diff options
Diffstat (limited to 'examples/finetune/convert-finetune-checkpoint-to-gguf.py')
| -rw-r--r-- | examples/finetune/convert-finetune-checkpoint-to-gguf.py | 487 |
1 files changed, 0 insertions, 487 deletions
diff --git a/examples/finetune/convert-finetune-checkpoint-to-gguf.py b/examples/finetune/convert-finetune-checkpoint-to-gguf.py deleted file mode 100644 index c8909091..00000000 --- a/examples/finetune/convert-finetune-checkpoint-to-gguf.py +++ /dev/null @@ -1,487 +0,0 @@ -#!/usr/bin/env python3 -# finetune checkpoint --> gguf conversion - -import argparse -import gguf -import struct -import numpy as np -from pathlib import Path - -# gguf constants -LLM_KV_OPTIMIZER_TYPE = "optimizer.type" -LLM_KV_OPTIMIZER_TYPE_ADAM = "adam" -LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs" -LLM_KV_OPTIMIZER_FILE_VERSION = "optimizer.file_version" -LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = "optimizer.convergence_past_count" -LLM_KV_OPTIMIZER_PARAMETER_COUNT = "optimizer.parameter_count" -LLM_KV_OPTIMIZER_ITERATION_COUNT = "optimizer.iteration_count" -LLM_KV_OPTIMIZER_JUST_INITIALIZED = "optimizer.just_initialized" -LLM_KV_OPTIMIZER_ADAM_BEST_LOSS = "optimizer.adam.best_loss" -LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = "optimizer.adam.previous_loss" -LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = "optimizer.adam.no_improvement_count" -LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count" -LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = "optimizer.lbfgs.best_loss" -LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = "optimizer.lbfgs.line_search_step" -LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = "optimizer.lbfgs.line_search_j" -LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = "optimizer.lbfgs.line_search_k" -LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = "optimizer.lbfgs.line_search_end" -LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count" - -LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = "optimizer.adam.first_moments" -LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = "optimizer.adam.second_moments" -LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values" - -LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = "optimizer.lbfgs.current_parameters" -LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters" -LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = "optimizer.lbfgs.current_gradients" -LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = "optimizer.lbfgs.previous_gradients" -LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = "optimizer.lbfgs.search_direction" -LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = "optimizer.lbfgs.past_loss_values" -LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = "optimizer.lbfgs.memory_alpha" -LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = "optimizer.lbfgs.memory_ys" -LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = "optimizer.lbfgs.memory_s" -LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = "optimizer.lbfgs.memory_y" - -LLM_KV_TRAINING_TYPE_TRAIN_MODEL = "train_model" -LLM_KV_TRAINING_TYPE_FINETUNE_LORA = "finetune_lora" -LLM_KV_TRAINING_TYPE = "training.type" -LLM_KV_TRAINING_FILE_VERSION = "training.file_version" -LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count" -LLM_KV_TRAINING_SAMPLE_COUNT = "training.sample_count" -LLM_KV_TRAINING_TOKEN_COUNT = "training.token_count" - -LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD = "training.lora.rank.token_embd" -LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = "training.lora.rank.output_norm" -LLM_KV_TRAINING_LORA_RANK_OUTPUT = "training.lora.rank.output" -LLM_KV_TRAINING_LORA_RANK_ATTN_NORM = "training.lora.rank.attn_norm" -LLM_KV_TRAINING_LORA_RANK_ATTN_Q = "training.lora.rank.attn_q" -LLM_KV_TRAINING_LORA_RANK_ATTN_K = "training.lora.rank.attn_k" -LLM_KV_TRAINING_LORA_RANK_ATTN_V = "training.lora.rank.attn_v" -LLM_KV_TRAINING_LORA_RANK_ATTN_OUT = "training.lora.rank.attn_output" -LLM_KV_TRAINING_LORA_RANK_FFN_NORM = "training.lora.rank.ffn_norm" -LLM_KV_TRAINING_LORA_RANK_FFN_GATE = "training.lora.rank.ffn_gate" -LLM_KV_TRAINING_LORA_RANK_FFN_DOWN = "training.lora.rank.ffn_down" -LLM_KV_TRAINING_LORA_RANK_FFN_UP = "training.lora.rank.ffn_up" - -class Tensor: - def __init__(self, dtype='f', ne=None): - if ne is None: - ne = [] - self.dtype = dtype - self.ne = ne - self.nbytes = 0 - if self.dtype == 'f': - if len(self.ne) == 0: - self.nbytes = 0 - else: - self.nbytes = int(np.product(self.ne)) * 4 - else: - raise