_persimmon(); - } break; case LLM_ARCH_REFACT: { result = llm.build_refact(); @@ -15992,7 +15713,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_FALCON: case LLM_ARCH_GROK: case LLM_ARCH_DBRX: - case LLM_ARCH_PERSIMMON: case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: case LLM_ARCH_STABLELM: diff --git a/requirements.txt b/requirements.txt index e7d14e16a..43f82dc2e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,4 +9,3 @@ -r ./requirements/requirements-convert-hf-to-gguf.txt -r ./requirements/requirements-convert-hf-to-gguf-update.txt -r ./requirements/requirements-convert-llama-ggml-to-gguf.txt --r ./requirements/requirements-convert-persimmon-to-gguf.txt diff --git a/requirements/requirements-convert-persimmon-to-gguf.txt b/requirements/requirements-convert-persimmon-to-gguf.txt deleted file mode 100644 index 6ac402610..000000000 --- a/requirements/requirements-convert-persimmon-to-gguf.txt +++ /dev/null @@ -1,2 +0,0 @@ --r ./requirements-convert.txt -torch~=2.1.1