ValueError(f"Unhandled data type '{self.dtype}'") - - def load(self, data, offset): - nd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - namelen = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - dtype = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - - assert(nd == len(self.ne)) - ne = [] - for d in range(nd): - n = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - ne.append(n) - - if tuple(ne) != tuple(self.ne): - raise ValueError(f"Tensor.load: Expected number of elements {str(self.ne)} does not match what is read from file {str(ne)}") - - if self.dtype == 'f': - assert(dtype == 0) - else: - raise ValueError(f"Unhandled data type '{self.dtype}'") - - self.name = bytes(data[offset:offset+namelen]); offset += namelen - # 32-byte alignment - offset += (0 - offset) & 31 - self.data = data[offset:offset+self.nbytes] - offset += self.nbytes - return offset - - def max_storage_size(self): - result = 0 - result += 4 # nd - result += 4 # namelen - result += 4 # dtype - result += len(self.ne)*8 # ne - result += 48 # name (maximum as of commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9) - result += 31 # 32-byte alignment - result += self.nbytes - return result - - def save_gguf(self, gguf_writer, name): - gguf_writer.add_tensor( - name=name, - tensor=self.data, - raw_shape=np.array(list(reversed(self.ne))), - raw_dtype=gguf.GGMLQuantizationType.F32) - -class OptimizationContext: - def __init__(self): - pass - - def load(self, data, offset): - self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0] - offset += 4 - - if self.version != 1: - raise ValueError('Invalid version of optimization context in checkpoint file') - - self.past = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.nx = struct.unpack('N', bytes(data[offset:offset + 8]))[0]; offset += 8 - self.iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]); offset += 4 - - self.adam_m = Tensor('f', [self.nx]) - self.adam_v = Tensor('f', [self.nx]) - self.adam_pf = Tensor('f', [self.past] if self.past > 0 else []) - - self.lbfgs_x = Tensor('f', [self.nx]) - self.lbfgs_xp = Tensor('f', [self.nx]) - self.lbfgs_g = Tensor('f', [self.nx]) - self.lbfgs_gp = Tensor('f', [self.nx]) - self.lbfgs_d = Tensor('f', [self.nx]) - self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else []) - self.lbfgs_lmal = Tensor('f', [self.lbfgs_m]) - self.lbfgs_lmys = Tensor('f', [self.lbfgs_m]) - self.lbfgs_lms = Tensor('f', [self.nx, self.lbfgs_m]) - self.lbfgs_lmy = Tensor('f', [self.nx, self.lbfgs_m]) - - # forgot to save type in version 1: - # guess self.type from number of remaining bytes - size_type_0 = 12 + sum([t.max_storage_size() for t in - [self.adam_m, self.adam_v] - +([self.adam_pf] if (self.past > 0) else [])]) - size_type_1 = 24 + sum([t.max_storage_size() for t in - [self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g, - self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf, - self.lbfgs_lmal, self.lbfgs_lmys, - self.lbfgs_lms, self.lbfgs_lmy] - +([self.lbfgs_pf] if (self.past > 0) else [])]) - # due to alignment padding the size might not by exact - # but the difference in size for both types is significant, - # so we can just use whichever is closest - remaining = len(data) - offset - if abs(remaining - size_type_0) < abs(remaining - size_type_1): - self.type = 0 - else: - self.type = 1 - - if self.type == 0: - offset = self.adam_m.load(data, offset) - offset = self.adam_v.load(data, offset) - offset = self.adam_pf.load(data,offset) - - self.adam_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.adam_fx_prev = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4 - - elif self.type == 1: - offset = self.lbfgs_x.load(data, offset) - offset = self.lbfgs_xp.load(data, offset) - offset = self.lbfgs_g.load(data, offset) - offset = self.lbfgs_gp.load(data, offset) - offset = self.lbfgs_d.load(data, offset) - offset = self.lbfgs_pf.load(data, offset) - offset = self.lbfgs_lmal.load(data, offset) - offset = self.lbfgs_lmys.load(data, offset) - offset = self.lbfgs_lms.load(data, offset) - offset = self.lbfgs_lmy.load(data, offset) - - self.lbfgs_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.lbfgs_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.lbfgs_j = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.lbfgs_k = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.lbfgs_end = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4 - - else: - raise ValueError(f"Invalid optimizer type '{self.type}'") - - return offset - - def save_gguf(self, gguf_writer): - gguf_writer.add_uint32(LLM_KV_OPTIMIZER_FILE_VERSION, 0) - gguf_writer.add_uint32(LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, self.past) - gguf_writer.add_uint64(LLM_KV_OPTIMIZER_PARAMETER_COUNT, self.nx) - gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter) - gguf_writer.add_bool(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized) - - if self.type == 0: - gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM) - gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, self.adam_fx_best) - gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, self.adam_fx_prev) - gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, self.adam_n_no_improvement) - - self.adam_m.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS) - self.adam_v.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS) - if self.past > 0: - self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES) - - elif self.type == 1: - gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS) - gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m) - gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best) - gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step) - gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j) - gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k) - gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end) - gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement) - - self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS) - self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS) - self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS) - self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS) - self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION) - if self.past > 0: - self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES) - self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA) - self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS) - self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S) - self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y) - else: - raise ValueError('Unknown optimizer type') - -class LoraParams: - def __init__(self): - pass - - def load(self, data, offset): - self.n_rank_attention_norm = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rank_wq = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rank_wk = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rank_wv = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rank_wo = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rank_ffn_norm = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rank_w1 = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rank_w2 = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rank_w3 = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rank_tok_embeddings = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rank_norm = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rank_output = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - return offset - - def save_gguf(self, gguf_writer): - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD, self.n_rank_tok_embeddings) - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM, self.n_rank_norm) - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_OUTPUT, self.n_rank_output) - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_NORM, self.n_rank_attention_norm) - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_Q, self.n_rank_wq) - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_K, self.n_rank_wk) - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_V, self.n_rank_wv) - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_OUT, self.n_rank_wo) - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_NORM, self.n_rank_ffn_norm) - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_GATE, self.n_rank_w1) - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_DOWN, self.n_rank_w2) - gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_UP, self.n_rank_w3) - -class ModelParams: - def __init__(self, n_ff = None): - self.n_ff = n_ff - - def load(self, data, offset): - self.n_vocab = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_embd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_mult = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_head = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_layer = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.n_rot = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - return offset - - def get_n_ff(self): - if self.n_ff is None: - # struct my_llama_model::get_n_ff in train-text-from-scratch.cpp commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9 - return ((2*(4*self.n_embd)//3 + self.n_mult - 1)//self.n_mult)*self.n_mult - else: - return self.n_ff - - def save_gguf(self, gguf_writer): - # self.n_vocab not saved - gguf_writer.add_embedding_length(self.n_embd) - gguf_writer.add_head_count(self.n_head) - gguf_writer.add_block_count(self.n_layer) - gguf_writer.add_rope_dimension_count(self.n_rot) - gguf_writer.add_feed_forward_length(self.get_n_ff()) - -def tensor_name(key, bid=None, suffix=".weight"): - return gguf.TENSOR_NAMES[key].format(bid=bid) + suffix - -class Layer: - def __init__(self, params, lora_params, bid): - self.bid = bid - self.att_norm_a = Tensor('f', [lora_params.n_rank_attention_norm, params.n_embd]) - self.att_norm_b = Tensor('f', [lora_params.n_rank_attention_norm, 1]) - self.wq_a = Tensor('f', [lora_params.n_rank_wq, params.n_embd]) - self.wq_b = Tensor('f', [lora_params.n_rank_wq, params.n_embd]) - self.wk_a = Tensor('f', [lora_params.n_rank_wk, params.n_embd]) - self.wk_b = Tensor('f', [lora_params.n_rank_wk, params.n_embd]) - self.wv_a = Tensor('f', [lora_params.n_rank_wv, params.n_embd]) - self.wv_b = Tensor('f', [lora_params.n_rank_wv, params.n_embd]) - self.wo_a = Tensor('f', [lora_params.n_rank_wo, params.n_embd]) - self.wo_b = Tensor('f', [lora_params.n_rank_wo, params.n_embd]) - self.ffn_norm_a = Tensor('f', [lora_params.n_rank_ffn_norm, params.n_embd]) - self.ffn_norm_b = Tensor('f', [lora_params.n_rank_ffn_norm, 1]) - self.w1_a = Tensor('f', [lora_params.n_rank_w1, params.n_embd]) - self.w1_b = Tensor('f', [lora_params.n_rank_w1, params.get_n_ff()]) - self.w2_a = Tensor('f', [lora_params.n_rank_w2, params.get_n_ff()]) - self.w2_b = Tensor('f', [lora_params.n_rank_w2, params.n_embd]) - self.w3_a = Tensor('f', [lora_params.n_rank_w3, params.n_embd]) - self.w3_b = Tensor('f', [lora_params.n_rank_w3, params.get_n_ff()]) - - def load(self, data, offset): - offset = self.att_norm_a.load(data, offset) - offset = self.att_norm_b.load(data, offset) - offset = self.wq_a.load(data, offset) - offset = self.wq_b.load(data, offset) - offset = self.wk_a.load(data, offset) - offset = self.wk_b.load(data, offset) - offset = self.wv_a.load(data, offset) - offset = self.wv_b.load(data, offset) - offset = self.wo_a.load(data, offset) - offset = self.wo_b.load(data, offset) - offset = self.ffn_norm_a.load(data, offset) - offset = self.ffn_norm_b.load(data, offset) - offset = self.w1_a.load(data, offset) - offset = self.w1_b.load(data, offset) - offset = self.w2_a.load(data, offset) - offset = self.w2_b.load(data, offset) - offset = self.w3_a.load(data, offset) - offset = self.w3_b.load(data, offset) - return offset - - def save_gguf(self, gguf_writer): - self.att_norm_a.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid, ".weight.lora_a")) - self.att_norm_b.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid, ".weight.lora_b")) - self.wq_a.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q, self.bid, ".weight.lora_a")) - self.wq_b.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q, self.bid, ".weight.lora_b")) - self.wk_a.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K, self.bid, ".weight.lora_a")) - self.wk_b.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K, self.bid, ".weight.lora_b")) - self.wv_a.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V, self.bid, ".weight.lora_a")) - self.wv_b.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V, self.bid, ".weight.lora_b")) - self.wo_a.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, self.bid, ".weight.lora_a")) - self.wo_b.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, self.bid, ".weight.lora_b")) - self.ffn_norm_a.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM, self.bid, ".weight.lora_a")) - self.ffn_norm_b.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM, self.bid, ".weight.lora_b")) - self.w1_a.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE, self.bid, ".weight.lora_a")) - self.w1_b.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE, self.bid, ".weight.lora_b")) - self.w2_a.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, self.bid, ".weight.lora_a")) - self.w2_b.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, self.bid, ".weight.lora_b")) - self.w3_a.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP, self.bid, ".weight.lora_a")) - self.w3_b.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP, self.bid, ".weight.lora_b")) - -class LoraModel: - def __init__(self, n_ff = None): - self.params = ModelParams(n_ff = n_ff) - self.lora_params = LoraParams() - self.layers = [] - - def load(self, data, offset): - offset = self.params.load(data, offset) - offset = self.lora_params.load(data, offset) - - self.tok_embd_a = Tensor('f', [self.lora_params.n_rank_tok_embeddings, self.params.n_embd]) - self.tok_embd_b = Tensor('f', [self.lora_params.n_rank_tok_embeddings, self.params.n_vocab]) - self.norm_a = Tensor('f', [self.lora_params.n_rank_norm, self.params.n_embd]) - self.norm_b = Tensor('f', [self.lora_params.n_rank_norm, 1]) - self.output_a = Tensor('f', [self.lora_params.n_rank_output, self.params.n_embd]) - self.output_b = Tensor('f', [self.lora_params.n_rank_output, self.params.n_vocab]) - - offset = self.tok_embd_a.load(data, offset) - offset = self.tok_embd_b.load(data, offset) - offset = self.norm_a.load(data, offset) - offset = self.norm_b.load(data, offset) - offset = self.output_a.load(data, offset) - offset = self.output_b.load(data, offset) - - self.layers.clear() - for bid in range(self.params.n_layer): - layer = Layer(self.params, self.lora_params, bid) - offset = layer.load(data, offset) - self.layers.append(layer) - - return offset - - def save_gguf(self, gguf_writer): - self.params.save_gguf(gguf_writer) - self.lora_params.save_gguf(gguf_writer) - - self.tok_embd_a.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD, suffix=".weight.lora_a")) - self.tok_embd_b.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD, suffix=".weight.lora_b")) - self.norm_a.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM, suffix=".weight.lora_a")) - self.norm_b.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM, suffix=".weight.lora_b")) - self.output_a.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT, suffix=".weight.lora_a")) - self.output_b.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT, suffix=".weight.lora_b")) - - for layer in self.layers: - layer.save_gguf(gguf_writer) - -class LoraCheckpoint: - def __init__(self, n_ff = None): - self.model = LoraModel(n_ff = n_ff) - self.opt_ctx = OptimizationContext() - - def load(self, data, offset): - magic = bytes(reversed(data[offset:offset + 4])); offset += 4 - if magic != b'ggcl': - raise ValueError(f"File header magic indicates, that this is no finetune-lora checkpoint file. Expected 'ggcl', Got '{str(magic)}'") - - self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - if self.version != 0: - raise ValueError('Invalid version of checkpoint file') - - self.train_its = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.train_samples = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - self.train_tokens = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 - - offset = self.model.load(data, offset) - offset = self.opt_ctx.load(data, offset) - - return offset - - def save_gguf(self, gguf_writer): - gguf_writer.add_file_type(gguf.GGMLQuantizationType.F32) - gguf_writer.add_layer_norm_rms_eps(1e-5) - gguf_writer.add_uint32(LLM_KV_TRAINING_FILE_VERSION, 0) - gguf_writer.add_string(LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_FINETUNE_LORA) - gguf_writer.add_uint32(LLM_KV_TRAINING_ITERATION_COUNT, self.train_its) - gguf_writer.add_uint32(LLM_KV_TRAINING_SAMPLE_COUNT, self.train_samples) - gguf_writer.add_uint32(LLM_KV_TRAINING_TOKEN_COUNT, self.train_tokens) - self.model.save_gguf(gguf_writer) - self.opt_ctx.save_gguf(gguf_writer) - -def handle_args(): - parser = argparse.ArgumentParser(description = 'Convert finetune checkpoints to GGUF') - parser.add_argument('--input', '-i', type = Path, help = 'Input finetune checkpoint filename', required=True) - parser.add_argument('--output', '-o', type = Path, help = 'Output GGUF filename', required=True) - parser.add_argument('--ff', type = int, help = "Feedforward size, if not provided compute from n_mult. Provide this if you get 'ValueError: Tensor.load: Expected number of elements does not match what is read from file'", required=False) - return parser.parse_args() - -def main(): - cfg = handle_args() - print(cfg) - data = np.memmap(cfg.input, mode = 'r') - chk = LoraCheckpoint(n_ff = cfg.ff) - offset = 0 - offset = chk.load(data, offset) - # we should have read all available data - assert(offset == len(data)) - - gguf_writer = gguf.GGUFWriter(cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False) - chk.save_gguf(gguf_writer) - print(" gguf: write header") - gguf_writer.write_header_to_file() - print(" gguf: write metadata") - gguf_writer.write_kv_data_to_file() - print(" gguf: write tensors") - gguf_writer.write_tensors_to_file() - gguf_writer.close() - -if __name__ == '__main__': - main() |